Science, Faculty of
Resources, Environment and Sustainability (IRES), Institute for
DSpace
UBCV
Salter, Jonathan
Doctor of Philosophy - PhD
University of British Columbia
This dissertation has two primary research threads. The first is a case study analysis of three community scale energy planning engagement processes in British Columbia, including community energy and greenhouse gas emissions workshops conducted in the City of Vancouver as part of the Greenest City Conversations Project (GCCP), stakeholder workshops as part of the City of Victoria’s Community Energy and Emissions Plan (CEEP), and public workshops conducted in the City of Revelstoke that investigated the relationships between urban form, energy, and climate change. The second research thread investigates the use of a mental models methodology for evaluating the impacts of engagement on the workshop participants. The research first examines the literature on public engagement in sustainability planning, and the literature on mental models methodologies, developing the rationale for a mental models approach to evaluating engagement in community scale energy planning. The research then turns to the development and conduct of the mental models methodology, starting with initial practitioner interviews to describe the community scale energy domain. These interviews revealed key themes about energy and related subjects that contributed to both the GCCP workshop development, and the construction of the dissertation’s primary research instrument, the pre and post workshop questionnaires. Workshop participants existing mental models of energy are then analyzed based on the results of the pre-workshop questionnaires, using a Multiple Correspondence Analysis (MCA) and hierarchical clustering procedure. This analysis revealed the presence of six primary clusters of energy mental models amongst participants that were used to organize and interpret subsequent research results. The final phase of the research combines a case study and mental models evaluation of the three community energy engagement processes, beginning with the dissertation’s primary research vehicle (the GCCP workshops) and then comparing the three engagement processes for their impacts on participants’ mental models, and for the constituent case study components that defined the workshops. The final chapter then turns to a discussion of the implications of the research for future mental models, energy planning, and public engagement research and practice.
Energy in Place: A Case Study and Mental Models Analysis of Engagement in Community Scale Energy Planning. by Jonathan Salter MSc. The University of British Columbia, 2005. A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF Doctor of Philosophy in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Resource Management and Environmental Studies) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) November 2015 © Jonathan Salter, 2015 ii Abstract This dissertation has two primary research threads. The first is a case study analysis of three community scale energy planning engagement processes in British Columbia, including community energy and greenhouse gas emissions workshops conducted in the City of Vancouver as part of the Greenest City Conversations Project (GCCP), stakeholder workshops as part of the City of Victoria’s Community Energy and Emissions Plan (CEEP), and public workshops conducted in the City of Revelstoke that investigated the relationships between urban form, energy, and climate change. The second research thread investigates the use of a mental models methodology for evaluating the impacts of engagement on the workshop participants. The research first examines the literature on public engagement in sustainability planning, and the literature on mental models methodologies, developing the rationale for a mental models approach to evaluating engagement in community scale energy planning. The research then turns to the development and conduct of the mental models methodology, starting with initial practitioner interviews to describe the community scale energy domain. These interviews revealed key themes about energy and related subjects that contributed to both the GCCP workshop development, and the construction of the dissertation’s primary research instrument, the pre and post workshop questionnaires. Workshop participants existing mental models of energy are then analyzed based on the results of the pre-workshop questionnaires, using a Multiple Correspondence Analysis (MCA) and hierarchical clustering procedure. This analysis revealed the presence of six primary clusters of energy mental models amongst participants that were used to organize and interpret subsequent research results. The final phase of the research combines a case study and mental models evaluation of the three community energy engagement processes, beginning with the dissertation’s primary research vehicle (the GCCP workshops) and then comparing the three engagement processes for their impacts on participants’ mental models, and for the constituent case study components that defined the workshops. The final chapter then turns to a discussion of the implications of the research for future mental models, energy planning, and public engagement research and practice. iii Preface With the exception of Chapter 2 (documented below) all of the research included in this dissertation represents the original work of the author. In applied research involving the creation of engagement workshops, a research team is required to develop the workshop ideas, create the materials, and deliver the workshops. In the relevant chapters (6 and 7) the author’s contributions to the development and/or conduct of the workshops are clearly stated. For the GCCP workshops, which were the primary research vehicle for the dissertation (see Chapter 6), I was involved in all aspects of the workshop’s creation and delivery. The majority of the workshop development and materials creation was conducted jointly with my research colleague Ellen Pond, with a larger research team contributing additional materials. The development of the interviews (Chapter 4), questionnaires (Chapters 5-7), gathering of the data, analysis of the data, and interpretation of the data were all the sole work of the dissertation’s author, with review and advice from the dissertation committee. A version of Chapter 2 has been published elsewhere. Salter, J., Robinson, J., Wiek, A. (2010). Participatory methods of integrated assessment – a review. WIRE’s Climate Change, 1, 697- 717. I wrote the initial manuscript draft, and was responsible for approximately 75% of the finished document. John Robinson wrote the initial draft of section 2.3.2, and significant portions of section 2.4.4, as well as contributing revisions to the entire manuscript. Arnim Wiek contributed significantly to section 2.3.2, and also contributed manuscript revisions. This research project was approved by the UBC Behavioural Research Ethics Board under certificates #H10-03423 (Chapter 4 interviews), and #H12-00701 (Chapters 5, 6, 7 workshop questionnaires). Table 7.1 was originally published elsewhere in a non-copyrighted technical report (see Miller & Cavens, 2012), and is included here with permission of the authors. All other tables and figures are original creations for this project. iv Table of Contents 1.1 Context: Engagement on Sustainability Issues .............................................................................. 1 1.2 Context: Community Scale Energy Planning ................................................................................ 2 1.2.1 Implications for Energy Planning and Management ............................................................... 4 1.3 Context: Evaluation of Participatory Processes ............................................................................ 6 1.3.1 Integration of the Research Questions into the Study of Engagement Workshops .......... 8 1.4 Research Context: The Case Study Processes .............................................................................13 1.5 Research Design ...............................................................................................................................15 1.6 Dissertation Outline ........................................................................................................................18 2.1 Origins of Participatory Integrated Assessment .........................................................................23 2.2 Notes Towards a PIA Typology ....................................................................................................25 2.3 Doing PIA: Recent Examples ........................................................................................................27 2.3.1 Methods ........................................................................................................................................27 2.3.2 Participation..................................................................................................................................37 2.4 Cross-cutting Themes and Future Directions .............................................................................42 2.4.1 Qualitative/Quantitative Tensions in PIA ..............................................................................43 2.4.2 Interactivity in PIA ......................................................................................................................45 v 2.4.3 Institutions and Institutional Change .......................................................................................46 2.4.4 Crossroads: Choice, Uncertainty and Constraints ..................................................................48 2.5 Conclusions .......................................................................................................................................49 3.1 Introduction ......................................................................................................................................51 3.2 What are Mental Models? ...............................................................................................................52 3.3 What is the Value of a Mental Models Approach to Sustainability Planning? .......................54 3.3.1 The Connections Between Concepts in a Complex Space ...................................................55 3.3.2 The Relationship of Mental Models to Existing Components of Sustainability Planning……………………………………………………………………………………....57 3.3.3 The Use of Mental Models in Related Human-Environment Fields ..................................61 3.3.4 Discussion: Mental Models for Community Energy and Sustainability Planning Engagement................................................................................................................................................64 3.4 Two Important Concepts in the Study of Mental Models: Individual vs. Shared Models, and the Implications of Expertise ...............................................................................................................66 3.4.1 Individual vs. Shared Mental Models, and the Subject of Intersubjectivity .......................66 3.4.2 The Relationship of Expertise to the Study of Mental Models ...........................................67 3.5 Methods for Measuring Mental Models .......................................................................................68 3.5.1 Sorting and Paired Comparison Techniques ...........................................................................69 3.5.2 Visuospatial Methods ..................................................................................................................70 3.5.3 Statistical Methods .......................................................................................................................72 3.5.4 Participatory Model Construction Methods ............................................................................72 3.5.5 Interviews and Questionnaires ..................................................................................................73 3.5.6 Discussion: Methodologies for Studying Mental Models in Community Energy and Sustainability Planning Applications ......................................................................................................74 3.5.7 Rationale for the Mental Models Methodology Used in this Dissertation .........................74 3.6 Conclusions .......................................................................................................................................76 vi 4.1 Introduction ......................................................................................................................................78 4.2 Theoretical Context .........................................................................................................................79 4.2.1 Constructivism and Social Constructionism ...........................................................................79 4.2.2 Constructivist Learning Theory ................................................................................................80 4.2.3 Mental Models Theory ................................................................................................................80 4.3 Research Questions .........................................................................................................................81 4.4 Description of the Research Methodology ..................................................................................82 4.4.1 Interview Methodology ..............................................................................................................83 4.4.2 Interview Analysis .......................................................................................................................85 4.4.3 Interview Results .........................................................................................................................86 4.4.4 Questionnaire Design .............................................................................................................. 107 4.5 Conclusions .................................................................................................................................... 111 5.1 Introduction ................................................................................................................................... 114 5.1.1 Chapter Structure...................................................................................................................... 116 5.2 Collective Mental Models of Community Scale Energy ......................................................... 117 5.2.1 Methodology ............................................................................................................................. 117 5.2.2 Results and Discussion ............................................................................................................ 118 5.2.3 Participants’ Collective Mental Models of Energy: General Discussion ......................... 151 5.3 Multiple Correspondence Analysis: Exploration of Participants’ Mental Models and their Constituent Pieces ...................................................................................................................................... 153 5.3.1 Background ................................................................................................................................ 153 5.3.2 A Conceptual Description of MCA....................................................................................... 153 5.3.3 MCA Analysis Methodology ................................................................................................... 154 5.3.4 MCA Analysis: Results and Discussion ................................................................................ 159 5.3.5 Interpretation of MCA Dimensions ...................................................................................... 166 5.3.6 Agglomerated Hierarchical Clustering of Individuals From the MCA ............................ 183 5.4 Expert vs. Lay Mental Models of Community Scale Energy ................................................. 196 vii 5.4.1 Expert vs. Lay Mental Models: Methodology ...................................................................... 196 5.4.2 Expertise vs. Lay Mental Models: Results and Discussion ................................................ 200 5.5 Conclusions .................................................................................................................................... 210 5.5.1 Limitations of this Research ................................................................................................... 213 6.1 Introduction ................................................................................................................................... 215 6.1.1 Description of the GCCP Project.......................................................................................... 216 6.1.2 Description of the Greenest City by 2020 Action Plan and the Selected Goals ............ 217 6.2 The Workshop Development Process ...................................................................................... 217 6.2.1 Engaging with the City of Vancouver ................................................................................... 218 6.2.2 Key Principles for the Workshop Design............................................................................. 220 6.2.3 The Working Team .................................................................................................................. 221 6.2.4 Advisory Panel .......................................................................................................................... 222 6.3 Workshop Development ............................................................................................................. 224 6.3.1 Development of the Generic Neighbourhood: ‘The Sandbox’ ........................................ 224 6.3.2 Energy and GHG Modelling .................................................................................................. 226 6.3.3 Workshop Scenarios ................................................................................................................ 229 6.3.4 Visualization and Graphic Representation ........................................................................... 233 6.4 The Workshop Process ................................................................................................................ 239 6.4.1 Planners Workshop: CIRS ...................................................................................................... 239 6.4.2 Marpole Workshop .................................................................................................................. 240 6.4.3 Grandview Woodland Workshop .......................................................................................... 241 6.4.4 Workshop Structure ................................................................................................................. 242 6.4.5 Presentations ............................................................................................................................. 243 6.4.6 Exercises .................................................................................................................................... 245 6.5 Results and Discussion ................................................................................................................. 254 6.5.1 Demographic Data: Who Participated? ................................................................................ 254 viii 6.5.2 Evaluation of Workshop Materials ........................................................................................ 267 6.5.3 Mental Models: Results and Discussion ............................................................................... 299 6.5.4 Participants’ Workshop Evaluation Data: Results and Discussion .................................. 326 6.6 Conclusions .................................................................................................................................... 340 6.6.1 Workshop Design and Development.................................................................................... 340 6.6.2 Conducting the Workshops .................................................................................................... 347 6.6.3 Demographics ........................................................................................................................... 347 6.6.4 Workshop Materials ................................................................................................................. 348 6.6.5 Impacts on Mental Models ..................................................................................................... 349 6.6.6 Participants’ Workshop Evaluation ....................................................................................... 350 7.1 Victoria CEEP Process ................................................................................................................ 353 7.1.1 The CAN Tool .......................................................................................................................... 354 7.1.2 Victoria CEEP Taking Action on Climate Change Workshops ....................................... 359 7.2 Victoria Results and Discussion ................................................................................................. 362 7.2.1 Demographic Evaluation ........................................................................................................ 362 7.2.2 Victoria CEEP Workshop Evaluation .................................................................................. 364 7.2.3 Mental Models Evaluation ...................................................................................................... 375 7.3 Revelstoke Form Based Code Workshops ............................................................................... 392 7.3.1 Workshop Tools ....................................................................................................................... 393 7.3.2 The Workshop Process ........................................................................................................... 397 7.4 Workshop Results and Discussion ............................................................................................. 400 7.4.1 Demographic Data: Who Participated .................................................................................. 400 7.4.2 Workshop Evaluation .............................................................................................................. 403 7.4.3 Mental Models-Related Questions ......................................................................................... 406 7.5 Discussion: Comparing the Three Processes’ Mental Models Results ................................. 411 7.5.1 Who Participated: Mental Models Clusters .......................................................................... 411 ix 7.5.2 Self-Reported Energy Knowledge ......................................................................................... 415 7.5.3 Ranking of Applied Solutions ................................................................................................. 416 7.5.4 What was Surprising about Energy and GHG Emissions ................................................. 422 7.5.5 Energy in Place ......................................................................................................................... 425 7.5.6 On the Malleability of Participants’ Mental Models ........................................................... 428 7.6 Discussion: Comparing the Three Processes’ Case Study Components .............................. 430 7.6.1 Workshop Goals ....................................................................................................................... 430 7.6.2 Workshop Audience ................................................................................................................ 436 7.6.3 Scale ............................................................................................................................................ 437 7.6.4 Scenarios .................................................................................................................................... 440 7.6.5 Modeling .................................................................................................................................... 443 7.6.6 Representation Types ............................................................................................................... 445 7.6.7 Process Components ............................................................................................................... 449 7.6.8 Outcomes ................................................................................................................................... 452 7.7 Conclusions .................................................................................................................................... 454 7.7.1 Mental Models ........................................................................................................................... 455 7.7.2 Case Study Components ......................................................................................................... 457 8.1 Introduction ................................................................................................................................... 463 8.2 Dissertation Research Questions Revisited .............................................................................. 466 8.2.1 Research Question 1: Case Study Focus ............................................................................... 466 8.2.2 Research Question 2: Mental Models Focus ........................................................................ 470 8.3 Mental Models Methodology ...................................................................................................... 472 8.3.1 Mental Models Methodology: What Worked ....................................................................... 474 8.3.2 Mental Models Methodology: Limitations and Suggested Changes ................................. 477 8.3.3 What is the Promise of the Mental Models Methodology ................................................. 485 8.4 Energy Planning ............................................................................................................................ 491 x 8.4.1 Land Use and Density ............................................................................................................. 492 8.4.2 Transportation .......................................................................................................................... 495 8.4.3 Local/Renewable Energy Supply ........................................................................................... 497 8.4.4 Energy Zeitgeist ........................................................................................................................ 499 8.4.5 Energy in Place ......................................................................................................................... 500 8.5 The Process of Engagement ....................................................................................................... 505 8.6 Engagement Tools ........................................................................................................................ 513 8.7 Finally… A Note on the Broader Research Strategy ............................................................. 525 xi List of Tables Table 4.1: Aggregate coding for all participants organized by parent and child codes, and number of times coded. .........................................................................................................................................................88 Table 4.2: Participants' worldview coding references. Subjects 5 and 13 had the fewest worldviewe references (3) and subjects 7 (21) and 12 (26) had the most worldview references. ...............................93 Table 4.3: Descriptions and sources for worldview component codes used in the interview analysis. ...............................................................................................................................................................................94 Table 4.4: A comparison of reference frequency for two interview participants organized in alphabetical order. ........................................................................................................................................... 103 Table 5.1: A comparison of the cultural background data observed in this study versus the 2011 Vancouver census data by percent of population. ..................................................................................... 147 Table 5.2: Original and amalgamated categories for active variables from the MCA. ......................... 155 Table 5.3: A schematic example of a disjunctive data table. ..................................................................... 157 Table 5.4: The eigenvalues and percentage of variance explained for all dimensions from the MCA. ............................................................................................................................................................................ 165 Table 5.5: University of Strathclyde correlation coefficient categorization. .......................................... 167 Table 5.6: Variables associated with Dimension One from the MCA. .................................................. 167 Table 5.7: The relationship of categories to Dimension One. ................................................................. 169 Table 5.8: Active variables for the illustrative individuals. ........................................................................ 172 Table 5.9: Supplemental energy-related variables for the illustrative individuals. ................................. 173 Table 5.10: Supplemental demographic variables for the illustrative individuals.................................. 174 Table 5.11: Variables associated with Dimension Two. ............................................................................ 175 Table 5.12: Categories associated with dimension two of the MCA....................................................... 176 Table 5.13: Category associations with Cluster One. ................................................................................ 186 Table 5.14: Category associations with Cluster Two. ................................................................................ 187 Table 5.15: Category associations with Cluster Three. .............................................................................. 189 Table 5.16: Category associations with Cluster Four. ................................................................................ 190 Table 5.17: Category associations with Cluster Five. ................................................................................. 191 Table 5.18: Category associations with Cluster Six. ................................................................................... 192 Table 5.19: A table of the primary characteristics of the mental models clusters developed in the hierarchical analysis. ........................................................................................................................................ 193 Table 5.20: Participant self-reported knowledge levels by workshop percent of total workshop in parentheses. ...................................................................................................................................................... 197 Table 5.21: Amalgamated self-reported energy knowledge levels by workshop. .................................. 199 xii Table 5.22: Self-reported energy knowledge by highest level of education attained. ........................... 200 Table 5.23: Self-reported energy knowledge level by employment sector. ............................................ 201 Table 5.24: Chi-squared test results for questions with no significant relationships to expertise ...... 202 Table 5.25: Participants' ranking of who is most responsible for addressing energy issues by self-reported energy knowledge level. .................................................................................................................. 204 Table 5.26: Participants' choices of best approach for addressing energy issues by self-reported energy knowledge level (n=103). .................................................................................................................. 205 Table 5.27: Energy use correctness measure versus self-reported energy knowledge levels. ............. 207 Table 5.28: Participant correct identification of largest energy source by self-reported energy knowledge. ........................................................................................................................................................ 208 Table 5.29: Participant correct identification of largest energy source by workshop. .......................... 209 Table 6.1: Key community energy issues identified by the advisory panel as requiring citizen engagement. ...................................................................................................................................................... 223 Table 6.2: The storylines, assumptions and indicators associated with each of the three scenarios. . 234 Table 6.3: A comparison of the cultural background data observed in this study versus the 2011 Vancouver census data by percent of population. ..................................................................................... 263 Table 6.4: The number of groups that added particular features to their Name Your Neighbourhood worksheets. ....................................................................................................................................................... 275 Table 6.5: Participants' coded responses about what they value in their current community (n=62). ............................................................................................................................................................................ 276 Table 6.6: The number of participants from each mental model cluster in the Vancouver workshops. ............................................................................................................................................................................ 279 Table 6.7: The themes and theme groups discussed by participants when asked what they would change about their communities (n=62). ..................................................................................................... 282 Table 6.8: The usage of strategy cards by workshop (all strategy cards can be found in Appendix C). ............................................................................................................................................................................ 292 Table 6.9: The mean number of stars acheieved by working groups by workshop. ............................ 293 Table 6.10: The mean number of blocks of each land use type placed by participants in the three workshops (that replaced other land use types). ........................................................................................ 294 Table 6.11: The number of participants from each workshop that chose to live in a particular land use type. ............................................................................................................................................................. 297 Table 6.12: A comparison of mental models clusters to changed energy knowledge self-reports. .... 302 Table 6.13: Participants’ mean pre and post workshop scores for the public transportation applied solution by workshop. .................................................................................................................................... 305 xiii Table 6.14: Participants' mean pre and post workshop scores for the decreased unit size applied solution by workshop. .................................................................................................................................... 305 Table 6.15: Participants' mean pre and post workshop scores for the public transportation applied solution by mental models cluster. ............................................................................................................... 306 Table 6.16: Participants' mean pre and post workshop scores for the decreased unit size applied solution by mental models cluster. ............................................................................................................... 307 Table 6.17: Participants' coded response themes about energy options not covered in the workshops (n=34) ................................................................................................................................................................ 317 Table 6.18: Participant responses about why they felt energy planning would have a negative effect on their neighbourhood (n=7). ..................................................................................................................... 324 Table 6.19: The distribution of scores assigned to the workshop components. ................................... 328 Table 6.20: Distribution of scores for participants' media preferences .................................................. 330 Table 6.21: A table of the main themes, and key findings, for each of the evaluation components in this chapter. ...................................................................................................................................................... 343 Table 7.1: CAN tool strategies and key variables (table from Miller & Cavens 2012). ........................ 355 Table 7.2: The number and relative percentage of each mental models cluster from the Victoria workshops. ........................................................................................................................................................ 376 Table 7.3: Participants who changed their energy knowledge rating by mental models cluster. ........ 378 Table 7.4: The mean number of changed rankings by mental models cluster for the Victoria participants. ...................................................................................................................................................... 382 Table 7.5: Victoria participants' mean scores for the individual behaviour applied solution. ........... 382 Table 7.6: Participants' coded themes regarding specific challenges they saw for energy planning in Victoria (n=8). .................................................................................................................................................. 390 Table 7.7: A summary comparison of the mental models findings from the three processes. ........... 412 Table 7.8: Participant numbers and relative percentages for each mental models cluster in the Vancouver and Victoria workshops. ............................................................................................................ 413 Table 7.9: The cumulative pre and post workshop rank orders of the applied solutions for GCCP and Victoria. The grey colour highlights the top five ranks for each of the processes. ............................. 419 Table 7.10: The changes in mean score for effectiveness of applied energy solutions from the pre to post questionnaires for the GCCP and Victoria workshops. ................................................................... 420 Table 7.11: A summary of the three engagement processes according to workshop components. .. 432 xiv List of Figures Figure 1.1: The proposed mechanism for the interaction of participants' mental models with workshop engagement processes. ...................................................................................................................... 9 Figure 1.2: A diagrammatic representation of the dissertation’s research design, including the case study (blue) and mental models (orange) components. ...............................................................................17 Figure 1.3: A schematic representation of the dissertation’s structure, showing how the individual chapters relate to the dissertation’s two primary threads. ............................................................................20 Figure 2.1: A taxonomy of representation types used in sustainability planning contexts. ....................36 Figure 3.1: An energy concept map developed by Geography undergraduate students at UBC (courtesy Ellen Pond). .......................................................................................................................................71 Figure 4.1: A tree map showing the aggregate coding frequencies across participants for every code used in the interview analysis. Frequency is represented by the colour of the box (green is highest), the size of the box (larger=higher frequency) and the location of the box (upper left=higher frequency). ...........................................................................................................................................................87 Figure 4.2: A mental model concept map for participant 3. The width of the lines denotes the frequency of references. ................................................................................................................................. 105 Figure 4.3: A mental models concept map for participant 2. The width of the lines denotes the frequency of references. ................................................................................................................................. 106 Figure 5.1: Participant responses to the question of why we need to address energy issues (n=118). ............................................................................................................................................................................ 119 Figure 5.2: Participant responses about the level of change required to address energy issues (n=118). ............................................................................................................................................................................ 121 Figure 5.3: Participant responses about who is most responsible for responding to energy issues (n=118).............................................................................................................................................................. 123 Figure 5.4: Participant responses to who they feel is least responsible for responding to energy issues (n=118).............................................................................................................................................................. 125 Figure 5.5; Participant responses to the best approach to addressing energy issues at the community scale (n=118). ................................................................................................................................................... 126 Figure 5.6: Participant responses to the best applied solution for addressing energy issues at the community scale (n=115). .............................................................................................................................. 128 Figure 5.7: Participant responses to how difficult it would be for them to change their energy behaviour (n=84). ............................................................................................................................................ 130 Figure 5.8: Participant responses about how they foresee energy sources changing in the future (n=118).............................................................................................................................................................. 132 xv Figure 5.9: Participants' coded themes from the open-ended question asking why planning for energy at the community scale is important (n=118). ............................................................................................ 134 Figure 5.10: Participant responses regarding their self-reported level of energy knowledge (n=118). ............................................................................................................................................................................ 135 Figure 5.11: Participant responses to their level of optimism about our ability to address energy issues in the future (n=118). ..................................................................................................................................... 137 Figure 5.12: A measure of how many energy sectors participants correctly placed by workshop (n=118).............................................................................................................................................................. 139 Figure 5.13: Proportion of correct responses to largest energy source by workshop (n-118). ........... 141 Figure 5.14: Participant responses regarding their current age (n=118)................................................. 143 Figure 5.15: Participant responses regarding their gender (n=118). ....................................................... 144 Figure 5.16: Participant responses regarding their current level of income for the Vancouver workshops (n=66). .......................................................................................................................................... 145 Figure 5.17: Participant responses regarding their cultural background for the Vancouver workshops (n=66). ............................................................................................................................................................... 147 Figure 5.18: Participant responses regarding their primary language spoken at home for the Vancouver workshops (n=66)....................................................................................................................... 148 Figure 5.19: Participant responses regarding their highest level of education received for the Vancouver workshops (n=66)....................................................................................................................... 149 Figure 5.20: Participant responses regarding the employment sector they work in for the Victoria workshops (n=52). .......................................................................................................................................... 151 Figure 5.21: The cloud of active (red) and supplementary (green) variables on the first two dimensions of the MCA. ................................................................................................................................ 160 Figure 5.22: The MCA cloud of individuals for dimensions one and two. ............................................ 162 Figure 5.23: The cloud of active variable categories for the MCA on dimensions one and two. ...... 163 Figure 5.24: The active categories (red) and supplemental categories (grey) for dimensions one and two of the MCA. .............................................................................................................................................. 164 Figure 5.25: A schematic representation of the interpretation of the MCA dimensions..................... 178 Figure 5.26: Centers of mass and 95% confidence ellipses for energy knowledge and workshop attended. ............................................................................................................................................................ 181 Figure 5.27: Centers of mass and 95% confidence ellipses for cultural background and gender. ..... 182 Figure 5.28: Hierarchical cluster diagram for the first 18 dimensions of the MCA dataset ................ 185 Figure 5.29: representation of the six clusters on dimensions one and two of the MCA. .................. 195 Figure 6.1: A schematic diagram of the GCCP process. ........................................................................... 216 Figure 6.2: A schematic diagram of the advisory panel process. ............................................................. 223 xvi Figure 6.3: An oblique 3D representation of 'The Sandbox' (baseline condition). .............................. 225 Figure 6.4: A representation of The Sandbox and the larger constructed context area coloured by land use.............................................................................................................................................................. 226 Figure 6.5: A representation of the scenario space used to develop the GCCP scenarios. ................. 229 Figure 6.6: The strategy cards for each of the three scenarios. Larger versions can be seen in Appendix C. ..................................................................................................................................................... 232 Figure 6.7: The visualization types used in the GCCP workshops. ........................................................ 238 Figure 6.8: The Planners workshop in the BC Hydro Theatre, UBC. .................................................... 239 Figure 6.9: The Marpole workshop held at the Marpole United Church............................................... 240 Figure 6.10: The Grandview Woodland workshop held at the Aboriginal Friendship Centre. ......... 241 Figure 6.11: A diagram of the overall workshop structure. ...................................................................... 242 Figure 6.12: The energy system exercise. ..................................................................................................... 246 Figure 6.13: The 'Name Your Neighbourhood' exercise. ......................................................................... 247 Figure 6.14: An example of a completed Technology Scenario worksheet. .......................................... 249 Figure 6.15: Examples of the land use puzzle pieces participants used to design the future Sandbox. Larger versions can be seen in Appendix C. ............................................................................................... 251 Figure 6.16: The base Sandbox worksheet used in the 'design your neighbourhood exercise'. ......... 252 Figure 6.17: A completed Neighbourhood Design worksheet. ............................................................... 253 Figure 6.18: The age distribution of participants by workshop (n=61). ................................................. 257 Figure 6.19: The distribution of participants' gender by workshop (n=61). .......................................... 258 Figure 6.20: The distribution of income by workshop (n=61). ............................................................... 259 Figure 6.21: The distribution of participant education levels by workshop (n=61). ............................ 261 Figure 6.22: The distribution of cultural backgrounds for participants by workshop (n=61). ........... 262 Figure 6.23: The distribution of primary language by workshop (n=61). .............................................. 264 Figure 6.24: The proportion of participants taking part in various environmental activities by workshop (n=61). ............................................................................................................................................ 265 Figure 6.25: An example of a simple (a) and more complex (b) energy system diagram from exercise 1. ......................................................................................................................................................................... 268 Figure 6.26: The number of responses for each energy supply category from exercise one stratified by workshop. ......................................................................................................................................................... 269 Figure 6.27: The number of participant responses for categories of individual energy use for exercise one. .................................................................................................................................................................... 270 Figure 6.28: The number of responses for community energy use categories for exercise one stratified by workshop. ................................................................................................................................... 272 xvii Figure 6.29: A comparison of a simple (a) and more complex (b) 'Name Your Neighbourhood' worksheet. ......................................................................................................................................................... 274 Figure 6.30: What people value about their current community stratified by workshop. ................... 278 Figure 6.31: What participants value about their current community stratified by mental model cluster. ............................................................................................................................................................... 279 Figure 6.32: Participants' themes regarding what they would like to see changed in their community stratified by workshop (n=62). ...................................................................................................................... 284 Figure 6.33: Participants’ themes regarding what they would like to see changed in their community stratified by mental model cluster (n=62). ................................................................................................... 285 Figure 6.34: A comparison of a Marpole workshop Sandbox (top) and a Grandview workshop Sandbox (bottom)............................................................................................................................................ 296 Figure 6.35: Pre and post workshop responses for self-reported energy knowledge level (n=66). ... 301 Figure 6.36: Participants' mean rank scores for different applied solutions from the pre and post workshop questionnaires (n=59 pre, n=54 post)....................................................................................... 303 Figure 6.37: Participants' pre and post workshop selections of Vancouver's largest energy source (n=58) ................................................................................................................................................................ 308 Figure 6.38: Participants' pre and post workshop selections for Vancouver's primary energy source broken down by workshop (n=50). .............................................................................................................. 309 Figure 6.39: Participants pre and post workshop selections for Vancouver's dominant energy source broken down by mental models cluster (n=42). ......................................................................................... 310 Figure 6.40: Participants' coded responses about what surprised them about energy from the workshops (n=63). .......................................................................................................................................... 312 Figure 6.41: Participants' coded responses about what surprised them about energy from the workshops stratified and normalized by workshop (n=63). ..................................................................... 314 Figure 6.42: Participants' coded responses about what surprised them about energy from the workshops stratified and normalized by mental models cluster (n=47). ................................................ 315 Figure 6.43: Participants' response themes regarding why they felt planning for energy would be good for their community (n=42)........................................................................................................................... 320 Figure 6.44: Participant responses about how they felt planning for energy would have a positive effect on their neighbourhood stratified by workshop (n=42). ............................................................... 321 Figure 6.45: Participant responses about why they felt energy planning would be positive for their community stratified by mental models cluster (n=31). ........................................................................... 322 Figure 6.46: Participants' scoring of workshop components (n=35). ..................................................... 327 Figure 6.47: Participants' scores for media types (n=35). ......................................................................... 330 xviii Figure 6.48: The number of participant responses on which element of the workshops was most helpful: coded responses (n=41). .................................................................................................................. 332 Figure 6.49: Participant responses to what was most difficult about the workshop: coded responses (n=30). ............................................................................................................................................................... 336 Figure 6.50: The number of participant responses about whether The Sandbox worked as a tool (n=55). ............................................................................................................................................................... 338 Figure 7.1: A screenshot of the strategies pane of the Climate Action Navigator interface. .............. 357 Figure 7.2: An example of an individual strategy's interactive variables. ................................................ 359 Figure 7.3: An example of one of the breakout tables from the Victoria workshop. The CAN tool is on the LCD monitor. ...................................................................................................................................... 360 Figure 7.4: Participant employment sectors for the Victoria workshops (n=42). ................................ 363 Figure 7.5: Participants' mean scoring of workshop components (n=32). ............................................ 365 Figure 7.6: Victoria participants' coded, open-ended responses about what they found most helpful (n=39). ............................................................................................................................................................... 367 Figure 7.7: Victoria participants' coded, open-ended responses about what they found difficult (n=32). ............................................................................................................................................................... 369 Figure 7.8: Participants' coded responses regarding what they found useful about the CAN tool. .. 371 Figure 7.9: Participants' coded negative comments regarding the CAN tool. ....................................... 373 Figure 7.10: Victoria participants' pre and post workshop self-reported energy knowledge levels (n=45). ............................................................................................................................................................... 378 Figure 7.11: Victoria participants' pre and post workshop scoring of applied energy solutions (n=49). ............................................................................................................................................................................ 380 Figure 7.12: Victoria participants’ coded responses about what they found surprising about energy (n=40). ............................................................................................................................................................... 384 Figure 7.13: Participants’ coded responses regarding what surprised them about energy coded by mental models cluster (n=40) – note that clusters 1, 2 and 6 have only 1-2 participants each. .......... 387 Figure 7.14: Victoria participants' coded references regarding Victoria-specific energy opportunities (n=23). ............................................................................................................................................................... 389 Figure 7.15: The arrangement of the survey (1), individual scenario (2), touchtable and projected screen displays (3), and the final focus group (4) (image courtesy of the Elements Lab). .................. 395 Figure 7.16: The touchtable interface (bottom left), projected wall display (top left) for the Revelstoke workshops. ................................................................................................................................... 396 Figure 7.17: The different representation types available in the touchtable interface (image courtesy of the Elements Lab). ..................................................................................................................................... 397 xix Figure 7.18: An example of the paper-based neighbourhood scenario construction materials from the second workshop station. ............................................................................................................................... 398 Figure 7.19: Revelstoke participants' ages at the June workshop (n=46). .............................................. 401 Figure 7.20: Revelstoke participants' scoring of the September workshop components (n=13). ...... 404 Figure 7.21 : Revelstoke participants' coded themes about what stood out to them from the workshop (n=26) ............................................................................................................................................. 407 Figure 7.22: Participants' coded themes about what they would like Revelstoke to be like in the future (n=26). ............................................................................................................................................................... 409 Figure 7.23: The mean self-reported energy knowledge scores for the GCCP and Victoria workshops. ............................................................................................................................................................................ 415 Figure 7.24: The mean pre and post-workshop scores for the applied solutions options for the Victoria (n=45) and GCCP (n=53) processes. ............................................................................................ 417 Figure 7.25: A comparison of responses to the 'what was surprising' question across the three processes by the percentage of total references. This chart represents the top 5 themes from each of the workshops. ................................................................................................................................................. 422 Figure 7.26: A schematic diagram of the representation types used in the three processes using the representation framework developed in Chapter two. .............................................................................. 446 Figure 8.1: A schematic representation of the thesis chapters as realized, and their relationships to the dissertations' two main threads. .................................................................................................................... 465 Figure 8.2: Relevant aspects of the interfaces and visual representations from the three case study processes. .......................................................................................................................................................... 516 Figure 8.3: A schematic representation of a possible engagement process, leveraging both visually rich and dialogue-focused engagement modes. .......................................................................................... 519 Figure 8.4: A schematic representation of the continuum of applied research from controlled experimental conditions, to messy, in situ conditions. An estimate of the position of this dissertation’s research is included. ................................................................................................................ 525 Figure C.1: The worksheet for the GCCP workshop's 'Name Your Neighbourhood' exercise. ....... 577 Figure C.2: The Integrated Land Use Scenario worksheet from the GCCP workshops. .................... 578 Figure C.3: The Technology Scenario worksheet from the GCCP workshops. ................................... 579 Figure C.4: The Behavioural/Cultural Scenario worksheet from the GCCP workshops. .................. 580 Figure C.5: The Integrated Land Use strategy cards from the GCCP workshops (front left, back right). ................................................................................................................................................................. 581 Figure C.6: The Technology strategy cards from the GCCP workshops (front left, back right). ...... 582 Figure C.7: The Behavioural/Cultural strategy cards from the GCCP workshops (front left, back right). ................................................................................................................................................................. 583 xx List of Abbreviations CEEI – Community Energy and Emissions Inventory CEEP – Community Energy and Emissions Plan CAN Tool – Climate Action Navigator Tool DES – District Energy Systems GCAT – Greenest City Action Team GCCP – Greenest City Conversations Project GHG – Greenhouse Gases MCA – Multiple Correspondence Analysis OCP – Official Community Plan PCA – Principal Component Analysis UDB – Unified Development Bylaw VKT – Vehicle Kilometres Traveled xxi Acknowledgements I would like to thank my research committee for their invaluable contributions, both to the dissertation itself, and to the broader development of my understanding of the interdisciplinary fields that contributed to this research. To my supervisor, Dr. Stephen Sheppard, who, over the many years I have known him, has focused on guiding and supporting, rather than directing, the research interests of his students. To Dr. John Robinson, who has challenged me with theory and an incredible breadth of knowledge about sustainability, always encouraging me to consider different perspectives on the issues discussed in this dissertation. To Professor Ronald Kellett, who provided the incisive critique of a designer, and has grounded the research in the realities of practice. Each of my committee members have profoundly impacted what is written here, and what will come next. In my opinion, every graduate student should have a shadow committee of friends and colleagues that can provide additional perspective, critique, and review. I have been unusually lucky in that regard. First, I would like to thank Ellen Pond, without whose incredible skill and tireless work the GCCP workshops, that were the focal point of this research, would not have been possible. I would also like to thank Ellen for her challenging perspective, and detailed review of many of the chapters. The other two members of my shadow committee – Dr. Howard Harshaw and Cam Campbell are longtime friends and colleagues who have been instrumental in providing context and guidance on the process of research. For each of the three case studies described in this dissertation, there was a team of researchers and practitioners who made the research possible. For the GCCP workshops I would like to thank Ellen Pond, Kerri Klein, Sara Muir-Owen, Nicole Miller, Sara Barron, Courtney Miller, Glenis Canete, Lil Ronalds, Cathy Buckham, Andrew Pask and Lindsay Cole. For the Victoria CEEP workshops I would like to thank Duncan Cavens, Nicole Miller, Alison Ashcroft, Steve Young, Ellen Pond, Sara Muir-Owen, Courtney Miller, and Jordan Fisher. For the Revelstoke workshops I would like to thank Cynthia Girling, Ronald Kellett, Maged Senbel, Mike vander Laan, Ericka Lay, Tao Su, and John Guenther. xxii This PhD was made possible by the funding provided by multiple sources. Initial funding was provided by the Social Science and Humanities Research Council’s Canadian Graduate Scholarship program. Subsequent funding that supported the GCCP workshops was provided by the Greenest City Conversations Project, which was funded by the Pacific Institute for Climate Solutions (PICS), the Graphics and New Media (GRAND) National Centre of Excellence, MITACS, BC Hydro, and the City of Vancouver. Additional funding during the research and writing stages was provided by the Integrative Data-enabled Approaches to Sustainability across Scales (IDEASS) project, a Natural Sciences and Engineering Research Council Strategic Grant. Finally, I would like to thank all of the participants who made the research contained in this dissertation possible. To the interviewees, GCCP workshop attendees, Victoria workshop attendees and Revelstoke workshop attendees, thank you for your unheralded contributions to this research project. xxiii Dedication For my parents 1 Introduction and Context This dissertation began with two premises: 1. That a confluence of issues has led to a need to plan for energy at local scales, and that this more local energy planning could have significant effects on our communities. In the face of these effects, engagement is crucial to community scale energy planning. 2. That significant work remains to develop effective techniques for evaluating engagement processes. Many evaluation techniques tend to be piecemeal, and miss important connections across participants’ answers (i.e. individual’s answer patterns) that might give a more complete picture of people’s understanding and beliefs about complex subjects like energy. These premises form the two major threads of the dissertation – a case study analysis of community scale energy engagement processes, and a mental models approach to evaluating said processes. This chapter will introduce the context for these premises, develop them into the primary research questions, situate them in the research design and, finally, turn to a review of the dissertation’s structure. 1.1 Context: Engagement on Sustainability Issues From the broadest perspective, this research is about engagement as a critical input to planning for sustainability issues which lack clear, definable answers. Many of the the sustainability issues we face today have been labled ‘wicked problems’, with complex variables and implications for both environmental and human systems (Head, 2008). Faced with complex issues that have no ‘right’ answer, planning for the future becomes fundamentally a question about choices (Salter et al., 2010). In a democratic society, those choices should ultimately be the responsibility of the populace. At the local scale, choices about the future of a community should be made by the citizens. However, the complexity of many sustainability issues means that creating the circumstances for informed choices is difficult. Media efforts by governments, agencies and private companies present the most pervasive approach to engaging the populace about social issues, but lack the depth for a 2 more meaningful exploration of variables and consequences of different planning actions (Depoe et al., 2004). To address the need for more in-depth engagement on social issues, many engagement techniques have been employed. For example, the Greenest City Conversations Project (GCCP) (http://gcc.sites.olt.ubc.ca/), which was a significant contributor to the research in this dissertation, investigated engagement through multiple channels, such as social media, mobile applications, gaming, performance art, and public workshops. Common to all of these engagement activities are tools for conveying, eliciting, and interacting with information about sustainability, and processes within which that interaction takes place. The concept of ‘tools’ here is broad, and may range from a forum for the open discussion of sustainability issues (cf. Haas-Lyons, 2011), to spatial and interactive tools for exploring the sustainability implications of land use and urban form (cf. Girling et al., 2014). Similarly, the processes may vary from loose dialogical modes (cf. Bendor et al., 2012) to highly structured and deliberative processes (cf. Kasemir et al., 2003; Pahl-Wostl et al., 2000, Patel et al., 2007). Nevertheless, research in this area is unified by a desire to understand and improve methods of engagement that address the fundamental choice aspect of sustainability planning. It is within this research domain of investigating tools and processes of engagement for sustainability planning that the research in this dissertation is most firmly situated. The contributions of the work are aimed at improving the tools and processes of engagement on sustainability issues. It is from this domain that the beginning premises were derived, and to this domain that the research questions, research design, and subsequent findings are directed. Chapter 2 will further develop the background for this work, situating it in engagement efforts from sustainability planning and Participatory Integrated Assessment (PIA) literatures. 1.2 Context: Community Scale Energy Planning The specific focus of the engagement research in this dissertation is on community scale energy planning. Our concerns about energy and the subsequent need to think about energy at local, community scales, have been driven by a confluence of factors that have arisen over the last four decades. The concept of limited resources was perhaps first brought into the larger consciousness by the publication of The Club of Rome’s The Limits to Growth in 1972 (Meadows et al., 2004). This was followed by the energy crisis of the 1970s, which further illustrated the importance of energy in 3 the western world, and led to extensive research on energy and human behaviour (Gardner and Stern 1996; Nickerson, 2002). At around the same time recognition began to grow about the environmental impacts of our fossil fuel based energy system. Climate Change The primary driver behind recent concerns about energy demand and supply is climate change (Stern 2007). As evidence has mounted about the human causes of climate change, primarily through the use of fossil fuels for energy, there have been increasing calls for both energy demand reduction and a move away from fossil fuel-based supplies (IPCC, 2007). This has led to the recognition that we need to address energy and greenhouse gas emissions (GHGs) at all scales, from international agreements down to local mitigation and adaptation planning (Bizikova et al., 2007, Grover, 2008, Larsson, 2003). Peak Oil/Resource Limits A second dominant narrative about energy is the concept of peak oil, first hypothesized by M. King Hubbert in relation to US oil reserves, and later extrapolated to forecast the peaking of world oil production (Hubbert 1969). Peak oil is based on the concept that oil (and, indeed, all fossil fuels) are finite resources which will reach a point of peak extraction and then decline, with a net result of human demand outstripping the availability of supply (Campbell, 2002; Deffeyes, 2005; Hirsch, 2005). The concept of peak oil exists within a broader environmental discourse which Dryzek (2005) describes as ‘limits’, or ‘survivalism’, focused on limited resources as an ultimate determinant of the potential for human population and consumption. Many of the forecasts for the peaking of world oil production fall between the early 2000s and around 2050 (Deffeyes, 2005; Simmons, 2005), meaning that the peak oil discourse is also contributing to a focus on energy demand reduction and a move to alternative energy supplies. Rising Demand In the face of pressures to reduce our use of fossil fuels worldwide, demand for energy has actually been increasing (EIA, 2014). Increases in demand in China (Zhang & Cheng, 2009) and India (Muneer, 2007), as well as other developing countries, has meant that the slowing growth, or even decline, in GHG emissions in developed countries has been overshadowed by global increases. The rise in demand in these developing countries has also raised the spectre of energy poverty, energy security, and geopolitical concerns about energy. 4 Of broader interest to this dissertation, these different reasons for addressing energy issues could also be equated to different worldviews, different environmental discourses (Dryzek, 2005), and/or different mental models of energy. 1.2.1 Implications for Energy Planning and Management The net result of this confluence of factors impacting the discussion of energy has been a renewed focus on the subjects of energy demand reduction and moving to more renewable supplies. Demand reduction is a multi-scalar problem, ranging from federal regulations all the way down to the design and maintenance of individual building systems, and individual human behaviour. Many possible solutions for demand reduction do, however, have inherently spatial characteristics which are best addressed at local scales. For example, researchers have investigated the implications of land use and urban form on energy demand, both for its impacts on building energy demand, and for the significant effects it can have on transportation demand (Condon, 2008; Ewing et al., 2008; Salat, 2014). Energy supply in North America has traditionally been highly centralized, and based predominately on fossil fuels (Andrews, 2008; van Hoesen & Letendre, 2010). The centralization of energy supply has meant that, for those people that do not live near an energy plant, energy is largely invisible. Citizens have not typically had to think about energy beyond filling their gas tank or plugging in an appliance. Renewable energy sources, with the possible exception of large scale hydroelectric power, tend to be much more dispersed, often located within the communities they service (St. Denis & Parker, 2009). At the most local level, theories such as industrial ecology and regenerative design call for integration of energy supply into neighbourhoods or individual buildings (Cole & Robinson, 2013; McDonough & Braungart, 2002). This integration of supply with demand at smaller spatial scales suggests a need to plan for energy at these more local levels. It also suggests that people may have a much more immediate interaction with their energy. As this dissertation’s first premise states, one of the early reasons for pursuing a study of community scale energy planning was the recognition that this localization of energy could have significant implications for how we plan our communities, and subsequently how we live our lives (Giradet, 2004; Robinson et al., 2008). An additional component of that premise is that this impact on our communities insinuates not just technical changes, but also changes that could dramatically impact place and human values, and ultimately citizens’ well-being (Schweizer-Ries, 2008; Theodori, 2001). 5 Some examples of research about community scale energy management began arising in the 1990s, based largely on more technical aspects of energy (cf. Jaccard et al., 1997). In the 2000s, researchers began investigating community energy planning – integrating issues of energy supply with the planning of communities, including development regulations, land use zoning and transportation measures (Andrews, 2008; Ewing et al., 2008; Neves & Leal, 2010; Schweizer-Ries, 2008; St. Denis & Parker, 2009; van Hoesen et al 2010). At the inception of this dissertation project, however, the concept of community scale energy planning, particularly in North America, was still quite new. The ensuing years have seen a dramatic increase in the profile of community scale energy planning, in research and in practice. In British Columbia this was largely driven by the Provincial Government’s Bill 27, mandating that all municipalities in the province conduct an energy and GHG inventory, and develop management plans (Rutherford, 2009). These planning efforts have since been facilitated by the BC Ministry of Environment’s Community Energy and Emission Inventories (CEEI) for BC municipalities (Province of British Columbia, 2014), and BC Hydro’s Community Energy and Emissions Plan (CEEP) funding for communities to conduct inventories and develop energy and GHG plans (BC Hydro, 2014). This has led to a cottage industry of consultants, and applied research in BC on the topic of energy at more local scales. As a result, this dissertation was coincident with the rise of community scale energy planning in British Columbia, and with a number of research and real world planning activities centred around community scale energy. An assertion made earlier in this chapter, is that many of the current human-environment challenges we face are complex and intractable, having no definable ‘right’ answer, meaning the solutions we pursue are inherently ones of choice (Salter et al., 2010). When the subject under discussion is something as meaningful and immediate as the planning of our communities (Robinson et al., 2008), there is a need for citizen engagement to ensure that the choices made are negotiated amongst the people that live there. A move to planning for energy at more local scales, then, necessitates engagement with citizens. This adds a layer of complexity, as energy experts, planners and decision makers attempt to grapple with the already novel domain of community energy planning. This challenge does, however, present the opportunity for new approaches to participatory planning that support learning and the co-development of solutions (Innes & Booher, 2004). The objective of this dissertation was to study efforts to engage with citizens and stakeholders about community scale energy planning. Chapter 2 will situate public engagement on community scale energy planning more broadly in the sustainability and related PIA literature, delineating the methods and process components that typify these types of planning activities. 6 The first research question for this dissertation, building off the first premise stated at the start of this chapter, reflects a need to better understand the tools and processes of engagement. It also reflects a research need identified by van Asselt and Rijkens-Klomp (2002), and typified by Robison et al. (2006), for a more systematic observation and reflection on the conduct of engagement, and the impacts of the tools and processes used. These case study evaluations and reflections can be extremely important in the development of knowledge and the improvement of future processes. Therefore, the first research question for the dissertation is: Research Question One 1. What are the tools (e.g. models, scenarios, visualizations) and process components (e.g. presentations and interactive exercises) used to engage participants about community scale energy, and how do they impact the process and its outcomes? This question requires observation of the case study processes described in section 1.4, codification of the evaluation parameters, and reflection on the conduct of the processes. It will draw from the findings of related research projects as discussed in Chapter 2, and will be developed more extensively in Chapters 6 and 7. 1.3 Context: Evaluation of Participatory Processes The second premise for this dissertation, and for the resulting focus on the evaluation of engagement processes, is based on previous research which shared a common thread of developing and evaluating engagement tools and processes for human-environment issues (Salter, 2005; Salter et al., 2005; Salter et al., 2009), as well as an extensive review of international participatory processes (Salter et al., 2010). Reflection on that previous research, as well as a calls from other researchers for development of better approaches to evaluating engagement processes (e.g. van Asselt & Rijkens-Klomp, 2002; Wiek etl al., 2014) resulted in the recognition that there was still significant work to be done in the development of process evaluations. This is particularly true in the absence of a clear linear path between engagement and decision making and policy. In the absence of this clear path to developed policy, what Stirling (2006) refers to as substantive engagement, questions arise about how to demonstrate the value of engagement. 7 One value of engagement is the opportunity for learning (Innes & Booher, 2004; Pahl-Wostl et al., 2008), and what Wiek et al. (2014) call building ‘enhanced capacity’ with participants. This capacity can then lead to more informed future engagement, the development of new networks, and to collective action, such as support for policies that impact local energy or sustainability planning. Evaluation of engagement processes also has an important role in feeding back to the design of future engagement activities. While some of the largest barriers to participatory processes impacting policies and planning activities are clearly institutional (Innes & Booher, 2004; Robinson 2008), others may reflect inadequacies of current engagement activities – i.e. their inability to meaningfully engage people and deliver actionable information back to decision makers about what people want, and what their concerns are. As Chapter 3 will discuss in more detail, part of the problem with existing methods for evaluating processes is a tendency to treat individual issues in a piecemeal fashion, without revealing how connections between concepts and participants’ answers may have implications for planning on complex human-environment issues. Without a more comprehensive picture of how people think about these energy issues, we are at risk of having only a partial picture of the challenges and opportunities. For example, people who believe we need to address energy for climate change reasons and people that think we need to address energy issues for peak oil reasons may agree that we need demand reduction, but it is likely that their preferred solutions are very different. Exposure to research in related human-environment fields (cf. Jones et al., 2012; Morgan et al., 2002) and in the field of Human-Computer Interaction (HCI) (cf. Leiser, 1992; Norman, 1983) introduced me to the concept of mental models as a potential evaluation method for engagement processes. Chapter 3 will discuss several of the aspects of a mental models approach that makes it an intriguing avenue to explore. Primary among those aspects is its capacity to make connections between concepts to better understand people’s cohesive representations of complex issues such as community energy planning. A review of the relevant mental models literature resulted in the decision to develop and incorporate a mental models methodology for evaluating the case study workshops. This decision led to research question 2 (a and b): 8 Research Question Two (a and b) 2. a) What are participant’s existing mental models about community scale energy? b) What are the effects of participation in energy engagement processes on people’s mental models of community scale energy? The study of participants’ existing mental models is a more common research approach when using mental models methodologies (Jones et al., 2012). In a novel research domain such as community scale energy planning, this is useful to understand the existing mental models space – i.e. what are the prevailing thoughts and opinions about the concept of planning for energy in a community? Evaluating changes in participants’ mental models as a result of participation is less common, and perhaps more challenging, particularly in time-limited engagement activities such as workshops where it may be difficult to demonstrably change the way people think about a given subject. The purpose of looking for these changes in mental models was as an indicator of learning and enhanced capacity – what Chapter 2 refers to as ‘process outcomes’. Research question two will be further developed, including the addition of subquestions on the role of expertise and the development of shared mental models, in Chapter 4. 1.3.1 Integration of the Research Questions into the Study of Engagement Workshops This section provides a conceptual picture of the integration of the Research Questions into the cohesive study of engagement workshops, and the proposed mechanisms through which participants’ existing mental models and the components of engagement workshops interact to result in learning (see Figure 1.1). First of all, there is a need to define important terms for this hypothesized mechanism which will be used throughout this dissertation; including mental models, individual, shared and intersubjective mental models, learning and capacity building, and social learning. 9 Figure 1.1: The proposed mechanism for the interaction of participants' mental models with workshop engagement processes. 10 Mental Models Perhaps the most generalized definition of mental models is internal representations of the external world (Jones et al., 2012). More specifically, mental models are a hypothesized mechanism for reasoning, where people draw upon concepts, and the connections between those concepts in their memory systems, to develop simplified ‘models’ of how a system in the external world functions. A key distinction that separates different definitions of mental models is the role of cognition, values, beliefs and emotions in the formation and application of mental models. Early, cognitive science theories of mental models focused explicitly on mental models as cognitive constructs (cf. Johnson-Laird, 1983). In more recent, applied definitions of mental models (cf. Young, 2008) the role of values, beliefs and emotions in the formation and use of mental models is explicitly recognized. It is this latter definition of mental models that is used in this dissertation, recognizing that reasoning about complex human-environment issues is inextricably linked to values, beliefs and emotions. A more thorough examination of mental models will occur in Chapter 3. Individual, Shared and Intersubjective Mental Models Another important distinction in the study of mental models is whether the research is looking for individual’s mental models, or shared mental models – typically with a group under common conditions, such as within a company (Jonker et al., 2010; Mohammed et al., 2010). Because the workshop processes studied in this dissertation were time-limited, there was little opportunity for the formation of shared mental models of community scale energy. Therefore, this dissertation’s research is focused on individual mental models and changes to those individual models as an indication of learning and capacity building. An important concept that relates individual models and shared models is intersubjectivity – that is, rather than sharing the same mental model of the functioning of a system, participants may come, instead, to understand and accommodate other mental models, other perspectives (Barker et al., 2006). The individual/shared/intersubjective mental models distinction will be discussed in more detail in Chapter 3. To understand the proposed mechanism of mental models with workshop tools and processes outlined in Figure 1.1, it is primarily important to understand that this dissertation will focus on individual mental models. 11 Learning and Capacity Building This dissertation subscribes to a constructivist perspective on learning – that is, that rather than passive receivers of information, people are active in the learning process, constructing knowledge through interaction with problems, sources of information, and one another (Pritchard, 2014). A position taken by this dissertation is that modern engagement activities such as interactive workshops and design charrettes (cf. Girling et al., 2006; Salter et al., 2009) are, in fact, problem-based learning exercises that draw upon (either explicitly or implicitly) pedagogical techniques from constructivist learning theory. Mental models, I contend, can be seen as the mechanism for meaning construction. Therefore, the mental models that participants arrive at engagement activities with are important inputs to the learning process (Research Question 2a), and the mental models they leave with are an indication of the learning that occurred (Research Question 2b). Learning is one important component of capacity (Chircop, 1998) – that is, an improved capacity to interact in future planning and decision-making processes, and an improved capacity to make informed decisions. Therefore, a demonstration of learning as a result of taking part in early engagement activities is an indication of improved capacity amongst participants to take part in future decision-making activities (Wiek et al., 2014). Chapter 2 will expand upon the importance of this type of process outcome from engagement activities. Social Learning Social learning theory as described by Bandura (1971) was a reaction to conditioning models of learning, and suggested that people could learn by observation and modeling of other’s behavior. In engagement research, the meaning of social learing is different, focusing on the social context and interpersonal interactions as active components in the learning process (Pahl-Wostl et al., 2008). In this way, it reflects an important distinction between Piaget’s constructivist learning theory and the more social constructionist approach of Vygotsky (Liu & Matthews, 2005). To Piaget, the social was simply the environment within which learning occurred, while to Vygotsky the social was an active and extremely important component of the learning process (Liu & Matthews, 2005). An individual’s processing and incorporation of new information into their mental model, then, is not simply a matter of incorporating new information, rather it is also filtered and modified by the social setting in which that information is presented. While the study in this dissertation is focused on individual learning and individual mental models, it recognizes that learning is not an individual process. Aspects of social learning (and intersubjectivity) are important parts of the learning process. 12 The social and interactive components of the engagement processes studied in this dissertation are important contributors to learning and will be documented and analyzed in the case study analysis. Given these definitions of mental models, learning, capacity building and social learning, we can better understand the proposed mechanism described in Figure 1.1. Participants arrive at a workshop process with their existing mental models of community scale energy. Those mental models are made up of cognition, emotions, values and beliefs, and may be either strongly or poorly formed depending on their past exposure to the subject of energy. The workshops present a constructivist, problem-based learning environment, which hold as a broad goal the development of learning and enhanced capacity to contend with complex subject matter such as community-scale energy. This learning occurs within a given social context, which is an important constituent of the learning process. It is important to note that this social learning is not a one-way flow from the workshop developers to the participants. There are multiple possible answers to the complex issues presented and discussed, so the workshop developers and facilitators are also active learners. Important variables in the workshop interventions are the tools and process components used (see Figure 1.1). How these tools and processes vary can have important implications for what gets discussed, and how. Therefore, they are important variables in the learning process, and should be observed and documented in engagement activities (Research Question 1). Finally, there are the learning outcomes from the process of engagement. These may be individual learning outcomes, social learning outcomes, or even the development of shared mental models of the domain covered in the engagement activities. The focus of the research in this dissertation is on the individual learning outcomes for two primarly reasons. Firstly, in the types of time-limited engagement processes investigated in this dissertation, it is unlikely that shared mental models of a complex domain such as community scale energy planning would be developed. Secondly, it has been uncommon to look for changes in people’s mental models. Because this dissertation’s research is looking for changes to mental models, the decision was made to investigate the implications of social learning and intersubjectivity primarily in the case study component, and to focus the mental models evaluation on changes to individual mental models as a more manageable first step. It should be recognized, however, that changes in individual mental models are at least partially the result of social learning, as the diagram indicates. With further 13 development and refinement, future mental models studies may look more explicitly at the social learning outcomes of engagement activities. 1.4 Research Context: The Case Study Processes Given the research questions and the desire to situate the research in real-world engagement processes, the dissertation required the identification or development of applicable community scale energy engagement activities. The case study processes evaluated in this dissertation will be discussed in detail in Chapters 6 and 7. They are introduced here to provide initial context for the discussions that ensue in Chapters 2 through 5. The initial research vehicle for this dissertation was a series of community energy and emissions workshops developed by a team of researchers at the University of British Columbia as part of the broader Greenest City Conversations Project (GCCP). The GCCP was a multiyear project developed to investigate different modes of engagement on sustainability issues, including social media, serious games, performance art, mobile applications and community workshops. The project was developed in partnership with the City of Vancouver (CoV). The workshop channel of the GCCP worked with the CoV Community Planning Department to develop the workshops. Three workshops were conducted, one with CoV staff and planners, and two in Vancouver neighbourhoods with citizens. Further context on the GCCP workshops will be provided in Chapter 6. During the same timeframe as the development of the GCCP workshops, connections with energy planning consultants and another UBC research group provided the opportunity to replicate the case study and mental models components of the dissertation on additional processes. The first was a set of two expert and stakeholder workshops in the City of Victoria, BC for their Community Energy and Emissions Plan. These workshops used a novel interactive modeling interface to explore energy and emissions options for the City of Victoria. This process provided the opportunity to evaluate both a different tool and process, and the implications of engagement with an expert and stakeholder (versus citizens) audience. I took part in these workshops as one of the group facilitators. The second engagement process was a series of two, iterative workshops in the City of Revelstoke, BC. These workshops were focused on a study of density and urban form in the city, with a particular concentration on the energy and GHG implications. The workshops were conducted with local residents using a novel, collaborative touchtable interface that allowed participants to 14 interactively plan neighbourhoods and investigate associated energy metrics. This process provided yet another set of tools, a rural (Revelstoke) versus urban (GCCP and Victoria) community, and a much more concentrated focus on density and urban form than the other two processes. I took part in this process in a technical support role, and observed both workshops. Collectively, then, the GCCP workshops provided the opportunity for an in-depth case study and mental models evaluation of engagement in community scale energy planning workshops, and the Victoria and Revelstoke processes provided the opportunity to both replicate portions of the evaluation, and to compare multiple case studies. This comparison process provided a number of insights (to be discussed in Chapters 7 and 8) that would not have been possible otherwise. A Note on Invitation Procedures and Workshop Audiences As the original research vehicle, the GCCP workshops represent the intended audience for the case studies and accompanying mental models research – that is, a focus on citizens (publics) rather than experts or decision-makers. While the first workshop in the GCCP process was conducted with City of Vancouver (CoV) planners and staff to vet and improve materials, the two subsequent workshops were conducted in the communities through an open invitation that included neighbourhood flyers and advertisements on the CoV website. The intention of this invitation approach was to primarily attract citizens from within the communities, though no measures were taken to exclude any participants. Further discussion of the GCCP invitation procedures and the workshop demographics will occur in Chapters 5 and 6. The Victoria and Revelstoke workshop processes presented the opportunity to conduct a cross-case comparison, but without the same level of control over the workshop processes or the participants invited as the GCCP workshops. The Revestoke workshops had the same intended audience – a cross section of citizens from the Revelstoke community. Similar to the GCCP workshops, the Revelstoke invitations were open, presented through postings and advertisements in the local paper. Further discussion of the Revelstoke workshops’ demographics will occur in Chapter 7. The Victoria workshops were anomalous in this study, because they were invitation-only events intended for energy and climate change practitioners and local stakeholders. Invitations were sent out to practitioners and local stakeholders identified by the City of Victoria planning staff and the consulting firm that developed the workshops (C2MP). The Victoria workshops, therefore, 15 introduced an additional participation variable that made comparison between the processes more challenging. The Victoria workshops did, however, present two benefits that led to their inclusion. Firstly, they presented an opportunity to explore yet another case study, with different tools, processes of engagement, and energy subject matter. In a new field such as community scale energy planning, this could introduce valuable additional information that could inform research and practice. Secondly, the Victoria workshops provided an opportunity to more explicitly evaluate one of the sub-questions to Research Question Two – that is, “what are the implications of expertise on people’s mental models of energy?” More broadly, the Victoria and Revelstoke processes illustrate an important consideration of the applied, in situ research that will be further discussed in Chapter 2, and that is exemplified by this dissertation; that is the need to adapt research to real world processes where the research team has limited control over the conduct of engagement activities. In this case of this dissertation, I would argue that the benefits of the cross-case comparison outweighed the methodological complications presented by the varied invitation procedures and subsequently, the different audiences. 1.5 Research Design This section provides a brief overview of the research conducted in this dissertation as an organizing framework for the dissertation outline described in section 1.6. Figure 1.2 shows a diagrammatic representation of the dissertation’s research structure including the case study (Research Question 1) and mental models (Research Question 2a) and 2b)) components. The research began with the design and implementation of practitioner interviews (see Figure 1.2). The purpose of these interviews was to help the author describe the research space for community-scale energy planning. The interviews were transcribed and analyzed to reveal the practitioners’ mental models, and the presence of key energy concepts. These concepts were then used to develop the pre and post workshop questionnnaires that were implemented in the workshop processes (see Figure 1.2). The interview process, results, and subsequent questionnaire development will be discussed in Chapter 4. Full mental models questionnaires (pre and post) were implemented at both the GCCP and Victoria workshops. The Revelstoke workshops had an existing evaluation methodology, so a limited subset of questions were implemented in Revelstoke. 16 Participants’ existing mental models were analyzed using multivariate statistical techniques, based on their responses to the pre workshop questionnaires in the GCCP and Victoria processes (see Figure 1.2). Participants’ existing mental models of community scale energy (Research Question 2a) will be discussed in Chapter 5. Changes to participants’ mental models (Research Question 2b) for the GCCP and Victoria processes were evaluated by comparing their post-workshop questionnaire responses to their pre workshop questionnaire responses. Changes to GCCP workshop participants’ mental models are discussed in Chapter 6. A comparison of changes in participants’ mental models between the GCCP and Victoria workshops are discussed in Chapter 7. The case study evaluation of the 3 workshop processes (Research Question 1) was conducted through observation and participation in the 3 processes (see Figure 1.2). A comparison of the case study components for the three processes is discussed in Chapter 7. Finally, a synthesis of the findings from the case study (Research Question 1) and mental models evaluation (Research Question 2a and 2b) is discussed in Chapters 7 and 8. This research diagram will be referred to at key points throughout the dissertation to remind the reader how specific research results relate to the broader research framework. 17 Figure 1.2: A diagrammatic representation of the dissertation’s research design, including the case study (blue) and mental models (orange) components. 18 1.6 Dissertation Outline Figure 1.3 shows the structure of the dissertation, organizing the chapters based on the main premise and resulting research question they pertain to. In the early part of the dissertation, the chapters refer more exclusively to one of the two threads. Towards the end, those threads are integrated into a more cohesive discussion, with both mental models and case study evaluation components. The purpose of this chapter (Chapter 1) has been to briefly review the research context, the resulting research questions, and the research design. This context provides a frame within which the remainder of the dissertation chapters are situated. Chapter 2 is taken from a previously published journal article (Salter et al., 2010), which broadly surveyed the literature on participatory integrated assessment and related sustainability planning projects. The purpose of this chapter is to set the context for engagement on community scale energy planning. As such a new area of research, there is little writing about participation in energy planning. There are, however, clear ties between energy planning and the broader fields of sustainability planning and PIA. This chapter will discuss the methods (futures, modeling, visualization, etc.) that engagement activities employ, it will also discuss characteristics of participation, and cross-cutting themes which are equally applicable to community scale energy planning. In addition to developing the context for the dissertation, this chapter will help illuminate some of the important case study components which will be used for the evaluation of the processes later in the dissertation. Chapter 3 presents a literature review on mental models. It first establishes what mental models are, and the history of their development and use. It then makes the case for their applicability to the study of engagement for sustainability planning, based on their ability to make connections across concepts to better understand how people think about complex issues, their existing use in the study of components of sustainability planning (scenarios, computer models, human computer interaction and visual representations), and their use in related human-environment fields. The chapter then turns to a discussion of mental models elicitation techniques, building towards a rationale for the methodology used in this dissertation. Chapter 4 describes the development and conduct of initial mental models interviews with energy experts and engagement practitioners as a method of describing the space of community scale energy 19 planning. The outputs of these interviews, reviewed in Chapter 4, were used both in the design of the GCCP workshops, and in the development of the questionnaires that became the primary research instrument for the workshop evaluation. The chapter concludes with a discussion of the construction of the workshop questionnaires. Chapter 5 provides a detailed analysis of the results of the pre workshop questionnaires as applied to the Victoria CEEP and GCCP processes. This chapter covers research question 2 a), regarding participants’ existing mental models of energy. The chapter first reviews the results collectively, across participants, to provide descriptive context for the subsequent mental models analysis. The chapter then describes the use of Multiple Correspondence Analysis (MCA) and Hierarchical Clustering to look for answer patterns in the participant responses. It is these answer patterns that the dissertation uses as surrogates for participants’ mental models – i.e. concepts, and the connections between those concepts. The chapter uses the Hierarchical Clustering methodology to group participants into definable mental models types which are subsequently used in the rest of the dissertations’ analysis. Finally, the chapter turns to an examination of one of the subquestions to research question 2 a), that is, what are the implications of energy knowledge, or expertise, on participants’ mental models? Chapter 6 describes the primary research vehicle for this dissertation, the GCCP workshops. These workshops were developed by a team that included the dissertation’s author. Therefore, the workshop development process is discussed as part of the case study analysis component of the dissertation’s research. The chapter then turns to a four part evaluation. First, the materials generated by the participants are analyzed, in part as a reflection of their collective mental models of energy, but also for the case study analysis. These materials reveal key participant concepts about energy, but also reveal important findings about how participants interacted with the workshop materials and exercises. The analysis then turns to a brief demographic analysis of the participants. The next analysis component focuses specifically on participants’ mental models, using the mental models clusters developed in Chapter 5. The mental models analysis investigates post-workshop responses to repeated questions from the pre-workshop questionnaires, and looks at coded responses to open-ended questions about key energy mental models concepts. The analysis concludes with participants’ evaluation of the workshops themselves, focusing on aspects that were particularly important to the broader case study analysis. 20 Figure 1.3: A schematic representation of the dissertation’s structure, showing how the individual chapters relate to the dissertation’s two primary threads. 21 Chapter 7 introduces the two comparison case studies, The Victoria CEEP workshops, and the Revelstoke form-based code workshops. These workshops provided the opportunity to both compare case study components and (primarily in the case of the Victoria CEEP workshops) the opportunity to further evaluate the impacts of participation on mental models. The chapter first introduces each of the processes and then conducts an evaluation of relevant mental models questions and workshop evaluations. The chapter then turns to a cross-case comparison between the three case studies. First, it discusses the relevant mental models findings from the case studies, and then turns to a broader analysis of the components of the case studies using a set of important themes. The chapter concludes with a discussion of what differences between the processes suggest about engagement in community scale energy planning more broadly. Finally, Chapter 8 reflects on the findings of the dissertation. This chapter describes the research limitations, and how they could be addressed in future research projects. It also expands upon key research themes related to mental models, community scale energy planning, and public engagement that were derived from the mental models and case study evalutions. Chapter 8 is forward looking, focused on the implications of the research, and on future directions. What are the key take-away messages, and what research threads should be adopted for future research? As Figure 1 and this chapter synopsis suggest, the dissertation will now turn to the broader context of sustainability planning and participatory integrated assessment to inform the subject of participatory engagement. 22 The Components of PIA and Sustainability Planning: A Review This chapter was first published as a journal article, reviewing the field of Participatory Integrated Assessment (PIA) (Salter et al., 20101). There are strong connections between the fields of PIA and sustainability planning, with the terminology ‘Participatory Integrated Assessment’ and its application being largely a European phenomenon. Numerous North American examples that fit the PIA definition do not generally describe themselves as PIA. To substantiate this, many of the references used in the original paper were, in fact, from North American processes that originate from planning and/or design fields (cf. Al-Kodmany, 2000; Girling et al., 2006; Kellett et al., 2008; Salter et al., 2009; Sheppard et al., 2008). The distinction between PIA and what this dissertation is broadly referring to as ‘sustainability planning’, then, is largely one of tradition and nomenclature, as I believe a review of this chapter will make clear. All of the subjects highlighted in this review of PIA are directly applicable to sustainability planning. Two additional benefits recommend the inclusion of this paper in the broader dissertation. The first, as described in Chapter 1, is the nascent stage of development of community scale energy planning. While examples do exist of research on energy planning at these more local levels (cf. Anderson & Doig, 2000; Dusyk, 2013; Neves & Leal 2010; Schweizer-Ries, 2008; Liming et al., 2008; Walker et al., 2010), it is sparse and not yet at a stage where clear recommendations can be made for how to ‘do’ energy planning. One goal of this dissertation is to make a contribution to that literature. However, in the absence of an existing body of literature, learning from related sources – particularly ones so clearly synergistic with community scale energy planning as PIA, can be particularly instructive. The second reason why the inclusion of this chapter is strongly relevant to this dissertation is the profound impact that working on it had on my thinking about the issues surrounding sustainability planning and PIA more broadly, and community scale energy planning more specifically. I must acknowledge the contributions of my co-authors, Dr. John Robinson and Dr. Arnim Wiek, for the large role they played in the development of my thinking in assessment and planning. 1 The original paper upon which this chapter is based is: Salter J, Robinson J, Wiek A (2010). Participatory Integrated Assessment: a review. Wiley Interdisciplinary Reviews: Climate Change 1(5): 697-717. 23 This chapter has kept the original paper largely intact, with a few main additions. Firstly, both this introduction, and the chapter conclusions have been altered to situate it in the context of the dissertation. Secondly, a section has been added to the methods section (section 2.3.1) that reviews visual representation and, in particular, a framework for categorizing the types of visual representations we use in sustainability planning applications. This framework resonates with the case studies analyzed in this dissertation, and is used to categorize representations in Chapters 6 and 7. Finally, connections between the concepts discussed in this chapter and features of the case studies are made at relevant points to illustrate the applicability of those concepts to the research conducted in this dissertation. Many of these concepts will surface again throughout the dissertation, culminating with Chapter 8’s implications and future directions. 2.1 Origins of Participatory Integrated Assessment Participatory Integrated Assessment (PIA) has evolved from the broader field of Integrated Assessment (IA), and represents an interdisciplinary position about the understanding and governance of human-environment systems. Like IA, PIA is a problem-based research endeavor, differing, in that it explicitly recognizes the right of stakeholders to participate in assessment and/or planning processes. PIA, however, is not simply a reaction to people's desire for input into environmental and sustainability decision-making, it is based on the notion that the quality of these decisions is improved through broader representation of knowledge and values in the assessment process. This theoretical basis for PIA developed as a result of the experiences of previous IA research. Many of the components of IA existed in research and practice in environmental fields in advance of its recognition as an identifiable approach to addressing complex human-environment problems (Toth & Hizsnyiuk, 1998). In retrospect, pioneering research projects from the 1970s and 1980s such as the Climate Impacts Assessment Program (CIAP) (Grobecker et al., 1974) and the RAINS project (Alcalmo et al., 1990) are recognized as the precursors to the further development of IA in the 1980s and 1990s (Weyant et al., 1996; Rotmans and Dowlatabadi, 1998). These projects encapsulated what have come to be seen as the two fundamental tenets of IA (Bailey, 1997; Rotmans & Dowlatabadi, 1998): 1. a multi- or interdisciplinary approach to addressing complex problems 2. relevance of the project to policy and decision-making 24 The need for multi- or interdisciplinary approaches to assessing complex problems was recognized as the types of environmental problems being addressed by researchers, managers, and policy makers moved from primarily local, tractable problems, to global, complex ones (Rothman & Robinson, 1997). In related sustainability planning, this may be further complicated by a need to address these issues at local scales, where government and institutional resources are more limited (Andrews, 2008; Neves & Leal, 2010). The increasing scope and complexity of the issues at hand meant that no single discipline was equipped to adequately address them. What was required were multiple disciplines working either separately (multidisciplinary), or ideally together (interdisciplinary) to provide a more comprehensive picture of the nature of a given problem (Rotmans & Dowlatabadi, 1998). The real world implications of the human-environment problems being addressed by IA (e.g. climate change, acid rain, ozone depletion, water supply and water quality) also enforced a departure from the previous strictly knowledge-generating function of disciplinary research, to a focus on the creation of policy-relevant research results (Rotmans & Dowlatabadi, 1998). A number of methodologies arose to address the complexity of IA processes, and their need to provide information to policy makers - most notably policy exercises, scenario analysis and computer models (Brewer, 1986; Rotmans & Dowlatabadi, 1998; Weyant et al., 1996). In climate change research, and other environmental fields, computer models have proven to be a particularly valuable tool in systematizing and assembling quantitative knowledge from various disciplines to better understand the problem and its policy implications (Rothman & Robinson, 1997; Rotmans & Dowlatabadi, 1998; Weyant et al., 1996). These models, however, cannot adequately account for qualitative changes in societies, cultures and institutions that will have dramatic effects on how people interact with the environment, and environmental problems (Weyant et al, 1996). Nor can they easily encompass the normative and intentional dimensions of public policy problems. It is these qualitative and normative aspects that in large part led to the move towards Participatory Integrated Assessments. The development of PIA built on theoretical developments on the nature of science2 and its relationship to society. Post normal science (Functowicz & Ravetz, 1993), Mode2 science (Gibbons et al., 1994), civic/citizen science (Irwin, 1995), transdisciplinary research (Scholz et al., 2000), and 2 The terms science and scientist are used here in the inclusive sense, and apply to natural, social and humanities applications. 25 interactive social research (Shove & Rip, 2000), as new conceptions of the science-society interface, provided some guidance on research and decision-making in the face of uncertainty and broad social implications. These formulations of science and research recognized the validity and importance of alternative forms of knowledge, and of extended peer review as means of moderating the implications of uncertainty on society-relevant scientific activities (Functowicz & Ravetz, 1993; Gibbons, 2000). In the absence of demonstrable "right" answers, broader inclusion, and more diverse sources of knowledge may at least provide more robust solutions. The late 1990s saw the recognition of the connection between these new formulations of science, and the types of problems being addressed by IAs (Rothman & Robinson, 1997). While not universal, there were increasingly calls to more broadly engage with stakeholders and citizens in IA processes, and to assimilate their knowledge and values into the research and subsequent policy guidance (Rothman & Robinson, 1997; Toth & Hizsnyik, 1998). It was also at this time that the first explicitly participatory IA projects were undertaken (cf. Kasemir et al., 2000). Despite early adoption of IA as a method for addressing Climate Change (cf. Weyant et al., 1996), the Intergovernmental Panel on Climate Change (IPCC) has moved away from IA approaches in more recent assessments. Explicitly Participatory Integrated Assessment has never been part of the IPCC mandate, though some might argue that the broad, multidisciplinary, multi-sectoral approach of the IPCC is itself an inherently participatory process. Despite the lack of a demonstrable connection between the IPCC and PIA, there is a strong connection between PIA and efforts to address climate change. In many cases, PIA represents the way that local, regional and national authorities are attempting to address sustainability. Given the complexity of PIA processes, and the time that they require, the field of PIA is relatively new. The intention of this paper is to review the field of PIA, to identify common components of PIAs to date, and to illustrate cross-cutting themes that can be identified. 2.2 Notes Towards a PIA Typology Despite the relative youth of participatory integrated assessment (PIA) as a topic of study, previous reviews have attempted to document developments, and provide typological guidance – particularly in respect to processes of participation. Toth and Hizsnyik (1998) provides perhaps the first review of PIA, in a broader examination of Integrated Environmental Assessment (IEA). They document 26 methodologies for conducting PIAs, including participatory modeling, policy exercises, simulation gaming, and IA focus groups. In addition, they provide initial guidance for evaluating the quality of IA processes according to three criteria: scientific quality, methodological quality, and practicality. Van Asselt and Rijkens-Klomp (2002) developed a framework for categorizing participation methods in PIAs, based on two dimensions: the purpose of the assessment and the purpose of the participatory process. They documented key components of eight methodologies used in PIAs, and reviewed selected projects according to the methodology used. Kloprogge and van der Sluijs (2006) developed criteria for analyzing PIA processes based on whether stakeholder involvement was active or passive, constructed from a bottom-up or top-down perspective, and whether particular stages of the process were open or closed to participation. In addition to reviews and typologies directed at PIAs in particular, a number of other authors have reviewed and categorized participatory processes in related fields, with applications to PIA processes (cf. Rowe and Frewer, 2005; Reed, 2008; Tippett et al., 2007; van de Kerkhoff et al., 2008; Talwar et al, 2011). For the purpose of this review, PIA projects were analyzed based on three broad categories: methods, participation, and outcomes. The term ‘methods' can mean different things to different disciplines. In the context of this review, methods refers to both qualitative and quantitative techniques used in the participatory process to facilitate interaction between the various participants and stakeholders, including scientific models, future visioning (scenarios, backcasting, brainstorming), deliberation and debate, etc. Participation refers to the mechanisms of engagement (Kloprogge & van der Sluijs, 2006) such as focus groups or stakeholder workshops, but also to the stages of participation. Toth and Hizsnyik (1998) suggest that the appropriate stages for IA in broader decision-making processes are the response assessment as well as goal and strategy formulation stages. Stauffacher et al. (2009) analyze and critically discuss the dynamics of participation in more detail, based on Arnstein’s ‘ladder of participation’ (Arnstein, 1969). From a research perspective, Talwar et al. (2011) differentiate different levels of interactivity in public engagement research in part based on the stages in the research process where stakeholders are involved. Finally, there is the question of who participates 27 (cf. Brugha & Varvasovszky, 2000). How do PIA exercises choose stakeholders and ensure appropriate representation? One of the key components of IA is the strong connection to real-world, policy-relevant problems (Bailey, 1997; Robinson et al., 2006; Rothmans, 1998). An important component of outcomes, then, is how PIA exercises assist or inform policy. This maps to Toth and Hizsnyik’s (1998) criteria of practicality for IA exercises. Inherent to PIA and its theoretical and methodological basis, however, is a recognition that processes of engagement themselves have value, and lead to phenomena such as social learning (Pahl-Wostl et al., 2008; Tabara & Pahl-Wostl, 2007) that are not easily quantified or documented (Walter et al., 2007). This suggests that another outcome of PIA exercises is the impact of the process itself on the participants. 2.3 Doing PIA: Recent Examples 2.3.1 Methods Participatory processes provide the opportunity for scientists, policy-makers, stakeholders and citizens to meet, exchange information, deliberate, and share values. Methods, as defined here, often provide the interface through which participants communicate, learn from one another, and co-produce new knowledge. Ideally these tools provide necessary information and a common language through which to discuss the issues, without constraining creativity and the open exploration of ideas. While a review of past PIA exercises revealed a number of methods for engaging participants, many of them had limited applications (cf. repertory grid in Van de Kerkhof, et al., 2002; Van de Kerkhof, 2006). Two participation methods which have seen broad application, however, are the development of future visions, and the use of quantitative computer models. The ubiquity of these methods, and the various techniques used in applying them, provides the opportunity to categorize, and learn from previous PIA exercises. Future Visions One of the defining features of IA exercises is a focus on uncertain futures. Indeed, one normative argument for the inclusion of participation in IA is largely based on the concept that the future is 28 unknowable through conventional scientific means, and therefore IA needs to be a reflection of people’s anticipatory knowledge and informed choices. One of the most prevalent tools used in PIA exercises has been the analysis and co-development of future visions. Future visions have most commonly taken two different formats: 1. Forecasts – Predictions of future conditions based on the current state. Forecasts are a common approach in computer model-based IA processes. 2. Scenarios – Alternative configurations, or multiple possible (but incommensurate) futures. Scenarios can be either qualitative or quantitative in nature. Scenario analysis, popularized by Royal Dutch/Shell, has a long history of use as a tool for investigating possible futures (cf. Schwartz, 1992; Svedin & Aniansson, 1987; Wack, 1985a; 1985b) Scenario literature differentiates between descriptive (‘possible’) scenarios and normative (‘(un-) desirable’) scenarios (Nassauer & Correy, 2004; Van Notten et al., 2003). Normative scenarios can be crafted in different approaches, for instance, guided by goals or targets (Ravetz, 2000; Robinson, 2003; Rotmans & van Asselt, 2000), or based on value-laden assessments of descriptive scenarios (Sheate et al., 2008). A specific participatory challenge in PIA, has been investigating how to combine the normative components of stakeholder visions and values with quantitative and/or qualitative assessments of the implications of those visions as inputs to governance (Robinson & Tansey, 2006). PIA projects have attempted to meet this challenge in a number of different ways. Broadly, the approaches can be separated into two main types: qualitative generation of future visions and mixed qualitative/quantitative visions of the future. Proponents of the qualitative scenario analysis approach to future visioning (for an important early example, see Svedin & Aniansson, 1987) point to the method’s ability to foster creativity and surprise as its main strengths, and as a reason for not encumbering the process with computer models or excessive scientific information (Patel et al., 2008). Stakeholders are afforded greater control because they are not constrained by the cognitive frame of computer models, or their perceived authority (Siebenhuner & Barth, 2005). Tools for qualitative future visioning include collages (Kasemir et al., 2000; Kasemir et al., 2003; Patel et al., 2008, Tàbara et al., 2008), and construction of narrative storylines, sometimes with the 29 assistance of creative writers (Patel et al., 2008), or sketch artists (Al Kodmany, 2000). A parallel exercise in the design fields is the charrette process – an intensive, participatory process with a strong focus on producing creative solutions (Girling et al., 2006). Similar to the design charrette, one of the difficulties of strictly qualitative future visioning exercises is the challenge of ensuring the consistency and demonstrating the feasibility of the developed ideas within the participation session where they were devised (Kellett et al., 2008; Yohe et al., 1999). However, tools and procedures have been proposed to cope with this challenge (Schoemaker, 1993; Tietje, 2005). Mixed qualitative/quantitative scenario generation processes more explicitly incorporate a quantitative component, though they may take several different forms. Perhaps the most common approach, pioneered in policy exercise work (Brewer, 1986), is for scientists or decision-makers to pre-compute a range of scenarios to bring to a participation process for analysis, or as guides to discussion (e.g. Puy et al., 2008; van de Kerkhof et al., 2002). Van Asselt and Rijkens-Klomp (2002) call participation based on these processes ‘scenario workshops’ or ‘envisioning workshops’. The GCCP process discussed in Chapter 6 used a modified version of this approach, first exploring pre-computed scenarios, and then having participants combine those scenarios in a final participant-driven exercise. Criticism of this approach focuses on the cognitive streamlining it may engender (Siebenhuner & Barth, 2005) as researchers set the frame and boundaries of discussion. The iterative development of scenarios through qualitative scenario workshops and subsequent translation into quantitative models has also been a common practice in PIA processes (Beck et al., 2002; Rotmans et al., 2000; Shackley & Deanwood, 2003; Volkery et al., 2008); the strength of this approach is that it makes the research team responsive to the knowledge, values and wishes of the stakeholders, rather than the stakeholders reacting to pre-existing quantified scenarios (Holman et al., 2005). This iteration can, however, be problematic if the qualitative scenarios created by the stakeholders are not accurately translated into quantitative representations (van de Kerkhof, 2006). It also can create logistic problems based on the time and money available, and the level of commitment required by the participants to attend multiple iterative workshops (Volkery, et al., 2008). The Revelstoke case study discussed in Chapter 7, and in Girling et al. (2014b) used such an iterative approach in the development and analysis of scenarios. Another alternative method for generation of mixed qualitative/quantitative future visions is the use of models in the participation sessions (e.g. Robinson et al., 2006; Pahl-Wostl, et al., 2000; Cohen et al., 2006; Dahinden et al., 2003). A discussion of participatory modeling, facilitated modeling and interface-driven modeling follows in the next section; however, for the purposes of generating future 30 visions, these tools allow the interactive development of future visions within participation processes themselves. The CAN Tool, a key component of the Victoria CEEP processes discussed in Chapter 7 used this within-session modeling approach for the development of participant-driven scenarios. Robinson and Tansey (2006) suggest that much of the development in IA and PIA processes has focused on predictions or forecasts of future conditions. Such a forecasting approach can constrain participants’ thinking through a focus on base cases or extrapolation of current conditions (Bizikova et al., 2009); moreover, it constrains the ability to explore less likely but potentially more desirable futures. Backcasting is a form of scenario development that relies upon choice and implicitly incorporates narrative values, because the individual or group creating the scenarios focuses on desired end-points rather than predicting likely future conditions (Robinson, 2003). For this reason, backcasting has become a popular tool in PIAs (e.g. Carlsson-Kanyama et al., 2008; Mander et al., 2008; Quist and Vergragt, 2006; Patel et al., 2007; van de Kerkhof et al., 2002). In a backcasting exercise, participants will typically describe their desired end conditions, and then work backwards to milestones and policy decisions that will achieve that future (Dreborg, 1996). By working backwards from desired end points, the scenario creators are less constrained by current thinking or current conditions, allowing for more creativity in response assessments. Robinson (2003) describes a different kind of backcasting termed ‘second order’ or ‘interactive’ backcasting facilitated by an interactive workshop setting using computer software. In this form of backcasting, the participant does not need to know her desired endpoint, but rather can discover it through iteration and learning from interacting with the model. Models One of the defining features in the advent and rise of IA was the use of computer models to investigate and understand complex problems not tractable to controlled scientific conditions (Bailey, 1997). For scientists and stakeholders, computer models provide the opportunity to understand and learn about future conditions in unobservable systems. In essence, models become the testing grounds for hypotheses about the future (Holman et al., 2005). Because the focus of PIA is the sharing of knowledge between scientists and stakeholders, it is understandable that scientists would want to use models – the manner in which they comprehend the issues at hand – in participation processes (Pahl-Wostl et al., 2000; Siebenhuner & Barth, 2005). From the stakeholder side, models may provide an opportunity to get a better understanding of the system they are operating in. This may present both opportunities and concerns in participatory processes. 31 Use of models in PIA exercises can be separated into two main categories: direct use of models by participants (including facilitated use in workshops or focus groups), and indirect use. In indirect model-use processes, as described above, future visions may be pre-computed for presentation to participants (e.g. van de Kerkhof et al., 2002), or used between sessions to evaluate the feasibility of scenarios, storylines, strategies or policy suggestions made by participants (e.g. Beck et al., 2002; Rotmans et al., 2000; van Notten et al., 2003; Volkery et al., 2007). The benefits of indirect model use include allowing participation sessions to focus on qualitative and creative solutions (e.g. Patel et al., 2007; Volkery et al., 2008), sessions not being dictated by model iteration and run times, and the increased time provided for analysis when model runs are not limited by session time constraints. Problems with indirect model use can include the challenge to back up proposal’s consistency, feasibility, and plausibility within sessions (e.g. Kellet et al., 2008), maintenance of the ‘black-box’ perception of models and their assumptions, and the need to conduct multiple iterations of qualitative (participation) and quantitative (modeling) stages of a process (Volkery et al., 2008). For direct use of models in PIA processes, there is a recognition of the need to mediate between the expert-based models scientists use in analysis, and the participants (Carmichael et al., 2004; Dahinden et al., 2003; Siebenhuner & Barth, 2005). In PIA exercises, this mediation has been conducted in three main ways: participatory model building, facilitated model use, and interface-driven model design. Participatory modeling is perhaps the most intensive method of model interaction, as it does not augment a process, but rather is a process in-and-of itself. Participatory modeling had its genesis in the work of Holling (1978) and Walters (1986) on adaptive management, but has since been applied in other fields. Participatory modeling commonly takes the form of system dynamics models constructed directly by workshop participants (van Asselt and Rijkens-Klomp, 2002), where the flow-chart nature of the model interface allows participants to visually assemble their collective representations of the workings of a system. Participatory modeling has been extensively used in water management PIAs (e.g. Langsdale et al., 2006; Talwar et al., 2011), but has also been used in other applications, such as forest management (Mendoza and Prabhu, 2005). The main strengths of participatory modeling are the overt reliance on social learning and shared understanding of the workings of a system, and the transparency of assumptions built into the model (Langsdale et al., 2006). Participatory modeling does, however, require a significant investment of time from stakeholders, often requiring several sessions to develop an adequate representation of the system being modeled (Langsdale et al., 2006). Participatory modeling may also exclude participants who do 32 not conceptualize a given problem in a manner that is translatable into the chosen modeling framework. Researchers in educational psychology have demonstrated the variance in how experts and lay-people process and organize information (Kozma & Russell, 1997). In facilitated model use exercises, expert models are used by model facilitators and scientists in PIAs to explain scientific phenomena. Dahinden et al. (2003) describes the use of expert-based models in focus groups in the ULYSSES project. They found that the models were effective tools for communicating spatial, temporal and uncertainty aspects of climate change. They also found that the use of the models helped subjects recognize the capacity for collective actions to lead to different futures (Dahinden et al., 2003). However, subjects in the focus groups felt that the models required simplification to be more useful, and the researchers found the models to be restrictive because of their excessive focus on quantitative versus qualitative information. The difficulties of using expert-based models in citizen and stakeholder processes led to developments in model and simulation-gaming design specifically aimed at non-expert users (Carmichael et al., 2004; Dahinden et al., 2003; Pahl-Wostl et al., 2000). The move to the development of interface-driven models (Robinson, 2003) was in part a reaction to the unnecessary and unwieldy complexity of expert-based models for use in PIA exercises (Carmichael et al., 2004; Dahinden et al., 2003; Holman et al., 2008; Pahl-Wostl et al., 2000). Interface-driven model design requires simplification of existing models into what have been termed ‘reduced form models’ (Carmichael et al., 2004; Holman et al., 2008), or ‘executive summary models’ (Robinson et al., 2006). These simplifications are required in order to allow the interactive run-times necessary to maintain user engagement (Carmichael et al., 2004; Holman et al., 2008). The CAN Tool, described in Chapter 7 made use of ‘executive summary modeling’ techniques to achieve real-time performance with a large number of energy-related variables. One of the main challenges for interface-driven models is to maintain model credibility and accuracy while simplifying model structure (Carmichael et al., 2004; Holman et al., 2008). Interface–driven modeling, therefore, must balance the experience of the participant (run times, relevant information, coherent representation) with the accuracy of the underlying data and model structures. This has been described in terms of a tension between the need to be both ‘fun to use’ and ‘true to life’ (Robinson, et al, 2006). An extension of the 'fun to use' component of interface-driven models is investigation of the use of games in PIA exercises (cf. Valkering et al., 2009; http://futuredelta2.ca/). Another feature common to interface-driven models is the explicit inclusion of qualitative information. This was a need identified through the use of expert-based models in participatory 33 processes (Dahinden et al., 2003; Pahl-Wostl et al., 2000). Two examples of interface-driven modeling applications are the QUEST model developed at the University of British Columbia (Carmichael et al., 2004) and the Regional Impact Simulator (RIS) developed through a collaboration of universities and research institutes in the UK (Holman et al., 2008). Both QUEST and RIS include qualitative frames (storylines and/or worldviews) through which users can view the information (Robinson, 2003; Holman et al., 2008). The recognition inherent in the inclusion of these qualitative components is that peoples’ views of the future are determined as much by their values and beliefs as by their knowledge of quantitative information. By incorporating quantitative and qualitative information (as well as the broader context of social learning engendered by participation) people’s desired futures become an emergent property of the process – not one ‘right’ future, but many possible ones. From a development standpoint, the inclusion of this qualitative information presents a design challenge not present in conventional model design (Carmichael et al., 2004). The design of the GCCP workshop materials in Chapter 6, in particular the ‘name your neighbourhood’ exercise, took their cue from this theoretical position. In addition to the challenges presented by the development of interface-driven models, there is the question of their implementation in PIA processes. To date, most of the interface-driven modeling tools developed have been designed for individual use (Holman et al., 2008; Dempsey et al., 2005; Pahl Wostl et al., 2000). After initially focusing on individual use, the QUEST model was moved to facilitated group sessions based on experience and testing that suggested that even user-friendly models of sustainability benefited from expert facilitation (Robinson et al., 2006). The importance of facilitation in model use and related participatory Geographic Information Systems (GIS) exercises has been found in related fields (e.g. Jankowski and Nyerges, 2001; Salter et al., 2009; Schroth et al., 2013). The use of interface-driven models in PIA, then, raises contextual questions. Unlike participatory modeling, interface-driven models tend to augment, rather than subsume existing processes. That is, the models are used to generate future visions and begin a discussion which continues beyond the use of the model in the workshop. A factor that has emerged in IA in general, and modeling in particular, is the need to move from larger (in the case of climate change, global) scales, to more regional and local levels. For IA this is based on the need for the scientific information to be policy relevant (Easterling, 1997; Holman et al., 2005b). For PIAs there appears to be an added component, as stakeholder and citizen input to PIA exercises has indicated that participants want more local information with which to deliberate 34 (Dahinden et al., 2003; Holman et al., 2005; Robinson et al., 2006). In the climate change context, this requires downscaling of climate change models to provide credible information at regional (Cohen et al., 2006; Holman et al., 2005) and even local levels (Sheppard et al., 2008), and presents an added challenge in adapting IA tools to PIA exercises. Rotmans and Dowlatabadi (1998) suggest that pressure to make IA models spatially explicit and to provide higher data resolution create a significant challenge for the credibility of the resulting information. Visual Representation One tool that may be particularly important for discussions of sustainability at more local scales is visualization, particularly visual representations of possible futures (Sheppard 2012). These local scales combine people’s place attachments (Devine-Wright 2009, Devine-Wright & Howes 2010, Marshall et al. 2012) with the challenge of sustainability – understanding the implications of an as yet unknowable future. Similar to the narrative aspect of scenarios, which provide a qualitative way for people to envision future environments, visualization techniques provide a form of visual narrative, depicting these future conditions. There are, however, many different ways of using visual media to represent possible future states, from data displays like graphs and dashboards, to highly realistic experiential visualizations conveying the actual look and feel of these future conditions. Several researchers have developed frameworks for developing and analyzing visual representations (cf. Sheppard 1989, Appleyard 1976, Zube et al. 1987, Bishop and Lange 2005). Bishop and Lange (2005) for example, classified visualizations based on levels of realism, interactivity, dimensionality and spatial and temporal characteristics. Figure 2.1 depicts a novel framework for categorizing the types of visual representations used in planning engagement activities, with the goal of helping researchers and practitioners select appropriate visual media for their intended purposes. The corners of the triangle represent Experiential, Conceptual and Information Visualizations (example images given). The axes between the different main visualization types denote what is shared by visualizations that fall within that space. So, for example, visualizations that are hybrids between experiential and conceptual visualizations may have different levels of realism, but they are all inherently spatial in nature – i.e. they all represent geographical space. Visualizations that occupy the space between conceptual and information visualization types may be spatial, aspatial, or some mix between the two. What they share in common is a focus on conceptual or cognitive information, rather than experiential or affective. Finally, visualizations that fall between the experiential and information visualization types 35 tend to be hybrids, taking on characteristics of augmented reality. The experiential part of the graphic is used to convey sense of place, or elicit an emotional response, while the information display is used to augment the experiential with some other form of information. The simple act of annotating graphics to highlight particular features is a form of augmentation. Depending on the researcher or practitioners purpose, they may select a graphic, or mix of graphics, from different locations within the representation space. One of the reasons for the use of in situ experiential visualization is a focus on working with local, real world communities that are worried about how climate change and other sustainability issues may affect the sense of place and character of their communities (Pond et al. 2010). A potential drawback of experiential visualization is that, because the visualizations are so rich in visual information such as texture and colour, it can be harder to discern conceptual information from them. When the intention is to represent conceptual information that has a spatial component (e.g. a neighbourhood plan), more abstract forms of representation such as 2D mapping or 3D massing models are often more useful. If the information to be presented does not have an inherent spatial component, then graphical aids such as information visualization and information graphics are usually preferred. In these graphical forms, the spatial dimensions of the graphic are often used to represent conceptual, rather than physical, relationships between entities (Ware 1999). The well-established principles of graphing data is an example information visualization (Cleveland 1993). This framework will be used to categorize the graphics used in the GCCP, Victoria CEEP and Revelstoke workshops in Chapters 6 and 7 of the dissertation, but it is more broadly applicable to sustainability planning and PIA exercises where visual representations are a key component of the engagement activities, for visualizing quantitative information, and for envisioning future environments. 36 Figure 2.1: A taxonomy of representation types used in sustainability planning contexts. 37 2.3.2 Participation Stirling (2006) identifies three arguments for public participation: normative arguments, which focus on rights to participate and the process of democracy; substantive arguments, which posit that the quality of decisions is improved through participation; and instrumental arguments, which state that participation improves trust and acceptance of subsequent decisions. All three of these arguments can be identified in the rationale for conducting PIA exercises, varying in prominence based on the particularities of a given process. The strong role played by the substantive argument in the rationale for PIA reflects a theoretical tie with post normal science (Funtowicz & Ravetz, 1993), and differentiates PIA from participation processes in other domains (Fischer, 2000). Mechanisms In their seminal review of participation methods for PIA, van Asselt and Rijkens-Klomp (2002) stated that their was not enough systematic attention being paid to the selection and implementation of participation methods in PIA, and that more effort was required to explicitly state methods and lessons learned from participatory processes. Robinson et al. (2006) presents one such reflection based on the lessons from the GBFP PIA process. Of participation mechanisms that have been used in PIA processes, the two most common appear to be stakeholder workshops (e.g. Cohen et al., 2006; Patel et al., 2007; Robinson et al., 2006; Stalpers et al., 2008; van de Kerkhof et al., 2002; Volkery et al., 2008), and IA focus groups (Kasemir et al., 2000; Kasemir et al., 2003; Pahl-Wostl et al., 2000; Puy et al., 2008). Kasemir et al. (2003) provides a detailed description of the IA focus group mechanism. IA focus groups were built upon a more generalized focus group methodology (Krueger and Casey, 2009), departing in a number of ways, including increasing the length of the sessions, providing more structure to the conversations, creation of future visions with collages, and facilitated use of computer models (Kasemir et al., 2003). It is, perhaps, the systematization of the IA focus groups mechanism that has led to its adoption in other PIA exercises (e.g. Puy et al., 2008; Pahl-Wostl et al., 2000). 38 Most of the other PIA processes that were reviewed for this chapter can broadly be categorized as utilizing stakeholder workshops as their primary participation mechanism. While levels of documentation vary, it is difficult to determine systematic design components of the workshop mechanisms in most cases. Several papers do, however, provide more detail about specific methodologies used to engage users and evaluate processes. Van Wynsberghe et al. (2003) details the use of the value-focused-thinking model in GBFP workshops. Savelson et al. (2005) documents the use of the health promotion Precede/Proceed model to a study of local community sustainability. Van de Kerkhof (2006) provides detail on the use of the repertory grid and dialectical procedures to the COOL workshops. Finally, Wiek et al. (2014) has analyzed, compared, and evaluated several workshop-based participatory scenario studies on local/regional sustainability, focusing on methodological strengths and weaknesses. The still relatively selective nature of these discussions of methodology make it difficult to reach strong conclusions, however, by systematically documenting the conduct of participatory mechanisms, they provide the opportunity for peer review, debate, and the further development of methodology. Van Asselt and Rijkens-Klomp (2002) specifically state that participation methods, in their definition, are group exercises, negating the inclusion of interviews and other individual connections. However, many of the PIA projects reviewed here did include individual interviews and interactions with stakeholders, particularly in the early stages of PIA processes (e.g. Patel et al., 2007). While it could be argued that PIA processes to date have been exploratory exercises intent on examining how they should be conducted, the criticisms of van Asselt and Rijkens-Klomp (2002) still obtains – the field would benefit from a more systematic approach to the use of participation mechanisms, and from more reflections on lessons learned from previous efforts. The case study component of this dissertation is, in part, a reaction to this call for reflection on the components of engagement. Representation In general, PIA exercises have predominately involved stakeholder groups, with a few projects broadening the discussion to include citizens (e.g. Kasemir et al., 2000; Pahl-Wostl et al., 2000; Robinson et al., 2006). In most stakeholder participation processes, systematic efforts are made to involve interested and affected parties, as well as diverse viewpoints, often including independent thinkers – people from non-related policy or academic backgrounds. Patel et al. (2007), Volkery et al. 39 (2008), and Webb et al. (2009) provide discussions of methods, difficulties encountered, and innovative approaches in the recruitment of stakeholders. Stages In what has been referred to as ‘transmissive’ participation (Reed, 2008), stakeholders or citizens have commonly been treated as receivers of information at the end of a scientific or planning exercise. Their lack of control over the process and products is, at least in part, due to the stage in the process at which they were consulted. Talwar et al. (2011) provides a typology for user engagement research that is primarily determined by the stage at which users are engaged. Those research stages include: 1. Definition of the problem 2. Designing a research strategy 3. Creating results 4. Application of research results Talwar et al. (2011) define four levels of research interactivity based upon what stages in the process are open to user engagement and input. In their typology, interactivity is a directional quality, with low interactivity research activities primarily engaging with users at later stages, while progressively higher levels of interactivity include successively more stages, up to and including definition of the research problem itself. In a review of three case studies, the authors found that user involvement in the research design phase is perhaps the most problematic hurdle for interactive research – a problem of institutional barriers to be addressed in the cross-cutting themes section of this chapter (see section 2.4), and discussed at greater length in the co-production literature (cf. Polk, 2015). Kloprogge and van der Sluijs (2006) similarly identify the openness of different stages of a project to participation as an indication of the quality of the participation opportunities. In their assessment they analyzed PIA projects based on the stages in the risk management function that were open to user participation. Those stages are: 1. Problem framing 2. Risk assessment 3. Response option assessment 4. Goal setting 5. Strategy formulation 40 Unlike Talwar et al. (2011), Kloprogge and van der Sluijs do not infer directionality in their assessment, but rather estimated the level of openness of different PIAs at the different stages. By this measure, the ULYSSES project (Dahinden et al., 2003) was a more open project than the COOL project (van de Kerkhof, 2006) because it included participation at all stages of the risk management function, while the COOL project did not have stakeholder involvement in the problem framing and risk assessment components. The literature review conducted for this chapter suggests the majority of PIA projects have reflect Talwar et al.’s (2011) conclusions, it is more likely for participation to be absent in early problem definition and process design phases than later result creation and application stages. PIA has been at the forefront in applications attempting to implement participatory approaches in assessment processes. While there is still strong debate about the merits of participation in assessment (Fisher, 2000; Innes & Booher, 2004), many of the participatory approaches developed for PIA are being adopted in related fields of research and practice. Integrated Sustainability Assessment (cf. Rotmans et al., 2008; Tabara et al., 2008), Transition Management (cf. Grin et al., 2009), and Multiple Criteria Analysis (cf. Giampietro et al., 2006; Roca et al., 2008) represent three domains where participatory approaches are being implemented and extended. This widening application of participatory methods provides the opportunity to answer questions about the merits of participation in assessment and decision-making, and to refine participatory methods where they prove beneficial. Outcomes Policy Outcomes From its inception IA, and subsequently PIA, has been problem-focused and intended to impact and inform policy. It is valid to ask, then, what policy implications a given PIA has had. Kloprogge and van der Sluijs (2006) found no evidence of climate change PIAs linked directly to a policy process. They state that PIAs to date have been primarily focused on research, with the intention of improving the IA process. Previous non-participatory IA exercises have been incorporated into policy making processes. For example Rotmans and Dowlatabadi (1998) discusses the successful integration of the RAINS model into international acid rain negotiations. There are also limited examples of direct connections between PIA exercises and policy-making (e.g. Schloz et al., 2004; Walter et al., 2007; Kowalski et al., 2008). However, a review of the outcomes of PIA projects 41 conducted for this chapter generally supports Kloprogge and van der Sluijs’ contention. Outcomes of PIAs to date tend to involve reports, strategies and associated material to inform policy, but there is little evidence of demonstrable impact on a particular policy process. At least part of the lack of evident ties between PIA exercises and policy outcomes is the scarcity of evaluation frameworks and activities aimed at elucidating those ties. According to Kloprogge and van der Sluijs (2006), another part of the problem is what Gieryn (1983) called boundary work. In this instance the boundary exists between IAs and policy processes, and is comprised of the relationships that exist at that boundary. In other words, it is not necessarily the case that PIAs are not providing policy-relevant material, but perhaps that institutional barriers have prevented that material from being incorporated into the policy process in a transparent way. This suggests the need, recognized in previous research (cf. Cash, 2001; Cash et al., 2006), for boundary or bridging organizations to facilitate translation between PIA exercises and policy-making. In the PIA exercises reviewed for this paper, bridging most commonly appears to have been the work of the research teams. This is perhaps another reflection of the difficulties inherent in PIA research in current academic settings, where activities such as bridging or facilitating have no recognized value under traditional metrics of academic performance. The disconnect between PIA exercises and policy-making may also reflect more generally a phenomena that Robinson et al. (2006) describe in specific reference to user interactions with the QUEST model in the GBFP project: that being the tension between the model being 'true to life' versus 'fun to use'. Traditionally, PIA project have erred on the side of being 'true to life' which has, perhaps, limited their ability to engage policy-makers, and influence policy. The GCCP process discussed in Chapters 6 and 7 encountered many of the institutional challenges outlined in the PIA research in its attempts to work with the City of Vancouver. The resulting disconnect from policy outcomes substantiates the challenges of working across institutions. Process Outcomes In examining the tangible policy outcomes of PIA exercises, it is important not to lose sight of process goals and outcomes that are foundational to participation and interactive or Mode2 science (Gibbons, 2000). Perhaps the most important of the process outcomes is the development of social learning between researchers, policy makers, stakeholders and citizens (Pahl-Wostl et al., 2008). Social learning can not 42 only improve the quality of the decisions made, but can have long-term impacts beyond a particular process, in the form of forged relationships and the propagation of ideas through society. In this manner participation can form a reciprocal relationship with social capital supporting the interactive governance envisioned by Stoll-Kleeman et al. (2003). Social learning, and other process factors such as credibility generated by extended peer review, can be difficult to demonstrate and evaluate (Reed, 2008). In the PIA projects reviewed for this chapter, evaluations of social learning and the impacts of participation are largely anecdotal, however some projects did attempt to systematically evaluate the participation components (e.g. Dahinden et al., 2003; Querol et al., 2003; Kok et al., 2008; Walter et al., 2007). Kok et al. (2008) provides a good example of an attempt to systematize diverse qualitative information generated in the MedAction PIA. A goal of the research in this dissertation is to contribute to the evaluation of process outcomes of engagement in energy planning, with broader applications to sustainability planning and PIA exercises. Robinson (2008) suggests that problem-based transdisciplinary research may require the development of new metrics by which to evaluate researchers, such as a measure of impacts on society. However, he also recognizes that under current institutions, academics are beholden to existing metrics of publication and advancement. Development and/or application of research and evaluation methodologies that allow for the assessment of participation processes could serve both metrics. Wiek et al. (2009) have designed, consolidated, and applied a comprehensive evaluative framework, following the aforementioned calls by Robinson (2008) and others. The framework and empirical study have been based on an earlier study on the societal effects of PIA research in Switzerland (Walter et al., 2007). The framework allows the evaluation of a broad spectrum of outcomes and effects that can be attributed to participatory research, including PIA. The comparative empirical study from Canada captures the societal effects of selected participatory sustainability and climate change research projects, including key elements of PIA. The study concludes with suggestions on how to transform institutional structures to enable and better support PIA. 2.4 Cross-cutting Themes and Future Directions A review of recent PIA projects and the associated literature reveals a number of cross-cutting themes that deserve further attention, and which have significant implications for applying the learnings from PIA to sustainability and energy planning processes. The authors have identified four cross-cutting themes that we believe merit further discussion, including the tension between 43 qualitative and quantitative information, levels of interactivity, the importance of institutions and institutional change, and the space created by the intersection of choice, uncertainty, and constraints. This section presents a broader discussion of these themes. 2.4.1 Qualitative/Quantitative Tensions in PIA The tension between qualitative and quantitative information predates PIA, and was explicitly recognized in earlier reviews of IA (e.g. Rotmans & Dowlatabadi, 1998; Weyant et al., 1996). This tension is present in many domains, particularly where scientific or technical information interfaces with human values. Efforts to incorporate qualitative information into IAs, and specifically computer models, proved difficult, and it was recognized that many human dimensions of environmental problems were not amenable to quantification (Rotmans & Dowlatabadi, 1998). As noted above, PIA was largely a reaction to this revealed obstacle of incorporating qualitative information in IAs. The development of creative storylines and visions of the future provided the opportunity for participants to represent their desires for future conditions in their own terms. While PIAs have unquestionably increased the representation of qualitative information in assessments, the qualitative/quantitative tension remains. In fact, that tension could be said to permeate all of the components discussed in the previous section of this chapter. For methods, the selection of techniques for constructing and evaluating future visions are very much a product of the research team's (or broader partnership's) perspectives on the value of qualitative and quantitative information. The relative centrality of computer models (quantitative), scenario methods (primarily qualitative), as well as methods such as repertory grid (van de Kerkhof, 2006) and value-focused thinking (van Wynsberghe et al., 2003) (both qualitative) reflect a position on the relative roles of qualitative and quantitative information in PIA. The mechanism of participation, choice of stakeholders (e.g. experts vs. citizens or independent thinkers), and the stages of stakeholder engagement also reveal a position about the importance of qualitative and quantitative information. These components dictate who participates, at what stages they have input, and how their knowledge and values are integrated into the process. If solutions to complex human-environment problems are an emergent property of PIA exercises, then who participates, how they participate, and at what stages they participate are crucially important to the impact of qualitative and quantitative information in resulting policy guidance. 44 For policy outcomes, the qualitative/quantitative tension is less explicit, but exists in the form of the relative weights given to qualitative and quantitative information in the interpretation of policy guidance, and the development of policies. Given the relative youth of PIA, the dearth of examples of direct links between PIAs and policy exercises, and the scarcity of existing evaluations of PIA-policy connections, it is difficult to assess this relationship. Process outcomes are directly linked to the qualitative/quantitative tension. Perhaps the key process outcome of PIAs is the social learning they engender (Pahl-Wostl et al., 2008). This social learning can be seen as an explicit recognition of both the existence of the qualitative/quantitative tension, and as an attempt to bridge that tension through the co-development of knowledge between researchers and stakeholders. The attempts made in the development of interface-driven models to account for qualitative and normative components presents a specific example of researchers recognizing and attempting to address the qualitative/quantitative tension (Carmichael et al., 2004; Holman et al., 2008a). In the GBFP project, this was known as the 'true to life'/'fun to use' tension, and was instrumental in several of the design components of the models and user interface (Carmichael et al., 2004). Despite this attempt to directly address the qualitative and quantitative dimensions of sustainability within QUEST, there was still significant disagreement and debate within the research team about the use of models, and the balance of those dimensions (Robinson & Tansey, 2006; Robinson et al., 2006). This type of deliberation is likely to be a mainstay of PIA exercises as researchers and partners negotiate the importance and integration of qualitative and quantitative information sources. When explicit attempts to combine qualitative and quantitative approaches are not taken, academic disciplines and/or research team expertise often dicate the dominant approach, with social science led teams focusing more on qualitative approaches, and physical or natural science teams focusing on quantitative approaches. In IA, the default position has largely been a quantitative approach, bassed on the field’s foundation in computer modeling, with the IPCC scenario processes presenting a strong example. In some design and planning-focused sustainability projects, the opposite has often been true, based on the more qualitative and creative aspects of design charrettes and related participatory planning processes (Kellett et al., 2008). Development of tools and processes that allow for the creative development of future alternatives (qualitative), as well as ways to measure the performance and trade-offs of those alternatives (quantitative) remains an important research direction for PIA, sustainability planning, 45 and related fields. In this way, the qualitative/quantitative tension is an important driver of future research. 2.4.2 Interactivity in PIA A second cross-cutting theme identified from a review of PIA is the importance of interactivity. Interactivity in this context refers to the relationships between the research team, and other actors or audiences involved in the PIA process (Robinson & Tansey, 2006). As PIA intends to address complex problems in human-environment systems and to create socially robust and solution-oriented knowledge (these are the critical assumptions), PIA needs to adopt and embrace the concept of interactive social research (ISR). The main implication is to move from the predominately transmissive or extractive models of engagement to the co-development of knowledge (Robinson & Tansey, 2006; Shove & Rip, 2000; Talwar et al., 2011), negotiating and integrating inputs from partners outside the research team at various stages of the research process (Polk, 2015). Although consensus emerges around the argument that participation in PIA requires interactive participation, there is still room for interpretation when and how and to what extent interactivity is required. Robinson and Tansey (2006) distinguish between weak ISR and strong ISR. Strong ISR is characterized by more active participation by users or partners in the research process itself, potentially including definition of the research questions and designing the research process (Talwar et al., 2011). Based on this distinction, it is possible to categorize some of the reivewed PIA projects as weak ISR (e.g. Catalonia Bioenergy PIA; COOL), and others as strong ISR projects (e.g. GBFP; VISIONS). Similarly, Stauffacher et al. (2009) analyze and critically discuss the dynamics of participation regarding stages and intensity of interactivity. The GCCP process discussed in Chapter 6 attempted to introduce some of these interactive social research concepts into the design of the workshops, providing some insights into the challenges of engaging partners in this way – particularly in the early stages of a project. The degree of interactivity in PIA is not a given, but rather is dependent on contextual factors (Talwar et al., 2011). For example, in the COOL project (van de Kerkhof, et al., 2002; van de Kerkhof, 2006), the research question was specifically set as an investigation of technological means for achieving an 80% reduction in GHG emissions for the Netherlands by the year 2050. In this circumstance, one might argue that participant interaction at the problem definition stage was not required, based on the context of the overall project. Yet, considering important process outcomes 46 (problem ownership etc.), even in this allegedly predetermined project, a critical degree of stakeholder interactivity during the problem definition stage seems to be favorable if not necessary. Conversely, in the GBFP project, the general research goal of investigating sustainable futures for the region provided much more leeway for strong interaction with partners in the early stages of the research process (Robinson and Tansey, 2006). The critical issue that incites the debate about higher and lower degrees if interactivity is the additional effort (time, resources, competence, etc.) that is required if interactivity is to be enhanced. The collaborating partners may have very different positions or interests concerning the research topic as well as broader participation, project timelines, and deliverables (Robinson & Tansey, 2006). Adequately addressing the differences and maintaining the value of the process for all parties requires negotiation about boundaries. Increased complexity comes at the additional cost that it can strain or even overburden a project. Robinson and Tansey (2006) state that the GBFP project with its high level of interactivity at times "groaned under the weight of its own complexity". Considering the regular constraints that PIA projects have to cope with (in an institutional environment that is not adapted to this new form of research), additional transaction costs are usually compensated by personal engagement and unpaid volunteering. Yet, the field of PIA is in the process of professionalization, for instance, specifically trained facilitators (interface managers) can ensure an effective and efficient process aiming to keep interactive participation high and the related transaction costs low. In conclusion, it seems to be uncontested that PIA requires a critical degree of interactivity for accomplishing the ambitious outcomes envisioned (including process outcomes like emergence of ownership, social learning, etc.). Acknowledging this convergence, there is still plenty of room for methodological studies that would provide empirically and conceptually informed insights when and how and to what extent interactivity is required considering project goals and means. Along the same line, it seems to be timely to contribute to a typological ‘meta-study’ on existing interactive methods about how they could fulfill the specific functions outlined – instead of simply extending the pool of interactive methods. This article provides first steps towards such a typology. 2.4.3 Institutions and Institutional Change Institutions interact with, facilitate, and hinder PIAs in a number of different ways. Academic institutions, and associated funding agencies, have yet to fully embrace the problem-based ISR 47 embodied by PIAs. This is largely because the traditional measures of academic productivity (e.g. publication, development of students) are not always well served by interactive research with partner responsibilities and deliverables (Robinson, 2008). Yet, the very conduct of ISR and PIA are dependent upon support from academic and funding agencies (Robinson & Tansey, 2006). Government and decision-making institutions interact with PIAs at the policy guidance and policy implementation interface. Despite isolated examples of PIA impacts on policy (Schloz et al., 2004; Kowalski et al., 2008), there still appear to be barriers to institutional uptake and application of the products of PIA (Kloprogge & van der Sluijs, 2005). Van der Kerkhof (2006) identifies another connection between institutions and PIA processes - that being the choice of institutional mechanisms for implementing policies. For example, the choice between reliance on markets and/or reliance on government institutions has significant impact on the methods proposed through PIAs to address environmental problems. Perhaps the overriding connection between PIA and institutions is the need to institutionalize PIA itself. Through the process of conducting a PIA connections are made, relationships forged, and novel approaches to addressing impediments are devised. Yet the realities of funding and time constraints mean that continuity is lost. PIAs become ad hoc, one-off exercises where much of the long-term value is lost by the cessation of projects and the necessities of the next funding cycle. As Talwar et al. (2011) state, the fact that risky ISR projects exist at all is a testament to the ingenuity and risk-taking of individual researchers. To address this disconnect between PIAs and organizational structures, institutional change is required. Appropriate support needs to be provided to address the procedural constraints of time, resources and academic advancement so that researchers who want to focus on interactive, problem-based research are provided the necessary tools. Coincident with the continued development of PIA as a field, there is a need to investigate the organizational and institutional arrangements needed to support PIA (Talwar et al., 2011). Negotiation of the relationship between PIA researchers, partners and institutions will continue to be an important theme of research in the field. It is notable that the only one of the three case studies described in this dissertation that has carried forward into new applications and new interactions with municipal partners is the Victoria CEEP workshops, which were delivered by consultants rather than academic researchers. 48 2.4.4 Crossroads: Choice, Uncertainty and Constraints A final cross-cutting theme identified by the authors in the conduct of PIA is the relationship, integral to the field, between uncertainty, choices, and constraints. The manner in which different PIA exercises have addressed this relationship, and navigated the space created by these three factors, accounts for much of the richness derived from PIA. Uncertainty is a mainstay of complex problems. Bizikova et al. (2009) note that the traditional approach to uncertainty has been to push for more and better analysis. PIA, however, was born of the recognition that complex human-environment problems are not solvable through scientific means - with uncertainty in natural and man-made systems, and human choice playing roles in the intractability of the problems. The development of "scenario planning" was largely a reaction to the uncertainty present in complex systems (Wack, 1985a). Wack (1985a) states that uncertainty must be accepted as a structural feature of complex problems. Scenarios present an opportunity to explore that uncertainty, and to expand our mental models (Berkhout et al., 2002). Scenarios are not meant to represent reality, but rather to explore possibility in such a way that they change decision-makers' representations of the systems in question (Wack, 1985a). The use of scenarios (and other future visioning methods) in PIA, then, largely serves to acknowledge and address uncertainty. In the face of uncertainty, decisions cannot be made based on the ‘right’ answers, but rather become a question of choices. Decision-makers must choose from amongst many possible futures. In PIA, the range of decision-makers is expanded to include stakeholders and citizens, on the premise that better decisions will be made through more inclusive choices. The application of backcasting approaches to PIA has served to make the importance of choice explicit, by focusing on desired future conditions, rather than extrapolation from current ones. The prevalence of backcasting approaches in PIA exercises is a reflection of the recognition of the centrality of choice in addressing uncertainty in complex human-environment problems (e.g. Carlsson-Kanyama et al., 2008; Mander et al., 2008;; Patel et al., 2007; Quist and Vergracht, 2006; van de Kerkhof et al., 2002). Complex human-environment problems, however, are not defined solely by uncertain future conditions, and decision-maker choices - they are bounded by real-world constraints. Not all futures are possible given limitations of resources, capacities, etc. This may be particularly true at more local levels, where governments and institutions have fewer resources at their disposal than at larger scales of organization. In order to generate plausible scenarios, and decision-relevant information, PIA exercises must recognize and account for constraints. In IA, and subsequently PIA, quantitative 49 computer models have been used to identify constraints and analyze the plausibility of scenarios against those constraints. Quantitative modeling, then, both constrains choice (i.e. through a dependence on available data and what can be modeled), and bounds uncertainty to increase the relevance of knowledge generated in PIA exercises. One of the key roles of PIA is to explore the space, and account for the pressures, generated by the interplay of uncertainty, choices and constraints. Scenarios, backcasting, and computer models are methods that have been developed to facilitate navigation of that space. PIA exercises to date have wrestled with how to best incorporate those methods into processes to facilitate the co-generation of knowledge, and the development of desired futures. The challenge of adequately addressing the space defined by uncertainty, choices, and constraints remains as a focal concern in the design and conduct of PIA exercises. All of the cross-cutting themes discussed in this section can provide insight on the conduct and results of the PIA processes reviewed. Additionally, these themes can be instructive in the design and development of future PIA processes, and more broadly applied to sustainability planning and, in the context of this dissertation, energy planning processes. 2.5 Conclusions Participatory Integrated Assessment is a young field, but it has already contributed significantly to the understanding of complex human-environment systems. In particular, it is the "P" in PIA that has elucidated a number of issues regarding the difficult social and normative aspects of human-environment interactions. By explicitly recognizing that the complex problems being addressed by PIA exercises are not solvable by scientific means, and moving to a model of co-developing knowledge with stakeholders and citizens (Polk, 2015), PIA has moved to the forefront of fields grappling with the concepts of Post Normal Science (Funtowicz & Ravetz, 1993), and Mode2 science (Gibbons et al., 1994). A review of PIA exercises demonstrates that they have moved to embrace key characteristics of Post Normal Science and Mode2 science, such as reflexivity, transgression of institutional boundaries, extended peer review, focus on process, and the co-production of knowledge. Not surprisingly, PIA exercises have struggled with the added complexity that the incorporation of these concepts entails, particularly when meshed with already difficult problems such as climate change or sustainability (cf. 50 Robinson et al., 2006; Rotmans et al., 2000; Shackley & Deanwood, 2003). By conducting these exercises, and documenting the processes and findings, however, past PIA efforts provide the opportunity for learning and future development. This review reveals some key components of previous PIA exercises, as well as cross-cutting themes in the field that represent challenges to be addressed by future PIAs. Overt recognition of these challenges, and movement to address them, could help future PIAs cope with the added complexity of broad, meaningful participation in decision-making for human-environment systems. This chapter has described the methods, outcomes and important cross-cutting themes from PIA and related sustainability planning processes. Casting this wider net builds further context to the discussion of community scale energy planning, providing guidance for the conduct and evaluation of engagement processes. The themes developed in this chapter permeate the case studies described in this dissertation, and will be revisited in multiple forms in the case study chapters (Chapters 6 and 7), and the dissertation’s conclusion (Chapter 8). Situating this dissertation in the broader PIA and sustainability planning literature informed the case study analysis used to examine research question 1: 1. What are the tools (e.g. models, scenarios, visualizations) and process components (e.g. presentations and interactive exercises) used to engage participants about energy, and how do they impact the process? The dissertation will now turn to developing research question 2: 2. What are people’s existing mental models of community scale energy? Is there evidence of changes in their mental models, or interactions between their mental models and the process of engagement? First, Chapter 3 will review the literature on mental models to develop the rationale for their use in this dissertation. Then, Chapters 4 and 5 will turn to a discussion of the methodology developed, and the results. In Chapter 6, the two research questions and corresponding case study and mental models threads will converge more thoroughly, each informing the other in the broader investigation of community scale energy planning. The purpose of this chapter was to broadly set the stage for the examination conducted in this dissertation. 51 Mental Models Applications - A Review 3.1 Introduction A common thread in the literature on human-environment systems is the need to understand the psychological, social and cultural components that lead to particular sustainability behaviours, be they individual behaviours, such as recycling, or collective behaviours, such as voting for sustainable policies (Smith et al., 1994; Stern 2000). Psychology research has a long history of examining the relationship between sustainability and various psychological factors, such as knowledge, attitudes, beliefs, worldviews, etc. (Nickerson, 2002). This research has provided significant insight into how people perceive and interact with their environments but the complexity of human-environment systems means that significant knowledge gaps remain (Gardner & Stern, 1996). This chapter will review mental models research and, in particular, the application of mental models as a research methodology for determining how people think about complex subjects. Lynam et al. (2012) suggest that one reason for the proliferation of mental models in various fields is the intuitive sense it makes as a conceptual framework for understanding how people think, and how that impacts their domain-relevant actions and behaviors. A number of fields have been working to operationalize agreed upon, context specific definitions of mental models, as well as effective methodologies for their research needs. Application of mental models in the evaluation and design of participatory processes in community energy and sustainability planning can benefit from decades of research in other fields (cf. Langan-Fox et al., 2000, Mohammed et al., 2010; Wilson, 2000). The chapter begins with a discussion of what mental models are, including the early development of the concept. It then turns to an investigation of what their value is (or could be) for energy and sustainability planning. This section develops the argument for the use of mental models in sustainability planning, based on their ability to reveal connections and interactions amongst concepts, their relationship to key components of scenario planning, and their use in related human-environment research. The third section will review two key concepts in mental models research that have potential implications for their use in sustainability planning: the distinction between individual and shared (or group) mental models, and the relationship of mental models to expertise. The final section will focus on methods for evaluating mental models, culminating with a justification for the mental models methodology developed and used in this dissertation. 52 3.2 What are Mental Models? At their most basic level, mental models can be thought of as internal, cognitive representations of the outside world (Jones et al., 2012). Kenneth Craik (1943) first proposed the idea that people think by manipulating these internal representations. Philip Johnson-Laird later formalized and greatly expanded this idea in his book “Mental Models” (1983). Johnson-Laird (1983) developed his theory of mental models largely in response to his dissatisfaction with previous theories of cognition that relied on an innate system of logic to explain humans’ capacity to reason. He set out to describe how the use of internal representations could better explain our capacity to reason and draw inferences. To do so, Johnson-Laird took an interdisciplinary approach, drawing on cognitive science, experimental psychology, linguistics and computer science. In the decades since Johnson-Laird formalized his concept of mental models, it has gained support in cognitive science and psychology as an explanation for how humans think (Jones et al. 2012). This is likely due, in part, to how it mirrors the corresponding development of computers and computer modeling, but it also reflects how mental models map onto other established and popular theories, such as schema theory (Bartlett, 1932), constructivist learning theory (cf. Bruner, 1961; Piaget, 1950, von Glasserfield, 1989; Vygotskii, 1978), and the dual coding theory of cognition (Kahneman, 2011). Perhaps the field that has done the most to develop and popularize the application of mental models is operational research (cf. Mohammed et al., 2010; Senge, 1990; 1994). The focus of mental models in organizational research is on how people (generally within companies) utilize their mental models to make decisions and plan strategically for the future (Senge, 1990). This research trajectory has direct ramifications for energy and sustainability planning, which has the same goal of navigating complexity and making decisions about the future. It is not surprising, then, that researchers with strong connections to organizational research, systems thinking, and mental models, have branched out to address the challenge of sustainability more specifically (cf. Ehrenfeld, 2008; Senge, 2010). In organizational research, much of the focus is on the shared mental models that develop as a result of teams working together over long periods of time, and how those shared mental models can be either adaptive, or maladaptive to organizational performance (Jonker et al., 2010; Langan-Fox et al.; 2000, Senge, 1990). Perhaps an added challenge for mental models research in the sustainability planning (and broader engagement) domain, is the ephemeral nature of engagement. People who are brought together to discuss, or deliberate on, sustainability issues do not have the time necessary to 53 develop shared mental models of the concepts they are discussing. The subject of individual, shared and intersubjective mental models will be discussed in 3.4. Another area of mental models research that has garnered significant attention, is the study of expert versus lay models (cf. Morgan et al., 2002; Smith, 2006). Expertise is thought to represent the combination of low-level schemas into ever-more complex networks (Sweller et al., 1998). In this way, an experts’ schemas, and associated mental models, can be thought of as containing not only a larger number of concepts than a lay persons, but also a larger number of connections between concepts. A focus on the special role of expertise was a feature of public engagement theories during the latter part of the 20th Century, including the public understanding of science and related information deficit model (Kurath & Gisler, 2009; Owens, 2000). While more modern theories of engagement have shifted to democratic, participative and deliberative modes (Kurath & Gisler, 2009; Wynne, 1996), the question of expertise is still relevant, if only to demonstrate synergies or disconnects that can exist between experts and multiple publics when planning decisions are being made. Section 3.4.2 will discuss the subject of mental models and expertise in more detail. Finally, an initial assertion of mental models theory was that it was strictly a cognitive, knowledge-based structure or set of routines (Johnson-Laird, 1983). Young (2008), on the other hand, contends that emotion, beliefs, values, etc. are inextricably linked with mental models. Previous psychological research on phenomena such as cognitive dissonance (Festinger, 1957) and the primacy of affect in perception (Zajonc, 1980) make strong arguments for the importance of non-cognitive factors in the way we reason about the world. The position taken in this dissertation more closely aligns with the positions of Young (2008) and Mohammed et al. (2010) that emotions, beliefs and values permeate our mental models. We need to recognize that, even in a deliberative process like sustainability planning, emotions and values are an inextricable part of the mental models that people bring to engagement exercises. With the increasingly local nature of energy and sustainability planning introduced in Chapter 1, it is likely that emotions and values based on place attachment will play an even more central role (Devine-Wright, 2009; Devine-Wright & Howes, 2010; Marshall et al., 2012). The importance of mental models to the research conducted in this dissertation, and to planning more broadly, is that our internal models of how the external world functions are central to how we think about and plan for the future (Jones et al., 2012; Lynam et al., 2012). Indeed, the centrality of the mental models concept in the early development of scenario analysis was a direct result of researchers and practitioners recognizing that the most important role of scenarios was to question 54 and expand our limited mental models about complex phenomena, and more importantly, about the future (Wack, 1985a). An understanding of the multiplicity of existing mental models in the broader publics may help us better navigate planning and decision-making for the future of our communities. It may also help us understand the types of interventions and engagement activities we should use to expand (build capacity) and accommodate those mental models in participatory processes. The next section of this chapter will expand upon the discussion of mental models’ value to sustainability planning activities. It will discuss their merits in determining the connections between important concepts, and how their use in research on components of sustainability planning and in related human-environment fields provides further support for exploring their application in sustainability planning. 3.3 What is the Value of a Mental Models Approach to Sustainability Planning? Chapter 2 made the case that many of the complex human-environment issues we are facing (including, but not limited to, climate change) are complex, with no single ‘right’ answer. In the face of this, an information deficit model of engagement – i.e. that we can change how people think and behave, simply through the provision of the correct information – is inadequate (Kurath & Gisler, 2009; Owens, 2000; Wynne, 2001). With the shift away from the information deficit model, new models for addressing sustainability issues are being developed. One approach that has been proffered is a procedural approach to sustainability (Robinson & Cole, 2015), which sees sustainability as an emergent property resulting from people negotiating desired futures. In this model, sustainability is seen as an essentially contested concept – i.e., like truth or beauty, it has no right answer, but rather many positions (Connelly, 2007). These positions from which people negotiate, it could be argued, are inextricably linked with their mental models of the subject at hand – whether that be broad, in the case of sustainability, or more narrowly focused, as in the case of energy. The mental models that people arrive with, then, have a significant impact on how sustainability is negotiated, and how engagement proceeds. In an engagement process that is truly participatory, the challenges and solutions considered, and ultimately the solutions chosen will all be impacted by the mental models of the participants. Therefore, 55 understanding the participants’ existing mental models can be an important component of the broader engagement process. Knowing these mental models, we can both design better processes (Lynam & Brown, 2012) and begin to look for changes in people’s mental models as one indicator of the impact of participation - what Chapter 2 called process outcomes, and what Wiek et al. (2014) call enhanced capacity. There are, however, many ways to evaluate people’s knowledge, attitudes, perceptions, beliefs and values. What makes mental models particularly well suited to understanding people’s thinking about complex sustainability concepts? My argument has three main parts: 1) that it is mental models’ theoretical and methodological focus on the connection between concepts, 2) its pre-existing relationship with research on components of sustainability planning (including computer models, scenario analysis, and human-computer interaction and visual representation), and 3) its use in related human-environment research fields that support its use in a sustainability planning context. 3.3.1 The Connections Between Concepts in a Complex Space The second premise which formed the basis for this dissertation was: 1. That significant work remains to develop effective techniques for evaluating engagement processes. Many evaluation techniques tend to be piecemeal, and miss important connections across participants’ answers (i.e. answer patterns) that might give a more complete picture of people’s understanding and beliefs about complex subjects. Frequently we collect results in such a way that we emphasize that ‘x% of participants believed y’, gathering participant responses by question to determine prevailing views on a given topic. This is not an indictment of such a methodology methodology (indeed, Chapter 5 begins with that approach as a way of characterizing the broader findings), rather it is to say that this approach is inadequate on its own to understand the complexity of human thought and its implications for decision making. Taking a summative question-by-question approach tells us what people collectively think, without telling us much about why they think it, or how individual’s answers on one question impact or predict their answers on other questions. In other words, it loses some of the richness available in the data about individual’s reasoning, and their answer patterns. Hoffman et al. (2014 pg. 13016) state that “Mental models complement more traditional approaches to understanding environmental behavior by highlighting the interdependent relationships among attitudes, norms, values and beliefs”. 56 As an example from the energy field, participants with many different concepts of the reasons to address energy (e.g. climate change, peak oil, economic reasons, geopolitical reasons, etc.) may all agree that we need to focus on demand reduction. However, it is likely that people from these different positions will have very different ideas about how we should achieve that demand reduction. The ideas of Kunstler (2006) and Hienberg (2004) are very different from those of Hawken et al. (1999) and McDonough & Braungart (2002). Dryzek (2005) calls these broadly different worldviews ‘environmental discourses’. In Hoffman et al.’s (2014) framework of mental models, they would be considered the core goals or concepts in a mental model – i.e. the abstract concepts that are far less amenable to change. In this example, then, it is not enough to know that ‘80% of participants supported the need for energy demand reduction’, we need to know more about how that individual concept connects to other concepts or beliefs about why we need demand reduction, who is responsible for addressing it, and how we should achieve it. From the outset, mental models research has been focused on these connections between concepts (Doyle & Ford, 1998) and, as many of the examples discussed in 3.5 will illustrate, mental models methods have been devised primarily to reveal these connections. What can these connections tell us about people’s thinking about complex systems? Are there particularly important concepts (i.e. keystone concepts) that we can use as heuristics to infer other components of people’s mental models? For example, does knowing that people place the onus for energy management at the Federal Government level tell us something more fundamental about their approach to addressing energy issues? Can we categorize people according to their beliefs about keystone concepts, such that we can infer the types of solutions they would support? If we can better understand the variety of mental models about energy in the broader publics, we may be able to better design solutions and engagement activities that meet multiple objectives – a lofty goal, but one worth pursuing. One of the primary reasons for the selection of mental models as a methodology for this dissertation, then, is its demonstrated capacity in different fields to uncover key concepts, and the connections between those concepts, and to reveal how the resulting mental models impact people’s interactions and decision making. When dealing with complex human-environment systems, a better understanding of how these concepts and connections are organized into multiple mental models has the potential to inform planning practice, and to better design engagement processes. 57 3.3.2 The Relationship of Mental Models to Existing Components of Sustainability Planning Chapter 2 discussed sustainability planning and participatory integrated assessment, and revealed many of the common components and tools of these fields. Some of the key unifying themes in this research include intractable problems, complex human-environment systems, and a focus on an unknowable future. To address these themes, researchers have turned to a set of methods that help us simplify and explore the complex space described by sustainability. Included amongst these methods are computer models, scenario analysis, interactive interfaces, and visual representations of the future (Salter et al., 2010). Mental models research (to a greater or lesser degree) has been connected to the study of each of these tools. A discussion of this research, then, can be instructive in better understanding the potential link between mental models research and sustainability planning. This discussion forms the basis for this chapter’s second argument for adopting a mental models methodology for this dissertation: the existing relationship between mental models and components of sustainability planning. Modeling Increasingly, computer models are an important way in which we understand our world in all its complexity (Weisberg, 2013). This is, perhaps, best illustrated by the topic of, and debate about, climate change, where computer models have taken centre stage (Edwards, 2010). The subsequent recognition of the power of computer models to help us understand complex subjects has led to the development of model based learning methods in science education (cf. Clement & Rea-Ramirez, 2008; Seel, 1995), which explicitly link the use of external models and representations as a way to help learners construct internal representations – mental models (Clement, 2008). Mental models have a reciprocal relationship with computer models. Computer models, after all, are externalizations of an individual’s or group’s mental model(s) of the structure and/or functioning of a system (Weisberg, 2013). In essence, a computer model is a 2nd order model of the actual system, translated first into people’s mental models, and then formalized with a set of rules into a computer. In some circumstances, this link is recognized explicitly, as in the case of systems dynamics models (Doyle and Ford 1999) and the related participatory modelling (eg. Cohen et al., 2006, Langsdale et al., 2006; Jones et al., 2009). The goal in these processes is to externalize people’s mental models into 58 an agreed upon representation of the system being studied. The act of externalizing and representing their mental models becomes the explicit goal and the process of engagement. This close link between mental models and computer models suggests that a mental models methodology could have key benefits for studying engagement activities that make extensive use of computer models to explore sustainability. Understanding the breadth of mental models amongst stakeholders, decision makers and citizens has the potential to help us develop more complete computer models (i.e. models that can better account for more of the important variables). Conversely, focusing on understanding mental models, and changes in those models as a result of engagement with computer models, can tell us something about the gaps in knowledge and concept connection amongst the engagement participants. This connection between mental models and computer models, however, does present some challenges. A potential limitation of computer models is their reliance on available data (Edwards, 2010). This means that what is modeled is often determined by external factors, rather than what is most important or most desirable to model. This raises the possibility of a disconnect between what is presented and what people believe is important. The qualitative/quantitative tension discussed in Chapter 2 is one example of this, where a reliance on quantitative, more easily modelable, data can overshadow important qualitative information in assessment and planning processes. A second question, worth considering, is whether the mental models concept of cognition is, in fact, a byproduct of the concurrent development of computer systems and computer models? Are we focusing on mental models elicitation approaches because it is an appealing concept (Lynam et al., 2012), or because it has real value as a way of understanding how people think, plan and make decisions? While this chapter argues the former, it is an important question to revisit as mental models research proceeds. Human Computer Interaction and Visual Representation The fields of sustainability planning and participatory integrated assessment are increasingly turning to the use of interactive computer systems as a method of engaging with people about sustainability issues and possible futures (cf. Carmichael et al., 2004; Dahinden et al., 2003; Holman et al., 2005; Pahl Wostl et al., 2000; Robinson et al., 2006, Shaw et al., 2009). Two of the three case studies in this dissertation used novel computer interfaces to explore participants’ interactions with energy issues (Girling et al., 2014a; Miller & Cavens, 2012). The study of mental models in human-computer 59 interaction research, therefore, is relevant to the adaptation of mental models to the evaluation of engagement activities, which are using interactive interfaces as part of engagement. Donald Norman (1983) is widely credited with bringing a mental models approach to designing and evaluating computer interfaces. Norman noted that the system designers’ “conceptualization of a target system should not be confused with the mental model that a user creates of that system” (Norman, 1983). As this quote suggests, application of mental models to HCI is based on the assumption that knowledge of how users internally represent systems will lead to the design of better systems (Ackerman & Tauber, 1987). In HCI there are two main approaches to accounting for users’ mental models. The first focuses on the effective development of instructional material to allow users to create a conceptual model of the functioning of a particular piece of software (Green, 1987). In this approach, the system itself is not flexible to users’ mental models, rather efforts are made to help users build an effective mental model of the system. The second approach focuses on actually changing the system or interface to accommodate for different mental models or learning styles. This may be accomplished either through user studies in the initial design phase (Schneiderman, 1998), or through the inclusion of adaptive interface technologies that change the interface to accommodate users with different mental models or learning styles – for example, users with different levels of expertise (cf. Schneider-Hufschmidt et al., 1993 van Wynesberghe et al., 2003). In both applications, the focus of mental models research in HCI is on using it to help design better systems for people to interact with. This is relevant to sustainability planning in the design of interfaces used in engagement, but also can be extrapolated to the design of materials and process components in engagement more broadly. As an example, the results of the mental models interviews in Chapter 4 had a direct impact on the development of the GCCP workshops (the case study that did not use an interactive interface). In addition to the relatively large body of research on mental models and interactive interfaces in HCI, there is also significant research focused on how people learn from multimedia information displays (cf. Mayer et al., 1999; Mayer & Moreno, 2002). The makeup and limitations of the human memory system play an important role in this research, including concepts such as cognitive load (Sweller, 1990), the components of working memory (Mayer et al., 1999), and schema theory (Kozma 60 & Russell, 1997). Increasingly, mental models are also being explored (cf. Malamed, 2009). Researchers are interested in the interaction between external (information display) and internal (mental model) representations, and the effects of external representations on the development of shared mental models. As Chapter 2 discussed, and has been reviewed elsewhere (Bishop et al., 2013; Sheppard, 2012), visual representations are one of the important tools we use to help imagine possible future environments. Understanding how people’s mental models interact with these representations could have significant impacts on our development of engagement materials. Research in HCI and visual representation, then, can inform the design and development of engagement tools for sustainability planning. Conversely, an understanding of how participants’ mental models are changed through the use of interactive interfaces in situ can have a significant impact on the fields of HCI and visual representation (Bishop et al., 2013). The Revelstoke research described in Chapter 7, and in Girling et al (2014a; 2014b) represents just such a collaboration between sustainability planning and HCI researchers. Scenario Analysis From the earliest days of its development, scenario analysis has been inextricably linked with the concept of mental models (Chermack, 2011; van der Heijden, 2005; Wack, 1985). Wack (1985), one of the founders of scenario analysis at Royal Dutch Shell, has said that the goal of scenarios is to challenge and expand mental models. The success of Shell’s reaction to the energy crisis of the 1970’s has been largely attributed to the impacts of scenario analysis on the corporation’s mental models, and subsequent strategic planning. In the intervening years, researchers such as Senge (1990), Chermack (2011; Chermack and van der Merwe, 2003), and van der Hiejden (2005) have described frameworks for the development and study of scenarios, all of which make explicit links to the concept of mental models. Chermack and van der Merwe (2003) draw further connections to constructivist learning theory, situating scenarios as a stimulus, and mental models as the mechanism through which individuals construct meaning about a given subject. As Chapter 2 discussed, scenarios have become an extremely important tool in sustainability planning and integrated assessment, because they provide a means for us to engage with the future. All three of the case studies discussed later in this dissertation make use of a scenarios approach to explore 61 future alternatives, this is emblematic of the wider field of sustainability planning (cf. Berkhout et al., 2002; Kok et al., 2006; Patel et al., 2007; Robinson et al., 2006). The connection of scenario analysis to mental models, then, is clear. If the purpose of scenarios is to challenge and alter mental models (Wack, 1985), then knowing something about people’s existing mental models, and how engagement (with scenarios and associated tools) impacts those models is an important engagement outcome. This is true whether those outcomes are substantive (Stirling 2006), impacting policy via decision making, or process outcomes and enhanced capacity (Wiek et al., 2014) developed via social learning. 3.3.3 The Use of Mental Models in Related Human-Environment Fields In addition to the more explicit connection of mental models to important components of sustainability planning, there is also a substantial literature on the application of mental models in related human-environment fields. The application of mental models methodologies in these fields can be instructive for adapting mental models and their elicitation techniques to sustainability planning. Environmental Risk Perhaps the most established body of literature on the use of mental models in a human-environment field closely related to sustainability planning, is the work in environmental risk (cf. Morgan et al., 2002; Bostrom et al., 1993; 1994; Smith, 2006). This research has focused primarily on identifying gaps in lay people’s knowledge of systems of risk, through comparison with experts’ models of the same systems (Morgan et al., 2002). For example, Morgan et al. (2002) describes a project that compared lay mental models of the risks posed by radon to expert models, identifying key concepts that were missing from lay models which presented health risks to homeowners. In related work on climate change (cf. Bostrom et al., 1994; Kempton,1997; Sterman and Booth-Sweeney, 2007), the focus has similarly been on identifying gaps or flaws in people’s understanding of climate change concepts. For example, Sterman and Booth-Sweeney (2007) used the concepts of stocks and flows and mass balance principles to study graduate students mental models of climate change concepts. They found that students mental models ignored basic processes, leading to a wait and see attitude about climate change. Similarly, Bostrom et al. (1994) found that participants in their study confused global warming with ozone depletion, and weather with climate. 62 The overarching framework of engagement within which this research is situated is the public understanding of science (PUS), and the related information deficit model (Kurath & Gisler, 2009). The PUS model – i.e. the argument that the challenge of public acceptance can be solved by providing people with accurate information about scientific processes - has been widely criticized in the literature (Kurath & Gisler, 2009; Owen, 2000; Wynne, 1996; 2001). This is perhaps particularly true in the environmental risk literature where Wynne (1996; 2001), Jasanoff (1998) and others have argued specifically against this expert/lay distinction in engagement and assessment. The focus of engagement research in more recent times has seen a shift or ‘democratic turn’ towards more inclusive forms of engagement (Wynne, 1996). It is within this more deliberative and participatory model that the research in this dissertation is situated, making the adoption of the methods used in the environmental risk literature on mental models problematic. Nevertheless, there are aspects of the methodologies used in the early environmental risk applications of mental models that are useful to this dissertation. In particular, the use of early interviews as a method for describing the research space, and developing subsequent research steps was very helpful in the overall research design (to be described in Chapter 4). So, while the focus of the research, and particularly the goal of engagement in this literature is not applicable for my purposes, their methodological development is a valuable addition. Natural Resource Management More recent work on mental models of human-environment systems has been focused on the implications of mental models for decision-making and participation in engagement exercises. Lynam and Brown (2012) introduce a special issue of Ecology and Society that focuses on the application of mental models research to decision making processes in South Africa and France. This research looked at both individual mental models, and shared mental models of water use and management (Etienne et al., 2012; Jones et al., 2012; Lynam et al., 2012; Stone-Jovicich et al., 2012). The primary focus of this research was on testing methods and evaluating existing individual and shared mental models of participants and stakeholders. For example, Stone-Jovovich et al. (2012) conducted mental models research using cultural consensus analysis to look for shared, multicentric or fragmented mental models of water systems in the Crocodile River catchment. They found that neither conservationists, nor irrigators shared a 63 cohesive mental model of the water system, but did identify key concepts of agreement between the groups as inputs to future engagement. In a similar study, Methevet et al. (2012) used cultural consensus analysis to investigate the mental models of different stakeholder groups in the Rhone River delta. Of interest to this dissertation, they demonstrated learning, and changes to participants’ mental models, as well as the modest development of shared mental models. They caution, however, that they found lower than expected levels of agreement between stakeholder groups. In their synopsis of the program of research, Lynam and Brown (2012) reflect on broad findings as guidance to other researchers looking to incorporate mental models into their research projects. While they felt that mental models could be considered an integral part of moving environmental governance towards more participatory approaches, they also encountered challenges in applying the methodology. One of the common threads was the amount of time and resources required to conduct mental models research, as well as the time required for participants to take part in the research. Lynam and Brown (2012) indicate that a focus of future research on mental models in human-environment research should be on streamlining the methods. This recommendation had substantial impact on the methodology used in this dissertation. The selection of questionnaires as a methodology for this dissertation was partly a response to this need for streamlining, despite the challenges that can be presented by the use of questionnaires to elicit mental models (see section 3.5.5). Sustainable Agriculture A very recent article on the use of mental models to study farmers’ sustainable practices (Hoffman et al., 2014), has both developed an instructive framework for conceptualizing mental models, and used a novel method for analyzing mental models data. Their research developed a composite mental model of sustainable agriculture for California wine growers, and identified key concepts that farmers used in decision making about their practices, including economic, environmental and future-focused concepts. Their framework (supported by the findings of the research) suggests that mental models are hierarchical, made up of abstract core goals, which are much more difficult to change, and peripheral concrete strategies, which are more amenable to change (Hoffman et al., 2014). They found that farmers whose core goals were consistent with sustainability cognition were more likely to take part in extension activities and to practice sustainable agriculture. While the research discussed in 64 Chapters 4 and 5 of this dissertation did not benefit from a review of the work of Hoffman et al., it anticipates a similar relationship, with the core worldview concepts, and the more peripheral concepts about energy use and solutions. Similarly, there are some connections between the mental models maps constructed in Chapter 4, and the methodology developed by Hoffman et al. (2014), with the distinction that the network analysis described by Hoffman et al. is more complex, and (it appears) more scalable. Had this literature been available during the construction of the methodology used in this dissertation, it likely would have had an impact on the research. However, for reasons to be discussed in Chapters 5 and 8, I remain convinced that the core Multiple Correspondence Analysis (MCA) has strong promise in the evaluation of mental models. Hoffman et al.’s research focus was on understanding the components of group mental models of sustainable agriculture among farmers, which is less applicable to the research conducted in this dissertation, where multiple publics (not definable groups) make up the study subjects. Nevertheless, the framework and methodology described by Hoffman et al. has ramifications for future research, as will be discussed in Chapter 8. 3.3.4 Discussion: Mental Models for Community Energy and Sustainability Planning Engagement Based on the review of past applications of mental models to the study components of sustainability planning and related fields of research and practice, some conclusions can be drawn for applying them in community energy, and more broadly, sustainability planning engagement. From the review of public engagement practices in Chapter 2, and more specifically the procedural approach to sustainability (Robinson, 2004; Robinson & Cole, 2015) there is a strong argument that complex human-environment issues, particularly in a futures context, do not have a definable ‘right’ answers. While particular aspects of sustainability planning may be verifiable and supported by strong evidence (e.g. the need to address energy demand and reduce GHG emissions), there are multiple ways that those issues could be addressed. Therefore, the goals of mental models evaluation in this domain should not be about looking for and evaluating flaws in participants’ mental models (i.e. teaching people the right answer), rather it should be about trying to reveal the plurality of models that exist in the population to better understand people’s thoughts, values, choices, and intentions. When discussing complex problems such as sustainability or climate change, and in particular aspects 65 of an as yet unknowable future, knowledge of the facts are less central than choices about a desired future state. Squaring those choices with the evidence-based need to address sustainability issues is the challenge of future planning for energy and, more broadly, sustainability. Wilson (2000) indicates that mental models use in human factors research took over a decade to operationalize a set of methods for evaluation and a generally agreed upon definition of terms. Lynam et al. (2012) documents similar challenges in the application of mental models to human-environment systems. To date, most of the research on mental models in human-environment systems has been focused on individual mental models (cf. Bostrom et al., 1994; Kempton, 1997; Morgan et al., 2006). Because engagement in sustainability planning is largely about collective actions such as planning and decision making, and in particular on collective choices, it seems that future work on shared, or perhaps intersubjective, mental models would be warranted, though the challenges of ephemeral engagement practices may make this difficult. The research discussed in this dissertation focuses primarily on individual’s mental models, but does attempt to look for indications of the development of shared models from the process of participation. In those circumstances where mental models have been used as an evaluation technique the focus was on learning the contents of people’s mental models as an end goal. In the research outlined in this dissertation, mental models were used both as an evaluation technique and as a cue to the design of the GCCP workshops. The design guidance took the form of inputs on key components of community energy that should be covered in the workshops – this was limited to the qualitative mental models interviews with practitioners (see Chapter 4). Another potential for mental models evaluation hinted at in more recent human-environment research is the possibility of demonstrating changes in mental models as a result of participation in engagement (Lynam et al., 2012). In their framework of mental models Lynam et al. (2012) point to the dual coding theory of cognition popularized by the work of Kahneman (2011). Dual coding theory postulates that in brief, single contact interactions where participants are not highly motivated, the associative cognitive strategy would most likely be used. This strategy is less likely to lead to a shift in mental models than the deeper, more deliberative rule-based cognitive strategy. Lynam et al.’s (2012) framework, then, would suggest that it will be difficult to shift mental models of community energy planning in the types of workshop processes discussed in this dissertation. This may have implications for the use of mental models in these settings, but it may also have broader implications for the types of engagement we are currently undertaking in human-environment issues. In other words, a failure to demonstrate changes in participants’ mental models may not reflect a 66 flaw in the sensitivity of mental models methodologies, it may reflect a flaw in the limited contact forms of public engagement we are currently undertaking. Based on the findings of this dissertation, this subject will be returned to in Chapter 8. 3.4 Two Important Concepts in the Study of Mental Models: Individual vs. Shared Models, and the Implications of Expertise There are two topics that have been part of the larger discussion of mental models in this chapter, but that bear further discussion, because of their implications for the study of mental models, and their use in supplemental research questions in this dissertation (see Chapters 4 and 5). Those subjects are the distinction between individual and shared mental models (and the more nuanced notion of intersubjectivity), and the relevance of expertise for the study of mental models. These subjects represent important sidebar issues to the larger structure of this chapter, and merit their own discussion here as a preamble to their use in the dissertation’s methodology. 3.4.1 Individual vs. Shared Mental Models, and the Subject of Intersubjectivity One of the primary distinctions in how different fields have applied mental models is whether they focus on individual mental models or shared mental models. Research focused on individual mental models is interested in how individuals think about or interact with the world. This may be to understand people’s conceptions of unfamiliar topics such as climate change (cf. Bostrom et al., 1994; Kempton, 1997) or it may be an input to future research stages as is often the case in HCI (cf. Leiser 1992). Research domains that focus on shared mental models are interested in how teams come to hold mental models in common, and how those shared models impact the success of an organization (Jonker et al., 2010). An important distinction when discussing shared mental models is between identical or near-identical models, which may lead to the problem of groupthink (Esser, 1998) and actually decrease the performance of the team, and a more nuanced version where the shared model contains well-understood methods for translating between team members’ mental models (Mohammed et al., 2010). A better name for the latter type of shared mental models might be intersubjectivity (Barker et al., 2006). In other words, what is desired is not the sharing of a particular way of thinking about the world, rather it is a method or set of heuristics for translating across 67 different ways of thinking about the world – a shorthand for understanding other team members’ mental models. The notion of intersubjectivity resonates with procedural theories of sustainability, in particular the idea of sustainability as the emergent property of the interactions amongst stakeholders (Robinson, 2004; Robinson & Cole, 2015). As it relates to sustainability planning, and broad public engagement, it is unlikely that a goal of developing a shared model of a particular system or issue is a realistic goal. In most research that has focused on shared models, the groups have been associated either through repeated exposure in the workplace (e.g. Mohammed et al., 2010), or a unifying interest and set of principles (e.g. Hoffman et al., 2014). Unless engagement activities occur iteratively over a protracted period of time, as is the case in some participatory modeling activities (cf. Cohen et al., 2006), it is unlikely that participants would have sufficient time to develop a shared mental model of energy or sustainability. For that reason, the research in this dissertation took an individual approach to the study of mental models, looking for definable sub-groups in the larger group of participants. As a goal, the fostering of intersubjectivity – i.e. the recognition and acceptance of differences in other participants’ mental models (Barker et al., 2006) seems like a more realistic, and perhaps more appealing goal of engagement. 3.4.2 The Relationship of Expertise to the Study of Mental Models As section 3.3.3 discussed, the study of the differences between expert and lay mental models was a focus of research on mental models of environmental risk (Morgan et al., 2002; Smith, 2006). In the context of current theories about public engagement, this research is problematic, in that it subscribes to the notion that the goal of engagement activities should be to fill gaps in lay models of risk via the provision of correct (i.e. expert derived) information (Kurath & Gisler, 2009). In addition to the substantial body of research that indicates that the simple presentation of information has little impact on people’s behavior (Gardner & Stern, 1996), there is also the notion that complex sustainability issues do not have easily definable ‘right’ answers. In other words, when discussing a community’s options for addressing energy issues, even experts have, at best, informed opinions. To be clear, this is not to suggest that there is no value in expertise, rather the contention is that domain experts are one (potentially multiple) important group of voices in the broader discussion about sustainable futures. 68 In this context, perhaps the more interesting question about the role of studying expertise is in studying possible discrepancies between what experts and practitioners think ought to be done to address energy, and what broader publics want to be done. A useful example from the Vancouver context is the contentious issue of density (cf. Bula, 2014; Campbell, 2014). City of Vancouver planners and decision makers over a number of different iterations, have proposed density as a potential solution to sustainability issues and the delivery of services, such as transit access. This discussion of density has met with significant resistance in public engagement activities (e.g. Bula, 2014), demonstrating a disconnect between the City’s mental model(s), and the public’s. Understanding the basis for this disconnect – i.e. the discrepancies in expert’s and decision maker’s models of density and land use, and those of the resistant publics’ – is an interesting question for engagement research, even in the more democratic and participatory models of engagement. To that end, this dissertation did look for variance in expert and lay mental models of community scale energy in Chapter 5. Additionally, in Chapter 6, multiple data sources (including the case study evaluation) look specifically at this issue of density, and what factors might be contributing to the resistance to density amongst Vancouver citizens. The question of expert versus lay mental models, then, still has important implications, despite a turn away from an information deficit model of engagement. 3.5 Methods for Measuring Mental Models Section 3.3.4 developed the theoretical argument for applying mental models to engagement in sustainability planning. However, it is not simply a matter of selecting the mental models methodology and applying it to the study of these engagement processes. There is no single agreed upon methodology for eliciting them (Cooke, 1995; Jonassen & Hoan Cho, 2008; Jones et al., 2012). Jonassen and Hoan Cho (2008) suggest that this is the result of mental models complexity and multifaceted nature, and that researchers tend to focus their evaluations on the aspects of mental models that best address their problem space. So, for example, a human computer interaction researcher might use think-aloud protocols intended to elicit a user’s thought patterns as they interact with a computer interface, while a marketing researcher might use structured interviews to try to elicit a customer’s feedback about a product. This section of the chapter will briefly review different elicitation techniques that have been used in mental models research, their intended foci, and perceived strengths and weaknesses of the different 69 techniques. Jones et al. (2012) describes a framework for categorizing mental models elicitation techniques based on whether they directly or indirectly (i.e. through researcher interpretation) elicit participants’ mental models. The categorization here will instead focus on key features of collection and analysis procedures used, and their applicability to the study of time limited workshops. 3.5.1 Sorting and Paired Comparison Techniques One of the most commonly applied families of evaluation techniques in mental models involves the repeated evaluation of similarity between two or more concepts. Depending on the technique selected, those concepts may be developed by the researcher, or elicited from the participant (Langan-Fox et al., 2000). In a one-on-one laboratory-like setting, the participant is presented with two or more concepts from the research domain and asked to spatially arrange, or rate the concepts based on their similarity. Through repetition of this task, a mental model for the participant is constructed that reveals the structure of their understanding of the research domain. Card sorting (Mohammed et al., 2010) represents the most basic form of sorting task, where the participant is presented with concept cards and is asked to spatially arrange them by similarity or relatedness. The result of the participants’ card sort would then be captured for later analysis. In pairwise comparisons, two concepts are presented to the participant and rated for similarity. This task is repeated for all pairs with the similarity ratings used to construct the participants mental model of the domain (Langan-Fox et al., 2000). Repertory Grid is a highly structured elicitation technique based on Kelly’s (1955) construct theory. The basic approach to the repertory grid technique is to present participants with triads of elements that represent key subject areas within a given field. So, for example, in energy planning research, three elements might be ‘demand management’, ‘renewable energy’ and ‘land use planning’. The participant then needs to develop a rationale for why two of the elements are more related than a third. Triads are continued until all relevant combinations have been exhausted (Tan & Hunter, 2002). The strength of the repertory grid system is that it is a well established methodology that is both structured and replicable. It has seen its widest application in management research (Tan & Hunter, 2002), but has also been applied in climate change (van de Kerkhof, 2006; van de Kerkhof et al., 2002) and environmental risk research (Smith, 2006). 70 Other sorting methodologies such as F-sort and Q-sort have been less frequently applied to mental models research (Kearney & Kaplan, 1997). Pros of the various sorting and comparison techniques include their long track record and established validity, and their applicability to cross-cultural studies (Langan-Fox et al., 2000). Limitations of the approaches include their repetitive nature leading to participant fatigue, and the fact that in most applications they need to be conducted serially – i.e. through one-on-one sessions with the researcher. For single contact engagement activities such as workshops, the serial nature of sorting and paired ratings evaluations would make them prohibitive, because the researcher, or research team, is severely limited by participant time. 3.5.2 Visuospatial Methods When talking about mental models, it is common to differentiate between content (knowledge) and structure (the organization of that knowledge) (Mohammed et al., 2010). For some mental models evaluation techniques, the researcher attempts to infer the model’s structure by interpreting the results of participant responses. Visuospatial techniques attempt to elicit model structure directly from the participant by having them create spatial relationships between concepts through drawing or arranging items (Lynam et al., 2012). Perhaps the most direct form of visuospatial methodology is concept mapping. Concept mapping calls for the participant to construct a spatial representation of their mental model of a system by drawing concepts and the relationships between them. Figure 2 depicts a concept map of a group of UBC Geography students’ conception of the energy system. This concept map conveys the key components of visuospatial methods, including both important concepts, and the connections between those concepts described visually by the study subjects. 71 Figure 3.1: An energy concept map developed by Geography undergraduate students at UBC (courtesy Ellen Pond). Chang used concept mapping to demonstrate differences between the mental models of blood sugar homeostasis in grade seven and grade nine students. Kearney & Kaplan (1997) describe the Conceptual Content Cognitive Map (3CM). This methodology calls for participants to identify the concepts they believe are important in a particular domain and then to arrange those concepts in a spatial representation of their mental model of the domain. The validity of the constructs generated by the 3CM has been demonstrated in empirical research (Kearney & Kaplan, 1997). The main advantage of visuospatial methods are their explicit recognition of the importance of the structure of mental models and their reliance on the participants themselves to describe that structure. A disadvantage, perhaps, is the potential impact that learning styles may play in the elicited material. The presence of visual versus verbal learning styles and their impact on spatial reasoning are well established (Mayer & Massa, 2003). Similar to the sorting and ratings tasks, another limitation of visualspatial methods is that they are time-intensive, making it necessary to include them as a major component of any corresponding engagement activity. Chapter 6 describes the use of a group exercise that made use of this visuospatial technique as one component of the case study evaluation of the GCCP process. 72 3.5.3 Statistical Methods Several methods have been developed to statistically analyze the content and structure of mental models (Langan-Fox et al., 2000; Weller, 2007). These methods use multivariate statistical methods to look for relationships between variables or between participants to evaluate model structure, or the presence of shared mental models. Langan-Fox et al. (2000) discusses the use of both multidimensional scaling (MDS) and the distance ratio formula (DR) to investigate relationships beween elicited concepts. Weller (2007) describes the Cultural Consensus Analysis method which uses factor analysis and cluster analysis to investigate the levels of agreement or sharing between different partcipants’ mental models. The Cultural Consensus Analysis method has been applied to mental models of water usage in human-environment research (Stone-Jovicich et al., 2011). The benefit of statistical methods is that they can be applied post hoc to a variety of different elicitation techniques, with the caveat that the results need to meet specific assumptions. For example, the Consensus Analysis technique is best performed on yes/no data or pile sorts, though it can be applied to other data (Weller, 2007). The promise of statistical methods for real world engagement evaluation is the possibility of using limited, structured elicitation techniques such as questionnaires and applying statistical methods to derive information about mental models structure. 3.5.4 Participatory Model Construction Methods One method that does not fit neatly into any of the other categories developed in this section is the Actors, Resources, Dynamics, and Interactions method (ADRI) described by Etienne et al. (2012). This is a companion modeling approach that calls for participants to co-construct a shared conceptual model of a system. Through the collaborative construction of the representation of the system, participants’ delineate a shared model. The ADRI process requires multiple workshops with the same participants and not only evaluates the process of engagement, but becomes the engagement activity itself. Evaluation of the ADRI in human-environment research in South Africa and France provided promising results, but was described as taxing by participants (Etienne et al., 2011). The ADRI may be most applicable in human-environment research that is already focused on participatory modeling approaches (e.g. Cohen et al., 2006; Langsdale et al., 2006). 73 3.5.5 Interviews and Questionnaires Interviews are a popular methodology for eliciting mental models, particularly in circumstances where the domain being studied is ill-defined (Morgan et al., 2002; Kearney & Kaplan, 1997). Interviews can range from open-ended to highly-structured depending on the intent of the elicitation (Kearney & Kaplan, 1997; Young, 2008). The benefit of interview techniques is that they do not constrain the participants as much as some of the more formalized techniques such as sorting. This allows participants to cover a broader range of concepts, including those that researchers may not have anticipated in early research design phases. Kearney and Kaplan (1997) view interviews in the early stages of a research project as key inputs to process design and methodology development. Kempton (1997), Bostrom et al. (1994) and Morgan et al. (2002) used interviews as a way to describe the conceptual space inhabited by climate change and environmental risk. In the case of Morgan et al. (2002) the outcomes of the interviews were used to construct influence diagrams (concept maps with procedural information) to investigate expert versus lay mental models of risk. Young (2008) uses interviews as her elicitation methodology for product design and marketing research. Kearney and Kaplan (1997) identify the lack of quantitative data as a key drawback of interviews. However, as Hoffman et al. (2014) demonstrates, quantitative data can be generated from interview transcripts through the use of thematic coding techniques. Like many of the other mental models elicitation techniques, however, interviews are time intensive, limiting the number of participants that can be studied in a given period of time. Similar to other techniques discussed in this section, then, interviews are a difficult methodology to insert into time-limited engagement exercises. Questionnaires have been used in mental models research in HCI (Green, 1990) and organizational research (Mohammed et al., 2010). The main advantage of questionnaires is that they are easier to administer to large groups of participants under time constraints. Lynam et al. (2012) emphasize the importance of situational variables in the evaluation of mental models, and state that elicitation techniques should occur under the same circumstances that the researcher is interested in learning about, because of the ephemeral nature of mental models. Questionnaires, then, have the advantage of being administered either during or immediately after an engagement activity, accounting for situational variables. The main criticism of questionnaires as a mental models elicitation technique is that they focus on the content of models, and are less well suited to evaluating the structure of those models (Mohammed et al., 2010). Some of the statistical techniques described in section 3.5.3 can help to 74 uncover structure in participants’ mental models from questionnaire data, but it is an open question whether they can achieve the same results as more time intensive techniques such as visuospatial representations. Questionnaires are a common form of evaluation in participatory planning activities, making the possibility of adapting them to the study of mental models a useful pursuit. 3.5.6 Discussion: Methodologies for Studying Mental Models in Community Energy and Sustainability Planning Applications As this section describes, there are a wide array of techniques that have been used in the elicitation of mental models across disciplines. As of this writing, none of those techniques has achieved a preferred status, largely due to the context specific nature of mental models research. Guidance for community energy and sustainability planning applications would include the need to assess the characteristics of the particular engagement process to be conducted, and the associated constraints presented (e.g. limited access to participants, time constraints, etc.). Guidance from Kearney and Kaplan (1997) about the importance of early process interviews with people with domain specific knowledge seem particularly applicable in planning applications where specific domain and local knowledge may uncover important concepts that researchers do not anticipate. These interviews may be particularly important in the realm of community energy planning, due to the novel dimensions planning for energy at this scale may entail (e.g. local renewable generation, district energy, land use implications, interactions of policy levers for different levels of government, etc.). Additional guidance is highly dependent on the nature of the engagement activities. For example, participatory modeling processes (e.g. Cohen et al., 2006; Langsdale et al., 2006; Jones et al., 2009) seem particularly well suited to connected techniques such as the ADRI. More dialogical processes may be able to use techniques such as visuospatial concept mapping or group sorting exercises as a form of structure to guide discussion. In the case of engagement processes with well developed existing structures, many of the more time-intensive techniques reviewed here will be problematic. This was the case with the community scale energy planning processes discussed in this dissertation, and had a strong design influence on the development of the methodology. 3.5.7 Rationale for the Mental Models Methodology Used in this Dissertation The methodology used in the mental models component of this dissertation drew from multiple sources in the review of elicitation techniques described in section 3.5. In some circumstances, this 75 was necessitated by realities of the processes to which the research was attached. For example, in all three case studies described in Chapters 6 and 7, contact with participants was limited to between two and five hours. In all but the Revelstoke case study (Girling et al., 2014b), there was only one contact with the participants. During the limited engagement workshops, there were a number of presentation and interactive engagement activities, leaving little time for the elicitation of mental models. In addition to the strong influence of the available elicitation time, the novelty of the research domain (studying community scale energy planning) also played a significant role in the development of the selected methodology. As a result of these situational variables, four key decisions were made about the resulting methodology: 1 Because of the novel research domain, and based on guidance from Kearney & Kaplan (1997) and Morgan et al. (2002), initial semi-structured interviews were conducted with practitioners and energy experts to help describe the research space and develop subsequent steps in the methodology. These semi-structured interviews are discussed in detail in Chapter 4. 2 Because of the limited time available for the mental models elicitation techniques at the community scale energy planning workshops, pre and post workshop questionnaires were developed based on the interview results from Chapter 4. These questionnaires were applied at a series of workshops associated with the Victoria Community Energy and Emissions Plan (CEEP), and the Greenest City Conversations Project workshops in Vancouver. Chapter 5 discusses the results of the pre workshop questionnaires, and assessment of participants existing mental model. 3 The use of the questionnaire format suggested the need for a technique, or techniques, to analyze the connections in participants mental models, based on the limitations of the questionnaire format described in section 3.5.5 As a result, the statistical techniques of Multiple Correspondence Analysis and Hierarchical Clustering were used to look for relationships in data from the pre workshop questionnaires. The premise of this application of MCA and clustering was that participants’ answer patterns, and subsequently the clusters they fell into, could be seen as surrogates for (or representations of) their mental model of community scale energy. A discussion of the MCA and clustering procedures can be found in Chapter 5. 76 4 Finally, there was a desire to look for changes in participants’ mental models as a result of participation. To look for these changes, portions of the post-workshop questionnaires repeated questions from the pre-workshop questionnaires. Changes in participants’ answers were collated based on the mental models clusters they were members of (based on the findings of Chapter 5). This examination of changes in mental models was limited by a number of factors, to be discussed in the corresponding chapters (Chapters 6 and 7). The resulting methodology has a number of novel characteristics, including the use of MCA and Hierarchical Clustering as a method of looking for the important components of mental models of energy, and for identifiable groups of mental models. In the remaining chapters of this dissertation, this methodology will be discussed in more detail, the results reviewed and, finally, the limitations and future directions discussed. 3.6 Conclusions This chapter reviewed the beginnings of the theory of mental models in cognitive science and how it moved from a theory of cognition, to a methodology for eliciting people’s mental models in a number of research fields and practical applications. A rationale was then developed for the application of mental models to the study of engagement in sustainability planning, based on mental models’ strength in revealing the connections and interactions between concepts, the existing relationship of mental models research with key components of sustainability planning, and findings from mental models research in related human-environment fields. The chapter also reviewed elicitation techniques, suggesting their strengths and weaknesses for use in sustainability planning engagement exercises. Finally, the chapter briefly reviewed the methodology developed for this dissertation, connecting it to the findings from the review of elicitation techniques, and the realities of connecting research to real-world participatory planning exercises. The next two chapters will build upon the mental models literature review developed here to describe the mental models methodology selected and developed for this dissertation in more detail, and to review the results of the evaluation. Starting first with the mental models interviews used to inform both the development of the community energy planning workshops, and the evaluation questionnaires (Chapter 4), and then moving to a discussion of the pre-workshop questionnaires as applied to the GCCP workshops in Vancouver and the Victoria CEEP workshops (Chapter 5). 77 Chapters 6 and 7 will combine the mental models evaluation results regarding changes in participants’ models with a broader case study analysis and description. Finally, in Chapter 8, the successes and failures of the developed mental models evaluation procedures will be discussed, as well as future directions for developing this methodology and applying it in future engagement activities. 78 Mental Model Practitioner Interviews - Towards the Development of the Mental Models Methodology. 4.1 Introduction Chapter 2 developed the rationale for the inclusion of participatory processes in community energy, and more broadly, sustainability planning and PIA. Chapter 2 also reviewed the challenges inherent in trying to demonstrate the value of participation in complex, multi-level decision making, where it is difficult to isolate changes in policy or behavior directly resulting from participatory processes. Chapter 3 reviewed the use of mental models in a variety of fields, including more recent work in human-environment research, where researchers are looking at how participants’ mental models factor into decision making (Jones et al. 2012). Chapter 3 also made the case for the use of mental models as a methodology to demonstrate the effects of participation in the absence of a clear, linear connection between participation and resulting planning or policies. In this chapter, the evaluation methodology used in this dissertation will be introduced, starting with the mental models interviews conducted with a selection of energy experts and practitioners (see Figure 1.2). The purpose of this evaluation was two-fold: firstly, the evaluation was intended to describe existing mental models of energy at the community scale (Research Question 2 a)). Secondly, the evaluation was intended to demonstrate (if present) changes in participants’ mental models as a result of participation in community energy planning workshops (Research Question 2 b)). The context for the research, to be described in detail in Chapters 6 and 7, were a series of community energy and climate change planning workshops held in three different BC municipalities. The primary focus was on community energy planning workshops developed and held in the City of Vancouver in 2012 as part of the Greenest Cities Conversations Project (GCCP). These workshops and the evaluation results will be described in Chapter 6. Comparison case studies were also evaluated for Community Energy and Emissions Planning (CEEP) workshops in the City of Victoria, and a partial comparison was conducted for climate change and urban form planning workshops in the City of Revelstoke. These comparison case studies and the results of their evaluations will be described in Chapter 7. 79 There were four main reasons for the selection of workshops as the participatory mechanism in this study. Firstly, there is substantial existing expertise at the Collaborative for Advanced Landscape Planning (CALP) at UBC and associated research groups in developing and conducting workshops (cf. Salter et al., 2009; Sheppard et al., 2011). This allowed the project to draw upon that existing expertise to develop materials and process components that would not have been available with other participation techniques (for example, social media campaigns). Secondly, workshops represent the dominant form of engagement in sustainability planning, PIA, etc. (see Chapter 2). Therefore, workshops would be representative of broader engagement efforts related to the subject of community scale energy planning. Thirdly, the types of collaborative, problem-based learning frequently applied in planning workshops and design charrettes (cf. Girling et al., 2006) are consistent with constructivist learning theory, and therefore have as one goal the development of participant learning and capacity. Finally, there is a practical reason; the workshops developed for this research project were part of the larger multi-channel Greenest City Conversations Project, which needed a more intensive, workshop-like engagement process to compare and contrast to other channels such as social media, interactive gaming, and performance art (see Figure 6.1). The combination of these factors led to the selection of workshops as the participation mechanism for this study. 4.2 Theoretical Context 4.2.1 Constructivism and Social Constructionism In the broadest terms, the theoretical framework for this research comes from a Constructivist and Social Constructionist perspective. Constructivist, because the focus is on how individuals construct meaning about the world around them (Crotty, 1998). This is consistent with the psychological approach taken in this research, and with the mental models approach more specifically, which focus on individuals and how they make sense of the world. The theoretical framework, however, extends into the realm of Social Constructionism, in that it recognizes the role that sociocultural factors play in development of that meaning (Crotty, 1998). As Fish (1990) states, the meanings we assign to objects, and the world more broadly, are significantly impacted by the social and the conventional. Indeed one of the bases for participation in planning and other contexts is the notion of a collaborative development of meaning – a shared mental model (Jonker et al., 2010). 80 4.2.2 Constructivist Learning Theory Building upon the epistemology of constructivism, researchers in educational psychology developed constructivist learning theory, and with it learning techniques based on active, experiential processes (cf. Bruner, 1961; Piaget, 1950; von Glasserfield, 1989; Vygotskii, 1978). Borrowing the constructivist perspective that people learn by interpreting new information and new experiences within the context of existing knowledge and values, constructivist learning theory developed a host of new instructional techniques based on experiential and problem-based learning (Gagnon and Collay, 2000). Many of the techniques used in participatory planning are consistent with the constructivist learning approach, with a focus on collaborative exercises such as participatory mapping, brainstorming and facilitated dialogue (cf. Girling et al., 2006, Kasemir et al., 2003, Patel et al., 2007). Constructivist learning is also the basis for significant research on collaborative use of interactive and multimedia tools of the types frequently used in participatory planning (e.g. Mayer &Moreno, 2002; Mayer et al., 1999) 4.2.3 Mental Models Theory If Constructivism and Social Constructionism represent epistemological theories about our interactions with the world, and Constructivist Learning Theory represents an attempt to tie those broader theories to methods of instruction in educational settings, Mental Models theory is an attempt to explain the mechanics of how individuals construct that meaning. While the earliest works on mental models do not specifically reference constructivist learning theory (Johnson-Laird, 1983), mental models are consistent with the tenets of constructivist learning. More recent research does specifically tie mental models and constructivist learning theory (e.g. Chermack & Van der Merwe, 2003), and posits that mental models are the mechanism for the construction of meaning. In addition, as Chapter 3 discussed, connections to components of sustainability planning and to related human-evironment research lead to the hypothesis that mental models could be a useful methodology for evaluating the emerging field of participation in sustainability planning. 81 4.3 Research Questions The purpose of the mental models component of this dissertation was to define the mental models that stakeholders, citizens, municipal staff and practitioners use to conceptualize energy at the community scale, and to evaluate the impacts that participatory processes have on those mental models as an indicator of individual and shared learning. The following two questions (and associated sub-questions) represent the main themes of the mental models exploration undertaken in this research – i.e. Research Question 2 of the broader dissertation research. 2. A) What are participant’s existing mental models about community-scale energy? i. Is there evidence of a link between energy expertise and participants’ mental models? The majority of mental models research (see section 3.3.3) has been directed towards an assessment of people’s existing mental models. For this dissertation, an assessment of existing mental models of energy was interesting for two primary reasons. Firstly, as a new research domain (Andrews, 2008; Neves & Leal, 2010), little is known about how people conceptualize energy at the local scale. Secondly, an understanding of people’s existing models of energy is a necessary pre-condition to evaluating changes in those models (see research question 2 b)). Finally, there is an established literature on the differences between expert and lay mental models of different domains (cf. Morgan et al., 2002; Smith, 2006). Though the focus on lay versus expert in this prior research was directed towards ‘correcting’ lay models, which is inconsistent with this dissertations position on engagement, the differences between lay and expert models are still important. These differences may tell us something about the disconnects between practioners and citizens that lead to discord in planning processes. Therefore, the subquestion about the link between expertise and energy mental models can augment our understanding of energy at the local scale. 2. B) What are the effects of participation in energy engagement processes on people’s mental models of community-scale energy? i. Is there evidence for individual learning as reflected by changes in participants’ mental models? ii. Is there evidence of the emergence of “new” knowledge or shared learning as a result of participation in the process? 82 iii. Is there evidence of convergence in participants’ mental models towards more shared mental models of community-level energy? iv. Is there evidence about what aspects of the workshop process drive changes in mental models? Research question two and its sub-questions refer to the use of the mental models methodology as an evaluation technique for assessing the impacts of participation in a workshop. How do participants’ mental models change as a result of participation in the case study processes? Mental models here are being used as a measure of shared learning, and an indicator of social learning and capacity building – longer term goals of engagement and participation. This is a more challenging use of mental models methods, and is much less common in the literature (Jones et al., 2011; Mohammed et al., 2010; Wilson & Rutherford, 1989). 4.4 Description of the Research Methodology As Chapter 3 revealed, methodologies for eliciting mental models tend to be intensive in nature. Strategies such as repertory grid (Tan & Hunter, 2002), interview procedures (Young, 2008) and concept mapping (Chang, 2007) usually occur one on one with a researcher over a protracted period of time. This often means that the number of participants in a study is comparatively small, and that inserting mental models methodologies into real-world participatory processes can be challenging. Limited examples exist of attempts to adapt mental models methodologies to instruments that are less intensive and more amenable to evaluation of real-world processes where participants have limited time to devote to evaluation procedures. In this study, methodologies were adapted from Morgan et al (2002), Young (2008), and Bostrom et al. (1994). In their procedure, Morgan et al. used expert interviews to describe the mental models space for environmental risk issues. That space could then be used to develop materials for communication with a broader audience. Young (2008) similarly uses semi-structured interview procedures to reveal participant’s mental models for broader application in the design and marketing of products. Bostrom et al. (1994) used a series of exploratory studies to develop a highly structured interview process (similar to questionnaires) in their study of mental models of climate change. The research design used in this project adapted a similar two-part strategy based on first describing the mental models ‘space’ through more intensive semi-structured interviews with energy experts, 83 planners, and non-governmental organizations (NGOs). The results of those interviews, described later in this chapter, were used to construct pre and post workshop questionnaires with questions designed to elicit workshop participants’ concepts on important mental models components (see Figure 1.2). The pre-test questionnaire focused on aspects of participants’ existing mental models, while the post-test questionnaire looked for changes in their mental models, in addition to general workshop evaluations and demographic information. 4.4.1 Interview Methodology Sampling for the community energy planning mental models interviews used the snowball sub-strategy of the purposeful sampling model (Creswell, 2008). Sampling attempted to identify participants from a range of community energy related fields to elicit and define a more complete set of concepts about community-level energy. Previous research on the elicitation of mental models has shown that small samples of four to six respondents can be adequate to accurately represent the concepts held by a given population (Gaines, 1989), and that larger numbers will often produce redundant data (Smith, 2006). As a result of the purposeful sampling methodology and snowball sampling methods, 13 participants were identified and interviewed for this research project. Initial plans were to interview between 15-20 subjects, however two realities of inserting research into real world processes interfered. Firstly, delays in project initiation were followed by a contracted timeframe between the selection of the locations and dates for the accompanying workshops and the actual workshops themselves. This led to a shorter available interview period, particularly since the interviews needed to be completed before workshop questionnaire construction could begin. Another limitation of the interview procedures was the exclusion of non-expert Vancouver citizens. The initial research design called for using Vancouver’s Greenest City Action Plan email list to solicit participation by Vancouver citizens. Delays in this procedure, when coupled with the short interview timeframe prevented the inclusion of non-expert citizens. While not all of the interviewees were energy experts, their proximity to energy-related issues in their work would not qualify them as true non-expert mental models of energy at the community scale. The selection criteria for the interviewees focused on practitioners that are working on energy issues at the municipal or community scale. This included planners who are including aspects of energy in 84 neigbourhood and Official Community Plans (OCPs) as a result of the BC Government’s Bill 27 (Rutherford 2009), as well as energy experts from various levels of government, NGOs, and private practice that are focusing on energy at the local level. The focus of the interviewees work on energy ranged from facilitating engagement on individual energy use with citizens to detailed, bottom-up spatial modeling of energy demand in different sectors. The selected participants, though not an exhaustive sample, are representative of the types of practitioners who would be involved in a Community Energy and Emissions Plan (CEEP) or related energy-focused community planning process. The resulting sample of interviewees consisted of: • Two university researchers with a focus on energy modeling and climate change mitigation/adaptation • One employee of a provincial climate change organization with an extensive background in community sustainability planning • One employee of a BC NGO with a focus on facilitation and public engagement, whose work includes engagement on energy and climate change issues • One energy planner with the City of Vancouver • Two community planners with the City of Vancouver • One Province of BC government energy expert • One Federal Government energy expert • Two energy experts from a provincial energy-focused NGO • One Community Energy Manager with BC Hydro • One energy expert with a national environmental and energy policy NGO Berger (2000) identifies four types of interviews: informal interviews, unstructured interviews, semi-structured interviews and structured interviews. Because the intention of mental model interviews is to as completely as possible elicit participants’ mental models in an identified domain, a semi-structured or structured interview technique is most commonly used. For the interviews used in this study, a semi-structured approach was taken to ensure coverage of key community energy planning concepts, but also to allow for pursuit of interesting diversions into related topics, given the relatively unexplored nature of this emerging practice. As the results will show, some of these diversions revealed important themes that later became key parts of the evaluation questionnaires. The resulting interview script can be seen in Appendix A. The script began with very broad questions about energy with the participants encouraged to speak expansively. Participants were informed that if key concepts were covered in the early broad questions, more specific questions later in the interview could be skipped. 85 The script started with questions intended to elicit participants’ fundamental understanding of energy and systems related to energy. This began at the highest level, asking participants to describe what came to mind when they heard the word energy, and then working down in scale to the city-level, and finally to their individual interactions with energy. The script then covered more specific questions about energy in the City of Vancouver context, such as reasons why we should be addressing energy as a planning issue, concepts of energy demand, supply and transmission, ways to reduce energy use, ways that energy supply might change in the future, concepts of the localization of energy planning, and issues surrounding responsibility for addressing energy issues. These more detailed questions were developed from a review of the literature and conversations with energy experts, and then reviewed and revised with the research committee. In the interview debrief, participants were encouraged to discuss any subjects related to energy planning at the community scale that we had missed, or any areas of specific interest to them. Several of the participants used this opportunity to further explore specifics of energy and community planning that they felt were particularly important. 4.4.2 Interview Analysis All of the interviews were tape-recorded and later transcribed using the NVivo 10 (QSR International, 2013) Computer Assisted Qualitative Data Analysis Software (CAQDAS). The interview transcripts were coded using a predefined, rather than a grounded coding scheme, based on the fact that many of the concepts being coded for were known (Miles and Huberman 1994), and the interviews had followed semi-structured approach. Codes were revised, however, during the coding process to allow for inclusion of new codes and elimination of inappropriate codes (Miles & Huberman, 1994). The purpose of the interviews was to perform two tasks. Firstly to reveal key themes and concepts as inputs to the workshop design and the workshop questionnaires, and secondly to reveal the mental models of community scale energy of the energy experts and planners. The interview analysis focused on these two tasks. 86 Firstly, the frequency of references to particular themes or concepts were used as one approximation of the importance of those concepts in the interviewees’ conceptions of energy. These reference frequencies were cross-referenced with coded answers to reveal qualitative information about the context in which the concepts were discussed. The resulting analysis provided information about the categories and subcategories of concepts that should be included in the questionnaires. Secondly, connections between codes in the participants’ coded transcripts were used to develop concept maps showing the frequency of references to a given concept, and its connections to other concepts – a visual representation of that participant’s mental model. The connections between codes were made based on the number of times they were mentioned together in the same answer. So, for example, if a participant referenced both demand reduction and solar thermal in an answer about possible big moves to address energy issues, those items would have a connection frequency of one. A caveat to this analysis step is that this concept map represents a model of a model (i.e. the researcher’s interpretation of the participants’ responses), and is open to criticism and reinterpretation. 4.4.3 Interview Results The interviews conducted in this study represent an interim result, in that their purpose was to inform later parts of both the workshop creation, and evaluation processes. Application of the interview results to the workshop development process took the form of an extended peer review as described by Funtowicz & Ravetz (1993). Extended peer review is the process of inviting people from outside a project’s working team to advise and comment on procedures during the research to encompass a broader range of views, particularly for complex, multi-faceted human-environment problems. For the evaluation procedures, the results of the interviews identified key themes, in some cases ones that had not previously been identified, that should be included in the developed questionnaires. Therefore, the results of the interviews were a crucial input to subsequent methodology step. 87 Figure 4.1: A tree map showing the aggregate coding frequencies across participants for every code used in the interview analysis. Frequency is represented by the colour of the box (green is highest), the size of the box (larger=higher frequency) and the location of the box (upper left=higher frequency). 88 Response Frequencies for Key Concepts Figure 4.1 shows a tree map view of the aggregate concepts coded from all of the interview participants. This representation is not sensitive to the number of times a particular respondent used the codes, but rather represents all participants’ codes. The colour scheme runs from dark orange for concepts with very few references, to dark green for concepts referred to the most frequently. A potentially confusing feature of tree map data visualizations is that it represents the frequency of codes with the colour, size, and arrangement of the boxes in the display. So a topic that was coded frequently will be darker green, have a larger box, and be positioned towards the upper left of its respective container. Table 4.1 displays the same information as the tree map in a numerical format. The organizing hierarchies were created by the researcher, and are not a function of the NVivo software. For example, the parent code Demand represented by the sub-table in Table 4.1 and the large encompassing DEMAND box in Figure 4.1 was a coding hierarchy assigned by the researcher. Table 4.1: Aggregate coding for all participants organized by parent and child codes, and number of times coded. Code # Code # Demand 103 Supply 113 Building 65 Hydroelectric 44 Behaviour 27 Other Electric 31 Efficiency 23 Solar Thermal 29 Demand Reduction 22 Renewables 29 Consumption 19 Biomass 26 Food 13 PV 12 Residential 11 Waste Heat 4 Commercial 10 Wind 9 Industrial 7 Fossil Fuels 34 Embedded Energy 10 Gasoline and Diesel 15 Transportation 58 Natural Gas 22 Public Transit 19 Coal 7 Electric Vehicles 17 Active Transportation 9 Single Occupant Vehicles 5 89 Code # Code # Systems 114 Worldview 101 Energy System 73 Economic 59 Sociocultural Systems 47 Social-humanistic 36 Governance Systems 34 Pessimism 36 Natural Systems 26 Limits 23 Economic Systems 7 Optimism 8 Ecological Systems 4 Authoritarian 7 Resource Management 6 Ecocentric 6 Reasons to Address 75 Anthropocentric 5 Climate Change 72 Sustainable Development 4 Environmental Degredation 22 Social Justice 20 Energy Security 20 Land Use 24 Economic Reasons 17 DES 42 Resource Depletion 11 Urban form 40 Sustainability 6 Density 18 Progress 4 Intergenerational Justice 1 Responsibility for Action 56 Non-hierarchical Municipal 57 Solutions 90 Provincial 21 Policy and Regulation 37 Multiple 15 Challenges 36 Business 15 Planning 36 Federal 13 Collective Action 34 Individual 10 Technology 34 Regional 9 Individual Action 32 Sense of Place 21 Localization 17 Scale 60 Social Capital 15 City 54 Transformational Change 14 Local 24 Incremental Change 14 Regional Scale 20 Global 10 Provincial Scale 7 90 In the coding scheme used there were parent codes and child codes (and in rare instances grandchild codes). Therefore, when a child code was coded, the parent was also coded, this accounts for the high reference numbers associated with particular codes. Parent codes included: systems, demand, supply, reasons to address (energy issues), responsibility for action, worldview, scale and land use. So, for example, if a participant referred to solar thermal energy, both solar thermal and supply would receive a coding reference. This accounts for the dark green colour associated with parent codes in Figure 4.1 and the high number of coding references in Table 4.1. Some basic information can be inferred from the simple frequencies. The most common reference was to systems (114). While this may be surprising, it is likely the product of the participants involved in the study, who focus primarily on energy and land use planning issues, both of which inhabit and interact with systems regularly. It should also be noted that while the system referred to most was the energy system (75), there were also references to natural systems (26), economic systems (7), Governance Systems (34) and Sociocultural Systems (47). These numbers do not add up to the total number of systems references, because participants may have mentioned multiple systems in a given coding block. The most common reference for reasons why we should be addressing energy issues was, unsurprisingly, climate change (72), followed by resource depletion (23), environmental degredation (22), energy security (20), social justice (20), economic reasons (17), resilience (9), progress (4), and intergenerational justice (1). Despite the more common focus on energy demand amongst most energy experts (Palensky 2011), there were more references to energy supply (113), than energy demand (103). Another interesting point from the coding references is the fact that the interviewees spent more time focused on solutions to energy issues (90) then on the challenges energy issues present (36). For a more detailed review of the interview codings, in particular as they apply to the design of the workshop questionnaires, the coding results will be broken down by important themes. 91 Energy and Associated Themes Based on the coding frequencies as expressed in Figure 4.1 and Table 4.1 certain important themes emerged. Categorizing energy into the traditional headings of supply and demand, and talking about sectors such as residential, commercial and industrial did introduce a cognitive frame to the interviews that might have constrained some participants. While efforts were made to avoid the jargon associated with energy expertise (i.e. referring to ‘where our energy comes from’ as opposed to ‘supply’), these categories still may have impacted the resulting discussions. 1. Supply From participants’ discussions of supply, certain themes became evident. The most commonly referenced code in the supply category was hydroelectric (44), to be distinguished from electricity more broadly. An additional code (other electric) was used to capture discussion about coal, natural gas or nuclear generation of electricity. Some of the references to hydroelectricity were primarily in discussion of the current energy system in Vancouver, however a common theme was the special status that Vancouver, and BC more generally, have with the abundance of low GHG power provided by hydroelectricity. So I know the one that comes out often is "well if we want to reduce greenhouse gas emissions" which is of course the legislated action for municipalities... its not energy really, not for the municipalities, its GHGs, at least explicitly, and you know there is always that... ‘well, we have GHG free hydroelectric power, so why wouldn't we just put everything on hydro and then we would have reduced our GHGs to zero’ Subject 3 Well apart from the heebie jeebies it gives to some organizations and I'm sure, and rightfully so to some extent, BC Hydro as far as increasing their overall load, as long as one plugs in on off peak load times (electric vehicles), those kind of concerns can be significantly mitigated, and as such you switch from a traditional fuel base to get your cars around, to fundamentally a hydro based electricity system, so bring it on! Subject 10 There was also frequent discussion of renewables broadly (29) and solar thermal (29) and biomass (26) more specifically. The discussion of solar thermal and biomass reflected a focus on generating energy more locally and exploiting local opportunities. I think it depends on the time frame that you are looking at. I think if the City had its way we would be actually using more biomass then we currently are. As far as I can tell, there has not really been a lot of consideration for solar. Both passive solar and even solar thermal, I mean it’s in there a little bit, but not, not at the scale that I think we are going to need it at, which I think is pretty much 100% deployment. Subject 1 92 There is no reason why we can't localize 80% (of our thermal energy). Between solar hot water, air source heat pumps, geoexchange, all those... you sprinkle a little bit of electricity on them to pump fluid around, then you add in a bit of biomass... There is no reason why we need to be bringing natural gas down from the Peace region or Alberta. Subject 5 As the above quotes suggest, there was significant cross-referencing between supply and solutions, where 40 of the 90 solutions codes co-occurred with supply codes. 2. Demand Participants spoke more about building demand (65) than transportation demand (58), though that difference isn’t large in such a small sample. When discussing transportation and buildings, the focus seemed to reflect the sentiment that municipalities in general, and Vancouver in particular, because it has its own building code, have more control over buildings and urban form then they do over transportation. There was also the notion that working on buildings is easier, because they are less impacted by individual behavior. And so that land use question... and in terms of jurisdiction and a city... we don't have jurisdiction over implementing California tailpipe emission standards. So then narrowing it down and where is our circle of influence, and okay buildings, it really came down to buildings. Subject 12 Hmmm, I think transportation would have a bigger impact, but buildings is easier to accomplish, because it has somewhat less to do with behavior Subject 3 Another theme that developed related to the issue of local or municipal influence, and where the circle should be drawn when talking about community energy. Does community energy planning include issues such as consumption (22) or embedded energy (10)? There's two things just to be aware of. One of them that has come up a couple of times is the potential to influence energy use beyond the boundaries that are accounted for. So with CEEI that's fuel burned within the jurisdiction, but depending on the procurement choices that a local government makes, they can significantly influence the embedded energy. If we go to greener concrete or alternatives, or alternatives to asphalt, as two high carbon examples. There are opportunities to influence and move things through supply chains using purchasing power large public sector organizations, to create broader changes. Subject 5 Yeah, I think it’s critical at this scale for lots of reasons, but I also wonder, personally, how much is possible at this scale without larger commitments from kind of a broader jurisdiction. So, I mean its really hard to ignore the fact that we.... like the energy from our consumption behaviors, like buying products from China, and buying food from Mexico, those are not really accounted for in the municipal action plan. Subject 2 93 Because it is not just the consuming of the product itself, it is actually the economy and activity and the culture that's based around the consumption of that product that also creates GHGs and uses energy and you know our whole society is... and those things aren't being accounted for. Subject 8 3. Worldview While there were no specific questions directed at eliciting participants’ worldviews, it was an anticipated finding from the research, because of the central role worldviews and associated discourses play in people’s construction of meaning (Dryzek, 1997). Table 4.2 shows the number of coding references relating to worldview for each of the participants. The lowest number of references came from subjects five and 13, each of whom only made comments that suggested their worldviews three times during the sessions. On the other side of the scale, subjects seven and twelve had the highest number of worldview references with 21 and 26, respectively. Table 4.2: Participants' worldview coding references. Subjects 5 and 13 had the fewest worldviewe references (3) and subjects 7 (21) and 12 (26) had the most worldview references. Subject 1 2 3 4 5 6 7 Worldview References 7 13 6 9 3 15 21 Subject 8 9 10 11 12 13 Worldview References 7 7 8 14 26 3 Worldview components varied from subject to subject, leading to the following codes (see Table 4.3): 94 Table 4.3: Descriptions and sources for worldview component codes used in the interview analysis. Code Description Source Optimism Participant demonstrated optimism about our ability to address energy concerns. There is a significant body of literature on the impacts of optimism and pessimism on people’s behaviors and decisions. For a review, see Chang (2001). Pessimism Participant demonstrated pessimism about our ability to address energy concerns. See optimism above. Eco-centrism Participant demonstrates an ecocentric outlook, focusing on nature-centred values. Eco-centrism and Anthropocentrism are used in the New Ecological Paradigm (Dunlap and van Liere 1978) as indicators of worldview as it pertains to environmental issues. Anthropocentric Participant demonstrates an anthropocentric outlook, focusing on human-centred values. See eco-centrism above. Economic The participant expresses a view that economic means such as the market, appropriate pricing of resources, or internalizing externalities are the best approach to addressing energy issues. This worldview component maps to Dryzek’s (1997) Economic Rationalism environmental discourse. Social-Humanistic (This worldview component may be better labeled ‘Social Equity’.) The participant expresses a view that both the reasons to address energy issues, and the best solutions should be focused on issues of social justice and equity. In this conception, we should be addressing energy to improve people’s quality of life. Feagan (1993) refers to Social Equity as a strong component of worldviews in his analysis of sustainability worldviews. Limits The participant expresses a belief that there are real limits to resources and the planet’s capacity to accommodate waste products, and that we are in some way approaching those limits. This worldview component maps onto Dryzek’s (1997) Survivalism environmental discourse, and is prevalent in much of the literature on peak oil (cf. Deffeyes 2005) 95 Code Description Source Sustainable Development The participant expresses a belief in sustainability as a form of progress towards a better future. There are elements of industrial ecology here, with references to Paul Hawken, Amory Lovins, etc. (see e.g. Hawken 2005, Hawken et al. 2000) This worldview maps onto Dryzek’s (1997) Sustainable Development environmental discourse, but also has components of his related Ecological Modernization environmental discourse. Authoritarian The participant expresses a belief that energy and related climate change issues would be best addressed if decision-makers took a more authoritarian approach, taking action without public buy-in. This worldview maps onto Dryzek’s (1997) Adminstrative Rationalism environmental discourse, which holds the view that important environmental decisions should be left to the experts. It also tracks with the large body of literature on authoritarian personalities and worldviews in the social psychology literature (cf. Jost et al. 2003). The most common worldview component or discourse revealed by the participants was economic (59). Twelve of the respondents referred to economic drivers or economic solutions at some point in their interviews. Participants six (15 references), seven (8 references) and twelve (15 references) all demonstrated strong economic rationales for energy challenges and solutions. Subject six tied economic aspects to sustainable development (1) and progress (1). Yeah, I think the supply side is exciting. So, I think there's a pretty clear trend from the classic utility grid system, the centralized supply and distribution network cascading down from transmission to distribution to consumption for both thermal and electricity, to a distributed grid. And I think the market economy is going to drive that, and I think there are cases around the world where its not just talk, we can see that happening. There's money in it for people…. So more distributed generation, and maybe even distributed total demand management... distributed, you know where private owners would control private distribution networks. Subject 6 Participants seven (9) and twelve (15), on the other hand, tied economic aspects more closely with pessimism. Subjects seven and twelve were much more focused on the need to internalize externalities and use pricing to influence behavior because other methods were proving ineffective. 96 But on the other side when you work in the real world, you realize how extreme that view is, and how the world is determined by economics, and the way to go forward is to internalize all the externalities, you know CO2 and all of that, and that's the only way that we are going to survive as a species. Subject 7 And I also know from a behavioral perspective, just from myself, economics can change my behavior, its really the only thing... climate change is such a nebulous concept, we can't ground it and feel it... really, is it going to happen and what’s it going to look like and feel like.... and it’s like, yeah Toronto is having a warm winter, but they'll probably have a cold one next year. Its like we know it, but we don't feel it. I don't know. It’s too hard to grasp, but its not... but when gas gets really expensive, people change. Subject 12 The second most common worldview component was pessimism (7 participants, 36 references), perhaps reflecting the concerns about energy and climate change possessed by those that work on these subjects on a daily basis. You should interview less cynical people... Although I'd like to believe otherwise, I'm just going to be super honest, my own experiences... tell me that for the most part people are going to act selfishly for their own best interest and their own level of comfort and for how much they can get for themselves. Subject 3 Conversely, optimism was only evident for five respondents with eight total coding references. If one or two municipalities begin to do this well, and others see what's possible, then actually you can create this cascade effect that rolls out from there. So I think there is a role for Vancouver or other municipalities to play in pushing that agenda forward and being champions of it. Subject 11 A third common worldview component was what Dryzek (1997) calls limits (7 respondents), the focus on the finite supply of resources and humanity’s profligate use of them. This argument is perhaps best exemplified in the energy field by peak oil (Campbell, 2002; Deffeyes, 2005). Several of the participants expressed sentiments consistent with a limits-based worldview. At the heart I think its quite clear not only with energy usage, but with resource usage of all kinds that we are living beyond our means... People are recognizing those Limits to Growth that the Club of Rome talked about actually maybe are there, and are not just a rhetorical item. Subject 11 I think we are going to reach the end of oil... they are both important, but I think we are going to reach the end of oil before we'll die as a species from climate, but we'll die as a species because we will all be killing each other for energy. Subject 12 97 A particularly interesting worldview component that arose in nine participants’ responses was a more humanistic or social equity lens (36 references). Rather than focusing on energy per se, these responses tied energy to quality of life, health and social justice issues. The answer is yes. I see there being a very strong correlation between feeling like I'm in a place that is animating its principles of energy efficiency, and doesn't... not just being told statistically that we are just reducing emissions by this percentage. That... there's kind of like an embodied experience of walking through a neighbourhood and seeing less traffic, feeling like you have more of an opportunity for exchange with people, its quieter...you feel like the air's healthy to breathe, so there's some pretty... like sensational, your senses you can feel it that way. Subject 2 While the importance of worldviews to mental models and people’s interactions with concepts such as community energy planning were anticipated in this research, the findings from the interviews in terms of the various constituent pieces of participants’ worldviews were stronger than anticipated. These worldview components proved very useful in the construction of the workshop questionnaires. 1. Land Use and Urban Form Another theme that received significant attention in the interviews was the importance of, and opportunity presented by, land use and urban form. Several participants (8) identified urban form as perhaps the biggest lever in addressing energy use issues at the community scale. Overall, there were 40 references to urban form, and 18 references to density. Participant responses varied from 11 references to urban form on the high end (subject 1), to zero references on the low end (subject 7). References to density ranged from five on the high end (subject 3), to zero on the low end (several). A particularly interesting finding in relation to density, was that none of the three staff members from the City of Vancouver made any references to it, perhaps reflecting a shared mental model within the city relating to the dangers of using the density message (Smith, 2008; Bula, 2014). The big elephant in the room, of course, is the whole issue of land use and transportation and the nexus between them. And, where you build your compact complete communities and how energy efficient you make them is really a cornerstone of energy use within/across the City of Vancouver. Subject 10 So the only other one I would say is the... the area that I think is really misunderstood is the connections between land use, density and energy, in a couple of ways. First, many people don't understand that increased density has the opportunity to reduce emissions significantly. Mostly through transportation, but also because you can bring in district energy systems. Then, actually I also find that, in the sustainability world people have embraced density and high density as a solution, and I actually think that there is a cutoff point where high density is also not going to solve the problem, because of issues like passive solar, and where you start to build tall towers that have really poor energy 98 performance and actually are energy hogs... so somewhere in there there is a middle ground that I think is a very good solution. Subject 1 Subject one’s reference to the energy-related problems associated with towers was a subtheme of the urban form topic. Five of the participants made specific reference to good versus bad density, and the inherent problems of glass towers as an urban form component. The focus on urban form could be seen as an artifact of the researchers and practitioners accessible through the snowball sampling methodology used in this study; however, several of the people interviewed did not come from a spatial planning or design background. The focus on land use as a key lever for energy planning was relatively ubiquitous, even amongst those with a more technology or economic focus. 2. Sense of Place An emergent theme that was not anticipated before the interviews began, and that was not specifically elicited by any of the interview questions, was the connection between place-related issues and community energy planning. The overall number of references to sense of place issues was not high at 21, however nine of the 13 participants made reference to sense of place issues in relation to community scale energy planning. I think that they can be completely complimentary. I think if a place is designed... I think in fact that a sense of place is something that is connected to natural energy systems, I guess, or clean energy systems, simply because you know, designing in relationship to the access of the sun, and the access to solar energy, those things actually inform our comfort in place and space on this earth, so connecting with those vital energy systems I think connects us with place and creates better habitats I guess, better places for people Subject 8 Well, I've spent a fair bit of time in Europe, and everything over there isn't perfect, they are in a big economic problem at the moment, but just in terms of that thing of sense of place, social connectivity, sense of well being, having lived in Sweden and Germany, and travelled a number of places over there where there are more mixed use urban environments, where there is a mix of services, where residential and commercial areas are connected by rail, bus links, bike paths, etc. I would say that that type of urban planning not only means there is less energy throughput and associated costs, but enables those social co-benefits... sense of place, improved social connectivity, and general sense of wellbeing. Subject 9 3. Collective versus Individual Actions While interview participants were not asked specifically about individual versus collective actions to address energy issues, multiple questions did approach the issue and the subject came up 99 frequently in departures from the interview script. Responses on the efficacy of individual and collective actions were mixed, with the general consensus being that both were necessary to address concerns over energy demand in the future. Perhaps the most common refrain in the discussion of collective and individual action was the challenge involved in implementing either approach. There was a general consensus that energy experts and planners know some of the things that should be happening, but they are unsure how to enact them under current sociocultural and political conditions. ideally for me it would be a setup where it would be very easy for people to.. for their top choices to also be less energy consumptive and have less energy impacts on the environment. So, I actually look to a smaller focused group of people to have that set of knowledge and to help set policy so that it makes it easier for the wider population to be informed at times to support the policies and support the politicians and support the direction , but not need the amount of knowledge and intensity that I have. Subject 4 I think the collective one is much harder to define. The individual one is much easier to define. You could change your furnace, you could put in solar hot water, you could better insulate your house, you could figure out how to drive less, you know, get rid of your car, blah, blah, blah. But moving to the neighbourhood or collective scale is much harder to understand, and yet, I think, I think that the place that that becomes important is that the.... the individual changes I think are not going to happen unless they are somehow undertaken collectively. Subject 1 Yeah, and I think, because... ultimately there is a bit of a cycle with this collective action and individual actions. So collective decisions have implications for individual actions in the future, even if they don't now, necessarily, but what’s the use of building densified communities if no one wants to live there? What's the use of building bike lanes downtown if no one’s on them? So, I mean ultimately, when it comes down to it, we are going to have to have citizens using less energy or consuming differently. Subject 2 4. Energy Zeitgeist The final theme to be discussed here are concepts that arose frequently seemingly because they are in the current zeitgeist around energy in the City of Vancouver, and the Province of British Columbia more broadly. Two topics that came up consistently in the interviews when the discussion turned to solutions were District Energy Systems (DES) and Electric Vehicles. Overall, there were 42 references to district energy systems in the 13 interviews. All of the participants save the federal government employee referenced DES at least once in their interviews. Perhaps more telling is the fact that when the ‘solutions’ code was cross-referenced with DES there were 14 references, only two less than when solutions was cross-referenced with 100 demand. Given the number of solutions generally associated with demand reduction and efficiency, this suggests that DES are currently receiving a large amount of attention. Well, I think and I hope that the role of DES or neighbourhood energy systems will be far more significant, and I think they have a great opportunity to, on the one hand reduce our carbon emissions, and on the other hand be examples of really, actually clear and obvious examples of innovative approaches to using energy that will actually help people understand in a deeper way what energy is all about. So I guess one example would be the SE False Creek neighbourhood energy centre... Subject 13 DES comes up over and over, and I, in my own work as well... I mean I certainly support it, but its interesting to see that... if the level of hope and expectation that has been placed on that, when we are not achieving the density that we need over large areas of Vancouver and other municipalities Subject 3 Electric vehicles received fewer overall references at 17, and were discussed by fewer participants (7). They were, however, a clear agenda item for particular energy experts. Yeah, and the question is how does transportation interact with that with electric vehicles or smart grid, and that’s about 20-30 years down the road, which is okay because until then there aren't going to be whole lot of electric vehicles anyways. I'd be surprised if it is much more than 20,000 EVs in Vancouver before 2016 or 2020. Subject 5 And I know that a lot of people think that we can move to electricity, but I still have yet to see anybody running real modeling numbers on what kind of extraordinary demand that is going to place on the grid. And we're already maxed out on our current renewable grid, and I think it could just... I would really like to see the modeling... I suspect that it is completely impossible to meet the demand that would come from... if we just went with our current everybody drives a car and now they plug their car in, we are going to be in real trouble. Subject 1 While items as specific as district energy systems and electric vehicles are not key determinants of a mental model, their primacy in energy discussions in Vancouver suggest, at least, a shared interest in them amongst practitioners, and also suggests that they should be incorporated into the questionnaires used to evaluate the community energy planning workshops, to determine if they receive the same attention and support amongst citizens. 101 Individual Mental Models of Community Scale Energy While the main intent of the mental models interviews was to reveal key constructs and themes for inclusion in the design of the workshops and the evaluation questionnaires, they also provided the opportunity to investigate the community energy mental models of practitioners. A comparison of two of the participants with significantly different answering patterns is conducted here. Table 4.4 depicts the difference in coding frequencies between two participants in the mental models workshops. The coding frequencies suggest that, despite being led through the same interview process with similar questions and themes, the resulting mental models of community scale energy were significantly different. Figure 4.2 and Figure 4.3 present concept mapping representations of these same participants’ codings, showing the frequency of the participants’ connections to individual concepts (black lines), as well as showing the frequency of co-occurrence between concepts by the width of the connecting grey line. These concept mapped diagrams represent the researcher’s interpretation of the participants’ mental models based on the coded transcripts. They present one method for assessing the variance between participants based on the contents of their interview responses. Figure 4.2 shows the concept map of participant three from the interviews. This participant is an energy expert with a focus on building energy demand and urban form. This focus is revealed by the width of the lines running to the Demand and Land Use categories. Participant three also spoke frequently about transportation behavior under the demand category, and hydroelectric and solar thermal sources under the supply category. Participant three focused primarily on the technical aspects of energy, with fewer references to reasons to address energy and fewer revelations of worldview components. Figure 4.2 also shows the connections between participant three’s primary focus (demand) and other codes. From these connections, it is possible to see that the participant spoke of demand primarily in relation to other technical aspects of energy, such as supply, land use and the energy system; with fewer connections to topics such as reasons to address energy issues, or responsibility for action. While it would be possible to connect all of the codes in the diagram based on concurrent coding in the participants’ transcripts, this would greatly reduce the legibility of the diagram and was, therefore, omitted here. Figure 4.3 show’s participant two’s mental model concept map. Many of the same categories and individual topics are present on both participants’ concept maps, a fact largely attributable to the semi-structured nature of the interviews, and the researcher’s focus on covering key topics. There is, 102 however, a visible difference in answering patterns, with participant two speaking much more frequently about issues of worldview and reasons to address energy. This participant is a public engagement expert, with relatively less expertise on technical aspects of energy. This participant focused much more on why we need to be addressing energy issues, and the impacts of energy on social justice and people’s quality of life. This participant spoke much less frequently about technical aspects of energy such as demand and supply. Participant two’s focus on systems is an interesting revelation of the diagram, however they did speak more frequently about sociocultural and governance systems than energy systems. Similar to Figure 4.2 , Figure 4.3 shows the connections between participant two’s primary code (worldview) and other codes. Of note here are the strong connections to systems and, in particular, sociocultural systems, as well as reasons to address energy issues, particularly social justice. A comparison of the two diagrams, then, demonstrates different mental models about the same subject matter. Participant three takes an expert-based, technical approach focused on demand, supply and land use, and participant two takes a human-focused approach concentrated on why we should be addressing energy issues, and how they relate to sociocultural factors such as social justice, sense of place and quality of life. The recently published Hoffman et al. (2014) uses a conceptually similar approach, though it utilized a more sophisticated network analysis methodology to automate the analysis. Future iterations of this research could use a similar network analysis approach to streamline the analysis, while maintaining the benefits of the approach used here (primarily the value of spatializing and visualizing participants’ mental models). A topic for future research, is how the disparate mental models of participants as revealed in Figure 4.2 and Figure 4.3 would impact not only their processing of energy-related information, but also the types of engagement activities that would best facilitate their participation around issues of energy or sustainability. 103 Table 4.4: A comparison of reference frequency for two interview participants organized in alphabetical order. Subject 3 Subject 2 Code References Code References Active Transportation 3 Active Transportation 0 Authoritarian 1 Authoritarian 0 Behaviour 7 Behaviour 4 Biomass 2 Building 3 Building 14 Business 1 Business 2 Business 0 Challenges 7 Challenges 0 City 2 City 1 Climate Change 4 Climate Change 6 Coal 1 Coal 0 Collective Action 1 Collective Action 6 Consumption 1 Consumption 4 Demand 21 Demand 2 Demand Reduction 2 Demand Reduction 1 Density 5 Density 1 DES 5 DES 5 Economic 1 Economic 4 Education 2 Education 2 Efficiency 3 Efficiency 1 Electric Vehicles 2 Electric Vehicles 0 Embedded Energy 3 Embedded Energy 0 Energy System 6 Energy System 6 Environmental Degredation 1 Environmental Degredation 0 Federal 1 Federal 0 Fossil Fuels 4 Fossil Fuels 3 Governance Systems 1 Governance Systems 8 Hydroelectric 7 Hydroelectric 2 Implementation 1 Implementation 0 Incremental Change 1 Incremental Change 0 Individual 2 Individual 1 Limits 1 Limits 0 Local 4 Local 2 Multiple 1 Multiple 2 Municipal 4 Municipal 3 Natural Gas 3 Natural Gas 1 Natural Systems 1 Natural Systems 2 Other Electric 3 Other Electric 1 Pessimism 9 Pessimism 0 Planning 2 Planning 1 Policy and Regulation 1 Policy and Regulation 6 Provincial 1 Provincial 1 Provincial 2 Provincial 0 Public Transit 6 Public Transit 1 PV 1 PV 1 Reasons Why 3 Reasons Why 13 Regional 2 Regional 1 Renewables 1 Renewables 1 Responsibility for Action 6 Responsibility for Action 8 RW Triangle 1 RW Triangle 1 Scale 6 Scale 3 104 Subject 3 Subject 2 Code References Code References Sociocultural Systems 4 Sociocultural Systems 14 Solar Thermal 5 Solar Thermal 2 Solutions 5 Solutions 8 Supply 13 Supply 8 Systems 7 Systems 16 Technology 5 Technology 3 Transformational Change 2 Transformational Change 3 Transportation 10 Transportation 3 Urban form 8 Unexpected Connections 2 Waste Heat 2 Urban form 1 Worldview 6 Worldview 16 Background 0 Background 1 Capacity Building 0 Capacity Building 3 Ecological Systems 0 Ecological Systems 1 Economic 0 Economic 1 Economic Systems 0 Economic Systems 2 Energy Concern 0 Energy Concern 1 Environmental Concern 0 Environmental Concern 2 Food 0 Food 2 Gasoline and Diesel 0 Gasoline and Diesel 1 Geothermal 0 Geothermal 1 Global 0 Global 3 Incremental Change 0 Incremental Change 1 Individual Action 0 Individual Action 5 Land Use 0 Land Use 2 Localization 0 Localization 2 Progress 0 Progress 2 Sense of Place 0 Sense of Place 5 Social Capital 0 Social Capital 4 Social Justice 0 Social Justice 8 Social-humanistic 0 Social-humanistic 18 Sustainability 0 Sustainability 4 Wind 0 Wind 3 105 Figure 4.2: A mental model concept map for participant 3. The width of the lines denotes the frequency of references. 106 Figure 4.3: A mental models concept map for participant 2. The width of the lines denotes the frequency of references. 107 4.4.4 Questionnaire Design Based on the interview results and, in particular, the revealed themes and concepts, the next step in the methodology was to design the pre and post workshop questionnaires (see Figure 1.2). The complete pre and post-test questionnaires can be seen in Appendix B. The fundamental research design for the workshop questionnaires made use of a concurrent mixed methods approach (Creswell, 2003). A mixed methods approach was selected based on past research experience, and a desire to examine both quantitative questions regarding how participants answered, as well as qualitative questions to try to determine why they answered in particular ways (Tashakkori & Teddli, 1998). Quantitative questions took two forms: 1. Multiple choice questions based on mental models constructs developed from the mental models interviews and literature research 2. Ranking questions asking participants to rank concepts based on relative importance. Ranking questions were selected over ratings, based on the preference in social research for ranking when participants are asked to develop valuations (Alwin & Krosnick, 1985), and the tendency in rating questions for participants to rate all items highly (i.e. to provide little differentiation in the values assigned). Ranking questions do, however, create more cognitive load. Qualitative questions took the form of open-ended, short answer questions and questions asking participants to list items, such as their daily energy-consuming activities. Quantitative and qualitative questions were interspersed throughout the questionnaires, depending on the purpose of a given questionnaire section. While some of the themes and questions for the questionnaires preceded the mental models interviews, a large portion of both the pre and post workshop questionnaires were developed directly from the interview results. 108 Pre-Workshop Questionnaire The full pre-workshop questionnaire can be seen in Appendix B. Section One of the pre-workshop questionnaire asked the participants about broad questions related to their worldviews, sustainability and energy planning. Questions developed from the interview results included a question about their worldviews related to energy, a question about the reasons we should be addressing energy issues and a question about who should be responsible for addressing energy issues. Interviewee responses to these questions proved to be important determinants of how they answered more specific questions about energy. For example, subject 3’s (see Figure 2) focus on technical aspects of energy and general pessimism about people’s energy behaviors led to a focus on buildings as a primary source of energy solutions. In fact, subject three specifically stated that concerns about behavior led to their belief that buildings could be a bigger part of the solution than transportation (see Demand in section 4.2.2). Eliciting workshop participants’ worldviews relating to energy, then, is an important component in interpreting their more specific energy answers. Section Two of the pre-workshop questionnaire asked participants about their individual energy use, following the theme of individual versus collective action found in the interview results. There was a clear relationship between interview subjects’ energy consciousness and their individual energy behaviors. Only one subject (subject 8) stated that working on energy or related sustainability issues did not permeate their everyday interactions with energy and the choices they made. Section two, then, provided the opportunity to assess workshop participants’ understanding of their own energy use, but also to assess connections between their worldviews and collective energy beliefs and their individual energy actions. Section Three of the pre-workshop questionnaire focused on two themes from the interview results. Firstly, participants were asked about elements in their community related to sense of place. Questions about sense of place were included in the pre-workshop questionnaire as a result of the emergence of place as an important issue in the interviews. The purpose of these questions was to look at whether what participants’ valued about their neighbourhoods were complimentary or in conflict with some of the energy solutions proposed. For reasons to be discussed in more detail in Chapter 7, these sense of place questions were not included on the Victoria workshop questionnaires. Secondly, participants were asked about collective energy actions within their communities. In this section of the questionnaire the participants were also asked about their perceptions of their own knowledge levels about energy issues. Questions about collective energy actions got to the heart of 109 the GCCP’s focus on collective, community actions and, therefore, could be thought of as the core of the questionnaire evaluation. In aggregate, the questions on the pre-workshop questionnaire were designed to allow for analysis of participants’ cumulative answer patterns. While the more limited questionnaire format did not allow for as complete a picture of mental models as the semi-structured interviews, the design goal was to develop as complete an elicitation as possible of the participants’ mental models of energy. Post-Workshop Questionnaire The full post-workshop questionnaire can be seen in Appendix B. Section One of the post-workshop questionnaire was designed to allow participants to evaluate the workshops themselves. The workshops were part of a larger research project, and were, in themselves, a research activity involving multiple researchers. The workshop evaluation questions allowed those researchers, the larger GCCP team, and the City of Vancouver to evaluate the efficacy of the approach taken and the tools used. Workshop evaluation questions included rankings of workshop components, and open-ended questions about what they found valuable or difficult about the workshops. Section Two of the post-workshop questionnaire asked the participants to revisit community-scale energy questions from the pre-workshop questionnaire. This section was designed to look for changes in the participants’ mental models of energy as a result of participating in the workshops, and contained some of the same questions used in the pre-workshop questionnaire, but also introduced new questions designed to look for changes in participants’ mental models. The new questions added in section two similarly focused on the themes developed from the interviews, in particular how the participants’ believed energy use and supply would change in the future. Section Three of the post-workshop questionnaire was designed to collect demographic data, both for use in the evaluation of the workshop channel of the GCCP project, but also to allow a cross-channel comparison with other channels of the GCCP project. The pre workshop questionnaires will be discussed in greater detail in Chapter 5. The post workshop questionnaires will be discussed in more detail in Chapters 6 and 7. 110 Challenges Associated with the Questionnaire Design and Delivery One of the early challenges in constructing the workshop questionnaires was to try to include enough questions, and enough of the right types of questions, to meaningfully elicit participants’ mental models. As stated previously in this chapter, most mental models evaluation methodologies involve more intensive methodologies in more controlled settings, and with longer timeframes. This was exacerbated by two factors. Firstly, the need to include both direct workshop evaluation questions and demographic questions in the post-workshop questionnaires, and secondly pressures from both the internal research team and the municipal staff to reduce the length and difficulty of the questionnaires. The questionnaires were dry-run with research colleagues. They were also first applied to a workshop with City of Vancouver staff. In addition, timing of the Victoria CEEP case study meant that the workshop questionnaires were applied to that process before they were applied to the GCCP process. In all circumstances, there were reports that the questionnaires were too difficult and time-consuming. Part of this pressure is likely common to any evaluation methodology applied to real-world participatory processes. In fact, the methodology used in the Revelstoke case study (see Chapter 7), which diverged from that used in the GCCP and Victoria CEEP case studies, received similar complaints that it required too much time. A challenge for future mental models instrument design, then, is to balance the need to elicit as complete a picture as possible of participants’ mental models, while at the same time not causing participant fatigue and impacting their enjoyment of the workshop process. Findings from the research in this dissertation may prove instructive in that regard. Application of the workshop questionnaires, as well as results, analysis and discussion will occur in Chapter 5 (existing mental models), Chapter 6 (GCCP) and Chapter 7 (Victoria and Revelstoke comparison). 111 4.5 Conclusions This chapter has reviewed the mental models interviews conducted as an interim step in this dissertation’s methodology. The results of these interviews were subsequently used in the development of the research methodology for the participatory workshop case studies in this dissertation, as well as in the construction of the workshops themselves. Construction of the workshop questionnaires was also discussed, including the rationale for sections of the questionnaires, and the question types building off of the interview results. A more detailed discussion of the individual questions, as well as the results, will be discussed in Chapters 5, 6, and 7. Challenges associated with the questionnaire design and delivery were also discussed. Challenges and limitations of the methodology, and the broader research project will be revisited in the conclusions in Chapter 8. The purpose of the mental models interviews with practitioners was to describe the community energy planning ‘space’. That is, the interconnected concepts that describe the domain within which community energy planning takes place. Analysis and coding of the participants’ transcripts revealed a number of important themes, some of which were anticipated, and others which were more surprising. All of the themes revealed by the interviews contributed to the development of the GCCP workshops, and the workshop questionnaires. The interviewees overwhelming response regarding the reason we should be planning for energy was climate change, suggesting that, amongst practitioners, it represents the dominant discourse, with discourses such as peak oil or energy security being less common. Participants focused relatively equally on issues of energy supply and energy demand. Supply discussions focused on BC’s ‘special status’ resulting from our large hydroelectric resources. The supply discussion also, to a lesser degree, focused on the potential of renewable energy resources. The subject of demand was characterized by discussions of where municipalities have influence on energy demand, leading to a focus on building demand and, more broadly, land use as the ‘easier’ aspects for municipalities to control. Many participants identified land use as the largest lever for municipal energy planning. An interesting finding in this regard was that none of the three City of Vancouver participants referenced density in their interviews. This density theme, and its contentious nature, will continue throughout the chapters that follow. 112 Participants focused more on solutions to energy issues than they did on challenges, though a common refrain was that most of these solutions (whether individual or collectively focused) will be difficult to implement. A theme that arose from analysis of the transcripts was the presence of an ‘energy zeitgeist’ – a common set of specific solutions (in this case DES and electric vehicles) that seemed to be popular amongst practitioners at the time of the interviews. As a result, these specific ‘zeitgeist’ solutions were incorporated into the GCCP workshops and the questionnaires. A surprising theme that developed over the course of the interviews was the connection of energy issues to quality of life and sense of place concerns. This began with one of the early interviews and, demonstrating a strength of the semi-structured interview approach, was pursued as a theme in subsequent interviews. This energy/sense of place theme became a key component of the GCCP workshop development and the workshop questionnaires, and perhaps best illustrates the value of the interviews as a component of the overall research design. An argument of post normal science (Funtowicz & Ravetz, 1993) and Interactive Social Research (Robinson & Tansey, 2006; Talwar et al., 2011) is that extended peer review and a broadening of the research team at early stages of the research process can improve the inclusiveness and quality of the resulting research. While these interviews did not include citizens (a failing of execution rather than intent), they did include a broader cross-section of perspectives early in the development of both the engagement activities, and the research methodology. The inclusion of the interviews demonstrably impacted the development of subsequent research steps, with the inclusion of the sense of place exercises in the GCCP workshops, and the sense of place questions on the questionnaires being illustrative of that fact. Finally, the development of the mental models maps for the two participants (see Figure 4.2 and Figure 4.3) was an important exercise in the overall development of the methodology. This interim step in the methodology hinted at the potential for using carefully constructed questionnaires and multivariate analysis methods to reveal similarly differentiated response patterns amongst a larger subject pool in a more automated way, making it amenable to use in a workshop setting. Chapter 5 will build upon this finding, using the pre workshop questionnaires developed from the findings of these interviews, and Multiple Correspondence Analysis (MCA) to look for differentiated patterns of responses similar to those revealed between Figure 4.2 and Figure 4.3. 113 The mental models interviews discussed in this chapter, then, were a valuable step in the development of the overall evaluation methodology, and in the development of the GCCP workshops and their materials. The results of these interviews do not represent an end product of this dissertation’s research, but rather an important input to subsequent stages of the research methodology. Nevertheless, conducting and analyzing these interviews was a valuable exercise that, in and of itself, was instructive about the complex space occupied by community scale energy planning. 114 Existing Mental Models of Community Energy 5.1 Introduction One result of community energy planning’s nascent stage of development, is a fragmented understanding of the challenges and solutions it represents (Andrews, 2008). Therefore, one of the key research questions for this dissertations was “what are people’s existing mental models of energy at the community scale?” This research question is exploratory in nature, because there was very little literature from which to draw. While some corollaries to other sustainability planning activities were possible (cf. Design Centre for Sustainability, 2008; Dodd, 2008; Girling et al., 2006; Hopkins, 2008; Salter et al., 2010) it was difficult to formulate specific, testable hypotheses about the content of participants’ mental models of energy at the community scale. In the absence of specific existing hypotheses, the decision was made to focus on an exploratory research design. Further supporting this research design was the desire to use qualitative research methods to allow participants to include information that was not anticipated by the initial questions. One notable exception to the more general exploratory research design was a supplementary research question focused specifically on the impacts of expertise3 on observed mental models of energy, based on previous mental models research that has revealed an important role for expertise (Morgan et al., 2002; Smith 2006). For example, Morgan et al. (2002) found that experts in environmental risk had much more complex and interconnected mental models of risk. While the research in this dissertation is not concerned with using expert models to determine holes or flaws in lay models, the role of expertise is still germane to the study of engagement, because it can be indicative of disconnects between practitioners and multiple publics. These disconnects could be important to planning engagement that is focused on exploration of possible futures. For the exploratory analysis conducted in this dissertation, a Multiple Correspondence Analysis (MCA) approach was selected (LeRoux & Rouanet, 2010). The viability of MCA as an evaluation methodology is contingent on its ability to reveal the components of mental models. It is the contention of this dissertation that mental models can be elicited as the connection between thematic concepts in a particular domain. In the case of this dissertation, energy planning at the community scale provides the domain. Bordieu, a key proponent of MCA in sociological research, has said that 3 My use of the term ‘expertise’ in this dissertation follows from Fischer (2000), with expertise in a domain (in this case energy) defined by domain-specific knowledge. 115 the key strength of MCA is that it works in relations (Bordieu & Wacquant 1992). For the purposes of this dissertation, the relations we are looking for are coherent response patterns on the questions from the pre-workshop surveys that can be interpreted as mental models of energy at the community scale. A review of the mental models literature did not reveal the use of Multiple Correspondence Analysis (MCA) in previous research (see Chapter 3). While there is limited precedence for the use of multivariate statistical techniques to investigate mental models (Langan-Fox et al., 2000; Weller, 2007), the application of MCA and resulting use of categorical data presents some new opportunities for mental models research. MCA was developed specifically for use with survey data (LeRoux & Rouanet, 2010), meaning it is applicable to elicitation techniques that are less intensive and time consuming than many of the methods described in Chapter Three. This raises the possibility of conducting mental models research on a larger number of participants, and in real-world, time-limited engagement activities. Chapter 4 described the development of the workshop questionnaires used in this study, based on the qualitative analysis of interviews conducted with both experts and practitioners who were in some way involved in the field of community energy planning. The pre-workshop questionnaire was broken into three main parts: 1. Section One of the pre-workshop questionnaire asked the participants broad questions related to their worldviews, sustainability and energy planning. 2. Section Two of the pre-workshop questionnaire asked participants primarily about their individual energy use. 3. Section Three of the pre-workshop questionnaire asked participants about collective energy actions within their communities. In addition to the three sections from the pre-workshop questionnaires, the demographic section of the post-workshop questionnaires were used in the analysis in this chapter, to examine relationships between participants’ mental models and key demographic variables. The analysis in this chapter will focus primarily on answers to questions from sections one and three, because the intent of the analyses is to assess participants’ existing mental models of energy at the community scale. This focus was reinforced by the use of the MCA technique in this study, because 116 it requires that the input questions cover consistent concepts. The Multiple Correspondence Analysis will be described in detail in section 5.3. 5.1.1 Chapter Structure The analysis in this chapter is divided into three main sections, increasing in specificity. In the first section (Section 5.2) a descriptive analysis is conducted on the collective participant responses. This analysis will reveal general response patterns, such as dominant, or infrequently chosen categories from the survey’s questions. This section will also document and explain the survey research questions used, and provide a shorthand nomenclature for each question that will be used throughout the subsequent analyses. The second analysis is the Multiple Correspondence Analysis. The purpose of the MCA was to use multivariate methods to look for particular response patterns and associations between questions. MCA is closely related to Principle Component Analysis (PCA), in that it attempts to reduce the dimensionality of a multivariate dataset by creating dimensions that amalgamate variables. These dimensions (which may be thought of as ‘supervariables’) are made up of the combination of variables, such that each dimension explains more of the variance in the original dataset than any variable can on its own (Husson et al., 2011). So, for example, if it was found that responses on optimism/pessimism shared characteristics with responses on responsibility for addressing energy (a hypothetical relationship), then a dimension may be constructed by MCA that combines optimism/pessimism with responsibility. The goal of MCA (similar to PCA) is to find the fewest number of these combinations of variables (dimensions) that can explain the variance in the dataset. A key distinction between MCA and PCA, is that MCA is used for categorical data. Another strong component of MCA is its usefulness for visualizing large data sets to reveal patterns and associations in the data (Greenacre, 2006). The MCA will be discussed in Section 5.3. The final analysis conducted was a series of univariate, Chi-square analyses to look specifically for effects of expertise on other variables. Contingency tables and Chi-square analyses were conducted to determine if relationships existed between self-reported energy expertise and other key variables, such as preferred energy solutions. The Univariate analyses will be discussed in Section 5.4. 117 5.2 Collective Mental Models of Community Scale Energy This section will review a descriptive analysis of, the participants’ collective responses to questions from the pre-workshop questionnaire, and a subset of the demographic data. The intention of this section is to look for the collective response patterns, both within and across workshops, and to look for particular dominant, or uncommon responses. 5.2.1 Methodology The methodology for this section utilizes a stratified (by workshop) within-subjects design and a simple descriptive analysis of the stacked bar charts developed for each of the questions from the questionnaire. For the purposes of consistency, the questions reviewed in this section will be the same subset as that used in the MCA analysis in Section 5.3. For MCA, the questions that are used to construct the dimensions of the MCA are called active variables. In order to be selected as an active question for MCA, a question needs to meet some basic assumptions. Firstly, it needs to be thematically consistent with the subject being analyzed (LeRoux & Rouanat, 2010). In the case of this study, it needed to be thematically related to energy at the community scale. There were a number of individual energy use questions on the questionnaire that were omitted from this analysis for that reason. Relating individual energy use mental models to participants’ community scale energy mental models may prove instructive for future research, but the decision was made in this study to focus on the community scale. Secondly, the active questions should have a relatively consistent number of categories, to prevent a question with a high number of categories from disproportionately affecting the variance in the MCA. For example, a question with eight possible answers will contribute significantly more to the construction of MCA dimensions than a question with three possible answers. The reasons for these assumptions will be described in more detail in section 5.3.3. Once the dimensions have been constructed, supplemental variables can be added to determine how they relate to the existing dimensions (Husson et al., 2011). Common supplemental variables include demographics such as age or gender, but they are not limited to demographic data. Any question from the same data pool (i.e. the same participants) can be associated with the active variables. In this study demographic variables and additional community scale energy variables were included in the supplemental variable category to determine their relationships to the revealed mental models. 118 Some of the energy variables were included as supplemental variables for mechanical reasons, such as too many null values, or large or small numbers of categories, while others were included as supplemental variables because they were thematically inconsistent (i.e. they did not reflect how the participant thought about community scale energy directly). The specifics of MCA will be discussed in more detail in Section 5.3. For the purposes of the collective mental model analysis discussed here, it is sufficient to understand that the active/supplemental distinction from MCA is being used to organize the data in this section. The brief description of the collective responses for each question will include a justification for that questions inclusion in the MCA analysis. The title for each question in the collective response section will also include a short form title for the question in parentheses, which will be used to refer to that question (variable) throughout the rest of the chapter. A Note on Null Values For the collective mental models section the null values, that is, those questions where certain participants did not provide an answer, are reported on the bar charts. These values may represent difficulty in answering a particular question, language barriers, time constraints or motivational issues. They are reported here because the intent of this section is to provide a complete picture of the cumulative answer trends. Section 5.3.3 Reports how these null values impacted the MCA, and how they were accounted for. 5.2.2 Results and Discussion Active Variables The questions reviewed in this section are those that were used in the construction of the dimensions of the MCA. Reasons to Address (Reasons_to_Address) The second question on the survey was a multiple choice question with seven possible options. The purpose of this question was to determine why participants think planning for energy is important. 119 Presumably, those that attend public or stakeholder engagement activities are either self-motivated, or encouraged to attend by their place of employment. Someone who attends because they are concerned about climate change may have a very different mental model of energy than someone that attends because they are concerned about the future of energy supply. The question asked was: Please circle the item that you think is the most important reason why we need to address energy concerns in our communities. a) Limitations of available energy supply b) Political reasons – energy disputes are a major source of conflict between nations c) Climate change concerns d) Environmental damage caused by our use of energy e) Fairness to future generations who may need the energy we are using f) Fairness to people in the world who have less access to energy supplies g) I do not think that we need to be concerned about energy issues Figure 5.1: Participant responses to the question of why we need to address energy issues (n=118). Climate change was the dominant response as the reason to address energy issues (see Figure 5.1). However, this is driven largely by the Victoria workshops where 21 of 52 participants chose climate 0510152025303540% of total responsesVictoria Planners Marpole Grandview 120 change. In contrast, for the Vancouver workshops the highest number of responses was for environmental damage (32 of 66 respondents). Environmental damage was a broad category meant to imply impacts such as ecological damage from fossil fuel extraction, transportation and use. It is possible that respondents chose it simply because of its broad applicability (i.e. climate change might be included in Environmental Damage). The Victoria workshops were advertised as Victoria Climate Action Plan workshops, which may either have primed participants, or caused them to self-select their attendance. The Vancouver workshops were advertised as Community Scale Energy and GHG workshops, with a larger focus specifically on energy. This may have either attracted a different audience, or not had the same priming effect as the Victoria invitation. Resource limits (15 responses) and fairness to future generations (11 responses) were the next highest selections, with geopolitical reasons (3) and fairness to the world (2) registering the fewest responses. Level of Change (Level_of_Change) The next question asked respondents to choose a statement that best fit their opinion of the magnitude of the changes required to address energy issues. The purpose of this question was to elicit participants’ beliefs about the severity of energy concerns, as well as to determine something about their worldviews and environmental discourses. Dryzek (2005) discusses the impacts of different environmental discourses on people’s view of environmental problems and chosen solutions. Similarly, Catton and Dunlap’s New Ecological Paradigm (Catton & Dunlap 1978), focuses on the importance of worldviews on people’s environmental behavior. This question provided the opportunity to look for a worldview or discourse consistency in participants’ response patterns. The question asked was: Please choose the statement that best fits your beliefs about the required size of the changes society needs to make about how we use energy, and where our energy comes from. 121 a) We do not need to make any changes. Concerns about the availability of energy are unfounded. We have lots of available energy sources. (no change) b) Energy is an important issue, but energy concerns can be solved through economic means by raising energy costs, leading to new technologies and more efficient use of energy. (economic) c) Energy is an important issue, and will require some changes in how we plan our communities, and the policies and regulations we set. For example, developers of new buildings could be required to make those buildings carbon and/or energy neutral. (planning and policy) d) Energy and other sustainability issues are extremely important. We need to make large changes in our economic and political systems in order to address the serious threats of climate change and energy shortages. (transformative) Figure 5.2: Participant responses about the level of change required to address energy issues (n=118). As Figure 5.2 show, participants overwhelmingly felt that we need transformative changes to our systems in order to address energy issues (77/118 participants). No participants felt that ‘no change’ was required, despite the vocal climate change and resource depletion skeptics evident in the media 010203040506070No Change Economic Planning andPolicyTransformative Null% of total respondentsVictoria Planners Marpole Grandview 122 (Painter & Ashe, 2012). This suggests that those who do not feel that we need to address energy and associated emissions issues do not attend public engagement events on the subject. If these events are intended to provide a public venue for deliberative democracy, however, then the absence of those that deny the importance of energy planning is problematic. Planning and policy level changes were a distant second choice (30/118 respondents), with economic level changes being a relatively rare third selection (6/118 respondents). Similar to the skeptical audience, there is evidence in the literature for a strong position that market mechanisms can address climate change and resource depletion issues (cf. Field and Olewiler, 2011; Hawken et al., 1999; Kolk & Pinske, 2004). The relatively small number of respondents that made this selection is further evidence that participatory planning events are not attracting all voices and views in representative numbers. The Level_of_Change question is the best surrogate in this question set for the severity of the perceived problem. The results of this question suggest that there is a prevailing opinion that we cannot achieve the changes required through existing mechanisms. The results of this question bring into question the nature of the tools and processes used in public engagement on energy, and more broadly sustainability planning, which are generally focused on incremental ‘practical’ solutions. This finding supports the notion that the tools and processes we are using to address energy and climate change issues with stakeholders and the interested public should allow for creative and unusual possibilities that better fit a transformative narrative. This theme will be returned to in chapter 8 of this dissertation. Who is Most Responsible (Most_Responsible) The next question on the questionnaire asked the respondents to rank who was most responsible for addressing energy issues from most to least responsible on a six-point scale. The purpose of this question was to elicit participants’ beliefs about who should be addressing energy issues. Decades of efforts have attempted to place the onus on the individual, with very limited results (Gardner & Stern 2002; Hines et al., 1986/1987). This has left the question of who is most responsible largely unanswered, as evidenced by the experts’ responses to this question in the mental models interviews in Chapter 4. 123 The question asked was: Who do you think should be responsible for responding to energy issues, such as finding new sources of energy, or reducing our overall use of energy? Please rank the following groups from 1 to 6, with 1 being the most responsible, and 6 being the least responsible for responding to energy issues. a) Businesses b) The Provincial Government c) Municipal (City) Governments d) Groups of citizens and/or neighbourhoods e) Individual citizens f) Federal Government Because MCA cannot make use of ranked data in its raw form, the data from this question was parsed to reveal who participants felt was most responsible (rank 1). Figure 5.3: Participant responses about who is most responsible for responding to energy issues (n=118). 05101520253035404550Business ProvincialGovtMunicipalGovtGroups ofCitizensIndividuals FederalGovtNull% of total respondentsVictoria Planners Marpole Grandview 124 As Figure 5.3 shows, participants overwhelmingly felt that the Federal Government was most responsible for responding to energy issues (55/118 respondents). The provincial government was second (15/118 respondents), while municipal governments and individuals were tied (10/118 respondents). Groups of citizens was the least popular choice (4/118 respondents). This finding may reflect the global nature of issues such as climate change and fossil fuel supplies. Indeed, several respondents made specific reference to ‘Stephen Harper’s government’ as a barrier to effectively addressing climate change and energy issues in their open-ended responses. This focus on the Federal Government may prove problematic in efforts to mobilize action and energy solutions at more local scales. Why make significant efforts at the local level when the Federal Government has more tools and power at its disposal? A follow-up study as BC communities move into the implementation phase of the Community Energy and Emissions Planning (CEEP) process would be instructive. Have subsequent efforts changed people’s perceptions about the role of local governments and citizens? Who is Least Responsible (Least_Responsible) In addition to parsing the question about who was most responsible (rank 1), it was also parsed to determine who participants thought was least responsible (rank 6). The intent of including the Least_Responsible question was to look for directionality in participants’ response patterns. For example, knowing that a participant selected business as most responsible and the Federal Government as least responsible provides us with a clearer picture of how they view the energy issue. Figure 5.4 shows that participants broadly felt that either individuals (42/118 respondents) or groups of citizens (23/118 respondents) were least responsible for responding to energy issues, followed by the federal government (12/188 respondents) and business (6/118 respondents). The provincial government (1/188 respondents) and municipal governments (2/118 respondents) were the least popular choices for who was least responsible. 125 Figure 5.4: Participant responses to who they feel is least responsible for responding to energy issues (n=118). Perhaps the most interesting thing about the response pattern for who is least responsible is that it was not simply the inverse of who is most responsible. The Federal Government, which was the dominant answer to who is most responsible (55/118 respondents), was the third highest choice for who is least responsible (12/118 respondents). While individuals, which was the most popular choice for who is least responsible (42/118 respondents) was tied for third as the most responsible (10/118 respondents). This discrepancy suggests the presence of different individual response patterns. Best Conceptual Approach to Energy Issues (Approach) This question focused on broad scale approaches to addressing energy issues. The intent of the question was for participants to identify their general concept of the best way forward. Do we use technology and innovate our way to new energy sources and more efficient use, or do we ask people to change their habits? Do we implement regulations on emission standards or fuel efficiency, or do we implement Smart growth planning practices to make sustainable lives easier? This question was deliberately conceptual, with more specific energy solutions to follow. 051015202530354045Business ProvincialGovtMunicipalGovtGroups ofCitizensIndividuals FederalGovtNull% of total respondentsVictoria Planners Marpole Grandview 126 Similar to the Most_Responsible question, this question was adapted to use only their top ranked solution so that it was more amenable to analysis using MCA. Many respondents did not fill in all five ranks, so using a ‘worst approach’ variable was not a viable option. The question asked was: What approaches do you think will best help us address future energy concerns, for example helping us use energy more efficiently, or changing to new energy sources? Please rank the following five options from 1 to 5, with 1 being the most successful approach to addressing future energy issues, and 5 being the least successful. a) Improvements in technology b) Changes in people’s behaviour c) Changes in government policies and regulations d) Government incentives or subsidies e) Changes in how we plan communities Figure 5.5; Participant responses to the best approach to addressing energy issues at the community scale (n=118). 051015202530354045Technology Behaviour Policy Incentives Planning Null% of total respondentsVictoria Planners Marpole Grandview 127 Figure 5.5 shows the participants’ responses on preferred conceptual approach. The most popular approach to addressing energy issues was government policy (50/188 repondents), followed by behaviour (22/118 respondents), technology (15/118 respondents), planning (13/118 respondents) and, finally, incentives (5/118 respondents). Perhaps the popularity of policy and regulations was because it could be used as an umbrella for the other categories, mandating changes in technology, incentivizing behavior, regulating planning, etc. In that way, policy could be seen as the safe choice. An interesting finding from this question is that behaviour was the second most popular choice, despite the decades of research that have suggested the limitations of this approach. Also, despite the fact that the participants were attending workshops with a specific planning focus, the vast majority of them did not feel that planning was the most efficacious approach to addressing energy issues. The responses to this question also suggest a connection to the Most_Responsible question, as the primary tool wielded by the federal government is policy and regulation. Applied Energy Solutions (Applied_Solution) This question increased the specificity about energy issues and asked participants to identify particular energy solutions that they felt could best be used to address energy issues. The solutions included were developed from the mental models interviews in Chapter 4, as well as from the energy literature. The basis for inclusion was primarily about the visibility of particular solutions at the time of the workshops, rather than an actual measure of their efficacy (i.e. the options for this question may not reflect the biggest energy saving moves). This relates to the ‘energy zeitgeist’ concept from Chapter 4, which noted that, at any one time, particular technologies or solutions seem to have primacy in energy discussions. At the time of this study, electric vehicles and district energy systems were very popular topics amongst practitioners in energy-related fields. Again, this question was adapted from its original ranking format to an identified best solution in order to incorporate it in the MCA. Participants were only asked to rank five of the seven possible answers, so a worst solution could not be used in the MCA. 128 The question asked was: Please rank your top 5 approaches for addressing energy concerns in your community, with 1 being the method with the largest potential impact, and 5 being the method with the smallest potential impact. a) Increasing public transportation options b) Increasing local sources of energy such as solar c) Decreasing the size of people’s residences d) Making technical changes, such as energy retrofits on existing buildings, changing building standards, or electric vehicles e) Changing Individual’s energy behaviours f) Collective options, such as district energy systems g) Providing incentives for people and businesses to conserve energy Figure 5.6: Participant responses to the best applied solution for addressing energy issues at the community scale (n=115). 051015202530% of total respondentsVictoria Planners Marpole Grandview 129 As Figure 5.6 shows, the most popular response to this question was technical changes (31/115 respondents), followed by public transportation (25/115 respondents), individual behaviour (16/115 respondents), collective options (13/115 respondents), incentives and unit size (tied at 7/115 respondents) and, finally local supply (6/115 respondents). The ‘technical changes’ option coupled several popular solutions, including building retrofits, and electric vehicles. As mentioned, electric vehicles in particular, were very popular at the time the workshops were conducted. This pairing of popular options as examples in the wording of the question may have influenced participants’ responses. Another popular applied solution – district energy systems – was an example of a collective option, and therefore may have had a similar impact on the popularity of that option. Once again, individual behavior was a relatively popular choice. Interestingly, despite the frequent media attention provided about renewable energy sources (cf. Bouw, 2013; Griffen, 2014), local supply was the least popular choice for an applied solution. In future applications of this type of questionnaire, the wording for this question could be improved to more clearly define the categories and to prevent popular examples of solutions from colouring the observed results. Nevertheless, this question provided the best reflection of specific, applied solutions in the questionnaire, and was therefore important to the final analysis. Difficulty to Change Energy Behaviour (Difficulty) This question was part of Section Two of the questionnaire that focused on individual energy use, and asked participants to rate how difficult it would be for them to change their individual energy behaviours. It was, however, a useful indicator of how difficult it would be to change our energy use collectively – i.e. in most cases one might expect that people would see collective changes as related to their individual changes. This question was one of the few ordinal questions in the questionnaire, asking participants to rate difficulty on a five-point scale. The question asked was: How important is energy to you in your daily life? In other words, if you were required to reduce your energy use significantly, how difficult would it be for you to carry out the activities in your daily life? Please circle the best answer. 130 a) Very difficult b) Somewhat difficult c) Neutral – neither difficult nor easy d) Somewhat easy e) Very easy Figure 5.7: Participant responses to how difficult it would be for them to change their energy behaviour (n=84). Due to an error in printing the questionnaires for the second day of the Victoria workshops, this question was omitted from that workshop. As a result, the overall n for this question was 84. As seen in Figure 5.7, the largest number of participants felt it would be somewhat difficult to change their energy use behaviours (43/84 respondents), followed by neutral (18/84 respondents), very difficult (11/84 respondents), somewhat easy (10/84 respondents) and very easy (2/84 respondents). Combining the ‘difficult’ categories (54/84 respondents) and ‘easy’ categories (12/84 respondents) suggests that the vast majority of respondents felt it would be challenging to reduce their energy use. Similar to the Level_of_Change question, then, this question is indicative of the challenge that participants felt that we face in trying to address future energy issues. This negative framing of the future of energy at the community scale may have a detrimental effect on people’s willingness to 0102030405060Very Difficult SomewhatDifficultNeutral SomewhatEasyVery Easy Null% of total respondentsVictoria Planners Marpole Grandview 131 participate in planning processes. It has long been understood that defense mechanisms such as cognitive dissonance (Festinger, 1957) are a reaction to unpleasant or conflicting information. If participants perceive energy futures as a hardship, they will probably be less likely to engage in energy planning activities. Future Energy Source Changes (Source_Change) The final question included as an active variable for the MCA was an open-ended question that asked participants to identify how they thought their community’s sources of energy would change in the future. The question asked was: Do you think that the sources of energy for your community are going to change in the future (let’s say by 2050)? If so, please briefly state how you think the energy sources in your community will change. Participant responses were transcribed and then coded in NVivo based on the dominant theme expressed. So, for example: Yes. The cost will increase; however, new technology will continue to balance and introduce ways to consume wisely and carefully. (GCCP Planners Participant 23) Was coded as ‘Technology’, and: More 'high footprint' fossil fuels (e.g. oil sands, fracking, gas, coal). (GCCP Grandview Participant 1). Was coded as ‘Alternative Fossil Fuels’. Figure 5.8 shows the participants’ dominant themes from their coded open-ended answers. The largest number of participants felt that energy sources would transition to renewables (39/118 respondents), followed by decreased fossil fuel use (16/118 respondents), changes in technology (14/118 respondents), moving to a mix of sources (4/118 respondents), improving efficiency of use (4/118 respondents), moving to alternative fossil fuels (3/118 respondents), promoting heat recovery (2/118 respondents) and, finally, promoting demand reduction (1/118 respondents). 132 Figure 5.8: Participant responses about how they foresee energy sources changing in the future (n=118). A move to renewable energy sources was the dominant theme from this question. Perhaps more surprising are the variability of answers, including those such as ‘technology’, ‘efficiency’, ‘heat recovery’ and ‘demand reduction’ that do not refer to energy sources per se. This could reflect either a lack of understanding of the common demand/supply way of characterizing energy use, or a more systematic way of looking at the energy system. In other words, waste energy recovered through efficiencies, technology, etc., could be thought of as a new energy source in a more complex picture of the energy system. In addition to the energy information it provides, this question demonstrates another strength of MCA; that it can accommodate coded open-answer questions, allowing for questions where participants’ responses have not been prompted. 051015202530354045% of total respondentsVictoria Planners Marpole Grandview 133 Supplemental Variables The preceding eight questions made up the active variables for the MCA. That is, the variables that were used in the construction of the MCA dimensions, reflecting relationships in the participants’ answer patterns. In addition to those variables, there were a number of other questions from the survey that might provide valuable insight about the participants and their response patterns. For a variety of reasons (most commonly a large number of null values) these questions were not included in the active variables. In this section, the collective trends in the supplemental variables will be described, and a justification will be provided for their inclusion as supplementary variables, but not active variables. Energy Importance The first question on the pre-workshop questionnaire was an open-ended question intended to capture the participants’ initial thoughts about community scale energy planning, before they were primed by reading available answers to subsequent multiple choice questions. The question asked was: Do you believe that planning for the future of energy use and energy supply in your community is an important topic for people to be working on? Please state briefly why you believe we should or should not be planning for energy in your community. The participants’ answers were open-ended. All of the participants’ responses were transcribed and coded for dominant themes using NVivo. For example, We need to take care of our health and to do so, we need to take care of the earth - conserve what we take and minimize our footprint. (Grandview Participant 36) Was coded with the dominant theme of health. 134 Figure 5.9: Participants' coded themes from the open-ended question asking why planning for energy at the community scale is important (n=118). Figure 5.9 Shows the dominant themes extracted from all of the participants open-ended answers to question one, stratified by workshop. Participant answers were quite varied, and ranged from very general themes such as sustainability to more specific responses such as fossil fuels. There were also a number of themes more tangentially related to energy, such as capacity building and planning. As Figure 5.9 displays, the most common response was climate change, though that was strongly influenced by the Victoria workshops. This question was included as a supplemental question because it provided more free-form responses about the importance of energy. Would participants’ answers such as ‘quality of life’ or ‘health’ relate to other variables in the MCA? This question was not included as an active variable because of the large number of response categories. A large number of infrequent categories can impact the power of an MCA (LeRoux & Rouanet, 2010). By including it as a supplemental variable, it could still be used in the interpretation of results, without potentially skewing the MCA itself. 02468101214161820Number of ReferencesVictoria Planners Marpole Grandview 135 Energy Knowledge (Energy_Knowledge) This question asked participants to rate their level of community scale energy knowledge on a five-point ordinal scale. The question asked was: How would you rate your knowledge of the energy system (where energy comes from, how it moves, and how it is used) in your community? Please circle the best answer. a) I have very little knowledge of the energy system in my community b) I have some knowledge of the energy system in my community, but not a lot c) I would say I have an average level of knowledge about the energy system in my community d) I have a fair amount of knowledge about the energy system in my community e) I would consider myself an expert on the energy system in my community Figure 5.10: Participant responses regarding their self-reported level of energy knowledge (n=118). This question provided the best opportunity to assess level of energy expertise and to separate expert vs. lay responses about energy. It was included in the supplemental variables, because energy 05101520253035Very Limited Some Average Fair Amount Expert Null% of total respondentsVictoria Planners Marpole Grandview 136 knowledge is not necessarily related to a person’s mental model. In other words, people with similar levels of energy knowledge may have very different conceptions of energy and how we should be managing it in the future. Energy knowledge is an interesting supplemental variable, however, because it can tell us whether particular models are related to expertise. Previous research on mental models has focused on the effects of expertise (cf. Morgan et al., 2006; Schneider-Hufschmidt et al., 1993; Smith 2006). This subject will be discussed in more detail in Section 5.4. Figure 5.10 shows that the largest number of participants felt they had an average level of energy knowledge (35/118 respondents), followed by a fair amount (33/118 respondents), some (22/118 respondents), very limited (17/118 respondents) and, finally, expert (4/118 respondents). Very few respondents felt they had an expert level of knowledge, despite the number of participants from the Victoria (and to a lesser extent Grandview) workshops that worked in energy-related fields. This may simply reflect modesty on the part of some participants. If judgements were made by third parties, the number of participants rated as experts would likely have been higher. This would have resulted in a more normal looking distribution of energy knowledge in Figure 5.10. The number of participants on the limited end (‘very limited’ or ‘some’) was 39, while the number of participants on the knowledgeable end (‘fair amount’ or ‘expert’) was 37. The large number of participants who rated themselves to have a ‘fair amount’ of energy knowledge (n=35) was driven primarily by the Victoria and Grandview workshops (see Figure 5.10). Energy experts were specifically invited to the Victoria workshops by the organizers. For the Grandview workshops, advertising techniques seem to have resulted in a higher proportion of participants who were energy experts rather than local citizens (see Chapter 6 for a more detailed discussion of this point). Optimism vs. Pessimism (Optimism) This question asked participants if they were optimistic or pessimistic about our ability to address energy issues. This question was included because it reflected whether participants saw energy issues as a surmountable challenge (or possibly an opportunity) or a threat to future well-being. It was not included as an active variable, because it only had three answer categories and test runs demonstrated 137 that it reduced the variance explained by the constructed dimensions of the MCA without contributing to the subsequent interpretation. The question asked was: In general, would you consider yourself optimistic or pessimistic about our capacity to address energy and other sustainability issues in the future? a) Optimistic b) Pessimistic c) Neither Figure 5.11: Participant responses to their level of optimism about our ability to address energy issues in the future (n=118). Figure 5.11 shows that the majority of participants at all of the workshops rated themselves as optimistic about our ability to address energy and other sustainability challenges in the future (58/118 participants). ‘Neither’ was the second most popular category (31/118 participants), followed by pessimistic (22/118 participants). 0102030405060Optimistic Pessimistic Neither Null% of total respondentsVictoria Planners Marpole Grandview 138 Relating the results of this question to the Level_of_Change and Difficulty questions in the active variables section, it may be surprising to see that most participants were optimistic about our ability to meet energy and sustainability challenges. Optimism bias is a known phenomenon (cf. Klein & Helweg-Larsen, 2002), which may account for this result. Another possibility is that respondents were not optimistic, but that they rated themselves as optimistic due to the stigma sometimes attached to excess pessimism (Helweg-Larsen et al., 2002). Taking these results at face value, it would appear that participants are quite positive about our ability to address energy issues in the future. Measure of Energy Use Knowledge (Energy_Use_Correct) This measure was developed from a question that asked participants to identify, in rank order, the energy use sectors in their community. Participants were assigned a score from 1-5 based on how many of the sectors they placed in the correct order. A score of four was not possible, because correctly ranking four of the options would have resulted in the fifth also being correctly ranked. This measure was included in the analysis for this chapter as an indication of actual versus self-reported local energy knowledge. In other words, to provide a form of ‘objective’ measure of participants’ actual energy knowledge. The limitations of this question, and the energy source question that follows, as representations of actual local energy knowledge is acknowledged. It represents a type of recall knowledge that is often associated with superficial learning, rather than deep understanding (Mayer, 1998). However, at the time of the questionnaire construction, community-scale energy planning was very new, and it was difficult to formulate knowledge-eliciting questions that participants (expert or non-expert) might be expected to know. The implication of these questions will be discussed in more detail in Chapters 6 and 7. The question asked was: Please rank the following energy users in your community from 1 to 5, with 1 being the largest user of energy in your community, and 5 being the smallest user of energy in your community. 139 a) Industrial users like factories b) Transportation users, like cars, trucks and buses c) Residential users like houses and apartments d) Commercial users like shops and businesses e) Municipal users like police, fire and other City services. Figure 5.12: A measure of how many energy sectors participants correctly placed by workshop (n=118). As Figure 5.12 shows there is a skewed distribution of correct responses, with the largest proportion of participants getting one correct answer (41/118 respondents), followed by two correct (24/118 respondents), zero correct (23/118 respondents), three correct (19/118 respondents) and five correct (3/118 respondents). Only respondents from the Victoria workshops put all five energy sectors in the correct order. 0510152025303540Zero One Two Three Five Null% of total respondentsGrandview Marpole Planners Victoria 140 The actual sector breakdowns are different for Vancouver and Victoria. In Vancouver, the actual sector order is: Commercial > Residential > Transportation > Industrial > Municipal While in Victoria it is: Commercial > Transportation > Residential > Industrial > Municipal. All data for these rankings come from NRCAN’s Office of Energy Efficiency (NRCAN, 2014) and the Province of British Columbia’s Community Energy and Emissions Inventories (Province of British Columbia, 2014). The primary error participants made was underestimating the role of Commercial energy use. Based on the mean rank value for each sector, the workshop participants ranked the sectors as follows: Victoria: Transportation > Residential > Commercial > Industrial > Municipal Planners: Residential > Transportation > Commercial > Industrial > Municipal Marpole: Transportation > Residential > Commercial = Industrial > Municipal Grandview: Transportation > Residential > Industrial > Commercial > Municipal This measure simply looked for sectors placed in the correct order, so it was not sensitive to respondents being off by one sector (e.g. if they ranked industrial as 1, but then correctly ordered the other four). There are also questions about the actual partitioning of the NRCAN and CEEI numbers. In the OEE and CEEI numbers, Commercial includes small and medium industrial applications, which will inflate its energy use. However, commercial and industrial transportation are included in the ‘Transportation’ sector, which will decrease their energy use numbers. Clearly, how the data are organized has an impact on the sector ordering and subsequently participants ‘correctness’ ratings. This emphasizes the limitation of this measure as a true reflection of community-scale energy knowledge. While this measure has considerable limitations, it does reflect domain specific energy knowledge – i.e. an energy expert is more likely to know the energy demand sectors and how the data are organized. Therefore, it is reasonable to expect that energy experts would be more likely to provide correct energy use sector rankings. 141 Measure of Energy Source Knowledge (Energy_Source_Correct) This measure simply indicated if the participants correctly identified the largest energy source for their community. In Vancouver, the largest energy source is Natural Gas (Province of British Columbia, 2014). In Victoria, the largest energy source is Electricity (Province of British Columbia, 2014). This question was used as another measure of actual versus self-reported energy knowledge. The question asked was: 1. Please circle the energy source that you think provides the largest amount of energy to your community. a) Gasoline and diesel b) Electricity c) Natural Gas d) Propane e) Solar (hot water or electricity) f) Geothermal g) Heating Oil Figure 5.13: Proportion of correct responses to largest energy source by workshop (n-118). 0102030405060correct incorrect Null% of total respondentsGrandview Marpole Planners Victoria 142 As Figure 5.13 shows more participants were incorrect (66/118 respondents) than correct (44/118 respondents) in identifying their city’s largest energy source. The chart clearly shows that there is a strong workshop bias in this answer. This will be discussed in more detail in section 5.4.2. This measure has similar limitations to those discussed for the Energy_Use_Correct measure, however it does provide one data point about the participants’ actual, versus reported energy knowledge. These measure were included as supplementary variables so that they did not affect the construction of the MCA dimensions, but could be related to those dimensions post hoc. Demographic Variables In addition to the three energy-related supplemental variables, there were a number of demographic variables that might have some relationship to people’s mental models of energy. The Greenest City Conversations Project had an extensive list of demographic variables, with the intent of comparing across the engagement channels to assess differences in audiences (see Chapter 6). The Victoria CEEP organizers felt that many of these demographic variables were not appropriate for their workshops, so they were removed from the Victoria questionnaires. The only common demographic variables across the Vancouver and Victoria workshops were Age and Gender. However, null values in supplemental variables do not impact the constructed dimensions of an MCA, and therefore they can be included in an analysis. The decision was made, therefore, to include the common demographic variables of Age and Gender, but also to include workshop-specific variables, including Education Level, Income, Cultural Background and Primary Language from the Vancouver workshops, and Employment Sector from the Victoria workshops. Age (Age) This question asked participants to report their age according to six age ranges: under 19, 19-24, 25-44, 45-64, 65-74, over 75. This research project did not have ethics approval to survey respondents 143 under the age of 19, so that category was not applicable, resulting in five active categories for this research. Figure 5.14: Participant responses regarding their current age (n=118). Figure 5.14 shows that the largest proportion of participants were from the 25-44 age range (57/118 respondents), followed by 45-64 (40/118 respondents), 65-74 (7/118 respondents) and, finally, 19-24 (3/118 respondents). Notable discrepancies by workshop included the 25-44 age range at the Planners workshop (13/16 participants at the Planners workshop) and the 65-74 age range at the Marpole workshop (4/20 participants at the Marpole workshop, 4/7 total participants from that age range across all workshops). The discrepancy at the Planners workshop was the result of it being comprised primarily of young to mid-career planning staff from the City of Vancouver. The discrepancy at the Marpole workshop was because it was the most representative of the local community and had the highest proportion of local citizens in attendance (see Chapter 6). 010203040506019-24 25-44 45-64 65-74 75+ Null% of total respondentsVictoria Planners Marpole Grandview 144 Gender (Gender) This question asked participants to identify their gender using three categories: female, male or other. A more inclusive accounting of gender would have been preferred, but limitations of data capture mechanisms in some of the other channels of the GCCP made open-ended responses impractical. Figure 5.15: Participant responses regarding their gender (n=118). There were more males (60/118 respondents) than females (45/118 respondents) that participated in the workshops as a whole. Only two participants listed their gender as other. Of note is the effect of the Victoria workshops, which had 29 males vs. 17 females and no others. This is likely due to a discrepancy in gender representation in the types of management positions of stakeholders that were invited to the workshops. In comparison, there was a much closer gender split in the Vancouver workshops (28 female, 31 male, 2 other), which were open invitations. 0102030405060Female Male Other Null% of total respondentsVictoria Planners Marpole Grandview 145 Income (Income) This question asked participants for their current incomes based on seven categories: under $20,000, $20,000-$34,999, $35,000 - $49,999, $50,000 - $64,999, $65,000 - $79,999, above $80,000, prefer not to say. This question was only asked on the Vancouver questionnaires (n=66). Figure 5.16: Participant responses regarding their current level of income for the Vancouver workshops (n=66). By far, the largest number of participants made over $80,000/year (26/66 respondents), followed by $20,000-$34,999 (12/66 respondents), $65,000-$79,000 (8/66 respondents), $35,000-$49,000 (4/66 respondents), and under $20,000 and $50,000-$64,000 tied (3/66 respondents). Additionally, 5/66 respondents preferred not to disclose their incomes. The income categories used in this study were developed for the GCCP as a whole. Considering the high cost of living in Vancouver, and the corresponding need for higher incomes, it is likely that these income categories were set too low. This might ameliorate the clear discrepancy towards the highest income bracket. Regardless of the problems with income categorization, however, there is a 051015202530354045% of total respondentsGrandview Marpole Planners 146 clear over-sampling of the highest income bracket. The tendency for more affluent citizens to take part in engagement activities is well known (Knapton & Myers, 2005; Scheufele, 2011). It is possible that this oversampling would also lead to discrepancies in participants’ choices on questions such as preferred solutions. Cultural Background (Cultural_Background) This question asked participants to identify their cultural background based on a predefined set of options, but also provided them with the opportunity to fill in an option that wasn’t present. The questions asked was: What is your cultural background (check all that apply): _____ First Nations / Aboriginal _____ Caucasian / European _____ Latino / Hispanic _____ African _____ South Asian (e.g. India, Pakistan, Sri Lanka) _____ East Asian (e.g. China, Japan, Korea) _____ South East Asian (e.g. Philippines, Indonesia, Thailand) This question was limited to the Vancouver workshop questionnaires, so the n=66. As Figure 5.17 shows, the majority of the participants were of Caucasian/European descent (38/118 respondents), followed by East Asian (13/118 respondents), South Asian (3/118 respondents), and First Nations/Aboriginal, Latino/Hispanic, African, Southeast Asia and West Indian with one respondent each. As Table 1 displays, there was not a large discrepancy between the data observed in this study and the general demographic trends for cultural background in the City of Vancouver (Statistics Canada, 2011). The main discrepancies include an oversampling of people of Caucasian/European descent, and an undersampling of people of East Asian descent. Given the relatively small sample size, and 147 the potential for language barrier issues, the cultural background demographic profile of the workshops is encouraging. Figure 5.17: Participant responses regarding their cultural background for the Vancouver workshops (n=66). Table 5.1: A comparison of the cultural background data observed in this study versus the 2011 Vancouver census data by percent of population. First Nations Aboriginal Caucasian European Latino Hispanic African South Asian East Asian South East Asian West Indian Observed 2 58 2 2 5 20 2 2 Census 2 46 2 1 6 31 6 NA Primary Language (Language) The original language demographic question on the Vancouver workshops questionnaires asked participants to identify their primary language spoken at home. For the purposes of this study, the main point of interest regarding language, was the potential for a language barrier, leading some participants to not understand either the workshop proceedings, or the questionnaires themselves. 010203040506070% of total respondentsGrandview Marpole Planners 148 Therefore the language data was parsed to reveal whether the participant was either English or non-English speaking. Figure 5.18: Participant responses regarding their primary language spoken at home for the Vancouver workshops (n=66). Figure 5.18 shows the proportion of English versus non-English speaking participants. Of the 66 participants in the Vancouver workshops, 42 (64%) were primarily English speaking, while 17 (17%) spoke a different language at home. This raises the possibility that some participants may have had difficulty with either the workshop material or the questionnaires. Some of the null values discussed in section 5.2.1 may be the result of this language barrier. Unfortunately, there were insufficient resources to develop workshop or evaluation materials in different languages. Highest Education Level Achieved (Education) The final demographic variable from the Vancouver workshops that was included as a supplemental variable in the MCA was the participants’ highest level of education received. The question asked was: 010203040506070English Non-english Null% of total respondentsGrandview Marpole Planners 149 What is the highest level of education you completed? a. Part of elementary school b. Completed elementary school c. Part of high school d. Completed high school e. Some college or university f. Received a college or technical school certificate g. Received a university bachelor's degree h. Some postgraduate training j. Received a postgraduate university degree This question was limited to the Vancouver questionnaires, and therefore had an n of 66. Figure 5.19: Participant responses regarding their highest level of education received for the Vancouver workshops (n=66). 0102030405060% of total respondentsGrandview Marpole Planners 150 Fully half of the participants in the Vancouver workshops had postgraduate degrees (33/66 respondents), an additional 10 respondents had some postgraduate experience, 12 had Bachelor’s degrees, 2 had college certificates and 3 had some college or university (see Figure 5.19). None of the participants in the Vancouver workshops reported a highest education level attained at or below the high school level. It is clear from Figure 5.19 that the participants in the Vancouver workshops were highly educated. This was most pronounced for the Planners (13 postgraduate degrees) and Grandview (12 postgraduate degrees) workshops. For the planners workshops that could be explained by the fact that many planning degrees in Canada are at the Master’s level. For the Grandview workshop, the previously discussed influx of non-resident energy experts is likely the cause. Regardless of these mitigating factors, it is clear that the workshops are not a representative sample of the education level in the populace of Vancouver, and the results should be evaluated with that in mind. Employment Sector (Employment Sector) The final demographic question for the Victoria workshops asked the participants to select the employment sector they work in. The question asked was: Employment Sector (circle the most appropriate answer) a. Government (Federal, Provincial, Municipal) b. Energy Sector c. Construction Sector d. Academia e. Consulting f. Business Owner g. Non-profit Sector h. None of the above This question was only asked on the Victoria questionnaires, so the n was 52. 151 Figure 5.20: Participant responses regarding the employment sector they work in for the Victoria workshops (n=52). Figure 5.20 shows that the largest portion of the participants in the Victoria workshops worked in the Government sector (15/52 respondents), followed by consulting (13/52 respondents), non-profit (5/42 respondents), business owners (3/52 respondents), and energy sector employees (3/52 respondents). Two participants wrote in answers, resulting in both manufacturing and the financial sectors having one response each. One participant selected none of the above without filling in a different employment sector. The Victoria workshops were an invitation only event that focused on energy experts and local stakeholders. As such, the employment sector demographics seem to reflect their intended audience. This demographic variable is strongly related with energy expertise, and was included for that reason. 5.2.3 Participants’ Collective Mental Models of Energy: General Discussion Surveying across the included active and supplemental variable questions, a collective trend emerges. Participants were most likely to think that climate change is our primary reason for addressing energy, that we need wholesale changes to our economic and political systems to address future 05101520253035% of total respondents 152 energy challenges, and that the federal government is the most responsible for meeting those challenges. Despite the importance of energy, and participants’ perceptions about the difficulty of changing their own energy behaviours, they were optimistic about our ability to address energy issues. Participants broadly felt that changes to policies and regulations are the best approach to addressing energy concerns, and that technical solutions such as building retrofits, or electric vehicles are the best applied solutions. Additionally, participants felt that we are most likely to move to more renewable energy sources to meet our future needs for supply. Participants at the workshops had a relatively even split between those with considerable energy knowledge, and those with limited energy knowledge. Description of these predominant trends are useful in understanding the broad landscape of opinion about energy futures, but it neglects the differences in people’s perceptions revealed by the graphs in the preceding section. While some trends were very strong (e.g. the opinion that we need transformational change), they were not universal. Amalgamated results downplay the underlying variance in a dataset. In a deliberative democracy, this variance is important, because it may reflect cohesive mental models of particular participants that are not being represented. More importantly, it may discourage those with different attitudes, beliefs or mental models from participating in deliberative or dialogical processes. So, while the description of the collective trends in Section 5.2 are an important means of understanding broad opinions, there is also value in trying to understand the more specific answer patterns of individual respondents. Were the dissenting voices the same across questions, or was the dissent essentially random noise? This question is particularly important to the question of participants’ mental models. A person’s mental model should not be a random assortment of opinions; rather it should, at least, be an internally coherent picture of an event or phenomena. In essence, the collective, descriptive trends revealed in Section 5.2 form the context within which to further examine particular individual answer patterns and the relationship between variables. The next section of this chapter turns to an examination of response patterns and the relationships between the questions described in Section 5.2. 153 5.3 Multiple Correspondence Analysis: Exploration of Participants’ Mental Models and their Constituent Pieces 5.3.1 Background Multiple Correspondence Analysis had its genesis in the 1960s, developed by the French statistician J. P. Benzecri. Benzecri built upon earlier work by R.A. Fisher and others to develop MCA as a descriptive method for examining relationships amongst categorical variables (LeRoux & Rouanet, 2010). The methodology saw extensive use in France, and more broadly Europe, during the 1970s and 1980s in fields such as sociology, epidemiology and market research, but has only recently gained more prominence internationally. Perhaps the chief proponent of the methodology is the French sociologist Pierre Bourdieu. Bourdieu came to use MCA almost exclusively in his research and stated: I use Correspondence Analysis very much, because I think that it is essentially a relational procedure whose philosophy fully expresses what in my view constitutes social reality. It is a procedure that ‘thinks’ in relations. (Bourdieu & Wacquant, 1992) This affinity of MCA for identifying and examining systems of relationships was the primary reason for it being included in this dissertation, as the primary goal of the mental models evaluation was to investigate the relationships of concepts that make up people’s mental models of energy. 5.3.2 A Conceptual Description of MCA Multiple Correspondence Analysis is one methodology in the broader category of Geometric Data Analysis (LeRoux & Rouanet, 2010). These methodologies are based on linear algebra, and describe relationships among individuals and variables based on geometric relationships in a multidimensional space. The mathematics behind Multiple Correspondence Analysis bear a strong relationship to those used in the more popular Principle Components Analysis (PCA), with the exception that PCA is focused on quantitative variables. Like PCA, MCA uses matrix algebra and the calculation of eigenvalues to determine the relationship between variables and the contribution of dimensionally reduced eigenvectors to explain the importance of different variables to revealed patterns. 154 There are two primary methodological requirements for conducting MCA. Firstly, there is the need for question homogeneity or thematic similarity – i.e. the variables input into the analysis must cover some cohesive research frame. Secondly, the researcher should attempt to be as exhaustive as possible in the development of the question set. These requirements are far less proscriptive then other statistical techniques, and it remains a strength of MCA that it can be used to reveal, rather than confirm, the latent relationships in a data set (LeRoux & Rouanet, 2010). This makes MCA particularly well suited to exploratory research of the type conducted in this dissertation. This affinity for exploratory analysis is a reflection of Benzecri’s affiliation with the inductive philosophy of statistical analysis, where the focus is on making the data model fit the data, rather than the other way around. 5.3.3 MCA Analysis Methodology MCA was developed, largely, as a means of analyzing categorical survey data (LeRoux & Rouanet, 2010). The basic dataset for an MCA is an Individual (rows) by Questions (columns) matrix. The Questions are also referred to as Variables. The options for each Question (e.g. a), b), c)) in a multiple choice question are the categories. So, for example, the graph of the category cloud in Figure 5.23 displays all of the possible answers for all of the questions used in the analysis. The specific objectives of using MCA in this analysis are threefold: 1. To look for patterns in the participants’ responses that partially represent their mental models of energy. Partially, because any subset of questions cannot reveal a complete picture of those mental models, rather the intent is to identify important model constituents. 2. To look for relationships between the questions asked. For example, does knowing what a participant thinks is the reason we should be concerned about energy tell us something about their preferred solutions? Similarly, does a participants’ level of energy knowledge impact how they think about energy? 3. To visualize the relationships between participants and questions as a means of visual data analysis. Do visual patterns exist in the data that facilitate the data analysis? 155 Data Preparation The first step in conducting an MCA is constructing or selecting a set of questions to be used. This includes both the active questions and the supplemental questions. Section 5.2.1 discussed the question selection process and documented the active and supplemental variable questions used in this analysis. Category Amalgamation for Rare Values In MCA, very rare categories can greatly impact the construction of the dimensions. This is beneficial to a point, as rare answers can help differentiate between patterns of responses. If a category is too rare, however, it may come to dominate the construction of dimensions, thereby limiting the explanatory power of those dimensions (Husson et al., 2011). As a rule of thumb, a category with less than 5% of the total responses should be amalgamated with another category whenever possible (LeRoux & Rouanet, 2010). In the current study, five of the eight questions had at least one category with fewer than 5% of the total responses. Table 5.2 Shows the rare categories and how they were amalgamated for the MCA. Table 5.2: Original and amalgamated categories for active variables from the MCA. Question Original Categories Amalgamated Category Reason_to_Address Fairness_to_World Fairness Future_Generations Level_of_Change No_Change Removed Most_Responsible Individuals Citizens Groups_of_Citizens Least_Responsible Federal_Govt Levels_of_Govt Provincial_Govt Municipal_Govt Difficulty Very_Easy Easy Somewhat_Easy 156 Using the amalgamated categories, all categories of the active variables had an response rate above the 5% threshold. Accounting for Null Values The presence of null values (no answer) in MCA can cause the first developed dimensions to be dominated by these relatively rare non-answers. This greatly impacts the explanatory power of the MCA. There are three main ways to account for null values in MCA (Husson et al., 2011): 1. Leave the null values in and interpret around them 2. Remove all of the null values at the risk of systematically excluding participants 3. Use mathematical means to impute the missing values based on the participant’s response patterns and the response patterns of like individuals. A difficulty of the imputation methodology is that it creates a new matrix from which the MCA is conducted. This prevents the use of supplementary variables, because they cannot be added to the analysis after this new matrix is created (and including them beforehand results them becoming active variables). The inclusion of supplementary variables was an important component of the analysis for this chapter. For that reason, the decision was made to remove all of the individuals with null values on the active variables. This led to a decrease in n from 118 to 80. A demographic analysis was conducted on the eliminated participants to determine if there was any systematic bias. There did not appear to be any systematic bias in the null value participants, with the possible exception of Primary_Language. It is possible that a language barrier led to some of the null values. Conducting the MCA Analysis The MCA conducted in this study used the FactoMineR (Husson et al., 2011) package of the R statistical programming language. The MCA was run on a data table of individuals versus active and supplementary questions using the geometric MCA technique. The MCA was also conducted using the Burt Table for comparison of the variance explained by the constructed dimensions. The outputs of the MCA included a list of the developed dimensions and their associated eigenvalues (Table 5.4), which denote how much of the variance of the total dataset each dimension accounts for. The outputs also included a graphical representation of the active and supplemental variables against the first two dimensions (Figure 5.21), the cloud of individuals (Figure 5.22) and the cloud of 157 categories (Figure 5.23 for active categories and 5.24 for active and supplemental categories). Using the dimdesc function of FactoMineR, the variable and category associations with the developed dimensions were calculated. Variance Explained in MCA vs. PCA or Factor Analysis One of the things that a researcher with experience in PCA or Factor Analysis will notice when reviewing MCA results is the greatly decreased variance explained by the developed dimensions (Greenacre, 2006). There are two main reasons for this discrepancy, both related to the categorical nature of the input data. Firstly, MCA is calculated from the geometric cloud rather than linear best-fit models of the components as in PCA. This increases the inertia of the dataset and decreases the variance explained by any one dimension (LeRoux & Rouanet, 2010). Secondly, the methodology for utilizing categorical data in multivariate analysis involves constructing a disjunctive table of the categories, such that each category is represented as a binary number of 0 (category not selected) or 1 (category selected). Table 5.3 Shows an example of a disjunctive table for an individuals by question matrix. Table 5.3: A schematic example of a disjunctive data table. Question 1 Individual Category A Category B Category C Category D Individual 1 0 1 0 0 Individual 2 1 0 0 0 Individual 3 0 1 0 0 Individual 4 0 0 0 1 The result of the disjunctive table is to greatly increase the inertia of the MCA dataset, which consequently decreases the variance explained. As a result, MCA notoriously undervalues the variance accounted for by its constructed dimensions (Greenacre, 2006; LeRoux & Rouanet, 2010). There are methods for correcting the variance explained in an MCA when the intention is to conduct further analyses based on the variance explained (Abdi & Valentin, 2007; Greenacre, 2006), however, because the MCA conducted in this study was primarily focused on a description of participant mental models and not further statistical inference, these mathematical corrections were not performed. The MCA was run using the Burt table method, which is one form of variance 158 correction (Husson et al., 2011). The Burt table numbers will be reported, though the base MCA will be used in all analyses. Dimension Selection Choosing the number of dimensions to interpret is more of an art than a science in MCA. There are, however, a set of guidelines to aid in the process (LeRoux & Rouanet, 2010): 1. Look for the point where the eigenvalues have the greatest decrease between dimensions 2. Determine what level of cumulative variance explained is sufficient for the current analysis 3. Examine the dimensions for interpretability. Include those dimensions that are easily interpreted. Dimension Interpretation There are four main tools for interpreting the dimensions developed in an MCA. Firstly, there is the table of variable relationships to the constructed dimensions. This tells the researcher what questions from the survey define the constructed dimension. Secondly, there is the table of category relationships. This table defines what categories of a given question were most strongly related to the dimension, and if that relationship was positive or negative (indicating what end of the axis that category inhabits). Thirdly, the category interpretation can be augmented with the visual cloud of categories for the dimensions being studied. This graphic visually represents the geometric location of the categories along the axes of the dimensions. Finally, the cloud of individuals shows the location of each respondent along the developed axes. From this information, illustrative individuals can be selected, and their answer patterns analyzed to further clarify what the dimensions are showing. This combined interpretation provides a picture of what the dimensions are measuring. Confidence Ellipses Based on the cloud of individuals and their responses on any given question, it is possible to plot the barycenter (center of mass of the individuals who made a particular category selection) and the 95% confidence ellipses around that barycenter. This graphic is a useful way of determining if there is a significant relationship between a given variable and the constructed dimensions. If confidence ellipses plotted for categories of a variable along the MCA dimensions do not overlap, then there is strong evidence that that variable is interacting with the dimensions. This is particularly useful for 159 interpreting the supplementary variables, which are not used in the construction of the dimensions – i.e. they are not inherently related to the dimensions through the mathematical procedures that constructed the dimensions. Confidence ellipses were plotted for key supplementary variables in this study. Supplementary Analysis: Agglomerated Hierarchical Clustering Agglomerated hierarchical clustering is a technique that can be conducted post hoc on either PCA or MCA datasets (Husson et al., 2011). This technique uses information from the MCA to cluster groups of individuals according to response pattern characteristics across a researcher specified number of dimensions. The number of clusters is largely determined by the number of dimensions included in the clustering procedure. One strength of the agglomerated hierarchical clustering technique is that it can include more dimension information than originally interpreted in the MCA analysis, meaning that the clusters will be developed from a larger proportion of the overall variance in the dataset. This may reveal patterns in the data missed in the original MCA interpretation. A hierarchical agglomerated clustering analysis was conducted post hoc on the MCA dataset in this study. 5.3.4 MCA Analysis: Results and Discussion This section documents the results of the MCA and their implications regarding participants’ mental models of energy at the community scale. Dimension Construction The first stage of the MCA analysis produced the cloud of variables (see Figure 5.21) the cloud of individuals (see Figure 5.22), and the cloud of categories (see Figure 5.23) for the MCA. From Figure 5.21 we can see that, for Dimension One on the horizontal axis, the dominant variables are Level of Change, Most Responsible, Approach, and Reason to Address. For Dimension Two on the vertical axis, the dominant variables are Approach, Least Responsible, Most Responsible and Applied Solution. The red variables are the active variables, while the green variables are the supplementary variables. Of the supplemental variables, only Energy Importance, and to a lesser extent gender, extend noticeably from the origin on either dimension. The stronger relationships of some of the active variables than the supplemental variables would be expected, to some degree, 160 because they were used in constructing the dimensions, however that is not a given. A supplemental variable with a strong association to one or more of the dimensions could be positioned much further from the origin than active variables. For example, if age was a supplemental variable in a dataset measuring risk-taking behavior, one might expect it to demonstrate a strong relationship to the developed dimensions. Figure 5.21: The cloud of active (red) and supplementary (green) variables on the first two dimensions of the MCA. A visual analysis of the cloud of individuals (see Figure 5.22) suggests a triangular pattern that extends farther to the right (positive end) of the origin on Dimension One than it does to the left (negative end). Individual’s geometric locations are the result of their answer patterns on the active variables. Individuals that are more distant from the dimension’s origin chose rarer categories to the active variable questions. 161 Figure 5.23 shows the cloud of active variable categories. Figure 5.24 shows the active variable categories augmented with the supplemental variable categories (in grey). Rarer categories (e.g. Level_of_Change_Economic, Approach_Incentives, Source_Change_Alternative_FF) occupy the periphery of the category cloud, and account for a disproportionate amount of the cloud variance. Figure 5.23 and Figure 5.24 provide useful aids for interpreting the MCA dimensions, and will be returned to later in the discussion. Selection of MCA Dimensions to Interpret The MCA developed a total of 32 dimensions, accounting for all of the variance in the dataset (see Table 5.4). The first five dimensions account for approximately 34% of the variance, and the first ten dimensions account for approximately 56% of the variance. The large number of dimensions required to account for all of the variance is common in MCA for the reasons explained in section 5.3.3. Dimension selection is based on three primary criteria: 1. Decreases in eigenvalue and cumulative variance 2. Overall variance explained 3. Interpretability of the components of a dimension The rule of thumb for eigenvalues and cumulative variance is to look for large changes in the decreasing values. Table 5.4 shows the eigenvalues and variance explained for all 32 calculated dimensions. The largest single decrease in eigenvalue and percentage of variance occurs between dimensions two and three, with regular, small decreases in value occurring from that point on. The eigenvalue method, then, would suggest interpreting two dimensions. 162 Figure 5.22: The MCA cloud of individuals for dimensions one and two. 163 Figure 5.23: The cloud of active variable categories for the MCA on dimensions one and two. 164 Figure 5.24: The active categories (red) and supplemental categories (grey) for dimensions one and two of the MCA. 165 Table 5.4: The eigenvalues and percentage of variance explained for all dimensions from the MCA. Dimension eigenvalue percentage of variance cumulative variance dim 1 0.32 8.1 8.1 dim 2 0.31 7.81 15.91 dim 3 0.25 6.27 22.18 dim 4 0.24 6.06 28.24 dim 5 0.22 5.53 33.77 dim 6 0.2 4.96 38.73 dim 7 0.19 4.81 43.54 dim 8 0.18 4.45 47.99 dim 9 0.18 4.4 52.39 dim 10 0.16 4.03 56.42 dim 11 0.15 3.81 60.24 dim 12 0.14 3.6 63.83 dim 13 0.13 3.31 67.15 dim 14 0.13 3.21 70.36 dim 15 0.12 2.92 73.28 dim 16 0.1 2.6 75.88 dim 17 0.1 2.41 78.29 dim 18 0.09 2.34 80.63 dim 19 0.09 2.26 82.89 dim 20 0.09 2.15 85.04 dim 21 0.08 2.08 87.12 dim 22 0.07 1.87 88.99 dim 23 0.07 1.7 90.69 dim 24 0.06 1.56 92.25 dim 25 0.06 1.43 93.68 dim 26 0.05 1.21 94.89 dim 27 0.05 1.14 96.03 dim 28 0.04 1.07 97.1 dim 29 0.04 0.88 97.98 dim 30 0.03 0.83 98.81 dim 31 0.03 0.74 99.55 dim 32 0.02 0.45 100 166 Reviewing the dimensions for interpretability also suggest examining the first two dimensions. In the subsequent dimensions the patterns are less distinct and harder to interpret. In some cases the dimensions tend to be dominated by single individuals, which can happen in circumstances where very rare responses dominate the variance of a dimension. The first two dimensions account for 15.91% of the variance in the MCA, or 28.6% if the Burt table method is used to calculate the MCA. Recall that the variance explained in MCA is substantially lower than in PCA, for both data and mechanical reasons (Abdi & Valentin, 2007; Greenacre, 2006). As a comparison, a commonly used example of MCA that examines patterns of tea drinkers’ according to tea type, purchase location, tea cost, etc., has a cumulative variance explained of 17.99% for the first two dimensions (Husson et al., 2011). Therefore, while the variance explained in the first two dimensions of this MCA is not particularly strong, it is not outside the bounds of normal use for the analysis technique. Section 5.5.1 will return to this point, with suggestions about how question construction and data collection could improve the variance explained. However, for the exploratory and descriptive analysis conducted in this chapter, the decision was made to rely on the decrease in eigenvalues, and the interpretability of dimensions to arrive at the decision to interpret two dimensions. 5.3.5 Interpretation of MCA Dimensions Dimension One Interpretation of the dimensions will first look at the numerical associations with the dimensions, starting with the variables and then followed by the categories. The interpretation will then use the cloud of categories from Figure 5.23 to augment and fill out the interpretation of the dimensions. Variables The first value in Table 5.6 is the coefficient of determination (R2), which represents the strength of relationship between two elements as a proportionate correlation. Relationships will be described using the Univerisity of Strathclyde’s Humanities and Social Science’s correlation categorizations seen in Table 5.5 (University of Strathclyde, 2014). The p value shows the probability of the displayed relationship occurring by chance. All p values reported in Table 5.6 are significant at the 0.05 level, 167 meaning there is a 95% confidence level that the reported value shows a real, rather than erroneous, relationship. Dimension one shows a strong relationship with the variables Level_of_Change required and who is Most_Responsible for addressing energy issues (see Table 5.6). This dimension also has a moderate relationship with the participants’ selected conceptual Approach to addressing energy issues. Finally, there is a weak to moderate relationship to participants’ chosen Reasons_to_Address energy issues (see Table 5.6). Table 5.5: University of Strathclyde correlation coefficient categorization. Strength of Correlation Value of Correlation Coefficient Perfect 1 Very Strong 0.8-0.9 Strong 0.5-0.8 Moderate 0.3-0.5 Modest 0.1-0.3 Weak >0.1 Zero 0 Table 5.6: Variables associated with Dimension One from the MCA. Variables R2 p.value Level_of_Change 0.573693 5.55E-15 Most_Responsible 0.5297896 1.07E-11 Approach 0.4239277 1.76E-08 Reason_to_Address 0.293619 7.19E-06 Least_Responsible 0.2231869 2.35E-04 Source_Change 0.2296394 3.33E-03 Applied_Solution 0.1849748 1.78E-02 How_Difficult 0.1338769 2.75E-02 Based on the associated variables, this dimension appears to reflect participants’ high-level beliefs about the problem presented by energy issues and the conceptual approaches to addressing them. That is, what the problem is (Reason_to_Address), how large it is (Level_of_Change), who should be responsible for addressing the problem (Most_Responsible), and how they should attempt to address 168 it (Approach). The dimension has a much weaker relationship to more specific questions about energy, such as Applied_Solutions and Source_Change. This dimension could be interpreted to represent participants’ broader beliefs, such as political affiliations, which have previously been connected to attitudes about climate change and other sustainability issues (Dunlap et al., 2000; Whitmarsh, 2011). In this context, this suggests that higher order beliefs are the best dimension reductions for the participants’ answer patterns. In other words, if you wanted to predict a participant’s answers about future energy solutions, the best attribute to know about them would be their more general worldview. Categories It is important to note that the categories that account for the greatest amount of variance on a dimension will be those that were infrequently selected (LeRoux & Rouanat, 2010). Therefore, the strongest category associations, while informative, should not be misinterpreted as defining the dimension. Rather, they are a reflection of the more extreme cases of the variables which construct the dimensions. Table 5.7 shows the categories strongly associated with Dimension One. The standard practice when interpreting MCA dimensions is to use categories whose estimated relationships are stronger than the mean relationship estimate for all significant categories (LeRoux & Rouanat, 2010). For Dimension One, the mean estimate was 0.54. Category relationships with a dimension can be either positive or negative, denoting their location on either side of the dimension origin. The strength of the relationship is determined by the estimated value. As Table 5.7 and Figure 5.23 show, there were both strong positive and negative category relationships with Dimension One. The strongest category relationship with Dimension One was a positive relationship with Most_Responsible_Business, followed by Level_of_Change_Economic and Approach_Technology. The strongest negative relationships were with Level_of_Change_Transformative, Most_Responsible_Municipal_Government and Source_Change_Efficiency. 169 Table 5.7: The relationship of categories to Dimension One. Category Estimate p.value Level_of_Change_Economic 1.1529573 2.96E-15 Most_Responsible_Business 1.2241484 4.11E-13 Approach_Technology 0.8684492 1.07E-09 Reason_to_Address_Limits 0.5565594 1.11E-06 Least_Responsible_Groups of Citizens 0.5013617 2.53E-05 Source_Change_Decreased FF 0.5551881 2.75E-04 Energy_Importance_Systems 1.1796799 2.31E-03 Applied_Solution_Public Transportation 0.3687744 4.41E-03 Applied_Solution_Collective Option 0.3879926 1.88E-02 Energy_Knowledge_Fair Amount 0.251509 3.74E-02 Reason_to_Address_Climate Change -0.1993684 2.32E-02 Approach_Behaviour -0.2570782 1.96E-02 Age_25-44 -0.3169403 1.62E-02 Least_Responsible_Business -0.44187 9.53E-03 Approach_Incentives -0.5250365 5.70E-03 Source_Change_Efficiency -0.7018006 3.75E-03 How_Difficult_Neutral -0.43593 2.68E-03 Reason_to_Address_Environmental Damage -0.2988453 2.04E-03 Most_Responsible_Citizens -0.4179622 4.56E-04 Most_Responsible_Municipal Govt -0.6258142 3.15E-06 Level_of_Change_Planning and Policy -0.4986042 1.36E-08 Level_of_Change_Transformative -0.6543531 4.00E-10 The right, or positive side of Dimension One is fairly straightforward to interpret. It is anchored by participants’ who felt that we can work within current economic systems to address energy issues. They feel that businesses have a large role to play in addressing energy issues, and that technology is our best approach. Their primary reason for wanting to address energy issues is limited resources, suggesting they want to ensure a consistent supply. In essence, these participants feel that we should innovate our way forward on energy, rather than using planning, policy or behavioural approaches. Figure 5.22 shows less differentiation on the left side of the origin. The primary association with the negative end of Dimension One was Source_Change_Efficiency (Table 5.7). This is likely an outlier, as it was selected by only 4 participants. Figure 5.23 shows that Source_Change_Efficiency lies in the extreme bottom corner of the category cloud, indicating that it accounts for a significant amount 170 of variance. The next two categories associated with the negative end of Dimension One were Level_of_Change_Transformative, and Most_Responsible_Municipal_Govt. The differentiation on this end of the dimension appears to be driven by how big the changes are that are necessary to address energy issues. While Level_of_Change_Economic could be seen as the least disruptive level of change required, Level_of_Change_Transformative is the most disruptive. Therefore, it is reasonable to assume that they could anchor opposite ends of a dimension (i.e. that other beliefs and ideas about energy are strongly tied to a participant’s beliefs about change). The connection of Most_Responsible_Municipal_Government could suggest a commitment to local action, and a review of Figure 5.23 Shows that Approach_Planning also occupies the left, or negative end of Dimension One. The cluster analysis performed in later in this section will help clarify the interpretation of the negative end of Dimension One. For now, it is sufficient to say that it is strongly influenced by those who felt we need transformative change. Illustrative Individuals The use of illustrative individuals is a recommended technique in the conduct of MCA to better clarify what the dimensions are measuring (LeRoux and Rouanat 2010). For the purposes of the illustrative individuals analysis, a structured visual analysis approach was used to select the individuals. The first individual selected for each end of a dimension was the individual who was farthest out on the dimension axis (or immediately beside the axis if no participant straddled it). This individual might be thought of as the archetypal individual, because they do not stray into one of the two possible quadrants that may result in influence from the other dimension’s axis. The next two individuals for an axis were then selected such that one individual came from each of the two possible adjacent quadrants. Figure 5.22 shows the cloud of individuals with a graphical indication of the illustrative individuals for each end of each axis. Tables 5.8 through 5.10 show the answer patterns for each of the selected illustrative individuals. For Dimension One, participant 76 (see Table 5.8 and Figure 5.22) is the archetypal individual for the positive end of the dimension, with participant 10 occupying the upper quadrant and participant 56 occupying the lower quadrant. Of note is the distance towards the origin it was necessary to travel to find an individual in the lower quadrant. This suggests that individual 56 may not be a strong representative of the positive end of Dimension One. As Table 5.8 shows, all three participants agree on the necessary level of change (economic), while participants 76 and 10 agree on the Reason_to_Address (limited resources), Most_Responsible (business) and Approach 171 (technology). Participants 76 and 56 agree on who is Least_Responsible (individuals). With the exception of certain aspects of participant 56’s responses, the illustrative individuals appear to corroborate the idea that the positive end of Dimension One reflects a view that energy issues are an economic problem that can be best addressed through markets, and that business should be primarily responsible for addressing them. Participant 78 is the archetypal individual for the negative end of Dimension One, while participant 73 occupies the upper quadrant and participant 12 occupies the lower quadrant (Figure 5.22). All three individuals agreed that energy issues require transformative change, and all three felt that government (municipal for 12, 73 and provincial for 78) should be responsible for addressing energy issues. Two of the three thought the main reason to address energy issues was environmental damage (78 and 73) and two felt businesses were the Least_Responsible (78 and 73). The illustrative individuals, then, appear to corroborate the analysis that the negative end of Dimension One is about transformative change and government responsibility. Based on an analysis of the variables, categories, category cloud and illustrative individuals, then, Dimension One seems to be differentiating between energy issues as an economic problem with economic solutions (and consequent responsibility) and energy issues being a transformative problem requiring government intervention and resultant policies. The negative end of Dimension One is less well-formed than the positive end, suggesting, perhaps, that the questions asked were not sensitive enough to pick up variations at that end of the dimension, or that those participants’ mental models may not be as strongly developed. 172 Table 5.8: Active variables for the illustrative individuals.Dimension One Positive Relationship Participant Reason to Address Level of Change Most Responsible Least Responsible Approach Applied Solution Source Change How Difficult? 10 Limits Economic Business Groups Technology Collective Option Renewables Somewhat Diff 76 Limits Economic Business Individuals Technology Public Transp Decreased FF Easy 56 Climate Change Economic Federal Govt Individuals Policy Incentives Decreased FF Very Diff Negative Relationship Participant Reason to Address Level of Change Most Responsible Least Responsible Approach Applied Solution Source Change How Difficult? 78 Environ Damage Transformative Provincial Govt Business Planning Indiv Behaviour Efficiency Very Diff 12 Climate Change Transformative Municipal Govt Levels of Govt Incentives Technical Changes Mix of Sources Neutral 73 Environ Damage Transformative Municipal Govt Business Behaviour Renewables Renewables Neutral Dimension Two Positive Relationship Participant Reason to Address Level of Change Most Responsible Least Responsible Approach Applied Solution Source Change How Difficult? 58 Environ Damage Planning and Policy Citizens Groups Behaviour Indiv Behaviour Decreased FF Easy 16 Climate Change Transformative Business Groups Behaviour Indiv Behaviour Renewables Null 29 Fairness Transformative Citizens Levels of Govt Behaviour Indiv Behaviour Alternative FF Null Negative Relationship Participant Reason to Address Level of Change Most Responsible Least Responsible Approach Applied Solution Source Change How Difficult? 32 Climate Change Transformative Federal Govt Individuals Policy Collective Option Mix of Sources Somewhat Diff 71 Climate Change Transformative Federal Govt Individuals Incentives Collective Option Renewables Very Difficult 42 Limits Transformative Federal Govt Individuals Policy Collective Options Null Somewhat Diff 173 Table 5.9: Supplemental energy-related variables for the illustrative individuals. Dimension One Positive Relationship Participant Energy Knowledge Energy Use Correct Source Correct Energy Importance Optimism 10 Fair Amount Three y Economic Reasons Optimistic 76 Fair Amount One n Systems Optimistic 56 Average One n Future Generations English Negative Relationship Participant Energy Knowledge Energy Use Correct Source Correct Energy Importance Optimism 78 Fair Amount Zero n Environmental Impact Optimistic 12 Fair Amount Five n Economic Reasons Optimistic 73 Very Limited Zero n Sustainability Neither Dimension Two Positive Relationship Participant Energy Knowledge Energy Use Correct Source Correct Energy Importance Optimism 58 Average One n Environmental Impact Optimistic 16 Very Limited One y Systems Neither 29 Fair Amount Two y Future Generations Pessimistic Negative Relationship Participant Energy Knowledge Energy Use Correct Source Correct Energy Importance Optimism 32 Fair Amount Two n Resilience Pessimistic 71 Some One n Resource Depletion Optimistic 42 Very Limited Zero y Resilience Neither 174 Table 5.10: Supplemental demographic variables for the illustrative individualsDimension One Positive Relationship Participant Workshop Age Gender Income Cultural Background Primary Language Employment Education 10 Victoria Null Null Null Null Null Null Null 76 Grandview 19-24 M above $80,000 Caucasian/European English Null Some Postgrad 56 Marpole 65-74 M $65,000-$79,000 Caucasian/European English Null Bachelor's Degree Negative Relationship Participant Workshop Age Gender Income Cultural Background Primary Language Employment Education 78 Grandview 19-24 M $20,000-$34,999 East Asian English Null Some Postgrad 12 Victoria 25-44 F Null Null Null Government Null 73 Grandview 25-44 F above $80,000 Caucasian/European English Null Some Postgraduate Dimension Two Positive Relationship Participant Workshop Age Gender Income Cultural Background Primary Language Employment Education 58 Marpole 25-44 M above $80,000 Caucasian/European English Null Postgrad Degree 16 Victoria 45-64 M Null Null Null Consulting Null 29 Victoria 25-44 M Null Null Null Non-profit Null Negative Relationship Participant Workshop Age Gender Income Cultural Background Primary Language Employment Education 32 Planners 25-44 F $65,000-$79,000 Caucasian/European English Null Postgrad Degree 71 Grandview 25-44 F $50,000-$64,000 Caucasian/European English Null Some Postgrad 42 Planners 25-44 M above $80,000 Caucasian/European English Null Postgrad Degree 175 Dimension Two Variables The strongest variable relationship with Dimension Two was the conceptual approach to addressing energy issues (see Table 5.11). This dimension also had a strong connection to who is Least_Responsible for addressing energy issues, and a moderate relationship to who is Most_Responsible for addressing energy issues and what is the preferred Applied_Solution. From a variable perspective, this dimension appears to be focused on issues of who and how to address energy issues. Another way to characterize it is that Dimension Two has a solution focus. Table 5.11: Variables associated with Dimension Two. Dimension 2 Variables R2 p.value Approach 0.4912249 1.92E-10 Level_of_Change 0.3808547 9.64E-09 Most_Responsible 0.3928387 4.47E-07 Reason_to_Address 0.3889249 5.59E-07 Least_Responsible 0.3714431 1.49E-06 Source_Change 0.281232 9.04E-04 Gender 0.1530976 5.31E-03 How_Difficult 0.1783516 1.11E-02 Categories Table 5.12 shows the categories that were related to Dimension Two. The strongest relationship with Dimension Two was Source_Change_Alternative_FF (Alternative Fossil Fuels). This was a rare category (3 individuals), which likely accounts for its particularly strong relationship. Most_Responsible_Citizens, Approach_Behaviour, Applied_Solution_Individual_Behaviour, and Least_Responsible_Levels_of_Government also had strong relationships to the positive end of Dimension Two. On the negative end of the dimension, there were strong relationships to Approach_Incentives, Source_Change_Efficiency, Least_Responsible_Individuals, 176 Energy_Importance_Resilence, Applied_Solution_Unit Size and Reason_to_Address_Climate Change. Table 5.12: Categories associated with dimension two of the MCA. Category Estimate p.value Approach_Behaviour 0.7456298 1.78E-11 Most_Responsible_Citizens 0.6424206 5.01E-06 Least_Responsible_Levels of Govt 0.4449049 3.18E-05 Applied_Solution_Individual Behaviour 0.6017661 1.71E-04 Source_Change_Alternative FF 0.8345183 3.01E-03 Planning and Policy 0.3093587 4.60E-03 How_Difficult_Null 0.3179451 1.02E-02 Approach_Technology 0.2567545 2.12E-02 Reason_to_Address_Fairness 0.2449435 4.25E-02 Energy_Importance_Resilience -0.5903734 3.70E-02 Energy_Importance_Climate Change -0.3296316 3.32E-02 How_Difficult_Very Difficult -0.3388625 3.24E-02 Applied_Solution_Collective Option -0.3850587 1.00E-02 Applied_Solution_Unit Size -0.471732 9.32E-03 Approach_Policy -0.2091839 6.49E-03 Source_Change_Efficiency -0.7056613 4.20E-03 Most_Responsible_Federal Govt -0.388389 1.75E-05 Approach_Incentives -0.779729 6.98E-06 Reason_to_Address_Climate Change -0.4665186 4.99E-07 Least_Responsible_Individuals -0.5819923 4.87E-12 The categories associated with the positive end of Dimension Two are quite clear: they focus on participants who felt that individuals should be responsible for addressing energy issues rather than levels of government. These participants also felt that individual behaviour was the best approach for addressing energy issues. Interestingly, the negative association with Reason_to_Address_Climate_Change, and strong positive association with Source_Change_Alternative_FF suggests that this end of Dimension Two is occupied by those who are concerned about continued energy supply, rather than any environmental impacts of energy extraction or use. 177 The negative end of Dimension Two is less clear from a review of the categories in Table 5.12, but becomes clearer with the addition of a visual analysis of Figures 5.23 and 5.24. Approaches that fall below the origin of Dimension Two include Approach_Planning and Approach_Policy, and Applied_Solutions that fall below the origin include Applied_Solution_Collective_Options and Applied_Solutions_Public_Transportation. Most_Responsible_Federal_Government and Most_Responsible_Provincial_Government also fall below the origin on Dimension Two, though Most_Responsible_Federal_Govt seems to be more strongly related to Dimension Two and Most_Responsible_Provincial_Govt more strongly related to the negative end of Dimension One (see Figure 5.23). A combination of variable, category and visual analysis, then, leads to the interpretation that Dimension Two is juxtaposing individual responsibility and action on the positive end of the dimension, with government responsibility and collective actions on the negative end of the dimension. Illustrative Individuals Tables 5.8 through 5.10 show the illustrative individuals for Dimension Two. Participant 58 was the archetypal participant for the positive end of Dimension Two, with participant 16 occupying the right quadrant and participant 29 occupying the left quadrant. As Table 5.8 shows, there was agreement between all three participants on Approach_Behaviour and Applied_Solution_Individual_Behaviour. Two of the three selected individuals as the Most_Responsible (confirmed by consulting the original data tables before Individuals and Groups of Citizens were amalgamated into Citizens). Individual 16 from the right quadrant selected business as the most responsible, suggesting the interplay between Dimensions One and Two in the right quadrant. Overall, the illustrative individual pattern for the positive end of Dimension Two corroborates that it is focused on individual responsibility and action. Participant 32 was the archetypal individual for the negative end of Dimension Two, with participant 71 occupying the left quadrant and participant 42 occupying the right quadrant (see Figure 5.22). All three individuals agreed on the Level_of_Change_Transformative, Most_Responsible_Federal_Government, and Applied_Solution_Collective_Option. These illustrative individuals appear to support the idea that the negative end of Dimension Two focuses on more collective responsibility and actions. 178 Figure 5.25: A schematic representation of the interpretation of the MCA dimensions. Based on an analysis of the variables, categories, category cloud and illustrative individuals, then, Dimension Two seems to be differentiating between individual versus collective mental models for addressing energy issues at the community scale. Figure 5.25 presents a summary of the dimensions constructed in the MCA labeled according to the dimension interpretations. What we can say from the dimensions and associated variables and categories, is that knowing where a person sits on the transformative to economic change and individual versus collective action dimensions will be a strong predictor of how they will answer questions related to energy at the community scale. In other words, these factors are particularly important to the way that people conceptualize energy from a community planning perspective. 179 Relationship of Supplemental Variables to the Constructed Dimensions The purpose of including the supplemental variables in the MCA was to examine whether variables that were not specifically part of participants’ mental models of energy at the community scale (as assessed in this study) were in some way related to them. For example, a person’s income is not an energy concept, but it may have a significant impact on how they think about energy issues. MCA allows supplementary variables to be related to the developed dimensions after those dimensions have been constructed using the active variables. The supplementary variables can then be analyzed numerically for their relationships to the constructed dimensions, as can be seen for certain supplemental variable categories in Table 5.7 and Table 5.12. The supplementary variables can also be graphed onto the cloud of individuals to provide a visual indication of the strength of the relationship. Using this graphing technique, the center of mass, or barycenter, of the individuals that belong to a category of the supplemental variable can be plotted, as well as the 95% confidence ellipse for that barycenter. Therefore, if two categories’ ellipses do not overlap, there is a strong indication of a significant relationship between that supplementary variable and the underlying MCA dimensions. Figure 5.26 and Figure 5.27 show the relationships between four of the supplemental variables and the constructed dimensions. In Figure 5.26, the participants self-reported energy knowledge and the workshop they attended are plotted against the MCA dimensions. One of the supplemental research questions for this study was to determine if there was a relationship between expertise and mental models of energy. Self-reported energy knowledge provided one proxy for level of expertise. There was also an observed difference in expertise across the workshops. The Victoria workshops were invitation only, and brought together energy experts and local stakeholders. The Planners workshop focused on community planners and sustainability staff from the City of Vancouver. Finally, the Marpole and Grandview Woodland workshops were open invitation with a focus on drawing citizens from those Vancouver neighbourhoods. Due to differences in workshop advertising (see discussion in Chapter 6), it was observed that more energy experts attended the Grandview workshop than the Marpole workshop. In fact, based on participant observation, the general trend in energy expertise could be approximated as: Victoria ≥ Grandview > Planners > Marpole 180 An investigation of Figure 5.26 shows no discernable relationship between either the participants’ self-reported energy knowledge or the workshop they attended, and the constructed dimensions of the MCA. There is considerable overlap in all of the confidence ellipses for both variables, and the barycenters for each category (denoted by the squares in the same colour as the category labels) congregate around the dimension origins. The question of the impact of energy knowledge on participant responses will be returned to in more detail in section 5.4, however, this first level analysis suggests that expertise has little effect on participants’ mental models as portrayed on the MCA dimensions. Confidence interval ellipses were plotted for each of the supplemental variables, though most variables did not demonstrate significant relationships. One more pairing, however, is worth interpreting here. Figure 5.27 shows the barycenters and confidence ellipses for cultural background and gender. Gender shows the strongest relationship to the constructed dimensions, with the confidence ellipses for males and females touching, but barely overlapping. There were not a sufficient number of Gender_Other participants to construct a confidence ellipse for that category. Recalling that the lower left quadrant of the dimension plot roughly equates to collective action, transformative change and government responsibility, and the upper right quadrant roughly equates to economic level of change, individual or business responsibility, and individual action, an interpretation of the gender data can be formed. According to this graphic, male participants trended towards the individual/economic end of the dimensions, and female participants trended towards the collective/transformative/government end of the dimensions. 181 Figure 5.26: Centers of mass and 95% confidence ellipses for energy knowledge and workshop attended. 182 Figure 5.27: Centers of mass and 95% confidence ellipses for cultural background and gender. 183 The cultural background graph shows a similar, though weaker, relationship to the gender graph, with people of Caucasian/European descent trending slightly towards the individual/economic end of the dimensions, and other cultural backgrounds trending towards the collective/transformational end. It must be noted that only the Caucasian/European and East Asian categories had sufficient numbers to construct confidence ellipses, and that those ellipses overlap. Therefore no strong claims can be made about the significance of this finding. Rather, the suggestion of a cultural impact on mental models of energy bears further investigation. Is there a tendency for people from less individualistic cultural backgrounds to prefer more collective solutions? If so, what are the ramifications for energy planning in multicultural communities? An analysis of a subset of the supplemental variables suggests that there may be an impact of gender and, more equivocally, cultural background on participants’ mental models of community scale energy. This finding further supports the dimension construction findings, which suggest that it is more fundamental beliefs and worldviews that impact how people think about energy, and less the specifics of energy knowledge. This finding resonates strongly with the mental models framework proposed by Hoffman et al. (2014), who suggested that mental models are made up of core, more abstract, and inflexible elements (such as worldviews) and external, flexible elements (for example, preferences for energy solutions). 5.3.6 Agglomerated Hierarchical Clustering of Individuals From the MCA Through the use of the MCA it was possible to determine some of the main variables and categories that influenced participant answer patterns. The documentation of the illustrative individuals further revealed the kinds of answers patterns that contributed to the creation of the MCA dimensions. These analyses have been instructive, and have provided a glimpse into the answer patterns equated with participants’ mental models of community scale energy. One final analysis, however, will provide a better understanding of those answer patterns, by more specifically focusing on the individuals in the MCA. Agglomerated Hierarchical Cluster Analysis is a common follow-up analysis to MCA, and can help to more effectively group sets of individuals on common characteristics (Husson et al., 2011). The hierarchical clustering is performed on the completed MCA, and can be performed using any number of MCA dimensions. The number of dimensions used can impact the number of clusters 184 generated. It is common practice to limit the number of dimensions used to eliminate noise that is commonly associated with the higher order dimensions (Husson et al., 2011). The intention in the cluster analysis performed here was to use a higher number of dimensions than were interpreted in the original MCA to potentially recover some of the variance from the original dataset. The final decision, based on some trial and error was to use sufficient dimensions to account for 80% of the variance in the original MCA, resulting in the use of 18 dimensions (see Table 5.4). Figure 5.28 shows the hierarchical cluster tree diagram for the resulting analysis. The bar chart in the upper right hand corner of the figure shows the inertia explained by successive clusters, and FactoMineR’s suggested number of clusters to interpret. The number of clusters to interpret are determined by decreases in the inertia explained for the dataset. The suggested number of clusters to interpret was 6; as denoted by the horizontal bar crossing the vertical cluster hierarchies in Figure 5.28. This cluster number is determined from the data, rather than by the researcher. The end of each cluster line indicates the numbered individual. This number corresponds to the individuals labeled in the individual cloud (see Figure 5.22). The cluster positions on the hierarchical tree diagram and the corresponding label number are not meaningful – i.e. there is no suggested order effect to the clusters, they are simply groupings of individuals on common characteristics. Cluster Interpretation This section will describe the interpretation of each of the six clusters according to their associated variable categories. It will also briefly discuss the ramifications of each cluster for public engagement activities related to community scale energy planning. Figure 5.29 shows the positions of the six developed clusters against the first two dimensions of the MCA. 185 Figure 5.28: Hierarchical cluster diagram for the first 18 dimensions of the MCA dataset 186 Cluster One: Limited Energy Knowledge (4 individuals) Table 5.13 shows the categories associated with cluster one. Only categories with a p < 0.05 are included in the interpretation. The first column of the table (Cla/Mod) shows the percentage of the participants who selected that category that are in cluster one. Conversely, the second column (Mod/Cla) shows the percentage of people in the cluster who selected that category. These columns tell us something about the cluster membership. For example, based on row one, we can see that 100% of the people who chose renewables as the best Applied_Solution are in this cluster, and 80% of the people in this cluster chose renewables as the best Applied_Solution. Table 5.13: Category associations with Cluster One. Cluster 1 Cla/Mod Mod/Cla p.value v.test Applied_Solution_Renewables 100.00 80.00 3.16E-06 4.660045 How_Difficult_Neutral 33.33 80.00 1.47E-03 3.18132 Energy_Use_Correctness=Zero 25.00 80.00 5.21E-03 2.793838 Gender_F 16.13 100.00 7.07E-03 2.693631 Energy_Knowledge=Very Limited 23.08 60.00 3.04E-02 2.164886 Source_Change_Mix of Sources 40.00 40.00 3.04E-02 2.164467 Gender=Gender_M 0.00 0.00 1.81E-02 -2.362909 Other characteristics of this group include that they were generally neutral about how difficult it would be for them to change their energy habits, the majority of them reported they had very limited energy knowledge, and 80% of them did not correctly identify the proper order for any of the energy use sectors in their community. While the clusters were constructed from the active variables and the resulting MCA dimensions, the supplemental variables were related to the clusters post hoc. Therefore, it was also possible to determine that all of the participants from this cluster were female. Perhaps the most interesting thing about this cluster, is that it is the one circumstance in which self-reported and/or observed energy knowledge is strongly identified with a mental model pattern. This cluster suggests that member participants’ knowledge of energy was so limited that it impacted their 187 capacity to answer the questionnaire – i.e. they had no strongly definable answer pattern because their answers were not the result of a consistent model. The fact that 100% of participants that selected renewables as the best Applied_Solution fell into this cluster is interesting, because it suggests that the dominant media coverage of renewable energy sources (as opposed to other energy solutions) may have caused low knowledge individuals to identify with this option. This mental model of energy might suggest the need for capacity building or energy literacy activities in any public engagement process around energy. Low knowledge participants may need to develop a basic understanding of energy at the community scale in order to fully participate. Cluster Two: Efficient Urban Design (5 individuals) Cluster two is a very specific group of individuals, separated distinctly by the rarity of their selections. The inclusion of Approach_Planning, Applied_Solution_Unit_Size and Source_Change_Efficiency (see Table 5.14) suggests that these participants are focused on improved energy efficiency through urban design and planning. Planning and design goals such as densification or smart growth might be associated with this mental model. Table 5.14: Category associations with Cluster Two. The categories associated with this cluster or mental model were well addressed in all of the workshop processes discussed in Chapter 7. This suggests that, while this mental model appears to be rare (at lest in the sample for this study), it is well served by current tools and processes for planning for community scale energy. Cluster 2 Cla/Mod Mod/Cla p.value v.test Source_Change_Efficiency 100.00 100.00 6.32E-07 4.981088 Approach_Planning 27.27 75.00 7.62E-03 2.668644 Applied_Solution_Unit Size 33.33 50.00 2.75E-02 2.204242 Cultural.Background_Latino/Hispanic 100.00 25.00 5.00E-02 1.959964 188 Cluster Three: The Muddled Middle Ground (27 individuals) Cluster three is the second largest cluster and may be the most difficult to interpret. In a way, it appears to be a conglomeration of categories that did not fit into other, well-defined clusters. One possibility is that this cluster highlights individuals without a well-formed mental model of energy, or strong definable worldviews. The group is made up roughly ¾ of those that believe that energy issues require a planning and policy level of change, and about ¼ of those who believe it requires transformative change. The dominant Applied_Solution selected by the group was technical changes, and none of the cluster participants selected individual behavior or collective options (recall that these two options were the polarizing categories along Dimension 2). Perhaps the most distinctive characteristic of this cluster is that 80% of those who said that energy is important for quality of life reasons, and 72% of those who said that the reason to address energy was fairness are in this group. One possible explanation for the lack of clarity in this group is that energy is not their focus at all – i.e. that they don’t think about energy as a factor in their lives or communities. Another possible explanation is simply that the questions on the questionnaire ‘missed’ these people – i.e. that they lacked the necessary resolution to further define this group. It is telling that this group congregates around the origins for both Dimension One and Dimension Two (see Figure 5.29) indicating no strong relationship with either dimension. It is difficult to say what the ramifications of this cluster or mental model are for engagement on community scale energy issues. If it is true that these individuals simply do not think about energy as it relates to their community, it may support the idea that community engagement should be focused more on what people value and want in their communities, and less on specific issues such as energy or climate change mitigation. This point will be returned to in Chapter 6, as questions on the Vancouver questionnaires asked participants specifically about quality of life aspects of their communities. 189 Table 5.15: Category associations with Cluster Three. Cluster 3 Cla/Mod Mod/Cla p.value v.test Level_of_Change=Planning and Policy 70.83 73.91 1.80E-07 5.218865 Applied_Solution_Technical Changes 53.85 60.87 1.02E-03 3.28571 Reason_to_Address_Fairness 72.73 34.78 1.63E-03 3.150321 Cultural.Background_Caucasian/European 46.67 60.87 8.26E-03 2.641308 How_Difficult_Somewhat Difficult 43.75 60.87 1.93E-02 2.338736 Energy_Importance_Quality of Life 80.00 17.39 2.38E-02 2.260423 Optimism=Optimistic 40.00 69.57 2.97E-02 2.174015 Approach_Behaviour 50.00 34.78 4.96E-02 1.963183 Applied_Solution_Individual Behaviour 0.00 0.00 3.88E-02 -2.06636 Applied_Solution_Public Transportation 10.00 8.70 3.12E-02 -2.154757 Applied_Solution_Collective Option 0.00 0.00 2.62E-02 -2.22283 Reason_to_Address_Climate Change 3.23 4.35 2.43E-05 -4.221554 Level_of_Change=Transformative 11.76 26.09 1.62E-05 -4.311513 Cluster Four: Power to the Individual (10 individuals) The defining feature of cluster four is the importance placed on individual behavior (see Table 5.16). Both the predominate Applied_Solution (78%) and Approach (56%) to addressing energy issues for this group is behavior-based. Interestingly, 89% of these individuals attended the Victoria workshops and none of them attended the Grandview workshop. Another interesting connection is that 67% of them thought that Group_of_Citizens were the Least_Responsible, suggesting that they were truly focused on individual behavior. Finally, the inclusion of all participants that identified alternative fossil fuels as the way that our energy sources will change, is indicative that this group is not focused on energy for climate change or environmental impact reasons. Comparing the clustering process to the construction of the dimensions in the MCA, this cluster does not seem to be as strongly defined here as it was in the dimension interpretation, suggesting that both processes are valuable to interpretation of the data. 190 Table 5.16: Category associations with Cluster Four. Cluster 4 Cla/Mod Mod/Cla p.value v.test Applied_Solution_Individual Behaviour 77.78 77.78 3.91E-07 5.073148 Source_Change_Alternative FF 100.00 33.33 1.02E-03 3.28429 Workshop=Victoria 25.81 88.89 1.84E-03 3.114793 Least_Responsible_Groups of Citizens 31.58 66.67 5.05E-03 2.804045 Employment.Sector_Non-profit 60.00 33.33 9.44E-03 2.595776 Approach=Approach_Behaviour 31.25 55.56 1.51E-02 2.431141 Energy_Importance_Local Level 66.67 22.22 3.32E-02 2.130203 Workshop=GC_Grandview 0.00 0.00 4.59E-02 -1.996127 Least_Responsible_Individuals 2.27 11.11 6.55E-03 -2.718724 The fact that 89% of these individuals attended the invitation only Victoria workshops suggests the possibility that they attended primarily as part of their job. In other words, would people who consider energy to be an individual-focused subject willingly attend an engagement process with a focus on collectively addressing energy issues? Consequently, this group may be under-represented in participatory planning activities. None of the workshop processes discussed in Chapter 7 had a strong thread relating to individual energy behaviours. A more inclusive planning process should consider scenarios and solutions that address more individual-focused participants. Cluster Five: National Action (29 individuals) While cluster four was more strongly defined by the dimensions than the clustering procedures, cluster five was not visible using just the MCA procedures. This cluster is consistent with observations at the workshops, but the original dimensions did not clearly define them. This suggests the importance of the post hoc cluster analysis, as this cluster is the largest group, and an important one to interpret. Cluster five is defined by the notion that we need transformative change to address energy issues, that the problem is strongly associated with climate change, and that the best approach is Federal Government policy (see Table 5.17). This cluster is strongly representative of a large segment of the population who are concerned with climate change. They share the perspective that we need sweeping changes to our systems to address 191 climate change, and they place the onus on the Federal Government. This mental model should be well represented at public engagement events, because these participants are motivated to take action. The focus on federal policy, however, is incongruent with the recent localization of energy and climate planning efforts. Their interests may be better served at engagement events if the impacts of federal policies such as emission standards or fuel efficiency could be modeled and downscaled to reflect local outcomes. For example, a federal regulation scenario could be used to discuss the local implications of national action. Table 5.17: Category associations with Cluster Five. Cluster 5 Cla/Mod Mod/Cla p.value v.test Least_Responsible_Individuals 63.64 84.85 5.83E-06 4.532356 Level_of_Change=Transformative 58.82 90.91 1.52E-05 4.325936 Approach_Policy 65.79 75.76 2.51E-05 4.214249 Reason_to_Address_Climate Change 64.52 60.61 9.96E-04 3.291718 Applied_Solution_Collective Option 90.00 27.27 1.21E-03 3.235723 Most_Responsible_Federal Govt 54.55 72.73 8.51E-03 2.631074 Applied_Solution_Public Transportation 65.00 39.39 1.62E-02 2.404394 Least_Responsible_Groups of Citizens 21.05 12.12 4.35E-02 -2.01931 Applied_Solution_Unit Size 0.00 0.00 3.57E-02 -2.09996 Least_Responsible_Business 0.00 0.00 3.57E-02 -2.09996 Most_Responsible_Citizens 10.00 3.03 3.36E-02 -2.124829 Approach_Technology 9.09 3.03 1.96E-02 -2.333285 Least_Responsible_Levels of Govt 9.09 3.03 1.96E-02 -2.333285 Reason_to_Address_Fairness 9.09 3.03 1.96E-02 -2.333285 Gender_M 27.91 36.36 1.05E-02 -2.560342 Approach_Behaviour 6.25 3.03 1.03E-03 -3.281767 Level_of_Change=Planning and Policy 12.50 9.09 5.25E-04 -3.46745 Cluster Six: Let the Market Decide (5 individuals) Cluster six represents the positive end of Dimension One. This cluster is defined a strong business focus and the idea that energy issues can best be addressed through market mechanisms. They feel 192 that the dominant reason that we need to address energy is limited resources, and approximately half of the cluster members feel that technology is the best Approach to addressing energy issues. In essence, this group believes that businesses can innovate us out of energy concerns. Sustainability engagement activities, like those discussed in Chapter 7, do speak to this cluster’s focus on technological solutions, but they are less successful at addressing the business ramifications of planning activities. Aspects of sustainability planning such as costing are infrequently modeled or discussed. It could be argued that members of this cluster have a strong voice through the influence of developers on planning activities, however their concerns are not always well addressed within engagement activities. Better modeling of costs and potential innovation impacts on energy futures may help incorporate this cluster’s views into planning initiatives. Table 5.18: Category associations with Cluster Six. Cluster Analysis Discussion Performing the cluster analysis on the MCA provided a more nuanced view of the types of mental models present in the participant pool. Some of the mental models revealed, such as clusters one and five, were not apparent from the analysis of the main MCA dimensions, but became apparent when the cluster analysis took into account a greater proportion of the MCA’s variance. Other clusters, such as cluster three, were still difficult to interpret, even with the higher level of resolution provided by the cluster analysis. Cluster 6 Cla/Mod Mod/Cla p.value v.test Level_of_Change=Economic 100.00 83.33 2.50E-07 5.158013 Most_Responsible_Business 66.67 66.67 1.38E-04 3.812107 Source_Change_Decreased FF 38.46 83.33 2.98E-04 3.616709 Reason_to_Address_Limits 33.33 83.33 6.83E-04 3.396363 Approach_Technology 27.27 50.00 3.41E-02 2.118455 Gender=Gender_F 0.00 0.00 4.65E-02 -1.990507 Level_of_Change=Transformative 1.96 16.67 2.33E-02 -2.268212 193 Table 5.19: A table of the primary characteristics of the mental models clusters developed in the hierarchical analysis. Cluster n Description Answer Patterns Representativeness Implications for Engagement Design 1 Limited Energy Knowledge 4 Participants with limited knowledge of the energy system. Primarily from the GCCP workshops (75%). Limited n so cannot generalize, but participant's responses were consistent with the cluster definition (i.e. indicated a lack of energy knowledge) This cluster should be more prevalent in the general population. Low attendance was likely due to a lack of interest in energy/planning. This cluster's responses suggest a need for energy literacy engagement early in planning and decision making processes. 2 Efficient Urban Development 5 A very small and specific group of participants that focused on demand reduction through planning and land use. Limited n so cannot generalize, but participants responses were consistent with the cluster definition (i.e. a focus on land use and urban form) This cluster is likely a small proportion of the larger population, because of the specialized nature of its mental model. This cluster's interests and perspective were well served by both the GCCP and Revelstoke processes, where land use was a key component. The less-spatial Victoria workshop may not have met their expectations. 3 Muddled Middle Ground 26 This cluster is ill-defined, but appears to be made up of participants that lack a strong mental model of energy. There were many more of these participants from the public GCCP workshops (81%) than the more expert Victoria workshops (19%). This cluster’s dispersed answer patterns may be explained by the fact that it was a catch-all for participants who lacked defining mental model. Based on the larger proportion of cluster 3 participants from the public GCCP workshops, I would speculate that this cluster makes up a significant proportion of the larger population. Similar to cluster 1, this cluster suggests the need for energy literacy engagement. Another possibility, is that this cluster was missed by the questionnaires. 4 Power to the Individual 10 These participants were defined by the belief that individual behaviour is the best way to address energy issues. Most of these participants attended the This cluster's response patterns were broadly consistent with the cluster's definition (i.e. a focus on individual and behaviour based Based on external literature, and the large behaviour-based focus in prior energy campaigns, this cluster should be more prevalent in the broader population. An open question about this cluster The presence of this cluster suggests a need to include individual as well as collective strategies in energy planning. It also suggests the need to address the limitations of 194 Cluster n Description Answer Patterns Representativeness Implications for Engagement Design expert-focused Victoria workshops (89%) solutions), though the larger n at the Victoria workshops led to a more dispersed answer pattern. is how prevalent it is in the expert community. If the Victoria workshops were representative, then many energy experts may still believe in behaviour-based interventions. behaviour-based interventions in engagement and communication materials. 5 National Action 28 This was the largest overall cluster. This cluster was defined by a focus on Federal Government policy interventions to address climate change. This national focus may be incongruent with the more local energy planning practices investigated in this thesis. Similar to cluster 3, this cluster's answer patterns were dispersed. One possible explanation for that is that they thought about energy issues at larger scales than the workshop materials covered. This cluster appears to represent a broad section of the larger population with concerns about climate change. The malleability of their post-workshop answers suggests they are not dogmatic, but rather generally concerned. This cluster's responses reinforce the need to discuss energy at local scales. Additionally, it suggests that local planning efforts should address the potential impacts of larger-scale actions such as vehicle emission standards on local energy use and emissions. 6 Let the Market Decide 5 This cluster is defined by the belief that energy issues are economic problems with economic solutions. Their focus on technological solutions and business responsibility suggests a need to innovate our way to energy futures. Limited n so cannot generalize, but participants, responses were consistent with the cluster definition. Cluster 6's answer patterns may have been the most distinct, with a consistent focus on costs and effectiveness. This cluster likely makes up a much larger proportion of the population than the workshop numbers suggest, based on the literature and its presence in the media. They may not attend planning engagement activities because of a lack of trust in their effectiveness. This cluster's focus on economic and technological solutions suggests a need to account for costs (as the Victoria workshops did) and to engage about solutions that require business involvement and present business opportunities. 195 Figure 5.29: representation of the six clusters on dimensions one and two of the MCA. 196 The combination of the MCA and cluster analysis was methodologically successful, in that coherent answer patterns (this study’s surrogate for mental models) were revealed. Improvements in the questions asked and a broader participant pool could greatly improve the resulting analysis, but these are not shortcomings of the analysis methodology. Table 5.19 shows the primary characteristics of the clusters developed in the hierarchical cluster analysis. Perhaps the primary benefit of conducting the cluster analysis is the development of coherent groupings of individuals that appear to make intuitive sense, based on observation of the workshop processes, and broader discourses on environmental and planning issues (cf. Dryzek, 2005). These clusters provide a useful shorthand for thinking about the different ways that people conceptualize energy. Therefore, the clusters will be a key component of the analysis conducted in Chapters 6 and 7. Figure 5.29 shows how the six clusters related to the two dimensions of the MCA. 5.4 Expert vs. Lay Mental Models of Community Scale Energy As mentioned in the introduction to this chapter, this dissertation used an exploratory research design based on the lack of prior research on people’s mental models of energy. However, one factor that was hypothesized to be important to people’s mental models of energy at the community scale was their pre-existing knowledge, or expertise, about energy. Extending beyond mental models research, there has been research in other fields that demonstrates fundamental differences in how experts and non-experts process domain specific information (cf. Kozma & Russell; Lowe 2003; 2004). The effects of expertise on participants’ mental models of energy, then, became a supplementary research question for this dissertation. This section of the chapter documents univariate analyses of energy knowledge versus other key variables to determine if energy knowledge led participants to hold different energy concepts. Recall that the multivariate analysis of knowledge versus the MCA dimensions did not reveal a definable relationship (see Figure 5.26). 5.4.1 Expert vs. Lay Mental Models: Methodology Measurement of participant expertise was captured through self-reported energy knowledge levels on a 5 level scale. The question asked of participants was: 197 How would you rate your knowledge of the energy system (where energy comes from, how it moves, and how it is used) in your community? Please circle the best answer. a) I have very little knowledge of the energy system in my community b) I have some knowledge of the energy system in my community, but not a lot c) I would say I have an average level of knowledge about the energy system in my community d) I have a fair amount of knowledge about the energy system in my community e) I would consider myself an expert on the energy system in my community Table 5.20: Participant self-reported knowledge levels by workshop percent of total workshop in parentheses. Knowledge Level Victoria Planners Marpole Grandview Knowledge Totals Expert 1 (1.9) 1 (6.3) 0 (0.0) 2 (6.7) 4 (3.4) Fair Amount 17 (32.7) 4 (25.0) 2 (10.0) 10 (33.3) 33 (28.0) Average 12 (23.1) 5 (31.3) 11 (55.0) 7 (33.3) 35 (29.7) Some 10 (19.2) 2 (12.5) 2(10.0) 8 (26.7) 22 (18.6) Very Limited 8 (15.4) 4 (25) 3 (15.0) 2 (6.7) 17 (14.4) Null 4 (7.7) 0 (0) 2(10.0) 1 (3.3) 7 (5.9) Workshop Totals 52 16 20 30 118 Table 5.20 shows the recorded knowledge levels for all participants stratified by the workshop they took part in. The number of individuals is followed by the percent of individuals for each workshop in parentheses. Only four participants across the workshops considered themselves to be experts on energy at the community scale, 33 felt they had a “Fair Amount” of knowledge, 35 felt they had an “Average” amount of knowledge, 22 felt they had “Some”, and 17 felt they had “Very Limited” knowledge of the energy system. Seven participants failed to answer the question about their perceived knowledge level. The Grandview workshop had the highest proportion of participants with either an “Expert” or “Fair Amount” knowledge level (see Table 5.20), followed by the Victoria workshops, the Vancouver Planners workshop and, finally, the Marpole community workshop. This recorded stratification matched researcher expectations based on observations at the workshops. Further analysis and discussion of the demographics of the various workshops will be conducted in Chapters 6 and 7. For the purposes of the analysis conducted in this section, only the counts of participants in each knowledge level are relevant. 198 All of the data captured from the questionnaires were either qualitative/open-ended, or categorical in nature. The analysis of the effects of expertise on participants’ mental models was confined to the categorical data, to make use of well-defined evaluation methodologies and statistics. Because the data were categorical, contingency tables and the chi-squared statistic for measuring the independence of two variables were the chosen methodology (Field et al., 2012; Zar, 1996). Pearson’s chi-square (Pearson, 1900) compares the observed frequency of all combinations of two categorical variables to the expected frequency for those variables based on chance. The resulting Chi-squared statistic is then compared against a table of critical values, based on a selected probability, to determine if there is a significant relationship between the two variables. One assumption of the chi-squared test, is that the expected frequency for each cell of a contingency table will be above five. If this assumption is violated, then the calculated chi-squared statistic will be inaccurate (Field et al., 2012). Based on the high degrees of freedom (levels of possible variable combinations) in the questions used in this study, coupled with the moderate overall n for the study (118 participants) it was likely that this assumption would be violated for many questions. This required the use of the Fisher Exact test (Fisher, 1922) to account for the low expected frequencies in many cells of the contingency tables. Even with the use of the Fisher’s Exact test, the relatively high degrees of freedom for particular questions (ranging from 4 to 16) meant that particular cells of the contingency table had very low observed and expected values. In order to address this, self-reported knowledge levels were amalgamated from five categories to three as follows: Expert, Fair Amount -> Fair Amount Average -> Average Very Limited, Some -> Limited This led to the new distribution of self-reported energy knowledge levels seen in Table 5.21. 199 Table 5.21: Amalgamated self-reported energy knowledge levels by workshop. Knowledge Level Victoria Planners Marpole Grandview Knowledge Totals Fair Amount 18 5 2 12 37 Average 12 5 11 7 35 Limited 18 6 5 10 39 Workshop Totals 48 16 18 29 111 The methodology for analyzing the impacts of expertise on participants’ mental models, then, involved the development of contingency tables with knowledge as the independent variable and a series of other categorical variables as the dependent variables, using the Fisher’s Exact test to assess whether their was a significant relationship between participants’ knowledge levels and the dependent variables. The categorical dependent variables were selected for this analysis to be consistent with both the descriptive analysis of the collective trends and the MCA analysis from preceding sections of this chapter. The questions selected for analysis included: • The most important reason we should be planning for energy at the community scale • The level of change required to address energy issues • How optimistic participants were about our ability to address energy issues • Who is most responsible for addressing energy issues at the community scale • What conceptual approach we should take to addressing energy issues at the community scale • What is the best specific solution for addressing energy issues at the community scale • How difficult it would be for the participant to decrease their energy use • A tabulated score for each participant based on how accurately they ranked energy use for particular sectors • A correct or incorrect score for each participant based on whether or not they identified their city’s largest energy source In addition, two demographic variables were compared to self-reported knowledge levels, based on their relationship to knowledge. From the Vancouver workshop demographic data, level of education was compared to knowledge level to determine if expertise was associated with higher education levels. From the Victoria workshop demographic data, employment sector was compared to knowledge level to determine if expertise was related to where participants worked. 200 5.4.2 Expertise vs. Lay Mental Models: Results and Discussion The first step in the expert versus lay mental model analysis was to determine whether or not self-reported levels of energy knowledge were related to other demographic variables that might indicate levels of knowledge or expertise. Vancouver: Energy Knowledge versus Education Level For the Vancouver workshops, the most related demographic variable was level of education. Table 5.22 shows the contingency table of observed and expected values for all combinations of self-reported energy knowledge and highest education level obtained. Table 5.22: Self-reported energy knowledge by highest level of education attained. Knowledge Level Some College College Bachelor's Some Postgraduate Postgraduate Knowledge Total Fair Amount Observed 0 0 2 4 12 20 Expected 0.93 0.62 3.41 3.1 9.93 Average Observed 1 0 2 2 14 18 Expected 1.03 0.69 3.79 3.45 11.03 Limited Observed 2 2 6 4 6 20 Expected 1.03 0.69 3.79 3.45 11.03 Education Total 11 2 32 3 10 58 The n for this question was 58, because it was only included on the Vancouver questionnaires. The degrees of freedom for the question were 8, and the calculated Chi-squared value was 12.15. Using the Fisher Exact test revealed a p value of 0.14, indicating that there is no significant relationship between self-reported energy knowledge and highest level of education obtained at the 0.05 level. This result is not particularly surprising because, despite the high levels of education seen in the workshop participants, there is no indication that their educations were energy or sustainability related. High levels of education are commonly seen in participants in workshop-like participation activities (Knapton & Myers, 2005; Scheufele, 2010). This is not necessarily an indication that people with relevant expertise are more likely to attend these events. Of note is the fact that this data does not include Victoria CEEP participants who were invited specifically because they were either energy experts or local stakeholders. In that more directed participation activity there may have been a stronger connection between self-reported energy knowledge and highest level of education attained. 201 Victoria: Energy Knowledge versus Employment Sector For the Victoria workshops, the most relevant demographic variable was employment sector. Table 5.23 shows the contingency table of observed and expected values for all combinations of self-reported energy knowledge and employment sector. Table 5.23: Self-reported energy knowledge level by employment sector. Knowledge Level Business Owner Construction Consulting Energy Sector Financial Govt Manufacture Non-profit None of above Fair Amount Observed 0 0 7 1 0 5 0 2 0 Expected 1.13 0.38 4.5 1.13 0.38 4.88 0.38 1.88 0.38 Average Observed 3 1 1 0 0 6 0 1 0 Expected 0.9 0.3 3.6 0.9 0.3 3.9 0.3 1.5 0.3 Limited Observed 0 0 4 2 1 2 1 2 1 Expected 0.98 0.33 3.9 0.98 0.33 4.23 0.33 1.6 0.33 The n for this question was 40, because it was only administered in the Victoria questionnaire and there were a number of null responses. The degrees of freedom were 16 and the calculated Chi-square statistic was 23.39. The Fisher Exact calculated probability was 0.066, indicating that it was not significant at the 0.05 level. This calculation was, however, quite close to significance at the 0.05 level and, given the relatively low n and high degrees of freedom caused by the large number of categories in this variable, it seems possible that a type II4 error is occurring here and a relationship actually does exist. Of particular note are the high proportion of consultants in the “Fair Amount” knowledge level (47% of all “Fair Amount” respondents). Consultants in the “Fair Amount” category had the largest divergence between their observed and expected values, and therefore the highest contribution to the calculated Chi-squared value. Given the invitee list, it is very likely that these consultants were from energy-related fields. This is supported by researcher observations – there were a number of energy or sustainability-related consultants at the Victoria events. 4 A type II error denotes a circumstance where an actual effect exists, but is not revealed by the testing procedures. 202 A curious result in this data is the number of energy sector employees that rated their energy knowledge as “Limited” (2 of 3 energy sector employees). This is a very small sample, but suggests that, perhaps, something about the particular structure of the workshop subject matter was new to them. The questions was phrased specifically to address energy at the community scale, which suggests that these energy sector employees were not used to thinking about energy at that scale in their day to day work activities. Energy Knowledge vs. Categorical Variables: No Significant Relationship For a number of the questions outlined in section 5.4.1, no significant relationship was found with self-reported energy knowledge. Table 5.24 shows the question theme, n, degrees of freedom, calculated Chi-squared value and Fisher Exact calculated p value. Table 5.24: Chi-squared test results for questions with no significant relationships to expertise Question n degrees of freedom Chi-squared p value Reason to Address 98 10 8.61 0.57 Level of Change 111 4 6.65 0.13 Optimism 109 4 1.26 0.88 Applied Solution 107 14 13.9 0.54 How Difficult 84 8 10.56 0.17 Of these questions, the required level of change (none, economic, planning and policy, transformative) was the closest to reaching significance, driven by a large number of respondents with “Average” energy knowledge choosing “Planning and Policy” change and a higher than expected number of “Fair Amount” energy knowledge participants choosing “Economic” change than expected. At over twice the p = 0.05 value, however, it is difficult to justify analyzing this result. Significant non-results in this table include “Optimism”, “Reason to Address” and “Applied Solution”, whose p values clearly indicate that there is no discernable relationship with self-reported energy knowledge. Perhaps most surprising is “Applied Solution”, because the possibilities were relatively specific: 203 Please rank your top 5 approaches for addressing energy concerns in your community, with 1 being the method with the largest potential impact, and 5 being the method with the smallest potential impact. a) Increasing public transportation options b) Increasing local sources of energy such as solar c) Decreasing the size of people’s residences d) Making technical changes, such as energy retrofits on existing buildings, or electric vehicles e) Changing Individual’s energy behaviours f) Collective options, such as district energy systems g) Providing incentives for people and businesses to conserve energy Perhaps this non-result is a reflection that, in this early stage of energy planning at the community scale, even the energy experts are unclear about which solutions are best. Energy Knowledge vs. Categorical Variables: Significant and Near Significant Relationships The two variables that demonstrated a significant, or near-significant relationship with self-reported energy knowledge were who was Most_Responsible for addressing energy issues, and the conceptual Approach to addressing energy issues. Table 5.25 shows the contingency table for who is most responsible for addressing energy issues. The n for this question was 100, the degrees of freedom were 10 and the calculated Chi-square statistic was 20.01. The Fisher Exact p value for this question was 0.029, suggesting that there is a significant relationship between self-reported energy knowledge and who the participant thinks should be responsible for addressing energy issues. 204 Table 5.25: Participants' ranking of who is most responsible for addressing energy issues by self-reported energy knowledge level. Knowledge Level Individuals Groups of Citizens Businesses Municipal Govt Provincial Govt Federal Govt Fair Amount Observed 2 0 4 3 11 15 Expected 3.5 1.4 2.45 3.5 5.25 18.9 Average Observed 5 3 1 4 3 16 Expected 3.2 1.28 2.24 3.2 4.8 17.28 Limited Observed 3 1 2 3 1 23 Expected 3.3 1.32 2.31 3.3 4.95 17.82 A review of the contingency table suggests that the largest discrepancies between observed and expected values (and therefore the largest contributors to the significant result) are in the “Fair Amount x Provincial Government” cell, and the “Limited x Federal Government” cell. In other words, more people with high levels of energy knowledge place the onus for addressing energy on the provincial government, and more people with limited energy knowledge place the onus on the federal government. One contributing factor to this finding may be the effects of the Province of British Columbia’s Bill 27 (Province of British Columbia, 2008), which is the context in which municipalities in BC are planning for greenhouse gas emissions (GHGs) and, consequently, energy. Energy experts and those that work in energy-related fields would have been aware of Bill 27s ramifications, whereas lay participants in the study may not have been. While Bill 27 mandates municipal planning and action on emissions, it is a provincial legislation, and therefore a provincial responsibility (e.g. if a subsequent government changed the legislation, it would have wide-ranging effects on planning for energy at the community scale). Another contributing factor to the association between high levels of energy knowledge and provincial responsibility for addressing energy issues, is a better understanding of legislative mechanisms, such as provincial utilities, fuel taxes, etc. Those actively involved with planning for energy in BC are likely much more aware of the provinces influence on energy issues than lay audiences. Lay participants may be more likely to place the onus for energy management on the Federal Government because of the association (explicit in the Victoria example and implicit in the Vancouver workshops) between energy and GHGs. The preponderance of news information on climate change places responsibility on the Federal Government, with issues such as adherence to the Kyoto protocol and 205 federal support for Alberta’s oil sands projects. It is possible that lay participants in the Vancouver and Victoria workshops specifically related the energy issue to climate change. In fact, there were several written comments in the open-ended questions that specifically referenced pessimism ‘while Harper’s government is in power’. This disconnect between lay and expert opinions about responsibility is an important one, because it has ramifications for who people are depending on to address community scale energy issues. Table 5.26 shows the contingency table for self-reported energy knowledge versus the participants’ preferred conceptual approach to addressing energy issues at the community scale. Table 5.26: Participants' choices of best approach for addressing energy issues by self-reported energy knowledge level (n=103). Knowledge Level Behaviour Incentives Planning Policy Technology Fair Amount Observed 2 3 6 16 8 Expected 7.14 1.7 4.42 16.65 5.1 Average Observed 11 1 5 14 2 Expected 6.73 1.6 4.17 15.7 4.81 Limited Observed 8 1 2 19 5 Expected 7.14 1.7 4.42 16.65 5.1 The n for this question was 103, with 8 degrees of freedom and a calculated Chi-square statistic of 13.91. The Fisher Exact p value for this contingency table was 0.058, meaning it was not significant at a 0.05 level, however it was very close to reaching the significant level, and will be interpreted with the caveat that it does not strictly meet the criteria, particularly in light of the moderate n and relatively high degrees of freedom for this contingency table. For those with a “Fair Amount” of energy knowledge, the most notable departures between observed and expected values are in the “Behaviour” and “Technology” categories. Those with a “Fair Amount” of energy knowledge were less likely to choose behavior as a viable approach addressing energy issues, and more likely to select technology. Conversely, those with an “Average” level of energy knowledge (and to a lesser degree, those with a “Limited” level of energy knowledge) were more likely to select behavior and less likely to choose technology as a viable approach than would be expected. 206 This appears to reflect expert knowledge of the literature on behavioural approaches to demand reduction (cf. Gardner & Stern, 1996; Hines et al., 1986/87). While lay audiences often interpret calls to ‘save energy’ as a need to introduce hardship through changing their energy use behaviours, experts who know the vast literature on the subject may look for less intrusive approaches. Experts’ gravitation to the technology approach may reflect a belief that any measures taken to address energy demand or supply need to accommodate existing energy behaviours; for example, maintaining existing car ownership and mobility behaviours through the use of electric vehicles. It should be noted that the technology vs. behavior phenomenon seen in Table 8 is secondary to the preponderance of participants from each knowledge level who selected “Policy” as their best approach to addressing energy issues. This raises the question of what types of policies different participants were thinking of? Perhaps experts were thinking of policies that mandate or encourage efficiency and technology adoption, whereas lay participants were thinking of policies such as use taxes that promote curtailment and changed energy use behaviours? This finding suggests a need for a finer grained investigation of the policy approaches in future studies. It also suggests that further efforts are required to address public concerns that energy planning means induced hardship. If experts are looking for solutions that specifically avoid requiring curtailment or behavior change, then the interested public should be made aware of that fact. Self-Reported Energy Knowledge vs. Measures of Energy Knowledge: Unexpected Non-Results One of the reasons why a mental models approach was taken to the development of the workshop questionnaires was a desire to move beyond questions of opinion or attitude to try to assess actual participant knowledge of the energy system. Questions that addressed participant knowledge included assessments of their individual energy use and supply (not included in this chapter) and questions about energy demand and supply in their communities. In particular, participants were asked to rank the energy users in their city from highest to lowest and to identify the largest energy source for their city. As a reminder, for Vancouver, the order of energy use by sector is Commercial/Institutional > Residential>Transportation > Industrial > Municipal (NRCAN, 2014; Province of British Columbia, 2014). For Victoria the order of energy use by sector is: Commercial/Institutional > Transportation> Residential > Industrial > Municipal (Province of British Columbia, 2014). 207 For the purposes of this study a simple measure of correctness was constructed based upon the number of sectors that participants correctly placed in their proper position. Participants could receive a score from 1 to 5 (excluding the value 4). Table 5.27 shows the contingency table for the “Energy Use Correctness” measure by the self-reported energy knowledge levels. Table 5.27: Energy use correctness measure versus self-reported energy knowledge levels. Knowledge Level 0 1 2 3 5 Fair Amount Observed 8 12 7 8 2 Expected 8 13.67 8 6.33 1 Average Observed 5 12 11 6 1 Expected 7.57 12.93 7.57 5.99 0.95 Limited Observed 11 17 6 5 0 Expected 8.43 14.41 8.43 6.68 1.05 The n for this contingency table was 111, the degrees of freedom were 8 and the calculated Chi-square statistic was 7.69. The Fisher Exact p value was 0.48 indicating that there was no significant relationship between the variables at the 0.05 level. While the “Fair Amount” of energy knowledge participants did have the most number of 3 and 5 correct scores, and the “Limited” energy knowledge participants had the lowest number of 3 and 5, and the highest number of zero scores, the relatively high p value clearly suggests that there is no definitive trend in the participants’ answer patterns. This question provides an interesting non-result, as the expert participants were statistically no better than the lay participants in correctly stratifying energy use by sector. While it can be argued that this measure is not a strong indicator of energy knowledge, it is notable that the discussion of energy sectors is an example of expert jargon, and it might be expected that lay participants would perform worse than expected based on their lack of familiarity with the sectors nomenclature. Therefore, the fact that experts showed no discernable improvement over lay values is notable. 208 One explanation for the result that the experts did not perform better in this measure is the localized nature of the question. As previously noted, planning for energy at the community scale is a new phenomenon (Andrews, 2008; Neves & Leal, 2010), and those experts at the workshops may not have been familiar with the city-specific energy use numbers. The growing use of tools such as the BC MoE’s CEEI reports (Province of British Columbia, 2014) may have improved expert familiarity with sector-based use patterns in the intervening years. Similar to the energy use by sector numbers, there was a difference between Vancouver and Victoria in terms of the primary energy source. For Vancouver, the largest energy source is Natural Gas, while Victoria’s largest energy source is electricity (Province of British Columbia, 2014). Participants were given a simple ‘correct’ or ‘incorrect’ score based on their identification of their city’s largest energy source. Table 5.28 shows the participant’s largest energy source scores versus their self-reported energy knowledge. Table 5.28: Participant correct identification of largest energy source by self-reported energy knowledge. Knowledge Correct Incorrect Fair Amount Observed 16 21 Expected 14.6 22.4 Average Observed 12 22 Expected 13.41 20.59 Limited Observed 15 23 Expected 14.99 23.01 The n for this contingency table was 109, the degrees of freedom were 2 and the calculated Chi-squared statistic was 0.47. The Fisher Exact calculated p value was 0.78, indicating that it was not significant at the 0.05 level. As Table 5.28 shows, the observed values were not much different than the expected values. In other words, the distribution is what might be expected by chance. Similar to the energy use correctness score contingency table in Table 5.27, then, there is no discernable relationship between participants’ energy knowledge levels and their ability to identify their city’s major 209 energy source. This is another a notable non-result, because it suggests that experts are no better at identifying their cities primary energy source than lay participants. An interesting related finding in this case comes from looking at the relationship between the participants’ correct identification of their city’s primary energy source and the workshop they participated in (see Table 5.29). Table 5.29: Participant correct identification of largest energy source by workshop. Workshop Correct Incorrect Victoria Observed 33 13 Expected 18.15 27.85 Planners Observed 6 10 Expected 6.31 9.69 Marpole Observed 3 15 Expected 7.1 10.9 Grandview Observed 1 28 Expected 11.44 17.56 The n for the contingency table in Table 11 is 109, the degrees of freedom are 3 and the calculated Chi-squared statistic is 39.75. The Fisher’s Exact calculated p value for this contingency table is p < 0.001, indicating that it is significant at the 0.05 level. As Table 5.29 shows the reason for this significant result is the disparity between the Victoria and the Vancouver (Planners, Marpole, Grandview) workshops. Recalling that the primary energy source in Vancouver is Natural Gas, and the primary energy source in Victoria is electricity, the reason for the disparity appears to be that participants have ‘Hydro on the brain’ – i.e. the strong presence of BC Hydro as a provincial utility strongly influences people’s perception that it is the largest supplier of energy. Indeed, a large proportion of the participants in the Vancouver study that got the primary energy source wrong incorrectly identified electricity as the dominant energy source (57%). This result seems to support the notion that the invisibility of our energy sources and our lack of connection to them in our day-to-day lives means that we do not pay attention to where our energy is 210 coming from. This may be exacerbated in BC by the relatively strong media presence of BC Hydro versus other energy providers such as Terasen Gas, or companies supplying other petroleum products. And, apparently, expertise in the field of energy is not an insulator from these effects. General Discussion of the Relationship between Expertise and Participants Mental Models It is surprising that the only strong relationships demonstrated with participants self-reported knowledge of energy at the community scale were their perceptions of who was most responsible for addressing energy issues (Table 5.25) and their preferred conceptual approach to addressing energy (see Table 5.26). In combination, these variables might be described as a high level ‘who’ and ‘how’ to address energy, with little agreement on the specifics. The primary research was collected in 2012 when the BC CEEP processes were at their peak. In the interim, most communities have completed their CEEP processes and have moved on towards the implementation phase. It would be interesting to conduct this research again with expert and lay audiences to determine if there has been some consolidation on approaches and solutions, as well as familiarization with the energy system at more local scales. The data discussed here seems anomalous – that is, those with high levels of energy knowledge would be expected to have different ideas about energy than the lay public. In particular, they should be expected to have a better grasp on aspects of the local energy system than was revealed in Table 5.27 and Table 5.28 . While the effects of expertise on mental models was not a primary research question in this study, the results do provide an interesting piece of the puzzle for evaluating mental models of energy and their impacts on participation in sustainability planning activities. 5.5 Conclusions This chapter has focused on answering one of the main research questions for this dissertation, that is, “what are participants’ existing mental models of energy at the community scale?” To accomplish this, the chapter focused on three levels of analysis: a collective, descriptive analysis of the general trends in participant responses, a multivariate analysis of participants’ response patterns (or mental models), and a more specific look at the implications of expertise on mental models of energy. 211 The descriptive analysis of dominant trends in participant responses provided an overall picture of how people are thinking about energy issues at the community scale. This type of descriptive analysis represents standard practice for analyzing questionnaires associated with engagement activities. The intent of the mental models research in this dissertation, however, was to both construct the questionnaires and analyze the data in a way that would better reveal response patterns across different questions. As stated in the chapter introduction, these response patterns provide surrogate measures for participants’ mental models, because they suggest something about how the participants connect concepts and construct meaning. Use of MCA and the subsequent cluster analysis allowed for a systematic investigation of the data, revealing patterns that were not apparent in the data’s raw form. These patterns included the construction of two MCA dimensions, one of which juxtaposed economic measures for addressing energy issues with transformative, government measures. The other dimension contrasted individual behavior with collective action (see Figure 5.25). An analysis of supplemental variables revealed a suggested relationship between gender and the MCA dimensions, with male participants trending towards the economic and individual, and female participants trending towards the transformative and collective. A weaker relationship existed between cultural backgrounds and the constructed dimensions, with people of Caucasian/European descent trending towards the individual and economic, and people of other cultural backgrounds trending towards the transformative and collective. No definable relationship was found with measures of energy knowledge, or the workshop participants attended. The cluster analysis further elucidated patterns in the data, resulting in the development of six clusters, or mental models: Limited Energy Knowledge, Efficient Urban Design, Muddled Middle Ground, Power to the Individual, National Action, and Let the Market Decide (see Table 5.19). The ramifications of these mental models clusters for participatory planning processes were briefly discussed. These clusters will be used in the rest of this dissertation when discussing the mental models findings, because they provide a useful shorthand for organizing and interpreting the findings. Finally, the analysis turned to a more specific univariate analysis of the effects of knowledge on constituents of mental models of energy. This question was a supplementary research question for the dissertation. The analysis found very modest results, suggesting that planning for energy at the community scale is not yet well enough developed to lead to consensus amongst experts about the path forward. It may also suggest that mental models of community scale energy, as with other sustainability 212 related issues (Hoffman et al., 2014; Whitmarsh, 2011), are dictated by more fundamental characteristics of people’s belief systems. The findings of the MCA analysis seem to support this latter point. More broadly, the research conducted in this chapter has three main applications for engagement research on community scale energy planning. The most immediate application of this research is as a way to organize and interpret the post-workshop results that will be discussed in chapters 6 and 7. While some methodological limitations (to be discussed in chapters 6, 7, and 8) did not allow for as complete a pre/post mental models evaluation as initially anticipated, the mental models clusters developed in this chapter did provide an organizing framework for interpreting the post-workshop results. The MCA and hierarchical clusters also have applications beyond this specific research project. One contribution of the mental models clusters is a better understanding of the representativeness of the workshop participants. If the goal of engagement is to encourage inclusive dialogue amongst participants with different perspectives, then the prevalence and representativeness of different mental models at engagement processes is important. While it isn’t possible to accurately assess the representativeness of the mental models present at the GCCP and Victoria CEEP workshops without a broader survey of BC citizens, it seems likely that groups such as the “Let the Market Decide” and “Limited Energy Knowledge” were under-represented at the workshops, while groups such as the “Efficient Urban Development” and “National Action” clusters may have been over-represented. This has implications for participant recruitment and workshop advertising if the intention is to encourage inclusivity and a plurality of voices. A related point is that understanding the existing mental models of participants, particularly as codified into coherent groups, could help to better craft engagement processes and materials to more comprehensively address different constituents’ concerns. For example, is the dearth of “Let the Market Decide” participants a reflection of the lack of costing and trade-off information at planning engagement activities? Also, despite a common focus on collective planning and policy actions, should energy scenarios more specifically address individual behaviours to account for participants with a “Power to the Individual” mental model, at least as a way of addressing their concerns and preferred approach? The connection of mental models to scenario analysis and strategic planning dates back to the inception of scenario planning (Wack, 1985a). The initial notion was that scenario analysis was a tool to challenge and extend existing mental models. Moving in the opposite direction, by first evaluating the constituents of existing mental models, may help us better understand the concepts and connections that scenarios and related engagement materials should be addressing, leading to better, more tailored engagement activities. 213 Codifying mental models using techniques such as the MCA and subsequent cluster analysis discussed in this chapter could facilitate this process by identifying coherent, understandable groupings of mental models with which to work. The research conducted in this chapter, though limited in a number of ways (to be discussed in section 5.5.1) holds promise for future research that will further investigate the implications of mental models for the development of future engagement processes. 5.5.1 Limitations of this Research It has been reiterated throughout this chapter that this research was conceived of as an exploratory, descriptive study. The intent was to illuminate aspects of mental models of energy at the community scale that could be used to form more specific hypotheses for future studies. As such, there was a clear recognition that mistakes would be made and limitations would be experienced. The MCA and cluster analysis methodologies themselves performed very well, and present the opportunity to assess mental models more broadly through the use of survey instruments. As discussed in Chapter 3, prior to this study mental models evaluation has been a much more intensive process, not conducive to time-limited real-world engagement activities. The limitations in this study are not related to the analysis methodology, but rather to the data collection procedures. Construction of the questionnaires was performed with the guidance of the mental models interviews discussed in Chapter 4, and with the advice of academics and practitioners working in the community planning field. However, there were some limitations of the constructed questionnaires that should be addressed in any future research. For example, the questionnaires were not sufficiently resolute in assessing participants preferred approaches and solutions to energy issues. Options such as resilience, regenerative design, waste reduction/heat recovery, etc. were not represented. A series of questions that more specifically addresses these issues would give a better picture of people’s mental models of energy. Another problem was the different versions of the questionnaire used at different events. This was, in part, unavoidable, because the research was beholden to our municipal partners and a larger research project that had a common set of demographic variables. Future research should, however, endeavor to maintain a consistent set of questions to increase the explanatory power of the final dataset. 214 Finally, construction of the questionnaires to better fit the assumptions and requirements of the MCA methodology would help to improve the variance explained in the final analysis, and provide a more comprehensive picture of the participants’ mental models. This research has presented a step in developing a methodology for assessing coherent mental models of sustainability topics for participatory planning, and can be instructive for future research activities that may want to adapt the methodology. The findings of this chapter will be returned to in three locations in the remainder of this dissertation. Firstly, the mental models elucidated by the analysis in this chapter will be compared to post-workshop questionnaires to determine if people’s opinions changed on particular energy issues, and if so, what models were most likely to change. This analysis will occur for the Vancouver workshops in Chapter 6, and the Victoria workshops in Chapter 7. Implications of the mental models for public engagement design, and more specifically the tools and process components used, will be discussed in Chapter 7. Finally, the concluding chapter (Chapter 8) will reflect on the ‘so what’ component of the mental models research. Based on the findings of chapters 4 through 7, what can we say about the value of understanding people’s mental models of energy, and how might it contribute to better public engagement activities in the future? 215 GCCP Community Energy and Emissions Planning Workshops 6.1 Introduction The workshops developed for the GCCP process were central to this dissertation. While the additional participatory processes described in chapter seven provided the opportunity to examine related cases containing different tools and process components, the GCCP workshops were developed with the dissertation’s topic in mind, drawing from both the design function of mental models and constructivist learning theory, and from the results of the mental models interviews described in chapter four. The primary research questions addressed by Chapter 6 include: 1. What are the tools (e.g. models, scenarios, visualizations) and process components (e.g. presentations and interactive exercises) used to engage participants about community scale energy, and how do they impact the process and its outcomes? 2. b) What are the effects of participation in energy engagement processes on people’s mental models of community scale energy? There was a strong preference for the PhD research to involve real-world planning activities, rather than contrived engagement exercises developed solely to evaluate the mental models. To achieve this, the available engagement process needed to be part of a real planning process, with a focus on community-scale energy planning. The process also needed to engage with publics and community residents representative of those who would participate in community energy planning or broader sustainability initiatives – what might broadly be defined as the ‘interested public’. While not crucial to the dissertation research, an emphasis was also put on the use of workshops or charrette-type processes, both due to expertise in the supporting research infrastructure at CALP, and to their amenability to the application of constructivist learning techniques more broadly, and scenarios, models and interactivity more specifically (see e.g. Girling et al., 2006; Salter et al., 2009; Schroth et al., 2013). For the broader research team, the goals of the workshops included developing a new methodology for engaging with citizens on community scale energy issues. This involved adapting energy modeling techniques (see Miller, 2013; Miller et al., 2009; Pond et al., 2011) and developing presentation materials and novel engagement exercises specifically focused on energy at a local scale. For the City of Vancouver, the primary goal was to leverage the relationship with the research team to develop baseline energy 216 modeling for the neighbourhoods being studied, and to investigate the implications of energy for upcoming planning initiatives in those neighbourhoods. 6.1.1 Description of the GCCP Project In 2010 a team of researchers developed a research proposal to examine the impacts of different channels of engagement on participants’ experience of sustainability in the City of Vancouver (CoV). The Greenest City Conversations Project was undertaken in association with the CoV’s Greenest City 2020 Action Plan (City of Vancouver, 2012) and was a joint effort between researchers at the University of British Columba (UBC) and Simon Fraser University (SFU) (http://gcc.sites.olt.ubc.ca/). One goal of the project was to fund research on engagement within specific engagement channels, including social media, interactive gaming, performance art, mobile technology applications and participatory workshops. A second, more ambitious goal of the overall project, (which was not fully realized) was to compare across the different engagement channels to look at how characteristics of the different channels impacted engagement characteristics, such as demographics, media use, types of interactions, the amount of dialogue versus goal-directed deliberation, etc. Figure 6.1 depicts the GCCP process schematically, including the engagement channels and the cross channel evaluation. Figure 6.1: A schematic diagram of the GCCP process. 217 6.1.2 Description of the Greenest City by 2020 Action Plan and the Selected Goals The context for the GCCP was the City of Vancouver’s initiative to become the greenest city in the world by 2020. This initiative led to the development of the Greenest City Action Plan (City of Vancouver, 2012). In 2009, the City of Vancouver convened the Greenest City Action Team (GCAT), a panel of experts that advised the City on how it could meet its goal of becoming the greenest city in the world by 2020. The GCAT researched best practices from around the world and made recommendations to council, culminating in the document ‘Our Bright Green Future’ (City of Vancouver, 2010). City staff and advisory panels were then tasked with operationalizing the GCAT recommendations into a plan with defined goals and measureable targets. In 2010, the Greenest City Action Plan (GCAP) was released containing goal areas and targets for the City to achieve. The ten identified goal areas included: Green Economy, Climate Leadership, Green Buildings, Green Transportation, Zero Waste, Access to Nature, Lighter Footprint, Clean Water, Clean Air, and Local Food. The GCCP intersected with the GCAP as the City was preparing to launch an ambitious public engagement strategy to elicit citizen input on the GCAP. The GCCP chose to adopt three of the GCAP goals to form the basis of the engagement and related research carried out in the engagement channels (see Figure 6.1). The selected GCAP goals were: Climate Leadership, Green Buildings, and Green Transportation – all three falling under the City’s broad focus on carbon management (City of Vancouver, 2012). The next step in the GCCP process was to work with the City to situate the GCCP engagement activities. The level of connection between the different GCCP channels and the CoV varied. For example, the Performance Art and Mobile Application channels had no direct contact with CoV staff during the development of their engagement activities, while the Social Media and Workshop channels worked closely with CoV staff in developing their engagement processes. 6.2 The Workshop Development Process The workshop development process took place over a two-year period, starting with initial discussions with the City of Vancouver in the winter of 2010, and culminating with the conduct of three Community Energy and GHG Emissions workshops in the spring and summer of 2012. 218 The development of the workshops was comprised of a number of important steps, from determining how to position the workshops in the broader frame of CoV engagement efforts, to recruiting the working team and advisory panel, defining the workshop scope and materials, and developing the workshop materials themselves. 6.2.1 Engaging with the City of Vancouver The main point of contact between the GCCP team and the City of Vancouver occurred through the CoV’s Sustainability Office, which housed the team responsible for planning and implementing the GCAP. The City of Vancouver and the University of British Columbia have a memorandum of understanding to encourage research activities within the City. As a result, there was strong interest and support from the City to develop a working relationship with the GCCP. This led to a number of meetings between City staff and GCCP researchers to determine the best course of action. The workshop team initially worked with the Greenest City Planner within the CoV Sustainability Office to try to incorporate the Community Energy and GHG Emissions workshops into the broader Greenest City Action Plan engagement activities. The City of Vancouver developed an ambitious agenda for engaging with citizens about the Greenest City Action Plan in advance of the plan being presented to council. As is often the case with the marriage of academic research with real-world processes, however, there was a timing conflict, as the City’s engagement activities were occurring over a brief window, leaving no time for the development of research instruments or workshop materials. Following the brief City engagement window, the Sustainability Office submitted the GCAP to council, and moved on to an implementation phase that did not include public engagement activities. Based on these incompatibilities, the suggestion was made that the workshop team engage with the CoV’s Community Planning Office, who were in the process of initiating new neighbourhood plans for three Vancouver neighbourhoods: the West End, Marpole and Grandview Woodland. These neighbourhood plans were required to address the 10 goal areas from the GCAP. The Community Planning Office was receptive to the idea of holding the workshops in association with their neighbourhood planning efforts, but was concerned that the Community Energy and GHG Emissions workshops would be held in the neighbourhoods in advance of their own engagement efforts. 219 Specifically, they were concerned about ‘putting the cart before the horse’ and upsetting residents with information and imagery suggesting radical changes to their neighbourhoods. Previous efforts to address energy-related topics such as the densification of land use (cf. Smith, 2008) had caused backlash from neighbourhood groups, and the City had concerns that research activities could have a similar effect. The solution to this problem resulted in one of the more interesting case study components of the GCCP workshops – the use of a generic neighbourhood that, while possessing many of the same characteristics of the actual Vancouver neighbourhoods undergoing planning processes, was not recognizable to participants as ‘my neighbourhood’. The idea of the generic neighbourhood was that it would allow for the conceptual exploration of many energy and GHG emissions topics in a setting that participants could understand, without instigating concern about changes to places with emotional investment. Previous work at CALP and affiliated research groups has focused on in situ research with real communities, so there was some trepidation about using a generic neighbourhood. However, the use of a generic neighbourhood also presented some opportunities, such as the dramatic reduction of data collection, modeling and visualization workloads, which are traditionally time and labour-intensive activities. The use of the generic neighbourhood, therefore, was embraced as a research opportunity. Could a generic neighbourhood be used effectively in workshops intended to address local issues? One of the challenges associated with the use of the generic neighbourhood concept was developing a neighbourhood that would address the urban form components of the actual different neighbourhoods involved. The West End has a significantly different urban form than Marpole and Grandview Woodland, with more towers and different types of commercial properties. While Marpole and Grandview Woodland also have their urban form differences, their general patterns of single family residential, multi-family residential, commercial and industrial result in a comparable fabric. As a result, the decision was made to focus on the Marpole and Grandview Woodland neighbourhoods for the development of the generic neighbourhood and, consequently, for the engagement processes. With the neighbourhoods and general context decided, as well as an agreement to proceed with the City, the workshop team began development of the workshops and associated materials, checking in with the Community Planning Office of the CoV at key stages to review materials and workshop design components. 220 6.2.2 Key Principles for the Workshop Design One of the overriding themes of the GCCP was a focus on procedural sustainability principles (cf. Robinson, 2004; Robinson & Cole, 2015), particularly the impacts of emergence during engagement, and during the design of the engagement activities. Different channels implemented emergence in different ways (Haas-Lyons, 2011; Tanenbaum et al., 2011). For the workshop channel, efforts were made to allow for emergence in the workshops through the use of interactive exercises, but also during the workshop design and development phases through the use of a broader working team and an advisory panel. Despite a focus on emergence, there were four key guiding principles that were pre-conceived parts of the workshop design. Those principles were: a focus on systems thinking, a focus on collective rather than individual energy actions, the explicit inclusion of values in the workshops, and a focus on interactivity. The focus on systems thinking reflects more recent thoughts and practice about sustainability issues (cf. Ehrenfeld, 2008; Meadows, 2008). Research areas and practical applications such as regenerative design see systems thinking as a crucial element in understanding sustainability and the interaction of systems such as energy, water and biota (Reed, 2007). Systems thinking may be particularly important for the understanding of ‘invisible’ phenomena such as energy where it is difficult to tie individual energy action to broader implications. In addition, many of the proposed solutions to energy issues have systemic implications. Complex interactions such as the relationship between district energy and density, or land use form and solar access require a systemic understanding. While the intent of the workshops was not to convert all of the participants into energy system experts, the hope was that the workshops would impart an appreciation of the interrelationship between factors such as land use and transportation. The focus on collective action was, in part, a broader GCCP goal, but was also a focus for the researchers developing the workshops. Decades of energy research have demonstrated the difficulty of changing people’s individual energy behaviours (cf. Gardner & Stern, 1996). In addition, many of the individual actions people can take within a given land use context will have a much smaller impact than collective changes to things like transit systems or changes in urban form (Robinson et al., 2008). There is some indication that focusing on lower effort, collective behaviours such as supporting policy or voting for sustainable initiatives can have a larger impact on energy use and sustainability (Stern, 2000). The focus of the workshops, then, would be on collective changes that could be made to participants’ neighbourhoods to address energy and GHG emissions. 221 Another interesting development from the mental models interviews described in chapter 4, and revealed by the mental models described in chapter five, is the importance of broader beliefs and values such as sense of place to people’s conceptions of future communities. In addition to sense of place issues, things people value such as health, safety and a sense of community are important to their conceptions of the future. For this reason, there was a specific goal of including values in the workshops. While the inability to address or visualize the participants’ actual communities made this principle more difficult to accommodate, measures were still taken to explicitly include values (see section 6.3). Finally, there was a focus on including interactivity in the workshops, to ensure that they were not simply one-way presentations about energy, but rather an exercise in which the audience were true participants in the investigation of energy issues, and the development of a future vision for the generic neighbourhood. The principle of interactivity comes from constructivist learning theory and its focus on active, social learning (Prichard, 2014), from a procedural sustainability approach which is inherently focused on the process of arriving at sustainable communities, and also from the experience of the research team in planning activities such as design charrettes, which call for participants to be actively involved, e.g. through discussion, drawing, participatory mapping, etc. (Girling et al., 2006; Robinson et al., 2006; Salter et al., 2010) In other words, the inclusion of interactivity in the workshops is the result of both supporting research evidence and practical experience. The inclusion of these four principles provided a broad framework for the workshops, while still allowing ample room for the workshop design to emerge from the input of both the workshop working team and the advisory panel. 6.2.3 The Working Team Due to budget limitations for the workshop channel, the majority of the production work was completed by a two-person team, including a senior CALP research scientist with extensive energy and public engagement expertise, and this dissertation’s author. However, budget was allocated to bring other key personnel into a working group to help devise workshop strategy, provide energy modeling expertise, and brainstorm about workshop flow, content and the interactive exercises. Working team meetings were held bi-weekly during the workshop development phase, with modeling support and material review occurring in the intervening time. 222 The working team also became the facilitators for the workshop exercises, ensuring that the facilitators were not only facilitation experts, but content experts as well. This content expertise was noted and appreciated by workshop participants (see section 6.5.2). The working team was comprised of two members with landscape architecture training, with extensive experience in sustainability planning and developing and running design charrettes; one energy modeler with a focus on urban form impacts on community energy who had also developed and run energy planning and design charrettes; and a public engagement expert with experience running both energy and climate change engagement activities. This team increased the breadth of expertise available for working out the details of the workshop design, and was instrumental in developing the scenarios, modeled data and exercises used in the GCCP workshops. 6.2.4 Advisory Panel In addition to the working team, the workshop channel recruited an advisory panel to provide extended peer review to the workshop development process. The advisory panel provided elements of interactive social research (Robinson & Tansey, 2006) to the process, because they were engaged early, and at key phases of the workshop development. Figure 6.2 shows a schematic of the advisory panel meetings. The first mini-workshop focused on defining the problem of energy at the Vancouver neighbourhood scale, as well as key sub-problems in areas such as transportation and residential energy use. The second mini-workshop focused on process components, including exercises and presentations, and tools, such as scenarios, modeling and visualization that might be included in the workshops. The final planned mini workshop did not occur, because of time constraints, but materials and the workshop plan were reviewed with individual advisory panel members. In addition, after the GCCP community energy and emissions workshops were developed, they were piloted for planners and sustainability staff with the CoV, providing a review of materials, with subsequent minor material revisions prior to the two public workshops. 223 Figure 6.2: A schematic diagram of the advisory panel process. The advisory panel was made up of participants from the Community Energy Association, BC Hydro, the Pacific Consortium for Climate Solutions, CALP and multiple departments from the City of Vancouver. Altogether 13 people took part in one or more of the advisory panel meetings. The first mini-workshop identified a number of key issues that the Community Energy and GHG Emissions workshops should cover (see Table 6.1). These issues formed the basis for the development of the workshop scenarios and associated energy strategies, as well as the necessary modeling. Table 6.1: Key community energy issues identified by the advisory panel as requiring citizen engagement. Key Issues Developed with Advisory Panel Percentage of total CoV 2020 GHG reductions (numbers from the Greenest City Action Plan) Integrated Land Use • Density (what does it mean?) • Road re-allocation • District energy systems (biomass) 7% 23% 14% Retrofits (residential buildings) 21% Renewable Energy • Biomass (air quality) • Other local renewables 14% Total 65% of CoV reductions require citizen engagement, support, participation Non-Spatial Issues • Financial • Health • Social Justice 224 The second mini workshop reviewed workshop process components and tools and developed a number of recommendations, including: • Limiting the workshops to 3 hours • Engaging participants in problem-solving • Fostering dialogue about social values (possibly using role playing) • Having energy experts for participants to consult with during the workshop • Using a design panel review format for participant neighbourhood design ideas • Exploring the idea of iterative workshops: first on Integrated Land Use, second with visualizations/visuals and review/re-design • Use visualizations, because they have proven to be powerful tools in past CALP work • Scale: work at neighbourhood scale Many of these recommendations were incorporated in the final workshop. The recommendations that were excluded either conflicted with other design goals or were cost or time prohibitive. 6.3 Workshop Development While the CALP research group has developed and conducted a large number of public engagement workshops in the past, the focus on energy, the need to construct a generic neighbourhood, and adherence to the four key workshop design principles meant that the Community Energy and GHG Emission workshops were an entirely new process. So, while the GCCP workshops could borrow from existing CALP approaches such as Local Climate Change Visioning (Pond et al., 2010), new methods needed to be developed. In this section, the development of key workshop components will be outlined, before discussing the structure of the workshops themselves. 6.3.1 Development of the Generic Neighbourhood: ‘The Sandbox’ Perhaps the most novel component of the workshops was the use of the generic neighbourhood concept. The basic idea behind the generic neighbourhood, or ‘Sandbox’ was to incorporate land use and urban form elements that were representative of Marpole and Grandview Woodland and, by extension, many other neighbourhoods within Vancouver. This Sandbox neighbourhood needed to have both spatial characteristics for mapping and visualization, and data characteristics, such as building age, building construction, building systems, etc. for energy and GHG modeling purposes. 225 Using ArcGIS and Vancouver’s Open Data, The Sandbox was constructed by analyzing and then standardizing parcel and building footprint data from representative blocks in Marpole and Grandview-Woodland to build a 30 block neighbourhood. The 30 block size was used so that the neighbourhood was large enough to incorporate different land use patterns (e.g. single family, multi-family, commercial, etc) and important concerns such as transit access and walkability, while at the same time being small enough that it was easy to comprehend and to work with in the workshops’ design exercises. Figure 6.3: An oblique 3D representation of 'The Sandbox' (baseline condition). Each quarter block in the Sandbox is interchangeable, unlike City blocks which have considerably more variation. Figure 6.3 shows an oblique 3D view of the constructed sandbox with the different land use designations indicated by the colour of the parcels. The Sandbox is predominately made up of single-family residential blocks (22.5 blocks) with a concentrated low-rise multi-family area (6.5 blocks) and a main strip with single storey commercial (3/4 block) and mixed-use (3/4 block) land uses. The neighbourhood also has a full block of ‘community’ land use, which represent an elementary school, community centre or park. Industrial areas were not included to simplify the exercises. 226 In addition to the 30 block area of The Sandbox neigbourhood, a larger contextual area was also constructed (see Figure 6.4). This context area is similar in size to both Marpole and Grandview Woodland, and has similar land use and services as those neighbourhoods. The context area allowed for the incorporation of features such as transit routes, skytrain access and an industrial area that would not have been feasible for an area as small as The Sandbox. Based on the features of the context area and its location within the City, it was possible to make modeling assumptions on variables such as transit ridership, active transportation and vehicle kilometres travelled. Figure 6.4: A representation of The Sandbox and the larger constructed context area coloured by land use. 6.3.2 Energy and GHG Modelling The primary function of modeling is to help us understand aspects of our world (Weisberg, 2013). In sustainability engagement exercises, modeling is often used to help us better understand the implications of different choices (cf. Carmichael et al., 2004; Holman et al., 2005). This may be particularly useful when broaching new subject areas, such as planning for energy at local scales, where the implications of 227 different actions are poorly understood. Energy modeling was used in the GCCP workshops to represent both the current, and potential future, energy and GHG implications of the generic neighbourhood. An additional benefit of the energy modeling was the provision of baseline energy and GHG numbers for Marpole and Grandview Woodland to the City of Vancouver – resources that the community planning department did not currently have for those neighbourhoods. The first step in the modeling process was to model the existing Marpole and Grandview Woodland neighbourhoods. Building energy and GHG modeling was based off of existing work by Miller (2013) which uses building archetypes and patterns of land use to estimate energy use and GHGs for aggregate areas. Miller’s (2013) modeling techniques were modified by Pond (2013) using ArcGIS and supplementary algorithms in Microsoft Excel to adapt them to the Marpole, Grandview Woodland, and Sandbox contexts. The building modeling technique uses BC Assessment Data to derive representative mixes of building age (and thus associated construction materials and systems) and the number of suites and duplexes, as well as multi-family houses and apartments. These data were then coupled with GIS footprint data about building size, and model factors and assumptions about variables such as number of units, unit size, people per unit (derived from census data broken down by neighbourhood and building type), and building energy use to derive modeling numbers for energy use and GHG emissions at the quarter block scale. These quarter blocks can then be assembled to model energy use and GHG emissions at larger scales. Commercial buildings were modeled using square meters and archetype energy numbers. Mixed-use buildings were modeled using a mix of the residential and commercial modeling techniques. Modeling for future changes under various scenarios applied assumptions about reductions in energy use by each action (e.g. land use conversion, district energy, retrofits, additional units and laneway homes) to the baseline model. The assumptions were based on available literature, expert opinion, and prior modeling work at CALP (e.g. Pond et al., 2011). As with the baseline, these results were calculated on a quarter block scale. Transportation energy and GHG modeling was adapted from work by Miller & Cavens (2012) for their Climate Action Navigator modeling tool. This tool will be discussed in more detail in Chapter 7. The model uses regional data on variables such as vehicle ownership, average vehicle kilometres traveled (VKT), transit ridership, and active transportation to estimate transportation energy use and GHG emissions. Transportation modeling is notoriously complex, because of the difficulty of accounting for vehicle trips into or out of a particular region and the difficulty of accurately estimating VKTs. For that 228 reason, the transportation modeling in both the Climate Action Navigator and particularly for the GCCP workshops was simplified. Based on the building and transportation models, energy and GHG emission estimates were constructed for Marpole and Grandview Woodland and vetted with City of Vancouver planning and engineering staff. The modeling methodology was then applied to the Sandbox neighbourhood, to generate base numbers for describing the energy use in the Sandbox, and for the later development and application of the future scenarios. In all applications of numerical models, the model’s purpose is the primary determinant of model inputs, outputs and complexity. A model developed to assist engineers in optimizing building Heating, Venting and Air Conditioning (HVAC) will, of necessity, have higher complexity and specificity than a model that is developed as an educational tool about building energy requirements. Modelling tools that have been simplified to accommodate less technical applications, or to improve model run times and interactivity have been called ‘executive summary models’ (Robinson et al. 2006). There have been numerous executive summary models developed for individual sustainability applications (cf. Carmichael et al., 2004; Holman et al., 2005). The modeling done for the GCCP fits into the executive summary modeling framework, as it was intended to estimate energy use and GHG emissions with a reasonable level of accuracy, and to be able to provide defensible estimates of trends in energy use and GHG emissions with the application of different strategies across the building and transportation sectors. In other words, while the modeling conducted would not be detailed enough for the CoV to use to determine the benefits of a specific policy decision such as adding transit service or funding a retrofit initiative, it was sufficiently accurate to engage with citizens and energy experts about current and projected energy use and GHG emissions, and about strategies to mitigate them. The model in this sense helps participants to conceptualize the system and system interactions, so that they can understand magnitudes and interactions that are important factors in planning neighbourhoods to address energy and GHG concerns. An assertion of the work for the GCCP and related engagement efforts is that fostering this understanding of the energy, or related sustainability, systems will help people provide more informed input into planning decisions. 229 6.3.3 Workshop Scenarios In Chapter two, the history and importance of scenarios to sustainability planning in general, and public engagement in particular, was discussed. Based upon the research interests of the working team, and previous work at CALP (see e.g. Salter et al., 2009; Schroth et al., 2013; Sheppard et al., 2011), there was an interest in further exploring the use of scenarios in the GCCP workshops. Community scale energy planning is, by its nature, a future focused activity and the likelihood that that planning could fundamentally alter key aspects of citizen’s everyday lives makes the construction of meaningful future narratives a valuable tool. Figure 6.5: A representation of the scenario space used to develop the GCCP scenarios. One of the important goals of the GCCP workshops was to build citizen capacity to think about and incorporate energy and GHG emissions issues into future community planning activities for Marpole and Grandview Woodland. All of the workshop components and materials were developed with that intention as the primary design cue. Therefore, the working team convened to discuss the scenario development strategy, with a focus on developing three or four coherent narratives to explore with the workshop participants. As part of that discussion, the main energy and GHG neighbourhood ‘levers’ 230 were explored, those being changes to land use and urban form, technological changes, and changes to the way people behave in their use of energy. Levers in this instance defines broad categories of approaches to the energy issue. So, for example, the technology lever may include more efficient heating and cooling systems for buildings, the integrated land use lever may include increasing density to facilitate more transit access, and the behavioural/cultural lever may include people changing their behaviours to include more active transportation options. After investigating several possible approaches to combining these levers into coherent scenario storylines, the decision was made to focus on each lever individually, rather than combining them as is more conventionally done in scenario planning. In this approach the ‘scenarios’ would reflect what a future neighbourhood might look like if one of the energy and emissions levers had been pushed to the exclusion of the two others. Figure 6.5 shows a schematic representation of what combined scenarios might look like, versus the single axis scenarios decided upon for the GCCP workshops. A key feature in the definition of scenarios is that they are plausible visions of the future (Van der Heihden, 2005). By that criteria, the ‘scenarios’ developed for the GCCP were not, in fact, scenarios, but rather explorations of limited pathways. For the purposes of this discussion, they will be referred to as scenarios, but the distinction needs to be made that they do not fit the strict criteria for scenarios. The main benefit that the working team perceived in the individual levers approach was the ability to communicate, and have participants discuss, the relative benefits and costs of each of the levers. What does a future neighbourhood look like if we do as much as we can to save energy through land use changes? How significant are the energy and GHG emission changes we would get from that approach? How would that compare to a neighbourhood that only focuses on updating technology to improve efficiency or promote fuel switching? A potential weakness of this approach is that it ignores inevitable interactions between the levers (e.g. is DES facilitated by density a land use or a technology solution?). This potential for interactions was recognized at the time, and accepted as a potential challenge of the scenario strategy. The focus on one lever at a time worked well with the broader workshop constructivist learning goal of building or ‘scaffolding’ participants’ capacity over the course of the workshop culminating in the final interactive exercise. The participants could investigate the effects of the individual levers in the presented scenarios’ and then combine those levers in the final exercise to create their own, more plausible scenario. For the purposes of comparison across the scenarios, each scenario was subdivided into three strategies for achieving energy and GHG emission reductions. Two of these strategies focused on buildings, and 231 one focused on transportation, with the strategies for each scenario being determined by actions consistent with the overall scenario theme. Figure 6.6 shows the strategy cards for each of the three scenarios. The Integrated Land Use scenario, in effect, had four strategies, because participants were provided with two levels of densification in the land use strategy. In order to measure and present the impacts of the various strategies and overall scenarios, the strategies had to be modeled. This modeling used the same basic form as that presented in Section 6.3.2 with the energy and GHG emission reductions of each strategy being modeled against the base case neighbourhood. The energy and GHG emission savings of some measures (e.g. building retrofits and urban form changes) were drawn directly from Miller’s (2013) energy modeling, while other numbers (e.g. electric vehicles and transit ridership) had to be derived from primary research sources and integrated into the modeling (Pond, 2013). A further simplification to the modeling data was also implemented at this point. The simplification involved converting the numbers from the GHG emission reductions associated with particular strategies into an icon system, in this case a series of stars, with each star equaling approximately a 5% reduction in carbon dioxide equivalent (CO2e) from the base case Sandbox neighbourhood. The purpose of the stars was to make it easier for participants, most of whom would have little to no experience with GHG emissions data, to understand and quantify the relative merits of different strategies. The star system also made it easy to quantify the City’s 2020 targets (approximately 4 stars by 2020), and to set a goal for the final exercise (12 stars). Emissions in CO2e was selected, because of the nature of the base modeling, which focused more on emissions than energy use. The number of stars associated with each strategy can be seen in the upper right corner of the strategy cards in Figure 6.6. Participants were informed of both the City’s targets for 2020 and the final exercise target (12 stars). The stars achieved by each working group were developed by simple addition, though the facilitators negotiated with the participants to adjust the final tallies based on possible interactions between strategies. For example, if a group played both the electric vehicle and the increased transit access strategies, facilitators would adjust the total number of stars allotted downwards to accommodate the potential competition of these strategies. The strategy cards (see Figure 6.6) did not provide trade-offs beyond the star rating system, although the land use cards (see Figure 6.15) provided unit numbers, energy use and population. Focusing on only one trade-off was potentially a weakness, as the cards did not themselves provide information for a more comprehensive assessment of pros and cons or costs and benefits that could tie more tightly to values. Pros and cons were instead weighted by participants in their discussions of the scenarios. This weakness 232 may have been part of the challenge of a single workshop, which represents a truncated planning process, set up for shared learning and exploration rather than any final decision-making. A few cards did require dependencies: transit improvements and district energy, for example, could not be used unless integrated land use was also chosen. Figure 6.6: The strategy cards for each of the three scenarios. Larger versions can be seen in Appendix C. The resulting scenarios were presented to the participants using a narrative storyline, an explanation of the assumptions and the strategies that composed the scenarios, and modeled performance measures for population supported, energy use, GHG emissions and transportation mode split (see Table 6.2). The scenarios were an integral part of the workshop and its intention of building participant capacity leading to the final ‘Design Your Neighbourhood’ exercise. In the workshop structure, explanation of the scenarios was given both as a presentation and as an exercise component. 233 6.3.4 Visualization and Graphic Representation Chapter two developed a framework for representation types used in land use planning processes (Figure 2.1). This taxonomy can be used not only to classify existing visualizations, but also to help determine what visualization types to use for particular applications. For the GCCP workshops, the intent was to communicate concepts of the energy system at the neighbourhood scale to build capacity for participants to engage in later stages of the neighbourhood planning process. Another key factor in the selection of visualization media was the use of the generic neighbourhood. Because citizens would not be working on their neighbourhood, both the need, and perhaps the demand, for experiential visualizations would be reduced – i.e. they would be less concerned about the visual character of energy planning interventions if they were not occurring on their street. For these reasons, the visualization materials selected and developed for the GCCP workshops were primarily of the conceptual and information visualization types. There were four major visualization techniques used in the GCCP workshops: 1. Two-dimensional mapping – this was used primarily to communicate land use changes associated with the integrated land use scenario. It was also used in the early part of the workshop to explain some of the modeling parameters such as building age and transportation factors. 2. Three-dimensional massing – this was used to communicate scale and neighbourhood attributes to compare the Sandbox to Marpole and Grandview Woodland. 3. Three-dimensional conceptual – This was the main visualization type used in the workshops. It formed the basis for most of the discussions of the Sandbox, and was used to represent the Sandbox in three of the four exercises. This form mixed more identifiable building types (articulated models, rather than massings) with cognitive information about land use designations, and was used primarily from an aerial oblique viewpoint. 4. Hybrid conceptual/experiential street scenes – these scenes were used in the presentation of the scenarios to convey the experience of different strategies in representative streetscapes. The idea of these visualizations was to allow the participants to relate the visual impacts of energy strategies to their own neighbourhoods. In order to further illustrate the experiential aspects of the different strategies, these visualizations used a ground level viewpoint. 234 Table 6.2: The storylines, assumptions and indicators associated with each of the three scenarios. Scenario Existing Sandbox Storyline The existing sandbox was constructed by analyzing the fabric of the existing Marpole and Grandview Woodland neighbourhoods. The developed Sandbox is meant to depict a typical residential neighbourhood outside Vancouver’s downtown core. Assumptions 30 block neighbourhood 23.25 blocks single family 6.5 blocks lowrise multi-family 0.75 blocks single story commercial 0.75 blocks mixed use 1 block community (park, school, etc.) 1 north/south bus route along main commercial street Indicators Population: 5586 people Percent of baseline: 100% Total Energy: 331,529 GJ change from baseline: 0% Per Capita Energy: 59 GJ change from baseline: 0% Total Emissions:15,440 tCO2e change from baseline: 0% Per Capita Emissions: 2.7 tCO2e change from baseline: 0% Mode Split Car/SUV 64% Transit 20% Walk 13% Bicycle 3% 235 Scenario Integrated Land Use Storyline Over the next 25 years, the neighbourhood undergoes re-development towards integrated land use, with low-rise apartments replacing single-family, and more mixed-use (commercial ground floor and residential above) along the main street. These changes support a transportation mode shift to public transit, reduces energy demand and GHGs per person, and allows for a District Energy Plant (wood-burning and natural gas). While overall neighbourhood emissions rise due to the increase in population, the per person emissions drop. The energy and GHG benefit of this scenario would occur if people from lower density, higher energy and GHG emission communities in the region relocate to this neighbourhood. Assumptions Almost 8 full blocks of single-family would re-develop to new low-rise multi-family apartments. Three mid-rise buildings (10-12 storeys) would replace one existing quarter-block of low-rise apartments. Mixed-use would fill the commercial street frontage, through redevelopment of the existing single-family and single-storey commercial along the main street. Two quarter-blocks would be converted to parks, to provide additional greenspace amenities for the increased population. Indicators Population: 8674 people Percent of baseline: 155% Total Energy: 429,611 GJ change from baseline: +30% Per Capita Energy: 50 GJ change from baseline: -17% Total Emissions:18,388 tCO2e change from baseline: +19% Per Capita Emissions: 2.1 tCO2e change from baseline: -23% Mode Split Car/SUV 53% Transit 22% Walk 18% Bicycle 3% 236 Scenario Technology Storyline Over the next 25 years, many small changes add up to reduce energy use and greenhouse gas emissions. Home and business owners pay for technology changes in order to improve energy efficiency and shift to more renewable energy sources. Existing buildings are retrofit to reduce demand (e.g. better windows, insulation); furnaces are upgraded to higher efficiencies; home and business owners add heating systems like air-to-air heat pumps and on-demand hot water. Solar hot water becomes common, meeting 30% of hot water demand in single-family housing areas and 15% in apartments. Electric vehicles are adopted, leading to an assumed market penetration rate of 40% by 2040. While electric vehicles reduce transportation-related greenhouse gas emissions, they do not reduce congestion and other health-related impacts of single-occupancy vehicles. Urban form and lifestyle remains essentially the same, as does the neighbourhood’s population - Vancouver’s population growth is accommodated elsewhere. Both overall neighbourhood and per capita emissions decrease. Assumptions There are no re-development changes at larger scales - changes occur on a building-by-building and vehicle-by-vehicle basis, incorporating better technology. Indicators Population: 5,586 people Percent of baseline: 100% Total Energy: 244,565 GJ change from baseline: -26% Per Capita Energy: 50 GJ change from baseline: -26% Total Emissions:10,198tCO2e change from baseline: -34% Per Capita Emissions: 1.8 tCO2e change from baseline: -34% Mode Split Car 38% EV 38% Transit 22% Walk 18% Bicycle 3% 237 Scenario Behavioural/Cultural Storyline Over the next 25 years, people change their behaviour to reduce energy demand, such as wearing sweaters and turning down thermostats. Households live in smaller units - the single-family areas add suites to single-family homes, convert single-family homes to multi-unit houses, and add laneway homes. Neighbourhood population increases by 24% while preserving the current “look” of the neighbourhood. For transportation, people use more active transport, and more people use public transit. Residents walk further to get to the bus/skytrain. People also ride their bikes further - the current 5km zone around downtown with higher cycling rates extends out to 7km around downtown. Behavioural changes are supported by cultural shifts, including changes in urban design and road allocation: for example. Urban form remains essentially the same. Overall neighbourhood and per capita emissions decrease. Assumptions There are no re-development changes at the block scale; however, changes do occur to the quality of public space and roadways, to support a more active lifestyle (walking, cycling). Indicators Population: 6,868 people Percent of baseline: 124% Total Energy: 298,584 GJ change from baseline: -10% Per Capita Energy: 43 GJ change from baseline: -27% Total Emissions:13,593 tCO2e change from baseline: -12%% Per Capita Emissions: 2.0 tCO2e change from baseline: -28% Mode Split Car/SUV 38% Transit 20% Walk 18% Bicycle 12% 238 Figure 6.7 shows representative examples of the four visualization types from the GCCP materials. Figure 6.7: The visualization types used in the GCCP workshops. As will be discussed in section 6.5.3, the graphics and visualizations used in the workshops were well received, and frequently cited for their usefulness in conceptualizing the energy and GHG emissions issues discussed in the workshops. 239 6.4 The Workshop Process There were some variations in the three workshops conducted. Firstly, the Planners workshop was used to review materials before presenting them to citizens. The Planners workshop used the Marpole materials, because the Marpole workshop was the first citizen workshop conducted. The Marpole and Grandview Woodland workshops had preliminary materials specific to their communities to help participants relate their own community to the Sandbox. 6.4.1 Planners Workshop: CIRS Figure 6.8: The Planners workshop in the BC Hydro Theatre, UBC. The first of the three workshops was conducted with 16 planners and city staff from the CoV. The purpose of this workshop was to provide one final vetting of materials with the City before conducting the workshops with citizens. The planners’ workshop was conducted in the BC Hydro 240 Theatre in the Centre for Interactive Research on Sustainability at UBC. This facility was designed with the intent of conducting sustainability-related public engagement activities and has a number of technologies and capabilities to facilitate these interactions. Participants in the Planners’ workshop went through the same workshop as the Marpole citizens, i.e. the workshop used Marpole images and data at the front end. The pre and post workshop questionnaires were administered, providing structured feedback by the City staff on the workshop. Anecdotal and observational feedback was also elicited from City staff to allow necessary changes to be made to the workshop process and materials prior to the citizen workshops. Figure 6.8 shows a photograph of the Planners workshop being held in the BC Hydro Theatre. As a result of the Planners workshop, some minor changes were made to the workshop materials, primarily focused on reducing jargon and technical language. 6.4.2 Marpole Workshop Figure 6.9: The Marpole workshop held at the Marpole United Church. Marpole is a large neighbourhood in Vancouver’s south end, adjacent to the Fraser River. Marpole is characterized by large areas of single-family residential, with concentrated areas of duplex, low and 241 mid rise multi-family. Most of the multi-family is of the 3-4 storey walk-up variety, characteristic of building from the 1960s and 70’s. Due to this housing stock, Marpole remains one of Vancouver’s most affordable rental areas. Most commercial establishments are of the single storey variety, though mixed-use buildings are increasingly being built along the major routes. Marpole is predominately a bedroom neighbourhood, with most residents leaving the neighbourhood for work. Demographic characteristics of the Marpole neighbourhood are discussed in section 6.5.1. The Marpole workshop was held with 21 participants at the Marpole United Church, a centrally located facility within the neighbourhood. Figure 6.9 shows a photograph from the Marpole workshop. 6.4.3 Grandview Woodland Workshop Figure 6.10: The Grandview Woodland workshop held at the Aboriginal Friendship Centre. Grandview Woodland is a neighbourhood in Vancouver’s northeast end adjacent to Burrard Inlet. Grandview Woodland has similar land use characteristics to Marpole, with predominately single- 242 family residential interspersed with concentrated low and mid-rise multi-family and single storey commercial. Of note, many of the single-family residential buildings have been converted to multi-family through the addition of rental suites and duplexes; most of the “single-family” homes, therefore, have at least two units, and many are full multi-family conversions with three to seven units. Similar to Marpole, most residents of Grandview Woodland work outside the neighbourhood. Demographics variables regarding Grandview Woodland will be discussed in section 6.5.1. The Grandview Woodland workshop was held with 32 participants in the Aboriginal Friendship Centre. A photograph of the workshop can be seen in Figure 6.10. 6.4.4 Workshop Structure Figure 6.11: A diagram of the overall workshop structure. The GCCP workshops were five hours in length, with a central lunch break. Pre and post session questionnaires were included in the five hours. Figure 6.11 provides a schematic diagram of the workshop structure. There were two primary goals in determining the workshops structure. Firstly, there was the aforementioned goal of ‘scaffolding’ to build the participants’ capacity over the course of the workshop, culminating in the most challenging exercise of designing a future neighbourhood. Secondly, the presentations were interspersed with interactive exercises to more thoroughly involve the participants in the learning process. The resulting structure alternates between presentation and 243 interactive exercises, building to the final ‘Design Your Neighbourhood’ activity. Upon entering the workshops, participants were greeted by one of the workshop team and given the pre-workshop questionnaire to fill out. Participants were also given two coloured dots to place on an aerial photo of the neighbourhood: one for where they live, and one for their favourite place. This was done to begin the spatial orientation of participants, provide a conversation entry point, and give more than one activity during the questionnaire time. Once all participants had filled out the questionnaire, the introductory presentation started, explaining the goals of the workshop and the introductory material. Following the introductory presentation, participants worked on their first small group interactive exercise (Triple Energy Brainstorm). After the results of that exercise were presented back to the group, the second presentation was delivered, discussing the components of the energy system in Vancouver. Following that presentation, the small groups worked on the ‘Name Your Neighbourhood’ exercise, which focused on exploring what they valued about a neighbourhood using the Sandbox. After the ‘Name Your Neighbourhood’ exercise, there was a half hour break for lunch. Following the lunch break, the day’s final presentation was delivered, explaining the energy and GHG scenarios and associated strategies for the Sandbox. At the end of this presentation, participants were asked to explore and report back on one of the three scenarios. Finally, participants were asked to draw upon their experiences from the workshop to design a neighbourhood that incorporated energy, land use, and quality of life features that their group felt were important. The presentations and interactive exercises are described in more detail in sections 6.4.5 and 6.4.6. 6.4.5 Presentations One of the design goals for the workshops, illustrated by the interactivity principle discussed in Section 6.1.2, was a desire to limit the amount of one-way presentation of information. As much as possible, the workshop team tried to focus on interactive exercises to allow the participants to construct their own knowledge about energy and GHG emissions. It was not possible to avoid presentation altogether, however, in part because there was a need to bring participants with vastly different energy and GHG knowledge onto a more level playing field to interact during the exercises. The presence of cluster one (Low Energy Knowledge), revealed by the 244 analysis in chapter five further supports the need for some learning component to build participant capacity to engage. There were also significant time constraints in the workshops, meaning that for some information, it was faster to develop directed presentations, rather than relying on those subjects to emerge during discussion and deliberation. Balancing presentations and interactivity is more of an art than a science. Workshop evaluations suggested that, if anything, participants may have preferred more presentation time to better situate the accompanying exercises (see section 6.5). As shown in Figure 6.11, the presentation material was broken into three segments, separated by exercises relating to the content from the previous presentation. The purpose of the first presentation was to introduce the subject matter, discuss why community energy is important, and to briefly discuss energy and GHGs as they related to the participants’ neighbourhoods. So, for example, at the Marpole workshop, the first presentation discussed the results of the base energy and GHG modeling for Marpole. The presentation ended with the introduction of the Sandbox, and slides intended to relate the Sandbox to the citizens’ neighbourhood on characteristics such as size, transportation access and land use and urban form. The second presentation focused on the energy system, working down from the BC scale, to the scale of Vancouver, and finally to the scale of the local neighbourhood and the Sandbox. The purpose of this presentation was to introduce the idea of energy as a system, and to help participants understand where their energy comes from, and what it is used for. The presentation looked at energy from both a supply and demand perspective, deconstructing energy needs and energy sources at each scale. The final presentation gave a detailed breakdown of the three scenarios, as well as the individual strategies within those scenarios. The purpose of this presentation was to help participants understand the scenario storylines and the strategies that accompanied each scenario. The two exercises following this presentation were reliant on the scenarios and strategies, so it was important to acquaint the participants with the material, before they delved more deeply into evaluating and applying the strategies. For all of the presentations, there was a main screen and a secondary screen (see Figure 6.8, Figure 6.9, and Figure 6.10). The main screen showed the primary content, while the second screen was used to keep important content up for longer periods of time, to present 245 complimentary content (e.g. legends for complex images), or to remind participants of the workshop structure. 6.4.6 Exercises As Figure 6.11 shows, there were four exercises in the GCCP workshops. Similar to the presentations and the overall workshop structure, the design strategy was to incorporate exercises that built upon each other to develop capacity within the participants to complete the final exercise of designing a future neighbourhood. The early exercises were also intended to foster communication between group members and explore trade-offs across the strategies and scenarios. Exercises were all conducted at the workshop tables, which varied in size from six to nine participants. Participants were assigned to the tables randomly upon entering the workshops, and stayed at the same tables throughout. There were three tables at the Planners and Marpole workshops, and five tables at the Grandview workshop. Each of the tables had a facilitator who conducted the exercises. The Energy System Exercise The first exercise was a conceptual exploration of the energy system, using a simple post-it notes and diagram approach. This exercise was allotted 25 minutes (based on the working team’s previous experience with similar exercises), with the intent of it being a quick, immersive introduction to the energy system. Participants were asked to individually identify how they used energy in their everyday lives, how energy was used in their community, and where their energy comes from. Participants filled these out on different coloured post-its (coloured by category – blue for supply, pink for individual energy use, and orange for community energy use), and then individually placed them on a large piece of paper (see Figure 6.12 ). As each individual placed their post-its, they were asked to arrange them in proximity to other post-its that they felt were related. Once all of the post-its had been placed, the group discussed groupings and rearranged post-its as necessary. The final step of this exercise encouraged participants to further discuss relationships that were apparent on the diagram, and to use markers to draw relationships and add concepts. 246 Figure 6.12: The energy system exercise. A common concern amongst the working team, affiliated researchers, and the City of Vancouver staff was that this exercise was too simple for adult participants. However, those that took part quickly realized that it moved from simple to complex very quickly when participants placed their post-its and started considering the relationships between them. How do individual energy uses relate to community energy uses? What energy supplies are associated with particular energy uses? This exercise also served as a rough surrogate for participants’ collective mental models of energy, and is quite similar to techniques for eliciting mental models discussed in Chapter 3 (cf. Chang, 2007; Kearney & Kaplan, 1997). The purpose of this exercise was to encourage participants to start thinking about energy as a system, and recognizing the interconnections between individual and community demand, and more broadly between demand and supply. 247 The ‘Name Your Neighbourhood’ Exercise The second exercise was an attempt to address one of the four key design principles for the workshops: the explicit inclusion of values in the energy discussion. Addressing this principle was made significantly more difficult by the focus on a generic neighbourood, because many of the values and concerns participants had about their own neigbourhoods were not necessarily evoked by the Sandbox. Figure 6.13: The 'Name Your Neighbourhood' exercise. The ‘Name Your Neighbourhood’ exercise was designed to address this values shortcomings of the Sandbox by having participants identify and draw features that they felt were important in a sustainable neighbourhood of the future. Participants were asked to specifically consider aspects of transportation, neighbourhood character and buildings/land use, but they were not limited to these categories. 248 The visualization media used in this exercise was a 3D SketchUp rendering of the community that had been simplified to make drawing and diagramming easier (see Figure 6.13). In addition to the black and white 3D buildings, the parcel data was rendered as a uniform light green, and the blocks outside the neighbourhood were rendered as a light grey to facilitate writing in the margins. Participants were encouraged to pick up markers and draw features themselves, rather than relying on the facilitators. Group discussions drove what features made their way onto the worksheets. Participants repurposed many areas of the neighbourhood, making significant alterations to the base Sandbox. Group conversations during this exercise were quite animated, perhaps because, unlike with more technical energy discussions, all of the participants knew what to do in this exercise. In general, people know what they like about a neighbourhood, and what services and amenities they want access to. This exercise was well received, perhaps because of its familiarity, and because it spoke more to what residents really want from their neighbourhood. In other words, they may want their neighbourhood to be sustainable, but more broadly that is only part of wanting their neighbourhood to reflect what they value. Participants were given 25 minutes to complete this exercise, culminating with the requirement that they name their neighbourhoods as a final step in taking ownership and investing in the neighbourhood. While the time allotted was adequate (all groups did complete the exercise within the time provided), it is possible that more time might have allowed for further creativity – a possibility that might be explored in future iterations. Before breaking for lunch, a representative was asked to present the group’s neighbourhood, name and its features and amenities to the larger workshop. The Scenario Exploration Exercise The third exercise in the GCCP workshop took place after the scenario presentation, which first introduced the participants to the developed scenarios/pathways and associated strategies. This exercise was designed to have different working groups focus on individual scenarios. Each group was presented with a 3’x4’ worksheet that identified the scenario, its associated strategies, and the 2040 implications of following that scenario on indicators of energy use, GHG emissions, population, etc. Figure 6.14 shows a scenario worksheet for the technology scenario. 249 In this exercise, the participants were guided through the scenario and associated strategies by the facilitator, using the Sandbox neighbourhood with the scenario-specific land use zoning and strategies (see Figure 6.14). Participants were asked to identify what they found exciting (scenario strengths) about the scenario, and what they found concerning (scenario weaknesses) about the scenario through a guided discussion by the facilitator. Figure 6.14: An example of a completed Technology Scenario worksheet. At the end of the 25 minute exercise, a representative of each working group was asked to present their impressions of the strengths and weaknesses of the scenario they investigated back to the larger workshop. This ‘report-back’ was intended to prime the groups that had worked on one of the other scenarios with information and insights that might help them incorporate the strategies in the final exercise. So, for example, a group that worked on the Integrated Land Use scenario could learn from the report-back of the Technology and Behavioural/Cultural scenario groups, and make better informed decisions about incorporating strategies from those scenarios in the final ‘Design Your Neighbourhood’ exercise. 250 This exercise was the least preferred of the four exercises in the workshops. There was less interactivity in the exercise, because the participants were primarily learning about the scenarios and strategies. Because of time limitations, the participants were also only assigned to learn about one scenario, meaning that they had less understanding of the strategies associated with the other scenarios when the entered the final exercise. Despite these limitations, the workshop design team still felt that the scenario exercise was a necessary part of the GCCP workshops. In applications of similar workshops that have occurred since the GCCP, removing the scenario exercise to save time led to some confusion during the ‘Design Your Neighbourhood’ exercise, particularly about trade-offs between strategies (Pond, 2013). Maintaining the scenario exercise content, while improving the exercise’s interactivity, might improve its reception. The ‘Design Your Neighbourhood’ Exercise The fourth and final exercise was the culmination of all of the presentations and exercises that preceded it. In this exercise, participants were asked to design the final neighbourhood ( i.e. design the future) incorporating what they had learned about energy and GHG strategies, as well as the value and character elements they developed in exercise two, Name Your Neighbourhood. The two main goals for teams during the Design Your Neighbourhood exercise were to achieve 12 stars worth of GHG reductions (approximately 60% reduction from the base case), while at the same time designing a neighbourhood that participants would collectively want to live in. The latter goal proved more challenging as some participants disagreed about strategies such as increasing density to facilitate DES or transit improvements, based on their impacts on the quality of life in the neighbhourhood. Participants stayed with their same working groups and were presented with a 3’ x 4’ plot of the Sandbox base case with parcels coloured by land use (see Figure 6.16, a larger version is also available in Appendix C). The instructions for this exercise asked participants to design a future neighbourhood that incorporated energy/GHG, land use, and quality of life features that they felt would result in a desirable place to live. For this exercise, participants had access to all of the strategy cards from each of the scenarios, as well as land use ‘puzzle’ cards that allowed them to redevelop 251 parts of the neighbourhood at the quarter block, half block and full block scales (see Figure 6.15). The participants could use any combination of the strategy cards from the three scenarios, provided they met the preconditions (e.g. density requirements for DES or transit improvements), and that they did not play competing cards (e.g. electric vehicles and transit improvements). The land use puzzle pieces came in quarter, half and full block sizes, and could be used by the participants to ‘redevelop’ blocks within the neighbourhood. On the backs of the puzzle cards (see Figure 6.15) relevant numbers such as population housed, energy use and GHG emissions were recorded, and a photographic example of the land use type was provided. A completed redesign of the neighbourhood can be seen in Figure 6.17. Figure 6.15: Examples of the land use puzzle pieces participants used to design the future Sandbox. Larger versions can be seen in Appendix C. Certain strategy cards required land use redevelopment to ‘play’. For example, a district energy system required at least four adjacent quarter blocks of multifamily residential or mixed use buildings, and the ‘Transit Improvements’ strategy card required at least a 25% increase in the neighbourhood’s population (i.e. the integrated land use card must also be ‘played’). These interactions were necessary to meet the assumptions of the underlying modeling. For example, a viable district energy system requires a particular density of dwelling types to be feasible. In a digital version of this type of game, playing a particular strategy may not become available until the conditions are met; in this analogue 252 approach, the conditions were written on the strategy cards, and the table facilitators were responsible for enforcing the rules. Figure 6.16: The base Sandbox worksheet used in the 'design your neighbourhood exercise'. Participants were also given a supply of blank strategy cards they could use to make their own energy and GHG reduction strategies. The number of stars to assign to these strategies was determined by discussion and negotiation within the group and with session facilitators. While there was no way to model or verify the stars assigned to these novel strategies, the workshop team did not want to limit participant creativity. In an iterative workshop design, the participant strategies could be modeled between workshops, to measure their implications. The Revelstoke workshops discussed in Chapter 7 used this iterative approach. 253 Figure 6.17: A completed Neighbourhood Design worksheet. Trade-offs existed between the available strategies from the different scenarios. For example, the ‘Electric Vehicles’ and ‘Transit Improvements’ cards had competing assumptions about how many residents would move from single occupant vehicles to public transit. The facilitators for each working group were aware of these trade-offs, and addressed them with participants if participants tried to play strategies with conflicting assumptions. How the groups addressed these conflicts and trade-offs was the result of discussions and negotiations. In some cases, groups decided not to play competing strategies, while in others they reduced the stars assigned to each strategy to approximate partial application of each. Complex game logic such as that incorporated in Sim City’s GlassBox engine could better accommodate these types of decisions and trade-offs in a digital application. With the limitations of the paper-based approach, that logic is off-loaded onto discussions amongst participants and facilitators. 254 The final activity of the ‘Design Your Neighbourhood’ exercise was for each participant to identify where in their group’s neighbourhood they would want to live. This was another activity aimed at having participants identify with the Sandbox. 6.5 Results and Discussion The result from the workshops can be broken down into four sections: the demographics of who participated, the analysis of the completed workshop materials, the exit survey’s workshop evaluation section, and the interaction of participants’ mental models with the workshop process. 6.5.1 Demographic Data: Who Participated? One of the key components of the broader GCCP was to compare demographic data across different engagement channels. In order to achieve this, some standardization of demographic data was required for all of the research instruments used in the individual studies in the GCCP. These demographic variables were developed by a sub-committee of the GCCP researcher team. A more detailed analysis of this data is currently being conducted by another researcher. In the context of the research described here, the demographic data provides two main services. Firstly, it helps describe who the participants in the workshops were, as an indicator of the validity of the research conducted. Were the participants residents of the neighbourhood? Are they representative of the neighbourhood in terms of age, ethnicity, income, education, etc.? Secondly, the demographic data provided more context for the mental models research described in Chapter 5, and later in this chapter. Are particular mental models associated with identifiable demographic groups? As chapter five indicated, there is some evidence that mental models are correlated with demographic data such as gender and cultural background. 255 Postal Code The workshop channel was unique within the GCCP because the engagement activities occurred in, and focused on, particular neighbourhoods within Vancouver. This is largely the result of the type of research conducted at CALP, focusing on local and spatial solutions to sustainability issues (cf. Salter et al., 2009; Shaw et al., 2009; Sheppard et al., 2011). The only other channel in the GCCP with a definable spatial extent was the Facebook channel, which focused on the CoV Transportation Plan. This plan, however, covered all of Vancouver and was therefore less concerned with where within the City participants resided. The postal code demographic variable, therefore, was particularly useful for the workshop channel, because it provided a mechanism to determine if participants in the workshops resided in the neighbourhoods being studied. The first three digits of postal codes within the CoV largely correspond to the City’s identified neighbourhoods (Canada Post, 2013), so this data made it possible to determine if participants lived within the study neighbourhood, adjoining neighbourhoods, or some other geographic location within the CoV. The Planners workshop was not conducted in a particular neighbourhood and therefore the postal code information for the workshop is not included here. The two neighbourhood workshops demonstrated very different patterns for participants’ postal codes. The Marpole workshop had a much higher percentage of residents from the neighbourhood with 11 of 19 (58%) being from Marpole proper and 14 of 19 (73%) being from either Marpole or the neighbourhoods that immediately bordered Marpole. In contrast, the Grandview Woodland workshop had a much lower participation rate from the neighbourhood proper at only 5 of 26 (19%) participants. Broadening out to include the neighbourhoods immediately bordering on Grandview Woodland, increased that number to 11 of 26 (42%). The best explanation for the discrepancy between the two workshops was the way in which they were advertised. For the Marpole workshop advertising was conducted through the City of Vancouver’s website, as well as through paper flyers posted throughout Marpole. This resulted in 21 participants (19 entry and exit surveys). In an effort to increase participant numbers for the Grandview Woodland workshop advertising efforts were increased to include UBC websites, email 256 groups and social media. Therefore, more people from outside the neighbourhood were exposed to the advertising. The differences in audience were palpable, as many of the participants in the Grandview Woodland workshop were either people from energy-related fields, or people with an abiding interest in the topic. The presence of a large number of participants from outside the neighbourhood was something that some residents commented on in the exit surveys (see section 6.5.4). One of the goals in the development of the materials for the GCCP workshops was to make the process more generalizable to a broader Vancouver audience, both for research purposes and at the request of CoV planners. Therefore, participants from outside the neighbourhood should still have been able to participate fully in the process. The results from the workshops, however, should be interpreted in the context of the different neighbourhood participation rates. Responses from Marpole are more likely to reflect specific concerns about the future of that neighbourhood. Age Each of the workshops had a distinct age distribution (see Figure 6.18). Not surprisingly, the Planners workshop had the most skewed age breakdown, with nearly 87% of participants coming from the 25-44 age range. The participants at the planners workshop were predominately CoV staff, and therefore of working age. These staff members were also predominately in their early to mid careers, explaining the skew to the 25-44 age range. For some demographic categories the CoV has amalgamated census tracts to provide data specific to individual neighbourhoods. In those circumstances where it has done so, it is possible to compare the workshop data to the broader demographics of the study neighbourhoods. Therefore, we know that Marpole has a median age of 39.7 years, and that the dominant age demographic for the neighbourhood is the 35-44 age range (Vancouver Economic Commission, 2013). The participants at the Marpole workshop trended older than the neighbourhood’s demographics, with 47% coming from the 45-64 age range and only 32% from the 25-44 age range. In addition, the Marpole workshop had the highest proportion of people over 64 at 21% (see Figure 6.18). 257 Figure 6.18: The age distribution of participants by workshop (n=61). Grandview Woodland has a median age of 37.6, with the 30-44 age group being the most dominant age demographic at approximately 20% (City of Vancouver 2013a). The 25-44 age group was over-represented at the Grandview Woodland workshop at 67% of participants, while the 45-64 age group was very close to the neighbourhood’s demographic breakdown at 21%. The Grandview Woodland workshop was the only workshop that had participants from the 19-24 age range. In retrospect, it would have been useful to have more resolute age category data, as the 25-44 age range may have blurred some of the age differences between Grandview Woodland and Marpole. There were noticeably more young people (perhaps 25-30 years) at the Grandview Woodland workshop. Gender Gender was elicited using the categories of female, male and other. The City of Vancouver’s demographics indicate the current binary split for the City is 49% male and 51% female (noting that the census data does not allow for the category “other”). Given the relatively small sample size for 0.010.020.030.040.050.060.070.080.090.0100.0a. under 19 b. 19-24 c. 25-44 d. 45-64 e. 65-74 f. over 75% of total respondentsPlanners Marpole Grandview/Woodland 258 the workshops, the gender breakdown of 49% male, 48% female and 3% other is quite close to the Vancouver numbers. Broken down by workshop (see Figure 6.19) it is apparent that the individual workshops were more skewed, with the Planners workshop having a higher proportion of females (60% female to 40% male), and the Marpole (42% female to 58% male) and Grandview Woodland (44% female to 48% male and 7% other) workshops having a greater proportion of males than the CoV numbers. Figure 6.19: The distribution of participants' gender by workshop (n=61). It is unlikely that the gender demographics had a significant impact on the conduct or results of the workshops, because the numbers were not heavily skewed in one direction or another. Inter-workshop distribution of participants at working group tables is likely to have impacted individual’s more than broader gender representation, however specific table distribution data is not available. Income From census data, Vancouver has a median income city-wide of $47,299. Both of the neighbourhoods where workshops were conducted had lower median incomes than the City median, with Marpole having a median income of $41,125 (City of Vancouver, 2013b) and Grandview Woodland having a substantially lower median income of $35,342 (City of Vancouver, 2013a). 010203040506070a. Male b. Female c. Other% of total respondentsPlanners Marpole Grandview Woodland 259 Figure 6.20 shows the categorized household income breakdown for the three workshops. The Planners workshop shows a roughly bimodal income breakdown. The participants in the $20,000-$35,000 range were likely students from UBC that attended the workshop, while those in the above $80,000 bracket were likely professional employees from the CoV. Figure 6.20: The distribution of income by workshop (n=61). Both the Marpole and Grandview Woodland participants show a more distributed income breakdown than the Planners workshop. The categories used for the GCCP cross-channel evaluation were different than those used in the CoV census data, so a direct comparison isn’t possible, however certain trends are apparent. In both the Marpole and Grandview Woodland workshop, the >$80,000 caegory is over-represented. In Marpole, 47.4% of participants had an income >$80,000, whereas the census data indicates that only 24% of Marpole residents fall into that category (City of Vancouver, 2013b). Other categories of Marpole participant income were consequently skewed downwards from the real neighbourhood representation. 010203040506070% of total respondentsPlanners Marpole Grandview Wooldand 260 Household income numbers for GW are not available in the neighbourhood profile, however the relatively low median household income of $35,342 (City of Vancouver, 2013a) suggest that the income categories should trend lower than Marpole. The $29.6% of participants with a household income >$80,000 is higher than the Marpole census data (24%) suggesting that the Grandview Woodland workshop participants were more affluent than the neighbourhood norm. One reason for this is the high proportion of Grandview Woodland participants from outside the neighbourhood. This is supported by the data, as seven of the eight participants with incomes >$80,000 were from outside the neighbourhood. While the workshop participants were clearly skewed towards the highest income category, all income categories were represented at both workshops. In addition, the data from the Marpole workshop shows the same bi-modal trend as that shown in the census data (Vancouver Economic Commission, 2013). Education Level The area where the GCCP workshop participants were least representative of the neighbourhood populations was education level. Figure 6.21 shows the education breakdown for the three workshops. Similar to age and income, the Planners workshop showed the most stratified distribution in education level, with 80% of the participants having a post graduate degree. Because many planning degrees in Canada are post-graduate this is not surprising. The education breakdowns in the Marpole and Grandview Woodland workshops are more surprising, as they were both heavily skewed towards higher education levels (see Figure 6.21). Through the Vancouver Economic Commission (2013) report, census education data is available for Marpole, though the categories are different than those used in the GCCP. From the census data, 20% of Marpole residents completed high school as their highest degree (versus 11% of Marpole workshop participants), 22% completed some college or other non-university degree (versus 11% of workshop participants), and 46% completed university, whether that be at the bachelors or the graduate level (versus 78% of workshop participants). The high level of undergraduate and graduate degrees for workshop participants likely impacted the workshop results. 261 Figure 6.21: The distribution of participant education levels by workshop (n=61). For Grandview Woodland the skew in education level is even more pronounced with a full 96% of participants having an undergraduate or graduate degree. Education level data is not available specifically for the Grandview Woodland neighbourhood, but the low median income (a demographic variable often associated with education level), would suggest that neighbourhood education levels are below the CoV norm. The high education levels in the Grandview Woodland workshop are largely attributable to participants who attended from outside the neighbourhood, as most of them were academics or professionals with a specific interest in energy and sustainability issues. Of all of the demographic variables measured, education level is the most problematic, due to its potential impacts on the study’s findings. Though it must be noted that the mental models analysis in Chapter 5 found very little relationship between education level and mental models clusters, a potential problem of the high levels of education demonstrated by the participants is the inconsistency with the design goals of the workshops. A key intent of the workshops was to build knowledge and capacity to participate over the course of the workshop. This ‘scaffolding’ approach 0.010.020.030.040.050.060.070.080.090.0% of total respondentsPlanners Marpole Grandview Woodland 262 could have been wasted if not counterproductive in a room full of energy experts and postgraduate scholars. It is unclear from the present study how much the education level of the participants was dictated by the methods of advertising the workshops. In future engagement activities, efforts should be undertaken to attract a broader cross section of the population, and to focus on neighbourhood residents. The tendency for more educated people to take part in engagement activities, however, is a known phenomenon (Knapton & Myers, 2005). Cultural Background As Figure 6.22 shows, the majority of participants at all three workshops were of Caucasion/European descent. This was most prevalent in the Planners workshop, where 73% of participants were of Caucasian/European descent and least prevalent in the Marpole workshop, where 56% of participants were of Caucasian/European descent. Figure 6.22: The distribution of cultural backgrounds for participants by workshop (n=61). 0.000.100.200.300.400.500.600.700.80proportion of total respondentsPlanners Marpole Grandview Woodland 263 Cultural background breakdowns are not available in the City of Vancouver Neighbourhood Profiles, however, Table 6.3 shows a comparison of the GCCP workshop cultural background data to the CoV census data. Based on this aggregate Vancouver data, the workshops performed relatively well. It should be noted, however, that Grandview Woodland is well known as a culturally diverse neighbourhood, and may depart substantially from the city norms. While the Grandview Woodland workshop did have representation from all but one of the cultural background categories, numbers from all but the Caucasian/European and East Asian categories were very low. For example, Grandview Woodland is 9.2% Aboriginal, but the workshop only had one participant (4%) from an aboriginal background (City of Vancouver, 2013b). Table 6.3: A comparison of the cultural background data observed in this study versus the 2011 Vancouver census data by percent of population. First Nations Aboriginal Caucasian European Latino Hispanic African South Asian East Asian South East Asian West Indian Observed 2 58 2 2 5 20 2 2 2011 Census 2 46 2 1 6 31 6 NA Some of the difficulty in attracting participants from different cultural backgrounds is likely due to a language barrier, but it is clear that future engagement activities need to specifically target key cultural demographics if they want to have more representative participant pools. Unfortunately, adapting materials, facilitation and presentation to accommodate different cultural groups and different languages is very expensive, and was beyond the resources available for this study. Language Spoken at Home Continuing the trend seen in the other demographic categories, the Planners workshop had the least diversity in language spoken at home (see Figure 6.23). Eighty percent of participants in the Planners workshop spoke English as a first language with the remaining 20% speaking Chinese (Mandarin or Cantonese). The Marpole workshop displayed a similar breakdown, with 72% of participants having English as a first language, and 22% having Chinese as a first language. One participant (6%) spoke Bangla at home. 264 The Grandview Woodland workshop had a wider array of primary languages with 73% speaking English at home, 12% French, 4% Chinese, 4% Farsi, 4% Hindi, and one participant that did not identify their primary language. Figure 6.23: The distribution of primary language by workshop (n=61). The language barrier may be the most difficult demographic hurdle to address, because the cost and time required to provide materials in multiple languages is prohibitive for most engagement activities. Participation in Environmental Activities Figure 6.24 shows the proportion of participants from each workshop that had taken part in various environmental activities over the past year. Not surprisingly, lower effort activities such as buying particular products or talking to people about environmental issues had the highest participation 0.000.100.200.300.400.500.600.700.800.90English Chinese French Farsi Hindi Spanish Bangla Otherproportion of total respondentsPlanners Marpole Grandview Woodland 265 rates, while contacting a politician or attending a demonstration (higher effort activities) had low participation rates. The most noticeable trend is that both the Marpole and Grandview participants had higher participation rates than the Planners workshop attendees. This is an indication that the types of people that attend engagement activities are more active on environmental issues based on intrinsic motivations rather than job requirements. It is also likely that municipal government employees do not want to take part in activities that are deemed political, like contacting a politician about an issue, or attending a demonstration, because of the potential impact on their employment. Figure 6.24: The proportion of participants taking part in various environmental activities by workshop (n=61). The participants of the Grandview Woodland workshop were more likely to participate in every activity except donating money. This may reflect the young, politically active, but less affluent reputation of the neighbourhood, but given the high number of attendees from outside the 0.0000.1000.2000.3000.4000.5000.6000.7000.8000.9001.000proportion of total respondentsPlanners Marpole Grandview Woodland 266 neighbourhood it is possible that some other factor is playing a role. The high level of environmental activities may be more reflective of the interest participants in the Grandview Woodland workshop had in environmental issues. Demographic Data Discussion The purpose of the demographic analysis conducted in the previous section was to reveal who attended the workshop processes, as well as to determine if the attendees were broadly representative of the neighbourhoods where the workshops were held. The opportunity to compare some of the demographic variables to other engagement channels in the GCCP offered the chance to see if any demographic anomalies were the result of the workshop form, or more broadly indicative of the types of people that participate in engagement activities around environmental issues. Taking the demographic data as a whole, we can draw some conclusions about the types of people that attended the workshops. They were primarily in their middle years, highly educated, relatively wealthy, primarily English speaking, and primarily of European descent. In other words, a criticism that may fairly be leveled at the GCCP workshops is that they attracted the ‘usual suspects’ of public engagement. Comparison of the attendee’s characteristics to data about the demographics of the neighbourhoods also demonstrates that there are clear discrepancies between who attended, and the broader makeup of Marpole and Grandview Woodland. The workshops did demonstrate characteristic of the neighbourhoods, for example Marpole does have an older population, and East Asians do make up the largest minority group, and Grandview Woodland does have a younger, more diverse population. However, the relative representations of these demographic characteristics in the workshops were muted in comparison to the real neighbourhoods. When compared with the other GCCP engagement channels, however, it becomes apparent that this is not a challenge unique to the workshop form. Despite suggestions that new technologies or alternate forms of engagement will increase the breadth of engagement opportunities, many of the demographic variables used in the cross-channel evaluation demonstrated similar problems for the other engagement forms. A notable exception to this may be the arts channel, which did not record as many demographic variables, but showed a clear discrepancy on income. Of note is the fact that the art events were not specifically aimed at sustainability issues. On a related note, the Facebook channel, which was the other strongly deliberative event showed very similar demographics to the workshops. In other words, this data raises the question of whether the failure to engage with under- 267 represented groups is a result of the form of engagement, or something more endemic to the population. Does it suggest that what people need is not some new way to engage, but rather more fundamental societal changes? To raise someone to the level of ‘interested public’ on issues like energy or climate change, do we first need to work on inclusiveness, poverty, education, etc. – in other words, human development. And in the meantime, should we continue to engage the ‘usual suspects’ as we try to address human-environment problems? These questions will be revisited in Chapter 8. 6.5.2 Evaluation of Workshop Materials The workshop exercises’ collaborative and interactive nature and the captured results (all exercises involved writing, drawing and/or placing materials) meant that they offered an opportunity to assess participants’ concepts about energy in a different way. It was not possible to determine which individuals (and therefore which mental model clusters) worked at particular tables, so these results represent a collective picture of participants’ mental models of energy. Because this analysis was conducted on the group products, the number of products to assess was relatively small. There were three working groups at the Planners workshop, three working groups at the Marpole workshop, and five working groups at the Grandview workshop; for a total of 11 products for each of the four workshop exercises. The products were evaluated qualitatively, using content analysis. The main goal of this analysis was to determine how groups approached the exercises, and to look for similarities and differences in the final products. In addition, any evident ties to the mental models clusters discussed in Chapter 5 were noted – i.e. what evidence is there of individual mental models manifesting in the workshop exercise products? Energy System Exercise As discussed in section 6.4.6, the purpose of this exercise was to start the participants thinking about energy, as well as to elicit their collective concepts of energy before they were exposed to the energy system presentation. The spatial arrangement of concepts (cognitive or mental mapping) is a common elicitation technique in mental models research (cf Chang, 2007; Jones et al., 2011; Kearney & Kaplan, 1997; Langan-Fox et al., 2000). As such, this exercise represents a form of collective 268 model elicitation, and an opportunity to triangulate some of the mental models concepts discussed in Chapter 5. Participants were asked to use different coloured post-it notes to identify: 1. Where their energy comes from 2. How they use energy individually 3. How their community uses energy At first glance, it was evident that there was a difference in complexity between the constructed diagrams of different groups. Figure 6.25 shows an example of a simple (a) and a more complex (b) energy diagram. The primary influences on the diagram’s complexity appear have been the table’s facilitator and the presence of energy expertise at the table. The impact of facilitation has been noted previously (Salter et al., 2009; Schroth et al., 2013). In the case of exercise one, facilitators that were more practiced at encouraging participants to engage, and at drawing and diagraming in participatory exercises, generally produced more intricate diagrams. Figure 6.25: An example of a simple (a) and more complex (b) energy system diagram from exercise 1. The impact of expertise on exercise one was evidenced by the presence of technical jargon on many of the more complex energy system diagrams, such as ‘resilience’, ‘embodied energy’, ‘energy intensity’ and ‘heat recovery’. The impact of expertise is notable, because there was little evidence of expertise impacting mental models of energy in Chapter 5. The interaction of expertise in exercise 269 one may reflect a level of comfort and engagement with the subject matter of energy and therefore a willingness to explore the issue more thoroughly in this first exercise. In other words, while energy expertise may not impact things such as preferred solutions to energy issues at the community scale, experts are more willing to engage with energy issues than lay people. Provided the energy experts did not attempt to dominate the conversation, their presence provided a useful, informed voice at the table. Breaking the diagrams down by the three questions participants were asked to address provides some further information from exercise one. Figure 6.26 shows a chart of participants’ response themes regarding where their energy comes from (categorized by the author). Figure 6.26: The number of responses for each energy supply category from exercise one stratified by workshop. Recall from Chapter 5 that most participants in Vancouver incorrectly identified electricity as the City’s primary energy source. The results of exercise one corroborate that finding. Each participant was asked to identify two energy sources; 39% of those identified sources were electricity, compared 01020304050607080number of responsesPlanners Marpole Grandview 270 to 20% for oil (including gasoline and diesel) and 16% for natural gas (which is the actual primary energy source for the city). Of those that identified electricity as an energy source, 64% labeled it as ‘Hydro’ or ‘Hydroelectric’. This further supports the postulation in Chapter 5 that one main reason why people think of electricity as the primary energy source for Vancouver is the strong media presence of BC Hydro. Another reason, is the relative visibility of plugging appliances, etc. in, vs. interacting with a furnace or hot water heater. Figure 6.27 shows a chart of participants’ responses about how they (individually) use energy (categorized by the author). Figure 6.27: The number of participant responses for categories of individual energy use for exercise one. Similar to energy supply, participants were asked to identify two individual energy uses on post-it notes and apply them (where they thought they fit) to the energy system diagram. There was little obvious variation between the workshops in participant responses about energy sources. Perhaps the 0102030405060number of responsesPlanners Marpole Grandview 271 most notable thing about individual energy use reporting is the prominence of cooking at all three workshops; further suggesting that the visibility of energy use is an important component of people’s perceptions of energy. Figure 6.28 shows the chart of participants’ responses about how their community uses energy. Recall from Chapter 5 that participants from the Vancouver workshops ranked the energy use sectors in the city as follows: Planners: Residential > Transportation > Commercial > Industrial > Municipal Marpole: Transportation > Residential > Commercial = Industrial > Municipal Grandview: Transportation > Residential > Industrial > Commercial > Municipal While the actual city breakdown is Commercial > Residential > Transportation > Industrial > Municipal. This is for primary vs. secondary energy use, though it is unlikely that many participants would make that distinction. The results of exercise one, then, corroborate the individual mental model findings from Chapter 5; transportation is widely viewed as the primary energy use in people’s communities. An interesting finding from this chart is that both streetlights and community centres (a category comprising responses of ice rinks, swimming pools and community centres) were common responses to community energy use. Similar to the cooking example from the individual energy use section, this suggests that visibility is a key component of how people think about energy use. It is not about what uses the most energy, but rather what they can see using energy. Decades of energy research has tried to address this problem of energy invisibility through the use of mechanisms such as energy labeling, though it has met with very limited success (cf. Stern, 1992). 272 Figure 6.28: The number of responses for community energy use categories for exercise one stratified by workshop. An analysis of the outputs of exercise one appear to corroborate the findings in Chapter 5, in particular as they relate to what is ‘front of mind’ when people think about where energy comes from, and how they use it. The primary goal of exercise one was to encourage participants to begin thinking about energy as an interconnected system. Multiple groups noted these interconnections on their diagrams, and participants spoke of the interconnections of the energy system in their exit questionnaires (see section 6.5.3), suggesting that this exercise had encouraged them to think about energy in a new way. The ‘Name Your Neighbourhood Exercise’ The purpose of the ‘Name Your Neighbourhood’ exercise was to explicitly include quality of life issues and what people value about their communities in the discussion of energy. 010203040506070number of responsesPlanners Marpole Grandview 273 Observation of the four workshop exercises suggested that the ‘Name Your Neighbourhood’ exercise was both the easiest and the most fun for the workshop participants. While they may have felt unqualified to give input about energy issues, participants knew what they liked and valued about the places they lived or frequented. Additionally, this was perhaps the least bounded of the exercises, allowing for more creative play. Similar to the energy system exercise, an evaluation of the ‘Name Your Neighbourhood’ worksheets demonstrated that some groups developed much more complex and detailed versions of the Sandbox than others. Four of the six facilitators that took part in the workshops were experienced design charrette facilitators, suggesting that their drawing and elicitation expertise may have played a role, however observation suggests that individuals and group dynamics at the working tables also played an important role. Figure 6.29 shows examples of simple (a) and complex (b) ‘Name Your Neighbourhood’ worksheets. An evaluation of the worksheets revealed that there were many similarities in the types of features that participants added to their ‘Name Your Neighbourhood’ worksheets. Table 6.4 shows the number of groups that added particular features to their worksheets; it also attempts to group individual themes into larger types based on coding of elements from the worksheets. The most common features included the addition of bike routes, community services such as community centres or schools, and more transit services. These features, in addition to other popular choices (see Table 6.4) reflect a primary focus on convenience – i.e. the provision of services that make people’s day-to-day lives easier. Another dominant type of features are ones related to neighbourhood character, such as street trees, community gardens, green laneways, heritage buildings, etc. The character category can clearly be further disassembled, as certain amenities such as greenspace can have leisure, social and restorative effects, while others such as heritage buildings can have broader, place-based import related to the neighbourhoods’ narrative (Miller, 2008). In addition to the dominant categories of convenience and character, other features participants added in the Name Your Neighbourhood exercise reflected a focus on safety (traffic calming) and equity or social factors (e.g. seniors housing). Table 6.4 was limited to features added by three or more groups, but there were many others that were less frequent, such as local jobs, artist/creative spaces, live/work, co-op housing, cultural sites, subway systems, etc. 274 Figure 6.29: A comparison of a simple (a) and more complex (b) 'Name Your Neighbourhood' worksheet. In general, groups seem to have embraced the idea of making the Sandbox their own, and adding features that were important to them. These features were then carried forward to the final exercise, where they incorporated them with energy design cues in the ‘Design Your Neighbourhood’ exercise. 275 Table 6.4: The number of groups that added particular features to their Name Your Neighbourhood worksheets. Neighbourhood Feature Number of Groups Type Bike routes 10 Convenience Community services 10 Convenience More transit 9 Convenience Street trees 8 Character Traffic calming 8 Safety Community gardens 8 Character/leisure More commercial 8 Convenience Walkable 7 Convenience Restaurants/cafes 7 Convenience Internal commercial 6 Convenience Green laneways 6 Character Diversity 5 Equity/social Greenspace 4 Character/leisure Food Production 4 Character/sustainability Seniors housing 3 Equity/social Heritage 3 Character Farmer's market 3 Character As mentioned in the workshop goals section in 6.2.2 a focus on values and quality of life issues was one of the primary goals of the workshop design. It was also a goal to evaluate participants’ values as they pertained to their own communities, based on the idea that their mental models of community scale energy should incorporate what they value about their community. To that end, two questions on the pre-workshop questionnaires were specifically focused on the ideas elicited collectively from groups in the ‘Name Your Neighbourhood’ exercise. Because of the clear connection of these questions to the ‘Name Your Neghbourhood’ exercise, and the possibility for them to shed further light on quality of life values in communities, they will be discussed here. What Do You Like Most about Your Community The first quality of life question in the pre-workshop questionnaire asked of participants was: What do you enjoy most about living in your community? 276 Participants’ responses were transcribed and coded in NVivo 10 according to the major themes expressed in their open-ended responses. Participants’ responses were also stratified, first by the workshop they attended, and then according to their mental models cluster developed in Chapter 5, to see if there were any discernable patterns in their responses. Table 6.5: Participants' coded responses about what they value in their current community (n=62). Theme Number of Participants Type Proximity 41 Convenience Services 26 Convenience Walkability 24 Convenience Shopping 21 Convenience People 19 People Greenspace 14 Sense of Place/Leisure Character 14 Sense of Place Transit 14 Convenience Interaction 10 People Sense of Community 10 People Leisure 7 Leisure Bicycling 6 Health and Safety Sustainability 3 Health and Safety Work 2 Convenience Health 2 Health and Safety Affordability 1 Equity Quality of Life 1 Quality of Life Safety 1 Health and Safety Table 6.5 shows the cumulative themes from participant responses about what they valued in their community. The number one theme was proximity to services, such as stores, transit and community centres. Proximity was followed by services, walkability and shopping. These themes could be combined with the transit theme (12 responses) to comprise a ‘convenience’ type, similar to what was seen in the ‘Name Your Neighbourhood’ exercise. Previous research, primarily on individual energy use behavior, has stressed the importance of convenience in people’s lives, and 277 resultant energy use (Shove, 2003). The findings here suggests that the importance of convenience scales up to a collective, neighbourhood scale. Convenience Type Responses Close to shops and services, work, amenities, friends, family. Overall convenience. Planners 14, cluster 3.5 Quiet, but still within walking distance of shops to meet daily needs. Fraser River Park. Marpole neighbourhood house community services. Marpole 19, cluster 2. The convenience; I can walk to do my groceries, run errands and find recreation and entertainment. If I can't walk, then I can bus or bike. Grandview 23, cluster 5. A second apparent grouping of themes from Table 6.5 is comprised of people (19 responses), interaction (10 responses), and sense of community (10 responses), which is titled the ‘people’ type. Answers with these themes focused on the value of meeting, talking and interacting with neighbours and community members. The ‘people’ group has a less direct connection to energy, other than as an asset in programs such as social marketing (McKenzie-Mohr & Smith, 2008) that are dependent on communication and norms to promote pro-social and pro-enviornmental behaviors. People Type Responses Interaction with people. Collaboration towards common goal of better, fairer living. Marpole 12, cluster 5. I can walk to everything and meet people I know along the way (for 'stop and chats')... like a sort of village where many people know each other (and often have similar social values/progressive outlook). Grandview 20, cluster 5. Additional types included sense of place (greenspace, character), health and safety (bicycling, sustainability, health, safety) and leisure (greenspace, leisure). Two themes, affordability and quality of life, were not obvious fits with any of the other themes and, as a result, they were given their own types (equity and quality of life respectively) 5 This short form nomenclature will be used in this chapter to indicate the participant and the mental models cluster they were assigned to in Chapter 5. So, in this circumstance, the participant attended the Planners workshop, and was categorized into cluster 3 (Muddled Middle Ground). 278 Figure 6.30 shows the coded themes for what participants’ valued in their communities stratified by the workshop they attended. The responses have been normalized as a percentage of responses from each individual workshop so that the larger n from the Grandview workshop does not skew the visual representation. Therefore, a value of 100 from any individual workshop, or 300 in total would be a perfect score, meaning that all respondents have responded with that theme. This normalization makes the trends more apparent. For example, we can see that proximity, while universally valued, was most important to participants at the Planners workshop; we can also see that the Marpole participants were under-represented in the ‘people’ categories of ‘people’ and ‘interaction’, and overrepresented in their desire for more ‘greenspace’. The Grandview participants, conversely, are over-represented in the ‘people’ and ‘interaction’ themes. Anecdotally, this is in keeping with the neighbourhoods, as Marpole is a more outward-focused bedroom community and Grandview has a greater in-neighbourhood focus, particularly with the popular and social Commercial Drive. Figure 6.31 shows the participants’ responses stratified by their mental models cluster from Chapter 5. Recall that Chapter 5 reviewed results from both the Vancouver and Victoria pre-workshop questionnaires. When segregated to just the Vancouver participants, the n for some clusters is quite low, as seen in Table 6.6. Figure 6.30: What people value about their current community stratified by workshop 050100150200250normalized % of by workshopPlanners Marpole Grandview 279 Table 6.6: The number of participants from each mental model cluster in the Vancouver workshops. Cluster Number of Participants Cluster 1 (Limited Energy Knowledge) 3 Cluster 2 (Efficient Urban Design) 2 Cluster 3 (Muddled Middle Ground) 21 Cluster 4 (Power to the Individual) 2 Cluster 5 (National Action) 16 Cluster 6 (Let the Markets Decide) 3 Total 47 The results displayed in Figure 6.31 and later in this chapter, then, should be interpreted with caution. At best, they suggest relationships, but would require further research with more participants to be substantiated. Similar to the results in Figure 6.30, the numbers in Figure 6.31 have been normalized as a percent of each cluster, such that 100% in a cluster or 600% in total would represent perfect representation for a particular theme. Figure 6.31: What participants value about their current community stratified by mental model cluster. 050100150200250300350400450500normalized % by clusterCluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 280 The most interesting trend is that the two respondents from the ‘power to the individual’ cluster chose only ‘convenience’ type themes and ‘greenspace’. Depending on their use of that greenspace (e.g. walking the dog, taking their kids to the park), it may also be a convenience feature. One of the questions from the diagram of the dimensions in Chapter 5 (see Figure 5.25) was whether or not a clear relationship existed between the dimensions. For example, were participants in the upper right quadrant both individual and business focused? In Figure 6.31 we see what appears to be a clear demarcation between the individual cluster (cluster 4) and the business cluster (cluster 6), with participants from the business cluster valuing ‘people’, ‘interaction’ and ‘character’ in their neighbourhoods, while participants from the individual cluster do not. Given the low n for both clusters it is difficult to make a strong conclusion, however, these results do suggest that these clusters are not simply permutations of the same types of people. Similar to Cluster 4, Cluster 1 (limited energy knowledge) seems to be focused on ‘convenience’ themes in their responses. The two largest clusters, Cluster 3 (muddled middle ground) and Cluster 5 (national action) have much more diverse answer patterns. This is largely because there are more of them (i.e. if there were more people from cluster 4, it is likely some of them would have selected non-convenience themes), but it does also seem consistent with the clusters’ definitions. Cluster 3 is defined by being ill-defined – i.e. the questions in the questionnaires failed to differentiate them as evidenced by their congregation around the origins of both dimension one and two in 5.29. Recall also, that the majority of people that stated quality of life reasons for energy being important fell into this cluster. In other words, Cluster 3 might be expected to have diverse quality of life values, both because they do not appear to be a homogenous group, and because they have shown prior affinity for quality of life issues. Cluster 5 (national action) is similarly diverse in its choice of themes, with 14 different themes. This cluster is defined by a focus on the Federal Government for intervention to solve climate change and energy issues, meaning that nothing in their definition suggests any particular position on more local matters. Therefore, a diversity of responses about what they value in their community is not inconsistent with what identifies them as a coherent cluster. Finally, cluster 2’s (efficient urban development) responses provide some evidence that a low n is not necessarily associated with a low number of themes. Cluster 2 had only three Vancouver participants, but reported nine themes. The relatively large number of themes, as well as the types of themes recorded (see Figure 6.31), are consistent with this cluster’s affinity for urban design and development. 281 A review of the clusters, though limited by the low n in some clusters, appears to show some understandable patterns. If, indeed, the factors affecting participants’ mental models about energy are more deep-seated values, then we would expect them to be reflected in other areas of life, such as what they value about their neighbourhood. The results in Figure 6.31 appear to partially corroborate that, as Cluster 4 focused more on individual convenience and cluster 2 focused on hallmarks of efficient urban development. For those clusters without a strong definable pattern (e.g. Clusters 3 and 5), characteristics that define the clusters do not have a strong relationship to local community matters. What Would You Change About Your Community The second quality of life question asked of the participants was: What would you most like to see changed in your community? Similar to the first quality of life question, participants’ response were then stratified by workshop and by mental models cluster, to look for discernable answer patterns in their response about what they would change in their community. Table 6.7 shows the cumulative response themes for the participants across workshops. Living choices (i.e. housing types), Sustainability (in the design and planning of their neighbourhood), and more transit access were the most popular choices. Grouping these themes is more difficult than for the first quality of life question, however Table 6.7 attempts to categorize them into five types, including ‘living’, ‘mobility’, ‘services’, ‘sustainability’, and ‘community’. These categories are different than those used for the previous question, but reflect nuances in the participants’ responses. For example, in the question about what they value in their current community, a participant may have valued the ‘convenience’ of walking to local services, while in the question about what they would change about their community, they may simply have said they wished the community was more ‘walkable’ (mobility). Also dissimilar from question one, the patterns or types of responses are not as obvious. For example, in Table 6.5 most of the ‘convenience’ themes were near the top in popularity, whereas in Table 6.7 they are more dispersed. Living choices was the single most referenced theme, but collectively the mobility type had the most responses. 282 Table 6.7: The themes and theme groups discussed by participants when asked what they would change about their communities (n=62). Theme Number of Participants Type Living Choices 13 Living Sustainability 12 Sustainability Transit 12 Mobility Independent Retailers 9 Services Shopping 9 Services Traffic 8 Mobility Services 8 Services Greenspace 7 Community Bicycling 6 Mobility Walkability 5 Mobility Interaction 5 Community Local Food 4 Sustainability Character 4 Community Affordability 4 Living Sense of Community 3 Community Local Jobs 3 Services Social Spaces 2 Community Health 2 Sustainability Aging in Place 1 Community Convenience 2 Services Parking 1 Services People 2 Community Recycling 1 Sustainability Living Choices House is not an energy saving building. I prefer more energy saving two or three level town-houses in the community. Marpole 14, cluster 3. I would like everyone to have a decent place to live. Grandview 16, cluster 5. 283 Mobility More appealing and safe pedestrian environment. More appealing and safe bicycle routes. Better transit. Marpole 13, no mental models cluster. I'd really like to see a rapid transit line down Hastings and reduced car congestion. Grandview 38, cluster 6. While participants did not speak extensively about sustainability when discussing what they liked about their current community, they were much more likely to talk about it when thinking about what they would change. Sustainability Awareness and understanding about our impact on the environment and our responsibility to change our behaviours. Marpole 12, cluster 5. I would like to see better uses of energy, where synergies between uses and also larger-scale plans integrate the types of energy uses. Grandview 37, cluster 2. Figure 6.32 shows the changes participants would like to see in their neighbourhoods stratified by workshop. This figure has the same normalization as that displayed in Figure 6.30. Perhaps the first thing to notice is that there was more diversity in themes in this question than in question one. One interesting finding from this question is the lack of interest in new ‘living choicies’ in Marpole. This was recently corroborated by Marpole residents’ resistance to even modest changes in density (Bula, 2014). Marpole does, however, show marked interest in more transit, retail shopping, services, and particularly greenspace, suggesting perhaps, that engagement efforts should focus on how these features could be facilitated by density supported by other built forms. 284 Figure 6.32: Participants' themes regarding what they would like to see changed in their community stratified by workshop (n=62). In Grandview, ‘living’, ‘sustainability’ and ‘mobility’ theme groups seem to be important considerations for the future, suggesting that residents in this neighbourhood may be willing to accept more density, provided it was coupled with services and affordability options. Another interesting point about Grandview is that its participants were the only ones to mention features such as local food and social spaces. Similar to the Name Your Neighbourood exercise, then, what people would like to change about their neighbourhoods is a reflection of the current neighbourood and its character. In some ways, they want more of what their neighbourhood currently supplies. 0102030405060Living ChoicesSustainableTransitIndependent retailersShoppingGreenspaceTrafficWalkability BicyclingServicesLocal foodInteractionCharacterAffordabilittySense of CommunityLocal JobsSocial SpacesHealthAging in placeConvenienceParkingPeopleRecyclingnormalized % by workshopPlanners Marpole Grandview 285 Figure 6.33: Participants’ themes regarding what they would like to see changed in their community stratified by mental model cluster (n=62). Figure 6.33 shows question two stratified by mental models cluster from Chapter 5. It is more difficult to see obvious patterns in this data than in Figure 6.31. This might suggest that people are better at recognizing what they currently like about their neighbourhood than they are at thinking about how they would like it to change in the future. Another data point which supports this idea is the number of coding references for the dominant themes in Table 6.7 versus Table 6.5. There are more references, and more commonality in references in the question asking participants about their current neighbourhood than in the question asking them about what they would change in the future. The most coherent cluster is cluster 2 (efficient urban development) whose themes included ‘living choices’, ‘sustainability’, ‘independent retailers’, ‘shopping’, ‘services’ and ‘local jobs’ – all consistent with smart growth-type urban planning systems (Jepson & Edwards, 2010). Maybe the most interesting, though, difficult to interpret cluster is cluster 4 (power to the individual) whose desired 02468101214Living ChoicesSustainableTransitIndependent retailersShoppingGreenspaceTrafficWalkability BicyclingServicesLocal foodInteractionCharacterAffordabilittySense of CommunityLocal JobsSocial SpacesHealthAging in placeConvenienceParkingPeopleRecyclingnormalized % by clusterCluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 286 features included ‘interaction’ and ‘sense of community’. This would seem to be at odds with their individual focus, and their focus on ‘convenience’ features in Figure 6.31. Perhaps the ill-defined patterns in this question are a reflection of the difficulty people have when thinking about the future (Tonn et al., 2006). If so, this provides some support for engaging with people further about desired futures. It also suggests some opportunity to sidestep strong mental models and dearly held beliefs when the discussion is about a fictional future world. This theme will be revisited in Chapter 8. Collectively, the ‘Name Your Neighbourhood’ exercise and the quality of life questions from the pre-workshop questionnaire suggest that participants were more comfortable and more capable when talking about what they like about a neighbourhood than when talking about a specific subject such as energy. In some cases, there is a definable relationship between their mental models and what they value (e.g. cluster 4 in Figure 6.31), while in others it is less obvious (e.g. Figure 6.33). The influences of their current neighbourhoods on what they value and what they would change are evident – raising a chicken or egg question. Do they live in that neighbourhood because it has features they value, or did they come to value them by living there (or possibly a combination of both)? Finally, the lower clarity on the question of what they would change about their communities, suggest that people are more definite about what they like in a current neighbourhood than they are in envisioning changes that would lead to a more desirable one. The Scenario Exploration Exercise The scenario exploration exercise proved to be the most difficult to position within the workshops. It was primarily a learning exercise to familiarize the participants with the scenarios and their constituent energy strategies, to build participant capacity to take part in the final ‘Design Your Neighbourhood’ exercise. The scenario exercise was the least interactive of the exercises, because the purpose was for participants to explore and evaluate a pre-existing scenario. The scenario exercise was made more difficult by its placement after the lunch break. Despite the apparent difficulties of the scenario exercise, the workshop team felt it was a necessary pedagogical intervention. As discussed in section 6.4.6, each of the working groups were given a single scenario to explore and evaluate according to two questions: 287 1. What about this scenario do you find exciting? 2. What about this scenario do you find concerning? Each group was then asked to report their findings back to the workshop to help the groups that had reviewed different scenarios understand the strategies, so they could incorporate them in the final exercise. Both the Planners and Marpole workshops had three working groups, meaning one group reviewed each of the three scenarios. The Grandview workshop had five working groups, meaning one of the scenarios could only be reviewed by one group. Based on prior experience from the Planners and Marpole workshops, the Technology scenario was selected as the scenario that would only be reviewed by one working group, because observation suggested that it was the easiest for participants to understand. In total across workshops, then, three groups reviewed the technology scenario, and four groups reviewed the integrated land use and behaviroural cultural scenarios. Technology Scenario The technology scenario made no land use changes to the Sandbox (and therefore no changes to the population), but instead focused on achieving energy and GHG reductions through the application of technological solutions, including building energy retrofits, heating and cooling system changes to individual buildings, and electric vehicles (see the technology scenario worksheet in Appendix C). The technology scenario worksheets had the smallest amount of participant notes of any of the worksheets. This may have been because it had the fewest visible changes to the Sandbox, because it was the easiest to understand, or because it was the least controversial (though anecdotally, the latter does not appear to be the case). Summarizing the groups’ input, what they found most exciting about the technology scenario was that it achieved large energy and GHG savings (the largest of the three scenarios for cumulative reductions) and did so within the current neighbourhood’s form. In other words, the technology scenario appeared to meet the necessary targets with limited effort and disruption. 288 Concerns about the technology scenario included that they felt it would require policy and incentives to implement (i.e. to encourage homeowners to invest in systems and retrofits), that it raised equity issues as retrofit costs could be off-loaded onto renters, and that it did not change the diversity of housing types within the Sandbox. The lack of extensive neighbourhood changes, therefore, was seen both as a positive and as a negative for the technology scenario. Integrated Land Use Scenario The integrated land use scenario made the most extensive visible changes to the neighbourood. The scenario worksheet represented the neighbourhood under the integrated land use 2 strategy, with an additional 8.25 blocks of low-rise multifamily, 0.25 blocks of mid-rise multifamily, and 1.75 blocks of new mixed use development. The focus of the integrated land use scenario was on reducing energy and GHGs city-wide by making land use changes in the neighbourhood to efficiently accommodate more people. In other words, the integrated land use scenario displaced population from less efficient neighbourood designs. The addition of population facilitated the construction of more efficient multifamily dwellings, as well as the addition of a biomass-fed district energy system and the addition of new transit services (see the integrated land use worksheet in Appendix C). There was more note writing on the integrated land use scenario worksheets than the technology scenario, but less than on the behavioural/cultural scenario worksheets. This might be seen as a surrogate for engagement with the scenario’s components, as excitement or concern should lead to more recording of ideas on the worksheets. Summarizing the input of the four groups that reviewed the integrated land use scenario indicates that what they found most exciting about it was the amount of housing diversity it provided, the fact that the added population leveraged more services (though there were some concerns about the type of services – e.g. box stores), and the fact that it generated energy locally through the DES (with some trepidation about the health implications of biomass burning). What concerned participants about the integrated land use scenario were issues of affordability of the new development and, particularly in Grandview, issues of gentrification (i.e. changing demographics and character). Another concern was about the effectiveness of the integrated land use scenario in actually displacing people from less-efficient neighbourhoods (rather than just adding overall population), and in the DES effectively reducing GHG emissions. Participants, then, seemed open 289 to the potential of the integrated land use scenario, but mistrustful of its real impacts. This finding may shed some further light on prior contentious density-related programs in Vancouver (cf. Smith 2008). People may not be resistant to density as a concept, but rather they may be mistrustful of how it will be implemented. The recent controversies over towers in the Mount Pleasant and Grandview Woodland neighbourhoods (Bula, 2014; Campbell, 2013; Lindsay, 2014) suggest that the type of density is of primary importance. The ‘Design your Neighbourhood’ exercise section will discuss this issue further. Behavioural/Cultural Scenario Visibly, the behavioural/cultural scenario made very few changes to the Sandbox. It added laneway houses to the single family blocks and an additional bus route along the sourthern boundary (see worksheet Appendix C). While the behavioural/cultural scenario did increase the Sandbox population, it still managed to decrease the neighbourhood’s total energy use and GHG emissions through a reliance on residents’ home energy use and transportation behaviours, and a cultural shift towards converting existing single family homes into multifamily residences. The four workshop groups that reviewed this scenario found it exciting because they felt it had positive health (more active lifestyle) and social (more diversity and affordability) impacts. They also felt that it was an effective route because the multifamily conversion of single family homes is already occurring for affordability reasons, particularly in the Grandview neighbourhood. Workshop groups felt that the BC scenario was exciting because it provided co-benefits towards other quality of life goals. What the workshop groups found concerning about the BC scenario was its reliance on behavior – which they noted is difficult to achieve. Similar to the technology scenario, groups felt that the BC scenario required policies and incentives to be effective. Finally, even those that supported the BC scenario’s shift to multifamily conversions and behavior change, acknowledged that it would require a cultural shift that may not be appealing to all residents. The BC scenario worksheets had the most notes on them – some expressing ideas that might facilitate the behavioural and cultural shift, such as ‘making transit free’. Perhaps participants saw the cultural and behavioral reliance of this scenario as being the most accessible to them as citizens, rather than technology providers or planners and developers. 290 One factor about the scenarios, mentioned in the modeling section in 6.3.2, was the absence of costing information (both initial and lifecycle costs). While the technology scenario was lauded for its low effort requirements, might that have been tempered by a realization of costs to individual home or vehicle owners? Costing would have provided further trade-off information for participants to weigh against energy and GHG reductions, and impacts on quality of life. As mentioned in the introduction to this section, this exercise was perceived as the least interesting by the internal workshop team, and was identified in the Planners workshops as a possible area to trim the workshop. However, it was cut from a subsequent application of the workshop, which led to some confusion in the application of the ‘Design Your Neighbourhood’ exercise – particularly in terms of the interactions and trade-offs between energy strategies (Pond, 2013). More broadly, arguments could be made about the scenarios not presenting holistic, plausible, and incommensurate pictures of the future, or, more generally, the value of presenting pre-developed scenarios at all (cf. Robinson et al., 2006; Salter et al., 2010). These topics are open subjects for future research and further debate. The rationale for the scenario methodology as used was a scaffolding, constructivist learning approach to building participant capacity to participate in the final ‘Design Your Neighbourhood’ exercise. The individual levers and scenario exploration exercises do appear to have helped participants understand the available strategies, however that does not ensure that they were the best possible approach. The research design for the GCCP workshops called for testing new approaches, and the feedback from the scenario review exercise does present some instructive findings. Similar to the ‘Name Your Neighbourhood’ exercise, there seems to have been a desire by participants to harmonize the energy strategies of the scenarios with deeper seated values and quality of life concerns. This has ramifications for engagement on sustainability issues that will be returned to in the conclusion of this chapter, and in Chapter 8. The Design Your Neighbourhood Exercise The final exercise of the workshops was what all prior presentations and exercises had been building towards. The intention of the previous exercises and presentations was to provide scaffolding for participants to build the capacity to take part in designing the future Sandbox to accommodate both energy and quality of life components. 291 The dual goals of the exercise were to: a) develop a neighbourhood that achieved 7-12 stars worth of GHG emission savings, and b) develop a neighbourhood that they would want to live in. Section 6.4.6 describes the final exercise methodology and its constituent components. Summarizing, groups of participants working at their respective tables were tasked with designing a future version of the Sandbox using land use puzzle pieces, scenario strategy cards and available markers to draw in any missing elements. Strategy Cards The mechanism for achieving 7-12 (35-60% reduction in Co2e) stars was placement of some combination of the provided scenario strategy cards, or the development of participants own strategy cards with star allotment being determined through group negotiation. For example, a group from the Grandview workshop developed a strategy card for ‘waste heat recovery’ that they assigned one star worth of GHG emission reduction. Table 6.8 shows the usage of each strategy card by workshop. The two strategy cards that were used by 10 of the 11 working groups were Transit Improvements and Improved HVAC Systems, followed by Integrated Land Use Level 2, District Energy Systems and Moving to Smaller Homes, which were each used by 9 of the 10 groups. With the possible exception of Moving to Smaller Homes, the most notable thing about the strategy cards that were used most often is the relatively low personal effort involved. In contrast, higher individual effort strategies such as Home Energy Behaviour (5) and Transportation Behaviour (7) were less frequently placed. A notable anomaly is Electric Vehicles, which falls into the low effort category, but were less frequently used (6 groups). This is also notable because of the prominence of electric vehicles in some energy strategies for BC (cf. Littlejohn, 2012). The primary reason why electric vehicles appear to have been a less popular strategy is the feeling of unease about their practicality. Participants were concerned about where the energy to power the EVs would come from, and about their implications for maintaining the single occupant vehicle, commuting lifestyle. Therefore, despite the large GHG emission reductions associated with this strategy (4 stars), it was one of the least common strategies used. 292 It is possible that the lack of costing information may have swayed participants towards strategies they would otherwise have avoided. For example, if participants knew how much a more efficient furnace would cost individual home owners, would they have been less likely to choose the HVAC Systems strategy? As noted in Chapter 5, this lack of modeled costing data may be particularly problematic for participants from Cluster 6 (let the market decide). Table 6.8: The usage of strategy cards by workshop (all strategy cards can be found in Appendix C). Scenario Strategy Planners Marpole Grandview Total Scenario Total Integrated Land Use Land Use 1 0 0 1 1 29 Land Use 2 3 2 4 9 District Energy 2 3 4 9 Transit Improvements 2 3 5 10 Technology Building Retrofits 2 2 3 7 23 Improved HVAC 3 3 4 10 Electric Vehicles 1 1 4 6 Behavioural/Cultural Home Energy Behaviour 1 1 3 5 21 Transportation Behaviour 2 2 3 7 Smaller Homes 3 2 4 9 Custom Strategy 1 2 3 6 Total Strategies Played 20 21 38 79 Mean Strategies Played 6.7 7.0 7.6 7.2 As Table 6.8 shows, six groups also developed their own custom strategies for use in the final exercise. Of those strategies, five concerned transportation, four concerned the use of alternative 293 energy sources (e.g. geothermal, solar, and waste streams), and one concerned land use (laneway houses). The most common strategy developed by participants was car sharing, which was used by three separate groups. Similar to electric vehicles and district energy systems, car sharing was, and remains, part of the energy zeitgeist, with a number of local care sharing services growing in popularity in Vancouver. Stars Achieved Through the combination of strategy cards illustrated in Table 6.8, all participant groups achieved the first goal of meeting a 7-12 star reduction in GHGs for the Sandbox. Table 6.9 shows the mean number of stars achieved by working groups in the three workshops. The Marpole workshops had the largest number of stars with a mean of 14, while the Planners were the most conservative with a mean of 10.8 stars. Table 6.9: The mean number of stars acheieved by working groups by workshop. Workshop Mean Stars Earned Planners 10.8 Marpole 14 Grandview 12.5 The conservatism of the Planners is understandable, as they are the group that is most familiar with the constraints and barriers to implementing the various strategies. As discussed in Chapter 5, invitation procedures resulted in the Marpole workshop having the most local residents, and the least energy expertise, which may account for their more optimistic GHG reduction numbers. This is corroborated by the neighbourhood design results that follow, as the Marpole participants made fewer land use changes to the Sandbox than the other two workshops – i.e. they achieved the largest GHG reductions for the Sandbox, while at the same time making the fewest changes to the neighbourhood form. 294 Sandbox Redesign The new design of the Sandbox, while augmented by drawing elements, was primarily achieved by placing the land use puzzle cards on the provided base map (see Figure 6.17). Table 6.10 shows the mean number of full blocks (32 single family parcels or 12 low-rise multifamily parcels) of each land use type placed by participants in the three workshops. In general, the Grandview workshop groups placed the most puzzle pieces (12 blocks) and the Marpole workshop groups placed the fewest puzzle pieces (6.1 blocks). To clarify, in order to be counted in this table, the placed puzzle piece must be replacing a different land use type. So, while both the Marpole and Grandview workshops were characterized by groups that almost fully covered the Sandbox in puzzle pieces (see Figure 6.17), in many cases they were simply adding the 3D puzzle representations on top of the same land use type. The reason why participants covered the entire Sandbox with puzzle cards appears to have been just the ‘game’ aspect of the exercise – i.e. they felt that completion required that they cover the entire ‘game board’. Table 6.10: The mean number of blocks of each land use type placed by participants in the three workshops (that replaced other land use types). Land Use Type Planners Marpole Grandview Community 1 1.2 2.35 Mixed Use 2.1 0.75 2.75 Low-Rise Multifamily 3.5 2.5 5.3 Mid-Rise Multifamily 1 1.25 1.2 Commercial 0.75 0.4 0.4 Total Blocks Added 8.35 6.1 12 Table 6.10 demonstrates numerically, what was visible observationally – the Grandview participants were bolder in their neighbourhood redesigns, and generally more comfortable with higher densities. Figure 6.34 shows examples of a Marpole worksheet and a Grandview worksheet. While there was variance within the workshops, Grandview working groups were more comfortable with density, and the Grandview example in Figure 6.34 illustrates this point. There is significantly more low-rise multifamily near the main north-south street, as well as more mixed use, low-rise multifamily and mid-rise multifamily along the neighbourhood’s southern boundary. In addition, participants in the Grandview Woodland group 295 cut up low-rise multifamily blocks and placed individual buildings at the intersections of several internal neighbourhood single family blocks. Conversely, the Marpole group (see Table 6.10) placed far less low-rise multifamily and isolated the low-rise multifamily, mid-rise multifamily and mixed-use buildings to the main north-south street and the neighbourhood’s boundary. While there are multiple possible explanations for the Grandview workshop participants’ willingness to incorporate density (recall that the Grandview workshop had fewer participants from the neighbourhood, and a higher proportion of energy experts), observations at the workshops suggest that the comfort with density might primarily be a reflection of the existing neighbourhood character of both Marpole and Grandview. Marpole is a bedroom community that is a lower density mix of single family and low-rise multifamily dwellings, while Grandview is a more urban environment with a higher diversity of both residents and dwelling types. Of note is the low proportion of single family homes in Grandview that remain truly single family. In other words, Grandview has already been undergoing significant changes in its housing stock and resident demographics. Several participants in the Marpole workshops expressed concern about adding density to the Sandbox, while participants at the Grandview workshops appeared more comfortable with diverse housing forms. An interesting design distinction between the Planners and the neighbourhood workshops was a tendency for the planners to place density along the arterials, while the neighbourhoods groups dispersed density throughout the Sandbox. In addition to the zoning difficulties of mixing single and multifamily, there is also a CoV strategy for locating density along major corridors to facilitate services and transit (cf. City of Vancouver, 2011). On the other hand, participants at the neighbourhood workshops, particularly in the Name Your Neighbourhood exercise, frequently referenced equity and diversity as neighbourhood goals. Perhaps in attempting to achieve this diversity and equity, participants at the neighbourhoood workshops attempted to more thoroughly integrate density into the broader fabric of the Sandbox. The lower image in Figure 6.34 illustrates this, as individual multi-family buildings were located at the corners of single-family blocks throughout the neighbourhood. 296 Figure 6.34: A comparison of a Marpole workshop Sandbox (top) and a Grandview workshop Sandbox (bottom). 297 In addition to the puzzle pieces, participants also drew in, or modified puzzle pieces to reflect, design elements from the Name Your Neighbourhood exercise to included important quality of life components in the Sandbox. For example, eight groups included dedicated bike routes and eight groups included commercial land use such as corner stores and daycares in the internal blocks of the Sandbox. Also popular were features such as traffic calming and green laneways. As a final assessment of the second goal of the final exercise (to create a neighbourhood they would want to live in), participants at the neighbourhood workshops were asked to place a dot to identify where in the Sandbox they would want to live. Table 6.11 shows the number of participants from each workshop that selected a given land use type. The results in this table further support the idea that Marpole and Grandview participants had different views on the desireability of different densities and housing types, as Marpole residents preferred to live in single family areas by 2:1, whereas a greater proportion of Grandview participants preferred multi-family dwellings (19:11). Table 6.11: The number of participants from each workshop that chose to live in a particular land use type. Workshop Single Family Low-rise MF Mid-rise MF Mixed Use Community Marpole 64% 32% 0 4% 0 Grandview 35% 39% 10% 13% 3% Total 48% 36% 5% 9% 2% While the finding that participants favoured their existing neighbourhood’s character and distribution of density is not surprising, it does have ramifications for the local nature of designing for sustainability more broadly, and energy more specifically. If community planning for energy is going to be sensitive to quality of life concerns, then a single approach, even in two neighbourhoods with surficially similar attributes, will not be sufficient. Planning and urban design will need to be responsive to neighbourhood-specific character and values – in other words, to place (Agnew, 1987). The CoV does plan in this manner, having distinct plans for different neighbourhoods within the City. This task is made more challenging, however, by the need to incorporate energy, and more broadly, sustainability goals. These challenges suggest the need for more integrated and regenerative design process (Cole et al., 2013; Reed, 2007) that can be responsive to place both from a sociocultural systems and a sustainability systems perspective. 298 Exercise Evaluation Discussion The evaluation of the workshop exercises and their resultant products provided additional insight over and above what was possible from evaluating individual participants’ mental models. The four exercises each had strengths and weaknesses and provided ample information for future workshop design processes. The energy system exercise was a simple task that successfully started participants thinking about their individual relationship to energy, as well as their community’s relationship to energy. This exercise had the strongest tie to the individual mental models research and some similar trends were seen in participants’ responses – for example a focus on visible energy sources and uses. The ‘Name Your Neighbourhood’ exercise was the easiest for participants to complete. It also appears to have been effective in encouraging the participants to identify with the Sandbox, so that they could incorporate things they value about their own communities into the neighbourhood design in exercise four. The two quality of life questions from the pre workshop questionnaire demonstrated that, similar to the energy system exercise, characteristics of participants’ individual mental models manifest on the group products. Themes such as convenience, services, and sense of place played prominent roles both in their individual responses and on the ‘Name Your Neighbourhood’ worksheets. The scenario exercise was the least interactive of the exercises. An investigation of other ways to communicate the scenarios and strategies might improve future processes, however, some way to help participants understand the scenario components was necessary. In general, participants seemed most engaged with the Behavioural/Cultural scenario, perhaps because it had the strongest connection to what they could do as individual citizens. Finally, the ‘Design Your Neighbourhood’ exercise was the culmination of the workshop’s presentations and exercises. It proved difficult for some participants because of the sheer amount of information they had to process and incorporate into their designs. There is fairly strong qualitative evidence that the participants’ existing neighbourhoods had a significant impact on their resultant designs, as the Grandview participants were more bold, and incorporated a broader range of features such as local food production and a focus on equity and diversity themes. This local variation is further evidence that energy and sustainability planning cannot be ‘one size fits all’, but rather must be sensitive to both the biophysical and sociocultural characteristics of place. The fact that 299 participants from the two workshops took different design approaches to the Sandbox is encouraging, because it suggests that the generic form did not streamline or limit their design choices. 6.5.3 Mental Models: Results and Discussion One of the primary goals of the workshop evaluation process was to look at the interaction between participants’ mental models of energy and the effects of participating in the workshops. There were two primary mechanisms for assessing the interaction of participants’ mental models and the workshop. The first mechanism was simply to look for changes in participants’ responses to pre and post workshop questionnaires. Did participating in the workshops change their mind about possible solutions to community scale issues, or inform them about energy sources? Additionally, could their responses to open-ended question be parsed to reveal further indications of the workshops interacting with or changing their mental models? The second mechanism was to stratify participants’ responses to questions by the mental models cluster they were placed in by the analysis in Chapter 5. Were their discernable influences of participants’ mental models on their answer patterns? Because the mental models clusters were constructed from both Vancouver and Victoria data, the n for some clusters was quite small for the questions discussed in this section (see Table 6.6). As such, the discussion of results is primarily qualitative and, at best, suggestive of a relationship. It was not possible to conduct a comprehensive pre/post workshop mental models analysis including an MCA like that conducted for existing mental models on the pre workshop questionnaires. Chapter 8 will discuss some of the limitations of this study, and point out ways to address those limitations in future research. As discussed in Chapter 5, the research conducted in this study was exploratory, and follow-up research with a larger number of subjects would be necessary to validate some of the possibilities described in this section. Other factors impacting the results discussed here were the time limitations to fill out the post-workshop questionnaires at the end of a five-hour workshop, and the necessity to include both workshop evaluation and demographic data questions on the post-workshop questionnaire. As a result, the available question set to assess mental models implications was limited. 300 The questions that will be discussed in this section include: pre and post workshop self-reported energy knowledge, pre and post workshop rankings of applied energy solutions, pre and post workshop assessments of their community’s dominant energy supply, participants’ open-ended responses about what surprised them about energy, other options for addressing energy issues, and how they feel planning for energy will affect their community. Self-Reported Energy Knowledge Self-reports are a straight-forward way to elicit information about changes in participant knowledge and attitudes as a result of an intervention. While the veracity of these self-reports is difficult to establish (Howard, 1994), it can be a useful measure when combined with other knowledge and opinion questions. An additional caveat about self-reported knowledge gains is the experimenter effect (Grice, 1999), as the goal of the researcher is plain to participants - i.e. it is evident that the researcher conducting an energy engagement event wants that workshop to have an effect on participants. Figure 6.35 shows the pre and post questionnaire results for self-reported energy knowledge. Because the data are on a 5-point Likert scale, they were treated as continuous, and a t-test was performed on their means to test for significance. The mean pre-questionnaire rating was 2.85, and the mean post-questionnaire rating was 3.47. The t-test was significant (p<0.01, df = 62) indicating that the post-workshop self-reports of energy knowledge were significantly higher than the pre-workshop self-reports. In total, 32 participants changed their rating, while 30 participants maintained their rating. Of those that changed their rating, 31 increased (became more knowledgeable) and one decreased (became less knowledgeable). 301 Figure 6.35: Pre and post workshop responses for self-reported energy knowledge level (n=66). Table 6.12 shows the interaction between mental models clusters and changes in self-reported energy knowledge. Cluster 1 might be expected to change the most, because they were defined by a low-level of energy knowledge in the cluster analysis. Conversely, Cluster 2 might be expected to show less knowledge change, because they were defined by an affinity for the type of efficient urban design discussed in the workshops. Neither of the two participants in cluster 4 changed their self-reported knowledge level, which may suggest an interaction with the mental models cluster, but may also be the effect of chance. The results for cluster 3 are interesting, because this question further supports the notion that the questionnaire ‘missed’ these respondents. They do not have universally low energy knowledge, despite their lack of a strong definable energy mental model, and they were unlikely to change their self-reported energy knowledge as a result of participation (suggesting that they were not simply uninformed about energy). This question is further evidence that future research should attempt to differentiate the clusters with a broader question set. 051015202530Very Limited Some Average Fair Amount Expert% of total responsesPre Post 302 Table 6.12: A comparison of mental models clusters to changed energy knowledge self-reports. Cluster Changed Rating Total in Cluster Percentage 1 – Limited Energy Knowledge 2 3 67 2 – Efficient Urban Development 1 3 33 3 – Muddled Middle Ground 7 19 37 4 – Power to the Individual 0 2 0 5 – National Action 9 13 69 6 – Let the Market Decide 1 2 50 The self-reported energy knowledge levels suggest that the workshops were successful in their goal to increase participant capacity to engage on community energy planning issues. In general, participants felt that the workshops had increased their energy knowledge. Pre and Post Workshop Rankings of Applied Solutions This question was intended to evaluate whether or not the workshops impacted participants’ opinions about the best applied solutions for addressing energy issues in their communities. If participants did change their opinions, were there discernable trends in their responses? Also, were there obvious interactions between the workshop they attended, or the mental models cluster they belonged to and their response patterns? The question asked of participants was: Please rank your top 5 approaches for addressing energy concerns in your community, with 1 being the method with the largest potential impact, and 5 being the method with the smallest potential impact. a) Increasing public transportation options b) Increasing local sources of energy such as solar c) Decreasing the size of people’s residences d) Making technical changes, such as energy retrofits e) Changing individual’s energy behaviours f) Collective options, such as district energy systems g) Providing incentives for people and businesses to conserve energy 303 Figure 6.36: Participants' mean rank scores for different applied solutions from the pre and post workshop questionnaires (n=59 pre, n=54 post). Figure 6.36 shows the participants’ scores with a score of 6 being the highest possible ranking and a score of 1 being the lowest possible ranking. As Figure 6.36 shows, only public transportation and decreased unit size increased in score from the pre to post workshop questionnaires. All other applied solutions became less popular as a result of participation in the workshops. Public transportation was the most popular option on the post workshop surveys, followed by technical changes, collective options, decreased unit size, individual behavior, incentives, and finally local energy generation. The analysis will focus on the public transportation and decreased unit size options, because they were the two which increased in popularity following the workshop. Of note, the choices on the post-workshop questionnaires more closely match the results of the modeling used in the workshops than the pre-workshop responses. This suggests that participants’ mental models of energy were impacted by the workshop content. A Wilcoxan paired sign rank test was performed in R on both the public transportation and decreased unit size options to determine if there was a statistically significant shift in the popularity of 0123456publictransportlocalenergydecreaseunit sizetechnicalchangesindividualbehaviorcollectiveoptionsincentivesmean scorepre post 304 those options. The shift in popularity for the public transportation option was significant at the p=0.05 level (p<0.001, n=49), while the shift in popularity for decreasing unit size was not (p=0.065, n=49). The modeling discussed in section 6.3.2 determined that the public transportation options would have a large impact on both energy use and GHG emission numbers for the Sandbox. As a result, the transportation related strategy cards for all three scenarios had a large number of associated stars (see Figure 6.6). The presentations did not emphasize the importance of transportation over other factors (for example there were eight transportation slides compared to seven building demand slides), however, the transportation information and strategies appear to have had a significant impact on the participants’ ranking of public transportation as a solution. In other words, this shift in popularity for the public transportation option might be seen as a predictable result of the presented information. The shift in popularity for decreasing unit sizes, while not statistically significant, is perhaps more surprising given the numerous examples of resistance to the discussion of density in Vancouver neighbourhoods (cf. Bula, 2014; Smith, 2008). This may be the result of discussing these issues using a generic neighbourhood and eliminating the emotional attachment people hold for their own neighbourhoods. It may also be an indication that citizens are less resistant to density than previously thought, with their opposition reflecting opposition to the types of density options and mistrust of the implementation process (cf. Bula, 2014; Lindsay, 2014). All of the other options for reducing energy use and GHG emissions decreased in popularity as a result of participation in the workshops, despite the fact that several of them (e.g. technical changes, individual behavior and collective options) received significant attention in the workshops and developed scenarios. Breaking the applied solution data down by workshop provides some interesting information. For both the public transportation (see Table 6.13) and decreased unit size (see Table 6.14) options the same trend is present. Participants from the Planners workshop had the largest increase in their mean scores for both public transportation and decreased unit size. While participants from the Grandview workshop had the smallest mean increase in scores. Marpole participants fell in the middle, but were closer in scoring to the Planners participants. These results seem to support the general trend that many participants from the Grandview workshop were energy experts that had strong, pre-formed and less malleable opinions about energy solutions. It is notable that Grandview 305 participants generally (both pre and post) scored decreased unit size lower than the other workshops, despite a noted affinity for increased density in the workshop exercises (see Section 6.5.2). One possible explanation is that the already high proportion of multifamily residences in the neighbourhood made them think it would be less impactful. It is also notable that the Planners participants’ chosen options changed the most, despite the fact that they work daily on community planning issues. One indication of how new the idea of planning for energy at this scale is. Table 6.13: Participants’ mean pre and post workshop scores for the public transportation applied solution by workshop. Workshop Pre Score Post Score Ranked Higher Ranked Lower No Change Planners 4.33 5.60 9 1 5 Marpole 3.75 4.75 9 3 4 Grandview 3.81 5.14 11 3 7 Table 6.14: Participants' mean pre and post workshop scores for the decreased unit size applied solution by workshop. Workshop Pre Score Post Score Ranked Higher Ranked Lower No Change Planners 2.53 3.13 7 2 6 Marpole 2.50 3.00 6 2 8 Grandview 2.38 2.52 7 5 9 While care should be taken in extrapolating too broadly from these results, they do present some interpretable trends. Surprisingly, the Planners participants were the most likely to increase their scores for both public transportation and decreased unit size, and the Grandview participants were least likely. Previously noted characteristics of the Grandview participants may explain this trend, as many of them came to the energy workshops with strong opinions on energy issues. The large shift in the Planners participants may suggests that there is still significant room for practitioner education and engagement on energy issues – i.e. before City planners engage citizens on proposed neighbourhood plans, perhaps they need better and more complete information about the sustainability ramification of different planning options. There are also some highlights from looking at the options that increased in popularity according to mental models cluster. Table 6.15 shows the shift in public transportation popularity by mental 306 models cluster. The caveat must be re-stated, that the n for certain clusters were quite low (see Table 6.6). Of note is that the low energy knowledge cluster had the largest mean score change, and that all three participants from that cluster increased their scores for public transportation. This suggests that the intended audience for these workshops – citizens with no particular affinity for energy as an issue – were impacted by the workshop materials. The other cluster with a large shift was cluster 3 (muddled middle ground). Cluster 4 (power to the individual) and 6 (let the market decide) had the smallest increase in scoring for the public transportation option. Overall, the order of change (largest to smallest) for the clusters was limited energy knowledge > muddled middle ground > efficient urban development > national action > power to the individual = let the market decide. Table 6.15: Participants' mean pre and post workshop scores for the public transportation applied solution by mental models cluster. Cluster Name Pre Score Post Score Ranked Higher Ranked Lower No Change 1 Limited Energy Knowledge 3.00 5.67 3 0 0 2 Efficient Urban Development 5.00 6.00 2 0 1 3 Muddled Middle Ground 3.15 5.15 13 1 5 4 Power to the Individual 5.50 6.00 1 0 1 5 National Action 4.36 5.07 6 3 5 6 Let the Market Decide 5.00 5.50 1 0 1 Table 6.16 shows the shift in decreased unit size popularity by mental models cluster. The most notable shift was that cluster 2 (efficient urban development) decreased their scoring for decreased unit size. This despite the fact that cluster 2 was defined by an affinity for smart growth-like land use solutions. 0may shed further light on this, as some participants were dismayed by land use’s smaller than anticipated impact on energy and GHGs in the workshop modeling. Cluster 5 (national action) had the largest increase in scoring for decreased unit size. This large cluster was defined by a focus on Federal scale interventions to address climate change, and may not previously have considered the implications of more local scale interventions. Overall, the order of change for the clusters for unit size from largest to smallest was limited energy knowledge > efficient urban development > national action > power to the individual = let the market decide > muddled middle ground. 307 Table 6.16: Participants' mean pre and post workshop scores for the decreased unit size applied solution by mental models cluster. Cluster Name Pre Score Post Score Ranked Higher Ranked Lower No Change 1 Limited Energy Knowledge 2.67 1.33 1 2 0 2 Efficient Urban Design 3.50 2.50 0 1 1 3 Muddled Middle Ground 2.85 3.25 6 3 11 4 Power to the Individual 2.50 3.00 1 0 1 5 National Action 2.21 2.93 7 2 5 6 Let the Market Decide 1.50 2.00 1 0 1 Pre and Post Workshop Opinions about Vancouver’s Primary Energy Source This question asked participants to choose the energy source that provided the largest amount of energy to the City of Vancouver. The purpose of the question to determine if the workshops changed how they viewed energy sources. The question asked was: Please circle the energy source that you think provides the largest amount of energy to the City of Vancouver. a) Gasoline and diesel b) Electricity c) Natural Gas d) Propane f) Solar (hot water or electricity) g) Geothermal h) Heating Oil Figure 6.36 shows the aggregate participant responses across workshops, demonstrating shifts away from electricity and natural gas and towards gasoline and diesel. Propane, solar and geothermal 308 received no votes on either the pre or post workshop questionnaires and are therefore not displayed in Figure 6.36. A Fisher’s exact test was conducted on the contingency table of the four options and revealed that there was a significant shift in participant responses from the pre workshop to post workshop questionnaires at the p=0.05 level (Chi2 = 6.89, df=3, p<0.05). These results seem to correlate with the applied solution question, as they demonstrate a similar focus on transportation and transportation fuels. This further corroborates the idea that one of the main effects of the workshops was to make participants focus on the energy and GHG implications of transportation. The results of this question also demonstrate the continued invisibility of natural gas, despite it being Vancouver’s largest energy source in GJ or energy. Clearly the workshops, despite explicitly stating natural gas’ primacy, caused participants to focus on transportation through the exercises, modeling and the strategy cards star system. Figure 6.37: Participants' pre and post workshop selections of Vancouver's largest energy source (n=58) Figure 6.38 shows the energy source data broken down by workshop and normalized as described in section 6.5.2. As Figure 6.38 shows, Marpole participants were the most likely to shift from 0510152025303540Electricity Gas and Diesel Natural Gas Heating Oil% of total responsesPre Post 309 electricity or natural gas to gasoline and diesel (5 participants). As previously stated, the Marpole workshops were the most representative of actual neighbourhood residents, so it might be expected that the workshops, as run, would have a similar effect on residents from other neighbourhoods. The Planners workshop participants showed a tendency to move away from natural gas and towards electricity. It is not clear from the results what might have caused this erroneous shift. Grandview participants appear to have been the least likely to shift their opinions of Vancouver’s dominant energy source, as they continued to favor electricity (15 participants), with a small (3 participant) shift to gasoline and diesel. The information presented on energy sources was the same across all workshops. That is, despite changes to certain workshop components to tie them to the individual neighbourhoods, the information on Vancouver’s energy system remained constant. This suggests that workshop differences in perceptions of Vancouver’s energy sources are the result of either the participants’ individual mental models, or the conversations that occurred at the working tables. Figure 6.38: Participants' pre and post workshop selections for Vancouver's primary energy source broken down by workshop (n=50). 020406080100120140160180Pre Post Pre Post Pre Post Pre PostElectricity Gas and Diesel Natural Gas Heating Oilnormalized % of responses by workshopPlanners Marpole Grandview 310 Figure 6.39 shows the participants’ selections of Vancouver’s dominant energy source broken down by the mental models cluster they belong to and normalized as described in section 6.5.2. This question represents a participants’ perception of fact – i.e. it is not about preferred option, but rather what they think the actual dominant source is. As such, it is more a question of energy knowledge than deeply held beliefs. Recall from Chapter 5 that the mental models clusters revealed by the MCA analysis were not strongly correlated to levels of knowledge, so we might expect there to be a weak relationship between this question and the mental models clusters. Figure 6.39: Participants pre and post workshop selections for Vancouver's dominant energy source broken down by mental models cluster (n=42). Cluster 1 (limited energy knowledge) was the only cluster associated with the participants’ self-assessed energy knowledge levels. Not surprisingly, two of the three participants from this cluster changed their votes for primary energy source, both moving to gasoline and diesel as the dominant source. Interestingly, all three participants of cluster 2 (efficient urban development) selected gasoline and diesel in the post workshop questionnaires. Recall that they also gave the highest score for public transportation as an applied solution in 0 (see Table 6.15). A follow-up question or 050100150200250300350400Pre Post Pre Post Pre Post Pre PostElectricity Gas and Diesel Natural Gas Heating Oilnormalized % of responses by cluster1 2 3 4 5 6 311 interview asking these participants how the workshops impacted their views on urban development and its relationship to transit might have provided interesting results. Another cluster worth mentioning is cluster 5 (national action). It is consistent that relatively large proportion (36%) viewed gasoline and diesel as Vancouver’s primary energy source before the workshop, because of the frequent association of GHGs with vehicle emissions. Cluster 3 (muddled middle ground) showed very little movement in selected energy source. Broadly, then, the workshops did have an impact on participants’ perceptions of Vancouver’s dominant energy source, though not in the anticipated direction. Statements of fact about Vancouver’s different energy sources appear to have been overpowered by the exercises, workshop materials (in particular the transportation-related strategy cards) and table conversations. There is some indication that this effect was largest for participants who did not come to the workshops with an abiding interest in, or knowledge of, energy issues. Both the Marpole workshop and the limited energy knowledge cluster showed relatively large moves away from electricity and natural gas and towards transportation fuels. Previous mental models research has demonstrated the interference that beliefs or other cognitive factors can have on mental models (cf. Sterman and Booth-Sweeney, 2007). These findings suggest an added need for caution in the presentation of materials and information in workshop processes, because the focus of workshop materials may have unintended consequences. The findings about people’s responses about Vancouver’s dominant energy source raises another question, however, which is the relative importance of participants understanding factual information. Is it more important for people to know what the numerically largest energy source is in the City of Vancouver, or for them to understand where they, and their communities, can have the largest impact on energy use and GHG emissions? From an educational psycology perspective, knowing factually correct responses is called recall, which is considered shallow learning (Mayer, 1998). Conversely, transfer – the ability to understand concepts and apply them in novel circumstances – is considered deeper and more valuable learning (Mayer, 1998). Ideally, workshops and other engagement activities would promote this type of transfer learning as one of their objectives. Therefore, it could be argued that the decreased accuracy of participants’ responses about Vancouver’s dominant energy source is less important than the result that they appeared to leave the workshops more focused on transportation issues, where they and their communities could have a larger impact on energy use and GHG emissions. 312 What Participants Found Surprising about Energy This was an open-ended question intended to capture what participants had learned about energy that surprised them. Many modern theories of learning identify surprise as a key factor in causing people to retain new information (cf. Schulz & Dickenson, 2000). Identifying what participants found surprising from the workshops might serve as an indicator of how the workshops would impact their future mental models of community scale energy. The question asked was: Did you learn anything today about energy that you found surprising? If so, please briefly explain what you found surprising. Participants’ responses were transcribed and coded in NVivo 10 according to the dominant themes of their responses. Figure 6.40 shows the aggregate responses of all participants’ across the three workshops. The most common responses related to aspects of the existing energy system that they had previously been unaware of. Existing Energy System Presentation on energy use and percentages, refinement of the information was useful - eg. Vancouver 5% of the energy use in BC. Grandview 19, Cluster 5 Figure 6.40: Participants' coded responses about what surprised them about energy from the workshops (n=63). 02468101214number of references 313 The proportions of energy demand sectors and how they convert to GHGs. Grandview 37, Cluster 2 In keeping with the trend seen in Figure 6.36, the impact of transportation was another common theme in participants’ responses. Transportation I didn't realize the high proportion of energy/ghg from transportation in Vancouver, outside of downtown core. Marpole 19, Cluster 2 I was surprised with how much of a major role transportation played in our scenarios. Grandview 45, no mental models cluster In addition to those that indicated what they had learned that surprised them, a number of participants (10 total) indicated that they had not learned anything new from the workshops. Additional elements that participants found surprising included information about energy sources, factors relating to the complexity of the interconnected energy system, the potential impacts of technologies such as electric vehicles and HVAC systems, trade-offs between possible solutions, the limited energy impacts of some land use types (e.g. industry), as well as some very specific items such as the inefficiency of high rise buildings and the examples set by some international cities. The wide array of things that participants’ identified as surprising suggests that the workshops did have the general effect of broadening people’s mental models of energy. Figure 6.41 shows the participants’ responses stratified by the workshop they attended and normalized as described in section 6.5.2. From Figure 6.41 we can see that more participants from the Planners and Grandview workshops indicated they had learned something surprising about the existing energy system, while more participants from the Planners and Marpole workshops indicated they had learned something surprising about the impacts of transportation. A higher proportion of participants from the Grandview workshop indicated that they had not learned anything surprising from the workshops, which corroborates the observation that many Grandview participants were energy experts. 314 Other notable observations include the fact that the inefficiency of high rises was a discussion limited to the Planners workshop, and the fact that no participants from the Planners workshop indicated that they had learned something new about energy sources. These findings illustrate the impacts of participant discussions at the workshops, as interaction with the same materials led to different points of focus. Figure 6.41: Participants' coded responses about what surprised them about energy from the workshops stratified and normalized by workshop (n=63). Figure 6.42 shows the participants’ responses about what they found surprising about energy stratified by their mental models cluster and normalized as described in section 6.5.2. As with other question from the post workshop questionnaires, the low n for some of the clusters means that the results should be interpreted with caution. Perhaps the most notable clusters from Figure 6.42 are clusters 1, 2 and 6. Cluster 1 indicated that they learned something surprising about the existing 0.010.020.030.040.050.060.0normalized % of responses by workshopPlanners Marpole Grandview 315 energy system, about the impacts of transportation, about energy sources and about DES. For these participants the surprising information was relatively basic in nature, which is consistent with the characterization of this cluster as having limited energy knowledge. Figure 6.42: Participants' coded responses about what surprised them about energy from the workshops stratified and normalized by mental models cluster (n=47). I didn't know about central energy systems. Grandview 36, Cluster 1 Transportation is a key contributor to increased GHGs. Planners 13, Cluster 1 Cluster 2 is notable because they had a high number of themes per participant, supporting the finding from Chapter 5 that these participants demonstrated a pre-existing affinity for the types of concepts being discussed at the workshops. 050100150200250normalized % of responses by clusterCluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 316 Some of the proportions of energy demand sectors and how they convert to GHGs. Also, the feasibility of some of the options in terms of cost, affordability and acceptance. Grandview 37, Cluster 2 In general, then, participants found a number of factors surprising about the workshops which partially supports the notion that the workshops increased the breadth of their mental models about energy. The dominant themes relating to the existing energy system and the impacts of transportation support the findings from other questions in this section, suggesting that a focus on transportation was a consistent, though not an intended, outcome of the workshop. Other Ways to Address Community Scale Energy The intent of this question was to provide participants with a venue to express solutions that were not part of the workshops. This was a mechanism to capture emergent ideas that would otherwise have been lost. The question asked of participants was: Can you think of other ways, not listed in question 6, that your community could address energy concerns in the future? Please briefly explain any ideas that you have. The wording of this question suggested it was optional and, perhaps as a result, only 34 of the 66 participants provided a response. Participant responses were transcribed and coded by dominant themes in NVivo 10. Table 6.17 shows the frequency of participant response themes by workshop. The most common theme related to aspects of planning (6 responses) such as designing for walkability, associating density with transit options, or designing complete streets. Planning Planning and zoning must take energy into account. Marpole 20, cluster 3. 1. Complete streets - max 1 lane mixed car each way. 2. Complete communities. Grandview 43, cluster 3. These aspects of planning were present in the workshops, so these participants were essentially emphasizing particular planning strategies. 317 Table 6.17: Participants' coded response themes about energy options not covered in the workshops (n=34) Theme Planners Marpole Grandview Planning 0 2 4 Car Share 2 0 1 Building Code 2 0 1 Education 1 1 1 Community Discussions 1 0 1 Geothermal 0 1 1 Local Food 0 0 2 Local Jobs 0 2 0 Ownership Options 0 0 2 Reduced Parking 0 0 2 Regulation 0 1 1 Taxation and Pricing 0 1 1 Walkability 2 0 0 Bike Share 0 1 0 Community Initiatives 0 0 1 Curtailment 0 1 0 Distributed Generation 1 0 0 Political Change 1 0 0 Total Themes Suggested 10 10 18 Mean Themes per Participant 1.25 1 1.2 The next most popular themes were car sharing (3) and building code changes (3). As mentioned in section 6.5.2, car sharing was (and remains) a popular part of the energy zeitgeist in Vancouver, explaining its popularity as a theme. References to the building code came primarily from the Planners workshop (2of 3 responses). Vancouver is unique amongst BC municipalities because it has its own building code. Changes to the building code, therefore, are a unique local opportunity. 318 Car Sharing and Building Codes Shared/co-op electric vehicles. Planners 13, cluster 1. EV car share proved to be a powerful tool. New building codes are significantly more efficient. Planners 22, cluster 5. Other suggestions included education (2), community discussion (2), local food (2), local jobs (2), geothermal (2), owenership options (2), reduced parking (2), regulation (2), taxation or pricing (2), walkability (2), bike sharing (1), distributed generation (1), curtailment (1), community initiatives (1) and political change (1). The mental models clusters data are further limited by this question’s low response rate. For example, cluster 1 (low energy knowledge) provided only one suggestion (car share) and cluster 4 did not provide any suggestions. Cluster 2’s themes of tax and pricing (1) and community initiatives (1) don’t appear to present a coherent picture for the cluster. Similar to other questions, cluster 3’s themes are dispersed. Cluster 5’s (national action) themes included walkability (2), car sharing (2), building code changes (1), reduced parking (2), education (1) and taxation and pricing (1). These themes seem to primarily focus on getting people out of their cars which is consistent with the clusters’s focus on Federal-scale GHG reductions. Cluster 6’s themes of local jobs (1) and ownership options (1) also seem strongly in keeping with the cluster’s focus on business or economic solutions. Most of the themes expressed by participants reflect common ideas from various energy research literature or practical applications. Some are notable, because they do reflect omissions from the GCCP workshops, for example taxation and pricing options and an explicit focus on building code implications. Other themes, such as geothermal or distributed generation, were conscious omissions, primarily because they are more detailed strategies than the workshops were intended to address. Perhaps the most interesting themes are those such as local food, local jobs, ownership options and community initiatives that have a less direct energy tie, but strong social and cultural implications. 319 Local Food, Local Jobs and Ownership Options Develop a culture of competencies in local food production (community gardens may not be the best way). Grandview 30, cluster 3. More businesses in the neighbourhood that employ residents of the community. Marpole 23, cluster 6. Collective ownership of houses, cars, etc., to promote individual buy in to energy efficient changes. Grandview 39, cluster 3. Energy planning currently has a strong technocratic focus that does not always look for co-benefits. Emerging fields such as regenerative design are taking a more holistic approach, which can incorporate these less technical solutions (cf. Cole et al. 2013, Robinson and Cole 2014), possibly garnering support from socially-focused citizens who might otherwise not be interested in energy planning. These non-energy themes support the notion that engagement on sustainability planning could garner wider interest, and possibly wider support, if it focused more on how design and planning for energy could address broader social goals. Energy Planning and Your Community Question 8 represented the summation question of the workshop evaluation. The question asked was: 8. Please briefly explain how you think that planning for energy and greenhouse gas reductions will have either a positive or negative impact on what you enjoy about your community. The overwhelming response was positive (42 responses) versus only 8 negative responses. Participant responses were transcribed and coded in NVivo 10 according to dominant response themes. Figure 6.43 summarizes the themes people used to explain why they thought planning for energy would have a positive impact on their communities. ‘Quality of life’ and ‘sense of place’ reasons were the dominant themes in participants’ responses, followed by ‘environmental impacts’ (primarily reduced pollution) and ‘social reasons’ such as increased interpersonal interactions and more activity on the neighbourhood streets. Six participants also made reference to ‘vibrance’, ‘vitality’ or ‘liveliness’ on neighbourhood streets that seems to 320 relate to social interactions and sense of place. Increases in active transportation (7) and transportation options such as transit, biking and electric vehicles (7) were also common choices. Figure 6.43: Participants' response themes regarding why they felt planning for energy would be good for their community (n=42). Quality of Life and Sense of Place Themes Positive - improve quality of living and environment. Planners 18, cluster 4. Positive, since it increases everyone's wellbeing for now and future generations. Marpole 12, cluster 5. People on the street, walking, biking, waiting for transit means more spontaneous interaction and safer neighbourhoods. Grandview 19, cluster 5. Environmental Impacts The community will benefit if the air is cleaner and we will be happier knowing we are not harming our world. Marpole 17, cluster 3. 024681012141618number of references 321 Less pollution - healthier people, more livable. Grandview 36, cluster 1. Vibrancy I think as we densify neighbourhoods and create communities that promote active transportation. Streets will get livlier and neighbourhoods more cohesive. Grandview 39, cluster 3. Breaking the positive responses down by workshop using the same normalization described in 6.5.2 reveals some modest trends (see Figure 6.44). All three workshops indicated ‘quality of life’ and ‘sense of place’ reasons for energy planning having a positive impact on their community. The Marpole workshop was disproportionately represented in the ‘environmental impacts’ and ‘future generations’ themes, suggesting that some participants viewed energy planning as a duty to the future. Figure 6.44: Participant responses about how they felt planning for energy would have a positive effect on their neighbourhood stratified by workshop (n=42). The Grandview workshop expressed more themes and focused more on immediate benefits such as ‘social reasons’, ‘diversity’, ‘vibrance’ and provided ‘services’. Given the demographic age differences between the communities and workshop attendees (see section 6.5.1) it could cautiously be speculated that the Marpole participants saw energy planning as something they ‘needed’ to do for 020406080100120normalized % of responses by workshopPlanners Marpole Grandview 322 future generations, and Grandview participants saw it as something they ‘wanted’ to do for immediate neighbourhood benefits. This could have implications for people’s enthusiasm about planning efforts. The Planners themes were dispersed, with ‘environmental impacts’ and ‘economic benefits’ of energy planning being the strongest perceived reasons why planning for energy would be good for communities. This may reflect what practitioners feel are the primary drivers for incorporating energy into their neighbourhood planning efforts. The differences between the three workshops may indicated a disconnect between how the City is ‘selling’ planning changes, and how citizens are thinking about them. Figure 6.45: Participant responses about why they felt energy planning would be positive for their community stratified by mental models cluster (n=31). Figure 6.45 shows the energy planning themes stratified by mental models cluster. Once again, the low n for certain clusters makes drawing strong conclusions infeasible. This is exacerbated by the fact that eight participants felt that energy planning would have a negative impact on their 050100150200250300normalized % of responses by clusterCluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 323 community (see Table 6.18). For example, there were two Vancouver participants from cluster 6, with one suggesting a positive impact and one a negative impact. Therefore, the strong ‘services’ connection from cluster 6 in Figure 6.45, though consistent with the business focus of the cluster, only represents one participant’s view. Cluster 1 participants (limited energy knowledge) selected ‘quality of life’, ‘health’ and ‘environmental impacts’ themes as the reasons why energy planning would be good for their communities. This might indicate their motivation for attending the workshops – i.e. that they know that energy planning is important to health and the environment more broadly, despite a lack of energy–specific knowledge. Cluster 2’s (efficient urban development) dispersed themes are perhaps a reflection of their ‘true believer’ status. They believe that sustainable urban design strategies will have numerous social, economic and environmental benefits for their communities. Of note, all cluster 2 respondents cited quality of life reasons for why energy planning would benefit their community. Similar to previous analyses, Figure 6.45 shows cluster 3’s responses were dispersed, with few respondents sharing the same themes. This is, in part, due to the higher n for cluster 3, and resulting increased opportunity for variance. As noted previously, however, it is also likely due to this groups “catch-all” status. Cluster 4 is interesting, because both participants from this cluster selected ‘environmental impacts’ of planning for energy as a reason why planning for energy would be good for their community. This is somewhat surprising, because of their responses in Chapter 5 suggested that they were not environmentally inclined. This result counters that assumption, suggesting that their individual focus may not preclude environmental attitudes, but rather represent a different idea about effective solutions. Finally, Cluster 5 shows a similarly dispersed pattern of responses to cluster 3, with slightly more responses for ‘sense of place’ and ‘active transportation’ themes (5 respondents each). A focus on active transportation is in keeping with the GHG reduction focus of this cluster, however, and corroborates findings of other questions in this section. 324 Negative Responses Eight of the participants felt that energy planning would have a negative impact on their communites. Table 6.18 shows the negative themes participants expressed stratified by mental models cluster. Only three of the six clusters were represented in the negative responses – clusters 3, 5 and 6. The presence of clusters 3 and 5 might largely be explained by their larger n and probability. Despite the low n cluster 6 may also be present as a result of chance, rather than a real effect. The most common responses for why energy planning would be detrimental to their community was that it would not be effective (and therefore wasteful), and the economic implications. Other concerns included the loss of individual transportation options, a loss of community character and concerns about density implications. Table 6.18: Participant responses about why they felt energy planning would have a negative effect on their neighbourhood (n=7). Cluster Not Effective Economic Individual Transportation Lost Character Density 3 0 1 1 1 0 5 2 2 0 0 1 6 1 0 0 0 0 Negative Examples Ecodensity looks like it will drive up price, not provide affordability with extra 'supply' and only increase total GHG even though more efficient. Grandview 44, cluster 5. Negative - more difficult to use automobile. Marpole 18, cluster 3. Similar to the ‘services’ focus for cluster 6 in Table 6.18, it is tempting to suggest that a concern about effectiveness here is consistent with cluster 6’s business focus. However, further research with a much larger subject pool is necessary before claims could be substantiated. More generally, the reasons given for concern about the impacts of energy planning on neighbourhoods are consistent with other publication sources. The density concern is well established in Vancouver (Bula, 2014; Smith, 2008) and a loss of local character seems to be a related 325 issue (see the Revelstoke example in Chapter 7). Similarly, the themes of effectiveness and economic concerns relate to a general mistrust that municipal government could effectively carry out the implementation of energy planning. In general, participants felt that energy planning would have a positive effect on their neighbourhoods, primarily for quality of life and sense of place reasons. There were some workshop variations, as Marpole residents appear to think of it more as a duty to future generations, whereas Grandview participants felt is would have immediate benefits. There were some understandable trends in mental models cluster responses (e.g. clusters 2 and 6), and some surprises (cluster 4). Cluster related responses may be as useful for helping to further define the clusters (e.g. cluster 4’s environmental attitudes) as they were at determining energy planning attitudes. Finally, the negative responses may partially explain some of the negative attitudes towards recent community planning efforts (cf. Bula, 2014; Smith, 2008). Participants may not be resistant to the concepts of sustainable land use planning, but rather they are mistrustful of how it will be implemented and, as a result, how effective it will be. This may suggest that engagement efforts would be better directed at securing citizens trust rather than focusing on energy planning concepts; what Stirling (2006) refers to as instrumental engagement. Mental Models Evaluation Discussion One of the two research questions stated at the beginning of this chapter was: How do participants’ mental models of community-scale energy interact with engagement activities? Is there evidence of changes in participants’ mental models, or interactions between their mental models and the workshop subject and materials? While limited by participants’ time and attention, and the necessity to ask demographic and workshop evaluation questions, participants’ answer patterns discussed in this section result in a qualified ‘yes’ to the second question posed here. Participants’ quantifiable answers regarding their self-reported energy knowledge levels, preferred energy solutions and opinions about Vancouver’s dominant energy source were all significantly changed through participation in the workshop. Perhaps more importantly, the open-ended questions about what surprised them about energy, other options for addressing energy issues, and how energy planning would impact their community, provided qualitative evidence about how their mental models were impacted. In addition, these 326 open-ended questions provided some evidence about the interaction of both the workshop they attended, and their mental models with the workshop content. The findings in this section are not definitive. Problems with subject numbers, limited time for questions, and the exploratory nature of the research made it difficult to identify clear relationships between the mental models clusters and participants’ response patterns in some circumstances. Similar to the workshop design, however, the novel elements of the mental models evaluation methodology can be instructive for future research. The Victoria CEEP example in Chapter 7 will help to further triangulate the mental models findings by providing more subjects to evaluate, as well as investigating the impacts of a different process. Chapter 8 will return to the question of the mental models evaluation and explore how that evaluation could be improved for use in future engagement activities. 6.5.4 Participants’ Workshop Evaluation Data: Results and Discussion In addition to the demographic data discussed in Section 6.5.1 and the mental models-related data discussed in section 6.5.3, a portion of the exit questionnaire was devoted to the participants’ evaluations of the workshops themselves. The questions in this section of the questionnaire were intended to help the researchers understand how the novel aspects of the GCCP workshop were received, both as indicators for future workshop improvements and as accompanying data for the mental models evaluation. Workshop Components The purpose of the first question was to evaluate which major components of the workshops were deemed most valuable by the participants. The question asked of participants was: Please rank the following components of today’s workshop from 1-4, with 1 being the most helpful component, and 4 being the least helpful component. 327 a) Group facilitators b) Presentations c) Interactive exercises d) Opportunity to discuss energy issues with fellow citizens The two major structural components of the workshops as discussed in sections 6.4.5 And 6.4.6 were the three presentations and the four exercises. Facilitators were added because of their noted importance in other public engagement research (Salter et al., 2009; Schroth et al., 2013). The opportunity for discussion with other citizens was included to account for non-deliberative interactions between participants. Figure 6.46: Participants' scoring of workshop components (n=35). Figure 6.46 shows the results for question one. A score of four is the highest possible score for this question. The interactive exercises had the highest ranking, with a mean score of 2.92, followed by the group facilitators with 2.83, the presentations with 2.61 and the opportunity to discuss with 1.92. 0.000.501.001.502.002.503.003.50a) Group facilitators b) Presentations c) Interactiveexercisesd) Opportunity todiscussmean score 328 Breaking the ranking down by the number of participants that gave a component a particular rank (see Table 6.19) shows that the presentations and interactive exercises actually tied for the highest ranking, but that the interactive exercises were much more likely to receive a ranking as the second most helpful component. The group facilitators were much more uniform in their distribution of ranks, giving them a higher mean score than the presentations. The opportunity to discuss energy issues was uniformly the lowest or second lowest ranked item for helpfulness. Table 6.19: The distribution of scores assigned to the workshop components. Score a) Group facilitators b) Presentations c) Interactive exercises d) Opportunity to discuss 4 10 12 12 3 3 11 5 15 6 2 13 11 2 11 1 2 9 8 16 Perhaps the most unusual thing about this data is the bimodal nature of the presentations rankings. Conventional wisdom in the development of public engagement activities is to limit, as much as possible, one-way information flow. Limiting the presentations was a design goal of the GCCP workshops. The bimodal ranking of the presentations may reflect learning styles of the participants, suggesting that they preferred to learn in a lecture, rather than interactive mode (Newble & Entwistle, 1986). It may also reflect elements of the presentations themselves, including the information, the presenter, and the design of the presentations. Despite the tie for the most helpful component, the interactive exercises were the most uniformly helpful element of the workshops. A significant amount of working team time and effort went into the development of the interactive exercises, and the workshop was structured around them. The group facilitators may be considered the benchmark in this ranking, because of previous research that has found that facilitators are frequently viewed as valuable components of participatory processes (see e.g. Salter et al., 2009; Schroth et al., 2013; Sheppard & Meitner, 2005; Wates, 2006). 329 Finally, the opportunity to discuss energy issues with other citizens may be expected to score lowest in the GCCP process, because opportunities for less structured dialogue were not frequent. The workshop was primarily deliberative in nature, and structured around presentations and goal-driven exercises. Workshop Media The second workshop evaluation question asked participants to rank the different media used in the workshops according to their usefulness. The participants ranked the media from 1-5 with 1 being the most useful. Similar to Figure 6.46: Participants' scoring of workshop components (n=35)., the ranks were converted to scores for the purposes of graphing (i.e. so that larger values equaled a better score). Scores went from 5-1, with 5 being the best score. Figure 6.47 shows the mean score for each of the media types. The question asked of participants was: Please rank the following media used in the workshop from 1-5, with 1 being the most useful media, and 5 being the least useful media. a) Presentation slides b) Neighbourhood visualizations c) Numbers – outputs from the energy modeling d) The strategy cards from the last exercise e) Drawing paper and sticky notes from the interactive exercises 330 Figure 6.47: Participants' scores for media types (n=35). The neighbourhood visualizations had the highest mean score at 3.72, followed by the presentation slides at 3.47, the drawing paper at 2.61, the strategy cards at 2.61, and the numbers at 2.58. Table 6.20 shows the distribution of scores for each of the media types. Table 6.20: Distribution of scores for participants' media preferences Score Presentation slides Neighbourhood visualizations Numbers Strategy cards Drawing paper, etc. 5 11 13 3 4 5 4 8 7 11 5 5 3 8 11 2 10 5 2 5 3 8 7 13 1 4 2 12 10 8 From the distribution of scores, it can be seen that the visualizations had both the highest mean score (3.72) and the most scores of 5 (13). The numbers had the lowest mean score (2.58). 0.000.501.001.502.002.503.003.504.00a) Presentationslidesb)Neighbourhoodvizc) Numbers d) Strategycardse) Drawingpaper, etc.mean score 331 Interestingly, the numbers also had the most scores of 4, suggesting that for many participants they were the second most useful media. The conceptual, high oblique visualizations were a dominant presentation media in the workshops. They were used to present much of the spatial data and were the platform for three of the four workshop exercises. Therefore, it is encouraging that they were rated highly by the participants, suggesting that they were easy to work with. Similar to the presentations in question one, the presentation slides performed well in the participants scoring of media, with the second highest mean score (3.47) and second highest number of 5 scores (11). Perhaps the most important message from the performance of the presentations is that lectures and one-way information flow is not inherently bad, provided it is balanced by opportunities for participants to interact. In other words, engagement designers should not shy away from the presentation format when it is consistent with a particular purpose. It is difficult to discern a pattern between the three lowest scoring media. The numbers (2.58), strategy cards (2.61) and drawing paper, etc. (2.61) were essentially tied in their mean scores. The only noticeable pattern from the distribution of scores is that participants seemed to either value the numbers (11 participants at a score of 4), or not value them at all (12 participants at a score of 1). This may again be a reflection or preferred learning styles. One difficulty of this question, which was anticipated before administering the questionnaires, was the confounding of media types. For example, the visualizations and numbers were embedded in the presentations and the strategy cards were tightly linked with the neighbourhood visualizations in the ‘Scenario’ and ‘Design Your Neighbourhood’ exercises. This may have led to some confusion in the ranking exercise for this question. While no participants remarked on this confusion (written notes on the questionnaires were common), it should be considered in interpretation of the results. What Was Helpful About the Workshop Question three of the workshop evaluation questions was an open-ended question asking participants to indicate what they had found most useful about the workshops. The question asked of participants was: 332 Is there something you found particularly helpful about today's workshop? If you answer yes, please briefly explain what you thought was helpful. Participants’ responses were transcribed in NVivo 10 and assigned codes based on categories pertaining to the workshops. Figure 6.48 shows the number of coding references for each of the categories. Participants may have mentioned more than one category in their responses, resulting in more coding references (83) than there were participants (41). Figure 6.48: The number of participant responses on which element of the workshops was most helpful: coded responses (n=41). The frequency of the coding references appear to be organized into discernable groupings. For example, both the information presented and the interactivity of the workshops received 18 coding references. These were the two major components of the workshops, as the information category can be tied to the presentations, while the interactivity category can be equated to the exercises. 02468101214161820number of references 333 Information The presentation was good (layering the supply, demand, emissions in that way made it easier to understand the important trade-offs). Marpole 25, cluster 4 Presentation on numbers and technologies. Marpole 18, no cluster Interactivity Yes. Exploring the connection between energy strategies and the repercussions on neighbourhood spaces. Grandview Woodland 37, cluster 2 Fantastic use of interactive ‘city planning’ images. The markable pages, coloured pieces to move and interactive models was great! Grandview Woodland 39, cluster 3 Combined Information and Interactivity Setting aside a considerable amount of time for learning and working through exercises that fit well with the presentations. Grandview Woodland 23, cluster 5 ‘Lectures’ were very informative and connected well with the exercises. Marpole 13, no cluster The next grouping includes discussion and the exercises at 12 references each. These could be interpreted as sub-categories of the broader interactivity code. Discussion For me, seeing different people have different perspectives and concerns. Grandview Woodland 18, cluster 3 Exercises I liked the last exercise the best. Marpole is a community on the first stages of densification and it would be a good idea to see some of the district energy systems implemented in the Marpole plan. Marpole 15, no cluster The next grouping includes the presentations (9 references) and graphics/visualizations (8 references) which collectively fit into a presentation media grouping. 334 Presentations Information, pie charts, good presentation, visually interesting and fun exercises. Grandview Woodland 20, cluster 5 Graphics/Visualization Hands on scenarios allow everyone to participate and get creative. Visual aids make a huge difference in improving understanding. Grandview Woodland 45, no cluster 3D visualization really helped. Marpole 16, cluster 3 Finally, the facilitators (4) and presenters (2) received the fewest coding references. These categories fit into a broader’people’ group. Facilitators It’s complex, so facilitators were critical and very helpful. Grandview Woodland 14, no cluster Presenters Good balance between material presentation and hands-on exercises. Excellent presentation and facilitation. Marpole 12, cluster 5 A synthesis of the participants’ answers suggests that people found the combination of the presentations and interactive exercises to be the most useful part of the workshops. Many participants commented that the movement between presented information and the interactive work sessions was well staged. Another common observation from the participants’ comments was that the visual materials were helpful in more easily understanding the complex subject matter. What Was Difficult About the Workshop The fourth question in the workshop evaluation section asked participants to identify something that they found particularly difficult about the workshop. The question asked of participants was: 335 Is there something you found particularly difficult, or unhelpful about today’s workshop? If so, please describe what was difficult, and why. Participants’ responses were transcribed in NVivo 10 and assigned codes based on categories derived from evaluating their responses. Figure 6.49 shows the number of coding references for each of the categories. Similar to question three, participants may have mentioned more than one category in their responses, resulting in more coding references (56) than there were participants (30). Of note, 11 fewer participants described unhelpful elements of the workshops than helpful elements. The ‘Design Your Neighbourhood’ exercise and group dynamics tied (8) as the most common difficulty that participants had with the workshop. The ‘Design Your Neighbourhood’ exercise appears to have caused participants difficulty primarily because of the complexity of the exercise. Drawing together strategies from the three scenarios (two of which participants had not worked with before) as well as quality of life considerations, and the need to work with others to negotiate the final neighbourhood design appears to have overwhelmed some participants. It is worthwhile to question, however, whether this perceived difficulty is necessarily a bad thing. Having to work through the difficult and complex exercise of designing a neighbourhood they would want to live in may have resulted in more learning than an easier, but less comprehensive exercise. Exercise Four The game was confusing. Grandview Woodland 18, no cluster The last exercise – how to score stars was vague and confusing. Objective wasn’t clear. Controversy and misunderstanding. Marpole 18, cluster 3 Other participants had difficulty with the ‘Design Your Neighbourhood’ exercise because they disagreed more fundamentally with the ‘play’ aspect of it.: Designing the ideal community was unnecessary and wasted time that could have been added to the energy saving scenarios. More information on technology (residential) and trade-offs, background. More life cycle costs. Grandview Woodland 33, cluster 3 I rarely find ‘play’ exercises to be very useful (too much fantasy, not enough thought). However, they do give participants a chance to discuss new knowledge. Marpole 25, cluster 4 336 Group dynamics (8) was a common difficulty that participants had for two main reasons. The first reason was one or more people in a working group dominating the discussion. The second reason, primarily found at the Grandview Woodland workshop, was the participation of people from outside the workshop neighbourhood. Figure 6.49: Participant responses to what was most difficult about the workshop: coded responses (n=30). Group Dynamics Some people took over the discussion. Grandview Woodland 26, no cluster Having participants not from our neighbourhood! Grandview Woodland 36, cluster 1 Having too little time (6) to absorb the information and meaningfully interact was one of the other themes from this question. This was related to participants who felt there was too much information (5) or that the information was too complex (2). It was a concern of the workshop team that there was a lot of information to fit into a relatively short period of time, but consultation with the CoV 0123456789number of references 337 staff and the advisory panel suggested that a five hour time frame was already pushing the boundaries of what participants would be willing to commit to on a weekend. Too Little Time/Too Much Information Yes, so much to discuss and very little time. Maybe a full day would be even more productive. Marpole 10, cluster 3 Absorbing/retaining all of the statistics was a bit overwhelming. Marpole 19, cluster 5 Some participants had comments or concerns relating to the workshop structure (5). In particular, some suggested that the flow of the workshop, or the workshop objectives were not clear. Workshop Structure I’d like a clearer explanation of the second half, building the community… I think a quick explanation of the process might help some of us to understand where we’re headed better. Marpole 17, cluster 3 Additional difficulties participants cited included the scenario exercise (2), some problem with the modeling (3), unclear instructions (4) and issues with the facilitators (3). A synthesis of the difficulties participants noted with the workshops suggests that there were problems with the sheer amount and complexity of information they were required to digest in a short time frame. This was perhaps most apparent when they were called upon to use that information in the final ‘Design Your Neighbourhood’ exercise. Another relationship that draws together multiple categories of what participants found difficult is the issue of group dynamics. The number of non-resident, energy experts in the Grandview Woodland workshops may account for the concern about people dominating the conversation. While it is not possible to deduce from the available data, it may also be that the participants that reacted negatively to the ‘play’ aspects of the ‘Name Your Neighbourhood exercise’ were experts that wanted to focus more on the technical features of the scenarios. Perhaps the take away messages from question four, then, is the need to continue to refine and, possibly, simplify the information and exercises presented to the participants; and the need to target advertising to ensure that the participants fit the profile that the workshop was designed for. 338 Generic Neighbourhood: The Sandbox The final question of the workshop evaluation section asked participants opinions about the generic neighbourhood concept, and how well it worked for the workshops. The question asked of participants was: One of the things we tried in today's workshop was the use of a generic neighbourhood (the Sandbox) to discuss the energy system, instead of focusing on your own neighbourhood. Do you think the use of this generic neighbourhood worked? Why, or why not? Participants overwhelmingly (over 2/3) thought that the Sandbox worked well in the workshops (see Figure 6.50). The primary reason why participants said they liked the Sandbox, was its ability to raise the discussion above specific concerns about their neighbourhood, or their street, so that they could think about the energy and GHG choices more conceptually. Figure 6.50: The number of participant responses about whether The Sandbox worked as a tool (n=55). Yes – it brought discussion to a higher level to avoid discussing specifics (focus more on concepts/strategies). Marpole 19, cluster 2 Yes, The Sandbox is an important exercise that people need to do before we tackle real existing neigbhourhoods. The three steps of pre-existing, retrofit and new neighbourhood is very useful. Grandview Woodland 19, cluster 5 These example quotes represent the general consensus from participants – that the generic neighbourhood allowed them to think more conceptually about the issues. Drawing upon the 0510152025303540Yes No Neithernumber of references 339 representation framework described in Chapter 2, the form of the visualizations of the Sandbox should also have contributed to that conceptual thinking, because the materials participants used were more abstract, 3D renderings from a high oblique angle. These findings suggest that the use of a generic neighbourhood could be helpful for early engagement with citizens to communicate concepts, before engaging with a community about proposed changes to their own neighbourhoods. Among the participants that did not think the Sandbox worked, the primary concern was the lack of realism, or applicability to the actual neighbourhoods. This, in effect, was the opposite side of the NIMBY coin from the people that found The Sandbox useful. Those who said the Sandbox was not useful focused on the difficulty of making such large changes in the real world, and also raised issues about the ‘play’ interaction The Sandbox facilitated. I think a specific neighbourhood would work better, since a lot of details have to be assumed. Taking a known area would highlight the difference. Grandview Woodland 38, cluster 6 No, because in real life people often get nervous/uncomfortable when they see their home neighbourhood changing. Grandview Woodland 45, no cluster In general, then, the Sandbox seems to have been well received, particularly as an early step in a multi-stage planning engagement process. This raises a number of interesting research and development opportunities. Firstly, could existing technologies such as SimCity or ESRI’s CityEngine be leveraged to facilitate a more malleable ‘Sandbox’ that could actually change physical form according to participants rules (e.g. changing streets or intersections)? SimCity is capable of a relatively high level of realism, and the logic incorporated in its simulation engine could accommodate the relatively simple rules developed for the Sandbox. ESRI’s CityEngine has many procedural rules, as well as the ability to be scripted, though it lacks many of the modeling capabilities of SimCity. Could external models be tied to CityEngine in such a way that a Sandbox neighbourhood could be manipulated in real time? The proliferation of real time visualization tools with associated modeling capabilities is an exciting development and the acceptance of the Sandbox in the GCCP workshops provides encouraging fodder for future developments in this area. A second research opportunity developing from the Sandbox is exploration of the effects of this type of conceptual conversation about sustainability and land use planning issues on later planning stages focused on actual neighbourhood changes. Following the subsequent Marpole and Grandview Woodland neighbourhood planning processes was beyond the scope of this dissertation, however, related research is ongoing at UBC. 340 Workshop Evaluation Discussion In general, the workshop evaluation questions from the exit surveys suggest that the workshops were well received by the participants. For those that did not appreciate the workshops, the primary issue seems to have been expectations. This was particularly true at the Grandview Woodland workshop where many participants were interested more in the subject matter (energy and GHGs) a the specific purpose of the workshops – building capacity amongst neighbourhood citizens to consider energy and GHG issues in subsequent planning activities. The workshops were not designed to be energy literacy instruments, and were also not designed to accommodate energy experts. In this sense, the differences between the Marpole and Grandview Woodland workshops were palpable. Workshop evaluations suggest that the balance of presentation and interactive exercises and the quality of the presentation and exercise materials were, broadly speaking, well received. Issues such as the sheer amount of information, the amount of time allotted for the workshops, and the instructions given for individual exercises should be evaluated in future applications of the workshop process. 6.6 Conclusions This chapter includes a number of evaluation components, including case study analysis of the process, as well as specific evaluation components including demographic data, analysis of the completed workshop materials, the mental models questions, and the participants’ evaluations of the process and its components. As a mnemonic device, Table 6.21 is included to provide a short form reference on the key findings for the specific evaluation components. The following sections will discuss some of these key findings in more detail. 6.6.1 Workshop Design and Development One of the most interesting findings from the design and development of the GCCP workshops was the impact of attempting to introduce emergence into the development process. Emergence was a fundamental principle of the larger GCCP project, and all of the channels were encouraged to 341 incorporate it in some way into their processes. Different channels engaged with emergence in different ways. For example the mobile channel used collaborative design in the development of its mobile application and the performance art channel included emergence as a major theme in the commissioning document to the four artists that developed works for the project. For the workshop channel, one approach was to incorporate emergence in the early design and development stages of the workshops, working with the CoV planning staff and an advisory panel. In the case of the workshops, the most noticeable effect of this attempt to incorporate emergence was a sense of confusion from our partners, who were expecting us to be much more proscriptive in our approach. One interpretation of this is that, in a strongly deliberative process like community planning, municipalities and other experts expect a more top-down process. It is unusual for consultants or government organizations to introduce elements of emergence, because of limited time and resources. Even events that contain elements of emergence, such as design charrettes, operate within broader structures that are largely developed by the delivering agency, with only periodic check-ins with the sponsoring funder or institution. Robinson (2008), and Salter et al. (2010) have previously noted the difficulties associated with incorporating elements of interactive social research into institutional systems that are not setup to accommodate them. The efforts to incorporate emergence into the GCCP workshops seems to provide more evidence of that difficulty. The challenge of incorporating emergence into the GCCP workshops underscores a broader challenge of academic researchers attempting to do real world research in cooperation with municipalities. UBC and the CoV have a memorandum of understanding to promote academic research within the City. Despite the clear desire to work together, there were a number of hurdles to getting the workshops up and running. The workshop team made multiple attempts within different groups in the City government structure before receiving the go-ahead to conduct the Community Energy and GHG Emissions workshops. This was not simply the result of resistance within the City, however, as limitations such as timelines and engagement process stages presented major roadblocks to initiating the research. The CoV was also understandably cautious about conducting workshops that represented large land use changes to existing neigbourhoods before they had conducted their own neighbourhood engagement efforts. The negotiated solution to this problem (the Sandbox) ended up being one of the more novel, and academically interesting, elements of the developed workshops. Negotiating the boundaries between institutions is a well-studied challenge of governance models (cf. Kloprogge & Van de Sluijs, 2006). The impacts on research careers, student timelines, etc. has also been noted (Robinson, 2008). However, the insights to be 342 gained from situating research in real-world settings present the opportunity for sustainability research to have more immediate impacts on planning practice. Another key aspect in the design and development of the GCCP workshops was the process of scenario development and how that affected the presentation of material and the conduct of the final exercise. As noted in section 6.3.3, the scenarios developed for the GCCP workshops were not truly scenarios, because they did not represent plausible narratives for the future. The scenarios, or pathways created for the GCCP workshops were primarily intended to help with the construction of understanding by the participants through demonstrating the potential impacts of isolating and pushing one particular lever, and subsequently allowing the participants to mix and match those levers. There was very little overt criticism of the scenarios in the workshop evaluations. Only one participant mentioned that they found them ‘reductionist’ and therefore limiting in the construction of a group scenario in the ‘Design Your Neighbourhood’ exercise. However, there is some question about the scenarios from the workshop development team, itself. Part of the value of scenarios is their ability to describe alternative, incommensurate, coherent and plausible stories of the future. There is value in the cohesion of a scenario narrative that was not present in the pathways developed for the GCCP workshops. While the pathways did appear to function well in helping participants quickly understand the energy concepts presented, there was a sense amongst the workshop team that some of the value of true scenarios in expanding our conception of what the future might look like, was lost. Future research might look at how the GCCP workshops would have been different had they used a more traditional scenario construction methodology. A criticism leveled at the workshop design from other researchers was the failure to include more positive pictures or stories of energy future such as those ascribed to the emerging research fields of industrial ecology and regenerative design (cf. Cole et al., 2013; McDonough & Braungart, 2002; Robinson & Cole, 2015). These approaches focus not only on limiting harm through efficiency or curtailment, but on systemic approaches that can improve the functioning of local natural systems (Hawken, 2005; McDonough & Braungart, 2002). This is a fair criticism and illustrates the importance, not only of having an external advisory panel or extended peer review mechanism, but also of selecting that advisory panel effectively. Inclusion of regenerative design concepts in community planning engagement activities may present the opportunity to motivate a broader audience. Significant work is required, however, to determine how to incorporate complex systems-based approaches into early conceptual stages of design and planning. To date regenerative design has been focused primarily on the building scale (Cole et al., 2013), though frameworks are being developed to accommodate multiple spatial scales. 343 Table 6.21: A table of the main themes, and key findings, for each of the evaluation components in this chapter. Evaluation Component General Theme Key Messages Demographics ‘The Usual Suspects’? - All 3 workshops had participants with above average income and education level. - Marpole had more participants from the neighbourhood and immediate surrounding area (73%) than Grandview Woodland (42%). Workshop Materials 1. Energy Systems Diagram 2. Name Your Neighbourhood 3. Scenario Exercise A focus on visible energy What people want from their neighbourhoods Some solutions are more engaging than others (participants were asked to identify as a group what they found exciting, and what they found concerning about the scenario they investigated) Supply – focus on ‘Hydro’ Demand – visible uses of energy (e.g. transportation, cooking, streetlights, community centres) Example themes include ‘convenience’ (e.g. bike routes, community services), ‘character’ (e.g. street trees and community gardens), ‘equity/social’ (e.g. diversity, senior housing) Sustainability was a common theme when talking about what they would change about their community. Technology exciting – very little change to the neighbourhood concerning – equity (cost of solutions) Integrated Land Use exciting – population increase leads to more services concerning – affordability and gentrification. Mistrust of execution. 344 Evaluation Component General Theme Key Messages 4. Design Your Neighbourhood The challenge of putting it all together… Behavioural/Cultural exciting – associated health and social benefits, some of it is already occurring concerning – A reliance on human behaviour The Planners participants achieved the fewest number of stars, Marpole achieved the most. The Grandview redesigns were the boldest, with the most added density (in keeping with the neighbourhoods character) The Planners placed density along the arterials, while the neighbourhood participants interspersed it throughout the Sandbox. Marpole - most wanted to live in single family Grandview – most wanted to live in multi-family Mental Models Results 1. Self-reported Energy Knowledge 2. Applied Energy Solutions Rankings Limited by low n, but there are indications of changed mental models Caveats about self-reports, but energy knowledge levels increased Post-workshop: transportation and unit size A statistically significant increase in self-reported energy knowledge. Participants from the ‘Limited Energy Knowledge’ and ‘National Action’ clusters were most likely to increase their self-reported knowledge levels. Transportation and decreased unit size were the only 2 solutions that increased their ranking from the pre to post workshop questionnaires (transportation shift was statistically significant). 345 Evaluation Component General Theme Key Messages 3. Community Energy Supply 4. What was surprising about energy? 5. Energy planning and your community Findings consistent with applied solutions (a shift towards transportation fuels) Existing energy system, transportation Participants made the connection between energy planning and quality of life The Planners participants were the most likely to change their rankings of transportation. A general movement towards transportation fuels as the largest energy source. This is consistent with the general focus on transportation impacts. A move away from the actual largest energy source (by GJ), Natural Gas. Participants were most surprised to learn about aspects of the existing energy system that they were unaware of, and to learn about the impacts of transportation. Experts and practitioners (from the Planners and Grandview workshops) were the most likely to express surprise about the existing energy system, suggesting a need to better understand energy at local scales. In general, people felt that planning for energy would have a positive impact on what they valued about their community Participants that did not think energy planning would be good for their community were primarily concerned about the planning’s effectiveness Grandview – energy planning consistent with current values Marpole – energy planning for future generations 346 Evaluation Component General Theme Key Messages Workshop evaluation Positive, with room for improvements Participants liked the interactive exercises, the use of visualization and the information presented the best. Concerns included group dynamics, and aspects of the ‘design your neighbourhood’ exercise (confusing, ‘play’) Strong support for the use of the Sandbox 347 6.6.2 Conducting the Workshops One of the observations from running the workshops that was borne out by the workshop evaluations was the sheer amount of material to get through in a relatively short five hour period. Something that would be worth investigating in future applications of the workshops or related work is how to either limit the information presented, or increase the timeframe. Several participants expressed an interest in increasing the workshop length to a full day, but it is unlikely that that was a universal sentiment. The consensus amongst the advisory panel and CoV staff was that five hours was already too long for a voluntary engagement exercise on a weekend. The second logistical issue that bears discussion was the effect of the different advertising for the Marpole and Grandview Woodland workshops. In an attempt to increase numbers at the Grandview Woodland workshop, advertising was extended to social media, email lists and websites that reached well outside the Grandview Woodland neighbourhood. As a result, there were many more participants from outside the neighbourhood than in the Marpole workshop. The consensus amongst the workshop team and facilitators was that the Marpole workshop was more successful, and that the participants (citizens rather than energy experts) were a much better fit for the design intent of the workshop. Future research activities should take into account the impacts that advertising on social media, and other far-ranging technologies might have on the demographics of participants. 6.6.3 Demographics Demographically, it could be argued that the workshops did, indeed, attract the ‘usual suspects’. Participants were predominately of working age, affluent, well-educated and of European descent. Comparison to the other GCCP channels, some of which have been hypothesized to reach very different audiences, suggests that this was not unique to the workshop channel (see Appendix C). The similarity of the two deliberative channels (workshops and Facebook) may suggest that the demographic homogeneity of the ‘usual suspects’ may be more a fea ture of who the ‘interested public’ is, rather than the engagement mechanisms. Perhaps more fundamental developmental or societal shifts are necessary to encourage other demographics to feel as though they have agency in determining the future of their city. 348 6.6.4 Workshop Materials Content analysis of the workshop materials provided the opportunity to look at the workshops and participants’ mental models in a different way. The questionnaires necessarily looked at individual reactions to the workshops, but the collective workshop materials provided the opportunity to investigate the intersubjective results of participants working together at a series of interactive exercises. Many of the findings about participants’ individual mental models, such as a focus on visible energy sources and use, were corroborated in the energy system diagramming exercise. This exercise also demonstrated a trend that continued throughout the workshops of different working groups having noticeably different levels of complexity in their exercise outputs. Observationally, the ‘Name Your Neighbourhood’ exercise was both the easiest and most fun for participants, suggesting that participants were quite aware of what they value about a neighbourhood, even if that neighbourhood is a fictional place (the Sandbox). The pre-workshop questionnaire’s quality of life questions further illustrated this point, as participants identified a diverse but related set of themes. There was some evidence from these questions of different mental models clusters valuing different things, which would be an interesting finding to follow up on with a larger number of participants. There was also some qualitative evidence that participants were better at identifying what they value about a current neighbourhood than they were at thinking about what they would like to change in the future. This uncertainty about the future supports the use of scenarios as a way of promoting thinking about alternative future worlds. The scenario exercise proved the most difficult, but subsequent use of the workshops without that exercise demonstrated its value as a pedagogical tool, and for illustrating scenario trade-offs (Pond, 2013). Participants seemed most engaged with the Behavioural/Cultural scenario, perhaps because its solutions were more accessible to them as individual citizens. Finally, the ‘Design Your Neighbourhood’ exercise led to a number of interesting findings. Firstly, there was a demonstrable difference in the way the three workshops approached the exercise, with the Planners being the most conservative, and the Grandview participants being the boldest. There was also a tendency to design the future Sandbox such that it reflected the neighbourhoods where the workshops took place. Marpole participants placed less density and tended to keep it out of the single-family areas, whereas Grandview participants place more density and interspersed it throughout the neighbourhood. Additional features such as local food and social spaces were also more common in Grandview than 349 Marpole. In essence, the ‘Design Your Neighbourhood’ exercise demonstrated the local nature of the planning exercise, and the fact that participants largely wanted more of the character, social interactions and services that their current neighbourhoods provide. The group products of the Victoria CEEP and Revelstoke form-based code workshops will be compared in Chapter 7. The implications of the materials and interactive exercises will also be revisited in Chapter 8 as the dissertation turns to a discussion of broader findings and future guidance for engagement activities. 6.6.5 Impacts on Mental Models The evaluation questions used to elicit participants’ mental models, and the interactions of the workshops with those models, were limited by two factors. Firstly, the need to incorporate demographic and workshop evaluation questions limited the number of questions that could feasibly be asked of participants. Secondly, the evaluation conducted in this study was exploratory, incorporating both novel methods (mental models, multiple correspondence analysis, etc.) and a relatively new subject domain (community scale energy planning). As a result, the breadth and depth of the questions posed could be greatly improved in future research. Provided that caveat, however, useful information can be derived from the questions posed. Firstly, there is evidence that participants’ mental models of energy at the community scale were altered by participation in the workshops. Participants self-assessed energy knowledge levels increased from the pre to post workshop questionnaires as one subjective measure of mental models change. In addition, there were significant shifts in both participants’ opinions about Vancouver’s dominant energy source, and their preferred solutions to energy issues. Participants’ responses to the open ended questions, particularly about what surprised them about energy, also suggest that the workshop information had an impact on their mental models. A broader question about the effects of this shift in energy mental models is a valid concern. Triangulating between the mental models related questions suggests that a dominant effect of the workshops was to cause participants to focus on transportation as both a root cause (as evidenced by the shift towards gasoline and diesel as Vancouver’s primary energy source) and as a primary solution (as evidenced by both the significant shift in scoring for public transportation as a solution, and as a focal point of what surprised participants about energy). If the primary message participants took away from 350 the workshops was a focus on the importance of transportation to energy and GHGs, is that a positive development for future planning initiatives? Another interesting finding from the mental models research was the difference between what participants walked away with and what was presented. Transportation was not a primary focus of the workshops, which, if anything, focused more on built form. In addition, changes to participant responses about Vancouver’s dominant energy source ran counter to the factual information stated in the presentations. In the case of the GCCP workshops, there was no agenda about what information participants were supposed to take away - i.e. there was no intention that they should ‘learn’ that solution a) is clearly better than solution b). The intention in the workshops and the broader GCCP was to explore a more emergent, dialogical approach, rather than a persuasive communication focus (Tanenbaum et al., 2011). However this may not be the case for more targeted energy literacy or public information-type engagement activities. The findings from this study suggest that the changes to participants’ mental models as a result of participation may not be those that a sponsoring institution or the engagement designers would have intended. This could have ramifications for energy-related engagement activities. In British Columbia, this may impact engagement with citizens as CEEP processes move towards implementation. The implications of the mental models research will be revisited in Chapter 7 where the Victoria CEEP process is analyzed as a comparison to the GCCP workshops. The broader implications will also be discussed in more detail in Chapter 8. 6.6.6 Participants’ Workshop Evaluation The workshop evaluation questions suggests some features of the GCCP workshops that were well received, and others that either need to be refined, or fundamentally changed. The use of the Sandbox in the workshops was a success. A large majority of the participants felt that it was a good idea, predominately for its ability to remove NIMBY concerns and concentrate discussion on energy and GHG reduction concepts. The ‘Name Your Neighbourhood’ exercise appears to have been a key component in encouraging participants to embrace the Sandbox. The visualizations and other graphical presentation media were also a clear success. Perhaps the major take away message about the visualizations and presentation media is the opportunity to improve public engagement through the careful preparation of materials. 351 Finally, the basic structure of the workshops that involved switching between presentations and interactive exercises seemed to have worked well for participants. The way that the presentations and exercises built upon one another was also well received. Other elements of the workshop structure seem to have been more problematic for some participants. The sheer volume of information, as well as the tight schedule left some participants feeling overwhelmed. In addition, there were specific elements of the information presented that some participants took issue with. This was the first time these workshops were staged – in ongoing work, some of the information and workshop elements are being reassessed (Pond, 2013). While the participants did not raise particular concerns about the scenarios, the working team itself had some concerns about the ‘push one lever’ technique. Subsequent work with this workshop structure may compare a more traditional scenario approach to leverage their ability to depict different, coherent pictures of the future. Another element of the workshops that might be re-evaluated is the use of the star system. Similar to the scenarios, there were few comments about the stars specifically. While they did prove useful for simplifying the conversation around energy and GHGs, combining and trading off stars was a less clear operation. Digital tools with embedded rules might alleviate some of the complexity with mixing strategies and also provide better transparency to the underlying models for those participants with concerns. Chapter 7 will revisit the GCCP workshops, comparing them to two other municipal processes that used modeling, visualization and interactivity to engage citizens and stakeholders on energy and GHG emissions. A comparison of the processes should provide a clearer picture of what tools and process components are effective for engagement on sustainability planning issues. In the case of the Victoria workshops, the interaction of participants’ mental models with the workshop information and structure will also be possible, allowing for the evaluation of differences between workshop types and their impacts on participants’ thinking about energy. 352 Energy Engagement Case Studies - A Comparison The GCCP workshops provided the primary research vehicle for this dissertation, however, two additional community-scale energy projects conducted in the same timeframe provided the opportunity to compare and contrast case study approaches and, in the case of the Victoria Community Energy and Emissions Plan (CEEP) workshops, the opportunity to evaluate existing mental models of energy (see Chapter 5) as well as the interaction of mental models with the workshop process (this chapter). The author took part in both the Victoria CEEP and Revelstoke Form-based Code workshop processes. For the Victoria workshops, I was one of six facilitators conducting interactive breakout sessions. For the Revelstoke workshops I helped with the workshops’ technical setup. Taking part in both processes provided the opportunity for case study observation, in addition to the more formal questionnaire-based evaluation. This chapter documents the case study and mental models comparisons between the Vancouver GCCP workshops, the Victoria CEEP stakeholder workshops and the Revelstoke Form-based Code workshops. First, the Victoria CEEP (sections 7.1 and 7.2) and Revelstoke Form-based Code (sections 7.3 and 7.4) processes will be discussed individually, documenting the context, process design and structure, engagement activities, and workshop and mental models evaluation results. Following the individual case study descriptions the GCCP, Victoria CEEP and, to a more limited extent, Revelstoke Form-based Code processes will be compared in regards to mental models findings. Finally, the three processes will be compared according to particular components of their engagement activities, such as models, scenarios, representation types, interactivity, outcomes, etc. The discussion sections (sections 7.5 and 7.6) will document key findings from the case study and mental models evaluations. The primary opportunities these additional workshops presented, were the opportunity to evaluate the mental models research with a larger audience, and the opportunity to explore and compare different approaches (including varying subject matter, process components and tools). Similar to Chapter 6, the research questions addressed in Chapter 7 include: 1. What are the tools (e.g. models, scenarios, visualizations) and process components (e.g. presentations and interactive exercises) used to engage participants about community scale energy, and how do they impact the process and its outcomes? 353 2. b) What are the effects of participation in energy engagement processes on people’s mental models of community scale energy? The chapter will begin with a discussion of the Victoria CEEP stakeholder workshops, because circumstances provided for a more complete evaluation of that process. The process descriptions and evaluation results for both the Victoria and Revelstoke processes will be more brief than the GCCP description in Chapter 6, because the intent is to use them as points of comparison. More detailed descriptions of the Victoria (Miller and Cavens 2012) and Revelstoke (Girling et al., 2014a; Girling et al., 2014b; Senbel, 2013) processes can be found elsewhere. 7.1 Victoria CEEP Process In 2008 the provincial government of British Columbia introduced Bill 27 (Rutherford, 2009), which amended the Local Government Act to require municipal governments to establish GHG reduction targets. As part of its provincial mandate to manage electricity supply, BC Hydro introduced the Community Energy and Emissions Plan (CEEP) program to provide funding and support to local governments to conduct comprehensive GHG emission reduction planning in their communities to meet their Bill 27 requirements (BC Hydro, 2014). Many municipalities hired outside consultants to analyze current emissions and develop strategies for achieving future targets as part of the CEEP program. The City of Victoria hired C2MP, a consulting firm with ties to UBC research groups in the School of Architecture and Landscape Architecture (SALA), and the Institute for Resources, Environment and Sustainability (IRES). These research ties provided the opportunity to replicate the mental models research constructed for the GCCP workshops on another community-scale energy planning process, and to observe an additional community-scale energy case study. The stakeholder workshops that provided the venue to replicate the mental models research occurred near the end of the broader CEEP process, and were intended to help C2MP and the City of Victoria establish targets in a number of key energy-related areas. One of the primary reasons why Victoria selected C2MP was the firm’s development of an interactive energy and GHG modeling tool – The Climate Action Navigator (CAN tool). The CAN tool was developed for use in interactive stakeholder workshops to facilitate discussion and exploration of energy strategies. As a result, the Taking Action on Climate Change stakeholder workshops conducted in the spring of 2012 provided the opportunity to investigate both the mental models and case study implications of a novel community-scale energy 354 planning process. No existing workshop evaluation methodologies were in place, therefore, I was able to implement a full pre/post workshop questionnaire to replicate the research conducted for the GCCP workshops. 7.1.1 The CAN Tool The CAN tool was developed both as an analysis tool, using the underlying modeling, and as an engagement tool, through the interactive, web-based interface. This section will first describe the modeling inputs and outputs, and then will discuss the tool’s interface, which was used as of the primary engagement mechanism for the ‘Taking Action on Climate Change’ stakeholder workshops. The CAN Tool Modeling Perhaps the strongest connection between the GCCP, Victoria and Revelstoke processes and their respective tools was the underlying modeling methodology. In all cases, the modeling methods were derived from prior research at UBC’s Elements Lab, Design Centre for Sustainability and Collaborative for Advanced Landscape Planning (cf. Condon et al., 2009; Miller, 2013; Miller & Cavens, 2008, Miller et al., 2009; Pond et al., 2011). In both the Victoria and the GCCP processes, the building and broader land use modeling was based on research by Miller (2013). This methodology uses a bottom-up approach, assembling building archetypes with associated energy characteristics into larger scale design patterns to approximate current, or potential future, land uses for a neighbourhood, community, city, or region. For the Victoria workshops, land use changes such as rezoning or large scale redevelopment were not part of the energy and GHG strategies considered, because the City of Victoria is largely built out. Therefore land use strategies were not part of the Official Community Plan (OCP) process to which the CEEP was attached (Miller & Cavens, 2012). Building-level strategies were separated into new construction (building replacement) and retrofitting of existing building stock. Building strategies included building envelope and HVAC system variables as well as retrofit uptake rates, and new construction replacement and growth rates. Transportation modeling for the Victoria CEEP focused on city-wide strategies and derived associated GHGs from vehicle kilometres traveled (VKTs), fuel types, and vehicle efficiency (Miller & Cavens 2012). Transportation energy use and emissions were limited to vehicles registered in the City of Victoria and did not consider vehicle trips into Victoria from outside the city. 355 Table 7.1: CAN tool strategies and key variables (table from Miller & Cavens 2012). Strategy Key Variables External Variables Growth Rate (population per year) Future Energy Prices ($/GJ) Demand Strategies Residential Retrofits (Ground Oriented) Degree of Retrofit [Low, Moderate, High] Uptake Rate (%) Residential Retrofits (Apartments) Commercial Retrofits New Construction (Ground Oriented) Construction Standards [Low, Moderate, High] Replacement and Growth Rates (%) New Construction (Apartments) New Construction (Commercial) Transportation Demand Management TDM Measures [Low, Moderate, High] Transit System Improvements Degree of Improvement (Low, Moderate, High) Transportation Behaviour Mode Share (%); Average Trip Length (km) Energy Distribution Strategies District Energy Zones System Extent; Connection Rate (%) Renewable Energy Supply Strategies – Building Scale Air Source Heat Pumps Degree of uptake (%) [Ground Oriented] Degree of uptake (%) [Apartments] Degree of uptake (%) [Commercial] Geoexchange Biomass Furnaces Solar Thermal Renewable Energy Supply Strategies – District Energy Waste Heat Capture Contribution to system (MWh) Sewer Heat Waste to Energy Biomass Geoexchange Other Vehicle Electrification Degree of uptake (%) Urban Forest Trees (#) Offsets Emission offset (% of total) Solid Waste Reduction Diversion rate 356 The Victoria modeling also considered the energy and GHG impacts of solid waste. The model assumed a constant methane capture rate based on current technology and modeled the GHG implications of different solid waste diversion rates (Miller & Cavens, 2012). The models district energy system (DES) module considered the spatial extent, new construction and existing building connection rates, and system type and fuel sources to derive energy and GHG data for different DES strategies (Miller & Cavens, 2012). The model also considered the GHG impacts of urban forest sequestration and the purchasing of carbon offsets, specifically through the BC crown corporation Pacific Carbon Trust (Miller & Cavens 2012). An additional feature of the CAN tool modeling was the calculation of costs per tonne of emissions reduction by each strategy from baseline/current trends levels. This costing model utilized a net present value (NPV) approach for the incremental costs of capital, operation and fuel for different strategies, based on the Levelized Cost of Energy method (Miller & Cavens, 2012). This method allowed for the comparison of strategies based on their relative dollar cost per tonne, and provided an additional trade-off mechanism between strategies. Table 7.1 provides a summary of the CAN tool’s energy and GHG strategies and the associated model variables. For a more detailed discussion of the model and its various components, see Miller and Cavens (2012). CAN Tool Interface Figure 7.1 shows the interface for the Climate Action Navigator. The CAN tool is web-based and operates in a browser, with data connections running to a central web server. The interface is broken into three main sections or tabs: assumptions, strategies and summary. Under the assumptions tab, the user can view the current model assumptions such as population growth rates and energy prices for various fuel sources. For the Victoria workshops, the main model assumptions were not editable by the participants. Under the strategies tab, the user can select strategies to apply via check boxes, investigate key variables of individual strategies, and interactively investigate the implications of applying different strategy combinations. 357 Under the summary tab, the user can view a summary of the current model results based on their strategy and variable selections. For each strategy, the summary page provides a table entry with cost, energy and GHG values, as well as a summary of the variable levels they have chosen. Interactivity The primary interaction mechanism is the strategies tab, which is shown in Figure 7.1. This tab shows the building, transportation, solid waste and offset strategies on the left, with associated costs, GHG reductions and energy savings for each strategy. Along the right side of the screen are summary tables and graphics that show the combined results of the users selected strategies. The user interacts with the interface by selecting the check box associated with individual strategies along the left hand side of the strategies window. Once an individual strategy has been selected, it can then be edited to expose key strategy variables. Figure 7.1: A screenshot of the strategies pane of the Climate Action Navigator interface. Figure 7.2 shows an example of an individual strategy’s information page with associated variables that can be changed to reflect less or more aggressive approaches to individual variables. For example, for apartment building retrofits (see Figure 7.2) participants can interactively select retrofit uptake rates for 358 target years, envelope retrofit levels, and heating and hot water systems. An envelope retrofit level of ‘none’ would specify no changes to the envelope, but a 25% reduction in appliance and lighting energy consumption, while an envelope retrofit level of ‘high’ would reduce heating load by 50% (near Energuide 85 levels) and appliance and lighting load by 40-50% (Miller & Cavens, 2012). While specific actions such as high efficiency windows or insulation R-values were not directly referenced in the CAN tool, they were related to percent energy savings in the breakout group discussions. Representation As mentioned in the modeling section (see section 7.1.1), land use modeling, while part of the underlying model structure, was not exposed to the CAN tool users. As a result, the CAN tool did not have a spatial representation component – i.e. there were no 2D or 3D representations of changing urban form or land use to accompany participants’ strategy selections. Map use was limited to the representation of district energy system locations inside that strategy’s settings page. The results of participants’ actions were conveyed to them with two main representations. The first representation type was interactive text and tables that changed to reflect their model inputs. On the strategies page participants could see the GHG increase or decrease and the average cost per tonne of GHG reduced for their cumulative strategy and variable selections in the page header (see Figure 7.1). They could also see additional costing information in a table on the right side of the interface. The second representation type was wedge graphs. The first graph (see Figure 7.1 top right) showed the cumulative emissions broken down by sectoral contributions. The second graph (see Figure 7.1 bottom right) showed emission reductions broken down by strategy. On both wedge graphs a 33% reduction level was represented – indicating the City of Victoria’s 2020 reduction target. 359 Figure 7.2: An example of an individual strategy's interactive variables. Using the text and wedge graph representations, the participants could interactively see the impacts of their choices as they were made. 7.1.2 Victoria CEEP Taking Action on Climate Change Workshops In the spring of 2012 The City of Victoria’s Sustainability Department and C2MP conducted two workshops with local business and community stakeholders, climate professionals, and industry experts at Victoria’s City Hall. The purpose of the workshops was to elicit stakeholder and expert input to help 360 the City and C2MP establish targets for individual energy and GHG strategies, as well as overall targets for the City’s GHG reduction efforts. In other words, these workshops were focused on substantive policy input from a select group of participants, rather than as broader engagement, consultation, or literacy events. There were two one-day workshops divided based on space in the venue, and the participants’ availability, rather than definable demographic characteristics. Therefore, both workshops were comprised of a mix of energy and GHG experts, and local stakeholders. The first workshop had 23 participants, while the second workshop had 29 participants. Participants self-selected seating locations for the morning session, with seating arranged into five tables of six to eight participants for the breakout exercises. Participants were presented with the pre and post workshop questionnaires at the registration table, and were given 15 minutes to fill out the pre workshop questionnaire prior to the start of the workshop. The workshop began with a 10 minute presentation providing the Victoria planning context, followed by a 20 minute presentation introducing the CAN tool and its functionality. Following a coffee beak, a 40 minute presentation introduced the individual energy strategies incorporated in the CAN tool, documenting their variables and the tool’s interactivity. Figure 7.3: An example of one of the breakout tables from the Victoria workshop. The CAN tool is on the LCD monitor. Prior to the lunch break, the first interactive exercise was conducted in the breakout groups. For this exercise, the groups were given 45 minutes to interact with the CAN tool with the goal of using all of the available strategies to achieve or better the City of Victoria’s committed goal of achieving a 33% reduction in GHG emissions from 2007 levels by the year 2020. Figure 7.3 shows an example of one of 361 the breakout session tables interacting with the CAN tool. For all of the working sessions a table facilitator controlled the CAN tool based on input from the table participants. The CAN tool was presented on a 40” LCD monitor at one end of the table, as represented in Figure 7.3. Dahinden et al. (2003) refer to this type of model interaction as ‘facilitated access’ – where the participants can see the model changing interactively based on their input, but do not have direct control of the interface. They differentiate it from ‘direct access’ where the participants themselves control the model, and ‘indirect access’, where participants are exposed only to model outputs, and have no interaction with variables or assumptions. For the Victoria workshops, the concern was that participants vying for control of the model might detract from the table conversation, or further ingrain problematic group dynamics. Following the first breakout session there was a lunch break, during which tables had the option to continue interacting with the model. Following the lunch break, each table reported back to the larger group to document the GHG reductions they have achieved, and the strategies and variable levels they had used. Each table’s constructed GHG scenario was then saved by the CAN tool for later analysis and incorporation into the final CEEP report. In addition to the model outputs, each table had a note taker to record qualitative information associated with each tables scenario (e.g. why certain strategies and variable levels were selected). Following the report-back from breakout session one, participants were asked to self-select the specific strategy area they wanted to explore in more detail. Tables were divided by dominant strategy themes, including building retrofits, new construction, district energy systems, and two tables that each discussed transportation strategies. In these strategy-specific groups, the participants completed a 2-part break out exercise that first examined their selected strategy and its variables and assumptions in detail, and then used the CAN tool to deliberate on a consensus target for the strategy. So, for example, at the DES table in the first part of the break out session, participants explored possible geographic locations for DES, discussed realistic connection rates for new construction and retrofits, and discussed different system types, such as geothermal, waste heat and biomass. This session allowed participants to familiarize themselves with the impacts that different DES variables would have on the strategy’s GHG emission reductions, as well as the cost trade-offs. In the second part of the breakout session, participants refined their positions though table discussion and model interaction, resulting in a consensus position. The consensus positions, as well as note taker observations from the different table discussions, were then reported back to the larger group and the individual strategy positions were saved by the CAN tool for later analysis. 362 Following the afternoon breakout session, a 10-minute wrap-up discussion was conducted to field any questions about, or criticisms of, the process from participants. After the discussion, participants filled out the post workshop questionnaire before leaving the venue. 7.2 Victoria Results and Discussion Similar to the GCCP workshops discussed in Chapter 6, the Victoria workshops provided the opportunity for a complete questionnaire evaluation. The results of the pre workshop questionnaires were used to construct the mental models clusters discussed in Chapter 5. The results discussed here were derived from the post workshop questionnaires and are comprised of three components: demographic data, post-workshop questions related to participants’ mental models of community scale energy (including pre/post question comparisons and open-ended responses), and participant evaluations of the workshops themselves. 7.2.1 Demographic Evaluation The Victoria CEEP workshop questionnaires included a greatly reduced demographic question set compared to the GCCP workshops. This was, in part, because there was no intention to compare the Victoria workshops to other engagement channels as was the case in the GCCP, and in part due to the fact that the intended participants at the Victoria workshops were invited energy experts and local business and community stakeholders, rather than representative citizens. Victoria workshop participants were asked to provide demographic information about their age, gender and occupation sector. Those data were discussed in more detail in Chapter 5, but will be summarized here. Age Participants of the Victoria workshops were strongly skewed towards the two dominant working age categories of 25-44 (43%) and 45-64 (50%). There was one participant in the 19-24 age range (2%) and 2 participants in the 65-74 age range (4%). This disproportionate representation of the 25 through 64 age range was expected for workshops that targeted invitations to local business and community stakeholders and climate and energy experts. 363 Gender The gender representation for the Victoria workshops was disproportionately male, with 29 males (63%) and 17 females (37%). This disproportionate male participation may be a result of the invitation procedures, but could also represent the underlying demographics of people working in the target sectors. Figure 7.4: Participant employment sectors for the Victoria workshops (n=42). Employment Sector Figure 5.20 shows that the largest portion of the participants in the Victoria workshops worked in the government sector (34%), followed by consulting (30%), non-profit (11%), business owners (7%), and energy sector employees (7%). The employment sector demographics reflect the intended audience with, perhaps, an over-representation of the government and consulting sectors. Assuming the government, consulting and energy sector employees were invited for their energy or climate change expertise, approximately 70% of the audience at the Victoria workshops could be considered energy experts. 3430117 752 2 20510152025303540% of total respondents 364 Summarizing the limited Victoria workshop demographic data we can see that workshop participants were primarily of working age, disproportionately male, and primarily from the government and consulting sectors. While the CAN tool was originally developed with online public engagement as an additional use case, broader consultation was not conducted for the Victoria CEEP (Miller & Cavens, 2012). Public consultation was a component of the Official Community Plan to which the CEEP was connected (City of Victoria, 2014), but that broader process was not evaluated as part of this dissertation. Despite the focused and technical nature of the CEEP workshops, there is some question whether the deliberation over future energy targets and strategies might have been changed and improved by representation from demographic groups not represented – for example secondary school student representatives, or seniors. There is a tradition in participatory integrated assessment processes, particularly in Europe, to include ‘free thinkers’ as a means of fostering creativity and avoiding the unquestioned acceptance of institutionalized ideas (cf. Patel et al., 2007). The question of audience will be returned to in the case study comparison in section 7.6.2. 7.2.2 Victoria CEEP Workshop Evaluation Similar to the GCCP workshop questionnaires, the first section of the Victoria post workshop questionnaire asked participants to evaluate various aspects of the workshops themselves. This workshop evaluation data was part of the case study evaluation conducted for this dissertation, and additionally provided feedback to C2MP and the City of Victoria on the workshop goals and structure, and the interactive CAN tool. This section will review the results of the workshop evaluation questions from the post workshop questionnaire. Ranking of Workshop Components Similar to the GCCP questionnaires, participants were asked to rank components of the Victoria workshops for their relative value to the workshop process. The question asked of participants was: Please rank the following components of today’s workshop from 1-5, with 1 being the most helpful component, and 5 being the least helpful component. 365 a) Group facilitators b) The interactive energy tool c) Presentation components d) Opportunity to discuss energy issues with other stakeholders e) The breakout exercises Participants’ ranks were converted to scores for the purposes of graphing by inverting the values such that a rank of one became a score of five, a rank of two a score of four, etc. Therefore, the highest possible mean score for a component would be five. Figure 7.5 shows participants mean scores for the workshop components. Figure 7.5: Participants' mean scoring of workshop components (n=32). Participants ranked the opportunity to discuss energy issues with other stakeholders as the most valuable component of the workshop. This was followed by the facilitators, the interactive CAN tool, the breakout exercises, and finally the presentations (see Figure 7.5). This ranking of components is quite 3.032.552.211.610.670.000.501.001.502.002.503.003.50opportunity todiscussfacilitators interactive tool breakoutexercisespresentationsmean score 366 different than that seen for similar components of the GCCP workshops (see Figure 6.45) where opportunity to discuss was the lowest ranked item. What was it about the Victoria workshops that led to such a high ranking for ‘opportunity to discuss’? Based on observation of the workshops, it appears that three factors contributed to the popularity of discussion. Firstly, there was the structure of the workshops, and the breakout exercises. The purpose of the exercises was to elicit participant feedback about individual strategy and cumulative targets for GHG emission and energy reductions. In terms of Fiorini’s (1989) classification of reasons for engagement, the Victoria workshops were strongly substantive in nature – i.e. focused on engaging with the stakeholders to improve the quality of the decisions made. As such, the purpose of the workshops was to promote discussion and consensus building, and the breakout exercise structure reflected that. The other two reasons for why discussion was the highest valued component in Victoria relate to the CAN tool. Firstly, the ‘facilitated access’ (Dahinden et al., 2003) to the model limited participants’ interaction with it, preventing them from becoming overly focused on the tool. In essence, all tool interactions had to be mediated by discussion, as participants instructed the facilitators about what they wanted to investigate. The sum effect of this was that participants used the tool as a form of ‘data Google’, querying it when necessary to inform their broader discussion. A second component of the CAN tool that may have promoted more discussion and less tool focus was the representation types it used. The CAN tool’s representation was limited to wedge graphs and text (see Figure 7.1). These simpler representation types may have contributed to participants’ reduced focus on the tool and increased focus on discussion. The value participants placed on discussion partially explains the ranking of the facilitators and the CAN tool as the second and third most valued items. Both the facilitators and the CAN tool contributed to the participants’ ability to discuss energy issues, either by moderating the discussion flow (facilitators), or contributing information (the CAN tool). The breakout sessions were ranked third, likely because they were less structured than the interactive exercises in the GCCP workshops. In other words, the breakout exercises in the Victoria workshops were largely a jumping off point for the discussions that were so highly valued. The very low ranking for the presentations was also inconsistent with the GCCP process (see Figure 6.45), where presentations were relatively highly ranked. Whereas the presentations were a focus of the GCCP process, with significant time and effort put into their preparation, the presentations for the 367 Victoria workshops were viewed primarily as a means of starting the discussion and familiarizing the participants with the CAN tool. This may be a result of the audience, as the Victoria workshop development team viewed the presentations less as a pedagogical or capacity building tool, because the participants already had significant energy knowledge. What was Helpful? This questions asked participants for their open-ended interpretations of what they found helpful about the workshops. The question asked was: Is there something you found particularly helpful about the today’s forum? If you answer yes, please briefly explain what you thought was helpful. Figure 7.6: Victoria participants' coded, open-ended responses about what they found most helpful (n=39). Participant responses were transcribed and coded in NVIVO 10 for dominant themes. Figure 7.6 shows the number of times particular themes were referenced in participants’ responses. Representative 312016128 75205101520253035number of references 368 examples of the top three responses are included below. Similar to Figure 7.5, discussion was clearly the most helpful component of the workshops based on participants’ open-ended responses: Yes, the ability to listen to and hear discussions from the different sectors and components. Victoria 1-11, cluster 36 High focus on dialogue. Victoria 1-22, cluster 2 The second most referenced theme was information or learning. This may have come either from the CAN tool, or from discussion with experts during the breakout sessions: Context around building retrofits and effect on energy use. Victoria 1-31, cluster 3 Background on GHG emission sources from tool. Discussion on financing and labeling policies. Victoria 2-17, cluster 5 The CAN tool was the third most frequently cited theme. The most commonly referenced aspect of the CAN tool was the real-time display of strategy and variable impacts: The tool's ability to demonstrate the impact of different choices. Victoria 1-16, no cluster. The uniqueness of the interactive energy tool and its empowering effect. Victoria 1-26, cluster 4 Additional themes included interactivity, the presence of expertise, hearing different perspectives, and the exercise facilitators – all supporting components of the deliberative discussions. Perhaps the most important finding from this question is the strong reinforcement of the value of discussion. When participants were not forced to rank multiple items, but rather to identify one key component of the workshops, discussion was an even clearer winner. What was Difficult? This question asked participants for their open-ended responses about what they found difficult during the workshops. The question asked was: 6 The text at the end of participants’ quotations indicates their participant number and the mental models cluster they were placed in by Chapter 5’s MCA. 369 Is there something you found particularly difficult, or unhelpful about today’s forum? If so, please describe what was difficult, and why. Figure 7.7: Victoria participants' coded, open-ended responses about what they found difficult (n=32). Participants’ responses were transcribed and coded for their dominant themes in NVivo 10. Figure 7.7 shows the number of times different themes were referenced in participants’ responses. The most commonly referenced component of the workshops was the CAN tool. The difficulties participants cited about the tool primarily referenced the inability to change model assumptions and the limited set of available options. A difficulty of modeling more broadly is that, by its very nature, it involves simplifying what it is meant to represent (Weisberg, 2013). By that nature, some things will always be left out. In modeling tools that expose the black box to the user, invariably the model is challenged and shortcomings pointed out. This appears to have been the case with the CAN tool, as it was widely praised, but also strongly criticized by a minority of participants who wanted more information and more options: 1197 6 5 4 3 3 2024681012number of references 370 The tool's limitation was its rigidity. In our group it restricted discussion of what policy tools the City would actually use. Victoria 1-16, no cluster. The analysis model is constrained in the scope and approach of GHG emission reduction strategies. Victoria 2-33, no cluster The second most commonly cited difficulty was with the workshop’s structure: Strange and frustrating to be asked to assert a number for how much uptake to expect BEFORE having conversation about 1) the conditions in Victoria 2) existing barriers, 3) political levers for change, 4) strategies to drive this, 5) tech and policy and regulation tools to influence or impair change. Victoria 2-25, no cluster Lack of outcomes for each session led to discussions going all over the map. Victoria 2-22, cluster 6 The third most common difficulty was missing or inaccurate information: Some of the information was not accurate or not municipal-specific enough. Victoria 1-11, cluster 3 The interactive energy tool - some of the assumptions and limited options (e.g. missed oil heating, which is a major source of emissions and home heating in this area). Victoria 2-4, cluster 4 Another concern that was reflected at both the GCCP and Victoria workshops was the impact of dominant individuals during the breakout sessions: Overpowered and opinionated individuals. Victoria 2-26, cluster 4 Managing dominant participants. Victoria 2-3, no cluster Dominant individuals are a feature of small group decision-making that can be difficult to address, particularly in face-to-face processes (Hiltz et al., 1986). Strong facilitation can mediate the effects of dominant individuals (Krueger & Casey, 2009), but it is difficult to completely nullify them. Therefore, some difficulty with group dynamics seems to be a necessary evil of engagement work. 371 Evaluating the CAN Tool The final question of the workshop evaluation section asked participants to provide open-ended evaluations of the CAN tool itself. This was the first time the CAN tool was used in an engagement exercise, so it was important to assess how well it worked. From the standpoint of the multiple case study evaluation conducted in this chapter, this question also provided the opportunity to assess the CAN tool in comparison to the tools used in the GCCP and Revelstoke processes. The question asked was: One of the new things we tried in today’s forum was the use of an interactive energy modeling tool to discuss the energy system. Please briefly state what you did, or did not, find useful about this tool. Participants’ responses about what they found useful or problematic about the CAN tool were transcribed and coded by dominant themes in NVivo 10. Participant responses were primarily positive (22/39 respondents), with a smaller number of negative (9/39 respondents) or mixed (13/39 respondents) comments about the CAN tool. Positive Comments Figure 7.8: Participants' coded responses regarding what they found useful about the CAN tool. 14872 20246810121416InteractiveChangesPromotedDiscussionNewInformationEase of Use PresentedTrade-offsnumber of references 372 Figure 7.8 shows the coded positive themes from the participants’ comments about the CAN tool. The most commonly cited benefit of the CAN tool was the ability to see real-time changes in GHG emission and energy use as a result of altering strategies and variables: Liked the immediate feedback on proposed changes. Victoria 1-1, no cluster The tool was great. It is very useful to be able to quickly and quantitatively see the actual (although modeled) impact of all the 'good ideas'. Victoria 2-11, cluster 4 The second most commonly cited benefit of the tool was its ability to promote discussion: Absolutely central to an interesting discussion. Victoria 1-21, cluster 3 Good tool, helped to facilitate discussion and prove/disprove concepts. Victoria 2-13, cluster 4 This finding is consistent with the interpretation of the scoring of workshop components, which suggested that the CAN tool was a strong contributing factor to the high value participants placed upon discussion. Other stated benefits of the tool included that it provided participants with new information and learning opportunities (7 references), that it was easy to use (2 references), and that it allowed participants to examine trade-offs between different strategies (2 references). Negative Comments Figure 7.9 shows the participants’ coded negative comments about the CAN tool. The most common problem that participants had with the CAN tool was with the underlying assumptions. Stated problems with the assumptions were of two main types. Firstly, some participants stated that particular model assumptions were incorrect and therefore produced erroneous results. Secondly, some participants were unhappy that the assumptions could not be challenged or edited in the CAN tool interface: 373 Figure 7.9: Participants' coded negative comments regarding the CAN tool. Some of the values were incorrect and it would have been great to edit it. Victoria 1-7, cluster 5 (The model) has built in assumptions that should be tweakable given the very loctional nature of some of the topics. Victoria 2-17, cluster 5 The second most common complaint about the CAN tool was that it required more detail in some areas: Understanding details of strategies more clearly would have helped. Victoria 1-1, cluster 3 Perhaps a little more precision on some choices other than "low, moderate, high". Victoria 2-8, cluster 3 In particular, participants seemed concerned about some of the detailed information associated with particular strategies – i.e. rather than selecting low, moderate or high retrofit intensities, they wanted to understand better what particular interventions (e.g. window replacement, insulation upgrades) comprised those intensities. While participants’ concerns about the presented level of detail may have been valid (i.e. more detail may have led to better decisions), it is also important to note that this desire for more 14114 42 20246810121416Problem withAssumptionsRequiredMore DetailIncorrectScaleNo Co-BenefitsNo Land Use Hard to Usenumber of references 374 detailed information is a common feature of engagement research. There will always be participants with particular expertise or pet issues that want more detailed information about a particular subject. Providing appropriate levels of detail is a constant negotiation for models used in engagement (Dahinden et al., 2003; Tansey et al., 2002). The next three complaints participants had about the CAN tool, while less prevalent, were more fundamental. Four participants felt that the CAN tool was operating at the wrong scale, and that it should operate at a more regional level to better capture energy and GHG issues such as transportation. Four participants also felt that the model was flawed because it did not consider co-benefits, particularly of some transportation interventions. They felt that this led to an over-estimation of the costs associated with several transportation actions. Finally, two participants specifically mentioned the lack of land use data as a fundamental problem with the CAN tool. Scale, Co-benefits and Land Use: Useful, but without tweaking for regional data beyond Victoria could lead to incorrect decisions (i.e. impact of regional transit improvements understated when only considering Victoria data). Victoria 1-19, no cluster Lacks reference to externalities, challenges to default $ per transportation in particular. Lack of integrated value representation for co-benefits in transportation. Victoria 2-12, cluster 5 The model does not consider smart growth land use policies, ignores the co-benefits of transport emission reductions strategies, and does not clearly define some strategies. Victoria 2-30, no cluster Workshop Evaluation Discussion The primary take-away from the workshop evaluation questions for the Victoria workshops is the high value that participants placed upon the discussions held in the breakout exercises. The participants also valued the workshop facilitators and the CAN tool, because they contributed to the quality and flow of the discussions. An interesting question arising from this finding is what elements of the Victoria workshops account for the high value placed on the discussion, particularly when compared to the GCCP and Revelstoke processes? Elements that may have played a role include the workshop audience (experts and stakeholders versus citizens), the workshops structure (looser discussion-focused breakout groups versus more directed exercises) and the tools used (‘facilitated access’ with a numerical model versus ‘direct access’ with more visually immersive tools). These components will be discussed in more detail in section 7.6. 375 The most common complaints participants had about the workshops related to the CAN tool and the workshop structure. For the CAN tool, participants were most concerned about the un-editable assumptions and the models lack of detail in some areas. More fundamental criticisms of the CAN tool related to the scale at which it operated, the lack of land use information and representation, and the lack of information about co-benefits of particular strategies. Conversely, participants praised the CAN tool for the value of interactively seeing the GHG and energy impacts of their decisions, and its ability to foster informed discussion. In other words, participants were supportive of the tool’s intent and design, but critical of the data inputs and outputs. 7.2.3 Mental Models Evaluation The Victoria questionnaires had a similar pre/post mental models question set to the GCCP questionnaires, with some modifications to reflect differences in the workshop goals and the information presented. This section will look at a subset of the mental models-related questions from the Victoria workshops for the purposes of comparison to the GCCP workshops. The questions discussed will include participants’ pre/post self-reported energy knowledge levels, participants’ pre/post rankings of different applied energy solutions, participants’ open-ended responses about what surprised them about energy, and participants’ open-ended responses about energy opportunities in Victoria (i.e. energy in place). The results of the mental models evaluation questions for Victoria will be compared to the GCCP results, and in a more limited way, the Revelstoke results, in section 7.5. Table 7.2 shows the breakdown of mental models cluster participants from the Victoria workshops. Chapter 5 documented the mental models cluster construction based on the Multiple Correspondence Analysis (MCA) and hierarchical clustering procedures. To meet the requirements of the MCA, participant questionnaires with missing values were eliminated from the analysis. As a result, the number of participants assigned to mental models clusters for both the GCCP and Victoria processes was a subset of the participants who filled out the workshop questionnaires. The Victoria workshops had a much larger number of questionnaires with missing values and, as a result, a smaller percentage of Victoria participants were assigned to mental models clusters (approximately 57% of participants that filled out the post workshop questionnaires). The reason for this higher attrition rate was likely a looser integration of the questionnaires into the workshop structure – i.e. participants were given less time and less encouragement to fill out the provided questionnaires. As a result, there were 26 participants that filled out the pre and post workshop questionnaires who had identifying mental models clusters. 376 Table 7.2: The number and relative percentage of each mental models cluster from the Victoria workshops. Cluster Victoria # Victoria % Cluster 1 (Limited Energy Knowledge) 1 4 Cluster 2 (Efficient Urban Development) 2 8 Cluster 3 (Muddled Middle Ground) 5 19 Cluster 4 (Power to the Individual) 7 27 Cluster 5 (National Action) 10 38 Cluster 6 (Let the Markets Decide) 1 4 Total 26 Partially as a result of the low overall n, clusters 1 (Limited Energy Knowledge), 2 (Efficient Urban Design), and 6 (Let the Market Decide) had very low participant numbers. The absence of the Limited Energy Knowledge cluster is likely a reflection of the expert and local stakeholder makeup of the Victoria workshops’ participant pool. Because there was no land use or spatial planning component to the Victoria workshops, people with the Efficient Urban Development mental model may have been less likely to be invited to the workshops. Observationally, this appeared to be true, as most of the government and consulting experts were from more technical energy and climate change fields, rather than planning or design backgrounds. The absence of participants from the Let the Market Decide cluster is more problematic, given the evidence that there is a strong contingent of people with this mental model in the broader population (cf. Kolk & Pinkse, 2004; Sandor et al., 2002). This suggests that participants with the Let the Market Decide mental model either were not invited, did not attend, or were less motivated to fill out the workshop questionnaires. Cluster 5 (National Action) had the largest representation, which is understandable, because this cluster was defined by a focus on federal action to address climate change. The main difference between the Victoria and GCCP workshop mental models representations occurred in clusters 3 (Muddled Middle Ground) and 4 (Power to the Individual). The Victoria workshops had a substantially larger proportion of participants with the Power to the Individual mental model, and a much smaller proportion of participants with the Muddled Middle Ground mental model. The absence of participants from the Muddled Middle Ground mental model in Victoria further supports the notion that this cluster is defined by a weakly formed mental model of energy at the community scale. A comparison of the GCCP and Victoria workshop mental models compositions will be made in section 7.5.1. 377 Self-reported Energy Knowledge This question was intended as a simple measure of participants’ self-assessed energy knowledge, recognizing the limitations of these types of self-reports (Howard, 1994). The post-workshop question asked of participants was: Has today’s workshop changed how you would rate your knowledge of the energy system (where energy comes from, how it moves, and how it is used) in Victoria? Please circle the most appropriate answer. f) I have very little knowledge of the energy system in Victoria g) I have some knowledge of the energy system in Victoria, but not a lot h) I would say I have an average level of knowledge about the energy system in Victoria i) I have a fair amount of knowledge about the energy system in Victoria j) I would consider myself an expert on the energy system in Victoria Figure 7.10 shows the participants’ pre and post workshop self-reported energy knowledge levels. Because the question asked participants to rate their knowledge on a 5-point Likert scale, the data were treated as continuous. The pre workshop mean was 2.80 and the post workshop mean was 3.5. A paired t-test conducted on the mean energy knowledge levels showed a significant shift from pre workshop to post workshop levels (p<0.001, df=45). At face value the results of this question suggest that many participants believed the workshop had increased their knowledge of energy in Victoria. Table 7.3 shows who changed their self-reported energy knowledge by mental models cluster. Interestingly, Cluster 5 (National Action) was the second most likely to change their knowledge rating in Victoria (70%), and the most likely to change the rating in the GCCP workshops (69%) (see Figure 6.34 in Chapter 6). This may reflect the local nature of the energy information presented at both workshops. In other words, they were motivated to attend based on larger scale climate change concerns, and felt they learned something new about local energy issues. Another interesting finding is that Cluster 3 (Muddled Middle Ground) showed a resistance to changing their self-reported energy knowledge levels in both the GCCP (37%) and Victoria (20%) processes. 378 Figure 7.10: Victoria participants' pre and post workshop self-reported energy knowledge levels (n=45). A cross-case comparison shows how closely the pre and post workshop self-reported energy knowledge levels tracked between the GCCP and Victoria workshops (see Figure 6.34 in Chapter 6). For the GCCP workshops, the mean values were 2.84 pre workshop, and 3.47 post workshop. Participants in the GCCP and Victoria workshops had both similar initial energy knowledge values and showed similar shifts in energy knowledge from the pre to post workshop questionnaires. A comparison of pre and post workshop self-reported energy knowledge values across processes, as well as possible explanations, will be discussed in section 7.5.2. Table 7.3: Participants who changed their energy knowledge rating by mental models cluster. Cluster Changed Rating Total in Cluster Percentage 1. Limited Energy Knowledge 1 1 100 2. Efficient Urban Development 1 2 50 3. Muddled Middle Ground 1 5 20 4. Power to the Individual 3 7 43 5. National Action 7 10 70 6. Let the Market Decide 0 1 0 8 1012 1511 4152330510152025Very Limited (1) Some (2) Average (3) Fair Amount (4) Expert (5)% of total respondentsPre Post 379 Applied Energy Solutions This question was intended to determine if participants’ valuation of different energy strategies changed as a result of participation in the Victoria workshops. The pre and post workshop question asked was: Please rank your top 5 approaches for addressing energy concerns in Victoria from the list below, with 1 being the method with the largest potential impact, and 5 being the method with the smallest potential impact of the approaches you choose. a) Improving public transportation options b) Increasing renewable sources of energy such as solar c) Reducing the size of people’s residences, for example through the creation of suites and carriage houses d) Making technical changes, such as energy retrofits on existing buildings e) Changing individual’s energy behaviours f) Collective options, such as district energy systems g) Providing incentives for people and businesses to conserve energy h) Setting higher standards for new building construction j) Reducing waste production Figure 7.11 shows the cumulative pre and post workshop scoring of applied energy solutions for all participants. For the purposes of graphing, the 1-5 ranks have been converted to scores, with 5 being the highest possible score. As Figure 7.11 shows, public transportation had the highest score both pre and post workshop, with very little change in score. This is interesting, because the costing data in the CAN tool showed GHG emission reductions from transportation to be a very expensive option. In other words, while the CAN tool showed the potential for large GHG reductions from transportation actions, the monetary trade-offs suggested that these actions were not cost effective. 380 Figure 7.11: Victoria participants' pre and post workshop scoring of applied energy solutions (n=49). Only local energy, decreased unit size and collective options (like DES) decreased in value from the pre to post workshop questionnaires. The drops in ranking for local energy and collective options are consistent with the modeling presented in the CAN tool, which suggested limited impacts for local energy generation and district energy systems (Miller & Cavens, 2012). The CAN tool did cover both building retrofits and new construction, but did not deal with land use, so there was no explicit coverage of decreasing unit sizes. This is an interesting counterpoint to the GCCP workshops, where decreased unit size was one of only two applied solutions (along with public transportation) that increased their score from the pre to post workshop questionnaires (see Figure 6.35). In Victoria, land use, zoning and density changes were not on the table and, therefore, were not discussed at length in the workshops. In addition, land use and density are highly spatial issues, and there was no spatial representation in the CAN tool. Whether as a result of affordances of the tool, the workshop goals and structure, or a combination of the two, there was a clear differentiation in the value placed on decreasing unit sizes between the GCCP and Victoria workshops. 2.940.820.652.711.981.331.431.063.040.240.413.022.670.722.371.2800.511.522.533.5mean scorepre post 381 Technical changes, individual behavior, incentives and building standards all increased in score from the pre to post workshop questionnaire, with incentives showing the largest increase in value. A Wilcoxon paired signed rank test showed that incentives was the only applied solution that was significantly different between the pre and post workshop questionnaires (V=451.5, p<0.05). This is instructive, because incentives were not part of the CAN tool modeling – i.e. it was not possible to implement incentives for any of the model strategies. The increased valuation of incentives may have caused participants to view government incentives as an effective means of mitigating the costs associated with particular strategies. In the GCCP workshops, where costing data was not provided, incentives were the second lowest scored solution (see Figure 6.35). This suggests that the affordances of the tools (the CAN Tool and suite of GCCP tools) had a strong outcomes chosen – i.e. that they impacted participants’ mental models in different ways. The importance of the costing data may also have affected the increase in score for the individual behavior option, because behavior change was given a cost per tonne for GHG emission reductions of $0.00. Similarly, ‘building standards’ for new construction could actually achieve a negative cost per tonne of GHG reduced, because operating costs were factored in, which may explain why it increased its score. These findings suggest that the costing information in the CAN tool had a noticeable impact on participants’ preferred applied solutions. Another feature worth mentioning about Figure 7.11 is the dispersed nature of the scoring changes. For Victoria, five of the eight options increased their scores. The reason for this may be the Victoria workshop’s structure. Recall that participants developed a comprehensive scenario in the morning breakout session, and spent the afternoon breakout sessions focused on one particular strategy. So, while the GCCP participants filled out the post workshop questionnaire after completing a scenario construction exercise for the Sandbox that involved utilizing and trading off all of the strategies, the Victoria participants filled out the post workshop questionnaires after focusing specifically on one strategy. Therefore, Victoria participants’ rankings of applied solutions may have been strongly influenced by the strategy they focused on in the afternoon session. Technical changes (building retrofits), building standards (new construction), public transportation (two transportation tables) and collective options (DES) were all focal strategies of the afternoon breakout sessions. It is notable that of these strategies, collective options (DES) is the only one that decreased its ranking, which is consistent with its underwhelming impact on GHG emissions and relatively high cost per tonne of GHG reduced, as discussed in the breakout exercises. 382 Table 7.4: The mean number of changed rankings by mental models cluster for the Victoria participants. Cluster Number in Cluster Mean Number of Changed Rankings 1 (Limited Energy Knowledge) 1 6 2 (Efficient Urban Development) 2 5.5 3 (Muddled Middle Ground) 6 4 4 (Power to the Individual) 6 4.5 5 (National Action) 11 4.9 6 (Let the Market Decide) 1 1 Table 7.4 shows the mean number of changed rankings by participants’ mental models clusters. The low numbers in clusters 1, 2 and 6 make it difficult to draw any conclusions from their values. Similar to the self-reported energy knowledge level data, the National Action cluster was most likely of the clusters with more than two participants to change their ranking of applied solutions, and the Muddled Middle Ground cluster was least likely. With the exception of cluster 6 (Let the Market Decide) all of the clusters made at least four (of a total possible of eight) mean ranking changes, suggesting that the workshops had a broad impact on participants choices of solutions. Table 7.5: Victoria participants' mean scores for the individual behaviour applied solution. Cluster Mean Pre Score Mean Post Score Score Change 1 (Limited Energy Knowledge) 2 4 2 2 (Efficient Urban Development) 2 1 -1 3 (Muddled Middle Ground) 2 2.67 0.67 4 (Power to the Individual) 3.67 3.5 -0.17 5 (National Action) 0.9 2.55 1.65 6 (Let the Market Decide) 0 0 0 Despite the evidence for broad changes in participants’ ranking of applied solutions, there is also evidence that their mental models impacted these effects. Table 7.5 shows the mental models clusters’ mean scores for the individual behavior applied solution. This applied solution was selected because it has the clearest connection to one of the mental models clusters (Power to the Individual). As Table 7.5 shows, the Power to the Individual cluster had the highest pre and post workshop scores for the individual behavior applied solution. In addition, the Power to the Individual cluster showed the smallest shift in score, indicating that their belief in the value of individual behavior solutions was not strongly impacted by the workshop information. By comparison, the National Action mental models cluster was 383 much more strongly affected by the workshop’s information about individual behavior, increasing their mean score by 1.65. The impacts of the workshops’ on participants’ ranking of energy solutions, then, was the result of both the information and participant interaction, and the participants’ pre-existing mental models. The Power to the Individual and National Action examples from Table 7.5 suggests that larger impacts can be seen when solutions don’t interact with existing ‘dearly held beliefs’. Summarizing, both the costing information and the workshop structure appear to have had a marked effect on participants’ post workshop ranking of applied energy solutions. This raises some questions regarding engagement on energy, and more broadly sustainability issues. It is understandable that participants’ in what Bendor (2013) calls an ‘analytical’ process (instrumental, deliberative and consensus building) would want costing information to make ‘rational’ choices about strategies, but what are the creative implications of relying so strongly on cost data? Participants in the Victoria workshops complained about the costing model’s inability to incorporate co-benefits and felt that, as a result, it undervalued transportation and other strategies. What other co-benefits, particularly qualitative ones that are difficult to value monetarily, might be overlooked because of a focus on costing data? Should there be some way of demonstrating other values in a tool like the Climate Action Navigator? Dahinden et al. (2003) in their review of interactions with climate change models suggest that models overemphasized the quantitative at the expense of important qualitative information. This echoes the qualitative versus quantitative tension discussed in Chapter 2 and in Salter et al. (2010). The effects of the workshop structure is also an interesting question for future research. The Victoria workshops movement from a morning exercise that combined multiple strategies to an afternoon exercise that focused on individual strategies appears to have led to very different results in the post workshop ranking of applied solutions than in the GCCP workshops. This raises a question about the structure of workshops relative to their intended goals. In other words, should engagement activities be working towards shared learning, shared mental models of energy and consensus, or is dissensus a completely valid result of engagement? It is interesting that the Victoria workshops, which were more substantive in their intent (i.e. they were designed to effect policy) ended with a less consensus-focused exercise, whereas the GCCP workshops, which were primarily normative in intent, culminated in a collective, consensus exercise. Finally, from a mental models perspective, the results of this question suggest a continued trend of particular mental models being less likely to change (Muddled Middle Ground), and of others strongly interacting with particular subject matter (Power to the Individual and the individual behavior applied solution). 384 What Surprised You About Energy? This open-ended question was used to capture what participants had learned from the workshops that they found surprising. Surprise is a strong indicator in the retention of novel information (cf. Schulz and Dickenson 2000). Understanding what participants found surprising, then, can be seen as one measure of changes to their mental models of community scale energy. The question asked of Victoria participants was: Did you learn anything today about energy that you found surprising? If so, please briefly explain what you found surprising. Participants’ responses were transcribed and coded for dominant themes in NVivo 10. Figure 7.12 shows the frequency of dominant themes from participants open-ended responses compared to responses7. Figure 7.12: Victoria participants’ coded responses about what they found surprising about energy (n=40). 7 It was possible for participants to have multiple themes in a single open-ended answer, therefore the number of references is greater than the number of respondents. 138 85 54 43 3 3 32 2 2 202468101214Number of References 385 Existing Energy System The dominant theme for the Victoria workshops was information about the existing energy system. This appears to reflect an existing lack of knowledge about the energy system at more local scales, but may also be augmented in the Victoria process by the fact that many of the energy and climate experts that attended the workshops were from outside the Capital Regional District. Pie chart of City of Victoria's GHG emissions by sector, as opposed to other cities. Victoria 1-18, cluster 5. Did not realize specific sources of GHGs for Victoria and how these differ from the region. Victoria 2-5, no cluster. Transportation Impacts The second dominant theme for the Victoria workshops was surprise about the impacts of transportation (see Figure 7.12). In the GCCP workshops all of the transportation responses referred to the large GHG emissions, and potential emission reductions, for transportation (see section 6.5.3). In Victoria, responses were mixed, with surprise about both the GHG emissions of transportation, and the high cost of GHG emission reductions in the transportation sector. Electric vehicles a non-starter due to disbelief by citizens that the tech is good enough (batteries and cost). Amazed that car culture is still so entrenched "can't live without my car" Victoria 1-30, no cluster The high cost of transit improvements for GHG reduction. Lack of integration to the regional context. Victoria 2-22, cluster 6. District Energy Systems District Energy Systems, while a component of both the GCCP and Victoria workshops, was much more prominent at the Victoria workshops, with an afternoon breakout session dedicated to exploring DES. This may account for the fact that DES was more frequently referenced as surprising in the Victoria workshops. Limited ability of district energy in this context/city to acheive GHG reductions. Victoria 1-22, cluster 2. Yes. That district energy is going to be more challenging than thought! Victoria 2-2, cluster 3 386 Similar to the GCCP workshops, there were also a large number of respondents that stated that they had not learned anything surprising about energy from the Victoria workshops (10 references). In addition to the common themes between the Victoria and GCCP workshops, there were a number of themes that were novel to the Victoria participants. Both waste impacts (4 references) and cost information (4 references) were likely the result of those factors being modeled in the CAN tool, but not present in the GCCP workshops. Themes such as new construction (3 references), commercial impacts (3 references) and building retrofits (2 references) were also more focal in the Victoria workshops than the GCCP workshops, perhaps explaining their presence in Victoria participants’ answers. Scale One theme from the Victoria workshops that was not present in the GCCP participants’ responses and which bears further discussion is the theme of scale. Four participants from the Victoria workshops directly referenced surprise or concern about the City of Victoria-confined scale of the workshops. In particular, participants referenced scale in relation to transportation: Yes, how little the City can do on its own and how it really has to be a regional/interjurisdictional approach. Victoria 1-16, no cluster. Scoping of GHG emissions was very geographically focused - i.e. Victoria focused and not regional focused. Victoria 2-26, cluster 4. The scale of the GCCP workshops was much smaller and yet no participants related the same concerns about it being inappropriately focused. This may reflect the impacts of working on the fictitious Sandbox rather than a real place, the predominately citizen vs. expert and stakeholder audience, or some real effect of working at a neighbourhood rather than a city scale. 387 Figure 7.13: Participants’ coded responses regarding what surprised them about energy coded by mental models cluster (n=40) – note that clusters 1, 2 and 6 have only 1-2 participants each. Figure 7.13 shows the participants’ responses about what surprised them about energy coded by the mental models cluster they belonged to. This data has been normalized similar to the data in Chapter 6, such that the cumulative responses for each cluster sums to 100%. Therefore, if all participants from all clusters referenced a particular theme, the total representation would be 600 on the graph. It is important to note again here that this does visually emphasize the responses of low n clusters. For example, only one respondent from each of Cluster 1 (Limited Energy Knowledge) and Cluster 6 (Let the Market Decide) responded to this question. Therefore Cluster 1 makes up a large visual proportion of the existing energy system theme, and Cluster 6 makes up a large visual proportion of the transportation impacts, scale, and cost themes. With that caveat in mind, it is notable that the Cluster 1 participant’s theme of surprise about the existing energy system, and the Cluster 6 participant’s discussion of cost are consistent with the clusters’ definitions. Similarly, Cluster 2’s (Efficient Urban Development) focus on DES is consistent with the 020406080100120140160180normalized % of respndents by clusterCluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 388 cluster’s focus on smart growth principles, because of the relationship between compact development and district systems (Hui, 2001). The Victoria data does provide the opportunity to explore the impacts of the clusters’ representations more thoroughly. In Chapter 6 I speculated that the more concentrated themes of clusters such as Cluster 2, Cluster 4 and Cluster 6 versus the more populous Cluster 3 and Cluster 5 may simply be the result of their low n’s in the GCCP data. Cluster 4 (Power to the Individual) had a larger n in the Victoria workshops (7 participants) and, as Figure 7.13 shows, its themes were more dispersed. Therefore, while the interpretation of Cluster 1, Cluster 2 and Cluster 6 in Figure 7.13 is encouragingly consistent with their cluster definitions, that interpretation should be viewed with caution. Much larger sample sizes would be necessary to substantiate the trends in mental models cluster responses for this question. The most salient finding to take away from the ‘what was surprising’ question from the Victoria questionnaires was how closely many of the responses tracked with the GCCP results (see section 6.5.3). In addition to the similarities, however, there were also process-specific results. In the case of Victoria, waste, new construction, commercial impacts and retrofits were all more prominent than in the GCCP workshops. A comparison between the GCCP, Victoria and Revelstoke ‘what was surprising’ results will be conducted in section 7.5.4. Energy Opportunities and Challenges in Victoria This question was meant to echo the question from the GCCP workshops that asked participants to relate energy planning to what they wanted for the future of their community (see section 6.5.3). Participants from the Victoria workshops, however, were not exclusively from Victoria and City of Victoria Sustainability Department staff felt that the question was not consistent with the aims of the workshop. Therefore, the question was changed to better reflect the workshop goals, while still maintaining a focus on energy in a place. The question asked of participants was: Are there any energy opportunities or challenges that you think are particularly important to Victoria? In other words, is there something special or unique about Victoria’s energy system? If so, please briefly explain. Participants’ responses were transcribed and coded in NVivo 10 for dominant themes. Participants tended to separate their responses into opportunities or challenges, with some participants referring to both. For the purposes of discussion, the opportunities and challenges have been separated. Figure 7.14 shows the opportunities participants identified as unique to Victoria. 389 Figure 7.14: Victoria participants' coded references regarding Victoria-specific energy opportunities (n=23). In part because of the wording of the question (asking about energy opportunities and challenges rather than their future vision of the community), but also likely due to the more technical nature of the workshops, most of the themes expressed centred around specific energy interventions. Buildings and Associated Themes The theme with the largest number of references was Buildings (7 references). Several themes associated with buildings were also mentioned, including HVAC Systems, Energy Sources, DES and Retrofitting. Opportunities for retrofits and hooking up existing buildings higher than other places in BC. Victoria 1-31, cluster 3 Optimal use of electricity is key - i.e. heat pumps. Victoria 2-13, cluster 4. 7 6 5 5 5 4 4 4 3 3 3 3 2 1 1 1012345678number of references 390 Transportation and Working Regionally Transportation (6 references) was the second most common theme. Transportation was often mentioned with the exhortation to act more regionally (4 references). This focus on scale, in particular the need to consider the City of Victoria in the region, was a common thread in the Victoria workshops. Opportunity to build on success of transportation programs. Leadership with other municipalities (esp. Oak Bay/Saanich/Esquimalt). Victoria 1-27, cluster 2. Smart growth policies that increase the portion of regional households that locate in accessible mult-modal neighbourhood, and much better walking, cycling and public transit services. Victoria 2-30, no cluster. Active Transportation, Neighbourhoods and People Despite the more common references to technical changes, there were also several suggestions that more closely approximated the focus on place and quality of life as stated in the GCCP answers. For example, themes of Active Transportation, Neighbourhoods, People, Village Centres and Quality of Life reflect the interface between energy and quality of life that was so prevalent in the GCCP responses. We are a bikable and walkable city. We should absolutely capitalize on this aspect of ourselves which is not readily available to many other cities. Victoria 2-11, cluster 4. Unique Victoria neighbourhoods - easier to shop, live, walk to work. Victoria 2-20, no cluster. Strong existing green culture - build on that. Meet the demand for workable, accessible alternative transport options. Make Victoria a showpiece - you're already half way there! Victoria 2-31, no cluster. Table 7.6: Participants' coded themes regarding specific challenges they saw for energy planning in Victoria (n=8). Buildings Energy Sources HVAC Systems Transportation Work Regionally DES Lots of Renters Low Growth 3 2 1 1 1 1 1 1 There were fewer participants (eight) who identified specific challenges for Victoria. Table 7.6 shows the coded themes for challenges participants anticipated for Victoria. Many of the themes were 391 common with the opportunity themes, but reflected difficulties Victoria might experience in implementing particular strategies. Yes, old infrastructure, lack of new construction, housing affordability vs. energy retrofits. Victoria 1-16, no cluster. Lack of ducting systems on electrically heated houses; continued use of home heating oil despite availability of natural gas. Victoria 2-4, cluster 4. Unique themes identified by participants were the high proportion of renters in the city, and low projected population growth. High proportion of renters. Who pays for improvements in a rental situation where the renter pays for energy? Victoria 2-15, cluster 4. Need to innovate in a place where population growth and development won't drive change. Victoria 1-22, cluster 2 Due to the combination of a relatively low response rate to the energy in Victoria question (31 respondents), the separation between opportunities (23 respondents) and challenges (8 respondents), and the relatively high number of responses from participants with no identified mental models cluster (14 respondents), stratifying this question by mental models cluster is not particularly informative. No participants from Cluster 1 answered this question, and no participants from Cluster 6 identified any opportunities. Perhaps the most mental model-consistent responses came from a participant in Cluster 6 (Let the Market Decide) when identifying Victoria-specific challenges: Cost/benefit investments in district energy/transit have a long return on investment - period. Victoria 2-22, cluster 6. And from a participant in Cluster 4 (Power to the Individual) when identifying Victoria-specific opportunities: I think from my experience with solar and the cost of gas here that all large domestic hot water users could economically install supplemental systems. Victoria 2-5, cluster 5. Participants of the Victoria workshops were largely optimistic about Victoria’s energy opportunities. Both opportunities and challenges tended to be technical and energy-focused, in part because of the framing of the question, but also likely due to the technical focus of the workshops. Despite this technical focus, however, a number of the participants themes did connect to the quality of life themes as expressed by participants of the GCCP workshops. Identifying the space where what 392 people value about, and aspire to, in their communities synergizes with sound energy planning seems like a profitable area for both research and practice. Systems such as smart growth and new urbanism explicitly attempt to make these connections, but have, to this point, had limited success (Godschalk, 2004; Downs, 2005). This will be explored more thoroughly in Chapter 8. 7.3 Revelstoke Form Based Code Workshops In the summer and fall of 2012, researchers from UBC’s School of Architecture and Landscape Architecture (SALA) and School of Community and Regional Planning (SCARP) conducted public engagement workshops in the City of Revelstoke in southeastern British Columbia to explore the implications of a proposed form-based planning code. Revelstoke is a small city of approximately 8000 residents, which, like other municipalities in British Columbia, was planning how to address the necessary GHG emission reductions required by the Provincial Government’s Bill 27 (Rutherford, 2009). As a key part of their measures to address Bill 27, Revelstoke’s planning department was proposing a Unified Development Bylaw (UDB) that would incorporate smart growth principles of zoning and land use to shape the growth and redevelopment of the city (Senbel et al., 2013). Ewing and Rong (2008) have suggested that, under the right circumstances, smart growth land use policies could reduce emissions by as much as 7-10% by the year 2050. In City-led public engagement efforts, however, residents expressed concern that the UDB would lead to development that was inconsistent with Revelstoke’s existing character (Senbel et al., 2013). A Pacific Institute for Climate Change funded research project was conducted in Revelstoke, by the UBC research group, using novel tools and engagement processes to explore the implications of the form-based code with Revelstoke residents. For the UBC research team, the goal was to determine “if and how visually clear, understandable, credible 2D and 3D visualization tied to metrics would affect people’s acceptance of smart growth design strategies aimed at reducing energy and emissions” (Girling et al., 2014b page 12). In terms of Fiorini’s (1989) and Stirling’s (2006) framework of public engagement, then, this was primarily an instrumental engagement activity, aimed at establishing trust for the form-based code. For the purposes of the workshops the UBC research team identified two neighbourhood centres that would be zoned for higher densities and mixed use commercial development under the proposed UDB. These neighbourhood centres formed the basis for the exploration exercises in the workshops. The two neighbourhood centres ranged from four to six blocks in size, with one centre located near the City’s 393 downtown and the other located in a more rural setting. Compared to the GCCP and Victoria processes, then, the Revelstoke workshops were focused on a smaller scale, and represented locations that were well known by all of the workshop participants (Senbel et al., 2013). 7.3.1 Workshop Tools The basis for the tools used in the Revelstoke workshops was elementsdb (www.elementsdb.sala.ubc.ca), a case-based database of urban land use elements, such as buildings, streets and open spaces. This database has been populated over the past two decades through case study research, and contains real, measured examples of various urban form elements. For neighbourhood planning, representative elements from the database can be configured to visualize and measure current, and potential future, neighbourhoods. Up until recently, neighbourhood designs using elementsdb would be developed by internal research and design teams, potentially informed by design charrette processes (cf. Girling et al., 2006), for subsequent presentation in planning documents, or at public events. Through a collaboration with UBC’s Computer Science Department (see Su, 2011) the Elements Lab has been investigating the implications of allowing practitioners, stakeholders and citizens to interact directly with elementsdb to generate their own neighbourhood designs using a collaborative, interactive touchtable (Girling et al., 2014a). The Revelstoke workshops represented the first opportunity to evaluate this touchtable interface for a neighbourhood design process involving citizens. Figure 7.16 shows both the touchtable interface (bottom image) and projected wall display (top image) for the Revelstoke workshops. The touchtable uses a federated architecture to connect web-based services to the table interface (Su, 2011). This federated architecture geolocates cases from elementsdb onto Google Maps and Google Earth to present both plan view and 3D perspective view representations of the cases in the real-world planning locations. Revelstoke Modeling Because of the case-based nature of elementsdb, and the touchtable interface’s focus on the placement of individual case elements (buildings, streets and open space elements), modeling data needs to be attached to cases in an archetypes approach. Each case has a 3-dimensional model associated with it, so dimensional information such as footprint, floor area, number of storeys, and number of dwelling units 394 can be calculated. Based on primary research for a location (e.g. census data), additional characteristics such as people per dwelling and jobs per commercial floor area can be calculated and aggregated for a particular design in real time. For the Revelstoke case study additional information was required to help participants gauge how their neighbourhood designs would impact energy use and GHG emissions. The research team first developed and calibrated building and transportation models for Revelstoke as a whole, and then downscaled this data to the case level so that individual elements could be substituted in new designs and their energy and GHG performance reflected in real-time metrics (Senbel et al., 2013). A Geographic Information Systems (GIS) spatial modeling approach was taken to calculating existing energy use and GHG emissions from Revelstoke buildings and transportation. Building energy was calculated using building footprints and parcel sizes. Using BC Assessment data, a simplified set of 13 zoning types was generated and assigned to all of the buildings in Revelstoke (Senbel et al., 2013). Floor area for each of the buildings was calculated using the building footprints and number of storeys. Energy intensity (MJ/m2 floor area) was then calculated using The Province of British Columbia’s Community Energy and Emissions Inventory (CEEI) data for Revelstoke (British Columbia, 2013) and Natural Resource Canada’s Energy Use Data Tables (NRCAN, 2007) for different dwelling types. This data was aggregated for the City of Revelstoke and calibrated against the existing CEEI data for the city. After calibrating the data, it was then adjusted for each of the 13 zoning types, resulting in energy intensity and GHG emissions data that could be calculated for individual building cases based on their floor area. For the purposes of future projections, new buildings placed by participants in their neighbourhood designs were assigned a 20% improvement in energy performance to reflect building standards and improved systems and appliances (Senbel et al., 2013). In addition, a density threshold was set that, if met or exceeded, would result in the installation of a district energy system, with a further 20% reduction in building energy use for the designed neighbourhood centre. Transportation modeling can be particularly difficult for small towns or cities where travel survey data may not be available. This is exacerbated when the intention is to reflect transportation data at even more resolute neighbourhood scales (Senbel et al., 2013). For the transportation modeling in Revelstoke, no neighbourhood scale data was available, so CEEI data for transportation emissions at the city scale were applied to the neighbourhood level census data. The transportation model calculated total vehicle kilometres traveled (VKT) for each fuel type, and used emission factors to calculate GHG emissions. This data was then coupled with population data to arrive at per capita and household transportation related emissions (Senbel et al., 2013). 395 Future transportation projections were treated similarly to the DES calculations. A threshold value for density was set as a trigger for transportation mode shift, such that achieving the threshold level of density would result in a shift away from personal vehicles and towards walking and transit use (Senbel et al., 2013). The resulting modeling of energy use and GHG emissions, then, was primarily dependent on urban form and the designs participants constructed in the workshop exercises. This focus on urban form is quite similar to the Integrated Land Use scenario and associated strategies in the GCCP workshops (see Figure 6.6 and Table 6.2), though it operated at a smaller scale, with individual buildings, rather than quarter to full block scale interventions. The digital integration of this model with the touchtable interface in the second workshop also allowed the data generated to be reflected in real time metrics of building and transportation energy use and GHG emissions, rather than the simplified star system used in the GCCP workshops. The Touchtable Interface A second new development for the Revelstoke workshops was the addition of energy, GHG, transportation and district energy modeling modules to associate those metrics with current conditions and participants’ neighbourhood designs. These modules required the development of new modeling protocols. Figure 7.15: The arrangement of the survey (1), individual scenario (2), touchtable and projected screen displays (3), and the final focus group (4) (image courtesy of the Elements Lab). 396 Figure 7.16: The touchtable interface (bottom left), projected wall display (top left) for the Revelstoke workshops. Figure 7.15 shows the physical arrangement of the touchtable and the associated projected display. Up to four participants can interact with the touchtable at any time, because of the table’s multitouch capabilities. The touchtable interface provides four case palettes, one along each side of the table (see Figure 7.16). Using the palettes, participants can drag cases (buildings, etc.) onto the plan view Google Map and locate them in their proposed design. The placed cases will be reflected visually on the 2D map and the associated 3D view in Google Earth on the wall display (see Figure 7.15). The associated case data is also displayed either in tooltips associated with the case on the 2D view, or in the cumulative metrics presented on the projected wall display (see Figure 7.16). 397 Cases placed in the interface can be represented either as land use zoning coloured massings, or as articulated 3D conceptual building models (see Figure 7.17). In the Revelstoke example, participants were limited to building cases (i.e. there were no street or open space cases available), and they were required to place buildings individually. Figure 7.17: The different representation types available in the touchtable interface (image courtesy of the Elements Lab). 7.3.2 The Workshop Process The Revelstoke workshops were an open invitation event intended for Revelstoke residents. Participants registered for the workshops in advance, because the workshops were structured so that only one small group (groups ranged from 2 to 4 participants) was using the single available touchtable at any one time. Participant groups were arranged primarily by their availability, with many of the groups signing up with friends, neighbours or relatives. Participants were sent a 2-page briefing document that discussed the workshop objectives and agenda. Unlike the GCCP and Victoria workshops, the Revelstoke process was split into two, iterative sessions. One session was conducted in June of 2012 and the subsequent session was held in September of 2012 with a subset of the same participants. In the June workshop participants focused only on the urban form implications of the UDB, with minimal associated data such as population and jobs created (Girling et al., 2014). In the September workshop participants were introduced to the energy and GHG emissions implications of their designs from the June session and provided with the opportunity to alter their designs. 398 Workshop One (June) The June workshops (workshops one) were conducted in a meeting room at a local hotel over a 2 day period. The workshop was separated into four stations, which each group completed sequentially. At station one, participants were asked to fill out a pre-survey using provided ipads. This survey elicited participant attitudes about climate change, energy, smart growth and buildings (Girling et al., 2014b). Figure 7.18: An example of the paper-based neighbourhood scenario construction materials from the second workshop station. At station two, participants were asked to develop their own individual scenario for one of the two neighbourhood centres using a paper based map and associated building cards (see Figure 7.18). All members of a group worked on the same neighbourhood centre. The building cards were constructed from elementsdb cases, so that the neighbourhood designs could be subsequently analysed, and also easily transferred to the touchtable interface. Building cards were colour coded by land use and participants were instructed to match their designs to the future land use designations on the maps based on their building preferences (see Figure 7.18). Participants were given 20 minutes to complete this exercise. At station three, participants each took their individual scenarios from station two over to the touchtable for reference. At the touchtable, participants deliberated to develop a collaborative design scenario for the same neighbourhood they had worked with at station two, using their individual designs for reference and negotiating an acceptable group scenario. For the June workshops, the touchtable interface and 399 associated wall display presented a limited subset of information to the participants, such as number of dwelling units, jobs and building height (Girling et al., 2014b). Both the individual paper-based exercise at station two, and the group touchtable exercise at station three, then, were focused on urban form and associated determinants such as neighbourhood character and the aesthetics of the buildings. Participants were given 45 minutes to complete the touchtable exercise, to account for the added complexity of learning the interface, and deliberating in a group. At station four, participants took part in a facilitated focus group debrief. The focus group asked participants to reflect on the workshop and the tools used. Participants were given 15-25 minutes to complete this station. Following their participation at station four, participants were free to leave the June workshop. Intervening Months During the intervening months between the June and September workshops, the research team analysed the designs of the 11 workshop groups, developing the energy and GHG emission results for each group scenario. The research team also implemented the energy and GHG metrics in the touchtable interface for use in the September workshop. In addition to the modeling and interface development, a second, post-workshop survey was developed for use at the September workshop. Workshop Two (September) All of the participants that attended the June workshop were invited to re-convene for the September workshop, conducted at the City of Revestoke recreation centre over a 2-day period. Because the intention was for participants to re-visit their June designs with additional information, participants were organized into the same groups. Similar to the June workshop, the September workshop was separated into four stations, though the station exercises differed. At station one, participants were shown a 20 minute presentation that reviewed the June workshop results, introduced energy and GHG emission concepts and their relationships to urban form, and discussed the City of Revelstoke’s emission reduction goals (Girling et al., 2014b). 400 At station two, participants were given print outs of their group’s June workshop design and asked to make any revisions or annotations they felt were necessary. While the participants made their own individual annotations and revisions, they were encouraged to discuss them as a group. At station three, participants re-convened at the touchtable to deliberate on their June designs using their individual revisions and annotations from station two, and the additional energy and GHG emissions information presented in the touchtable’s associated wall display (see Figure 7.16). The additional metrics included a building density measure with an indicated DES threshold, an overall and per capita GHG emissions estimate, and an estimation of household energy costs. Participants were given 30 minutes to complete the re-design of the neighbourhood centre. At station four, participants were provided with ipads and asked to fill out a post-workshop questionnaire that was similar in content to the June pre workshop survey. 7.4 Workshop Results and Discussion The Revelstoke workshops presented the opportunity to compare the GCCP and Victoria workshops to a third process with both similarities (e.g. subject matter, underlying modeling) and notable differences (e.g. iterative design, interactive touchtable, scale). The Revelstoke process, however, had existing research goals and an existing evaluation design making the inclusion of the full pre/post questionnaire as used in the GCCP and Victoria processes infeasible. Therefore, a very limited subset of the questions asked in the GCCP and Victoria workshops was available for analysis. In some cases, similar questions from the existing Revelstoke workshop evaluation will be used for comparison, but it was not possible to conduct a comparable workshop, or mental models evaluation. Nonetheless, as a case study of community scale energy planning engagement, and in the limited set of questions asked, there are some interesting points of comparison between the Revelstoke, GCCP and Victoria processes. 7.4.1 Demographic Data: Who Participated The Revelstoke workshops were open invitation events, though participation in the September workshop was limited to participants who had already taken part in the June workshops.One challenge of an iterative workshop design is participant attrition, which may reflect a loss of interest, or simply the difficulty of scheduling multiple events for multiple participants. Participant attrition was a concern of the Revelstoke workshops, as the June workshop had 46 participants, while the September workshop had 27. 401 While the Revelstoke surveys used their own demographic questions, there was some commonality with the questions asked on the GCCP and Victoria questionnaires. In particular, common demographics questions included participant age and gender. Age Figure 7.19 shows the participants’ age ranges for the June workshop. One of the workshop groups was formed from a class of students at the local secondary school, accounting for the five participants from the under 19 age class. The Revelstoke data was more resolute than the categories used in the GCCP and Victoria workshops (10 year age ranges rather than 20 year age ranges). Similar to the GCCP and Victoria workshops, however, the vast majority of the participants were from the 25-64 working age range (85% for Revelstoke, 83% for Victoria, and 91% for GCCP). Observationally, the participants from the Revelstoke workshops were younger than both the Victoria workshops and the Marpole workshop from the GCCP process. The Grandview and Revelstoke processes appeared to have the largest representations of participants younger than 30 years old, though the Grandview workshop had no participants from the under 19 age class. Figure 7.19: Revelstoke participants' ages at the June workshop (n=46). A trend continued by the Revelstoke workshops is the low, to non-existent participation of people from the 19-24 age range. The Victoria workshops had one participant from this age range, while the 110302017 172 205101520253035Under 19 19-24 25-34 35-44 45-54 55-64 65-74 75 andover% of total respondents 402 Grandview workshop had two participants from this age range. The Revelstoke workshops also only had two participants over the age of 65. Of all of the workshops studied, only the Marpole workshop had a significant number of participants from this age range (4 participants, or 21% of the workshop). The Revelstoke age data suggests that, in order to reach less represented age ranges (e.g. teens, early 20’s) it may be necessary to specifically recruit these groups through schools or local colleges. This does, however, introduce another demographic factor, as these participants would not be self-motivated to attend – i.e. questions could be raised about their representativeness of the ‘interested public’. A factor in favor of targeting younger audiences is the future focus of community energy planning - younger audiences may have a particular stake in the future of their communities. Gender Participants were asked to report their gender as female, male or ‘prefer not to say’. There were more female participants (54%) than male participants (46%). The Planners workshop in the GCCP process was the only other workshop that had more females than males. The Victoria workshops had the most skewed gender participation with 29 males (63%) and 17 females (37%). Recall from the cluster analysis in Chapter 5 (see Figure 5.27), that gender was correlated with dimension 2 of the MCA analysis (individual versus collective action). Therefore, relatively large discrepancies in gender representation might impact participant views on community scale energy solutions. The limited demographic data from both the Revelstoke and Victoria processes make a detailed demographic comparison between the processes infeasible. In particular, it would have been useful to have cultural background, language and environmental involvement data for both the Victoria and Revelstoke processes. What we can say, based on the available age and gender data, is that the invited stakeholder workshops in Victoria had more older working age (45-64) males than the open invitation workshops, that all of the workshops had difficulty attracting participants from the 19-24 age demographic, and that only the Marpole workshop had a significant proportion of participants from the over 65 age demographic. 403 7.4.2 Workshop Evaluation Similar to the demographic data and the mental models-related questions, there was a limited set of workshop evaluation results from the Revelstoke workshops that were relatable to the GCCP and Victoria workshops. The most direct comparison came from a question on the September workshop survey that asked participants to rank particular components of the workshop. Additionally, there were participants’ responses in the focus groups at the June workshop about the touchtable interface (Girling et al., 2014b). Ranking of Workshop Components This question was included to assess the relative perceived benefit of different workshop components at the September workshop. The question asked was: Please rank the top four most helpful workshop components. 1. Presentation on energy and neigbourhood design 2. Reflection exercise on personal preference 3. Opportunity to discuss energy issues with fellow Revelstoke residents 4. Wall display of neighbourhood scenarios 5. Touchtable map with menu of building choices 6. Real-time 3D view of neighbourhood scenarios 7. Real-time measurement tools In both the GCCP and Victoria workshops, a subset of participants misinterpreted the similar workshop component ranking question, and instead rated the components. For example, they may have rated three different components as a ‘1’, two components as a ‘2’, etc. This problem was greatly exacerbated in the Revelstoke survey, with half of the participants (13 of 27) rating rather than ranking the components. As a result, the useable n for this question was only the 13 participants who correctly ranked the components. Figure 7.20 shows the participants’ scoring of the workshop components. For the purposes of graphing, the original 1-4 ranks have been converted to scores, with a score of five being the highest possible score. 404 Figure 7.20: Revelstoke participants' scoring of the September workshop components (n=13). The energy and neighbourhood design presentation was clearly the highest ranked component of the workshop, followed by the 3D neighbourhood visualizations, the real-time metrics of their design’s performance, the touchtable’s 2D maps and building palettes and the opportunity to discuss energy issues with other participants. The wall display of their previously developed scenarios from the June workshop was not ranked by any participant. Two things are notable from these results. Firstly, like the GCCP workshops, the presentation elements and the 3D visualizations were both highly ranked. Like the GCCP workshops, a significant amount of time and design effort went into the construction of the presentation. Also similar to the GCCP workshops, the Revelstoke workshops were highly visual and spatial, relying on 2D and 3D representations of the neighbourhoods and graphical displays of the energy and GHG metrics. Secondly, like the GCCP workshops, and unlike the Victoria workshops (see Figure 7.5), participants ranked the opportunity to discuss energy issues with other Revelstoke residents very low. The Revelstoke data, then, corroborates the notion that something fundamentally different was happening between the highly visual and spatial GCCP and Revelstoke workshops, and the primarily discussion-based Victoria workshops. The main differences between the workshops appear to have been the 2.921.921 10.850.54000.511.522.533.5mean score 405 audience (expert/stakeholder vs. citizens), the visual media (text and graphs versus spatial 2D and 3D visualizations), and the particulars of the subject matter (energy without land use implications versus a predominate focus on land use implications for energy). These points will be discussed further in section 7.6. Focus Group Results This section will briefly review the results of the focus group discussions that occurred at the end of the June workshops. In particular, it will review comments from the focus groups that relate to the workshop evaluation sections of both the GCCP and Victoria workshop. For a more detailed description of the focus group results, see (Girling et al., 2014b). Workshop Tools On both the GCCP and Victoria exit questionnaires, participants were asked to describe what was helpful and what was difficult about the workshops. Additionally, a question from each of the GCCP and Victoria workshops asked specifically about a novel tool that was used in the workshops. For the GCCP workshops, that was the Sandbox generic neighbourhood. For the Victoria workshops, it was the CAN tool model and interactive interface. These results were elicited as open-ended questions and then transcribed and coded. For the Revelstoke workshops, this information was primarily recorded at the group level through the focus groups conducted at the end of the June workshops. It should be noted that the focus groups occurred before the energy information was presented at the September workshop, however, it is relevant here, because the focus of the tool discussion is about how the tools facilitated engagement in the exercises, rather than the energy subject matter per se. The novel tool used in the Revelstoke workshops was the touchtable interface and the connected real-time 3D visualizations and metrics. When asked about the specific tools used in the workshops, eight out of 12 groups stated that the touchtable, 3D visualzations and real-time metrics helped them understand the implications of their designs. More specifically, 10/12 groups felt that the 3D visualzations were particularly helpful in allowing them to understand the design implications for future neighbourhoods. Similar to the ranking exercise in section 7.4.2, then, the 3D visualizations were particularly important to the participants when discussed in the focus groups. This mirrors the findings of the GCCP workshop, where the neighbourhood visualizations were the highest scoring media type from the workshops (see 406 Figure 6.46). The visualizations in both processes were valued for their explanatory power in helping people understand concepts, and in visualizing future conditions. The most commonly referenced difficulty in the focus groups was lack of building options that participants felt were consistent with Revelstoke’s character (Girling et al. 2014b). In particular, participants wanted buildings with fewer storeys to reflect what they felt was the current character of the city. This focus on aesthetics and character are likely the result of the Revelstoke workshops’ focus on a smaller, neighbourhood scale. The focus was also influenced by the lack of energy and/or GHG emission data in the June workshop. Other difficulties included a level of disagreement with the proposed development scale, based on Revelstoke’s currently declining population (i.e. observed versus projected population changes), and a desire for costing data for the proposed developments (Girling et al., 2014b). 7.4.3 Mental Models-Related Questions While it was not possible to include the mental models pre/post workshop questions for the Revelstoke process, there were select questions on the September workshop’s questionnaire that related closely to similar questions asked on the GCCP and Victoria questionnaires. This section will briefly summarize these results as they compare to those from the GCCP and Victoria data. What Stood Out About the Workshop This was an open-ended question on the post-workshop questionnaire for the September workshop. It is included here, because it closely approximated the open-ended question on the GCCP and Victoria workshop questionnaires that asked participants what they found surprising about energy from the workshops. Indeed, many Revelstoke participants specifically referenced ‘surprise’ in their answers. Participants’ responses were transcribed and coded for dominant themes in NVivo 10. Figure 7.21 shows the number of coding references for participants’ dominant themes about what stood out to them from the workshop. It should be emphasized that this question was posed after the September workshop, when participants had revisited and revised their previous designs based on energy and GHG emission metrics. 407 Density and DES The dominant themes which participants discussed were density (14 references) and district energy systems (11) references. These themes were commonly mentioned together. In particular, participants expressed surprise at how much density was required to meet the DES threshold: I was surprised the amount of density needed to reach the district energy targets. Revelstoke5 How high the district energy threshold is. How hard it is to get my girlfriend to accept any amount of density. Revelstoke17 I hadn't really thought about how significantly the density of housing impacts the amount of energy used. Revelstoke21 This participant focus on density reflects the subject matter of the workshops more broadly (exploring the implications of the UDB) and the September workshop more specifically, where presented information and targets for DES and transportation mode shift focused on the impacts of density. Figure 7.21 : Revelstoke participants' coded themes about what stood out to them from the workshop (n=26) 141172 2 2 1 10246810121416number of references 408 Existing Energy System Similar to both the GCCP and Victoria workshops (see Figure 7.12), another common theme that stood out to participants in Revelstoke was aspects of the current energy system. As in the GCCP and Victoria cases, presentation of some of the fundamental aspects of the energy system, such as demand levels or supply sources, surprised some participants and conflicted with their assumptions. I was surprised to find the number that was presented as the average energy use in Revelstoke was so low. Revelstoke2 The amount of energy that Revelstoke consumes and how much it could go up! Revelstoke23 When compared with the GCCP and Victoria workshops, participants at the Revelstoke workshops presented fewer themes in their responses about what stood out from the workshops. This is understandable, considering the Revelstoke workshops were considerably shorter sessions, and were more focused in the energy and GHG emission topics they covered. Recall from Figure 6.39 and Figure 7.12 that the existing energy system and transportation impacts were the dominant ‘surprising’ themes in both the GCCP and Victoria workshops. The Revelstoke focus on density and DES, then, suggests a qualitative shift in what participants took away from the workshops. Though this was clearly influenced by the focus of the September workshop, it would be interesting to determine if it is also related to the scale of the discussion and/or affordances of the touchtable tool. Factors such as building aesthetics and character, and inter-building relationships are much more prominent at these small scales. It is also possible that the intimate knowledge participants had of the two study areas impacted their understanding about density. There were far fewer comments about density in the GCCP workshops where the generic neighbourhood was used. What Would You Like Revelstoke to be Like in the Future? This was an open-ended question on the September exit survey. Much like the ‘energy planning and your community’ question on the GCCP questionnaire, and the ‘energy in Victoria’ question on the Victoria workshop questionnaire, the intention of this question was to summarize how participants conceived of place and, in particular, whether participation in the workshops caused them to incorporate ideas of local energy and GHG emission planning in those conceptions. Participants’ open-ended responses were transcribed and coded for dominant themes in NVivo 10. Figure 7.22 shows the prevalence of different themes from the participant responses. 409 Density Density was the most commonly referenced theme (11 references). This was influenced by the September workshop, which focused intensively on the implications of density for energy use and emissions, and on its secondary effects on district energy systems and transportation mode split. In other words, when discussing their future city, the theme of density was primed by their recent participation. Figure 7.22: Participants' coded themes about what they would like Revelstoke to be like in the future (n=26). I would like the City planners to be more sensitive to neighborhood character and to encourage higher density in specific neighborhoods and allow higher density in areas currently not allowing it. I would like to see increased density (not necessarily high density) in most residential areas (such as carriage houses duplexes and 4 plexes). Revelstoke2 119 8 85 5 5 5 4 4 3 3 3 2 2 2024681012number of references 410 I think Revelstoke is already a relatively compact town… if homes were made more energy efficient then I really don't think neighbourhood design is the key factor. Revelstoke17 Character and Built Form The second most common theme, community character (9 references), was often placed in juxtaposition to the idea of increasing density. In particular, some participants were concerned that density would lead to built forms that were inconsistent with the community’s current character: Mixed use along arterials, supported by single-family/multi-family that respects our heritage character. Revelstoke3 Certain parts of town could have higher density providing there is better public transit to those areas. I disagree with large impersonal buildings. Revelstoke15 I would hope that we make our developments attractive and with some character so that Revelstoke maintains a certain image, thereby making it attractive to tourists and investment. Revelstoke20 Equity, Quality of Life and Affordability Of the remaining themes, perhaps the themes of equity (5 references), quality of life (3 references) and affordability (3 references) were the least directly related to the workshop’s focus on urban form and energy. Inclusive, respectful, efficient, affordable, appropriate and well maintained infrastructure, clean water, clean streets, safe, protected, accessible, with a wine cellar in every basement. Revelstoke14 I would like to see Revelstoke remain an inclusive community that is active in the progressive development of the city. Revesltoke8 I would like to see more long term planning for neighbourhood development that encourages community interactions and engagement, local food production, reduces the amount of travel necessary, and enables more sustainable life/living choices while enhancing people's integration with natural space. Revelstoke12 Finally, of note was one participant’s direct incorporation of the workshop tools into their vision of a future Revelstoke: This interactive and virtual reality format is always open to the public, measurable vs other neighbourhoods and cities in Canada and worldwide, real time and the final word with city planning. Residents and even visitors can pitch in thoughts of changes that can be made and subsequently voted on by the people. Revelstoke16 411 This comment is interesting, because it envisions a future where interactive engagement is an ongoing part of the procedural (see Robinson and Cole, 2014) negotiation and co-creation of sustainability for Revelstoke. Broadly speaking, participants’ responses about what they wanted from a future Revelstoke used the frames of density, urban form and sustainability from the workshops, but expanded them to include other aspects of place that they valued. Those might be physical characteristics such as walkability and transit options, or social and cultural, such as character, equity, engagement and quality of life. In essence, thinking of place and desired futures in the presence of information on density and energy cause participants to try to incorporate that information into their broader conception of a future Revelstoke. 7.5 Discussion: Comparing the Three Processes’ Mental Models Results Table 7.7 summarizes the findings for the three workshop processes across five key questions. Characteristics of the workshop processes prevented the standardization of question sets, therefore, as Table 7.7 shows, there were cases where questions were not asked in Revelstoke, and there were also cases where question formats differed. These differences will be discussed with the summaries for each question in the sections that follow. 7.5.1 Who Participated: Mental Models Clusters The first point of comparison relates to who participated from a mental models perspective. The first row of Table 7.7 shows the number of participants from each of Chapter 5’s mental models clusters at the GCCP and Victoria workshops, and Table 7.8 shows the relative percentages for each cluster. Because the full suite of pre workshop mental models questions could not be asked in Revelstoke, mental models clusters were not assigned to the participants from that process. 412 Table 7.7: A summary comparison of the mental models findings from the three processes. Question GCCP Victoria Revelstoke Mental Models Clusters (number of participants in each cluster per process) Limited Energy Knowledge - 3 Efficient Urban Development - 2 Muddled Middle Ground - 21 Power to the Individual – 2 National Action - 16 Let the Market Decide - 3 Limited Energy Knowledge - 1 Efficient Urban Development - 2 Muddled Middle Ground - 5 Power to the Individual – 7 National Action - 10 Let the Market Decide - 1 Unknown Self-reported energy knowledge Participants self-reported knowledge levels increased from a mean score of 2.84 to a mean score of 3.47. The Grandview workshop dramatically increased the overall GCCP score (see Figure 7.23). Participants self-reported knowledge levels increased from a mean score of 2.80 to a mean score of 3.50 Not Asked Ranking of Applied Solutions From the pre to post workshop questionnaires, only public transportation and decreased unit size improved their ranking. Local energy, technical changes, individual behavior, collective options and incentives all decreased. Technical changes was the highest ranked item on the pre workshop survey, public transportation was the highest ranked item on the post workshop survey. These findings were consistent with the modeling and information presented. From the pre to post workshop questionnaires, public transportation, technical changes, individual behavior, incentives and building standards all improved their rankings. Local energy, decreased unit size and collective options all decreased. Public transportation was the highest ranked item on the pre and post workshop surveys. The dispersed changes in ranking were influenced by the workshop structure (focus on individual strategies) Not Asked What surprised you about energy? The most common themes from participants’ responses related to characteristics of the existing energy system and the large transportation impacts. The most common themes from participants’ responses related to characteristics of the existing energy system and the size and cost of transportation impacts. The most common themes from the participants’ responses related to the implications of density for energy and GHGs and characteristics of the existing energy system. Energy in Place The most common themes were quality of life, sense of place, environmental impacts and social reasons. Participants related energy information from the workshops with what they valued about a community. Participant responses focused on technical opportunities and challenges for Victoria. Buildings and transportation were the dominant themes. Despite a focus on the technical, however, there were noticeable connections to quality of life themes. The most common themes were density and character. Participants related energy and GHG information from the workshops with what they valued about Revelstoke. 413 Table 7.8: Participant numbers and relative percentages for each mental models cluster in the Vancouver and Victoria workshops. Cluster GCCP # GCCP % Victoria # Victoria % Cluster 1 (Limited Energy Knowledge) 3 6 1 4 Cluster 2 (Efficient Urban Development) 2 4 2 8 Cluster 3 (Muddled Middle Ground) 21 45 5 19 Cluster 4 (Power to the Individual) 2 4 7 27 Cluster 5 (National Action) 16 34 10 38 Cluster 6 (Let the Markets Decide) 3 6 1 4 Total 47 26 The most noticeable difference between the GCCP and Victoria workshops was the large differences in the ‘muddled middle ground’ and ‘power to the individual’ clusters. The GCCP workshops had a significantly larger proportion of participants from Cluster 3. In fact, Cluster 3 was the single largest group in the GCCP workshops. Conversely, Victoria had a much smaller proportion of participants from Cluster 3, and a noticeably larger proportion of participants from Cluster 4. The shift in Cluster 3 between workshops is consistent with the interpretation that Cluster 3 participants are unified by a poorly formed mental model of energy. Under that interpretation, one would expect there to be more participants from this cluster at the citizen-focused GCCP workshops than the expert and stakeholder focused Victoria workshops. Further supporting this idea (when the GCCP data is broken down further by workshop), is the fact that the more expert Grandview workshop had fewer ‘muddled middle ground’ participants (23%) than either the Planners (50%) or Marpole (39%) workshops. The shift in the proportion of participants to the ‘power to the individual’ cluster between the GCCP and Victoria workshops is more difficult to interpret. Is there some reason to expect experts to have a more individualistic view of energy solutions than lay-people? One possibility is that people who attend an open invitation event on community planning are predisposed to more collective solutions, whereas experts attending a workshop as part of their job requirements might not be. In other words, participants with more individualistic views might be under-represented at community events. As Table 7.8 shows, there were very few participants from the ‘power to the individual’ cluster at the GCCP events, and none of those participants attended the more 414 expert Grandview workshop. The opposite conclusion is also possible – that experts are, indeed, more individualistic than the broader public. Cluster 5 (National Action) was relatively consistent at both workshop processes, making up approximately 1/3 in both. This may suggest that a focus on the Federal Government’s role in addressing climate change is a popular mental model of energy in the broader population, though further research would need to substantiate that claim. If ‘national action’ is a dominant mental model for addressing energy issues, it suggests significant work will be required to foster a more local approach. Encouragingly, the participants from the ‘national action’ mental model demonstrated a consistent willingness to change their opinions on energy issues and solutions (cf. Table 7.3 and Table 7.4). Clusters 1, 2 and 6 all had very limited representation at both processes. A question for future research is whether or not this is indicative of their representation in the larger population. Based on the Cluster definitions, I would speculate that Cluster 2 (Efficient Urban Development) is rare in the larger population, because it is defined by a specific knowledge set around urban planning. Clusters 1 (Limited Energy Knowledge) and 6 (Let the Market Decide) anecdotally seem like they are more prevalent in the larger population, suggesting that they did not attend the engagement events in representative numbers. Why might they stay away? For Cluster 1, they may simply feel unqualified or lack the motivation to engage with the subject of energy. For Cluster 6, there could be a number of reasons; one possibility, suggested by a Cluster 6 participants’ response to the ‘energy planning and your community’ question on the GCCP workshop questionnaire, is that they question the efficacy of planning for energy at the community scale. One benefit of the mental models methodology as described in Chapter 5, is that it worked with questionnaires, suggesting that it could be scaled up to much larger mail or online surveys, which could be more broadly representative and could further elucidate the characteristics of the 6 mental models clusters. It would have been informative to have mental models information from the Revelstoke process, both as a means to triangulate the results of the other two processes (in particular citizen versus expert models), and to see the impacts of a small, rural city versus larger, urban population centres. 415 7.5.2 Self-Reported Energy Knowledge The self-reported energy knowledge question was not asked of Revelstoke participants, therefore, it is only possible to compare the GCCP and Victoria workshops. As Table 7.7 shows, the shifts in self-reported energy knowledge for the two processes were very similar, with the GCCP mean score moving from 2.84 to 3.47, and the Victoria score moving from 2.80 to 3.50. It is noteworthy that the citizen GCCP participants had a higher pre workshop self-reported knowledge level than the expert/stakeholder Victoria participants. The explanation for this appears to have been the anomalous Grandview workshop, which, as previously stated, had a high proportion of expert participants. Figure 7.23 shows the pre and post workshop energy knowledge levels for each of the three GCCP workshops and the Victoria workshops. From this figure, it is clear that the Grandview workshop was responsible for raising the collective GCCP pre workshop energy knowledge levels above the Victoria workshops. Figure 7.23: The mean self-reported energy knowledge scores for the GCCP and Victoria workshops. 2.67 3.132.653.243.043.622.83.500.511.522.533.54Pre Postmean energy knowledge levelPlanners Marpole Grandview Victoria 416 Collectively, what does the self-reported energy knowledge information tell us? Is it truly a reflection of how much the participants learned as a result of participation? One interpretation of this data is that it demonstrates some of the problems with self-reports, such as what Podsakoff and Organ (1986) refer to as the ‘social desirability’ effect. This effect can cause participants to rate themselves higher on desirable traits (in this case knowledge levels). This may account for non-experts from the GCCP workshops rating their self-reported knowledge levels higher than participants from the Victoria workshops. It is also possible that participants’ conceptions of ‘knowledgeable’ differed. In other words, that experts had a different conception of what it meant to be knowledgeable about energy than non-experts (this may be particularly true depending on their perceptions of who they are comparing themselves to). Another difficulty is that factors such as participant knowledge of the experimenter’s intent might play a strong role in participants rating shift from pre to post workshop questionnaires (Grice, 1999). In other words, participants might think that increasing their self-reported energy knowledge level from the pre to post workshop questionnaire is helpful to the researcher and the workshop developers. Self-reports of this kind are common assessment tools for participatory exercises (cf. Cohen et al., 2012) and therefore allow for the comparison of results to other research. However, given the limited time and attention of engagement participants, there is some question as to their value as an assessment mechanism. This question was used in Chapter 5 to assess the impacts of energy knowledge on mental models, and one of the mental models clusters (cluster 1) was largely defined by low levels of energy knowledge, so some measure of knowledge is valuable. Perhaps a series of multiple choice or true/false questions about components of the energy system would be a better way to assess energy knowledge specifically, rather than (or in addition to) the more perceptual use a self- report question. 7.5.3 Ranking of Applied Solutions This question on the GCCP and Victoria workshop questionnaires asked participants to rank order a set of applied solutions to energy and GHG emissions issues by perceived effectiveness. This question was not asked on the Revelstoke questionnaires. On the Victoria questionnaires, the participants had a slightly larger set of options, because the Sustainability Department wanted to include building standards. Figure 7.24 shows the mean post workshop scores for each of the 417 applied solutions for the GCCP and Victoria processes. Recall that building standards was only an option on the Victoria workshop questionnaires. Figure 7.24: The mean pre and post-workshop scores for the applied solutions options for the Victoria (n=45) and GCCP (n=53) processes. There are two ways to interpret the data from this question. Firstly, there is simply the question of which solutions were ranked highest, and whether those rankings changed pre to post (see Figure 7.24). Secondly, there is question of which solutions showed the largest change pre to post, and whether those changes reflect the subject matter and deliberations from the workshops. Table 7.9 shows the pre and post workshop rankings for the GCCP and Victoria workshops. Recall that the participants were asked to rank the top five solutions in order, therefore the top five post workshop solutions for each process are highlighted in grey. For both the GCCP and the Victoria workshops, Public Transportation and Technical changes (which included building retrofits and systems, and electric vehicles) were ranked as the best solutions both pre and post. A focus on transportation is understandable, because it is a highly visible source of energy use and because of its 00.511.522.533.544.5mean scoreGCCP pre GCCP post Victoria pre Victoria post 418 strong association with climate change emissions (cf. Nicholson-Cole, 2005). The ranking of Technical Changes is more difficult to rationalize, in part because the phrasing of the option conflated building interventions with electric vehicles. Did people rank it highly because of the concept of technological change (i.e. progress), because of the building interventions, or because electric vehicles related it to transportation energy and emissions? In future iterations of this type of questionnaire, further unpacking of these solutions needs to be done to better differentiate what participants are focusing on. Chapter 8 will address a re-drafting of the mental models questionnaire based on the learnings from this dissertation. After the first two ranked items, the GCCP and Victoria workshops differ substantially in their ranking (see Table 7.9). Neither the 3rd ranked item (Collective Options) nor the 4th ranked item (Unit Size) from the GCCP workshops were ranked in the top five for Victoria. Similarly, the 4th ranked item for Victoria (Incentives) was not one of the top five ranked items for the GCCP. The absence of Collective Options and Unit Size from the Victoria rankings is consistent with the workshop’s subject matter. District Energy System (Collective Options) modeling suggested it would have a minimal impact on Victoria’s energy use or emissions, despite significant enthusiasm for it amongst the participants. Questions of land use, and therefore redevelopment and density (Unit Size), were not part of the Victoria workshops’ subject matter and were, therefore, not discussed. The GCCP workshops did not have costing information, which appears to account for the absence of Incentives in the workshop discussions and, therefore, its lower rank. In the GCCP workshops, 4/5 of the top ranked solutions changed rank from pre to post, while in the Victoria workshops only 1/5 solutions changed rank (see Table 7.9). This suggests that either the expert and local business stakeholder audience had more strongly held opinions about the possible solutions, or that the workshop structure was less impactful at changing their opinions (or both). It seems likely that participants that work with energy issues as part of their daily employment would have stronger ideas about effective solutions. This is partially corroborated by the considerably lower proportion of participants in Victoria from Cluster 3 (Muddled Middle Ground). This is also supported by the fact that, in the GCCP process, the Grandview workshop participants showed the smallest change in their scoring of public transportation from the pre to post questionnaires (see Table 6.13). In other words, the GCCP workshop with the most energy experts showed the lowest propensity to change their rankings. The workshop structure may also have played a role, because participants in Victoria spent more time focused on individual strategies (e.g. building retrofits or transportation), and less time 419 combining and trading off strategies over the course of the workshop. This lower level of interaction between strategies might have tempered participants’ ranking of items. Table 7.9: The cumulative pre and post workshop rank orders of the applied solutions for GCCP and Victoria. The grey colour highlights the top five ranks for each of the processes. GCCP Victoria Solution Pre Post Changed Rank? Pre Post Changed Rank? Public Transportation 2 1 Yes 1 1 No Local Energy 6 7 Yes 7 8 Yes Unit Size 7 4 Yes 8 7 Yes Technical Solutions 1 2 Yes 2 2 No Individual Behaviour 4 5 Yes 3 3 No Collective Options 3 3 No 5 6 Yes Incentives 5 6 Yes 4 4 No Building Standards 6 5 Yes The second way to compare applied energy solution rankings between workshops is to look at the direction and size of changes in scoring for the different applied solutions, and to relate that to the workshop content. Table 7.10 shows the changes in mean score for each applied energy solution from the pre to post workshops questionnaires for the GCCP and Victoria workshops. Positive changes (i.e. increased scores) are represented in white and negative changes (i.e. decreased scores) are represented in grey. The single largest change for either process was the positive shift in participant scores for public transportation in the GCCP workshops. This change reflected the large GHG emission reductions associated with public transportation strategies in the GCCP modeling. In other words, this large shift was consistent with the information presented in the workshop. In the Victoria workshops, despite large associated GHG emission reductions, public transportation strategies and variables had high capital and/or operating costs. This may account for the much smaller increase in score in Victoria. An interesting comparison between the two processes is to look at circumstances where the scoring of solutions moved in opposite directions for the GCCP and Victoria workshops. For example, in the GCCP workshops, the only solution other than public transportation that increased its score was decreased unit size (see Table 7.10). In Victoria, decreased unit size decreased its score, and was the 420 second lowest scored item in the post workshop questionnaires. In the GCCP workshops, density was a key component of the Integrated Land Use scenario and its associated strategies. In the Victoria workshops, land use and new development were not part of the workshops, and were not represented in the CAN tool. Table 7.10: The changes in mean score for effectiveness of applied energy solutions from the pre to post questionnaires for the GCCP and Victoria workshops. Solution GCCP Change Victoria Change public transport 1.34 0.1 local energy -0.23 -0.58 decreased unit size 0.55 -0.24 technical changes -0.42 0.31 individual behaviour -0.28 0.69 collective options -0.53 -0.61 incentives -0.17 0.94 building standards 0.22 In the Victoria workshops, incentives and individual behavior showed the largest positive scoring shifts, while in the GCCP workshops both of these solutions showed negative shifts. A strong candidate to explain these differences is the impact of the costing data in the Victoria CAN tool. Having the cost information associated with particular strategies such as building retrofits may have increased the value placed on incentives in the Victoria workshops. For individual behavior, the association of zero costs with behavioural actions likely made that option more appealing. Given the well-established difficulty of changing individual energy behaviours, this zero cost value seems unrealistic. While the action of changing behavior may have no cost, promoting and facilitating these actions would have a cost. When weighed against the number of people that actually change their behavior, the cost/benefit for individual behavior changes may be quite high. Another example suggesting the power of the costing data is the positive shift in the building standards solution. Because of the inclusion of operating costs, it was possible to achieve a negative cost for new construction with efficient envelopes and systems. A final note on the differences seen between the GCCP and Victoria processes in Table 7.10 is the impact of workshop structure. For the GCCP, participants moved from a more individual strategy focus in the scenario exercise to an integration and trade-off focus in the final ‘design your 421 neighbourhood’ exercise. This combination and trade-off component at the end of the workshops may have impacted their scoring of items. For example, in trying to achieve the most number of GHG emission reduction ‘stars’ participants may have come to recognize that increasing density facilitated the public transportation strategies that provided the largest savings. This would partially explain the more focused positive shifts in the GCCP process. For Victoria, the combine-and-trade-off exercise occurred in the morning, after which the balance of the workshops focused on exploring individual strategies in more depth. This workshop structure likely led participants to enter the post workshop questionnaire focused on the solution that they had been discussing in the afternoon. The net effect was a more dispersed pattern of scoring in the Victoria example. Collectively, the applied solution scoring data for the GCCP and Victoria processes suggest that participants’ pre to post ranking of applied energy solutions tracked with the subject matter and information presented in the respective workshops. In the GCCP workshops, the primary mechanism for assessing different strategies was the associated star system. Trade-offs primarily came in the form of accommodating quality of life factors they had developed in the ‘name your neighbourhood’ exercise earlier in the workshop. For the Victoria workshops, there was a much more salient trade-off mechanism, in the form of the associated costs of different GHG and energy reduction strategies. This costing data appears to have had a large effect on how the participants ranked energy solutions. Finally, the structure of the workshops may have played an important role in participants’ post workshop ranking. The GCCP participants ended the workshops with a integrative exercise that emphasized the superior GHG reductions of particular strategies, leading to large increases in their scoring. The Victoria participants, on the other hand, ended the workshops focused on specific strategies to the exclusion of others, which may have led to their more dispersed scoring. A comparison of these two workshop processes and their impacts on participants scoring of energy solutions, then, reveals the importance of trade-offs and workshops structure on participants’ opinions. This may have considerable impacts on their mental models of energy at the community scale that could be carried forward into subsequent planning or policy making activities. Of particular note is that, despite very similar underlying modeling of the energy and GHG repercussions of different solutions, the way that information was presented and interacted with led to dramatically different scoring of energy solutions. 422 7.5.4 What was Surprising about Energy and GHG Emissions This is the first question that was asked in all three processes (see Table 7.7), though the Revelstoke question differed slightly, in that it asked participants what ‘stood out’ rather than what was ‘surprising’. Figure 7.25: A comparison of responses to the 'what was surprising' question across the three processes by the percentage of total references. This chart represents the top 5 themes from each of the workshops. Figure 7.25 shows the percentage of total references to key themes for each of the three processes. The top five themes from each of the processes are represented. In many cases, themes were similar across the three workshops, for example the existing energy system, transportation impacts, DES and household energy demand were present in responses at all three processes. Other themes, such as energy sources, a systems approach to addressing energy issues, and trade-offs between values were present at two workshops (GCCP and Victoria). Finally, certain themes were specific to particular processes, most notably density at the Revelstoke process, but also the issue of scale at the Victoria process. 0510152025303540% of total referencesGCCP Victoria Revelstoke 423 In all three processes, surprise about aspects of the existing energy system was a common response. For both the GCCP and Victoria processes, the existing energy system was the most frequently referenced theme, while in Revelstoke it was the third most referenced theme. This focus on the existing energy system is consistent with the notion that thinking about, and planning for, energy at local scales is a new idea (Andrews, 2008; Neves & Leal, 2010). Even in the more expert audiences of the GCCP Planners and Grandview workshops, and the Victoria workshops, information about the existing energy system was most surprising. In fact, Planner and Grandview participants were more likely to identify aspects of the existing energy system as surprising than the more citizen-focused Marpole workshop (see Figure 6.40). One of the primary actions funded by the CEEP process in BC was the development of municipal inventories of energy and GHG emissions (BC Hydro 2014). The findings of this question suggest that those inventories could provide a valuable resource to help both practitioners and citizens better understand the energy uses and resources in their communities as a precursor to future planning. At the time of this study (2012), it appears that the existing energy systems in Vancouver, Victoria and Revelstoke were not well understood, even by those professionals tasked with planning for energy in their communities. In both the GCCP and Victoria processes, transportation impacts was the second most surprising theme, though there was a difference in focus between the processes. In the GCCP process, participants were uniformly surprised at the large impact transportation had on energy use and GHG emissions. This can be attributed to the large GHG emissions savings associated with transportation strategies in the modeling and resulting materials. In Victoria, the context for discussing transportation impacts was more nuanced, with some participants focusing on the large GHG emission impacts of transportation, while others focused on the high costs of transportation interventions (and therefore the lower potential for transportation to contribute to future GHG reductions). This more nuanced reaction to transportation impacts can be largely attributed to the cost model incorporated in the CAN tool. Of note, several participants in the Victoria workshops made reference to co-benefits of public transportation options, and the lack of active transportation in the CAN tool modeling as factors that could further ameliorate the high costs associated with transportation strategies. Determining what complexity of systems to incorporate and where to simplify is fundamental to the process of modeling (Weisberg, 2013). Only by exposing the model to peer, or more broadly public, review can appropriate adjustments be made. As a beta test of the CAN tool, then, it is understandable that issues were identified for further refinement. Nevertheless, the impacts of the tools used on the reaction of participants is notable. In the GCCP process, participants may have 424 left the workshops convinced that it was all about transportation, without having information about the cost associated with those interventions. In the Victoria workshops, participants may have left the workshops convinced that transportation strategies were not a cost-effective approach, without considering unmodeled co-benefits, the possible contributions of active transportation, or the regional implications of transportation actions. In the Revelstoke workshop, participants were most surprised about the associations between density and energy use/GHG emissions, and the required density to make district energy systems viable. The prevalence of these themes follows directly from the focus of the workshops. While participants were introduced to new information on energy use and GHG emissions at the September workshops, the primary focus was still on urban form and density, and the impacts of form and density on other factors such as DES and transportation mode splits. It is not surprising that this more concentrated and focused exploration of urban form and density resulted in participants expressing surprise about their implications. One question about the density focus in Revelstoke, is the extent to which it was tied to the recognizable locations the participants worked with. Density and DES were also components of the GCCP workshops, though in that process participants worked on a generic neighbourhood at a much larger scale (30 blocks versus 6 blocks in Revelstoke). In the GCCP workshops, DES and land use impacts (the theme most closely approximating density as expressed in Revelstoke) were the eighth and ninth most common themes, at only three references apiece. Was the issue of density more salient in the Revelstoke example because of the local examples, the scale of the interaction, or simply because it was more focal to the discussion? Girling et al. (2014b) expressed similar questions about the impact of the local examples versus the impact of the planning tool. This question has ramifications for the design of future tools and processes for planning engagement. In particular, it raises the question of when it is appropriate to use more recognized and thus experiential representations versus more conceptual representations. As mentioned previously, the question about what surprised participants from the workshop was intended as a surrogate for immediate learning, based on the established relationship between surprising information and retention (Schulz & Dikenson, 2000). Therefore, it might be expected that the thing, or things, that surprised participants would be the ideas about energy that would stick with them after they left the workshop. Relating that to the focus of the three workshops, then, it might be argued that the Revelstoke process was the most successful, because participants overwhelmingly referenced density as the most surprising theme – i.e. they left the workshop focused 425 on the intended concepts. The Revelstoke process did, however, have structural affordances that may have impacted that finding. For example the sessions were considerably shorter and were focused on a much smaller number of energy concepts. This is evidenced by the smaller number of themes discussed in the Revelstoke participants’ answers. Despite the broader focus of the GCCP and Victoria processes, however, there was a similar concentration on a few key themes. Of the themes related to solutions or strategies, transportation clearly had the most impact on participants’ mental models of energy. This may suggest a need to focus engagement efforts more specifically on building or waste sectors if the intention is to thoroughly explore planning or policy options for those sectors. However, this suggestion runs counter to the calls for a more systemic approach to planning and policy, and may only be applicable under circumstances where specific policies or approaches are being investigated (as in the case of the Revelstoke UDB). In all three workshops, the focus on the existing energy system suggests that participants were unaware of how energy works in a location. While this is not surprising given the largely invisible nature of the energy system, and the relatively new notion of planning for energy at more local scales, it does have ramifications for community energy planning. It suggests that there is significant value in energy literacy and capacity building efforts associated with community planning. What’s more, results from the GCCP Planners and Grandview workshops, and the Victoria workshops, suggests this need for energy literacy is true of practitioners as well as citizens. 7.5.5 Energy in Place The final mental models-related question used for comparison across the three processes related to the question of energy in a place. This question took different forms in Vancouver, Victoria and Revelstoke not by research design, but due to the realities of attaching research to different processes. The question as intended is reflected by the one asked of GCCP participants, which was: Please briefly explain how you think that planning for energy and greenhouse gas reductions will have either a positive or negative impact on what you enjoy about your community. The question asked in Victoria was changed to reflect special characteristics about the City relating to energy: 426 Are there any energy opportunities or challenges that you think are particularly important to Victoria? In other words, is there something special or unique about Victoria’s energy system? If so, please briefly explain. The question asked in Revelstoke in the existing evaluation survey was more similar to the Vancouver question than the Victoria question was, with the exception that it did not directly reference energy. As the responses showed in section 7.2.3, however, participants made the association to energy and density themselves, presumably as a result of the priming from the workshop. The question asked was: Briefly describe your vision for the future of neighbourhoods in Revelstoke. Table 7.7 provides a summary of the findings from the three workshops relating to energy in a place. The GCCP question was the most explicit in tying energy planning with the future of participants’ communities, so it is not surprising that participants’ responses reflected a direct connection between energy and themes such as quality of life, sense of place and social values. In general, GCCP participants felt that planning for energy would have a positive impact on what they enjoyed about their community (42 of 50 responses). Summarizing the results discussed in section 6.5.3, participants felt that energy-related strategies that led to more active transportation, transit, and local services would have the effect of increasing social interactions and what was referred to as the ‘vibrancy’, ‘vitality’ or ‘liveliness’ of the neighbourhood. Other themes discussed by participants, which had a less direct tie to energy were diversity and affordability – suggesting that the types of mixed density urban form and land use promoted by smart growth and similar systems (Jepson & Edwards, 2010) could have the added benefit of providing multiple housing types for lower income families, aging in place, etc. Only eight GCCP participants expressed negative associations between energy planning and what they enjoyed about their community. The most common concern was that energy planning would not be cost effective. More specific, but rarer, concerns included loss of the freedom of individual transportation, and loss of neighbourhood character. One of the main goals of the GCCP workshops was to more explicitly include what participants valued about their communities, primarily through the ‘name your neighbourhood’ exercise. The results of the energy planning and community values question suggests that participants were capable of making the connections between the energy and planning concepts discussed in the workshops and what they enjoyed about their neighbourhoods. 427 Question framing and workshop intent in Victoria led to a more technical focus in participants’ responses about energy in place. Similar to the GCCP process, participants were generally optimistic, and more likely to suggest opportunities (23/31 responses) than challenges (8/31 responses) for Victoria. Particular interventions for buildings and transportation facilitated by local characteristics were the most common themes; for example, opportunities to replace base board heating with heat pumps, or to work regionally with adjacent municipalities to expand transit options. Challenges for Victoria were framed in a similar manner, focusing on the existing building stock and energy sources (e.g. electric baseboard heating), the high proportion of rental units, low population growth, etc. Despite this tendency to report technical opportunities, though, there were a number of themes expressed that reflected a broader conception of place – i.e. place as not just a location with physical characteristics and resources, but also incorporating social and sense of place dimensions (Agnew 1987). Those themes included active transportation, neighbourhoods, village centres, people, and quality of life. In other words, even in the more technical, practitioner and stakeholder-focused Victoria workshops, the connection was made between planning for energy and the incorporation of a broader set of values. If the GCCP question represented the explicit combination of energy planning and community values, and the Victoria question represented a more exclusive focus on energy in a place, then the Revelstoke question could be seen as anchoring the community values end of the equation. Participants were asked about their future visions for neighbourhoods in Revelstoke, without a direct tie to energy planning. Despite the lack of an explicit energy question, most participants made reference to the energy-related information from the September workshops. As discussed in section 7.4.3 participants primarily focused on aspects of land use and urban form in their future visions. In particular, many participants framed their responses as a relationship between the density required for energy and GHG reductions and Revelstoke’s existing character. Many of the same themes expressed in the GCCP and Victoria workshops also arose in Revelstoke, such as active transportation, greenspace access, local services, environmental impacts and quality of life. Also similar to the GCCP and Victoria workshops, there was an expression of themes more abstracted from the issue of energy, such as equity and affordability. Similar to the other processes, then, participants used this question as an opportunity to relate the energy and related concepts from the Revelstoke workshops to what they valued about their community 428 more broadly. Interestingly, in Revelstoke, the shorter workshops and more focused subject matter resulted in a very strong connection of density to participants’ conceptions of a future Revelstoke. This may have ramifications for the design of engagement processes – for example, would an iterative set of shorter workshops that focused on specific components of energy or sustainability planning be a better way to build capacity for citizens to consider concepts? Conversely, could this more piecemeal approach prevent participants from recognizing the multifaceted and systemic nature of sustainability planning? One of the primary goals of this dissertation was to investigate the mental models of energy at the community scale. A fundamental precept of that investigation was a notion that planning for energy at the local scale required a different way of thinking that more explicitly recognized the challenges and affordances of place. Initially, this idea reflected the ecological and natural resource characteristics of place as discussed in industrial ecology (cf. Hawken et al., 1999; McDonough & Braungart, 2002), and more recently in regenerative design (Cole et al., 2013; Reed, 2007; Robinson & Cole, 2015). However, through involvement with the GCCP and its evolving discussions of procedural sustainability, and with design and planning researchers in UBC’s School of Architecture and Landscape Architecture, it has become apparent that the local scale also brings with it a different set of social and cultural expectations, challenges, and opportunities that directly impact energy planning. Responses to the questions on each of the GCCP, Victoria and Revelstoke questionnaires relating energy to place reflect that broader conception of integrating energy planning in a location. In all cases, even the highly technical workshops in Victoria, participants made the connection between energy and the social and sense of place characteristics of the community being discussed. This seems to be a fruitful area for future research to more carefully explore how energy planning, and more broadly sustainability planning, is consistent with, or in opposition to, what people value about their communities. Another, related question, is how to incorporate these more value-based characteristics of place into engagement tools and processes so that they can have a more explicit role in decision-making processes. 7.5.6 On the Malleability of Participants’ Mental Models Given the results discussed in sections 7.5.2 through 7.5.5, what can we say about the changes in participants’ mental models? Chapter 8 will discuss the mental models methodology used in this 429 dissertation and its limitations and implications in more detail. Suffice it to say here that the methodology as developed did not allow for a comprehensive look at whether participants’ mental models were changed by participation in the case study workshops. Complications related to participant numbers as described in Chapter 5, limitations of the questionnaires, and the challenge of trying to evaluate both existing mental models and changes to those models during the same processes limited the comprehensiveness of the findings. At best, we have a partial picture of how participants’ thinking about community scale energy planning was altered. A recent paper by Hoffman et al. (2014) provides a useful framework for the discussion of mental models more broadly and the specific case as discussed in this dissertation. In their framework they classify mental models as hierarchical structures, with more abstract but fundamental concepts at the centre (in the case of this dissertation that would equate to worldviews, allocation of responsibility, optimism, etc.) and more concrete concepts at the periphery (in the case of this dissertation, those concepts would equate to preferred energy solutions, attitudes about land use, etc.). In Hoffman et al.’s (2014) framework, the concrete peripheral concepts are much more amenable to change based on external factors such as engagement or learning. While this framework was not part of the development of the methodology for this dissertation, the methodology did anticipate a similar distinction between the less malleable core model components and the peripheral concrete or applied concepts. For this reason, the pre/post questionnaire format did not look for changes in core concepts such as worldviews or allocation of responsibility – recognizing that a one-day workshop would have little impact on those beliefs. The questions discussed in section 7.5 reflect a focus on the more peripheral, concrete concepts such as applied energy solutions, conceptual learning (what was surprising) and integration of energy with other thoughts and values about place (energy in place). The results of the questions summarized in Table 7.7 do provide evidence of changes to the peripheral concepts in participants’ mental models. In particular, there were statistically significant shifts in participants’ self-reported energy knowledge levels, and their preferred applied energy solutions for both the GCCP and Victoria workshops. There is also qualitative evidence from the ‘what was surprising’ open-ended questions that participants incorporated new ideas into their mental models of community scale energy, particularly information about the existing energy systems in the study communities, transportation (in Vancouver and Victoria), and density in Revelstoke. There is also evidence from the ‘energy in place’ open-ended responses that 430 participants incorporated energy concepts with broader community values into their mental models of place. These suggestions about changes to participants’ mental models, while incomplete, provide direction for future studies. The limitations of the methodology as implemented in this dissertation can be corrected in future implementations, and the framework developed by Hoffman et al. (2014) provides some direction for constructing a more robust mental models elicitation tool that connects the central, abstract mental model components with peripheral, concrete ones, and subsequently looks for changes in the peripheral concepts resulting from engagement. These themes will be returned to in section 8.2 of Chapter 8. 7.6 Discussion: Comparing the Three Processes’ Case Study Components The case study comparison conducted in this chapter served two main purposes. The first, discussed in section 7.5, was to provide other contexts and more participants with which to conduct the mental models evaluation. The second purpose was to examine the components of public engagement exercises as discussed in Chapter 2. This section will compare the GCCP, Victoria and Revelstoke processes according to their workshop goals, audience, scale, modeling, scenarios, representation types, process components (e.g. presentations and interactive exercises), and outcomes. Table 7.11 provides a summary comparison of these workshop components for the three processes. 7.6.1 Workshop Goals The three community-scale energy processes discussed in this chapter had different goals for their public engagement components. Stirling (2006), building upon previous research from Fiorino (1989) identified three primary reasons for public engagement. Normative public engagement focuses on citizens’ democratic right to participate in policy-making and governance. Normative public engagement stresses people’s right to contribute input and be heard. Instrumental public engagement focuses on building trust with citizens for policies and or planning actions. In instrumental public engagement, participants review and vet proposals to ensure that they meet standards, or serve the public good. Substantive public engagement focuses on using participant input to improve the products of decision-making. In substantive public engagement, participants’ 431 engagement meaningfully contributes to the policies or planning being discussed. As a result of the broader workshop goals, the three projects discussed in this chapter also differed on Fiorino (1989) and Stirling’s (2006) framework of public engagement types. For the GCCP process the public engagement goal was to build citizen capacity in the two Vancouver neighbourhoods (Marpole and Grandview Woodland) to incorporate energy issues in the upcoming neighbourhood planning processes. Initially, the public engagement goal of the GCCP was to more thoroughly incorporate the Community Energy and Emissions workshops into the City of Vancouver neighbourhood planning processes; however, timelines and institutional barriers prevented that from occurring. As a result, citizen participation in the workshops served a primarily normative function. The neighbourhood planners for the Marpole and Grandview Woodland neighbourhoods attended the workshops and heard participants input on energy planning in their communities, but there was no mechanism for this input to impact the subsequent planning. The most direct impact these workshops might have had on the neighbourhood plans was through capacity building amongst neighbourhood citizens who would subsequently attend public engagement activities conducted by the City of Vancouver. Bogner (2011) argues that these types of ‘participation experiments’, though an increasingly common approach to engaging publics on science and technology issues, are detrimental to the purposes of engagement, because of their artificial circumstances and limited resonance in decision-making. This point will be returned to in the discussion of public engagement in Chapter 8. The Revelstoke process was developed through discussions with the City of Revelstoke’s planning department. The City had experienced resistance to the proposed Unified Development Bylaw, and wanted to explore other avenues of engaging citizens on the implications of the bylaw for the future development of Revelstoke (Girling et al., 2014b). The public engagement goal for the Revelstoke workshops was to explore the implications of the UDB with Revelstoke residents. The research was conducted at arms-length from the City of Revelstoke government and planning department, and it was not the role of the research team to be proponents of the UDB; however, the instrumental function of either building trust, or legitimizing concerns, about the UDB was implicit in the workshops design. 432 Table 7.11: A summary of the three engagement processes according to workshop components. Element GCCP Victoria Revelstoke Workshop Goal To build citizen capacity to incorporate energy issues into upcoming CoV neighbourhood planning activities Normative engagement To work with climate experts and stakeholders to set strategy targets for Victoria’s CEEP process. Substantive engagement To investigate the implications of Revelstoke’s form-based code on future neighbourhoods, informed by energy information. Instrumental engagement Audience Vancouver citizens, City of Vancouver Planners. Energy and climate change experts from government, stakeholders from business and community groups Revelstoke citizens Scale Neighbourhood to community scale. Sandbox = 30 blocks. 2040 time horizon City scale City of Victoria municipal boundary 2020 (and limited 2041, 2080) time horizon Neighbourhood scale Small neighbourhood centres within the City of Revelstoke (4-6 city blocks) 2030 time horizon Scenarios 3 pre-developed limited pathways. - Technology - Integrated Land Use - Behavioural/Cultural Participants constructed a negotiated scenario in small groups. No pre-developed scenarios. Participants created their own interactive scenarios in small groups (6-10) during the morning exercise using the CAN tool. No pre-developed scenarios Participants created individual scenarios (June workshop) Participants negotiated a final scenario in small groups (2-6) (June workshop). Participants revised group scenarios (September workshop) 433 Element GCCP Victoria Revelstoke Modeling Building archetypes Building GHG reduction strategies Simple transportation modeling (VKTs) Indirect model access Building archetypes amalgamated into larger scale development patterns Building GHG reduction strategies Simple transportation modeling (VKTs) Waste and offsets modeling Costing data Direct Access (though mediated) Building archetypes Building GHG reduction strategies Simple transportation modeling (VKTs) Direct access (touchtable) Representation Conceptual Mapping 3D building massing 3D conceptual (articulated buildings) Hybrid Conceptual/Experiential Illustrative scenario street scenes Information Visualization Charts Diagrams Information Visualization Wedge charts of GHG emissions Conceptual 3D building massing 3D conceptual (articulated buildings) Information Visualization Charts Diagrams 434 Element GCCP Victoria Revelstoke Process Components 3 presentations (context, energy system, scenarios) 4 exercises: 1. Energy system exercise: group diagramming 2. Name Your Neighbourhood exercise: drawing and annotating a neighbourhood to reveal values. 3. Scenario exploration Design Your Neighbourhood exercise: participants used puzzle pieces and strategy cards to create a future scenario. 2 presentations (context, CAN tool instructions) 2 exercises: 1. Participants worked with the Climate Action Navigator in groups to develop future scenarios. Participants worked with the Climate Action Navigator in groups to investigate particular strategies in more detail 1 presentation (September workshop, design recap + energy/climate change information) 3 exercises: 1. Participants worked individually with paper-based puzzle pieces to construct neigbourhood scenarios. 2. Participants worked groups in at the touchtable to negotiate scenarios. At an iterative session, participants worked in small groups to review and alter their previous scenarios Outcomes Academic Workshop evaluation Mental Models evaluation Academic publications Planning and Policy None Academic Workshop evaluation Mental Models evaluation Planning and Policy Technical appendix to Victoria’s OCP Implementation? Academic Workshop evaluation Academic publications Planning and Policy Cancelation of the UDB by city council. 4. 2. 3. Author’s Role Co-development of workshop and all materials with a workshop team Development of evaluation methodology Table facilitator for scenario development and DES strategy discussion Development of evaluation methodology Technical support, limited workshop documentation Limited development of evaluation methodology 435 The Victoria workshops were developed within the strictures of BC Hydro’s CEEP process. Unlike the GCCP and Revelstoke processes, the workshops developers were not researchers, but rather consultants. The research discussed in this dissertation was added to the existing process and, other than the time required to fill out the questionnaires, had no impact on the process structure. As such, the Victoria process had one primary goal, which was to elicit climate expert and local business and community stakeholder input into energy and GHG-related targets for particular strategies in Victoria. This goal was substantive, as the expert and stakeholder engagement was intended to directly impact the actions proposed by the CEEP report. The differences in the workshop goals, and their relationship to Fiorino (1989) and Stirling’s (2006) framework resonated through the workshops’ design and execution. The GCCP workshops had a more extensive pedagogical element, with interactive exercises interspersed with information presentations. In addition, the normative, rather than instrumental or substantive engagement type provided more room for creative play, as evidenced by the energy system and ‘name your neigbourhood’ exercises. Because the workshops were not intended to explore or impact particular policies, there was more latitude in the subjects covered and the types of exercises used. The goals of the Revelstoke process and the implicit instrumental engagement type dictated a different workshop design and execution. Because the workshops focused more tightly on land use and urban form, the workshops could be shorter, the pedagogical interventions less extensive, and the exercises more directed. All three of the interactive exercises focused on developing or revising designs for neighbourhood centres, to explore the implications of density and built form. The engagement goal of the workshop was to explore how a rich engagement process impacted participants’ attitudes about density – particularly in the face of information about energy use and GHGs. Participant’s understanding and resultant levels of trust for the UDB, while not an explicit purpose of the workshops, was an understood potential byproduct. The Victoria workshops were designed around two breakout exercises that were used to elicit feedback from participants on targets, for first a comprehensive GHG reduction scenario, and second more specific targets for individual strategies. In part because of the expert and stakeholder audience, there was a much smaller pedagogical component. The primary design goal of the workshop, was to promote discussions amongst the participants leading to negotiated energy and GHG reduction targets. 436 7.6.2 Workshop Audience The main audience distinction between the three processes, of course, was that the Victoria workshops were invitation only events for energy and climate experts and Victoria business and community stakeholders, while both the GCCP and Revelstoke processes were open invitation events intended for local citizens. In reality, the audiences were more nuanced than that, particularly in the case of the GCCP workshops. Firstly, the Planners workshop was not an open invitation event, but rather a directed workshop for City of Vancouver planners and staff. Additionally, a small number of UBC participants attended the Planners workshop, because of their interest in the subject of community energy planning. Secondly, the methods of recruitment differed between the Marpole and Grandview workshops (see section 6.5.1), resulting in more people with energy expertise and interest attending the Grandview workshops. The difference in audience between the Marpole and Grandview workshops was palpable, and those differences were borne out by demographic and energy knowledge data. As a result, the level of energy knowledge across the three processes was more of a continuum. A subjective representation of that energy knowledge continuum from highest to lowest would be: Victoria ≥ Grandview > Planners > Revelstoke = Marpole Adapting workshop goals, materials and activities for the intended audience is critically important to engagement activities. This is particularly true with public engagement where participants may have disparate levels of knowledge about the subject matter, creating some need for capacity building. The very different levels of energy knowledge at the Grandview workshops made it more difficult to navigate, and some of the people with energy expertise were critical of the workshop, because they wanted to delve deeper into specific energy subjects (see section 6.5.4). This expert tendency to want to dig into the models and assumptions was also seen at the Victoria workshops, perhaps leading to some of the criticisms of the CAN tool (see section 7.2.2). Subjectively, the Victoria, Revelstoke and Marpole workshops all seemed well adapted to their intended audiences. The Victoria workshops primarily focused on facilitated and model-mediated discussions to elicit substantive feedback, while the Revelstoke and Marpole workshops used 437 pedagogical tools (presentations and early exercises) to build capacity for the more complex later exercises. . 7.6.3 Scale8 Taylor (1981) has described the urban scale as the ‘scale of experience’, that is, the scale at which humans experience everyday life. Several postmodern scholars have also suggested that local scales best represent plurality and difference in human societies (Herod, 2011). In concert with more instrumental reasons for thinking and planning for energy at local scales (Andrews, 2008; Neves & Leal 2010), the argument has been building for localizing climate change and energy planning (cf. Hulme, 2008; Sheppard, 2012). Previous participatory integrated assessment research has also found that participants preferred to work at more local (rather than regional) scales (Robinson et al., 2006). All three of the engagement activities discussed here worked at urban (i.e. city) or smaller spatial scales. For the Victoria and Revelstoke processes, the scale of engagement was largely determined by the existing subject matter. BC Hydro’s CEEP program is aimed at municipalities both in terms of funding and expected deliverables (BC Hydro, 2014), therefore, the Victoria process was required to work at the City of Victoria scale. The Revelstoke process had slightly more leeway in the chosen scale, however, the focus on urban form, density and Revelstoke’s UDB largely dictated a neighbourhood, or block approach. The less specific goals of the GCCP process, as well as the use of a generic, rather than existing neighbourhood, meant that that process was able to choose a scale appropriate for the subjects to be discussed in the workshops. The scale was selected to account for a diversity of residential and commercial properties, and for active transportation, proximity to services, and transit. As the processes and evaluation results have demonstrated, these different scales tend to amplify and diminish different attributes. The City of Victoria covers approximately 20 km2 and has 80,017 residents as of the 2011 census (Statistics Canada, 2011). By comparison, the Sandbox used in the GCCP workshops was 30 blocks in size with a baseline population of 5586 residents. The Revelstoke workshops used two neighbourhood centres ranging in size from 4-6 blocks. Baseline population numbers for the Revelstoke neighbourhoods are not available. 8 In this passage and in the broader dissertation, the colloquial version of scale is used, rather than the geographical version. That is, ‘smaller scale’ denotes a smaller, rather than a larger real-world area. 438 Scale interacted with the topics and strategies covered in the three workshops. For example, the Victoria workshop covered transportation more directly, because it modeled transportation at the same city scale as the other energy strategies. For both the GCCP and Revelstoke workshops, transportation was dealt with more obliquely. For example, in both cases mode shifts related to density changes had to interpolate changes outside the study areas (Senbel et al., 2013). In other words, even large changes in density in a small neighbourhood could not facilitate the installation of a new transit line if surrounding areas remained low density. In order to address this in the modeling and representation, both the GCCP and Revelstoke modeling had to assume density increases in adjacent neighbourhoods. This disconnect between representation of the smaller neighbourhoods and calculation based on external changes may have been challenging for participants. Despite what was perhaps a more complex interaction with scale in the GCCP and Revelstoke examples, the only comments on the incompatibility of scales came from the Victoria workshops, where several participants felt that transportation modeling should be scaled up to account for regional travel (see Figure 7.9). This may be a result of the fact that the Victoria workshops’ scale was determined by jurisdictional boundaries, rather than by the subject matter being discussed. The challenges of inter-jurisdictional and regional impacts on transportation were also noted by several interviewees in Chapter 4. It is possible that the smaller Sandbox and Revelstoke scales actually helped participants suspend their disbelief about the modeling such that they could focus on the transportation implications in the much smaller study areas. Scale also had a strong interaction with visual representation in the three processes. Representation differences will be discussed in 7.6.6. Temporal scales Like spatial scales, temporal scales are important to planning. This may be particularly true for sustainability issues such as climate change, because human perceptual scales of time are incongruent with the scales of climatic processes (Pahl et al., 2014). Tonn et al. (2006) found that people have difficulty envisioning the future past about 10-20 years. Conversely, scientific modeling of climate tends to work on 2050 and 2100 time horizons (cf. IPCC, 2007; Stern et al., 2006) because longer timeframes are necessary to express the potential long-term impacts of climate change. Engagement processes, particularly those that use models of future conditions, need to carefully consider human, 439 planning and climate timescales to select appropriate time horizons at which to work (Robinson et al., 2006). All three of the workshop processes discussed here represented current and potential future conditions. The GCCP workshops made reference to the 2020 time horizon in presentation material, because the City of Vancouver had set a goal to reduce GHG emissions by 33% from 2007 levels by 2020 (City of Vancouver, 2012). However, a time horizon of 2040 was used for the scenarios and workshops exercises, because the 2020 horizon was deemed too short to allow extensive neigbourhood changes. Changes to building stock and transportation infrastructure are difficult to achieve on short time horizons. The 2040 time horizon placed the future Sandbox beyond the 20 year time frame discussed by Tonn et al. (2006), however, visualizations of future conditions were used in an attempt to make the future more salient (Pahl et al., 2014; Sheppard, 2005; Sheppard, 2012). The Victoria workshops were structured to develop targets for energy and GHG strategies to allow the City of Victoria to achieve 33% reductions in GHG emissions from 2007 levels by 2020. For the morning exercise that used all of the available strategies, the 2020 time horizon was used, with the goal of the exercise being to find a mix of strategies that could achieve or beat the 33% target. In the afternoon sessions, that focused on specific strategies, some strategies had additional 2041 and 2080 time horizons that participants could engage with. These included the retrofit, new construction and transportation strategies. The focus of the Victoria workshops, therefore was on a shorter planning time horizon, but the CAN tool also provided the longest visible time horizon for context. The Revelstoke workshops used a 2030 time horizon to correspond to the City of Revelstoke’s goal to decrease GHG emissions by 6% from 2007 levels by 2030 (Senbel et al., 2013). The Revelstoke example, therefore, fell just within Tonn et al.’s (2006) 20 year time horizon. The spatial scale, recognizeable neighbourhoods and relatively short time horizon appear to have had an impact on the boldness of participants’ designs. Senbel et al. (2013) speculate that the modest density increases were, at least partially, the result of participants working on recognizable locations. While it is not possible from the workshop data, it would be interesting to determine the interactions of spatial and temporal scales with participants’ willingness to be bold and creative in the design of future places. Not surprisingly, the process that was most focused on developing actionable planning targets operated on the shortest time horizon (Victoria). Conversely, the process with the weakest connection to planning and policy had the longest focal time horizon (GCCP). One of the questions 440 arising from an investigation of the three processes for spatial and temporal scales is the question of appropriate scale. In the Victoria example, the spatial scale was determined by jurisdiction, and the temporal scale was determined by commitments made by the City of Victoria before the CEEP process began. In Revelstoke, the sub-neighbourhood scale was selected by the research team as an appropriate spatial scale for the discussion of density and urban form. The temporal time horizon, however, was determined by previous commitments by the City of Victoria. In the GCCP process, both the spatial and temporal scales were unconstrained by existing requirements. The spatial scale and time horizon used were arrived at through the working team and advisory panel’s deliberation over the appropriate scale at which to represent the energy and quality of life subjects selected for the workshops. It is not possible to unpack the implications of spatial and temporal scale and the effects of jurisdictional versus system-scaled engagement in these processes, but it does present questions for future research. Are there ‘optimal’ spatial and temporal scales for engaging with participants about sustainability issues? Does the optimal scale vary according to the way the questions are asked and the engagement process designed? And how are these ‘optimal’ scales interfered with by jurisdictional boundaries or externally developed timeframes? 7.6.4 Scenarios All three of the processes used scenarios to explore energy and GHG futures. The primary distinction between the processes was that the GCCP used pre-developed scenarios (more properly limited energy pathways) as a pedagogical tool, while both the Victoria and Revelstoke processes only used participant-generated scenarios. Victoria made the most limited use of scenarios. Participants working in groups in the morning breakout session used the facilitator-mediated CAN tool to develop a 2020 scenario to try to reach a 33% reduction in GHG emissions from 2007 levels. These scenarios were then reported back to the larger workshop, and saved for future analysis as part of the CEEP process. Despite their more limited use, then, the scenarios were a major substantive output of the workshops. In Revelstoke, participants first developed an individual scenario using maps and building cards, and then developed a group scenario at the touchtable by negotiating between their individual designs. At the subsequent September workshops, participants revisited and modified both their individual and group scenarios based on new information about energy and GHG emissions. The 11 scenarios 441 from the July workshop were analysed for research on the implications of density on GHG emission reductions in a small town setting (Senbel et al., 2013). In the GCCP process, the three limited pathways of Integrated Land Use, Technology and Behavioural/Cultural were first presented to the participants along with their accompanying energy strategies and metrics. Participants then performed a group exercise where they explored and evaluated one of the three pathways and presented their findings to the larger workshop. In the final exercise, participants combined what they had learned about the energy pathways and strategies with quality of life issues developed in the ‘name your neighbourhood’ exercise to develop their own group scenario. The resulting scenarios are analysed in section 6.5.2. In all three processes, the participant-generated scenarios represented important workshop outputs. In Victoria, the scenarios formed the partial basis for recommendations of the consulting firm to the City of Victoria (Miller & Cavens, 2012). In Revelstoke, the scenarios formed the basis for an analysis of the implications of density on GHG emission reductions in a rural town (Senbel et al., 2013). And in the GCCP process, the scenarios formed the basis for an analysis of the differences in neighbourhood designs at the Planners, Marpole and Grandview workshops. Perhaps the most salient question arising from the use of scenarios in the three processes is the value of presenting participants with pre-developed scenarios. One of the primary theoretical benefits of scenarios is their value as learning tools (Berkhout et al., 2002; Chermack, 2011; Chermack & van der Merwe, 2003; Patel et al., 2007; Senge, 1994). Relevant to this dissertation, researchers have argued that a key purpose of scenarios is to expand and/or change mental models (Chermack, 2011; Chermack & van der Merwe, 2003; Senge, 1990; 1994). Proponents of putting the scenario construction process exclusively in the hands of engagement participants (cf. Robinson et al., 2006) contend that the act of making choices and developing the scenarios help participants to learn about the underlying concepts and consequences of decisions. Inherent in this approach is the idea that participants will ‘game’ with decisions or make multiple scenarios so that they can understand the workings of the underlying concepts. The Victoria workshops, and the CAN tool, at least partially corroborate this idea, as participants did ‘game’ within strategies to see, for example, how ‘low’ versus ‘high’ retrofit rates would impact the city’s emission levels. Participants did not, however, develop multiple complete scenarios, because there was insufficient time. The time required to generate user-generated scenarios is a challenge of this approach (Robinson et al., 2006). 442 Proponents of presenting pre-developed scenarios contend that introducing coherent, varied scenarios to participants can help to ‘stimulate their imagination’ (Patel et al., 2007, page 557), and encourage them to consider ideas that they had not previously envisioned. Another potential strength of pre-developed scenarios is the strong narrative storylines that can be developed for the scenarios. The power of narrative as a pedagogical tool is well established in educational research (cf. Jackson, 1995). A review of participant responses, as well as observation of the process, suggests that the presentation of the pre-developed limited pathways was helpful for GCCP participants in understanding the energy concepts and how they might fit together: Particularly understanding what land-use/tech/behavior strategies could/should be implemented together. Marpole19 Seeing comparisons of various scenarios, e.g. energy use, bike transit for land use, technology. Helps to see actual impacts. Grandview44 Based on participation in all three processes, my contention is that presenting pre-developed scenarios can be a valuable learning tool. The strong narrative component and coherent pictures of the future seem to help participants grasp concepts quickly. This is particularly true if the scenarios are clear and well differentiated (incommensurable), because they can help participants better understand the scenario space – i.e. the range of future possibilities. For non-expert audiences, better understanding the space they are working within should be particularly helpful. I do not, however, contend that only presenting pre-developed scenarios is valuable. As Patel et al. (2007) suggest, the pre-developed scenarios should be used as tools to foster learning and broaden perspectives for a subsequent exercise where participants develop their own scenarios. Reviewing the Victoria and Revelstoke processes, it is possible that this same role can be filled by strong facilitation, interactive tools, and extensive discussion during the scenario construction process. All three of these processes used what could be considered a second order backcasting approach to the in-workshop construction of scenarios. That is, the interactive and iterative development of scenarios through trial and error with interactive systems. A challenge of this approach is the amount of time it requires to truly iterate on solutions – one reason (in addition to the complexity of developing real-time modeling and engagement tools) why the pre-production of scenarios remains a popular approach in futures studies. A comparison of these approaches is fodder for future research, as a review of the scenario literature did not reveal studies that looked at this question directly. 443 7.6.5 Modeling The modeling used in the GCCP, Victoria and Revelstoke processes is perhaps the strongest connection between the workshops. The bottom-up approach to building modeling has been under development at UBC for a number of years, and has been applied and vetted in a number of planning and engagement processes (Condon et al., 2009; Miller 2013; Miller et al., 2009). This modeling is particularly well adapted to representation from neighbourhood to city scales and to tabulating the impacts of building and urban form alternatives. The transportation modeling used in the three processes was also relatively similar, based on estimates of vehicle kilometres traveled (vkt), fuel types and emissions factors. Additionally, all three processes modeled factors associated with mode shifts, such as density, transit access and behavioural variables. One primary difference in the modeling between the processes was the integration of costs and, in particular, cost/emission trade-offs in the Victoria workshops. The Revelstoke modeling did present home energy costs, but did not reflect the overall costs of strategies, nor the cost/emission implications. As the Victoria results discussed earlier in this chapter revealed, the presence of the cost trade-off information had a marked effect on participants’ choices. There is some evidence that this could be particularly useful for participants with the ‘Let the Market Decide’ mental model who, in both the Victoria and GCCP processes, focused on the cost effectiveness of different interventions. The cost trade-off modeling also illustrates a broader point. Participants, particularly in the Victoria workshops, wanted to understand trade-offs and co-benefits. This introduces complexity into both the modeling and representation of data. This may be particularly true in the case of socio-economic information, which may be impossible to credibly represent in the future (Berkhout et al., 2002) except, perhaps, through the use of backcasting techniques (Dreborg, 1996; Robinson, 2003). As one representation of trade-off techniques, however, the costing data used in the Victoria example suggests the desire for trade-off information in the decision-making process. Another modeling-related difference in the three processes was the level of exposure of the models to the participants. The GCCP process had the least model exposure, because the energy and GHG values were pre-computed and placed on static worksheets and strategy cards. The distance from the model was further increased by converting the GHG emission levels from numeric values to a series 444 of representative stars (see Figure 6.6). The relationships between the participants and the model, therefore, was considerably more abstract. While the participants did tally stars in their final neighbourhood design scenarios, it is unlikely that they had a clear picture of what that meant in terms of emission reduction values. In the Revelstoke process, participants were exposed to a limited set of interactive metrics in the June workshop, and a broader set of energy and GHG-related metrics in the September workshop. These metrics, however, represented the cumulative impacts of their interactions. The metrics were also spatially separated from the touchtable interface on a secondary wall display, and video evidence suggests that many participants did not reference them frequently (Girling et al. 2014b) In the Revelstoke example, then, the model was generating real-time metrics, but interaction with those metrics was limited. In general, participants focused more on the interactive 2D planning interface on the touchtable and the 3D representation of their designs on the wall display. Salter (2005) found a similar tendency amongst participants in a user study to focus on landscape visualizations to the exclusion of associated graphical information. The Victoria process had the most direct exposure of the participants to the model. Indeed, the CAN tool interface was explicitly designed for interactive model use. Model use was, however, mediated by the facilitators. One of the impacts of exposing the model in this way seems to be an ever-increasing desire by participants for more variables, more data and more detail about ‘what is under the hood’. In other words, some of the expressed concerns about model assumptions, ‘incorrect’ values, missing variables, etc. in the Victoria questionnaires were a function of the CAN tool’s level of transparency rather than limitations in the modeling. This type of exposure to the modeling in limited time engagement exercises also necessitates simplification in the modeling, sometimes referred to as ‘executive summary modeling’ (Robinson et al., 2006). The CAN tool’s use in Victoria represented a beta test. In subsequent implementations of the tool, questions about appropriate levels of detail, exposure of assumptions, etc. are being tested and refined (Pond et al., 2014). Similar to other workshop components discussed in this section, it is difficult to express definitive findings about the level of model exposure in the three processes. In the GCCP example, participants accepted the use of the star system, and few made comments about wanting more direct links to the modeled data. However, there is also evidence that the participants focused on other aspects of the neighbourhood design to the exclusion of the modeling information. In Revelstoke, the connection to the model numbers was more explicit, but there was a similar tendency for 445 participants to focus on the visual representations of the neighbourhood to the exclusion of the data (Girling et al. 2014b). In the Victoria workshop, the model was front and centre, and participants grappled with the numbers and trade-offs, however there is some question about whether this more direct connection to the model might have led to model fixation – i.e. focusing on the function of the model rather than the underlying concepts. A review of the evaluation of workshop components for the three processes suggests that this is an area in need of future research. Clearly aspects such as direct model access, visual representation, interactivity, etc. had a dramatic impact on what participants valued about the workshops and what information they took away from them. 7.6.6 Representation Types Chapter two developed a framework for evaluating the types of representations used in planning engagement activities. Figure 7.26 locates the representations used in the GCCP, Victoria and Revelstoke processes on that framework. The discussion here will focus on the interaction of representation types with workshop purpose, audience and information/concepts discussed. As Figure 7.26 shows, the Victoria workshops had the fewest representation types and those representations focused primarily on cognitively abstract, but well established, representations of numerical data. The primary visual representation in Victoria was the wedge chart visible in Figure 7.1, which was augmented by maps (e.g. maps of the City of Victoria’ OCP, district energy zones, etc.) and charts, graphs, and diagrams from the accompanying presentation. In the Victoria process, the choice of representations appears to have been driven by three factors. Firstly, there was the audience, which was made up of energy and climate experts and local stakeholders. The difference between expert and non-expert use of representations is well established (cf, Kozma & Russell, 1997; Kozma et al., 2000). While non-experts tend to focus on the surficial aspects of a representation, experts look beyond the representation to the data being represented (Lowe, 2003; 2004). This is particularly true when accepted representation types are used, for example blueprints in architecture and construction (Kozma & Russell, 1997). In theory, then, the expert audience in Victoria could use the more abstract graph representation to understand the data being represented. In other words, they needed less representational help to understand what the model was telling them. 446 Figure 7.26: A schematic diagram of the representation types used in the three processes using the representation framework developed in Chapter two. The second factor driving the representation was the decision not to include land use in the discussion or in the list of possible interventions. This omission of land use made the discussion in Victoria far less spatial in nature. It would have been difficult for the CAN tool to interactively model land use without some additional spatial representation. The final factor that impacted the representation types was the novelty and complexity of the CAN tool. As mentioned previously, the Victoria CEEP was the initial test of the CAN tool. Adding in spatial and potentially 3-dimensional representations would have greatly contributed to the tools complexity, because the CAN tool’s design would have required the spatial representations to be interactive with the participants decisions about strategies and variables. For example, changes to new construction redevelopment rates would have had to be represented in 2D or 3D in real time. Therefore, given the audience, exclusion of land use, and the complexity of the tool, the choice of representation types for the Victoria workshops was appropriate. An interesting question arises from the choice of representation, however, because of the marked difference in participants’ valuation of workshop components in Victoria compared to the GCCP and Revelstoke processes. Was the high value placed on discussion partially the result of the less visually rich representations used in the Victoria workshops? 447 The Revelstoke workshops were largely designed for the purpose of evaluating a set of tools constructed around the concept of ‘measured visualization’ (Girling et al., 2014b). These tools have been tested with city planners, citizens and high school students (Girling et al., 2014a), using the same representation types, including 2D and 3D spatial representations of locations and urban form, real time graphical data displays of associated metrics, and augmenting charts, graphs and diagrams in associated presentations (see Figure 7.26). In the Revelstoke example, the audience was made up of citizens and the purpose was to explore the visual and GHG emission-related implications of urban form and density. In order to convey these concepts, the visual representations on the paper maps, touchtable, and associated 3D screen focused on a small neighbourhood scale representation. Two things are notable based on the workshop evaluations reported in Girling et al. (2014b). Firstly, many of the participants focused exclusively on the 2D and 3D representations of place, to the apparent exclusion of the associated data metrics. Secondly, the majority of participants choose to work with the more visually representative articulated building models, rather than the colour-coded (by zoning) building massings. A related finding, discussed in section 7.4.3, was the prevalent theme of character, particularly as it related to the proposed increases in density. Additionally, in the June workshop, many participants asked for a wider variety of building models that better fit Revelstoke’s character. In summary, the highly visual concepts of density and resultant character caused participants to focus on the visual representations, and for them to want more detailed visual information about the future character of the neighbourhoods. Speculating, participants may have preferred more realistic and experiential representations of the future neighbourhoods with which to make their decisions. Small, intimate scales and familiar locations are often associated with the desire for higher realism in representations (cf. Lange, 2001; McGaughey, 1998). As Figure 7.26 shows, the GCCP workshops had the widest variety of representation types. In addition to the representation types shared with the Revelstoke workshops, the GCCP workshops also used representative photographs from Marpole and Grandview to illustrate particular concepts, and hybrid experiential/information visualizations to explain what particular energy interventions might look like in participants’ neighbourhoods (see Figure 6.7). The representation types used in 448 the exercises and to convey most concepts, however, were very similar to those used in Revelstoke (differing primarily in aesthetic ways). The GCCP exercises focused on an area considerably larger than the Revelstoke exercises. Another notable point, is that the GCCP participants were working on a fictitious neighbourhood, rather than one they were intimately familiar with. While there were discussions of the desirability of density and the ramifications for community character in the GCCP workshops, there were no written comments discussing the inappropriateness of the representations. Of note, the GCCP representations were less detailed and more generic than those used in Revelstoke. An interesting question when comparing the representations in the Revelstoke and GCCP examples, then, is to what extent the more local scales and more familiar places dictate the need for more realistic representations. Additionally, what is the impact of this if the goal is to communicate concepts versus detailed designs or proposals? In Revelstoke, the goal of the workshops was to explore the concepts of the UDB and what they would mean for Revelstoke’s neighbourhoods, rather than to communicate proposed or realistic designs. However, the scale and familiarity of the representations may have impacted participants’ perceptions (Girling et al., 2014b). A seeming desire for more realistic representations in Revesltoke, then, may have resulted from unforeseen interactions of scale and familiarity. Assessing the representations used for appropriateness suggests that all three processes used representations that were appropriate for their purpose, audience and intended concepts. However, there were some interactions with the representations that would be interesting for further study. For the Victoria workshops, was the increased value placed on discussion at least in part the result of the lower visual immersion provided? In other words, would spatial and visually rich information about the impacts of participants choices cause participants to immerse themselves more in the tools and focus less on discussion? What would the impacts of that be on workshop results and participant satisfaction? In the Revelstoke example, would discussing the implications of density at a larger, less detailed scale, and potentially with an unrecognized neighbourhood, cause participants to focus more on the concept of density and less on its aesthetic and character implications? In that circumstance, would participants be more comfortable with the less realistic, more conceptual visualizations, and would they focus more on the associated metric information? There is some indication that this did occur in the GCCP example, but this raises the opposite concern. Would participants in the GCCP 449 workshops who were comfortable with many of the interventions as discussed in the Sandbox be strongly opposed to them if realistically represented in places they were familiar with? One possible answer to this, would be to use unfamiliar, conceptually represented places early in the decision-making process to discuss the concepts in a less charged environment, moving to more realistic and local representations as planning moves from conception to design. This suggests the need for a more nuanced view of representations and their appropriate application. 7.6.7 Process Components All three of the processes used presentations and interactive exercises as the primary activities in their workshops. There were, however, some significant differences in the integration of the presentations, and the style of the exercises. Presentations The GCCP workshops spent the most time on presentations as a proportion of the overall workshop length. There were three presentations, one introducing the workshop context and concepts, a second discussing the energy system at multiple scales, and a third providing details about the scenarios and strategies to be used. These presentations were interspersed with the interactive exercises. On the workshop review questions, the presentations were ranked third after the interactive exercises and facilitators; they were second behind the neighbourhood visualizations on the workshop media question (see section 6.5.4). Additionally, several participants identified the presentations as helpful elements of the workshops on the open-ended questions. The Revelstoke process only had one presentation, which occurred at the start of the September workshop. This presentation discussed the relationship between density, urban form, energy and GHG emissions. On the post-workshop evaluation, the presentation was ranked as the most helpful component of the workshop. The Victoria workshops had two presentations. The first presentation started the workshop, and established the Victoria context as well as baseline energy and GHG numbers. The second presentation came after the morning coffee break, and before the first breakout session. This 450 presentation focused on demonstrating the CAN tool. On the post workshop questionnaires, the presentations were ranked as the least helpful workshop component (see Figure 7.7). The very different reactions to the presentations in the Victoria, versus the GCCP and Revelstoke processes raises some questions. One possibility is that the audience had a large impact. Perhaps the experts, practitioners and stakeholders from the Victoria workshops were impatient to interact with the CAN tool, and therefore ranked the presentations poorly. Another possibility is that the time and relative effort put into the presentations in the three processes impacted participants’ reception of them. Having participated in the three processes, it is clear that the time and attention devoted to the presentations in the GCCP and Revelstoke processes was significant. For the Victoria workshops, the CAN tool was being developed right up until the workshops; when combined with the need to develop the workshop structure and familiarize the facilitators with the tool, there was very little time to work on the presentations. A common refrain in the public engagement literature is to limit the one-way flow of information. Practically, this takes the form of limiting presentations or lectures and providing more interactive elements. However, the findings from both the GCCP and Revelstoke suggest that presentations have their place. This may reflect differences in learning styles (Felder & Silverman, 1988) and, as Kleinman et al. (2011) suggest, it might also reflect the intrinsic motivation of some participants to attend public engagement events specifically to learn about science and new technologies. Another possible contributing factor, demonstrated in the results for all three processes, is a general lack of local scale energy understanding, or literacy, at the time these studies were conducted. Exercises Interactive exercises, either with digital tools in the case of Victoria and Revelstoke, or paper-based tools as in the GCCP and Revelstoke processes, made up a significant proportion of each of the workshops. The GCCP workshops had four interactive exercises. The first exercise used post it notes and diagramming to develop group concept maps of the energy system. The second exercise used a blank 3D representation of the Sandbox and asked groups to identify the elements of a neighbourhood that they valued. The third exercise was the least interactive, it asked participants to investigate one of the three scenarios. The final exercise incorporated all prior presentations and 451 exercises from the GCCP workshops, culminating in a final group scenario for the Sandbox. The interactive exercises were the highest ranked item on the post workshop questionnaires (see Figure 6.46). Additionally, many participants identified various aspects of the exercises in their open-ended evaluations of what was helpful from the workshops. The final exercise was the most complex, and perhaps the most controversial. While several participants identified it as their favorite activity, it was also commonly cited as a difficult component of the workshop (see section 6.6.6). This difficulty is not inherently bad, however, as causing participants to think and deliberate over difficult choices may, in fact, lead to more learning and empathy for other positions (intersubjectivity). However, one contributing factor to the difficulty, which should be improved in future workshops, is better participant instruction about expectations for the exercise. The Victoria workshop had two break-out sessions, which were both interactive sessions with the CAN tool. In the first session, groups developed a comprehensive set of strategies for Victoria, trying to achieve the 33% reduction in GHG emissions by 2020. In the second exercise, participants broke into strategy focus groups and used the CAN tool to more thoroughly investigate the implications of different variables. The breakout sessions themselves were the second lowest ranked item for helpfulness on the post-workshop questionnaires (see Figure 7.7). However, the highest ranked item was the opportunity to discuss energy issues with other stakeholders and experts. As section 7.2.2 shows, the interactive exercises, facilitators, and the CAN tool all significantly contributed to the participant discussions. The interactive exercises (or breakout sessions) in Victoria were less structured than the activities in the GCCP and Revelstoke workshops. This may have contributed to the lower ranking of the interactive exercises, as participants perceived them as open discussions. The Revelstoke process had three interactive exercises at the June workshops, and two interactive exercises at the September workshop. At the June workshop, the first exercise had participants develop their own neighbourhood design scenarios using the paper-based tools. The second exercise involved participants negotiating in small groups to translate their individual scenarios into a combined scenario on the touchtable. Finally, the third interactive exercise at the June workshop was a focus group download about the process, tools, etc. At the September workshop, the first interactive exercise had participants revisit their individual June scenarios with the new energy and GHG information from the presentation. The second interactive exercise had participants re-convene their June groups to revisit the June group scenario at the touchtable. The workshop evaluation question on the September questionnaire only referenced the 452 September workshop components. In the component evaluation, the second touchtable exercise was the third highest ranked item, while the second individual scenario exercise was not ranked by any participant (see Figure 7.20). The June workshop exercises, where participants developed their initial scenarios, were significantly more active. It seems likely that the exercises and the touchtable would have ranked more highly if surveyed after the June workshop. Taken as a whole, then, the evaluation tools suggest that the interactive exercises were not as highly valued as would be expected. Only the GCCP exercises were ranked as the most valuable part of their respective workshop. However, responses to the open-ended questions and observation of the workshops suggest that the ranking questions did not capture the whole story. In Victoria, the value placed on ‘discussion’ can be seen as a collective measure that included the importance of the CAN tool, facilitation, and the breakout exercises. In the Revelstoke example, measurement of participants’ perceptions after the September workshops likely had a dampening effect on their rankings. It was generally felt among the project team that the design-focused June workshops and exercises were more enthusiastic and better received. A conclusion from a review of the process components in the three processes, then, is that distributing well-prepared presentations with interactive exercises is a useful way to address both participants’ motivations for attending, and their different learning styles. 7.6.8 Outcomes The workshops’ process outcomes have been broadly discussed in this dissertation, both from a mental models perspective (for the GCCP and Victoria processes) and from a workshop evaluation perspective. There are, however, other outcomes from the engagement activities that are important to discuss. Firstly, there are the academic outcomes – how did the engagement activities, the tools used and the evaluation mechanisms contribute to the body of knowledge? Secondly, there are the planning and policy outcomes. What is the evidence that the engagement activities actually contributed to the development of policy or the conduct of planning? Academic Outcomes The GCCP workshops were developed, in part, as an avenue for this dissertation research. As such, journal articles derived from this dissertation are direct academic outcomes of the workshops. 453 Additionally, the workshops formed one channel of the broader GCCP, which was designed as a process to compare different engagement channels. While the full cross-channel evaluation that was envisioned did not emerge, journal articles drawing from the project’s findings are in process (cf. Bendor et al., 2012; Haas Lyons, 2011; Tanenbaum et al., 2011). Another academic outcome of the workshops stemmed directly from the concept of the Sandbox. While the idea of a fictional community to explore relationships between systems is not necessarily unique, the Sandbox did foster thinking at UBC about the advantages of working on a neighbourhood or community where land use, urban form, transportation networks, etc. could be modified to explore implications. In addition, a Sandbox neighbourhood can be constructed such that data limitations common to municipalities can be overcome. The idea of the Sandbox has been adopted into current research in the NSERC funded IDEASS (Integrative, Data-enabled Approaches to Sustainability) project (http://ideass.civil.ubc.ca/). The Victoria CEEP process was a consulting project with no intended connections to academic research. However, through existing research connections with the consulting firm (C2MP) theVictoria workshops became part of this dissertation. In addition, the modeling used in the Victoria workshops contributed to another PhD dissertation (Miller, 2013). Additionally, the modeling used in the CAN tool represents a continued evolution of research devised techniques. The Revelstoke workshops have, thus far, resulted in four academic publications and conference proceedings (Girling et al., 2014a; Girling et al., 2014b; Senbel et al., 2013; Van der Laan et al., 2013) and has contributed to two Master’s theses (Lay, 2013; Su, 2011). The Revelstoke process also served as an evaluation and development platform for the touchtable application. The touchtable has subsequently been used to engage with City of Vancouver planners and City of Richmond secondary students (Girling et al., 2014b). The touchtable and related applications are also being further developed and extended as part of the IDEASS project. Planning and Policy Outcomes The GCCP workshops did not have any discernable planning or policy outcomes. Project timelines and an arms-length relationship with the City of Vancouver Community Planning Department meant that the workshops were not incorporated into the broader neighbourhood planning activities. At 454 best, the GCCP workshops may have impacted the subsequent neighbourhood planning activities through the local residents that attended the workshops – i.e. through capacity building or social learning. However, this link is tenuous, as no longitudinal research was conducted (nor was it an intended part of this dissertation’s research). A missing connection to policy and planning is a definite weakness of the GCCP process. The Victoria workshops contributed substantively to the development of strategy targets for the Victoria CEEP (Miller & Cavens, 2012). The participant-generated scenarios and strategy targets were included in the CEEP report, with direct connections made to the developed consultant recommendations (Miller & Cavens, 2012). Subsequently, the CEEP recommendations were added as a technical appendix the City’s Official Community Plan (OCP). The CEEP proposals also became part of the City of Victoria’s broader Climate Action Program (City of Victoria, 2011). In February of 2013 the Unified Development Bylaw, which provided the context for the Revelstoke workshops, was abandoned by city council. While it is difficult to determine any specific role of the Revelstoke workshops in the UDB’s failure, it is notable that the workshops’ design was largely instrumental. The benefit to the research team was the opportunity to explore the use of new tools in the exploration of density and urban form, and the benefit to the community was the opportunity for residents to explore the implications of the UDB in new ways. As one form of public engagement in the broader engagement program conducted by the City of Revelstoke’s Planning Department, then, the Revelstoke workshops contributed to the City’s final determination (perhaps confirming concerns, rather than forming them). While Orlando (2013) suggests that the broader public was onside with the proposals of the UDB, a subset was particularly concerned about the implications of the proposed density. This example provides an unusually direct connection between a public engagement activity and subsequent policy or planning (Salter et al., 2010), and suggests that the workshops met their instrumental goals – i.e. exploration of the proposals answered participants’ questions about whether or not to trust the UDB. 7.7 Conclusions Similar to the discussion sections, the conclusions for this chapter can be broken down into two categories that directly address the two primary research questions for this dissertation. The first category will synthesize the mental models findings derived from a comparison of the GCCP and Victoria processes, augmented where applicable by the Revelstoke process. The second category will 455 synthesize the findings of the case study review, and what it suggests for future engagement research and practice. 7.7.1 Mental Models The case study comparison conducted in this chapter has led to four main findings about participants’ mental models. Those findings include: the differences between participants’ mental models at the GCCP and Victoria workshops (i.e. expert and stakeholders vs. citizen models), an existing lack of knowledge about community scale energy, the impacts of workshop subjects, tools and structure on participants’ mental models, and the promise of connecting mental models of energy with other important aspects of place. The primary finding about the mental models clusters when compared between the GCCP and Victoria processes was a significant shift between the ‘muddled middle ground’ cluster and the ‘power to the individual’ cluster. It is understandable that participants at citizen workshops would not have strongly formed and coherent mental models of energy, explaining why the ‘muddled middle ground’ cluster was so prevalent at the GCCP workshops (see Table 7.7). What is less clear, is why there was a strong contingent of the ‘power to the individual’ cluster at the Victoria workshops. Is this focus on individual energy responsibility and solutions more common among practitioners? If so, does it suggest a disconnect between those practitioners and the broader public? A larger public survey on energy issues would be useful, not only to determine the extent of individual-focused mental models, but more broadly the other clusters revealed in Chapter 5. What is the real incidence of people from the ‘limited energy knowledge’ and ‘let the market decide’ clusters that were poorly represented in the GCCP and Victoria workshops? Anecdotally, it would seem that these clusters (perhaps unlike the ‘efficient urban development ‘cluster) are more common in the larger population than the workshop participation rates would suggest. What actions could be taken to encourage the participation of these groups in engagement activities? A review of the questions from the three processes about what surprised participants about energy suggests that there is a significant lack of knowledge about the energy system at more local scales. Interestingly, the higher energy knowledge workshops (Victoria, Grandview, Planners) actually made more comments showing surprise about the existing energy system than the lower energy knowledge workshops (Marpole and Revelstoke). This finding suggests that the CEEP municipal inventories and community energy literacy efforts (e.g http://www.energyexplorer.ca/) are important steps 456 towards experts and practitioners better understanding energy at local scales, in preparation for subsequent community planning activities. The findings of this study also suggest that the presentation of information can be highly valued by participants in public engagement activities. Indeed, as Kleinman et al. (2011) have suggested, some participants may be particularly motivated to attend engagement activities specifically to learn about new science and technologies. An effective approach to balancing the delivery of information with the elicitation of participant knowledge and values, appears to include a balance of presentations and interactive elicitation. In accordance with information deficit critics, however (cf. Owens, 2000; Wynne, 2007), it can be said that we better understand how to present scientific and technical information than we do how to meaningfully account for local knowledge and values. The hard work remains in determining how to incorporate the things people value into institutional planning and decision-making. A review of the ‘applied solutions’ questions from the GCCP and Victoria workshops suggests that both workshops did have a marked effect on participants perceptions of those solutions. Participants rankings changed and did so in directions that were consistent with the subjects covered in the workshops. Additionally, the workshop structure appeared to play a role, as the GCCP workshops led to greater consensus amongst participants, while the Victoria workshops appeared to lead to greater dissensus. It is notable, however, that the salience of particular energy issues seemed to overpower others. In the GCCP and Victoria workshops, the large GHG emissions associated with transportation made it the focal solution in both cases. The cost trade-off data in the Victoria workshops did have an impact on participants ranking of transportation, but it was not enough to remove it as the preferred energy solution. In a corresponding way, the discussion of density, perhaps fueled by the small scale and familiarity of the represented neighbourhood centres, was a dominant focus of participants in the Revelstoke process. The dominance of these energy subjects and solutions may suggest a difficulty of taking a systemic approach to engagement activities. If, for example, the goal is to address the energy implications of solid waste or building retrofits, then including a discussion of the whole energy system might detract from the participants’ focus. Conversely, an encouraging finding, seen in all three processes, was a proficiency among participants for connecting energy concepts to other social and cultural values of place. This was, perhaps, most prevalent at the GCCP workshops, largely because the workshop structure and the format of the question was specifically directed at these connections. However, participants’ open-ended responses about energy in place at both the Revelstoke workshop and the Victoria workshop 457 demonstrated a tendency for participants to reflect on how planning for energy might impact, or be facilitated by, residents’ values. The explicit recognition of the importance and inter-relation of energy sustainability, quality of life, sense of place, and other human values is critical to a regenerative approach to designing and planning for the future. The findings from these processes suggests that it is not difficult for people to make these connections. 7.7.2 Case Study Components Bishop et al. (2013) discusses the merits of experimental lab studies versus in situ case studies for the evaluation of visualization interfaces. They conclude that, despite the inability to generalize findings from one-off case studies, it is through observation of tool use in real-world engagement activities that strengths, affordances and limitations can be best assessed. When studying the complex interactions of information, values, tools and processes, the limitations of a case study approach are often overweighed by the richness and unexpectedness of results. Reflection articles that describe processes and their experienced successes and challenges are uniquely suited to advancing knowledge in the domain of engagement on messy human-environment issues (cf. Patel et al., 2007; Robinson et al., 2006; Salter et al.; 2009). It was for this reason that the case study component was included in this dissertation research. In other words, while the hope was that the mental models research would provide a more concise picture of people’s thinking, and changes in that thinking fostered by engagement, the evaluation would be impoverished if it did not examine the broader case study components. Table 7.11 provides a categorized synopsis of the case study components for each of the three processes, however, the reality is that decisions made in these categories are not discrete. In particular, the broader decisions made in the beginning stages of developing an engagement process permeate all later decisions. Generally, the first decision made (if it is a decision, and not dictated by external factors or agents) is the workshop goal. It is notable that the process with the weakest external pressure on the workshop’s goal (GCCP) had the most freedom to experiment with exercises, scales, outcomes, etc. It is also notable, however, that this workshop had the weakest demonstrable link to the institutions of governance and resultant policy change. These findings suggest that engagement process developers need to determine whether their goals should be broad, transformative and arms-length from institutions, or targeted, incremental and more directly tied to policy and planning. I would argue that both approaches are necessary, and will discuss two ongoing research projects in Chapter 8 that are working from opposite ends of the incremental/practice and 458 transformative/creative spectrum. Another possibility, unrealized in the broader Greenest Cities Conversations Project example, is that a relationship of trust can be established between researchers and institutions providing a bridge between transformative engagement activities and decision-making. The GCCP experience suggests there is significant work required to transgress these boundaries (Robinson, 2008). Audience is another process component that is fundamental to the design and development of engagement activities. It was, in some ways, a happy accident that the more expert Victoria workshops and the expert/lay disparities in the GCCP workshops presented themselves for evaluation in this research. For example, the finding that experts and practitioners were more likely to indicate surprise about the workings of the existing energy system in local settings (see section 7.5.4) supports the notion that planning for energy at these more local scales does present a new way of thinking (Andrews, 2008; Neves & Leal, 2010). This suggests that municipal inventories and programs such as CEEPs (BC Hydro, 2014), and the Community Energy Explorer (http://www.energyexplorer.ca/) can be useful tools for building capacity amongst practitioners. From a workshop design and development perspective, the tendency for expert audiences to want tool transparency and the ability to drill into the assumptions and workings of the models suggests directions for future tool development. Some of the complaints about the tools and exercises from the Grandview GCCP workshop also indicate the problems that can arise when the workshop design and tools do not fit the actual audience. Iteration in tool and engagement design is necessary to better adapt tool affordances to audience expectations (cf. Pond et al., 2014). The three processes described here suggest that proscribed boundaries and/or political jurisdictions can present difficulties for discussing energy and sustainability subjects at meaningful scales (both spatial and temporal). In this way, scale, like workshop goals and audience, can have an impact on other workshop components. The Victoria and Revelstoke processes had elements of their scales dictated by external factors. In Victoria, this led to challenges regarding the scale of interventions such as transportation initiatives. In Revelstoke, scale may have contributed to a fixation on building types and character to the detriment of intended subjects (i.e. energy and GHG emission implications). The GCCP process had the most freedom to select appropriate spatial and temporal scales, and responses to the open-ended questions suggest that inappropriate scales were not a concern of the GCCP participants. This may suggest that early engagement activities in planning or decision-making processes could benefit from 459 positioning topics at meaningful, rather than jurisdictional scales. For example, the difficulties of addressing transportation at the municipal scale could be addressed by demonstrating regional forces and implications before focusing engagement on the sponsoring institutions jurisdiction of influence. Scenarios have become a de rigueur tool of energy and sustainability planning, because of the focus of this planning on future environments (Salter et al., 2010). One of the ongoing debates in the scenario domain is between primarily quantitative, model-driven scenarios, and qualitative narrative, or value-based scenarios. This qualitative/quantitative tension manifested itself in the processes discussed in this chapter, with the Victoria scenarios being strictly technical and quantitative, while the Revelstoke and GCCP workshop scenarios were quantitative/qualitative hybrids. The GCCP scenarios had the strongest qualitative component, with an early workshop scaffolding exercise directed specifically at helping participants think about what they value about a community. A related, but separate, issue about scenarios is the question of the value of pre-developed scenarios. In section 7.6.4 I made the case for pre-developed scenarios as a pedagogical aid for non-expert audiences to help define the scenario space. One of the values of pre-developed scenarios is the opportunity to create strong, qualitative narratives that can express what it might be like to live in a given future world (cf. Watson & Oliver, 2012). These qualitative narratives can add flesh to the bones of quantitative modeling to better describe what future environments might actually be like. A question arising from the case study review in this chapter, is the extent to which visual representations, coupled with real-time interactivity and iteration can act as surrogates for these strong pre-developed narratives. Recent research on serious games (cf. http://futuredelta2.ca/) may shed some light on this opportunity. At present, one of the challenges of interactive, iterative scenario construction, is the time required to iterate and learn. Until the time hurdle can be crossed, pre-developed narrative scenarios have a place in sustainability planning. From a modeling perspective, there were two key conclusions from the processes discussed in this chapter. The first, was a tendency for model transparency to breed ‘model fixation’, particularly in the Victoria workshops. In essence, the more you show participants (particularly experts) what is ‘under the hood’, the more they want to drill in and investigate parameters and assumptions. In some circumstances, this is undoubtedly appropriate. For example, in participatory modeling (cf. Cohen et al. 2006), the participation mechanism is the act of model construction. However, in planning or decision-making exercises, model fixation may detract from the broader goal of exploring alternatives. In a subsequent use of the CAN tool, some of the model transparency has been reduced in an effort to avoid fixation (Pond et al., 2014). 460 A second finding from the case study review of the modeling, was the value of trade-off information. The Victoria workshop was the only process to utilize direct, quantitative trade-off information in the form of emission reduction costs. This trade-off information had an impact on participants’ valuation of alternatives. Based on participants open-ended comments, it is clear that they would have liked more trade-off information and, in particular, information about synergistic co-benefits of particular interventions. For the representations used, the main conclusion involves the interaction of the representations with the processes’ goals, audience, subject matter and scales. The more expert-focused Victoria workshops used interactivity and limited representations in the CAN tool to augment primarily discussion focused engagement exercises. This type of engagement worked well for collaboratively setting substantive targets for particular strategies. Conversely, the citizen-focused GCCP and Revelstoke processes more strongly immersed participants in rich media and multiple representation types, resulting in a focus on the tools, with less value placed on discussion. In both the Revelstoke and GCCP processes, the focus was on exploring energy and planning implications for pedagogical and evaluative purposes. In these cases, the greater tool focus was, at least partly, based on a desire to better understand the implications of spatial planning choices. What are the implications of the discussion versus tool focus and the role that representation played in that focus? Should representational richness be varied at different points in the engagement process to foster these different types of interactions? For example, should visualizations be ‘put away’ during key deliberative moments to move the focus to discussion? Alternatively, can discussion-focused deliberations be trusted if the subject matter in question is highly spatial (e.g. the impacts of increased residential density)? Many of these questions are implicit in the development of engagement tools and workshop processes. Evaluating multiple case studies with varying tools and interactive exercises can serve to make these questions explicit in the development of future engagement. In terms of the process components – i.e. the presentations and interactive exercises that so frequently make up the bulk of workshop and/or charrette engagement – perhaps the key finding from this case study evaluation is the higher than expected value placed upon presentation elements. A possible explanation for this, as Kleinman et al. (2011) suggest, is an intrinsic motivation amongst the type of interested public participants that attend workshops, to learn about new science, technology and policies. It should be noted, however, that in all three of the processes evaluated 461 here, interactive exercises made up a much larger portion of the overall workshops. In other words, a better than expected ranking of presentation elements should not be taken to suggest that public engagement should predominately use a lecture format. Rather, it suggest that public engagement developers should not shy away from using presentation when they have other benefits – e.g. the explanation of local context, available energy resources, etc. Finally, there are conclusions about the processes’ outcomes. This chapter’s broader evaluation has been about the process outcomes – i.e. what is the impact of participation on those that participate (what did they take away from the process of engagement)? All three of the processes also made significant contributions to academic outcomes. The Victoria modeling (Miller, 2013), GCCP Sandbox and Revelstoke touchtable all represented novel research, and the results of the three case studies are being applied to the development of these tools. As Bishop et al. (2013) noted, it is only through real-world applications of these types of tools that they can be effectively assessed, leading to improved tools in future research and practice. Both the Revelstoke and Victoria processes also had relatively strong connections to policy outcomes. While it is not possible to quantify the contribution of the Revelstoke workshops to the eventual abandonment of the UDB (Orlando, 2013), it is likely that they played a role, along with the broader City of Revelstoke engagement activities. Instrumental engagement, after all, is not about selling particular ideas, but rather investigating them in an open process. The participant outputs from the Victoria workshops were recorded and directly included in the final CEEP report as partial justification for the consultants’ recommendations (Miller & Cavens, 2012). The final CEEP report became a technical appendix in the broader OCP. While evidence of implementation of CEEP strategies is not yet available, the reflection of participant input in official planning documents is a more direct indication of policy outcomes than is commonly seen. The lack of policy or planning outcomes in the GCCP process was a perceived weakness of that engagement activity, and raises the spectre of the ‘participation experiments’ criticized by Bogner (2012). Bogner’s broader thesis is valid, that with the evidence for declining social capital (Putnam, 2000), we should be endeavoring to make engagement meaningful, rather than just an academic exercise. A meaningful process, of course, does not necessitate substantive outcomes – there is clearly value in both normative and instrumental engagement activities. In the planning sphere, however, there is a risk that actions that look so concrete (e.g. manipulating density and transit access) can easily be misconstrued as having real implications for people’s communities. This may be 462 particularly true because the GCCP workshops occurred in actual neighbourhoods. It should be noted, however, that the disconnect between the GCCP workshops and the CoV’s neighbourhood planning was not intended. Rather, it reflects a persistent difficulty of connecting academic research with other institutions (Robinson 2008). The challenge of negotiating these boundaries is a constant of applied research activities, and reflects an ongoing need for engagement with municipal (and other) partners, to facilitate the bidirectional benefits that can come from real evaluation of these processes. The case study review presented in this chapter reveals how the answers to the questions posed in section 7.6 and Table 7.11 shaped engagement on energy issues and contributed to their success or failure. Undoubtedly, this is not an exhaustive list, and reflects a bias towards tool-based (e.g. models, visualization, interactivity) engagement. However, the prevalence of these types of tools in the investigation of sustainable future landscapes, and the explicit link between the tools and mental models (cf. Chermack, 2011; Chermack & van der Merwe, 2003; Senge, 1990 ) suggest they are worthy of further study. Chapter 8 will build upon the mental models and case study elements discussed in this chapter to explore broader implications and avenues for future research. 463 Implications and Future Directions The purpose of a PhD is to come to the end and know what the questions were – Carl Steinitz Esri Geodesign Summit (2014) 8.1 Introduction As Chapter 1 discussed, this dissertation was conceived based on two primary premises: 1. That a confluence of issues has led to a need to plan for energy at local scales, and that this more local energy planning could have significant effects on our communities. In the face of these effects, engagement is crucial to community scale energy planning. 2. That significant work remains to develop effective techniques for evaluating engagement processes. Many evaluation techniques tend to be piecemeal, and miss important connections across participants’ answers (i.e. individual’s answer patterns) that might give a more complete picture of people’s understanding and beliefs about complex subjects like energy. These two premises became the two major threads of this dissertation: a case study evaluation of engagement in community-scale energy planning, and the mental models evaluation methodology. From the beginning, the dissertation has been moving between these two threads (see Figure 8.1); at times focusing more on the mental models component (e.g. chapters 3, 4, and 5), and at times more on the energy planning at local scales component (e.g. chapters 2, 6, and 7). For reasons to be discussed in section 8.3, the threads did not weave into as coherent a pattern as initially anticipated. Perhaps this is to be expected when approaching a nascent research domain (community-scale energy) with a novel research methodology (a different approach to mental models elicitation). As Figure 8.1 shows, Chapters 6 and 7 were more strongly focused on the case study dissertation thread than initially intended (see Figure 1.3 for a comparison). Nevertheless, there were multiple key findings from both the case study and mental models components that have implications for future research, and the conduct of engagement in sustainability planning. The goal of this chapter is to place the dissertations findings in a broader context, highlighting and summarizing key outcomes of the research, and pointing towards resulting research questions and hypotheses for future research. Because of the breadth of material covered in chapters 4 through 7, 464 the structure of the dissertation has focused on including conclusions and key findings with the individual chapters. As a result, the majority of this chapter will be focused on extrapolation from the dissertations findings – that is, what might some of the key findings mean for future research and practice, and how have they changed my theories and concepts about how to do energy planning, and process evaluation. The chapter is organized into the discussion of six main topics. First, the the dissertation’s findings will be discussed, and summarized as they relate specifically to the two research questions (section 8.2). Second the mental models methodology, findings, limitations and directions for future research will be discussed (section 8.3). Third, the chapter will turn to a discussion of the implications of the findings for the future of community-scale energy planning (section 8.4). What are some of the key findings that may influence future energy planning efforts? Fourth, public, or civic engagement in sustainability will be discussed, focusing on broad questions about why we engage, with whom, and how we structure that engagement (section 8.5). The fifth section will focus on the tools of engagement, in particular the models, scenarios, representations and interactive components that are common in engagement processes that deal with complex human-environment systems (section 8.6). These tools are particularly important when we engage about desired futures. For each section, I will focus on a key theme, or themes, that I believe have particular import for how we plan, engage, or evaluate. In the relevant sections I will also review two current research projects that address some of the key issues identified in this dissertation. The purpose of the research in this dissertation was exploratory and, therefore, generative – leading towards more specific follow-up research. Therefore, for the themes of mental models, energy planning, engagement, and engagement tools each section will conclude with a succinct set of testable hypotheses or research questions for future research. The research in this dissertation was also applied, with a goal of providing guidance to practitioners. Therefore, each of the theme sections will also conclude with specific recommendations for practice. The final section will discuss the dissertation in the broader context of applied research. What is the value of situating this type of research in messy, real world engagement processes that can make operationalizing methodologies and interpreting results more difficult? 465 Figure 8.1: A schematic representation of the thesis chapters as realized, and their relationships to the dissertations' two main threads. 466 8.2 Dissertation Research Questions Revisited Before turning to a more in-depth discussion of the themes that arose from the findings in this dissertation, it is important to first revisit the dissertation’s research questions, briefly summarize how the research findings addressed them, and how those findings contribute to research on engagement in sustainability planning more broadly. 8.2.1 Research Question 1: Case Study Focus The first research question concerned the case study component of the research. It was: 1. What are the tools (e.g. models, scenarios, visualizations) and process components (e.g. presentations and interactive exercises) used to engage participants about community scale energy, and how do they impact the process and its outcomes? Tools The majority of the tools used were common to the three processes, including the use of numerical models of energy use and supply, future scenarios, and interactive tools that allowed participants to generate their own representations of future conditions. This is not surprising, given the prevalence of these tools (particularly models and scenarios) in engagement on sustainability issues (Salter et al., 2010). There were three primarly differences in the tools used between the three processes that are worth mentioning. Firstly, there was a difference of tool platforms, perhaps most notably reflected by the fact that the Victoria workshops used a single, interactive digital modeling tool, the GCCP workshops used a series of analog (i.e. pre-developed paper tools) and the Revelstoke process used a mix of analog and digital tools. Secondly, there were notable differences in the types of representation used in the three processes. Both the GCCP workshops and the Revelstoke workshops made extensive use of spatial visualizations of the study neighbourhoods, while the Victoria workshop had no visualizations, and thus no representation of spatial characteristics of the city. 467 Finally, there was a demonstrable difference in the way that scenarios were used in the three processes. The GCCP process was the only one that used pre-developed scenarios as a pedagogical device. All three of the processes allowed participants to develop their own scenarios. In the Victoria workshops, participants worked in a group to develop a collective scenario. In the Revelstoke workshops participants first used paper tools to develop an individual scenario, and then negotiated a group secenario at the touchtable planning tool. In the GCCP process participants’ ended the workshop by developing a group scenario, after first examining the pre-developed scenarios. An additional difference in the processes that was not specifically a tool difference, but impacted the types of tools and the way they were used, was the spatial scale of the workshop content. The Revelstoke process had the smallest spatial scale at 4-6 blocks, the GCCP process the next smallest at 30 blocks, and the Victoria workshops the largest at the scale of the City of Victoria. The focus on spatial issues and representations in the Revelstoke and GCCP workshops could be seen as a byproduct of the more intimate scales on which they focused, though other variables played a role (see section 7.6.6). In terms of the impacts of the tools on the processes, there were three major findings that provide fodder for future research. Firstly, there was the variance between the processes in the extent of the tool focus, with the Victoria being the most tool focused, and the GCCP process being the least tool focused. The tool focus in the Victoria workshops was apparent, as participants interrogated the model, challenged assumptions and registered complaints about solutions and trade-offs not represented in the model. This fixation on the workings of the model has been demonstrated in other model-focused engagement processes (cf. Kasemir et al., 2003). This meant that some facilitator and group time was spent discussing the tool, rather than the content. A question for future research is whether or not this model fixation is a significant distraction that negatively impacts engagement activities. Secondly, there was an apparent impact of trade-off information represented by the presence of costing data in the Victoria model. The GCCP workshops did not have costing data, and there were significant differences between the two processes on post workshop preferences for energy solutions that were consistent with the impacts of the Victoria costing data. This finding suggests that trade-off information is an important component in representing energy solutions in engagement processes. 468 Finally, there was strong evidence that the visual richness of the representations in the Revelstoke and GCCP processes had a significant impact on the value participants placed on discussion. In the Victoria workshops, discussion with other participants was the highest rated workshop element, while in the Revelstoke workshops it was the second lowest rated element, and in the GCCP workshops it was the lowest rated element. This finding suggests that rich visual displays may cause participants to focus more on the tools, and less on discussions with other participants. Further, more directed, research will be required to establish the veracity of this case study observation. More generally, what can we say about the impacts of the tools on the processes? It is not a stretch to say that the tools drove all three processes, filtered the information presented and interacted with, and had demonstrable effects on participant learning. Sections 8.4 and 8.5 will further discuss the implications of the engagement tools and develop testable hypotheses and research questions stemming from this case study analysis. Process Components Similar to the tools used, there was significant commonality in the process components of the workshops, with all three processes making use of presentations and interactive exercises. The Revelstoke process was unique in the use of both an individual-focused exercise (development of a planning scenario) and the use of a focus-group discussion. A notable difference in the processes based on participant ratings of workshop elements, was the value participants in the Revelstoke and GCCP workshops placed on the facilitator presentations. In the Revelstoke workshops the presentation was the highest rated element, and in the GCCP workshops the presentations were the second highest rated item. Conversely, in the Victoria workshops the presentations were the lowest rated item. Notable variables that may have caused these differences in ratings were the expert and practitioner audience at the Victoria workshops (perhaps suggesting they needed less contextual information) and the time and attention dedicated to the presentation development in the Revelstoke and GCCP processes. Another notable difference between the workshops that may have impacted the processes is the construction of the workshop exercises. As stated in the tools section, the GCCP workshops were the least tool focused. In fact, one of the reasons for using analog tools in the GCCP workshops was to tailor the tools to the individual exercises. In other words, the workshop design first identified the 469 necessary exercises, and then developed tools specifically for those exercises. The interactive exercises were the highest rated workshop element on the GCCP post workshop questionnaires. Conversely, the workshop exercises were the second lowest rated item in the Victoria workshop questionnaires. The major difference between the interactive exercises in the two processes was a more structured approach to the GCCP workshop exercises, which deliberated towards identified goals. The Victoria workshop exercises were significantly less structured, and focused on discussion. The case study component of the research in this dissertation provided observational evidence for the impacts of engagement tools, process components and related content on the outcomes of the research. This observational evidence has led to the development of future research questions and hypotheses which will be discussed in sections 8.3 to 8.5. Contributions to Research The case study component and associated research question from this dissertation contributed to applied, in situ research on community scale energy engagement, and more broadly sustainability engagement, in two primary ways. Firstly, community-scale energy planning is still in a nascent stage, and researchers and practitioners are still determining what aspects of energy are important at these more local scales. The modeling of DES, and the relatively disappointing potential that modeling revealed in the Victoria and GCCP processes, is indicative of findings from the research that can inform subsequent energy planning processes. Similarly, the exposure of the consistent connections between local energy planning and community values such as sense of place and quality of life have important implications for how we do community scale energy planning in the future. The case study research conducted in this dissertation, therefore, has contributed to our understanding of energy at local scales, and has identified areas for future research. The second major contribution of the case study research is to sustainability engagement more broadly. Van Asselt and Rijens-Klomp (2002), in their seminal paper on PIA, made a strong case for researchers and practitioners to be reflective on engagement processes; recognizing that in messy, applied research, it is often through structured reflection that the most valuable findings are revealed. Reading research that includes this form of structured reflection (cf. Robinson et al., 2006) provides significant guidance for the development of future processes that is not always available in more 470 specific, quantitative evaluations. The cross-case comparison in Chapter 7, therefore, can make significant contributions to applied sustainability engagement research. 8.2.2 Research Question 2: Mental Models Focus The second research question concerned the mental models component of the research, and was separated into two parts. The research questions were: 2. a) What are participant’s existing mental models about community scale energy? b) What are the effects of participation in energy engagement processes on people’s mental models of community scale energy? The mental models evaluation methodology was most effective at identifying existing mental models or energy. The MCA of the pre-workshop questionnaires revealed important components of participants’ mental models of energy. Those important components made up the first two dimensions of the developed MCA (see Figure 5.25). Dimension 1 was defined by how big participants felt the necessary level of change was to address energy issues, and who they felt was responsible for making those changes. Dimension 2 was defined by participants’ beliefs about whether energy issues required individual or collective action. The results of the MCA suggest that knowing these more fundamental aspects of participants’ mental models we could expect to predict how they would react to given energy solutions. This is consistent with the mental models framework proposed by Hoffman et al. (2014). A further analysis of participants’ existing mental models using a hieararchical clustering procedure revealed 6 dominant mental models types: limited energy knowledge, efficience urban development, muddled middle ground, national action, and let the market decide (see Table 5.19). These clusters appear to be plausible groups within society more broadly, though it is likely that some groups were over-represented at the workshops, and others were under-represented. It is also likely that there are other mental models groups in the broader population that this research did not reveal. Section 8.3 will discuss limitations and future directions of the mental models research in more detail. The mental models methodology was less successful at determining changes to participants’ mental models for reasons that will be discussed in section 8.3.2. At best, we can hint at apparent changes to 471 participants’ mental models and their causes as an exploratory first step towards a more directed and thorough investigation. In both the GCCP and Victoria workshops, participants’ preferences for particular applied energy solutions shifted significantly, and did so in ways that were consistent with the workshops’ information. Additionally, there was limited evidence that participants from particular mental models clusters changed their preferences in ways that were consistent with the definitions of the clusters (see section 6.5.3). Participants’ identification of Vancouver’s primary energy source also shifted from the pre to post workshop questionnaire, though it did so in a counterfactual way (i.e. away from natural gas and towards transportation fuels). Despite the fact that participants were less accurate in identifying the primary energy source after the workshops, their opinions were consistent with the workshop focus. In other words, while natural gas is the dominant energy source in Vancouver, the workshop material would have suggested to participants that they could have the largest impact by changing their transportation behaviours. This finding raises a question about the importance of factual information (in educational psychology terms, recall) versus participants’ capacity to internalize concepts that may be more important to energy planning (in educational psychology terms, transfer – or deep learning). Another indicator of learning and shifts to participants’ mental models came from an open-ended question that asked participants what surprised them about the workshops. In all three workshop processes, participants identified aspects of the existing energy system in their community as surprising information. This suggests that there is still significant learning, even by experts, to understand what community-scale energy planning means. In terms of participants’ mental models, this finding minimally suggests that the workshops established recognition of the unique aspects of planning for energy at this scale. Finally, in all three case studies open ended questions asking about the connection of energy planning to participants’ sense of place revealed a propensity amongst participants’ for connecting these concepts. This finding suggests that people’s mental models of energy at this scale contain other place values, and future evaluation methodologies should look for the connections between these concepts. 472 Research question 2b), then, did not provide definitive answers. Rather it provides fertile ground for the development of better methodologies, and more specific research questions and hypotheses, that can query the apparent mental models relationships only hinted at in this research. The mental models methodology limitations and recommendations for future work will be discussed next in section 8.3, culminating in more specific hypotheses for furture work. Contributions to Research The mental models research conducted in this dissertation made three primary contributions to research. First of all, to the author’s knowledge, this is the first mental models study to look specifically at energy. As such, it makes significant contributions to our knowledge of how people think about energy at local scales. Secondly, the dissertation developed a novel methodology for evaluating mental models in time-limited engagement processes, utilizing questionnaires and multivariate statistical techniques to reveal participants’ answer patterns as surrogates for their mental models. This evaluation methodology demonstrates promise for continued applications, particularly with the improvements to be discussed in section 8.3). Finally, while limited in its current application, the methodology used to look for changes in participants’ mental models did reveal some promising results that may fuel future research on mental models of community scale energy. True to the broader exploratory ethos of the dissertations research, the methodology was generative, leading to more operationalizable hypotheses and research questions that will be discussed in in the chapter sections that follow. 8.3 Mental Models Methodology As stated in the introduction to this chapter, the purpose of the chapter is not to provide conclusions regarding the specific findings of the individual chapters. Those conclusions occurred within each chapter. For the mental models results, the best summary of the conclusions can be found in Table 5.19 (summarizing the main characteristics of the developed mental models clusters), Table 6.21 (summarizing the main mental models findings from the GCCP workshops), and Table 7.7 473 (summarizing the main mental models findings from the cross-case comparison). The focus of the mental models discussion in this chapter has two parts. Firstly it will discuss the methodology as applied, and how it could be improved in future iterations. Secondly, particularly salient results will be discussed within the other themes that organize this chapter (energy planning, public engagement, and engagement tools). The mental models methodology used in this dissertation represented an exploratory approach towards adapting mental models research to workshops with temporally limited access to participants. As Chapter 3 discussed, mental models methodologies have traditionally been more intensive, using techniques such as semi-structured interviews, one-on-one sorting exercises, or cognitive mapping that require substantially more time from the participants (Jones et al., 2012). In many cases in the literature, the mental models methodology makes up a significant portion of the actual engagement with the participants in the studies. While constructing the engagement activities studied around a mental models methodology was one possibility for the research described in this dissertation, that idea was rejected for two main reasons. Firstly, it would not have been possible to prescribe a more intensive mental models methodology to either the Victoria or the Revelstoke case studies, which were designed and delivered by consultants, or other research groups. The Victoria and Revelstoke cases are indicative of the types of community-scale energy planning engagement (and more broadly sustainability engagement) being undertaken in many jurisdictions. Therefore, to make a mental models methodology widely applicable to sustainability planning, it needed to be amenable to the workings of existing engagement activities. A second, related reason why a more traditional mental models methodology was not followed was the nature of the domain being researched. As the case study evaluation in Chapter 7 revealed, there are a number of components common in sustainability engagement, including scenarios, modeling, participatory exercises, etc. One of the two primary research questions for this dissertation required exploration of these case study components of sustainability planning, therefore, a research methodology that dramatically changed those components was not feasible. For these reasons, the decision was made to try to adapt a mental models methodology to the more time-constrained conditions of workshop-based engagement. This decision led to the construction the methodology as described in Chapters 4 through 7. The methodology started with the mental models interviews with experts and practitioners to better describe the research space and develop the questionnaires in Chapter 4, carried on through the Multiple Correspondence Analysis and Hierarchical Clustering description of participants’ existing mental models of energy in Chapter 5, 474 and culminated in Chapters 6 and 7 with the evidence for interactions between participants’ existing mental models and the workshops, and evidence of changes to their mental models. Through the conduct of the mental models methodology, much was learned about the strengths and weaknesses of the approach, and about how it might be altered in future iterations to better serve its intended purpose. In particular, this exploratory study was instructive about how to more effectively structure the research and research instruments to answer one of the key questions from the dissertation: how are participants’ mental models of energy impacted by participation in engagement workshops (research question 2 b))? This section will first review what about the methodology worked well, before concentrating on limitations of the methodology and possible solutions. Finally, the section will conclude with a discussion of the promise of a mental models methodology for future research on energy and sustainability engagement. 8.3.1 Mental Models Methodology: What Worked As stated, evaluation of participants’ existing mental models of community-scale energy was more successful than evaluation of changes to those models. In particular, the mental models evaluation components that worked well included the mental models interviews described in Chapter 4, and the multivariate analysis of participants’ existing mental models in Chapter 5. These methodological successes can be built upon to improve the mental models methodology in future iterations. The mental models interviews were derived from work described by Bostrom et al. (1994), Kearney & Kaplan (1997), Morgan et al. (2002), and Young (2008), all of which used semi-structured interviews as a method of eliciting mental models and describing the mental models ‘space’ for a particular domain. In this dissertation, the mental models interviews used a similar semi-structured form to elicit the mental models of 13 experts and practitioners. The results of those mental models interviews were used both in the construction of the GCCP workshops and to develop the questionnaires used in the GCCP and Victoria workshops. In a more limited way, they also contributed to the construction of the Revelstoke workshop questionnaires. In a way, the mental model interviews served as a form of extended peer review (Funtowicz & Ravetz, 1993), by providing for the inclusion of community-scale energy concepts into the design of the GCCP workshops and the evaluation questionnaires that may not have been possible if the design had been the sole domain of the research team. The semi-structured format of the interviews 475 also allowed for a departure from the specific interview questions to pursue interesting subjects that arose. For example, the relationship of energy planning to other aspects of what people value about their communities. This led to the inclusion of questions on the GCCP workshop questionnaires that specifically addressed sense of place and quality of life issues. This relationship between energy and broader community values became a significant part of the analysis in Chapters 6 and 7, and form the basis of section 8.4.5, which looks forward to the potential for better integrating energy and sustainability planning into official community plans (OCPs) and other planning instruments. The use of the semi-structured interviews as a precursor to the design and development of both workshop components and evaluation materials, then, had a significant effect on how the research was carried out. Another aspect of the mental models interview methodology that functioned well was the use of the mental maps representing interview participants’ mental models of energy (see Figures 4.2 and 4.3, Chapter 4). These maps provide a visual representation of the participants’ mental models, including the prevalence of connections between concepts, and made differences between interviewees more apparent. In this dissertation, the maps were constructed manually, based on the results of the NVivo qualitative analysis of participants’ interview transcripts. The recent work by Hoffman et al. (2014) describes a quantitative network analysis methodology that can partially automate this process, as well as combine multiple participants’ models into a collective picture of the mental models space for a given domain. Similar to the MCA methodology described in Chapter 5, Hoffman et al.’s method allows for the description and analysis of answer patterns across participants, which was one of the driving factors behind the mental models methodology selection for this dissertation. These visual analysis tools, a hallmark of information visualization (cf. Ware, 1999) and visual analytics, provide the opportunity to parse large, complex data sets to reveal patterns, clusters, etc., which can be useful in deciding how to structure engagement materials and evaluation tools. For example, some of the interviewee’s insights regarding the connection of land use and urban form (see section 4.2.2) were instrumental in the spatial construction of the Sandbox for the GCCP workshops. Based on the research experience from this dissertation, I would argue that the early use of interviews as a way of understanding the research domain and helping to develop both engagement materials and research instruments is an effective tool. One caveat is that interviews with a broader cross section of people, preferably including citizens with no prior expertise in energy-related issues, could improve the scoping of the research space. While this was an intended part of the research design in the current study, time constraints and a lack of access to Vancouver or Victoria citizens prevented it. 476 The other aspect of the developed methodology that was successful in this application was the use of MCA, and subsequently Hierarchical Clustering to describe participants’ existing mental models of community-scale energy. Despite limited participant numbers and incomplete surveys, the MCA and clustering methodologies managed to delineate dimensions (economic versus transformational change and individual versus collective action) and clusters (see Table 5.19) that make intuitive sense when considering different people’s understanding of energy issues. Additionally, an evaluation of supplemental demographic variables suggested that gender, and possibly cultural background could play a role in people’s mental models of energy. As shows, male participants trended towards the individual and economic ends of dimensions one and two, and female participants towards the collective and transformative change ends. In the same figure, there is some evidence (though inconclusive) that people of European descent also trended towards the individual and economic ends, while other cultural groups trended towards the collective and transformative ends. In future engagement practice and research, these findings could play an important role in determining the content and structure of engagement. For example, the scenarios and strategies for the GCCP workshops focused primarily on collective actions that residents could take to address energy issues. While there is significant and growing support for a more collective approach to sustainability issues in the social practice and related literatures (e.g. Ostrom, 2014), the individual action pole of MCA dimension 2, and the presence of the ‘Power to the Individual’ mental models cluster suggest that engagement efforts need to, at least, address the individual/collective question. One possibility would be to explore the links between collective options like public transit access and individual behavior, such as taking the bus more frequently. The findings of the MCA and cluster analysis regarding different mental models of energy and the potential impacts of gender and cultural factors on those models may also be important for the recruitment of engagement participants. Truly inclusive engagement will require the presence of participants from different perspectives, some of which may be underrepresented in current engagement practices. For example, it seems likely that participants from clusters 6 ‘Let the Market Decide’, and 1 ‘Limited Energy Knowledge’ were underrepresented at both the GCCP and Victoria workshops based on anecdotal observation of broader publics. Additionally, cultural groups that may experience language barriers to participation in engagement activities may also be underrepresented. With the multicultural nature of large urban centres like Vancouver and Victoria, and the suggestion from Chapter 5 that cultural background may have an impact on mental models of energy, exclusion of particular demographics or perspectives is a problematic limitation of current engagement activities. Efforts may need to be focused on specifically targeting different cultural groups or sectors to increase the representativeness of engagement activities. Further analysis of 477 existing mental models of energy and their constituents may help target those perspectives that are not being adequately represented. Application of MCA and cluster analysis to a much broader sample, potentially through mail or internet surveys could provide a better indication of which mental models from the population are under, or overrepresented at engagement processes. A significant benefit of the questionnaire and MCA/cluster analysis methodology is that it could easily scale to a much larger subject pool. In fact, MCA has most commonly been applied to large survey methodologies (Husson et al., 2011; LeRoux & Rouanet, 2010). As an exploratory study, then, the mental models interviews, questionnaire construction and subsequent MCA/cluster analysis demonstrate promise for future research. There were, however, several limitations of the methodology that this study uncovered. The following section will discuss these limitations, as well as possible ways to address them in future research. 8.3.2 Mental Models Methodology: Limitations and Suggested Changes Given the exploratory nature of the research methodology developed for this dissertation, it should be expected that some of the components of the methodology would work better than others. In this section I will revisit some of the components of the methodology that I think were either unsuccessful, or had limited success. In each case, I will make suggestions for how they might be improved in future iterations. Broad Scale Methodological Issues The primary shortcoming of the research methodology was attempting to elicit both existing mental models of community-scale energy and changes to those models at the same time (i.e. the pre-workshop and post-workshops questionnaires were, of necessity, developed together). This approach did not allow me to learn from and refine the analysis of participants’ existing models before trying to tackle the more difficult task of looking for changes to those models. The pre-workshop questionnaires (See Appendix A) cast a wide net when looking for components of participants’ existing mental models, including worldview questions, individual energy relationship questions, community-scale energy questions and, in Vancouver, questions about the relationship of community to quality of life factors. In the post workshop questionnaire, the addition of workshop evaluation questions and demographic questions created a need to be selective about questions looking for pre/post relationships, and open-ended questions specifically directed at changes to participants’ mental models. This need to be selective in question creation, without knowing what 478 components were most important to people’s mental models of community-scale energy, meant that key questions were missed in the pre/post evaluation. For example, the centrality of participants’ opinions about who is responsible for addressing energy issues to the construction of the MCA dimensions (see section 5.3.4) suggests that it would have been instructive to know if participants’ opinions on that question were changed by participation in the workshops. We might not expect their highest ranked option to change (e.g. people from the ‘National Action’ mental models cluster would probably not change their opinion that the Federal Government is most responsible), however, their ranking of the other options might reflect a new conception of who can effectively address local energy issues. Would municipal governments and/or groups of citizens be seen as more effective agents for change after a workshop that focused on local responses to energy issues? As Chapter 3 revealed, most mental models evaluations have looked at existing mental models (cf. Jones et al., 2012). The addition of a methodology looking for changes in those models, particularly before first knowing what the important components of existing models were, greatly added to the complexity of the design discussed in this dissertation. Separating those steps in time, either through the use of iterative workshops like those used in Revelstoke, or through the construction of existing mental models through a broader mail or internet survey, would have allowed for analysis and reflection before constructing the questions looking for changes to mental models. In the case of the large mail or internet survey, the important components of participants’ mental models, as well as the dominant mental models clusters could have been used to construct a more concise pre-workshop questionnaire to efficiently assign participants to particular clusters (mental model groups). This more concise questionnaire could also improve response and completion rates (cf. Dillman et al., 1993; Edwards et al., 2002). While the simultaneous elicitation of both existing mental models, and changes to those models was instructive in a qualitative sense (i.e. useful information can be gleaned from the analysis of the available pre/post questions and the open-ended mental models questions), a more systematic mixed methodology approach, facilitated by a more thorough questionnaire construction should lead to more insight, and more generalizable results. A second limitation of the methodology used in this dissertation was the relatively low participant numbers. This was exacerbated by the low completion rates for the questionnaires (i.e. questionnaires where all of the relevant questions were not filled out). The low numbers meant that some of the clusters described in Chapter 5 (e.g. Limited Energy Knowledge, Efficient Urban Development and Let the Market Decide) had low membership. This was further complicated by 479 participants who either did not completely fill out the pre, or the post workshop questionnaire. Therefore, there were cases where participants who were classified into a mental models cluster did not fill out the relevant post workshop questions, and also cases where participants who could not be classified into a cluster (because they did not fill out the pre workshop questions) had complete post workshop surveys. While some measures can be taken to improve response and completion rates (Edwards et al. 2002), a more reliable solution would be to conduct the surveys with a much larger subject pool. A previously mentioned solution for the evaluation of existing mental models would be to conduct mail or internet surveys. For the post workshop surveys, subject pools could only be increased by conducting more, or larger, workshops. Connecting the research to a larger scale engagement process such as a municipality’s Official Community Plan or multiple Community Energy and Emissions Plans could provide a larger subject pool. Often, the true value of evaluation methodologies comes from their broad application, and the resulting ability to systematize their conduct, and compare results. The New Ecological Paradigm (Dunlap et al., 2000) is an example of a broadly applied methodology that has benefited from repeated application, generating a widely accepted evaluation tool for assessing environmental attitudes. The NEP was considered for the evaluation developed in this dissertation, but its specific focus on people’s environmental values, I would argue, make it ill-suited to the discussion of energy, which people may think about in ways that have no environmental connection (e.g. their own energy supply, energy costs, etc.). The benefit of a mental models approach, when effectively implemented, is the opportunity to tailor it to the particular subject matter at hand. Construction of the Questionnaires A second set of methodological limitations from this study relates to the construction of the questionnaires themselves. These limitations included the use of different questions for different processes, the subject matter of some of the questions asked, and a degree of confounding and/or conflating that occurred in some of the multiple choice and ranking questions. The Revelstoke example presents the clearest departure in questionnaire construction, however, this was a byproduct of the projects’ existing evaluation methodology, severely limiting the number of questions that could be asked. The Revelstoke process provided enough questionnaire carryover, along with it substantial value as a comparison case study, to offset the limitation of not being able to use a full questionnaire. 480 It is between the GCCP and the Victoria workshop processes that the differences in questionnaires was more problematic. This is perhaps best illustrated by the removal of the two quality of life questions from the Victoria workshop questionnaire: 2. What do you enjoy most about living in your community? 3. What would you most like to see changed in your community? These questions were removed, because the City staff from Victoria felt they were not germane to the subject matter of the workshops. In retrospect, as section 8.4.5 will discuss, the relationship between energy planning and quality of life features of communities became an important component of the findings from this dissertation. Having these same questions answered by the Victoria participants would have provided more subject numbers, as well as providing different perspectives from the experts and practitioners. This illustrates two points related to the application of research instruments in real-world planning activities. Firstly, as discussed in regards to the GCCP workshops and the City of Vancouver in Chapters 6 and 7, there is the issue of negotiating between academic and municipal institutions. Working across institutional boundaries is a significant challenge of applied research (Innes & Booher, 2004; Robinson, 2008; Salter et al., 2010). For the academics involved, it often requires alterations to their methodologies or research instruments that can lead to incomplete results. For the institutional partners (in this case the municipalities) the risk is participants misunderstanding the purpose of the research, or the perspectives represented. The case of density in both the GCCP and Victoria processes is illustrative of this point. The City of Vancouver, in particular, has had previous bad experiences with discussions of increasing urban density (Bula, 2014; Campbell, 2014; Smith, 2008). As a result, our municipal partners from the CoV were very reluctant for us to openly discuss density in our workshops and related research methodology. Similarly, the City of Victoria representatives requested we not discuss density in our questionnaires (though, recall from Chapter 7, that increasing density was not one of the energy options in the Victoria workshops). This led to the inclusion of the awkwardly phrased ‘reducing unit size’ option in the applied energy solution questions on both processes questionnaires. How should we address the challenge of negotiating research across institutional boundaries? Perhaps the best option is to continue working together to establish trust and to, more generally, institutionalize the practice of social research (Innes & Booher 2004; Robinson, 2008; Robinson & Tansey, 2006; Salter et al., 2010; Wiek et al., 2014). The University of British Columbia has established a Memorandum of Understanding (MOU) with the City of Vancouver for just such a 481 purpose. Hopefully, through continued work, methods can be developed to better allay the concerns of both researchers, and their municipal partners. The second point illustrated by the changes to the questionnaires between the GCCP and Victoria processes, is simply the importance of standardized question sets, and therefore standardized scales. While the questions about quality of life in a community may not have seemed relevant in the Victoria example, there is value in simply having the same data to work with across processes. As mentioned earlier, there is value in standardized scales such as the NEP, simply because they provide consistency and generalizability. As a caution to future research, then, a recommendation from this study is to negotiate harder about research instrument construction. This, of course, raises multiple issues about power relationships in applied research, particularly when one partner can be seen as more reliant on the relationship than the other (in this case, a PhD student reliant on the municipalities to provide the venue and conditions for the research). Another limitation in the construction of the questionnaires related specifically to the questions asked. The best illustration of this point is found in the questions related to individual energy use in section two of the pre workshop questionnaire. Despite the conceptual connection of individual energy use to the broader issue of community-scale energy, the results of these questions were not included in the analysis found in chapters 5 through 7. This was partly due to a decision to focus on the collective, community scale energy questions, but was reinforced by initial runs of the MCA that did not find strong relationships between the individual energy use questions and the other determinants of the MCA dimensions. The only question from this section that was used in the MCA was the question which asked participants how difficult they felt it would be for them to change their own energy use. This was seen as a surrogate for how difficult they perceived energy issues to be to solve at all scales. The remaining four questions from the individual energy use section were not used in the dissertation. The time required for participants to fill out this section could have been better spent on other questions more relevant to community-scale, collective energy issues and solutions. Specifically, more questions about possible energy solutions, unpacking broader categories into more specific solutions would have been helpful. This seems particularly true in relation to Hoffman et al.’s (2014) framework for mental models, which suggests that it is these more domain-specific and concrete aspects of people’s mental models that are amenable to change. This point is strongly related to the final limitation of the questionnaire construction: the confounding and conflation of particular options for solutions to energy issues. 482 It is difficult in any complex domain to separate out and evaluate individual components. The time and space limitations of the questionnaire format (as compared, say, to extended interviews) can make it even more difficult to appropriately differentiate responses. It was apparent during the questionnaire construction, and even more so in the analysis, that there was a possibility of conflation and confounding in particular multiple choice questions. This was particularly true in the question that asked participants to rank different solutions for energy issues. The question asked was (GCCP example given): Please rank your top 5 approaches for addressing energy concerns in your community, with 1 being the method with the largest potential impact, and 5 being the method with the smallest potential impact. a) Increasing public transportation options b) Increasing local sources of energy such as solar c) Decreasing the size of people’s residences d) Making technical changes, such as energy retrofits on existing buildings, or electric vehicles e) Changing Individual’s energy behaviours f) Collective options, such as district energy systems g) Providing incentives for people and businesses to conserve energy In this case, the use of examples, such as ‘energy retrofits’ or ‘electric vehicles’ demonstrates the conflation of transportation options (electric vehicles) with building options (retrofits). Therefore, it is not possible to parse participants’ answers to determine what type of technical change they are in favor of (recall that technical changes was the second ranked option in both the GCCP and Victoria processes). Another example from this question, that perhaps better represents confounding, is the linking of ‘Collective options’ to DES. This link downplays other collective options, such as car or bike sharing programs, which may have been much more popular (participants’ written responses from the GCCP and Victoria processes suggest that they were). Based, it seems, on the poor performance of DES in the models used, collective options faired poorly on post workshop rankings of solutions in both Vancouver and Victoria (see Figure 7.24). This confounding may be underplaying participants’ attitudes about other collective options. 483 Based on what was learned from the analysis in this dissertation, and the recognition that these solution questions represent an important piece of the energy puzzle (as well as the components of people’s mental models that may be most likely to change), future iterations of this type of study should further differentiate the options, perhaps starting with overview categories, and drilling down into those categories in subsequent questions. As an example, the top level question might be: Please rank your top 5 categories for addressing energy concerns in your community, with 1 being the method with the largest potential impact, and 5 being the method with the smallest potential impact. a) Changes to transportation b) Energy retrofits on existing buildings c) Changes to construction standards for new buildings d) Changes to how we manage our waste products e) New, renewable energy sources f) Technological innovations to make better use of energy And the follow-up question for transportation might be: Please rank your top 5 transportation solutions for addressing energy concerns in your community, with 1 being the method with the largest potential impact, and 5 being the method with the smallest potential impact. a) Improving public transit options b) Electric and hybrid vehicles c) Car and bike share programs d) Making our communities more walkable and bikeable (local services, safe routes) e) Better transportation planning to reduce traffic f) Other: Removing the individual energy use questions from the pre workshops questionnaires (see Appendix A), as well as those questions that did not prove important in constructing the MCA dimensions or subsequent mental models clusters (e.g. optimism/pessimism) would provide the necessary space to better focus the questionnaires on community/collective energy solutions. This refinement of the questionnaire is, of course, an anticipated outcome following any research project. Similar to the process of modeling, the development of research instruments requires iteration and refinement to better ‘fit’ the intended purpose. 484 A final note on the construction of the questionnaires, and more broadly the research instruments as applied in this dissertation, is a need to streamline the number of questions asked from a research quality perspective. A criticism that could be levied at the evaluation in this dissertation is that the large number of questions asked were bound to lead to some significant results – i.e. that the instruments used lacked precision in research focus. As an exploratory study, I believe this lack of precision is partially justified, as it was intended to develop more specific hypotheses for future work that will be more directed. There are, therefore, multiple reasons for further streamlining and directing the research instruments to focus on a smaller set of important community-scale energy concepts in future iterations. Expanding and Improving the MCA/Hierarchical Cluster Analysis While the MCA and Hierarchical Cluster analysis methodology described in Chapter 5 proved to be a valuable way to describe and visualize the answer patterns that this dissertation used as a representation of existing mental models, the limitation of those methods to the pre workshop questionnaires is seen as a limitation of this study. The differences in participants who filled out the pre vs. the post workshop questionnaires, meant that corresponding participant pools, and corresponding mental models clusters, were limited. In addition, the relatively small number of pre workshop community energy questions that were repeated in the post workshop questionnaires meant that a similar MCA would have been difficult to carry out. Like other aspects of the research carried out in this dissertation, the MCA and cluster analysis were exploratory exercises. It was an open question as to how they would perform, even in developing a picture of existing mental models. The results of Chapter 5, while admittedly limited in a number of ways, show the promise of extending the MCA and cluster analysis methodologies to future studies. In particular, the ability of MCA to account for categorical, and even carefully crafted open-ended question types (Husson et al. 2011) makes it a promising methodology for the types of social and applied research common in engagement work. A future iteration of the questionnaires used in this study should attempt to more systematically tie the post workshop results to the MCA dimensions and established hierarchical clusters. Experimentation may be required to determine if the post workshop responses should be used as active variables (i.e. establishing the geometric relationships and resultant dimensions) or supplementary variables (i.e. connected to the existing dimensions and clusters post hoc). 485 I would argue that the relationships revealed by the MCA and subsequent cluster analysis in Chapter 5, perhaps particularly by the ability to visualize those relationships, is a valuable way to understand the conceptual space within which people think about, and understand community-scale energy. Connecting that space to changes in people’s mental models as a result of participation in engagement activities may be an important way of demonstrating the social learning and capacity building outcomes of engagement efforts. A final limitation of the mental models methodology described in this thesis may be seen as a meta-level recommendation that supercedes the specific recommendations made above. This meta-level recommendation is to generally look for ways to streamline, automate and systematize the methodology and its component parts. The process of developing and conducting interviews, developing and administering the questionnaires, inputting the data, and finally analyzing the data using multiple univariate and multivariate approaches is an onerous one. The process as carried out would limit the applicability of the methodology in time and resource-limited applications. Lynam and Brown (2012) make a similar recommendation based on examples from a broad program of research on mental models’ use in natural resource management. Some of the challenges as demonstrated in this project are the result of fumbling through an exploratory first iteration, and could be streamlined simply as a result of the experience of conducting the research this time. However, there are other ways that the methodology could be streamlined. For example, the initial interviews with practitioners and energy experts may be unnecessary in future iterations, partly because it was conducted here, but also because the questionnaire results from this study could serve a similar purpose in future iterations. Questionnaire improvements suggested above should also serve to streamline the analysis process. Finally, repetition and refinement could serve, as demonstrated by the-well established NEP (Dunlap et al., 2000), to facilitate the future application of the methodology to new processes by creating an existing base of literature and practitioners upon which to draw. 8.3.3 What is the Promise of the Mental Models Methodology The depiction of the limitations of the mental models methodology as described in section 8.3.2 should not be seen as a more general condemnation of either the broader mental models field, or even the specific methodology discussed in this dissertation. In this section, I will discuss what I see as the promise of mental models methodologies more broadly, and the methodology as used in this dissertation more specifically, for the study of engagement in sustainability planning. 486 The Application of Mental Models in Related Fields Chapter 3 described the use of mental models in several fields related to sustainability planning, including scenario analysis (Chermack & van der Merwe, 2003; Senge, 1990), human-computer interaction (Leiser, 1992; Norman, 1983), natural resource management and sustainable agriculture (Hoffman et al., 2014; Jones et al., 2012), environmental risk (Morgan et al., 2002) and climate change (Bostrom et al., 1994). This use in related fields is not simply a matter of coincidence, rather, it is indicative of common threads that run through these domains, and which connect them to sustainability planning. In scenario analysis, for example, Chermack & Merwe (2003), Senge (1990; 1994) and others have made specific reference to mental models as a key component of the effectiveness of the scenario approach. As Chapter 2 discussed, and the case study analysis in Chapter 7 elaborated, the need to consider and plan for distant futures has made scenario analysis an indispensable component of sustainability, climate change, and related planning. Evaluating mental models of energy and other sustainability concepts, then, has something to contribute to the field of scenario analysis. In the case of existing mental models, they can tell us something about what is missing from how we currently conceptualize complex problems and their future implications. For example, as the findings in both Chapters 6 and 7 indicate, the relationship of energy and density, and people’s mental models about density, are complex. This suggests a need to develop scenarios that more broadly investigate density, perhaps using different designs such as corridor-focused vs. dispersed density, to explore the experience of these different possible futures. Evaluating how mental models change as a result of participation in engagement activities may also help us better understand the effectiveness of different scenario and futures approaches. Scenario processes are most commonly evaluated either for their quantitative outcomes (e.g. IPCC, 2000), or qualitatively in a case study approach that looks at their larger implications (cf. Kahane, 2012.). While both of these approaches are viable means for assessing scenarios, a mental models evaluation could add an important element – understanding how the scenario approach actually affected people’s thinking. An effectively designed mental models study may also be able to reveal intersubjectivity between participants’ mental models (Barker et al., 2006), or the development of shared mental models (Jonker et al., 2010). In essence, a mental models methodology could give us a better 487 understanding of how scenarios work to expand our thinking, and perhaps even the particular aspects of scenarios that are the most effective. In the GCCP study, a more specific mental models evaluation of the pedagogically-focused scenarios might have revealed whether this uncommon approach (using limited pathway scenarios as building blocks to help participants craft their own scenarios) was effective. Work on mental models in human-computer interaction has been focused on understanding how people use computer systems, and how those systems can be designed to facilitate more effective use. Similar to scenario analysis, the use of modeling, visualization and interactive computer tools has become a mainstay of sustainability planning and related engagement activities (cf. Dahinden et al., 2003; Salter et al., 2010). These tools allow for the quantitative and qualitative representation of possible futures, providing us a limited experience of what those futures might be like. In other words, these tools are the mechanisms through which we evaluate and represent the futures developed through scenario analysis. Similar to their use in HCI, then, mental models evaluation in sustainability planning holds the promise of helping us improve our tools, so that participants in engagement activities can more effectively understand the implications of choices. In the current study, evaluation of the tools used took a case study approach, at least in part because the Victoria and Revelstoke case studies became available after the questionnaires had been developed. Future studies could allow the specific tools to be part of the mental models evaluation, helping us better understand the impacts of the tools on participants mental models. For example, would the more dialogue-focused CAN Tool from the Victoria workshops lead to a different conception of energy issues than the more visual and spatial tools used in the GCCP and Revelstoke cases? Understanding more directly the interaction of the tools used with participants’ mental models of energy, or sustainability, could help us better design those tools. Prior work in HCI provides a template for constructing mental models evaluations to assess the effectiveness of tools (Tauber & Ackermann, 1991). Finally, the more recent and burgeoning use of mental models in natural resource management, environmental risk and climate change research suggests the promise of applying mental models methodologies to human-environment problems. Perhaps the reason for the increased popularity of mental models methodologies in human-environment systems, is the complex nature of the systems being studied. The strength of most, though not all, mental models methodologies is a focus on the connections between concepts – i.e. a multifaceted model of how people conceptualize complex problem spaces. Complex human-environment problems are defined by their multifaceted nature, involving not only the interaction of natural systems (e.g. hydrology, ecology, etc.), but also the 488 interaction of those natural systems with human systems (e.g. culture, economy, etc.). Sustainability planning, of necessity, must account for these interactions, making mental models methodologies a strong candidate for understanding how people conceive of those systems, and how engagement activities affect those conceptions. The specific methodology used in this dissertation provides some additional strengths to mental models evaluation that might make it easier to apply to sustainability planning engagement. Firstly, the adaptation of mental models to a questionnaire research instrument offers the opportunity to apply a comprehensive mental models methodology to a time-limited process, where access to participants may not allow for more intensive interview, sorting or mapping evaluations. Secondly, the MCA methodology used to analyze participants’ existing mental models of energy allowed for the use of categorical and open-ended questions on the questionnaires. The ability to perform multivariate analysis on categorical data provided the opportunity to analyze connections and answer patterns, while at the same time eliciting qualitative information necessary for the understanding of complex mental models (e.g. not just what a participant’s answer was, but also why they answered that way). This multivariate methodology is also scalable, because MCA was initially devised as a method for evaluating large surveys (Husson et al., 2011, LeRoux & Rouanet, 2010). A questionnaire or survey-based methodology for evaluating mental models could broaden the application of mental models evaluation to a wider array of circumstances. Finally, the use of the hierarchical cluster analysis methodology presented the opportunity to classify participants into mental models groups that may provide a useful heuristic for characterizing particular approaches, simplifying, somewhat, the analysis and application of results. This clustering, or classification, presents the opportunity to develop an NEP-like scale for energy and broader sustainability planning, which would allow for a systematized evaluation across projects and case studies. In order for this methodology to live up to its potential, however, it must first overcome the limitations as outlined in section 8.3.2. The concept of mental models appears to have broad, intuitive appeal (Lynam et al., 2012), perhaps in part because of its relationship to the metaphor of the computer at a time when they are so dominant culturally. Maybe because of this colloquial, intuitive appeal, mental models have been interpreted in many different ways, from studies that considered individual concepts as mental models (cf. Sterman & Booth-Sweeney, 2007), to those that looked at complicated connections between concepts (cf. Hoffman et al., 2014). The current study took the position that mental models could be represented as answer patterns in a particular domain, looking at the connections between concepts as they related to community-scale energy. The advent of tools for analyzing and visualizing these complex answer patterns presents the opportunity to evaluate complicated human- 489 environment systems without stripping away the complexity of people’s conceptions of these domains. This may allow for a better understanding of the cognitive, affective, belief and value components of people’s conceptions of human-environment issues. The larger purpose of these types of evaluation is, of course, to feed back to the design and development of better planning and engagement processes. The study represented in this dissertation presents one exploratory step along that path and, despite clear methodological limitations, sufficient promise was revealed to suggest that this mental models methodology should be explored further. Mental Models: Research Hypotheses Based on the mental models findings from this dissertation, what I learned from the process, and the recognized limitations and future promise of mental models as expressed in section 8.3, the following hypotheses present interesting (but not exhaustive) opportunities for future studies: Hypothesis (MCA dimensions) People’s beliefs about the level of required change to address energy, who is responsible for addressing energy, and whether or not solutions should be individually or collectively focused are key determinants of their preferences for energy planning actions. Hypothesis 1 would allow us to determine the veracity of the MCA results from Chapter 5. Does the dimension reduction performed by the MCA help us to more effectively predict the types of energy solutions people with a given mental model of energy will prefer? Hypothesis (place values and mental models) People’s place values (i.e. what they value about their community) are linked to their preferences for community-scale energy planning solutions. Hypothesis 2 would help to further establish the connections between local energy planning and people’s sense of place – a theme that has pervaded the dissertation from the early practitioner interviews. Testing this hypothesis would provide guidance about the structure of engagement activities. Should we be focused more on what people value, and less on energy metrics in engagement activities? 490 Hypothesis (changes to mental models) People’s mental models of energy can be changed by participation in time-limited engagement processes, provided those changes are to peripheral model components such as applied energy solutions. Hypothesis 3 would allow us to establish whether or not time-limited engagement activities can have demonstrable impacts on participants’ mental models. In other words, it could help us establish a learning-based process outcome of engagement activities. Based on the methodology limitations and subsequent learning from this dissertation (see section 8.3) I am confident that it is possible to develop pre/post questionnaire instruments that could identify shifts to components of participants’ mental models. I am also confident, based on the evidence from this dissertation, that these mental model shifts do occur to more peripheral, concrete concepts. Hypothesis (mental models clusters) There are more mental models types in the broader population than the 6 mental models clusters revealed in the cluster analysis of Chapter 5. By extension of this hypothesis, some mental models of energy were not represented at the workshop processes. Finally, Hypothesis 4 asserts that there are more mental models of energy than were represented at the workshops, and that a broader mail survey could be used to identify these other models. The significance of this hypothesis relates to representativeness at engagement activities. If there are more mental models types that are not attending engagement activities, then further efforts are required to determine how to make their voices heard in planning processes. Mental Models: Recommendations for Practice Based on the mental models components of this dissertation research, my primary recommendation to practitioners for developing engagement processes is to continue and expand efforts to ensure that a broad cross-section of perspectives (i.e. mental models) are represented at engagement activities. To do this, practitioners should continue to investigate ways to target under-represented groups. Chapter 6 discussed efforts by the City of Vancouver to include more cultural groups and to cross language barriers. The mental models research in this dissertation suggests that there are also barriers of perspective that are preventing some people from participating. 491 One way to approach this would be to use the evaluation outputs of research like this dissertation to ensure that workshops and other engagement activities speak to a wider range of issues. For example the mental models clusters from Chapter 5 could be used as inputs to the creation of future scenarios, envisioning economic (Let the Market Decide), individual-focused (Power to the Individual), federal policy intervention (National Action), etc., futures. Engagement activities could also take a lead from the Victoria workshops inclusion of costing data by presenting trade-offs and co-benefits that speak to the concerns of people with different perspectives. Collectively, recommendations to practice from the mental models component of this research involve better understanding the perspectives of multiple publics, and tailoring engagement activities to address (but not cater to) those perspectives. 8.4 Energy Planning This dissertation was not about energy planning per se; that is, it was not about what demand sectors provide the best opportunity for efficiencies, or what local energy sources have the highest potential for providing significant energy in the future. Rather, it was about the intersection of energy planning with public (and in Victoria stakeholder and expert) engagement. Nevertheless, there were a number of findings from the study that I think have real ramifications for how energy planning is done. Any planning activity, of course, lives at an intersection of technical possibilities, economic and political pressures and people’s wants and values (Innes & Booher, 2004). The way in which particular energy topics were received and did, or did not, resonate with the engagement audiences, therefore, has implications for how we do community scale energy planning in the future. This section will highlight some of the instructive findings from the case studies and mental models evaluation that have significant implications for community scale energy planning. This is not an exhaustive list of subjects that arose in the evaluations in Chapters 6 and 7, but rather a highlight of subjects that seem particularly impactful for future planning and engagement activities. The section will conclude with a discussion of ‘energy in place’, a theme that developed out of the case study and mental models research, and that carries over into the subsequent discussion of engagement and engagement tools. 492 8.4.1 Land Use and Density The three case studies discussed in Chapter 7 reflect very different approaches to the discussion of land use, and more specifically density, as it pertains to energy planning. In the Victoria case study, land use changes, such as zoning or large scale development were specifically off the table. The rationale for excluding density was that the City is already zoned, and is relatively dense for a North American city (Miller & Cavens, 2012). The CEEP process in Victoria also occurred after the OCP process, meaning that discussions of levers such as zoning and land use had been dealt with previously. The impacts of land use, density, and at more local scales, urban form and building siting, can have significant impacts on energy demand, and the potential for local supply (Ewing et al., 2008; Miller, 2013; Salat, 2014). Perhaps the salient question about land use and density from the Victoria example, is the implication of planning for energy without consideration for land use, density, and urban form changes. While opportunities to change zoning or institute large scale redevelopment may not always be feasible or desirable, should they not be part of the discussion of local opportunities – i.e. should we be removing potential levers from the discussion, particularly when the energy systems at these more local scales are poorly understood? In their open-ended answers, multiple participants expressed concern that land use was not included in the workshop. The GCCP and Revelstoke processes both did address land use and density directly. The differences in those two processes, and the participants’ reactions to discussions of density provide some fodder for future research, and raise questions about how we engage with people about issues such as land use and urban form, which have such spatial, visual and experiential qualities. In the GCCP example, the timeline and institutional barriers resulted in the development and use of the Sandbox neighbourhood. As discussed in Chapters 6 and 7, this also allowed for significant freedom in the size and constituent parts of the neigbourhood. The lack of participant familiarity with the neighbourhood being discussed also informed the use of more conceptual representations (see Figure 2.1 and Figure 6.7), because participants would not be comparing future design options to existing features that they were familiar with. It could be argued that the culmination of these decisions was a set of conditions that made the discussion of density and urban form changes less charged by NIMBY and character concerns – i.e. that it became more of a conceptual discussion of the merits of density. Another feature of the discussion in the GCCP, fueled by the ‘name your neighbourhood’ exercise and its explicit connection to what people value about a community, was a focus on the connections between density and quality of life issues. This was later reinforced by the 493 scenario strategies that required a level of land use change to procure services such as transit and district energy. Participants at the neighbourhood workshops (Marpole and Grandview Woodland) were more supportive of additional density than previous City of Vancouver efforts may have suggested (Bula, 2014; Smith, 2008). Indeed, participants’ neighbourhood designs (see section 6.5.2) and related comments suggest that it is not the concept of density per se that is the issue. Rather, it may be the types of density that the City is trying to promote that is the problem. It is notable how central the opposition to towers is in the resistance to density in previous Vancouver planning efforts (Bula, 2014; Campbell, 2014). There are many different ways to achieve density (Salat 2014), and participant designs that represented a more mixed density approach, with low rise and mid-rise buildings interspersed with single family dwellings (see Figure 6.33) suggest that people would be more welcoming of that type of urban form. This is quite different from the corridor and node density planning that the City of Vancouver is currently conducting (City of Vancouver, 2011). Clearly this may have economic and zoning implications, but it does suggest that previous resistance to density, and the City’s resultant aversion to discussing the topic, may not be the result of a persistent negative public view of the subject. Another possibility, is that it is, in fact, the method of engagement that is the primary problem, with residents consulted after the planning and decision making about new developments has already taken place (Bula, 2014). There is some evidence from City of Vancouver planning efforts in Marpole that this is, in fact, a significant part of the problem (Tromp, 2013). It should be noted, however, that the neighbourhood designs and participants’ opinions of density at the two neighbourhoods differed. While the Marpole participants did demonstrate a willingness to increase density in the Sandbox, their designs were less bold, and included less density than the Grandview Woodland designs (see Section 6.4.6). This is in keeping with the current character of the two neighbourhoods, and reveals that, even a conceptual discussion of density is informed by people’s current living arrangements. Previous controversy in Marpole over rezoning plans (Tromp, 2013), further suggest that differences in the neighbourhood designs are reflective of real differences in the makeup, and mental models of the residents. This foreshadows the discussion of ‘energy in place’ to be conducted in Section 8.4.5, and also relates to the discussion of density and urban form as they played out in the Revelstoke case study. As noted in Chapter 7, the Revelstoke case study was much more focused specifically on density, and its relationship to energy and GHGs, than the more comprehensive examination of energy 494 performed in the Victoria and GCCP examples. This specific focus on density, coupled with a significantly smaller scale, and the participants’ intimate knowledge of the two study areas led to a very different reaction to higher density options. In particular, participants were more resistant to the idea of raising density, particularly for community character reasons. Additionally, the discussion of the energy and GHG benefits of increased density at the September workshops had very little impact on participants’ designs (Girling et al., 2014b). The researchers that developed the Revelstoke case study speculated that the intimate scales and familiar locations had a dramatic impact on participants’ reactions to density, potentially lessening the effects that the energy and GHG information had on their designs. Another likely contributing factor is the rural character of Revelstoke. Residents of these smaller rural cities likely have a much lower threshold for density than the residents of Vancouver and Victoria. What does a comparison of the reactions to land use, density and urban form in the GCCP and Revelstoke examples suggest about the discussion of these topics in community scale energy planning engagement? One takeaway, not surprisingly, is that density is a harder sell in practice (i.e. in real, known locations) than it is in concept. It is notable, however, that density was not disagreeable in concept in the GCCP – i.e. that removed from experientially charged conditions, people were not resistant to the very idea of denser neighbourhoods. One possibility is that discussions of density and its possible forms could be conducted using scenarios and a sandbox neighbourhood, moving to representation of preferred options in citizens’ actual neigbhourhoods once they have identified those preferences. This could have the dual benefit of involving citizens in early, strategic stages of planning, and of diffusing NIMBY reactions while those strategic discussions take place. The GCCP example also suggests that there is value in tying density not just to its energy, GHG or sustainability benefits, but also to its provision of services and amenities. Rather than metrics that show the reduction in GHGs per capita, or the number of local jobs generated, the representation of a new corner store, or a new transit stop may make the discussion of density more palatable. Another possibility illustrated by the Revelstoke and GCCP examples, is the potential for earlier engagement that uses a generic neighbourhood like the Sandbox to discuss topics such as density and urban form (and the associated benefits) in a less charged form, leading to later engagement focused on the specifics of neighbourhood designs. Ultimately, planning and design are about actually changing the neigbourhoods that people inhabit, so it would be insufficient to only discuss controversial issues in conceptual terms. However, building up to that discussion, and including 495 residents in the earlier, strategic discussions of density and land use may alleviate some of the mistrust and resistance that arises when public information meetings are held demonstrating already developed designs for their neighbourhoods with dramatic proposed changes (Innes and Booher 2004). 8.4.2 Transportation Of the three case studies, only Victoria and Vancouver dealt with transportation explicitly. The Revelstoke study did include VKTs as an indicator of the energy performance of participants’ designs, but transportation was not an input to the design process. Perhaps the most interesting thing to note about transportation planning at the local scale, is how poorly it fits the jurisdictional boundaries within which municipalities have primary influence. As a result, there is an incongruence of scale between what is meaningful from a transportation modeling and transportation planning perspective, and from a municipal planning perspective. This, of course, has implications for what municipalities can accomplish on transportation issues when working on their own. As noted by one of the municipal planners in the mental models interviews in Chapter 4, the reason smaller municipalities are focused on buildings and land use, is because they lie within their sphere of influence (predominately through development incentives and zoning, as the building code is provincially controlled). This disconnect of scale also has implications for engaging with citizens about transportation energy, because transportation interventions in a community or city, such as public transit options, are often dependent on changes (e.g. population growth and densification) in areas outside that being discussed. Interestingly, in the case studies discussed in Chapter 7, it was the expert and stakeholder audience at the Victoria workshops that demonstrated the most difficulty accounting for the disconnect in scale. At the GCCP workshop, participants seemed comfortable with the notion that changes would need to occur outside of the Sandbox to accommodate the level of transportation changes proposed in their designs. This may be a function of the relative size of the study areas (Victoria being at the city scale, while the Sandbox was only 30 blocks), with the smaller area making the cognitive leap easier to make (i.e. it is clear that transportation interventions in such a small area cannot exist independently). Another likely contributing factor is a lack of systemic understanding of the transportation system by a non-expert audience. In other words, the citizens had an easier time 496 not focusing on the scale disconnects, because they were less primed to consider them when developing their designs and interpreting the material. In reality, transportation planning occurs at a more regional scale, but the case studies in this dissertation illustrate the broader issue, that one of the challenges of a more local approach to planning for energy and sustainability is a need to accommodate these scale incongruencies and interactions. Transportation is perhaps the most obvious, but as local energy generation increases, it is likely to demonstrate some of the same issues. Another salient impact of transportation on the case studies in Chapter 7 was its dominant impact on energy and GHGs. In the GCCP case study, transportation trumped all other solutions in the post workshop assessment of participants’ mental models, based largely on the demonstrated energy and GHG savings that were possible by playing the transportation-related strategy cards. The addition of costing information in the Victoria modeling had a dampening effect on the participants’ post-workshop evaluation of solutions, and in their comments, because it suggested that the cost per unit of GHGs reduced for transportation interventions would be prohibitive (Miller and Cavens 2012). However, even the high cost of transportation interventions could not remove it as the highest rated solution (see Figure 7.24). Several Victoria workshop participants rationalized this by stating that the models were not reporting the co-benefits of transportation interventions, or accurately representing the impacts of active transportation. The primacy of transportation in people’s post-workshop perceptions of energy solutions raises some questions about its impact on energy planning engagement. First of all, what are the implications of one particular topic holding so much sway on people’s perceptions of the issue? If they walk away from an engagement activity convinced that energy issues are all about transportation, or that transportation is the biggest problem, but too expensive to address, what are the ramifications for how they engage on energy issues in the future? This suggests the possibility that building capacity to think about energy issues in a more systematic way may be interfered with by particularly dominant issues. One possibility is to separate the individual subjects, such as buildings, transportation, waste and local renewable generation into different sessions or workshops. In the Victoria workshops, participants did spend the afternoon focused on specific topics, and in the Revelstoke workshops an iterative design was used, where energy issues were only discussed in the second workshop. This separation of energy topics may have prevented a specific focus on 497 transportation. For reasons to be addressed in the ‘energy in place’ section and the engagement section, however, I do not think this further subdividing of topics is an effective approach. The opposite approach, though more complicated and design intensive, would be to further integrate the transportation discussion with the other components of energy planning. For example, through the better modeling and representation of co-benefits, and the representation of transportation interdependencies (e.g. public transit’s need for density to be cost effective). To be able to model and represent the energy system in a more comprehensive, but also comprehensible way, places significantly more onus on practitioners to understand the energy system. In other words, we should not try to downplay the role of transportation so that other opportunities and solutions are given adequate attention. Rather, we should try to further integrate the solutions so that the connections between transportation, land use, buildings, waste streams and renewable energy sources are more clearly demonstrated. A focus on the importance of transportation is not problematic, provided that it is understood that transportation does not happen in a vacuum, and that it is dependent on many other systems. A similar case in ecology is keystone species – species that are particularly important indicators of the health of a system largely because of their interdependence with other components of that system (Krebs, 1994). The challenge to understand the nature and interdependencies of systems is a difficulty of sustainability more broadly (Meadows, 2008). It also raises the question of how much people need to actually understand the systems, versus understanding how the systems can coexist with what they need and value in their communities. This thread will be picked up in section 8.4. 8.4.3 Local/Renewable Energy Supply The issue of local, renewable energy supply was not focal to any of the three case studies. The Revelstoke case study did have a threshold density level that would lead to a biomass fed district energy system, but there was little focus placed upon this renewable supply. The Victoria CEEP had the largest number of potential renewables, with biomass and geoexchange district energy systems, and solar hot water systems. The GCCP strategy cards included solar hot water in the building retrofits, and a biomass-fed district energy system. While some form of local generation of renewable energy was present in each study, then, it was not central to any of the processes. The lack of local renewable energy options in the workshops may have had a negative impact on the ‘local energy’ item ranking in the post workshop questionnaires for both Victoria and Vancouver. Local energy options was the lowest ranked item for both of those workshop processes (see Figure 7.24). 498 However, it should be noted that local energy generation was also ranked poorly on the pre workshop questionnaires. This suggests that people are not currently considering the local generation of energy to be a viable answer to energy and GHG concerns – i.e. it is not currently part of their mental models of energy. One of the reasons for this in Vancouver, Victoria, and Revelstoke, is the province’s large supply of publically owned hydroelectric power. In fact, in Victoria, where a large proportion of units are heated with baseboard heating, a switch to a district energy system could actually lead to increased GHG emissions (Miller & Cavens, 2013). BC Hydro, however, has a mandate to decrease energy demand in the province to prevent taxing the current electricity system (see https://www.bchydro.com/news/conservation/2014/demand-side-management.html). In addition, proposed projects such as ‘site C’ have met with significant opposition, suggesting that large scale hydroelectric additions to the current grid are controversial (Moore, 2013). Proponents of local renewable generation also suggest that it is not simply about total available energy supply, but also local energy security and resilience (Schweizer-Ries, 2008; van Hoesen & Letendre, 2010). With the possible exceptions of biomass (air quality), and large scale wind projects (noise, bird fatalities, etc.), local renewable production also has the advantage of being largely uncontroversial. Why then is it receiving such little focus in community scale energy planning in BC? One difficulty is assessing local available energy resources (St. Denis & Parker, 2008). As energy and GHG mitigation at local scales is becoming a more prevalent concern, efforts are being undertaken to address this. For example the Community Energy Explorer (energyexplorer.ca) maps the solar, wind, biomass, small scale hydro, and heat recovery resources for the Metro Vancouver region using LiDAR, remote sensing and GIS data sources. Development of these types of inventories provides a resource for more local assessments of renewable energy potential which may be valuable to planners and decision-makers. As discussed in Chapter 4, energy supply was the most common reference from the participants in the mental models interviews. Despite this focus on supply (driven largely by discussion of the impacts of fossil fuels), renewable energy opportunities were not a major part of any of the case studies evaluated in this dissertation. If local renewable generation is to become a more significant part of community energy planning, with support and capital requirements it would need to be widely adopted, more information is needed to assess local potential, and engagement efforts will be needed to increase support for its adoption. A particular challenge for local governments with limited 499 resources will be justifying the expense of developing local renewable supplies, suggesting the resources such as the Community Energy Explorer would benefit from additional costing data. 8.4.4 Energy Zeitgeist Chapter 4 briefly developed the idea of an ‘energy zeitgeist’ in the professional and practitioner community – a popularity of particular subjects or solutions that seem to permeate the current discussions about energy planning at the community scale. Primary among the ‘zeitgeist’ topics at the time (2012) were electric vehicles and district energy systems. It is interesting, then, to see how these subjects fared in the case study processes. In both the Victoria and Vancouver processes, the relevant modeling for DES did not indicate large potential for decreasing energy use or mitigating GHG emissions. As mentioned in the previous section, one of the main reasons for this is the large installed base of baseboard heating, and the predominately hydroelectric supply to those systems. The modelling for DES in Revelstoke suggested a larger potential savings (20%), in part because of the colder winters, but also due to a different modelling methodology that assumed larger savings (Senbel et al. 2013). While the subject of DES was popular amongst participants at both the Victoria and GCCP workshops, its performance in the displayed modeling led to a low post workshop ranking (see Figure 7.24). While not quantified directly, this suggests that the tools and modeling had an impact on participants’ mental models, particularly as they related to DES as a significant solution. Recall from Hoffman et al. (2014), that it is just this kind of peripheral and concrete component of a mental model that their framework hypothesizes is alterable. Electric vehicles were most explicitly covered in the GCCP workshops, where they had their own strategy card in the Technology scenario (see Appendix C). Despite large associated GHG reductions (4 stars or approximately 20% reduction in total community emissions), the electric vehicle strategy was one of the least frequently played in the ‘design your neighbourhood’ exercise (see Table 6.8). There seemed to be some unease amongst participants about where the electricity was going to come from, whether the GHG reduction targets were realistic, and what a move to electric vehicles might mean for other aspects of sustainability (i.e. shifting fuel sources, but otherwise maintaining the status quo). This unease might simply reflect caution about a new technology (Egbue & Long, 2012), and it would be interesting to see studies on attitudes about electric vehicles in the wake of the success of Tesla and other automakers electric options. One 500 possible insight based on participants’ comments, and the strategies that GCCP participants created on their own, is that there may be more support for electric vehicles in a car sharing, rather than individual ownership arrangement. Another possibility is that a larger subject pool from the ‘Power to the Individual’ and ‘Let the Market Decide’ mental models clusters at the GCCP workshops may have increased the popularity of this option. The results for both DES and electric vehicles suggests some caution about subjects that capture the attention of the professional community and their potential impacts on subsequent planning. This may be particularly important in planning, where policies that accommodate particular approaches could have such significant impacts on long term infrastructure. For example, planning for a region based on individually owned electric vehicles has dramatically different implications than planning for a region based on active and public transportation. Another aspect of the ‘energy zeitgeist’ concept that does not reflect a specific technology, is the apparent prevalence, amongst practitioners and experts, of the opinion that individual behavior has a significant role to play in any future energy conservation. Evidence for this comes from the mental models clusters discussed in Chapter 5, where the expert and stakeholder Victoria workshops had a much larger proportion of the ‘Power to the Individual’ mental models cluster than the citizen focused GCCP workshops. Further evidence of this prevailing opinion comes from a recent beta test amongst practitioners for the Community Energy Explorer (energyexplorer.ca), which similarly found a practitioner focus on individual action. This focus on individual energy use in the professional community runs counter to decades of research suggesting the ineffectiveness of addressing environmental issues at the individual level (cf. Gardner & Stern, 1996; Hines et al., 1986/1987). Similar to the popularity of electric vehicles and district energy systems, this focus on individual energy behavior as a significant component of future community energy solutions has ramifications for the approach, and potential policies, developed. While the jury is largely out on the role of electric vehicles and district energy systems, the abundant literature on individual behavior is clear. Reliance on people to significantly curb their own energy use in the absence of facilitating technologies and systems is, at best, a very small piece of the community energy planning picture. 8.4.5 Energy in Place The concept of planning for energy at the local scale brings with it the need to consider the local context. Industrial ecology stresses the need to consider local natural systems in sustainability 501 planning - for example available resources and the ability of natural systems to process waste (Ehrenfeld & Gertler, 2008; McDonough & Braungart, 2002). Ecological design (cf. van der Ryn & Cowan, 1996), and more recently regenerative sustainability (cf. Robinson & Cole, 2015) add people and culture to the mix, recognizing that sustainability is inherently a complex, human-environment issue. Another domain of research which seems to have much to contribute to the discussion of ‘sustainability gone local’, is the place literature (cf. Devine-Wright, 2009; Devine-Wright et al., 2015; Manzo & Perkins, 2006). In place theory, ‘place’ is the culmination of all of the systems interacting in a particular area, including (and perhaps most importantly) the human systems. Importantly, place also includes the notion of an emotional attachment to that location (Agnew, 1987). So, when we talk about the challenges inherent with a move to more local planning for energy (Andrews, 2008, Neves & Leal, 2010), it is not just the need to inventory local resources, or to account for factors such as climate or material flows. It is also the need to account for local cultures, people, their wants and needs, and their connection to place – their community. Aspects of the case studies hinted at the need for energy planning to be responsive to variances of place. Perhaps the clearest example is the differences seen between the Marpole and Grandview workshops in the GCCP case study. Participant designs in the ‘design your neighbourhood’ exercise reflected differences in the local communities and culture (see section 6.4.6), and participant comments reflected the broader sense of place for each neighbourhood. For example, in Grandview, there were far more comments reflecting the desire for more social interaction and street life. Revelstoke also demonstrated this aspect of place, primarily in the participants’ comments about density and building choices being inconsistent with the city’s character (Girling et al., 2014b). These findings suggest that energy interventions at the neighbourhood or community scale will not be universal, nor simply dependent on local environmental factors. The notion that people, culture, place, local resources and affordances, etc., are key components of planning and policy is not new. In that sense, the addition of planning for energy at local scales does not represent a paradigm shift – i.e. there will not be fundamental changes in the way we plan our communities. However, the move of energy planning to local levels is emblematic of a broader reality of sustainability planning, which is that more complexity needs to be addressed at these local scales, where governments have fewer resources to accommode them (when compared to the provincial and federal levels). Therefore, while community scale energy planning may be thought of as a quantitative rather than qualitative change in how communities are planned, it still presents an added complexity which may be difficult for communities to accommodate. The relatively universal hiring of consultants to address the BC Hydro CEEP process (BC Hydro, 2014) and Bill 27 502 (Rutherford, 2009) reflect the fact that communities are ill-prepared for addressing energy issues on their own. However, in some of BC’s larger communities, internal capacity is being built through the development of the new position of Community Energy Managers, suggesting a recognition of the need for coordination of energy planning efforts. In addition to simply addressing the added complexity of energy, and more broadly sustainability planning, there is a need to consult and engage with citizens about some of the significant changes that will come to their communities if these planning measures are put into practice. How can municipalities adequately address these issues with their constituents without overburdening them with an endless array of individual subjects? In other words, how can people be engaged on the variety of sustainability and other community concerns without suffering participant burnout? Edward de Bono, the creator of the concept of lateral thinking, and author of several books on thinking (cf. de Bono, 1996) stated that the new millennium would be characterized by a different type of thinking. While the past millennium was characterized by thinking of ‘what is’ – i.e. judging and evaluating, de Bono feels that this new millennium will be characterized by thinking about ‘what can be’ (de Bono, 2000). De Bono labeled this new type of thinking ‘design thinking’, suggesting that the properties of design would be applied to a broader set of tasks, because it is well suited to dealing with complexity and incomplete knowledge (Zeisel, 2006). It is my contention that a design approach – i.e. one that is integrative rather than reductive, that looks for synergies in solutions rather than addressing problems piecemeal may be the best way to approach energy and sustainability planning at the local scale. The concepts of ecological design (van der Ryn & Cowan, 1996), regenerative design (Cole et al., 2013) and regenerative sustainability (Robinson & Cole, 2015) similarly support this more integrated approach. This focus on design could also find use in engagement, where careful design and development of engagement mechanisms would allow people to comment on aspects of community that are meaningful to them, rather than technical or policy issues that are divorced from their daily lives. The next section will focus on engagement and, in particular, what engagement sensitive to this concept of ‘energy in place’ might look like. Energy Planning: Reserch Hypotheses and Research Questions Energy planning at local scales is a new area of research. As such, it provides fertile ground for examination of people’s preferences for solutions, and the impacts of engagement interventions on those preferences. The hypotheses and research questions in this section focus on better 503 understanding people’s preferences for planning components, such as land use, transportation and technologies, and the impacts of trade-off information, as well as identifying linkages between sense of place values and community-scale energy planning. Hypothesis (land use) Presenting participants with representations of the same level of density using different arrangements and urban forms will lead to significantly different levels of preference. This notion has been discussed theoretically (cf. Campbell, 2007; Salat, 2014), but to my knowledge, has not yet been tested empirically. The use of 3D representations to present the same location with different configurations of density could further control for other variables to facilitate a specific focus on density arrangements. Do people prefer corridor and node density as proposed by the City of Vancouver, or a more dispersed density as represented in European cities such as Paris? Hyptothesis (trade-off and co-benefit information) Representation of trade-off and co-benefit information will have a significant impact on participants’ preferences for transportation and land use options. This hypothesis draws from the recommendation to practice from the mental models findings in section 8.3, and provides an opportunity to empirically evaluate the impacts of trade-off information on participants’ preferences for different energy planning options. Would information about the cost implications of improved transit options dissuade participants from selecting that solution? Would information about improved convenience within their neighbourhoods make density interventions more palatable? Research Question (energy zeitgeist) Is the popularity of particular energy solutions in professional fields related to their quantitative potential for energy savings, their ease of implementation? Modeling on the potential for district energy in the case study processes was mixed, with the Victoria and GCCP modeling suggesting, at best, moderate energy and GHG savings, and the Revelstoke modeling suggesting significant savings. For electric vehicles, the GCCP and Victoria modeling suggested large potential GHG savings, but slow adoption rates. In both circumstances, then, it 504 appears that the popularity of particular solutions (both DES and electric vehicles were prevalent in professional discourse at the time) did not reflect their potential for energy savings. One possibility is that practitioners favoured these interventions because they perceived lower barriers to implementation. That is, neither the structure of communities, nor our methods of transportation (low occupancy vehicles) would need to change to adopt these solutions. A better understanding of the true potential of energy solutions may help to better prioritize options. Hypothesis (energy in place) Acceptance for controversial planning subjects such as density will be higher when those subjects are discussed in community values terms (e.g. convenience, services, etc.) than in terms of energy use or GHG emission reductions. This hypothesis draws from the prevalent theme of ‘energy in place’ that has pervaded the dissertation, and aims to test the notion that people are better engaged about the things they really care about in their communities. This hypothesis could be tested by presenting density thresholds for added services such as daycares or local shopping, and comparing participants’ preferences to a similar exercise that presented energy and GHG metrics. Would participants be willing to accept more density when they perceive immediate quality of life improvements in their community? Research Question (energy in place) What is the relationship between energy and things people value in their community? Can we quantify the energy implications of a corner store, access to transit, or a local daycare? This research question relates directly to the preceding energy in place hypothesis. The goal of this research question is to direct modeling efforts towards developing metrics that may be more in line with how people think about their communities, and what they value. Is it possible to quantify the energy implications of a local daycare (in terms of reduced travel time, walkability, etc.) and, if so, does that type of metric resonate more with participants in engagement activities, leading to higher preference ratings for planning options such as density. Collectively, the hypotheses and research questions in this section can be summarized by their intent to direct research towards a better understanding of what people really value about their communities, and how those values relate to energy. This intent is reflective of the assertions made in section 8.4 (and foreshadowing section 8.5) that we should engage with people about what they 505 want from their future communities and, as practitioners and researchers, work to better understand the relationships between those values and sustainable practices. Recommendations for Practice: Energy Planning Based on the energy planning findings of the case studies from Chapter 6 and 7, the discussion of those findings in section 8.4 and the research directions discussed in the last section, there are two primary recommendations I would make to practitioners of community scale energy planning. Firstly, practitioners should seek to incorporate energy into a broader discussion of what people want from their communities, rather than making it the focal point of engagement activities. The assertion here is that participants will be more engaged, and more accepting of energy interventions if they are discussed in terms of the things that matter to them. Secondly, there is significant work to be done in modeling, analyzing and understanding the relationship of community features and community values to energy use and GHG emissions. Modeling the impacts of urban form on walkability, transit access, and local services may reveal alternative methods for increasing density that are more acceptable to citizens. Better representation of trade-off and co-benefit information may lead to solutions that more effectively accommodate multiple values. Given the nascent stage of development of community-scale energy planning, it is understandable that we do not yet know the answers to many of the questions. However, development of this knowledge in practitioner communities would allow us to ask and address the important questions about energy use and supply in our communities. 8.5 The Process of Engagement A broad discussion of public engagement is beyond the scope of this dissertation. Other theorists (cf. Fischer, 2000) and applied researchers (cf. Beirle & Cayford, 2002; Innes & Booher, 2004; Rowe & Frewer, 2000; 2004) have made the study of public engagement and its many facets the focus of their programs of research and, though most of the aspects of public engagement they address are applicable to the cases discussed in this dissertation, there are specifics of the engagement discussed in Chapters 6 and 7 that bear some emphasis here. What we are interested in about public engagement is its role in community energy planning and, more broadly, sustainability planning. 506 Perhaps the first questions to ask in any engagement activity is ‘why’, and ‘with whom’? Possibly the simplest, and most direct answer about why we need to engage with citizens about sustainability planning issues is that sustainability is fundamentally about choices (Salter et al., 2010). As Chapter 2 discussed, many of the complex human-environment issues we face are wicked problems. Unlike previous environmental issues such as ozone depletion, the direct connection between problem and solution is unclear when discussing sustainability. There are many possible approaches, meaning that ultimately it is a matter of choice. When that choice is so inextricably tied to the communities we live in – to place – the involvement of citizens is inevitable, whether it comes in the form of early involvement to negotiate, co-develop and choose future conditions (Innes & Booher, 2004), or late involvement, protesting the development of new high rises in their communities (e.g. Campbell, 2014). The case studies discussed in Chapters 6 and 7 are all, in their own ways, illustrative of the primacy of choice in community scale energy planning. Previously, I categorized the case studies according to their engagement goals on Fiorino (1989) and Stirling’s (2006) framework of engagement types. These engagement types also inform the types of choices that people make during engagement. The Revelstoke case study was primarily instrumental, investigating participants’ preferences for density in the wake of the City’s Unified Development Bylaw (UDB). The choices participants made, reflected in their designs, suggested a broad rejection of the density concepts of the UDB. The Victoria case study was primarily substantive, focused on developing effective energy and GHG strategies for the City. Participants’ choices about those strategies directly impacted recommendations made to the City of Victoria in the CEEP report. Finally, the GCCP example, largely as a result of challenges of integration with the City of Vancouver neighbourhood planning process, was primarily normative. In this process, participants’ choices became largely about social learning – i.e. working together to better understand energy in their communities through the choices they made about the design of the Sandbox. A larger goal of this type of process outcome (see Chapter 2) is to develop participants’ capacity to take part in subsequent engagement activities – i.e. to help them make informed choices. It is notable that in all three case studies, the workshop outputs were not consensual, but rather multiple. That is, people worked in small groups to develop alternative pictures of the future – again, 507 reflecting the primacy of choice. As the case studies reveal, one of the key organizing decisions in developing engagement activities is “what are the choices we want participants to investigate and provide input on?” The design of each of the three case studies, and their resulting affordances are largely a result of that end goal. A continuing challenge of engagement and participatory planning, however, is connecting people’s choices to planning and decision making outcomes in meaningful ways (Innes and Booher 2004). On the subject of who to engage with, the case studies also presented a variety of audiences. From the expert and practitioner audiences at the Victoria and GCCP Planners workshops, to the mixed expert/citizen audience at the Grandview workshop, and the predominately citizen workshops in GCCP Marpole and Revelstoke. This provided the opportunity to analyze how different audiences interacted with the subject of community scale energy, and with the tools and processes of engagement. Results from the post workshop questionnaires suggest that there is still significant work to be done engaging with experts and practitioners, particularly those charged with planning communities. As noted in section 7.5.4, the more expert participants were also the most likely to indicate surprise about aspects of the existing energy system, such as the proportions of energy coming from different sources. This suggests that how energy works at these more local scales is still poorly understood, even by experts and practitioners. If planners, consultants and energy sector workers are going to incorporate energy into their planning, and engage with citizens about the implications of energy, then work needs to be done to build capacity amongst practitioners about the nature of energy at local scales. This is not surprising, considering that even researchers working specifically on issues of energy have, at best, a partial understanding of all of the factors contributing to energy demand and supply. It is a common adage that the best building energy model only survives until people take occupancy (cf. Clevanger et al., 2014). Scaling this up to a community or city level, and adding in the complexities of time and system interactions, there is significant work to be done to better understand the implications of community planning for energy. British Columbia is fortunate, in that there are several provincial programs (e.g. the Ministry of Environment Climate Action Secretariat’s Community Energy and Emissions Inventory, and BC Hydro’s Community Energy and Emissions Plans), as well as user groups, such as the Community Energy and Emissions Modelling group (http://www.toolkit.bc.ca/ceem), that are addressing energy issues at the community scale. Research partnerships such as those undertaken in the GCCP (this dissertation) and in Girling et al. (2014a) also present the opportunity for both academic research and practitioner engagement. The subject of expert and practitioner engagement, then, seems uncontroversial. There is significant opportunity 508 for both researchers and practitioners to benefit from working together to understand community scale energy issues. The subject of interacting with the public on community scale energy issues is less cut and dried. Arguments against public engagement on a variety of societal and environmental issues can take one of two main forms. The first, largely a relic of the 20th Century (though perhaps more commonly held than literature or practice might suggest), is that lay publics should not be engaged on environmental, resource management, policy or planning issues, because they lack the expertise for meaningful input (Beirle & Cayford, 2002; Innes & Booher, 2004). This argument is rarely seen in modern practice, and has been roundly discredited in the literature. A more difficult argument against public engagement is that declining social capital (Putnam, 2000) and a concurrent rise in the desire to engage with people on a variety of social, scientific and environmental issues, is leading to too much engagement of an indiscriminate nature. Bogner (2012) refers to ‘participation experiments’ where participants are engaged for primarily academic, governmental or private sector research, with little connection to participant outcomes. While the initial intent of the GCCP workshops was a more direct connection to the City of Vancouver’s planning process, the actual conduct of the workshops could be criticized as being a ‘participant experiment’. This criticism illustrates a larger point. With so many potential issues to engage upon, fueled, in no small part, by our increasingly complex systems, how do we rationalize the opposing forces of declining social capital and civic engagement, with an increasing number of issues to engage with people about? One part of the problem is that increased complexity leads to increased specificity (Tainter, 1990) – both in terms of expertise on particular issues (e.g. energy, or water, or the impacts of biotechnology), and in the rate at which they enter public consciousness. So, in recent years, the subject of energy has become a dominant topic in community planning across British Columbia (and, indeed globally) driven, in no small part, by available funding from programs such as BC Hydro’s Sustainable Communities Program. This often leads to a staccato approach to engagement, where we engage and solicit input on individual subjects like energy, GHGs, or water availability and quality. 509 A metaphor from a forestry perspective, is the dominant form of public engagement in the latter part of the 20th Century in BC, which involved public meetings to solicit feedback on individual proposed cutblocks. The argument made here, is that it is much more important for people to engage about how they want the forests in their valley or their region managed at a strategic level, rather than focusing on each potential intervention so late in the game, divorced from the broader context. Innes and Booher (2004) recognize a similar phenomenon in planning in the United States, where the most common form of engagement is public hearings to solicit feedback after a plan has already been proposed. From a planning perspective, the forestry metaphor relates to points raised in section 8.4.5 (Energy in Place), and to a lesser extent in section 8.4.2 (Transportation). That is, that we should be engaging with people in a more holistic way about what they need, want and value about their communities, rather than individual issues as they arise. Granted, some of these issues, such as energy or climate change mitigation and adaptation, could have dramatic effects on the conduct of our everyday lives, and the complexion of the communities we live in. In this approach, significant onus would be placed upon planners and practitioners to understand and communicate the implications of design and planning options across a range of indicators. Onus would also be placed on practitioners to design options that integrate the amenities and services that people want in their communities with sustainability goals – admittedly, no small task. An obvious criticism of this approach is that it puts too much control back in the hands of experts. Wynne (2007), Jasanoff (1998) and others in the environmental risk field have demonstrated the potential perils of prioritizing expert knowledge over other kinds of knowledge, and advocates of post normal science (Funtowicz & Ravetz, 1993) Mode2 science (Gibbons, 2000) and citizen science (Irwin, 1995) have also argued effectively for expanding the input of other forms of knowledge, particularly in complex projects with clear social implications. I am not, in fact, arguing for a re-essentializing of expertise. I believe the arguments against it are compelling. What I am arguing for, is an understanding of the limits of people’s time and attention and a recognition that there is a responsibility for practitioners to use the time that participants dedicate to engagement activities effectively. I would argue that this effective use of time involves better understanding and integrating important sustainability goals with an array of values – a problem of effective design. 510 This is also not to suggest that we should cease engagement on sustainability issues until researchers, experts and practitioners have sufficient understanding of energy, sustainability and community values to effectively propose solutions. Rather, it suggests that this type of integrated design and planning is what we should be working towards. In the GCCP workshops, the ‘name your neighbourhood’ exercise was perhaps the most enjoyable for participants, and the most informative for the researchers, because it demonstrated some of the obvious links between what people value in their communities and potential energy interventions. More broadly, looking for the connections participants revealed between community, quality of life and sense of place in their open ended answers suggested a number of areas of synergy and overlap between what people want more of, and what energy planning needs (e.g. walkable communities, local services and jobs, access to public transit, etc.). While we are working towards a more integrated, regenerative design of communities, engagement with those communities should include discussions of these important synergies. Harkening back to the discussion of density in section 8.4.1, perhaps engagement should spend more time focused on how density leads to corner stores, public transit access, and parks, and less time discussing per m2 reductions in energy use. This approach attempts to account for people’s limited time (and potentially limited motivation) for engagement by integrating planning for multiple values through the effective design of engagement processes. Another, perhaps more ambitious approach to the public engagement conundrum, is to address the issue of declining social capital and civic engagement more directly. A current research project at UBC, the Sustainability in Imaginary Worlds (SIW) project, is investigating this approach by combining scenario analysis with performance art to re-imagine what sustainable futures might mean, and how we might experience them. The beginning question of this research project is “what if the challenge of sustainability is not to prove the world real, but rather to prove the world imaginary?” The SIW project could have significant implications for participatory processes. At the most fundamental level, this project is addressing the nature of engagement on sustainability issues. Given the challenges of public participation, how do we engage with people about such important issues as sustainability? The challenge of the SIW project, from an engagement perspective, is to make the experience both enjoyable and provocative. In the process of developing the experience (which is ongoing at the time of this writing), we will learn a great deal about the nature of engagement more broadly, which should have carry-on effects to future research and practice. 511 This two part approach - on the one hand investigating how to integrate sustainability planning to increase the value of engagement processes, and on the other hand looking for ways to make the process of engagement itself more engaging – represents a promising avenue for future research on public engagement in sustainability. Research Hypotheses: The Process of Engagement One of the early ideas behind the research in this dissertation was the notion that there is rarely a clear path between the outcomes of engagement and subsequent policy – what Stirling (2006) and Fiorino (1989) call substantive engagement outcomes. In this dissertation’s case studies, it is telling that the only process to show substantive outcomes was expert and practitioner focused. A challenge of future engagement research, then, is to demonstrate the value of normative and instrumental engagement, both as inputs to policy (e.g. a demonstration of trust for proposals) and as mechanisms for further developing social capital and citizen agency in the planning of our communities. Based on the case studies from this dissertation, and a broader review of participation literature, I would argue that two areas that require further research are when engagement occurs (Talwar et al., XX, PIA) and what the subject(s) of that engagement are. Hypothesis (early engagement) Participant satisfaction will be higher in engagement activities that occur at earlier, strategic and direction-setting stages, rather than later stages focused on specific developments. This hypothesis is broadly supported, in theoretical terms, in the engagement literature (cf. Beirle and Cayford, 2002; Innes & Booher, 2004; Rowe and Frewer, 2005), but a literature review did not find an explicit treatment of it in empirical terms. Perhaps the best way to analyze this hypothesis would be through a meta-analysis of public engagement research that has evaluated participant satisfaction, with stage in the planning process as the independent variable. Hypothesis (integrated engagement) Participants will be more engaged in processes that focus holistically on desireable or sustainable futures (i.e. are more integrative) than processes that focus specifically on a small subset of sustainability issues. 512 This hypothesis would need to be operationalized into a testable format. In particular, methods for evaluating level of engagement or level of agency would be required, and case study conditions would need to be identified that represented different levels of integration. Level of engagement on its own is not necessarily indicative of ‘good engagement’ – i.e. it is possible to engage with people about subjects that are riveting, but meaningless to planning practice. However, if engagement is taken seriously as an important input to planning, then efforts to encourage people to participate do have value. The SIW project discussed in section 8.5 is emblematic of research taking a bolder (if less immediately pragmatic) approach to addressing the challenges declining engagement. Broadly speaking, the hypotheses in this section are aimed at identifying ways to foster engagement on sustainability issues. Can declining civic involvement be counteracted by changing the ways we engage, involving people at more appropriate stages of the planning process, and speaking more systemically to sustainability as it impacts them and the future of their communities? Recommendations for Practice: The Process of Engagement Based on the engagement component of the research conducted in this dissertation, I have three primary recommendations for practice, two of which mirror the research hypotheses just discussed. Firstly, I believe planners and practitioners should endeavor to involve people earlier in the planning process, at the stage where broader strategy-setting is taking place. Rather than soliciting input on prepared strategies for energy savings in their communities, citizens’ community values should be treated as a crucial input to the development of those strategies. Tools such as the Sandbox and the accompanying ‘name your neighbourhood’ exercise from the GCCP workshops present some opportunities for soliciting citizens’ inputs at earlier conceptual phases without suggesting concrete plans for their neighbourhoods. Secondly, I believe that planners should, where possible, try to integrate multiple values in their engagement activities. This will require more background work by professionals to identify trade-offs, co-benefits and intersections of multiple values, meaning there is significant work to be done in understanding what local sustainability really means. Finally and relatedly, we should not cease engagement while we wait for a better understanding of local sustainability. Rather, we should take an adaptive management approach, learning about what 513 works and what doesn’t in engagement efforts, in what Robinson and Cole (2015) describe as a procedural approach to sustainability. Inherent in this approach is a need to be reflective about the outcomes of engagement activities, and to share those outcomes in both academic and professional settings. 8.6 Engagement Tools In the domain of sustainability planning research, any discussion of engagement would be incomplete without a discussion of engagement tools. As Chapter 2 revealed, and the three case studies in Chapter 7 illustrated, engagement tools are fundamental to how we conceive of, and engage with the future. The engagement tools discussed in chapters 6 and 7 were developed in parallel by a connected group of researchers at UBC – i.e. there was significant cross-pollination of ideas between the different tools. However, a broader survey of tools used in integrated assessment and sustainability planning (see Dahinden et al., 2003; Salter et al., 2010) indicates that there are many similarities in the types of tools being used more globally. In particular, computer models and scenarios are ubiquitous in futures and sustainability studies. Tools such as visualization and interactive interfaces, while less common, are growing in popularity, particularly as platforms such as Google Earth, ESRI’s CityEngine, and others evolve to make 3D and real-time interactivity more accessible to practitioners. Therefore, how we develop and use these tools can have significant impact on how, and how effectively, we engage with practitioners, decision-makers and publics on sustainability issues. The results of the case study analysis in Chapter 7 reveal some questions about the tools we use, and their future development. Notwithstanding advances in adaptive interfaces (cf. Jameson, 2002), there is still a need to develop tools targeted towards particular audiences. In the case of energy and sustainability planning, perhaps the most distinct audiences are experts, practitioners and decision-makers, and multiple publics. The following sections will discuss engagement tools as they pertain to these different audiences. 514 Expert Tools As section 8.4 discussed, evidence from chapters 6 and 7 suggest that there is still significant work to be done by experts and practitioners to understand how energy functions, and how we should plan for it, at local scales. Understanding how transportation, buildings, waste streams, local energy sources, etc., interact at local scales is complex. Adding to this, we need to understand how these interactions play out across both spatial and temporal scales. For example, how does initial building energy modeling compare to the ‘as built’ performance, and how does human occupancy change that building performance over the long term (Clevenger et al., 2014)? When that building is added into a larger district energy system, how do changes across its lifespan impact demands upon that system? Spatially, how does its location impact transportation planning for its occupants, and how does its siting and surrounding buildings impact its energy performance (Salat, 2014)? What local resources (e.g. solar, waste heat, etc.) exist, and how can they be accessed? In order to answer these questions, we need to acquire the data and develop the models necessary to explore them. One of the particular challenges of developing these tools is limited data availability from municipalities. Data limitations and their impacts on what gets modeled are a fundamental concern in modeling more broadly (Edwards, 2013). While the recording, maintaining and providing of spatial and aspatial data is improving at many municipalities, data limitations persist. In the face of these data limitations, there is a tendency to model and study based on the data we have. As a result, we often explore a more limited set of options. For example, accurate transportation modeling is very difficult without detailed trip surveys (Kitamura and Fujii, 1997), which smaller municipalities may not be able to acquire. An additional concern is the tendency for the tools of different professions to exist in silos. For example, architects and engineers tend to work in CAD and Building Information Models (BIMs), while planners and resource managers more commonly work in GIS. Starting in Europe, there is also a burgeoning industry focused on the development of intelligent 3D city models, based primarily (but not exclusively) on CityGML (Kolbe et al., 2005). Translation is possible, but not easy, between these different formats, but it is not simply a matter of translation. In the case of BIM models, the level of detail required for a full architectural and engineering model of an individual building is far too complex and data intensive to store in a city-wide model. Therefore, it is necessary to do an additional level of translation, maintaining the important information, and stripping out the extraneous information. Determining how to make these different models work together is an 515 additional level of overhead needed to investigate the implications of energy across spatial and temporal scales. One approach to try to alleviate these data issues, and to investigate interactions across scales based on different land uses and urban forms, is to build upon the Sandbox idea used in the GCCP case study. This sandbox could be constructed from known/measured components such that data limitations can be overcome. Additionally, parts of the sandbox could be swapped in and out to investigate the relationships between different features. How does inter-building shading impact solar access for photovoltaics or passive heating? How does a district energy system perform based on different density arrangements? This sandbox format would also allow for a controlled investigation of the interactions between different professional tools at a sub-city scale, so that translation routines can be developed, and data management practices put in place. Another continuing research project at UBC is investigating these interactions across spatial and temporal scales, from the building systems scale, to the community scale (see http://ideass.civil.ubc.ca/). The next stage of this project will leverage the Sandbox idea introduced in Chapter 6 to explore some of the spatial, temporal and scalar issues discussed here. My main contention about expert and practitioner engagement tools, then, is that we need to continue developing a set of tools and models that will allow us to study the interaction of factors at local scales that have energy, and broader sustainability implications. These tools need to facilitate interaction amongst disciplines that currently use tools from different traditions (e.g. design versus geospatial). Also, unlike the more traditional, larger scale econometric energy models (van Beek 1999), these local tools must be spatial, to account for important spatial interactions at local scales that drive energy use, and the potential for local energy supply. Public Engagement Tools In addition to the challenge of researchers, energy experts and practitioners understanding how energy functions at local scales, there is an additional burden on engagement tools to translate that understanding in ways that it can inform, and be informed by, citizen engagement. Elements of the expert tool discussion carry over to the public realm. In particular, the focus on solutions for which we have data, and that we can model, has implications which are just as important for public engagement as for practice and decision-making. In the GCCP process, some participants expressed frustration that solutions which they thought were important were not included amongst the 516 strategies. For example, the absence of parking policies and waste heat recovery strategies were specifically mentioned. Figure 8.2: Relevant aspects of the interfaces and visual representations from the three case study processes. In addition to the challenges shared with the development of expert tools, however, there are a number of factors of public engagement tools that are unique to this tool type. Observation of the participants’ use of the engagement tools in the three processes, as well as comments on the post 517 workshop questionnaires, revealed a number of topics that bear further study in future research. The most salient of those topics will be discussed here. One of the most notable differences in the engagement tools used in the three processes, is the level of visual immersion the different tools provided, and the apparent effect of that immersion on participant interactions. Two of the tool sets (GCCP and Revelstoke) were strongly spatial and visual in nature, with extensive use of 2D mapping and 3D visualizations. The third tool set (Victoria) was highly interactive, but much less visually rich, with only graphical representation of data (Figure 8.2). As Chapter 7 discussed, there was, at least, a correlation between the visual richness of the tools and the value placed on discussion in the accompanying exercises. That is, there was a negative correlation, where more immersive, visually rich tools were associated with a decreased value placed upon discussion. In the Victoria case study, discussion amongst participants was the highest rated aspect of the workshop (see Figure 7.6), and participant comments corroborated the ranking, with many participants stating that discussing energy issues with other workshop participants was the most valuable part of participating in the workshop (section 7.2.2). Conversely, in the GCCP and Revelstoke workshops, discussion was rated much lower. In the GCCP workshops, discussions with other participants was the lowest ranked aspect of the workshops (see Figure 6.45), while in the Revelstoke workshops it was the second lowest rated item (see Figure 7.20). This raises interesting questions about the use of visually rich tools in public engagement exercises. Firstly, what is the impact of more discussion versus more immersion in the representations and interactivity of tools? From a mental models perspective, what is the impact of participants focusing on the tools, rather than on each other’s perspectives? From the standpoint of developing shared mental models (Jonker et al., 2010) of community scale energy, it could be argued either way – i.e. that either a tool dominant or discussion dominant process might lead to the development of more shared models. If the goal, however, is intersubjectivity – i.e. not sharing, but rather understanding and taking other models into account (Barker et al., 2013), then a discussion, rather than tool-focused approach seems better suited. This is, of course, not a binary decision. It is not simply a matter of choosing between the different tool types and the different processes they engender. In fact, it is the possibility of mixing these modes – inserting visually rich materials at points when spatial concepts are important, and removing them at points when they may distract from important discussions – that may be the most interesting. 518 The larger GCCP project (i.e. the multiple engagement channels and the cross-channel evaluation as depicted in Figure 6.1) had a number of overarching themes that played out in various ways across the project. One of the unifying themes was a discussion of deliberation versus dialogue in engagement activities (Bendor et al., 2012). That is, what is the value of goal-directed deliberation versus a more open dialogue. A hypothesized benefit of encouraging more open dialogue is the fostering of emergence (Ehrenfeld, 2008; Robinson & Cole, 2015) – the expression and development of ideas that may not be part of the existing lexicon of options or solutions. Based on this notion of emergence, and more broadly a procedural view of engagement and sustainability (Robinson & Cole, 2015), there is value in promoting moments of dialogue that are not necessarily part of a goal-directed deliberation. Experience with the GCCP process, however, would suggest caution that these moments of dialogue and emergence appear to function better if they are bounded within a broader framework. That is, people seem to enjoy creative and unbounded dialogue more if they understand the structure within which they are working. In another domain, the apparent success of techniques such as brainstorming (Rawlinson, 1986) might reflect this movement between more and less directed modes. Metaphorically, this process calls to mind Gould’s (1992) version of evolutionary theory ‘punctuated equilibrium’, where long, relatively inactive periods of evolution are punctuated by highly active (evolutionarily creative?) periods of rapid change. What does this mean for the design of engagement tools and processes? Perhaps that exercises, and the tools they use, should alternate between visually rich and immersive examinations of topics with strong spatial, visual and experiential components, and facilitated discussions (perhaps augmented with interactive, but less visual tools such as the CAN Tool) so that participants can benefit from both the explanatory benefits of visual representations (Mayer, 2003) and the emergence and intersubjectivity encouraged by discussion (see Figure 8.3). Other researchers from the GCCP project (cf. Tanenbaum et al., 2011) similarly suggest that a current weakness of the emergent model is the difficulty of creating and sustaining the conditions for it to occur successfully in engagement processes. This punctuated approach represents on possibility for exploring the placement of emergence within a broader engagement structure to foster effective conditions emergent dialogue. 519 Figure 8.3: A schematic representation of a possible engagement process, leveraging both visually rich and dialogue-focused engagement modes. It should be noted, of course, that there are other, potentially confounding factors, at play here, including the audience type (primarily expert in Victoria, versus a public/expert mix in both the GCCP and Revelstoke processes), and the issues discussed (e.g. Victoria did not discuss land use and other strongly spatial characteristics of local energy). Even without these potential confounding factors, it would still not be possible at this juncture to assign causality – i.e. that it is the tools that are directly responsible for the focus on discussion. Still, the strong effects seen between the Victoria workshop exercises facilitated by the CAN Tool, and the GCCP and Revelstoke exercises facilitated by 2D and 3D visual representations, bears further study. In many ways, the use of these digital (or, at least digitally produced) tools is still in its infancy. There is much to explore about how they are used, and how they impact engagement processes (Bishop et al., 2013, Bishop & Lange, 2005; Sheppard, 2012). The discussion of visual representations relates back to the representation framework developed briefly in Chapter 2 (see Figure 2.1), and the discussion of the case study representations from Chapter 7 (see section 7.6.6 and Figure 7.26). This issue regards the use of different representation types for different purposes (e.g. conceptual understanding versus the experience of place) and our biases as content creators and researchers. The Revelstoke case study is perhaps best illustrative of this point. As stated, the Revelstoke case study investigated density relationships at intimate scales in known locations. In addition to the frequently expressed concerns about community character, there were also a number of requests (specifically at the June workshop) for building representations that were more in keeping with Revelstoke. Additionally, observation of participants’ use of the interactive touchtable revealed that the vast majority used the higher realism building models, rather than the thematically coloured models (Girling et al., 2014b). This suggests that, for a character- 520 related task such as exploring density, participants may have been looking for a higher realism, more experiential picture of what the future neighbourhoods would look like. In the GCCP case study, with an unknown neigbhourhood and larger scale, there were no comments suggesting the types of representations were inappropriate for their purpose. Many of these interactions of visual representation and underlying information are anecdotally known in the research and practice communities. For example, if you want to convey a conceptual understanding of a proposed plan, an aerial oblique viewpoint using a conceptual 3D representation may be the best method (see Figure 2.1). Conversely, if you want people to experience what a future community might feel like, a ground level viewpoint using highly realistic 3D or photomontage techniques is likely more effective. Over the years, some elements of representations of landscapes and urban form have been codified (see e.g. Appleyard, 1976; Bishop & Lange, 2005; Sheppard, 1989; Sheppard, 2012; Zube et al., 1987). Primarily, these frameworks have discussed important aspects of visualizations such as accuracy and representativeness, but there has been less work on elements of visual representation such as realism (Bishop & Lange, 2005 is a notable exception on realism), viewpoint, associated information, etc., and the underlying messages the creators are trying to convey. Notably, visual representation developers tend to have their own biases – favouring more or less realism, particular types of viewpoints, augmentation with non-visual information, etc. The argument made here, is that content creators, researchers and practitioners, should explore these biases, and think about the use of visual representations in a more systematic way. How are our representation media and their different affordances interacting with the messages we are communicating, and how might they be colouring the process of engagement? The visualization framework developed briefly in Chapter 2 and used to delineate the different representation types in Chapter 7 (section 7.6.6) represents an attempt to think more strategically about the way that we develop and use representations in engagement activities. This representation framework does not constitute a finished product, there is still significant work to be done to define its constituent parts and to evaluate representations as used in a broader cross-section of engagement activities. The framework as it currently exists, however, has been a useful tool for thinking about representations. For example, it was helpful in devising the suite of representations used in the GCCP project. It is, perhaps, not coincidental that the GCCP had the widest array of representation types of the case studies examined. 521 Accounting for the Qualitative and Emergent The final theme on public-engagement tools, is a need for those tools to do a better job of accounting for either qualitative aspects that are not modelable, or participant input that emerges during engagement. One result of the development and use of models and digital tools, is that their outputs tend to have a legitimacy that ideas put forth in the engagement exercises do not share (Dahinden et al., 2003). For example, in the Victoria workshops, several participants raised a criticism of the transportation model that it did not account for the co-benefits of transportation planning. This criticism was heard, but with no mechanism for accounting for it in the model, it became a footnote to the transportation recommendations in the subsequent report. Both the GCCP and the Revelstoke processes did include mechanisms for addressing participant input regarding options that were not represented in the accompanying modeling. In the GCCP process, participants were provided with blank strategy cards that they could fill in with their own proposals, with the associated ‘stars’ (levels of GHG reduction) being negotiated with the facilitator and other members of their group. So, for example, strategies such as car sharing and waste heat recovery were added to the final neighbourhood designs. In Revelstoke, the iterative nature of the workshops meant that input from the first workshop could be researched and added to the subsequent workshop. As a result, additional building models that were more in keeping with Reveltoke’s character were developed and added to the palate of choices for the second workshop. These approaches allowed the engagement activities to be responsive to participant input. I believe we should build upon them, and explore other avenues for ensuring that participant-generated content is not only accounted for, but provided with an agency more in line with the expert-generated models and data. One approach, building off the blank strategy cards from the GCCP process, would be to include a form of participant-defined ‘container’ in the engagement tools. For example, participants could define a ‘car share’ strategy which would be represented in the tool itself. One tool that has this functionality is CommunityViz (http://placeways.com/communityviz/index.html). Built on top of ESRI’s ArcGIS, CommunityViz allows participants to develop their own indicators, assign parameters and conduct sensitivity analyses. Similar to the strategy cards from the GCCP, participants could negotiate the characteristics of their solution. From a mental models perspective, this approach has some of the characteristics of participatory modeling (cf. Cohen et al., 2005; Doyle & Ford, 1998), where the engagement exercise consists of externalizing participants mental models 522 of the system in question, and negotiating the construction of a representative computer model, generally through the use of a system dynamics modeling tool. The challenge of this approach is making it flexible to the range of ideas that participants might generate, particularly more qualitative ideas. For example, how do we assign an energy or GHG value to ‘proximity to shopping’ in a time-limited, interactive workshop? Nevertheless, if the purpose of engagement processes is to meaningfully engage with people towards a co-creation of future conditions, then their input needs to be elicited in such a way that they understand that it is being seriously considered, and that it can actually be used in the generation of new results and materials. The themes discussed in this chapter, summarized into the categories of mental models, energy planning, public engagement, and engagement tools, present a large number of opportunities for future research, which could be addressed individually, or in concert to generate future research projects. One possibility would be to attach a new study to the next stage in BC Hydro’s Community Energy and Emissions Plans program, looking at implementation of the municipal plans that have been developed. This would present the necessary subject numbers, and a similar research context to build off of the findings of this dissertation, and the related findings from the Revelstoke research (Girling et al., 2014b; Senbel et al., 2013). Many other possibilities to expand on this research exist, but remain to be explored. Research Hypotheses: Engagement Tools Further development and evaluation of the tools of engagement presents the most specific opportunities for testable hypotheses. There were apparent effects of the tools used in the Victoria, GCCP and Revelstoke processes that should be further evaluated as inputs to subsequent tool development. In particular, the implications of visual immersion and interactivity, and the benefits of more explicitly incorporating the qualitative feedback of participants in the tools themselves appear to be fruitful areas for future study. 523 Hypothesis and Sub-hypotheses (visual immersion and interactivity) The level of visual immersion in planning tools is inversely related to the quantity of participant discussion and the value they place upon it. a. Visually immersive tools are superior for promoting understanding of spatial aspects of sustainability such as urban form than interactive but non-visual tools. b. Visually immersive tools reduce intersubjectivity – i.e. they reduce participant understanding of other participants’ perspectives. This hypothesis and its sub-hypotheses follow directly from the case-study observation and worshop evaluations discussed in Chapter 7. It was apparent, but not empirically demonstrated, that participants in the Victoria workshops, using the less visually immersive tool, placed a significantly higher value on discussion. Because there were mitigating variable, such as the expertise of participants and the workshop structure, it is not possible to define a causal relationship. A more controlled study of this phenomena may reveal the impacts of visually rich tools on discussion, comprehension of spatial planning concepts, and intersubjectivity. Hypotheses (visual realism) Participants’ desire for visual realism is directly related to the scale of representation and the scale of planning. Participants’ desire for visual realism is directly related to the familiarity of the represented environments. These hypotheses were derived from observation of the GCCP and Revelstoke processes. The GCCP process worked on the unfamiliar sandbox neighbourhood, and at a larger scale. The Revelstoke process worked on very familiar community locations and at a more intimate scale. In the Revelstoke workshops, participants indicated a desire for more realistic representation of those environments. Testing these hypotheses should reveal the effects, and possible interactions, that led to that desire for higher realism. 524 Hypothesis (accounting for qualitative input) Provisions made within interactive tools to record and/or evaluate participants’ qualitative input will increase participants’ sense of agency and satisfaction with engagement processes. Observation from the Victoria and GCCP workshops led to this hypothesis. In the GCCP workshops, participants were provided with blank strategy cards that they could fill in with their own strategies. As Chapter 6 discussed, this feature was popular and widely used. In the Victoria workshops, several open-ended comments referred to the rigidity of the CANTool, and their inability to challenge its assumptions and investigate alternative strategies. These findings suggest that making provisions for recording participant input in a transparent way could improve participant satisfaction. A further extension of this hypothesis, is that recording that input within the actual tools used may improve that agency by further suggesting to the participants that their ideas are being meaningfully captured. Recommendations for Practice: Engagement Tools From the case study evaluation of the tools used in the three workshops, the discussion of engagement tools in section 8.6, and the hypotheses stated above. I have two recommendations for practice that do not rely on future tool development. Firstly, engagement activities that make use of visually immersive tools such as future scenario visualizations or spatial planning tools should alternate modes between visually immersive exercises, and more discussion-based exercises. This alternation between modes may leverage the affordances of both types of interaction to both explain concepts, and allow participants to more fully explore other participants’s perspectives on those concepts. Secondly, practitioners should make provisions within engagement activities for recording participant input in transparent ways. This may be particularly important when participants raise issues or alternatives that are not represented in the tools being used. 525 8.7 Finally… A Note on the Broader Research Strategy This dissertation had many moving parts. In addition to the starting premises, the resulting research themes, and the developed research questions, there were multiple sub-themes that resulted from the case study approach as described in Chapters 6 and 7. Many of those themes and sub themes have been built upon in this chapter. This, I would argue, is one of the challenges and opportunities of applied, in situ research. Figure 8.4: A schematic representation of the continuum of applied research from controlled experimental conditions, to messy, in situ conditions. An estimate of the position of this dissertation’s research is included. Research on engagement in human-environment systems tends to exist on a continuum from more carefully controlled experimental conditions on one end, to messy, applied research on the other. Figure 8.4 shows a schematic representation of this continuum, and approximately locates this dissertation’s research along it – indicating a much closer relationship with the in situ end of the continuum. While there were elements of a more controlled approach in portions of the study (e.g. my participation in the development of the GCCP workshops and their materials), all three of the case study processes were fundamentally about developing real engagement opportunities, driven by participant interactions and outcomes. In other words, the research was subservient to the process, rather than the other way around. The benefit of more controlled conditions – e.g. running subjects through researcher-defined activities under carefully proscribed conditions – is the opportunity to operationalize variables and to try to control for confounding factors. This allows for a clearer picture of what component(s) of engagement, or engagement tools, are having a particular effect. As an example, Girling et al., (2014a) describes a separate study on the use of the touchtable interface from the Revelstoke process, using a form of user testing to more carefully control for external variables, and to better understand the affordances of the tool. The challenge of this type of research is that it necessarily removes much 526 of the richness of applied, real-world conditions. Because the goal is to evaluate specific aspects of a tool or process, it is also rarely tied to real world outcomes for the participants. As previously noted, Bognor (2012) refers to these types of engagement exercises as ‘participation experiments’, and suggests that they can be problematic depending on participants’ expectations for outcomes from the engagement. Controlled experimental conditions do, however, have an important place in engagement research. They can be particularly helpful in isolating effects of processes, the information presented, and the tools used, leading to specific design interventions and better processes and tools in the future. At the other end of the continuum is ‘messy’, in situ research, where researchers attempt to understand the dynamics of engagement processes over which they have much less control – generally through case studies, observation, or limited evaluation methodologies such as questionnaires. The primary benefit of this approach is a better understanding of the actual dynamics of engagement in a rich, real-world process, with definable outcomes for participants (dependent, of course, on the quality of engagement). The challenge of this approach to engagement research is a lack of control over the conduct of the process and an inability to delineate cause and effect. Bishop et al (2013) recognize a similar distinction between these two types in their review of interactive decision-support tools. They state that, while experimental conditions allow for the isolation of the effects of particular aspects of the tools, there is no substitute for actually observing the tool’s use in real, applied conditions. My own position, is that research projects all along the continuum are necessary, from carefully controlled experimental conditions, to messier case studies of actual engagement processes. Indeed, projects at different positions on the continuum can feed into one another to explore findings under different conditions. For example, the intriguing finding from Chapter 7 about the relationship between visually immersive tools and the value placed on discussion (see section 7.6) could subsequently be studied under more controlled conditions to untangle the effects. Was the high value placed on discussion actually an affordance of the CAN Tool, or did the audience (experts and stakeholders) and/or structure of the process have a larger effect? Answers to these questions, from more specific and controlled studies, lead to the design of better tools, for use in subsequent in situ research. In this dissertation’s research, a conscious decision was made to attach the research to the in situ end of the continuum, accepting the messiness that entailed. No doubt the mental models evaluation would have been easier to operationalize, and would have avoided several of the challenges discussed 527 in section 8.3.2 if it had been attached to a more controlled study. However, the broader ethos of this work is to better understand engagement on applied human-environment issues, with the goal of improving how we conduct engagement and, ultimately, how we plan our communities. For the mental models research to be applicable to these messy processes, it needs to be further streamlined and systematized so that it can accommodate the challenges they bring (Lynam et al., 2011). Section 8.3.2 attempted to describe ways that the mental models procedures could be changed to better fit these criteria. The case study evaluation of the processes investigated in this dissertation proved extremely valuable in uncovering elements of energy planning, engagement, and engagement tools that can be helpful to researchers and practitioners in future planning and engagement activities. This component of the dissertation took its lead from research needs described by van Asselt and Rijkens-Klomp (2002), and from previous examples of reflective research (cf. Robinson et al., 2006). One of the advantages of this approach is that it is generative – it points towards new avenues for research that may not have been foreseen without the struggle of carrying out research under applied conditions. One example discussed previously in this chapter is the more nuanced view of density, and people’s mental models of density, afforded by the GCCP and Revelstoke case studies – i.e. that resistance to density has multiple causes, but may be much more about the kinds of density, than the concept of density. The possibility of developing scenarios and processes to investigate this density relationship is an interesting avenue for future research. There are a number of other such findings to build off of from this research, many of which have been highlighted in this chapter. Additionally, reflection on the conduct of this type of applied research (cf. Robinson et al., 2006, Salter et al., 2009) can also be extremely valuable to other researchers and practitioners in the development of their own engagement processes; allowing them to learn from our mistakes, and build off our successes. Too often these reflective findings remain within the institutional memory of a given research group, rather than informing broader research and practice. This chapter began with a quote from Carl Steinitz of Harvard’s Graduate School of Design about the nature of a PhD that I think is particularly germane to this dissertation. At the end of the research, perhaps the most important finding is a better understanding of the questions we should be asking next. 528 References Abdi, H., & Vanetin, D. (2007). Multiple Correspondence Analysis. in Encyclopedia of Measurement and Statistics. Thousand Oaks, CA: Sage. Ackerman, D., & Tauber, M.J. eds. (1990). Mental Models and Human Computer Interaction 1. North-Holland, New York, NY. 387 pp. Agnew, J.A. 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Energy consumption, carbon emissions, and economic growth in China. Ecological Economics, 68(10), 2706-2712. 554 Appendix A Interview Script Date: Time: Subject ID: ENERGY AND THE CITY OF VANCOUVER INTERVIEW This interview is part of my PhD research at the University of British Columbia on energy planning at the city or community scale. The focus of the research is on how energy works in a particular place, and how the residents of that place relate to energy. I am interested in how you think about energy and its relationship to the city of Vancouver. This is not a test - don't worry about whether your ideas are right or wrong. I am interested in everything you think about these issues, and want to capture your ideas as completely as possible. We will start out with very broad questions about energy, and then move to more specific topics. Please cover as much information in the early questions as you feel is necessary, if your answers overlap the subject matter of later questions, we can skip those questions. 1. You’ve been invited to take part in this interview based on some relationship to the issue of community energy planning in Vancouver, could you briefly state what your relationship is to this issue? 2. Can you tell me what comes into your mind when I say energy? 3. Can you tell me what you know about how energy works in the city of Vancouver (Specific prompt: energy systems). 4. Do you interact with energy in your everyday life (Yes -> How?, No -> Prompts – ask questions about their daily life that relate to energy.) Now I am going to ask you several questions. Some of these may repeat things you have already said. I need to ask these questions to make sure that I cover everything. You can refer to the answers you have already given, if you think you have nothing more to add to a particular question. 555 REASONS TO ADDRESS ENERGY 5. Do you think energy is an important topic for the city of Vancouver government, and its residents to be working on (Yes -> Why? No -> Why not?)? a. Can you tell me some reasons why people are concerned about our use of energy? b. Can you tell me some reasons why people are concerned about where our energy comes from? SUPPLY 6. Can you tell me where Vancouver's energy comes from currently? a. Do you think the way(s) that Vancouver currently gets its energy is/are likely to change in the future (Yes -> How?, No -> Why not?)? b. What can you tell me about Green Energy or Renewable Energy? c. Do you think that Green Energy or Renewable Energy will affect Vancouver in the future (Yes -> How?, No -> Why not?)? DEMAND 7. Can you tell me about how energy is currently used in the city of Vancouver? a. What is more important about energy - where it comes from, or how we use it (Specific prompt: why)? b. What types of energy uses do you think use the most energy in the city of Vancouver? 556 TRANSMISSION 8. Can you tell me how energy gets to Vancouver, and how it moves in the city (Specific prompt: how does energy get to the energy users)? a. Do you think how energy moves in Vancouver will change in the future (Yes -> How?, No -> Why not?). CONSERVATION/DEMAND REDUCTION 9. Can you think of some ways that energy use in Vancouver can be reduced? a. What types of actions do you think would lead to the biggest energy savings? 10. Who do you think should be responsible for reducing energy use in Vancouver… (Specific prompt: the government, businesses, neighbourhoods, individual citizens)? a. What can the province of BC do to reduce energy use? b. What can the City of Vancouver do to reduce energy use? c. What can your neighbourhood do to reduce energy use? d. What can your household do to reduce energy use? CLOSING QUESTIONS/DEBRIEF 11. How was it to participate in this interview? 12. Were any questions unclear, or too difficult to answer? 13. Did any of the questions make you feel uncomfortable to answer, particularly because of your job? 14. Is there anything about energy or how it relates to the City of Vancouver that you haven't had a chance to speak about, and that you would like to? If so, what did we miss? 557 Appendix B Vancouver Questionnaires B.1 Pre-Workshop Questionnaire The University of British Columbia Session Number: Faculty of Forestry, Forest Resources Management 2nd Floor, Forest Sciences Centre Participant Number: 2045, 2424 Main Mall, Vancouver, B.C., V6T 1Z4 Entry Survey - Community Energy Planning Workshops We would like to thank you for participating in this study. Please remember that your identity will remain completely confidential, and the answers you provide will remain anonymous. If you feel uncomfortable with any question(s) you need not answer it (them). Your participation is purely voluntary. Do not write your name on this questionnaire. You have been assigned an anonymous participant number. Instructions 1. By filling out and returning this questionnaire, you are providing consent for your responses to be used in Dr. Stephen Sheppard and Mr. Jon Salter’s University of British Columbia research project, entitled: “Assessing Mental Models of the Energy System at the Municipal Scale: Energy Experts, Decision Makers, and Citizens”. 2. After filling out this survey, please return it to the provided envelope. You will be prompted at the end of today’s forum to fill out the exit survey. The two surveys can be returned together at the end of the forum. 3. This questionnaire is not a test of your knowledge - there are no right or wrong answers. We are interested in how you think about energy and greenhouse gas emissions, how they relate to your daily life, and to the City of Vancouver. To ensure the quality of the results, we urge you to answer the questions as completely as possible. If you want to add more information about any question please feel free to do so. Space is provided on the last page. 558 4. If you do not understand a question, or don't have an answer, feel free to answer 'don't know' and move on to the next question. Section One The questions in this section ask about your opinions and beliefs on broad issues about sustainability, energy, and the future of your community. 1. Do you believe that planning for the future of energy use and energy supply in your community is an important topic for people to be working on? Please state briefly why you believe we should or should not be planning for energy in your community. 2. Please circle the item that you think is the most important reason why we need to address energy concerns in our communities. h) Limitations of available energy supply i) Political reasons – energy disputes are a major source of conflict between nations j) Climate change concerns k) Environmental damage caused by our use of energy l) Fairness to future generations who may need the energy we are using m) Fairness to people in the world who have less access to energy supplies n) I do not think that we need to be concerned about energy issues 559 3. Please choose the statement that best fits your beliefs about the required size of the changes society needs to make about how we use energy, and where our energy comes from. e) We do not need to make any changes. Concerns about the availability of energy are unfounded. We have lots of available energy sources. f) Energy is an important issue, but energy concerns can be solved through economic means by raising energy costs, leading to new technologies and more efficient use of energy. g) Energy is an important issue, and will require some changes in how we plan our communities, and the policies and regulations we set. For example, developers of new buildings could be required to make those buildings carbon and/or energy neutral. h) Energy and other sustainability issues are extremely important. We need to make large changes in our economic and political systems in order to address the serious threats of climate change and energy shortages. 4. Who do you think should be responsible for responding to energy issues, such as finding new sources of energy, or reducing our overall use of energy? Please rank the following groups from 1 to 6, with 1 being the most responsible, and 6 being the least responsible for responding to energy issues. a) Businesses b) The Provincial Government c) Municipal (City) Governments d) Groups of citizens and/or neighbourhoods e) Individual citizens f) Federal Government 560 5. What approaches do you think will best help us address future energy concerns, for example helping us use energy more efficiently, or changing to new energy sources? Please rank the following five options from 1 to 5, with 1 being the most successful approach to addressing future energy issues, and 5 being the least successful. a) Improvements in technology b) Changes in people’s behaviour c) Changes in government policies and regulations d) Government incentives or subsidies e) Changes in how we plan communities 6. In general, would you consider yourself optimistic or pessimistic about our capacity to address energy and other sustainability issues in the future? d) Optimistic e) Pessimistic f) Neither Section Two The questions in this section ask about your relationship with energy - how do you use energy, and where the energy you use comes from? 1. List up to five activities you do in your daily life that require you to use energy. 1) 2) 3) 4) 5) 561 2. What is the thing that you do in your daily life that you think uses the most energy? 3. Please circle the energy source that provides you with the most energy in your daily life. a) Gasoline and/or diesel b) Hydroelectric power c) Natural gas d) Heating oil e) Solar f) Biofuels or biomass g) Nuclear h) Coal 4. How important is energy to you in your daily life? In other words, if you were required to reduce your energy use significantly, how difficult would it be for you to carry out the activities in your daily life? Please circle the best answer. f) Very difficult g) Somewhat difficult h) Neutral – neither difficult nor easy i) Somewhat easy j) Very easy 562 5. Do you have any concerns about how the future of energy use and energy supply may affect your daily life? If so, please state briefly what those concerns are, and how they might affect you. Section Three The questions in this section ask about energy and your community. How do people in your community use energy, where does that energy come from, and what might your community’s relationship be to energy in the future? Remember, there are no right or wrong answers to these questions. 4. What do you enjoy most about living in your community? 5. What would you most like to see changed in your community? 563 6. How would you rate your knowledge of the energy system (where energy comes from, how it moves, and how it is used) in your community? Please circle the best answer. k) I have very little knowledge of the energy system in my community l) I have some knowledge of the energy system in my community, but not a lot m) I would say I have an average level of knowledge about the energy system in my community n) I have a fair amount of knowledge about the energy system in my community o) I would consider myself an expert on the energy system in my community 7. Please rank the following energy users in your community from 1 to 5, with 1 being the largest user of energy in your community, and 5 being the smallest user of energy in your community. a) Industrial users like factories b) Transportation users, like cars, trucks and buses c) Residential users like houses and apartments d) Commercial users like shops and businesses e) Municipal users like police, fire and other City services. 564 8. Please rank the following energy sources to your community from 1 to 7, with 1 being the largest source of energy used in your community, and 7 being the smallest source of energy used in your community. a) Gasoline and diesel b) Electricity c) Natural Gas d) Propane e) Solar (hot water or electricity) f) Geothermal g) Heating Oil 9. Do you think that the sources of energy for your community are going to change in the future (let’s say by 2050)? If so, please briefly state how you think the energy sources in your community will change. 10. Please rank your top 5 approaches for addressing energy concerns in your community, with 1 being the method with the largest potential impact, and 5 being the method with the smallest potential impact. a) Increasing public transportation options b) Increasing local sources of energy such as solar c) Decreasing the size of people’s residences d) Making technical changes, such as energy retrofits on existing buildings, or electric vehicles e) Changing Individual’s energy behaviours f) Collective options, such as district energy systems g) Providing incentives for people and businesses to conserve energy 565 11. Can you think of other ways, not listed in question 7, that your community could address energy concerns in the future? Please briefly explain any ideas that you have. Thank you for participating in this research project. Your participation makes this research possible. Additional space is provided on the back of this page for you to add any additional comments. When you have completed the questionnaire to your satisfaction, please return it to the provided envelope. You will be prompted to fill out the exit survey at the end of today’s forum, and then to return both surveys to a workshop facilitator. Additional Space for Comments: 566 B.2 Post-Workshop Questionnaire The University of British Columbia Session Number: Faculty of Forestry, Forest Resources Management 2nd Floor, Forest Sciences Centre Participant Number: 2045, 2424 Main Mall, Vancouver, B.C., V6T 1Z4 Exit Survey - Community Energy Planning Workshop We would like to thank you for participating in this study. Please remember that your identity will remain completely confidential, and the answers you provide will remain anonymous. If you feel uncomfortable with any question(s) you need not answer it (them). Your participation is purely voluntary. Do not write your name on this questionnaire. You have been assigned an anonymous participant number. Instructions 5. By filling out and returning this questionnaire, you are providing consent for your responses to be used in Dr. Stephen Sheppard and Mr. Jon Salter’s University of British Columbia research project, entitled: “Assessing Mental Models of the Energy System at the Municipal Scale: Energy Experts, Decision Makers, and Citizens”. 6. After filling out this survey, please return it to the provided envelope, and return the envelope with both surveys to the provided drop-off container before leaving the forum. 7. This questionnaire is not a test of your knowledge - there are no right or wrong answers. We are interested in how you think about energy and greenhouse gas emissions, how they relate to your daily life, and to the City of Vancouver. To ensure the quality of the results, we urge you to answer the questions as completely as possible. If you want to add more information about any question please feel free to do so. Space is provided on the last page. 8. If you do not understand a question, or don't have an answer, feel free to answer 'don't know' and move on to the next question. 567 Section One The questions in this section ask you about the workshop itself. They are intended to help us understand what was useful or not useful about the workshop and its components. 1. Please rank the following components of today’s workshop from 1-4, with 1 being the most helpful component, and 4 being the least helpful component. a) Group facilitators b) Presentations c) Interactive exercises d) Opportunity to discuss energy issues with fellow citizens 2. Please rank the following media used in the workshop from 1-5, with 1 being the most useful media, and 5 being the least useful media. a) Presentation slides b) Neighbourhood visualizations c) Numbers – outputs from the energy modeling d) The strategy cards from the last exercise e) Drawing paper and sticky notes from the interactive exercises 3. Is there something you found particularly helpful about today's workshop? If you answer yes, please briefly explain what you thought was helpful. 568 4. Is there something you found particularly difficult, or unhelpful about today’s workshop? If so, please describe what was difficult, and why. 5. One of the things we tried in today's workshop was the use of a generic neighbourhood (‘the sandbox’) to discuss the energy system, instead of focusing on your own neighbourhood. Do you think the use of this generic neighbourhood worked? Why, or why not? Section Two The questions in this section ask you to revisit the issue of energy in your community after participating in the workshops. Some of the questions are similar to questions you answered in the pre-workshop questionnaire, but there are also some new questions. Remember, there are no right or wrong answers. 12. Did you learn anything today about energy that you found surprising? If so, please briefly explain what you found surprising. 569 13. How would you rate your knowledge of the energy system (where energy comes from, how it moves, and how it is used) in your community? Please circle the most appropriate answer. p) I have very little knowledge of the energy system in my community q) I have some knowledge of the energy system in my community, but not a lot r) I would say I have an average level of knowledge about the energy system in my community s) I have a fair amount of knowledge about the energy system in my community t) I would consider myself an expert on the energy system in my community 14. Please rank the following energy users in your community from 1 to 5, with 1 being the largest user of energy in your community, and 5 being the smallest user of energy in your community. a) Industrial users like factories b) Transportation users, like cars, trucks and buses c) Residential users like houses and apartments d) Commercial users like shops and businesses e) Municipal users like police, fire and other City services. 570 15. Please rank the following energy sources to your community from 1 to 7, with 1 being the largest source of energy used in your community, and 7 being the smallest source of energy used in your community. a) Gasoline and diesel b) Electricity c) Natural Gas d) Propane f) Solar (hot water or electricity) g) Geothermal h) Heating oil 16. Do you think that the sources of energy for your community are going to change in the future (let’s say by 2050)? If so, please briefly state how you think the energy sources in your community will change. 571 17. Please rank your top 5 approaches for addressing energy concerns in your community, with 1 being the method with the largest potential impact, and 5 being the method with the smallest potential impact. a) Increasing public transportation options b) Increasing local sources of energy such as solar c) Decreasing the size of people’s residences d) Making technical changes, such as energy retrofits on existing buildings e) Changing individual’s energy behaviours f) Collective options, such as district energy systems g) Providing incentives for people and businesses to conserve energy 18. Can you think of other ways, not listed in question 6, that your community could address energy concerns in the future? Please briefly explain any ideas that you have. 19. Please briefly explain how you think that planning for energy and greenhouse gas reductions will have either a positive or negative impact on what you enjoy about your community. 572 Section Three The questions in this section ask for general information about who you are. These workshops are part of the Greenest City Conversations Project that is looking at different ways to engage with people on sustainability issues. Filling out this section will allow us to make comparisons between engagement activities based on who participates in them. 1. First three characters of your home postal code 2. Have you participated in other Greenest City Conversations Project activities? a. no b. visited the website c. joined the FB group d. attended an arts event e. participated in a workshop f. participated in mobile device activity 3. Age a. under 19 b. 19-24 c. 25-44 d. 45-64 e. 65-74 f. over 75 4. Sex a. Male b. Female c. Other 573 5. Household income range a. under $20000 b. $20,000 - $34,999 c. $35,000 - $49,999 d. $50,000 - $64,999 e. $65,000 - $79,999 f. above $80000 g. prefer not to say 6. In the past 12 months have you (check all that apply): a. Contacted a politician or a local government official about environmental issues b. Signed a petition regarding sustainability issues c. Donated money to an environmental organization or group d. Boycotted certain products for environmental reasons e. Deliberately bought certain products for environmental reasons f. Volunteered to work on a community project related to an environmental issue g. Talked to people to change their mind about an environmental issue h. Talked to people to learn more about an environmental issue i. Been part of a demonstration or protest for an environmental issue 7. Please use this scale to rate your use of the following communication media. For questions 7 a) and b), you will be using this scale to rate how often you use certain media and technologies: 1 – Never 2 – Rarely 3 – Occasionally 4 – Regularly 5 – Frequently 6 – Very Frequently 7 – Constantly 574 a. Rate how often you use the following technologies (for any task): a) Smart Phone b) Tablet PC c) E-Book Reader d) Home Gaming Console e) Personal Computer f) Other technology If you stated f) Other technology, please describe it briefly: ________________________________________________________________ b. Rate how often you use the following media or services: a) Television b) Radio c) Twitter d) Social Networks e) Virtual Worlds f) Digital Games g) Internet (web browsing) h) Email i) Texting j) Blog 8. How did you find out about this activity or event? a) An organization b) Friends c) The City of Vancouver d) Through another GCCP activity e) Advertisement f) Other (specify): _________________ 575 9. What is your cultural background (check all that apply): _____ First Nations / Aboriginal _____ Caucasian / European _____ Latino / Hispanic _____ African _____ South Asian (e.g. India, Pakistan, Sri Lanka) _____ East Asian (e.g. China, Japan, Korea) _____ South East Asian (e.g. Philippines, Indonesia, Thailand) 10. Were you born in Canada? Yes____ No _____ If “No”, what year did you move to Canada? ________ 11. What language do you normally speak at home? English _____Other _____ If other, indicate which language do you speak at home? _________________ 12. What is the highest level of education you completed? a. Part of elementary school b. Completed elementary school c. Part of high school d. Completed high school e. Some college or university f. Received a college or technical school certificate g. Received a university bachelor's degree h. Some postgraduate training j. Received a postgraduate university degree Thank you for participating in this research project. Your participation makes this research possible. 576 Additional space is provided below for you to add any additional comments. When you have completed the questionnaire to your satisfaction, please return it to the provided envelope with the entry survey, and return the envelope to a workshop facilitator. Additional Space for Comments: 577 Appendix C GCCP Workshop Materials Figure C.1: The worksheet for the GCCP workshop's 'Name Your Neighbourhood' exercise. 578 Figure C.2: The Integrated Land Use Scenario worksheet from the GCCP workshops. 579 Figure C.3: The Technology Scenario worksheet from the GCCP workshops. 580 Figure C.4: The Behavioural/Cultural Scenario worksheet from the GCCP workshops. 581 Figure C.5: The Integrated Land Use strategy cards from the GCCP workshops (front left, back right). 582 Figure C.6: The Technology strategy cards from the GCCP workshops (front left, back right). 583 Figure C.7: The Behavioural/Cultural strategy cards from the GCCP workshops (front left, back right).
Thesis/Dissertation
eng
Resource Management and Environmental Studies
Vancouver : University of British Columbia Library
Attribution-NonCommercial-ShareAlike 2.5 Canada
Graduate
Energy in place : a case study and mental models analysis of engagement in community scale energy planning
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