Applied Science, Faculty of
Engineering, School of (Okanagan)
DSpace
UBCO
Alghamdi, Abdulaziz Saeed M.
Doctor of Philosophy - PhD
University of British Columbia
Canadian higher education institutions (HEIs) consume 60% of the total energy supplied to the educational sector, which ultimately contributes to significant greenhouse gas (GHG) emissions. In addition, HEIs consume a large amount of water for indoor use and research purposes. Existing tools developed for the sustainability assessment of HEIs, cover generic aspects such as operations, engagement, and innovation but often overlook key environmental accounting aspects like water, energy, and carbon (WEC) flows. Furthermore, academic buildings are the largest infrastructure units in HEIs and are sub-grouped under the commercial and institutional (C&I) buildings in Canada. HEIs buildings are intrinsically different from other C&I buildings in terms of energy and water consumption patterns. There is a dire need to assess the sustainability performance of HEIs and academic buildings as critical infrastructure units. This research aims to develop a framework for sustainability performance benchmarking of HEIs in terms of WEC flows. First, an inter-university benchmarking method was proposed to classify and benchmark 34 HEIs across Canada. The results indicate that small-sized HEIs perform better on average than their medium and large-sized counterparts. Furthermore, the size-based classification of the HEIs better justifies the relationship between the flows of energy and GHGs. A strong correlation coefficient of 0.97 between energy consumed and associated GHG emissions was observed for small-and-medium-sized HEIs. Later, an intra-university benchmarking was performed on 71 academic buildings using fuzzy clustering techniques to classify academic buildings. To account for the uncertainties in partitioning, the fuzzy c-mean algorithm was employed to classify the buildings based on the normalized WEC performances. The findings show that building activities such as laboratory intensive buildings are among the least sustainable as a cluster of buildings. The fuzzy clustering technique assists in highlighting the intrinsic characteristics and activities associated with the least performing buildings. Finally, a probabilistic fuzzy synthetic evaluation method was proposed to address aleatory and fuzziness (or vagueness) uncertainties raised in the intra-university benchmarking. The findings show that 32% of the buildings were classified into the most sustainable class during the summer, while in the winter season, none of the buildings were classified as most sustainable, indicating that heating significantly affects the energy performance of buildings. The proposed spatiotemporal analysis helps identify external variations such as the seasonal and occupant variations that affect the environmental performance in HEIs. The uncertainty analysis contributes to addressing commonly overlooked uncertainties associated with benchmarking techniques. The deliverables of this research identify the least performing HEIs in terms of WEC flows. The methodology proposed will improve communicating, setting targets, and improving the sustainability performance of HEIs.
PERFORMANCE ASSESSMENT AND BENCHMARKING OF HIGHER EDUCATION INSTITUTIONS AND ACADEMIC BUILDINGS IN CANADA: A SUSTAINABILITY PERSPECTIVE by Abdulaziz Saeed M. Alghamdi M.Eng., McMaster University, Canada 2012 B.Sc. Eng., King Abdulaziz University, Saudi Arabia 2005 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR IN PHILOSOPHY in THE COLLEGE OF GRADUATE STUDIES (Civil Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) October 2021 ©Abdulaziz Saeed M. Alghamdi, 2021 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, the dissertation entitled: Performance Assessment and Benchmarking of Higher Education Institutions and Academic Buildings in Canada: A Sustainability Perspective Submitted by Abdulaziz Saeed M. Alghamdi in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Civil Engineering Examining Committee: Rehan Sadiq, Ph.D. (SoE, UBC Okanagan) Supervisor Kasun Hewage, Ph.D. (SoE, UBC Okanagan) Co-supervisor Sumi Siddiqua, Ph.D. (SoE, UBC Okanagan) Supervisory Committee Member Shahria Alam, Ph.D. (SoE, UBC Okanagan) Supervisory Committee Member Kevin Hanna, Ph.D. (EEGS, UBC Okanagan) University Examiner Vikram Garaniya, Ph.D. (University of Tasmania) External Examiner iii Abstract Canadian higher education institutions (HEIs) consume 60% of the total energy supplied to the educational sector, which ultimately contributes to significant greenhouse gas (GHG) emissions. In addition, HEIs consume a large amount of water for indoor use and research purposes. Existing tools developed for the sustainability assessment of HEIs, cover generic aspects such as operations, engagement, and innovation but often overlook key environmental accounting aspects like water, energy, and carbon (WEC) flows. Furthermore, academic buildings are the largest infrastructure units in HEIs and are sub-grouped under the commercial and institutional (C&I) buildings in Canada. HEIs buildings are intrinsically different from other C&I buildings in terms of energy and water consumption patterns. There is a dire need to assess the sustainability performance of HEIs and academic buildings as critical infrastructure units. This research aims to develop a framework for sustainability performance benchmarking of HEIs in terms of WEC flows. First, an inter-university benchmarking method was proposed to classify and benchmark 34 HEIs across Canada. The results indicate that small-sized HEIs perform better on average than their medium and large-sized counterparts. Furthermore, the size-based classification of the HEIs better justifies the relationship between the flows of energy and GHGs. A strong correlation coefficient of 0.97 between energy consumed and associated GHG emissions was observed for small-and-medium-sized HEIs. Later, an intra-university benchmarking was performed on 71 academic buildings using fuzzy clustering techniques to classify academic buildings. To account for the uncertainties in partitioning, the fuzzy c-mean algorithm was employed to classify the buildings based on the normalized WEC performances. The findings show iv that building activities such as laboratory intensive buildings are among the least sustainable as a cluster of buildings. The fuzzy clustering technique assists in highlighting the intrinsic characteristics and activities associated with the least performing buildings. Finally, a probabilistic fuzzy synthetic evaluation method was proposed to address aleatory and fuzziness (or vagueness) uncertainties raised in the intra-university benchmarking. The findings show that 32% of the buildings were classified into the most sustainable class during the summer, while in the winter season, none of the buildings were classified as most sustainable, indicating that heating significantly affects the energy performance of buildings. The proposed spatiotemporal analysis helps identify external variations such as the seasonal and occupant variations that affect the environmental performance in HEIs. The uncertainty analysis contributes to addressing commonly overlooked uncertainties associated with benchmarking techniques. The deliverables of this research identify the least performing HEIs in terms of WEC flows. The methodology proposed will improve communicating, setting targets, and improving the sustainability performance of HEIs. v Lay Summary Reporting systems that provide the status of sustainability in universities and colleges focus on many factors that include social, environmental, and financial. Moreover, these tools fail to highlight the progress status of environmental sustainability in higher education institutions (HEIs), especially in the context of academic buildings. This research aims to help HEIs across Canada meet environmental commitments and emission reduction targets. It proposes a methodology for comparing sustainability in universities in terms of water, energy, and carbon flows. The framework consists of an industry-level performance benchmarking to rank 34 Canadian HEIs, and a method proposed to benchmark the performance of 71 academic buildings. And finally, a method is proposed to address uncertainties commonly involve in benchmarking techniques. This research includes classifying universities based on their total enrollment into small, medium, and large-sized universities. Later, the buildings are grouped into homogenous groups based on their water, energy, and carbon flows. Finally, a methodology to help address the uncertainties associated with benchmarking practices is proposed. This research aims to help higher education institutions across Canada to meet environmental commitments and emission reduction targets. By providing a pragmatic method to manage the adverse impacts associated with the water, energy, and carbon flows, this research helps decision-makers assess the environmental progress made in HEIs. Research findings enhance community engagement by providing tools to enable institutions to shift towards a sustainable campus. vi Preface I, Abdulaziz Saeed M. Alghamdi, developed the research concept presented in this thesis and conducted relevant analyses. This research was conducted under the academic supervision of Dr. Rehan Sadiq and Dr. Kasun Hewage, who reviewed the manuscripts provided critical feedback, and suggestions. Three journal articles have been published from the thesis research: 1. Alghamdi, A., Haider, H., Hewage. K., & Sadiq, R. (2019). Inter-University Sustainability Benchmarking for Canadian Higher Education Institutions: Water, Energy, and Carbon Flows for Technical-Level Decision-Making. Sustainability, 11(9), 2599 https://doi.org/10.3390/su11092599 2. Alghamdi, A., Guangji, H., Haider, H., Hewage, K., & Sadiq, R. (2020). Benchmarking of Water, Energy, and Carbon Flows in Academic Buildings: A Fuzzy Clustering Approach. Sustainability, 12(11), 4422 https://doi.org/10.3390/su12114422 3. Alghamdi, A., Guangji, H., Chhipi-Shrestha, G., Haider, H., Hewage, K., Sadiq, R., (2021). Investigating Spatiotemporal Variability of Water, Energy, and Carbon Flows A Probabilistic Fuzzy Synthetic Evaluation Framework for a Higher Education Institutions. Environments, 8(8), 72 https://doi.org/10.3390/environments8080072 vii Table of Contents Abstract ......................................................................................................................... iii Lay Summary ................................................................................................................. v Preface .......................................................................................................................... vi Table of Contents ........................................................................................................ vii List of Tables ................................................................................................................ xi List of Figures ............................................................................................................ xiv List of Symbols ........................................................................................................ xviii List of Abbreviations .................................................................................................. xix Acknowledgements .................................................................................................. xxiii Dedication .................................................................................................................. xxv Introduction ................................................................................................. 1 Background and Motivation............................................................................... 1 Research Gaps ................................................................................................. 6 Research Objectives ......................................................................................... 9 Thesis Organization ........................................................................................ 10 Meta Language ............................................................................................... 11 Research Methodology ............................................................................ 13 Literature Review ...................................................................................... 17 Higher Education Institutions (HEIs) ............................................................... 17 Declarations .................................................................................................... 27 Sustainability Reporting within HEIs ............................................................... 36 3.3.1 Global Reporting Initiative: A Generalized Approach .................................. 37 viii 3.3.2 Reporting in HEIs: A Customized Approach ................................................ 39 Selecting Indicators ......................................................................................... 50 Benchmarking ................................................................................................. 54 3.5.1 Inter-University Benchmarking .................................................................... 61 3.5.2 Intra-University Benchmarking .................................................................... 62 Classification and Clustering of Academic Buildings ....................................... 63 Trend Analysis ................................................................................................ 70 Decision Making .............................................................................................. 81 Uncertainty Analysis ....................................................................................... 83 Inter-University Benchmarking ............................................................... 84 Background ..................................................................................................... 84 Methods and Procedure .................................................................................. 90 4.2.1 Data Collection ............................................................................................ 91 4.2.2 Classification ............................................................................................... 95 4.2.3 Prediction Module ........................................................................................ 95 4.2.4 Normalization of Factors Affecting WEC Flows ........................................... 96 4.2.5 Ranking of HEIs based on WEC Flows ..................................................... 100 Results and Discussion ................................................................................. 101 4.3.1 Regression Models .................................................................................... 101 4.3.2 Ranking ..................................................................................................... 112 Summary....................................................................................................... 124 Intra-University Benchmarking ............................................................. 127 Background ................................................................................................... 127 ix Methods and Procedure ................................................................................ 128 5.2.1 Framework Development .......................................................................... 128 5.2.2 Case Study ................................................................................................ 130 Results and Discussion ................................................................................. 136 5.3.1 Benchmarking of Water, Energy, and Carbon Flows ................................. 136 5.3.2 Fuzzy Clustering ........................................................................................ 140 Summary....................................................................................................... 149 Spatiotemporal Variability of WEC Flows in HEIs ............................... 153 Background ................................................................................................... 153 Method and Procedure ................................................................................. 156 6.2.1 Framework ................................................................................................ 156 6.2.2 Evaluation of Water, Energy, and Carbon Emissions ................................ 157 6.2.3 Probabilistic Assessment........................................................................... 161 6.2.4 Fuzzy-based Assessment ......................................................................... 162 6.2.5 Case Study ................................................................................................ 168 Results and Discussion ................................................................................. 169 6.3.1 Water Use, Energy Use, and Carbon Emission Status ............................. 169 6.3.2 Probabilistic Assessment........................................................................... 178 6.3.3 Fuzzy-based assessment .......................................................................... 180 6.3.4 Ranking of buildings .................................................................................. 187 Summary....................................................................................................... 188 Conclusions and Recommendations .................................................... 191 Summary and Conclusions ........................................................................... 192 x Originality and Contributions ......................................................................... 193 Limitations ..................................................................................................... 195 Future Research ........................................................................................... 196 Bibliography .............................................................................................................. 198 Appendices ................................................................................................................ 223 Appendix A STARS ................................................................................................. 223 Appendix B Data from the STARS database ........................................................... 225 Appendix C Regression Parameters ........................................................................ 227 Appendix D Buildings at UBCO ............................................................................... 234 Appendix E Spatiotemporal parameter .................................................................... 235 xi List of Tables No table of figures entries found. Table 3.1: Top 21 Canadian GHG emitting HEIs in 2019. ............................................. 19 Table 3.2: Declaration addressing sustainability in higher education institutions adapted from Caeiro et al. (2013). .............................................................................................. 34 Table 3.3: Differences between reporting methods. ...................................................... 37 Table 3.4: HEIs participants per STARTS version report per year. ............................... 41 Table 3 5: Sustainability reporting systems. .................................................................. 41 Table 3.6: Reporting systems comparison from adapted from Shi et al. (2013). ........... 43 Table 3.7: Benefits and barriers to benchmarking. ........................................................ 58 Table 3.8: Types of benchmarking adapted from Elmuti & Kathawala (1997). .............. 60 Table 3.9: EUI in educational buildings. ........................................................................ 64 Table 3.10: Canada emission trend. ............................................................................. 71 Table 3.11: Estimated GHG trend results for the province of BC. ................................. 76 Table 3.12: Comparison between rating reports and performance in GHG reductions. 77 Table 3.13: UBC trend analysis results. ........................................................................ 80 Table 3.14: Second Nature carbon neutrality. ............................................................... 80 Table 4.1: Higher education institutions data. ............................................................... 93 Table 4.2: Statistical summary for the collected data. ................................................... 94 Table 4.3: Anderson-Darling Normality Test and the Johnson Transformation. .......... 101 Table 4.4: Johnson Transformation for the selected parameters. ............................... 107 Table 4.5: Linear regression coefficient. ...................................................................... 109 Table 4.6: Water consumption results. ........................................................................ 110 Table 4.7: Linear regression coefficients. .................................................................... 110 xii Table 4.8: GHG emission results. ............................................................................... 111 Table 4.9: Data and results of the University of British Columbia (UBCV). ................. 113 Table 4.10: Ten bottommost underperforming HEIs with their sources of fuels. ......... 115 Table 4.11: Empirical constants for power functions between floor area of HEIs buildings and WEC flows. ............................................................................................ 117 Table 5.1: Distribution of energy source for building heating in the UBCV. ................. 132 Table 5.2: Carbon emission factor by energy source. ................................................. 133 Table 5.3: Metabolic flow data for the building Alumni Centre, UBC. .......................... 133 Table 5.4: Cumulative distribution of functional benchmarking. .................................. 138 Table 5.5: A detailed fuzzy clustering analysis results. ............................................... 141 Table 5.6: Cluster centroids. ....................................................................................... 143 Table 5.7: Statistical summary of the cluster results. .................................................. 146 Table 5.8: Benchmarking results. ................................................................................ 146 Table 6.1: Random Indexes by Raharjo et al. (2006) .................................................. 167 Table 6.2: Nine-point intensity scale developed by Saaty (1980). ............................... 167 Table 6.3: Carbon-Sequestration parameters and results. .......................................... 177 Table 6.4: Fuzzy classes and their corresponding percentiles .................................... 179 Table 6.5. AHP weights and the scenarios. ................................................................. 181 Table 6.6. Temporal classification of the seasons. ...................................................... 182 Table 6.7. Spatiotemporal fuzzy classes of all the data. .............................................. 183 Table 6.8: Seasonal variation results Energy preference Scenarios. .......................... 183 Table 6.9: Seasonal variation results Carbon preference scenarios ........................... 184 Table 6.10. The three scenario ranks. ......................................................................... 187 xiii Table A1: groups/subgroups and points awarding system of the STARS reporting system obtained from the STARS technical manual ................................................... 223 Table B1: Data collected for Canadian HEIs in the STARS database ......................... 225 Table B2: The data collected for the HEIs in Canada from the STATS database ....... 225 Table C1: Regression coefficient for the area of buildings and water consumption .... 232 Table C2: Model summary for the area of buildings and water consumption .............. 232 Table C3: Regression coefficient for the area of buildings and energy consumption .. 232 Table C4: Model summary for the area of buildings and energy consumption ............ 232 Table C5: Regression coefficient for the area of buildings and GHG emissions ......... 233 Table C6: Model summary for the area of buildings and GHG emissions ................... 233 Table E1: Buildings investigated ................................................................................. 235 Table E2: Water calculation based on the area-ratio-method ..................................... 235 Table E3: Statistical summary of the calculated water values ..................................... 235 Table E4: Statistical summary of the reported EUI consumption ................................ 236 Table E5: Statistical summary for the CEI ................................................................... 236 Table E6: Probabilistic results and its statistical summary .......................................... 237 Table E7: Statistical summary for the EUI ................................................................... 237 Table E8: CEI results .................................................................................................. 238 Table E9: Conditional distribution by class .................................................................. 240 xiv List of Figures Figure 1.1: The organization of the thesis. .................................................................... 11 Figure 2.1: Phases of the research. .............................................................................. 16 Figure 3.1: Share of GHG emissions of the educational sector in Canada from 2007 to 2014. ............................................................................................................................. 22 Figure 3.2: Changes of GHG emissions in BC public sector from 2010 to 2018. .......... 23 Figure 3.3: NRC survey results of the education sector normalized energy consumption. ...................................................................................................................................... 26 Figure 3.4: Reports of HEIs in the STARS database per year. ..................................... 41 Figure 3.5: Reporting system comparison. .................................................................... 43 Figure 3.6: Summary of STARS size-based evaluation: a) distribution of reporting universities, b) performance of large-sized universities, c) performance of small- and medium-sized universities. ............................................................................................ 45 Figure 3.7: STARS scores vs. WEC scores averaged for 12 small-sized, 10 medium-sized, and 12 large-sized HEIs. ..................................................................................... 45 Figure 3.8: Number of indicators for residential neighborhoods. ................................... 51 Figure 3.9: Number and distribution of environmental sustainability indicators. ........... 52 Figure 3.10: The evolution of SD in HEIs. ..................................................................... 70 Figure 3.11: BC Public sector GHG performance from 2010-2018. .............................. 73 Figure 3.12: GHG for HEIs in BC. ................................................................................. 74 Figure 3.13: The province of BC GHG emission trend analysis (a) Trend line, (b) Residual plots. ............................................................................................................... 76 xv Figure 3.14: GHG trend analysis for the UBC, (a) Trend line analysis, (b) Residual plots. ...................................................................................................................................... 80 Figure 3.15: Integrated decision framework modified after Harmann et al. (2007). ....... 82 Figure 4.1: Primary energy use in a buildings life. ......................................................... 87 Figure 4.2: Inter-University Benchmarking Framework. ................................................ 91 Figure 4.3: Johnson Transformation for all the selected parameters. ......................... 107 Figure 4.4: Residual plots. ........................................................................................... 109 Figure 4.5: Parameter selection .................................................................................. 112 Figure 4.6: Sustainability ranking for all HEIs in Canada. The benchmark taken represents the overall average value. ......................................................................... 113 Figure 4.7: Relationship between energy consumed and GHG emissions for small-and-medium-sized HEIs in Canada. ................................................................................... 114 Figure 4.8: STARS ranking of Canadian HEIs by size. ............................................... 118 Figure 4.9: Comparison between STARS overall ranking and sustainability ranks based on WEC flows. ............................................................................................................. 119 Figure 4.10: Energy baseline targets and performance of Canadian HEIs based on STARS reporting. ........................................................................................................ 121 Figure 5.1: Fuzzy clustering-based environmental performance benchmarking of academic buildings. ..................................................................................................... 130 Figure 5.2: Box-plots of the metabolic flows ranges. ................................................... 137 Figure 5.3: The 50th and 75th percentile benchmarks for (a) water usage intensity (WUI), (b) energy usage intensity (EUI), (c) carbon usage intensity (CEI). ............................ 140 xvi Figure 5.4: within-cluster sum of square (WCSS) plot to select the number of clusters. .................................................................................................................................... 144 Figure 5.5: The layout of the three clusters. ................................................................ 145 Figure 5.6: GHG growth rate per province and the number of HEIs per province. ...... 148 Figure 5.7: Scalability of the framework. ..................................................................... 152 Figure 6.1: Probabilistic Fuzzy Synthetic Framework.. ................................................ 157 Figure 6.2. Triangular fuzzy membership functions. .................................................... 164 Figure 6.3. Fuzzification and Fuzzy memberships. ..................................................... 164 Figure 6.4: Student enrollment in the University of British Columbia (UBCO) in the past five years. .................................................................................................................... 168 Figure 6.5. Heating degree days and cooling degree days. ........................................ 169 Figure 6.6. Total campus water usage. ....................................................................... 170 Figure 6.7. The trend of water consumption. ............................................................... 171 Figure 6.8. Ratio-to-area and BEF box-plots. .............................................................. 172 Figure 6.9. Box-plots of EUIs calculated for different buildings on campus. ................ 174 Figure 6.10. Historic GHG emissions on the campus. ................................................. 175 Figure 6.11. Box-plots of the calculated CEI in the UBCO buildings. .......................... 176 Figure 6.12 Water-Energy-Carbon nexus of the supplied water at campus (a) water quantity, (b) energy from water usage, (c) carbon emission from the energy needed to use water at the campus level and its corresponding energy use. .............................. 179 Figure 6.13: Probabilistic Assessment distribution for the normalized WEC ............... 179 Figure 6.14. Fuzzy sets for the metabolic flows. ......................................................... 181 Figure 6.15: Temporal preference scenarios (a) Energy, (b) Carbon. ......................... 185 xvii Figure 6.16: Energy and Carbon preference scenarios classification per building. ..... 186 Figure 6.17: Results of scenario analysis, (a) Building per proposed class, (b) Academic buildings per class, (c) Residential buildings per class................................................ 187 No table of figures entries found. Figure C1: Parameter selection ................................................................................... 227 Figure C2: Residual plot for the WU regression .......................................................... 228 Figure C3: Regression plot for the WU regression ...................................................... 228 Figure C4: Confidence interval for the WU regression ................................................ 229 Figure C5: GHG parameter selection .......................................................................... 229 Figure C6: Interaction report for the CEI regression .................................................... 230 Figure C7: Residual plot for the CEI regression .......................................................... 230 Figure C8: Regression plot for the CEI regression ...................................................... 231 Figure C9: Residual plots for the area of buildings and water consumption. ............... 231 Figure C10: Residual plots for the area of buildings and energy consumption ............ 232 Figure C11: Residual plots for the area of buildings and GHG emissions. .................. 233 Figure D1: UBCO building usage of natural gas and electricity ................................... 234 xviii List of Symbols MU Metabolic Usage [ ]G U Utility of GHG [ ]A U Utility of Water Consumption [ ]Q U Utility of energy consumption [ ]S U Utility of the total heated area [ ]Q U Utility of climatic factors (HDD and CDD) iC Coefficient ijd Euclidean Distance ( )j ix Membership VI Validity Index sC Carbon sequestration iSOC Total sequestration measured iA Total Area /st ssC C Total carbon sequestration by trees and shrubs &t sN N Number of trees and shrubs nC Net carbon emissions EC Carbon emission related to energy usage sC Total sequestration by landscaping  Number of residential landscaping ( )A x Fuzzy set of X X Fuzzy universal set of variable x °C Degree Celsius xix List of Abbreviations AASHE Advancement of Sustainability in Higher Education ACUPCC American College and Universities Presidents’ Climate Commitments AHP Analytical Hierarchy Process AISHE Assessment Instrument for Sustainability in Higher Education AMAS Assessing Sustainability in Higher Education BC British Columbia BIQ Benchmarking Indicator Questions BTU British Thermal Unit BPIE Building Performance Institute of Europe BREEAM Building Research Establishment Environmental Assessment Method DOM Degree of Membership CDF Cumulative Distribution Function CEI Carbon Emission Intensity C&I Commercial and Institutional Buildings CO2 Carbon Dioxide CPI Continuous Performance Improvement CSR Corporate Social Responsibilities CSAF Campus Sustainability Assessment Framework CSRC College of Sustainability Reporting Card ECCC Environment and Climate Change Canada EF Ecological Footprint xx ELECTRE Elimination and Choice Translating Reality EUI Energy Usage Intensity FCA Fuzzy Clustering Analysis FCM Federation of Canadian Municipalities FSE Fuzzy Synthetic Evaluation FTE Full-time Equivalent GASU Graphical Assessment of Sustainability in Universities GHG Greenhouse Gas GM Green Metrics GRC Green Reporting Card GRI Global Reporting Initiative GDP Gross-Domestic Product GWP Global Warming Potential HDD & CDD Heating & Cooling Degree Day HEASC Higher Education Association Sustainability Consortium HEIs Higher Education Institutions HVAC Heating, Ventilation, & Air Conditioning IARU International Alliance of Research Universities IUESA Intra-University Environmental Sustainability Assessment IPCC Intergovernmental Panel on Climate Change LEED Leadership in Energy and Environmental Design MCDA Multi Criteria Decision Making xxi ECCC Ministry of Environment and Climate Change Canada MtCO2e Metric Tonnes of Carbon Dioxide Equivalence MF Metabolic Flows NRC National Research Council NGO Non-Profit Organizations PI Performance Indicators PPM Parts per Million SAQ Sustainability Assessment Questionnaire SCAS Sustainable Campus Assessment System SD Sustainability Development SMU Small and Medium-sized University SR Sustainability Reporting STARS Sustainability Tracking, Assessment and Rating System SUM Sustainable University Model TOPSIS Technique for Order Preference by Similarity to Ideal Solution UBC (V/O) University of British Columbia (Vancouver/Okanagan) UEMS University Environmental Management System UNCED United Nations Conference on Environment and Development USAT Unit-based Sustainability Assessment Tool VI Validity Index WRI World Reference Institute WEC Water, Energy, and Carbon xxii WSM Weighted Sum Method WUI Water Usage Intensity xxiii Acknowledgements I would like to acknowledge the support and guidance of my research supervisor Dr. Rehan Sadiq. This research would not have been complete without his strong mentorship. I am deeply thankful for all that he has done. He provided every support possible to ensure I operate without distraction. I am for that and much more very thankful. I want to extend my appreciation to my co-supervisor, Dr. Kasun Hewage, for his guidance, feedback, and meticulous attention to detail. Both professors helped me converge the ideas I had into world-class academic research. Their gratitude and inspiration helped me in ways I cannot express. I would also like to thank the committee members Dr. Sumi Siddiqua and Dr. Shahria Alam for their guidance throughout the years. I want to thank Dr. Husnain Haider, Dr. Guangji Hu, Dr. Mir Faizal, Dr. Leonard Lye, Dr. Haroon Mian, and Dr. Gyan Chhipi-Shrestha for their tremendous support and contributions that helped me in this research. I also want to acknowledge the School of Engineering administrative staff, Ms. Shannon Hohl, Ms. Stephanie Oslund, and Ms. Brittany Parr for always showing the utmost support when needed. The sustainability office at UBCO for helping me collect the data I needed. Mr. Colin Richardson was always supportive without hesitation. His character and enthusiasm are genuinely appreciated; I would like to extend my appreciation to Mr. Nikhil Agrawal, Mr. Derek Mahoney, & Mr. Glen McIntyre to contribute to the data collection process. The King Abdullah Bin Abdulaziz Scholarship program for funding this research. I am also thankful for the University Graduate Fellowship that was provided in the early stages of this research. xxiv To UBC for creating an ecosystem for ideas to flourish and minds to collaborate. My time here was fruitful spiritually, scientifically, and intellectually. To everyone who helped me along this journey. Your profound help, care, and guidance is greatly appreciated. To my colleagues Mohammad Alhashmi and Mohamed Hegazi for their encouragement, feedback, and long discussions throughout the years. Most importantly, I want to express my deepest gratitude to my father (Saeed), my mother (Sharifah), my siblings (Mohammed, Sarah, Amenah, Ammar, Razan, Seba, and Aseel), my wife (Lamesse), my children (Mariya, Omar, and Salma), and to Amr Alghamdi for all the support, affection, and care they showed. xxv Dedication This dissertation is dedicated first and foremost: To my parents Saeed and Sharifah For their enduring support. To my wife Lamesse, and to my children Mariya, Omar, and Salma Whose love, patience, and inspiration made this dissertation possible. 1 Introduction Background and Motivation Greenhouse gases (GHGs) are believed to be the primary contributor to global warming. Today the concentration of carbon dioxide in the atmosphere is at the highest level in the recorded times, exceeding 410 parts per million (ppm) [1]. This leads to different global warming phenomena, such as melting glaciers, heatwaves, sea-level rise, and ocean acidification. The recent increase in GHG emissions is mainly contributed by human activities such as energy generation using fossil fuels. To mitigate the adverse effects, an Intergovernmental Panel on Climate Change (IPCC) called on the world to reduce the release of anthropogenic emissions in the astrosphere to keep the rise in temperature within 1.5 °C from the pre-industrial baseline [2]. To meet this goal by 2030, the world is required to reduce emitting carbon in the atmosphere by 45% from the 2010 baseline level and gradually reduce it to reach a net-zero emission by 2050 [1]. In accordance with the Paris Accord 187, countries set voluntarily intended, nationally-determined contributions to curb emitting GHGs [3]. Countries like Canada pledged to reduce the emissions by 30% from the 2005 baseline level by 2030 [4]. To meet the goals of GHG emission reduction, Higher Education Institutions (HEIs) or universities (used interchangeably in this thesis) in different countries set their own goals to reduce GHG emissions. For example, in Canada, the University of British Columbia set its own goals to become carbon neutral by 2050 [5]. Energy generation is the most significant contributor to climate change. As of 2015, this sector is believed to be responsible for 42% of the global GHG emissions [6]. In Canada, the commercial & institutional (C&I) sector, which includes educational buildings, is the 2 third-largest electricity consumer. In 2017 this sector consumed 23.7% of the total electricity generated [7]. The building sector in Greece, for example, consumed 36% of the country’s energy consumption in the year 2011 [8]; In the UK, building energy usage accounts for 19% of UK’s emissions [9]; In Canada, the building sector is the third-largest GHG emitting source responsible for 12% of the nation’s total emissions [10]. Buildings emit the most significant GHGs during the operational phase over their life cycle [11-13]. As a result of high energy usage in the building sector globally, the building sector is believed to be responsible for 18.4% of the total GHG emissions [2,14]. Furthermore, the growth in energy consumption in the building sector is estimated to rise by 50% in the next three decades [15,16]. The associated increase of GHG emissions from the building sector due to energy consumption is alarming; for instance, Greece’s GHG emissions growth rate is at 4% per annum [8]. Energy consumption and GHG emissions are positively correlated. In the educational sector, a recent study showed a strong coefficient of correlation (i.e., R = 0.97) between energy consumption and GHG emissions in small- and medium-sized Canadian HEIs [17]. HEIs such as universities and colleges have been identified as one of the major sources of GHG emissions in urban settings [17]. In China, universities are the largest public sector contributing to the total GHG emissions by consuming 40% of the energy devoted to the public sectors [18]. Larsen et al. (2013) calculated the emissions per student at the Norwegian University of Technology and Science and found the emissions per student accounted for a substantial percentage of the total consumption per citizen. Furthermore, in terms of the investment-to-carbon ratio, universities have been found to spend more than most of the other service industries [19]. In Canada, HEIs consume 60% of the total 3 energy allocated to the educational sector [20], equivalent to approximately 430,000 Canadian households. Accordingly, HEIs have been recognized as one of the major contributors to GHG emissions in Canada [21,22]. They are also responsible for harmful impacts on the environment, such as water resource depletion. Brown and Southworth (2008) reported that buildings are responsible for 43% of the GHG emissions in the US [23]. Faulconbridge (2013) reported a 70% increase in anthropogenic gas emissions in urban areas like London compared to rural areas resulting from building operations [24,25]. The extent of the impact of this sector is the least understood to date [26]. A 2004 study on 351 HEIs, reported that i) academic buildings consumed 50 GJ of energy and emitted nearly 2.7 million MtCO2e; ii) universities on average consumed 2.04 GJ/m2; iii) colleges consumed 1.48 GJ/m2 [21]. Furthermore, this sector heavily depends on fossil fuels for its primary and secondary energy sources, with 65% of energy was supplied by natural gas and other fuels [21]. This is consistent with a report indicating that HEIs in China consume 30 million tons of standard coal to meet their energy demands [27]. Academic buildings consume more energy and water and release more carbon than other types of buildings on campus [28]. The educational sector in the US spends $7.5 billion on energy in a year; this cost is among the highest expenses the educational sector bears. Due to the limited resources, HEIs face challenges in implementing operational needs, preventative maintenance, and efficient interventions, which often leads to deteriorating equipment and results in high energy costs and environmental impacts [29]. A global increase in energy costs is another challenge faced by HEIs [15,30]. Universities generally own complex physical infrastructure, including buildings (educational, research, administrative, residential, etc.), green spaces, laboratories, 4 recreational facilities, and operational buildings (e.g., pumping stations, generator rooms). Based on the complexity of infrastructure and the number of occupants, the universities may act like small cities [31]. In terms of GHG emissions, universities are the largest emitters among the public sector around the world [32]. In addition, universities may pose other adverse environmental impacts on the surrounding ecosystem [33,34]. Among all types of buildings, the commercial and institutional buildings, which include higher education buildings, are classified as the most challenging due to their high energy consumption [35] and the uncertainties associated with the occupants’ energy use behavior [36]. For instance, it is reported that the number of users entering and exiting a university building in an hour is equal to the maximum occupancy of that building [37]; another study reported that 92% of the occupants in a university building are visitors [9]. Among many determinants that predict electricity consumption in schools were found to be area, age of the building, roof construction, and others [38]. It is believed that the energy demands in those buildings are the least understood among the non-domestic buildings due to the uncertainties in the number of occupants and the stochastic nature of occupants’ behavior with regards to energy consumption [36,39]. On the other hand, researchers have highlighted that building energy consumption is strongly connected to the building management system. Users (i.e., students) have a minor influence on the overall building energy consumption. Yet, they highlighted the importance of understanding occupants’ behavior as a means to help building managers apply better strategies and optimize energy performance [9]. To achieve sustainability in HEIs, uncertainties in the reporting methods should be addressed, and classification should be established to enable comparison between institutions within the same scope of work. 5 HEIs are mandated to educate citizens, engage communities, lead innovation, and serve as hubs for science and research [40]; moreover, they promote and implement sustainable development principles in their growth plans [41]. Due to the specific role of HEIs in fostering talent and educating future generations, it can be argued that universities should have the highest positive impact on the community than any other public sector [42-45]. Furthermore, universities significantly contribute to socio-economic development. For example, in Canada, 96 universities are home to 1.7 million students with a cumulative budget of $35 billion/year, which is ~2.79% of the nation’s gross domestic product (GDP) [46]; the universities also employ ~250,000 people [47]. The number of occupants in HEIs is constantly increasing in many countries around the world [46]. Enrollment in UK universities has increased by 33% over ten years from 1996/97-2005/06, the associated energy consumption has increased by a modest 2.7% between 2001-2006 [48,49]. In Canada, post-secondary institution enrollment increased by 60% between 2000–2019 [50]. In large-sized HEIs, higher water and energy demands are expected to contribute to more GHG emissions; however, they provide larger economies of scale than their smaller counterparts (e.g., reported as GHG emissions/student). For example, large HEIs can adopt a wide variety of sustainable solutions and technologies that small campuses cannot afford, e.g., biofuel generation as an alternate energy source. A large amount of waste is required. Smaller campuses can also face challenges of affordability of having skilled staff, though it is relatively easier to make decisions and implement strategies in smaller organizations [51]. Furthermore, programs designed to improve energy efficiency 6 in small- and medium-sized buildings do not substantially reduce the GHG emissions of these buildings. There are several reported reasons for this discrepancy, and one of them is due to the less participation of small- and medium-sized buildings [52]. There is no universally accepted standard to classify universities by the number of students. Therefore, there is a need to understand how classification in HEIs is attributed to consumption of water, energy, and carbon flows within HEIs. Research Gaps The literature review of the sustainability reporting in HEIs revealed several gaps in the current body of knowledge. These gaps are listed below. 1. Reductionist approach: There is a lack of technical-level approaches to classify and benchmark universities and buildings within universities in terms of water, energy, and carbon flows of HEIs. Sustainability is viewed as a multidisciplinary approach to meet the development of today without compromising the future generation’s ability to meet their own needs. To achieve this, many reporting systems use holistic approaches to report sustainability (i.e., social, economic, and environmental). These have failed to mimic sustainable development on campus (i.e., universities scored well overall and low in technical areas). This has led some researchers to emphasize the need to improve on holistic approaches by using reductionist approaches (i.e., through a singular dimension) [53]. There is a need to develop new methods to assess, benchmark, and track the sustainability performance of HEIs. To help decision-makers make better decisions on a HEIs performance. 2. Increasing energy consumption and GHG emissions in universities: 7 Universities are facing an uphill task of achieving sustainable and green campuses, are struggling to achieve self-imposed targets. Approximately 43% of the HEIs in Canada fall behind their targets in energy reductions [54]. This aligns with the UK data, where HEIs account for more than 5% of the non-domestic buildings and are responsible for roughly 14% of the emissions from the entire public sector. Similarly, approximately 2.7% increase in energy consumption has been observed in the UK during the years 2001–2006 [48]. Ward et al. (2008) reported that the UK HEIs have an average energy use intensity (EUI) of 293 kWh/m2 for the fiscal year 2001, and the number gradually decreased to 287 kWh/m2 by the year 2006, which is still substantially below the best practice benchmark of 162 kWh/m2. HEIs in the UK emitted 2.07 MtCO2e in 2005 and 2.16 MtCO2e the following year [48]. Altan et al. (2010) highlighted the need to establish clear carbon targets for HEIs in the UK [55]. HEIs are significant carbon emitters [56]; some HEIs are struggling to achieve emission reduction targets, and as a result are skeptical about existing actions of emissions reduction [57]. 3. Addressing growth: HEIs must address issues associated with increases in student number, electricity demand, and energy costs. The growth rates in these institutions are visible. In China, the total floor area of campus buildings grew five times between 1998 and 2011 [27]. In the UK, an increase of 33% in enrollment was found between 1996-2005 [49]. Similar trends are reported in Canada, where a 60% enrollment increase happened between 2000–2019 [50]. Aging buildings and the associated inefficient performances over time result in an additional set of uncertainties. Furthermore, emerging 8 regulations aimed at curbing carbon emissions will make it harder to set and achievable goals. The energy consumption performance deteriorates with time due to several building envelopes and HVAC units operating in HEIs [58]. Many HEIs buildings were constructed before energy codes were applicable [26]. It is reported that 55% of commercial building (including HEI buildings) projects before the construction did not model energy consumption in their design process and did not adhere to any code compliance or green certification [59]. Therefore, sustainability monitoring, reporting, and continuous improvements are needed in HEIs. These factors have a significant impact on an institution’s ability to set the targets needed to improve energy efficiency and reduce the associated impact in carbon emissions. These factors will limit the institution’s ability to maximize the benefit of the allocated resources needed to address the most critical problems. 4. Addressing uncertainties: HEIs sought to implement building rating systems such as the LEED to minimize the environmental impacts; however, studies have shown that many of these building rating systems fail to achieve their desired goals [60-62]. Agdas et al. (2015) investigated the EUI of 24 academic buildings in an HEI in the United States and found that Leadership in Energy and Environmental Design (LEED) certified buildings tend to use significantly higher energy than their counterparts at around 1044 kWh/m2 whereas, non-LEED buildings used 702 kWh/m2 [60]. Furthermore, Energy assessment of HEIs is challenging due to the uncertainties associated with the occupants' energy use behavior. Furthermore, floor area is among many prediction variables used to estimate energy consumption in HEI buildings [38]. Due to these 9 uncertainties, it is believed that energy demand in these buildings is the least understood among all non-domestic buildings [39]. Research Objectives The overall goal of this research is to develop a sustainability framework for performance assessment and benchmarking of higher education institutions and academic buildings. This research will focus only on the operational phase of academic buildings in HEIs. The specific objectives are: 1. Conduct a comprehensive and critical review of an academic and state-of-the-practice literature on-campus sustainability and its reporting mechanisms. 2. Develop an inter-university benchmark based on water, energy, and carbon flows (WEC) at comparable universities. 3. Establish an intra-university asset classification and benchmarking of WEC flows in academic buildings. 4. Propose and develop a spatiotemporal framework to address uncertainties in the WEC flows reporting for academic buildings. This research proposes a sustainability framework to help HEIs plan their transition towards reducing overall GHG emissions. The deliverables of this research will improve the communication of the sustainability status in HEIs, by improving the benchmarking practice in HEIs, and by addressing the uncertainties commonly associated with benchmarking techniques. This research will aid in decision-making by identifying the best and worst in class among HEIs. The outcomes of this research will enable HEIs to investigate the spatiotemporal factors that are associated with the least sustainability class in HEIs. 10 Thesis Organization As shown in Figure 1.1, this thesis consists of seven chapters, including an introduction, methodology, literature review, three main research chapters, and a conclusion chapter. Chapter 1: This chapter provides the background, motivation, research gaps, objectives and deliverables, and the proposed framework for this study. Chapter 2: This chapter provides the proposed methodology, including steps such as benchmarking, classification, and clustering, uncertainty analysis, and related techniques to achieve the objectives of this research. Chapter 3: This chapter provides a detailed literature review on the current status of HEIs declarations, reporting systems, benchmarking techniques, classification, and clustering methods. Chapter 4 to 6 presents detailed analyses addressing the overall goal, framework, methods used, solutions (results and discussions), and best practices and recommendations. Chapter 4: In this chapter, an Inter-University Benchmarking method is presented in terms of WEC utilization, and classification of HEIs is proposed. Chapter 5: In this chapter, an Intra-University benchmarking is performed on 71 buildings in two university campuses in British Columbia. Chapter 6: This chapter addresses uncertainty associated with benchmarking, and an integrated, probabilistic fuzzy synthetic benchmarking method is proposed. Chapter 7: In this chapter, the conclusion for the overall study is presented. Moreover, the originality, contributions, limitations, future work, and recommendations are also discussed. 11 Figure 1.1: The organization of the thesis. Meta Language This thesis contains specific technical vocabulary that is commonly used in the literature. To ensure consistent interpretation of the content in this thesis, the following technical terms are defined below: Benchmarking: Refers to the continuous process for evaluating and comparing services and products to facilitate improvements by determining best and worst in class. Indices and Indicators: Refer to the level of information carried out by an assessment or a reporting system. 12 Framework: Refers to the structure of the processes proposed. Sustainability Reporting: The act of communicating sustainable performance holistically. Methodology: A set of methods, rules, and postulates. Best practices: A set of best performers or best-in-class ranks. Classification & Clustering: The act of classifying and grouping something based on its attributes. Fuzzy logic: The technique applied to address linguistic uncertainty. Multi-Criteria Decision Making: The process of making informed and structured decision-making based on the consideration of multiple evaluation criteria. Probabilistic Assessment: The technique employed to address random uncertainty in the collected data. 13 Research Methodology This research aims to develop a framework for performance assessment and benchmarking of higher education institutions (HEIs) and academic buildings. Figure 2.1 shows the phases of the current research. The detailed methodology for each phase is shown in the corresponding chapters. The following is a summary of the methodology used in this research: ▪ First Phase: A literature review is conducted to summarize the chronological attempts for achieving a sustainable campus. Various declarations, sustainability reporting methods, and indicators used to assess the multi-dimensional status of sustainability are included in the scope of the review. This phase also includes data collection such as the water, energy, carbon (WEC), equivalency inputs, and climate inputs for universities across Canada. More detailed data were collected for the two campuses of the University of British Columbia. The data were collected from multiple sources to ensure the parameters and methods proposed can best represent the asset classes and enable the developed model to be applicable to other institutions worldwide. Moreover, the challenges associated with current sustainability reporting tools and benchmarking techniques were investigated to demonstrate the research needs in this study. The review is conducted in chapter three. ▪ Second Phase: An inter-university benchmarking method was developed to benchmark the performances of Canadian HEIs in terms of water use, energy consumption, and carbon emissions. This phase included the carbon equivalency calculations, statistical tests on the data to explore relationships between different 14 inputs, classifying HEIs in terms of WEC flows, developing a mathematical model to calculate utility functions and their coefficients, and finally benchmarking the HEIs in terms of environmental impacts and analyzing critical impact-contributing areas. The findings help in identifying the least performed HEIs. The detailed methodology is provided in Chapter 4, Section 4.2. ▪ Third Phase: Academics buildings are the most prominent infrastructure units in HEIs. Benchmarking academic buildings in HEIs faces two main challenges. The first challenge is the lack of an available national database required to compare and determine a set of best practices in academic buildings; the second challenge is relevant to the conventional benchmarking methods that may yield misleading benchmarking results. A fuzzy clustering analysis (FCA) is proposed to classify academic buildings and benchmark their environmental performance in WEC flows. By determining the environmental performance of academic buildings, the proposed fuzzy clustering-based method allows efficient resource allocation for buildings with low environmental performance. Moreover, the intra-university performance benchmarking method was applied to academic buildings in two UBC campuses. The buildings were classified into different performance groups with respect to their normalized water utilization, energy consumption, and carbon emissions. Finally, the buildings were ranked to determine the best and the worst in class and highlight the reasons for the underperformance. The detailed methodology for the proposed method is provided in Chapter 5, Section 5.2. ▪ Fourth Phase: A spatiotemporal probabilistic-fuzzy synthetic evaluation framework is proposed to address aleatory and epistemic uncertainties inherent in 15 benchmarking. Finally, scenarios based on student enrollment and seasonal variations in the data are investigated. The detailed methodology for the proposed method is provided in Chapter 6, Section 6.2. ▪ Fifth Phase: Is the compilation of the thesis. Three peer-reviewed papers have been published in a journal. Based on the main content covered in Phase 2, 3, and 4 of this research. 16 Figure 2.1: Phases of the research. 17 Literature Review Parts of this chapter have been published in the Sustainability journal as two articles titled “Inter-university sustainability benchmarking for Canadian higher education institutions: water, energy, and carbon flow for technical-level decision-making”; and “Benchmarking of water, energy, and carbon flows in academic buildings: a fuzzy clustering approach” Higher Education Institutions [17,28]. Higher Education Institutions (HEIs) HEIs such as universities and colleges are mandated to educate citizens, engage communities, lead innovation, and serve as hubs for science and research [40]. HEIs are among the largest public institutions in many countries around the world. Owing to the specific role in fostering talent, educating future generations, and shaping societies [63], universities are arguably considered organizations that have the highest positive impact on communities among various public sectors [42,44,45]. HEIs are also recognized as key drivers for the development of a sustainable society [64]. A recent study has examined sustainability in HEIs through a 7E framework that encompasses expenditure, existence, efficiency, effectiveness, economy, equity, and equality [65]. Universities also have significant socio-economic impacts on the surrounding communities. For instance, 96 Canadian HEIs with a cumulative budget of $35 billion/year employ around 250,000 people [47]; this amount is approximately 2.79% of the nation’s gross domestic production [46]. Furthermore, a study of 15,000 universities worldwide estimates that a 10% increase in a region's number of HEIs is associated with a 0.4% increase in future GDP per capital in that region. Universities impact the economy through innovation, human capital, and finally, through the support of debates, discussions, and 18 engagements [66]. For example, in a report published in 2017, the UK universities total economic activities equaled £95 billion for 2014-2015, supported 944,000 jobs, and generated £14.2 billion worth of tax for the government [67]. In the 21st century, sustainability development (SD) has been recognized as an essential concept in socio-economic and environmental entangled policies [68]. HEIs have been promoting and implementing sustainable development principles in their growth plans over the past few decades [41]. HEIs are assessing their overall sustainability in a broader context by situating the concepts of sustainability in all their functions, including academics (e.g., curriculum and research activities), engagement (e.g., employee orientation and community participation), planning and administration (e.g., participatory governance, diversity, and equity coordination), and operations (e.g., climate, buildings, energy, transportation, and water) [69]. Universities host complex physical infrastructure systems housing roads, water, and buildings that vary in function, operation, characteristics, and area. Large universities may therefore be regarded as small cities [31]. However, the performance of infrastructures in Canadian HEIs has yet to be adequately investigated. The National Science Foundation defined an engineered system as “a combination of components that work in synergy to collectively perform a useful function,” such as manufacturing processes, technologies to improve quality of service, and infrastructure systems [70]. In HEIs, engineered systems (e.g., infrastructure) and other activities (e.g., educational services, recreation and sports, and other extra-curricular activities) utilize immense natural resources to meet water and energy requirements and consequently emit carbon emissions. These operations are commonly associated with the adverse environmental impacts of GHG emissions that are 19 mainly created from its building stock. A report by the buildings performance institution of Europe (BPIE) in 2011 estimated that educational buildings ranked third in terms of building stock (17%) and use 12% of the total energy consumed by the building sectors across Europe [71]. In Canada, the Ministry of Environment and Climate Change Canada listed the top 9 emitting universities among the largest GHG emitting facilities in the country, as shown in Table 3-1 [72]. Table 3.1: Top 21 Canadian GHG emitting HEIs in 2019. No HEI GHG Emissions (kt CO2 eq) 1 University of Alberta 240,695 2 University of Calgary 71,494 3 York University 62,948 4 University of Saskatchewan 57,821 5 University of Toronto 57,460 6 The University of Western Ontario 53,848 7 Queen's University 46,996 8 University of Guelph 43,568 9 McMaster University 42,330 10 University of Waterloo 36,635 11 McGill Universty 33,234 12 University of Manitoba 31,855 13 University of Windsor 26,011 14 Brock University 22,965 15 Université Laval 22,807 16 University of Ottawa 19,842 17 Carleton University 19,319 18 Université de Montréal 18,480 19 University of Regina 14,260 20 University of New Brunswick 13,690 21 Fanshawe College of Applied Arts and Technology 2,988 In Canada, educational services have been sub-grouped under Canada’s largest activity category- the commercial and institutional (C&I) sector. Emissions from the Canadian C&I sector were predicted to increase 23% by 2020 from the 2011 reported values [73]. The C&I sector energy use increased 34% between 1990 and 2016. The commercial sector 20 uses 20.4% of the energy produced by natural gas in 2016 and 23.3% of the electricity [74]. The C&I sector consumed 10% of the secondary energy use in Canada and consequently emitted 9% of the nation’s GHG in 2013. The C&I sector energy consumption sector grew by 23% between 1990-2013, while the energy consumption in the residential sector grew by 7% during the same period; consequently, the growth in GHG emissions from the C&I sector grew by 4%, while the residential sector reduced GHG emissions by 9% during the same period [75]. The C&I sector is divided into several sub-sectors, which include retail, offices, health facilities, educational services, and a few others. The educational sector is the 3rd largest sub-sector in energy [76] and GHG emissions [22]. The educational services consumed 16% of the total energy supplied to the C&I sector at a staggering value of 134.7 petajoules in 2009. HEIs account for 61.4% of the total energy consumption by the educational sector; moreover, HEIs are among the most energy-intensive institutions within the C&I. The average EUI in 2009 of the C&I sector is 1.28 GJ/m2, and those of universities, colleges, and schools are 1.79 GJ/m2, 1.17 GJ/m2, and 0.82 GJ/m2, respectively [77]. These figures show improved energy efficiency compared to the 2003 figures shown in chapter 1 [21]. The energy consumption share of the C&I sector was 23.7%, with a total secondary energy consumption of 429.7 PJ as of 2017 [7]. The commercial sector saw a growth of 34% in energy consumption and a decrease of 8% in energy intensity. In comparison, the average growth in energy consumption in the residential, commercial, transportation, freight, and industrial sectors was 24%, while the average improvement of these sectors was 20% [74]. 21 The building sector in Canada comprises approximately 482,000 commercial and institutional buildings [77]. Energy consumed to feed these buildings results in nearly 111 MtCo2E, roughly 17% of the total country GHG emissions. Between 1990 and 2015, the total energy demand of C&I buildings increased by 34% compared to only an 8% increase in residential buildings; furthermore, the National Energy Board forecasts that energy demand will continue to increase through 2040. The area of the C&I buildings has grown by 50%, while EUI has reduced by 8% between 1990-2015; by 2030, the floor space for C&I buildings is projected to grow by 50%. GHG emissions of C&I buildings increased by 10% between 1990 and 2015, while residential buildings were reduced by 10% [78]. If this trend continues, these services will likely become the country’s largest emission-producing subgroup in the C&I category. For example, the education sector in British Columbia (BC) emitted 41.25% of the province's public sector emissions in 2018 [79]. Figure 3.1 shows the percentage of emissions from the educational services with respect to the entire C&I sector [20,80]. Most Canadian HEIs primarily rely on the national electricity grid, often produced by fossil fuels and associated with high GHG emissions. Therefore, HEIs are believed to have a significant contribution to the total national emissions in Canada. 22 Figure 3.1: Share of GHG emissions of the educational sector in Canada from 2007 to 2014. Globally, HEIs have been identified as one of the major sources of greenhouse gas (GHG) emissions in an urban setting. A study in the UK revealed that energy consumption increased by 2.7%, and the related carbon dioxide (CO2) emissions rose by 4.3% between 2005 and 2006. Overall, GHG emissions generated through HEIs in the UK soared from 1.78 to 2.05 Million tonnes of carbon dioxide equivalence (Mt CO2e) from 1990 to 2005 [48]. Similarly, in China, universities, as the largest public sector, consumed 40% of the energy used by the entire public sector [18,32]. Nevertheless, as universities proceed towards sustainability within their operations, the environmental dimension within sustainability is usually the least represented. For instance, in Portugal, a recent study found that the environmental dimension of sustainability is least represented after the social and economic dimensions of sustainability [81] HEIs face challenges in meeting the self-proposed reductions of GHG emissions and struggle to reduce energy usage beyond the baseline values reported. For example, the UBC set goals to reduce their emissions by 33% in 2015 and 67% by 2020, UBC was 10111213142007 2008 2009 2010 2011 2012 2013 2014% Contribution of GHG emissions23 only able to achieve a 28% reduction in its GHG by 2015 [17,82]; furthermore, the University of Alberta reported a 39% increase in their overall emissions between 2012/2013 and the baseline 2005/2006 [28,83,84]. In BC, the public sector is divided into five sections: the core government, schools, HEIs, crown, and health. Buildings emit nearly 80% of their entire lifecycle GHG emissions during their operational phase alone [11-13]. In BC, the reported GHG emissions inventory across the public sectors in BC varied, and universities on average achieved a reduction of roughly 8% over eight years, as shown in Figure 3.2 [85]; however, universities are still behind other public sectors in terms of energy use reduction. For instance, a recent study determined that 43% of the Canadian HEIs fell behind the baseline values of energy use [17,69]. Another study in the UK reported that energy and GHG-related emissions have increased in HEIs [48]. HEIs were reported to use 3-4.9 times more energy than schools [86]; thus, HEIs are critical organizations among the public sectors that require attention in GHG emission reduction. Figure 3.2: Changes of GHG emissions in BC public sector from 2010 to 2018. -31.4%-14.2%-7.9% -7.9%0.4%2.1%-32.0%-27.0%-22.0%-17.0%-12.0%-7.0%-2.0%3.0%Core Government Schools Total HEIs Crown Health24 The above studies depict an increase in energy consumption of HEIs, intangible improvements in achieving GHG commitments, and the common goal among universities to improve energy use efficiency. An ambitious GHG reduction goal calls for the sustainable development of HEIs. Addressing sustainable development is a crucial step to mitigate the environmental degradation caused by the growth of HEIs. In addition, owning to the specific role in fostering new generations, universities are looked upon to set an exemplary role leading sustainable development. HEIs exhibit external factors on the environment and exhibit internal factors accustomed to HEIs core functions such as teaching, researching, and educating the public. The innate nature of HEIs allows them to become facilitators of change entrusted by communities to lead, foster talent, and raise awareness of key discussions within their communities (e.g., sustainable development, environmental degradation, green campus, anthropogenic GHG emissions). In addition to their specific roles as education hubs for new generations and places for groundbreaking research, HEIs are key promoters of communications within their communities. Universities are reported to have a high moral responsibility towards enriching the discussion of a sustainable campus [44] and have a critical role to play in the progression of sustainable development in their communities due to their roles in knowledge-generating, shaping social and scientific paradigms, and influencing policy makers [87]. Therefore, HEIs are looked upon to lead communities by setting exemplary models of change within the institutional physical and metaphysical (pedagogical) boundaries [64,88]. Furthermore, HEIs are expected to be at the forefront of a sustainable development discussion [89] and serve as a bridge to link communities and the 25 advancement of human knowledge [90]. HEIs are arguably assumed to have a higher positive impact on their communities than any other public sector [42,44,45]. We can only reach a sustainable world if HEIs take a lead in demonstrating sustainability and promoting it within their campuses [91]. To reduce the associated emissions from a university’s operations, displacing fossil fuel usage and integrating renewable sources of energy in the university’s energy grid is an integral approach [92]. Energy itself is influenced by loads (i.e., HVAC systems), consumption patterns, climatic factors, and behavioral aspects challenging to quantify due to their stochastic nature [14,36]. Factors like users per building also play a significant role in determining the energy trend. The Energy Fact Book by the National Research Council highlighted some issues affecting energy consumption which include; activity (floor area), structure (change of the makeup in each sector), service level (auxiliary equipment), and energy efficiency [74]; for instance, it is reported that the number of users entering and exiting a university building in an hour is equal to the maximum occupancy that building [37]. Underpinning the occupancy behavior is highly challenging due to the random variability over time and due to the stochastic nature of occupants' behaviors [93]. Another critical factor that can increase the energy consumption of buildings is building age. Recently studies on different HEIs around the world reported contradicting information in this regard. A study found a correlation between the age of buildings and their energy consumption: newer buildings use less energy to meet the heating requirements [94]. However, another study has nullified this hypothesis (or has suggested that there is no relationship between building age and energy consumption) [36]. Moreover, studies have shown that climatic factors tend to correlate highly with energy 26 consumption [95]. Universities are expected to consume more energy than HEIs, due to their longer operational hours, higher loads (i.e., laboratory requirements), computer usage by students, and a higher number of students. A study reported that the energy consumption of buildings in HEIs across the US was greatly dependent on their climate regions [96]. The effect of climate on buildings’ energy consumption adds challenges in benchmarking the building performances and understanding the factors that greatly affect a building's performance. Buildings are responsible for over a third of the global energy consumption and the release of GHG emissions. The emissions from the building sector have been doubled since the 1970s [2]. As previously mention [76]. The average area of commercial and institutional buildings is 1136 m2. The average educational building is 5394 m2 which may explain the higher need for energy in educational buildings, among other sectors in the commercial and institutional sector. Furthermore, universities are reported with a higher normalized EUI within the education sector, as shown in Figure 3.3 [20]. Figure 3.3: NRC survey results of the education sector normalized energy consumption. Buildings also use large amounts of water; for example, educational facilities in the US use approximately 6% of the total water consumption of the commercial and institutional 0.821.171.79-0.150.350.851.351.85Primary and SecondarySchoolColleges UniversitiesEUI GJ/m227 facilities in the country [97]. Water flows are, in most cases, the least investigated performance of buildings, although some studies investigated the correlation between energy consumption and water flows in buildings [98]. Universities worldwide are committing to a timescale to reduce the dependency on fossil fuels [99]. Nevertheless, many universities cannot meet the challenge. This can be due to insufficient resources for adopting renewable energy sources and energy conservation measures to decrease funding [100]. Other barriers could be related to the business models used in HEIs. Thus there is a need to improve the decision-making process in universities to help them better meet sustainable development goals [99]. HEIs are complex physical infrastructures housing different types of buildings that vary in scope, operation, characteristics, and area with the complexity of the infrastructure and the number of occupants. HEIs may therefore be regarded as a small city [31]. Thus, HEIs are an integral part of modern society and the global landscape, contributing to high consumption and waste generation [101]. This implies a need to assess, manage, and improve the utilization of flows within a university to achieve sustainable development. For sustainability performance evaluation of HEIs, the first step is to review how HEIs began to address sustainability from the inception to making a meaningful change reflected in them. Declarations Attesting to declarations is the first noticeable momentum HEIs acted upon to deal with the plethora of attempts to define, initiate, engage, and discuss sustainability within HEIs. These declarations cover a holistic nature of sustainability issues and non-statutory, self-imposed, and non-binding memorandums. The first discussion was formally introduced 28 in principle 19 of the 26 principles of the Stockholm Declaration in 1972 [102]. Although the conference did not focus solely on sustainability in higher education, it did recognize the interconnection between the social and environmental degradation of the planet [87]. Moreover, in the United Nations 21st Agenda, universities were asked to reshape and engage communities in the sustainable development discussion. Universities, among other public sectors, were asked to consider: conducting systematic observations, developing national assessment and evaluation processes or programs, establishing necessary intersectoral and program linkages to determine the effects of environmental impacts on the ecosystem [41]. Many of the declarations have been endorsed and signed by HEIs. The first commitment towards sustainability in HEIs is the Talloires declaration [87]. The 1990 Talloires Declaration has over 275 signatories, and over 291 educational institutions have endorsed it [103]. As of 2005, The COPERNICUS is signed by over 320 HEIs [104]. The International Associate of Universities is signed by 800 HEIs and was pivotal to developing the Kyoto Declaration [105]. As of 2011, there were 31 declarations in the context of higher education, and 1400 universities have signed them [106]. Some of these declarations are the Stockholm Declaration in 1972, the World Declaration on Higher Education 1998, Barcelona Declaration in 2004, the Graz Declaration in 2005, the Sapporo Declaration in 2008, and the Lubeck Declaration in 2009 [107]. In HEIs, the sustainability concept was first introduced in the Stockholm Declaration in 1972 [102]. Later in 1990, the Talloires Declaration was signed by over 275 HEIs, which was the action plan that introduced sustainability and environmental literacy in HEIs. In 1992, the UNCED conference established the correlation between building and the 29 capacity for decision-making for sustainable developments, primarily in undeveloped nations [41]. The earth charter initiative in 2000 involved all levels of education institutions in discussions. The earth charter in 2000 inspired a new sense of global interdependence by identifying 16 broad-based principles, which were later applied to many organizations, cities, non-profit organizations (NGOs), and education sectors. The aim was to promote interdependence and shared responsibility for the wellbeing of the human and the larger living world [108]. In 2012, the Rio +20 declaration was signed by 78 universities, where the overwhelming majority were from Europe and only one university from North America. This declaration covers five aspects of a university: the greening of campuses, teaching sustainable development concepts, supporting sustainability efforts, fostering and engaging in international collaboration, and encouraging research on sustainable development issues [18]. The UNESCO launched a Global Action Plan in 2014 to generate and scale up actions in all levels and areas of education. This action plan identified five priorities: whole-institution approaches, policy support, youth, local communities, and educators. Overall, 272 universities signed the initiative and shared their action plans or achievements to address climate change, only 13% of North America [109]. More than 680 universities worldwide have signed the American college and university presidents’ climate commitment (AUPCC) agreement, requiring participating institutions to reduce greenhouse gas emissions [60]. Furthermore, the University Charta for Sustainable Development established the moral and ethical roles of HEIs, “…it is consequently their duty to propagate environmental literacy and to promote the practice of environmental ethics in society.”[104]. 30 The historical background of declarations on the sustainability of HEIs developed by different organizations around the globe is given in Table 3.2, modified from [42,87,110]. For several years, the emphasis of the declarations was on introducing, integrating, and implementing the concepts of sustainability in HEI curricula development, enhancing teaching techniques, securing research funding [111,112], promoting awareness in the general public [113], and developing regional and international networks to support the concept of sustainability in HEIs [111,114]. A review of the declarations revealed that the 2008 G8 University Summit Sapporo Sustainability Declaration (No 19, Table 3.2), held in Sapporo, Hokkaido (Japan), specified the principles that universities should develop sustainable (green) campuses as experimental models. The idea was appreciated in almost all subsequent declarations. As a result, universities, including Canadian HEIs, started to appraise the sustainability of their infrastructures in terms of natural resource utilization and associated environmental impacts. The declarations initiated essential questions for discussion such as what, how, and why sustainability should be a critical goal in universities [115]. It also helped shape a broad definition of a sustainable university and the role of a sustainable university towards its community. Out of the cradle of these declarations, a prominent definition of a sustainable university emerged as “… that through its guiding ethos, outlook and aspirations, governance, research, curriculum, community links, campus management, monitoring, and modus operandi seeks explicitly to explore, develop, contribute to, embody and manifest critically and reflexively the kind of values, concepts, and ideas, challenges and approaches that are emerging from the growing global sustainability discourse [116].” This definition is a generalized theme of sustainability in HEIs. It did not explicitly refer to 31 the direct and indirect impacts that HEIs bear on their communities in terms of resource utilization and underline the metabolic flows generated from the operations of a university (e.g., water, energy, and carbon). Whereas Velazquez et al. (2006) [117] define a sustainable university as “… a whole or as a part, that addresses, involves and promotes, on a global level, the minimization of negative environmental, economic, societal and health effects generated in the use of their resources in order to fulfill its functions of teaching, research outreach, and partnership, and stewardship in ways to help society make the transition to a sustainable life”. Declarations have different goals, agendas, and strategies with general themes of inclusion and encouragement, communication with the stakeholders, environmental degradation, ethical and moral obligations of HEIs towards sustainable development, and teaching environmental literacy [118]. The overall notion behind these declarations was to define sustainable developments in HEIs. Furthermore, working towards a better understanding of the general trends and frameworks that have emerged in sustainability in the context of HEIs [87]. Beyond the definition and moral obligations, it is unclear how these declarations aided the overall discussion. Wright (2002) described the implementation of these declarations as a “conceivable daunting and perhaps impossible task” [87]. On the one hand, the signing of a declaration does not lead to the implementation of it [106]; on the other hand, it is reported that these declarations have had a minor effect in translating the commitments to realities, beyond raising awareness which its unlikely to lead to improvements let alone meaningful change [119]. Accountability is a key issue that enables universities to commit to change. A declaration alone will not render a region carbon neutral or energy self-sufficient [120]. Furthermore, 32 the pace at which these declarations gained momentum is noticeably slow [106]; for example, the Talloires declaration received 20 signatories in 1990, and the number gradually increased to 275 HEIs by the year 2015 [87] and finally to 507 HEIs signatories as of March 2019 [121]. This number is relatively small compared to the overall number of HEIs worldwide (i.e., Valero conducted an economic impact on 15,000 universities in 78 countries in 2019 [66]). Although the declarations prompted HEIs to comply with the stated objectives, they were vague and did not measure the sustainability performance of HEIs at the operational level [122-124]. The declarations are voluntary and non-binding by nature, so some universities used these declarations to gain benefits; for example, the Royal Melbourne Institute of Technology is an example that appears to have benefited from signing declarations without achieving genuine progress [119]. It is also reported that beyond momentum, moral reasons, marketing, and communications, declarations in the context of HEIs also enabled universities to initiate the discussion around sharing information, assessing, and reporting performances [17,125]. This leads the path for researchers to request that HEIs must open themselves up to the scrutiny of progress to assess if the commitments signed have been fulfilled [119]. Sharp (2009) described the wave in which declarations have changed HEIs in a “piecemeal” fashion, and these commitments did not alter significant changes in the organizations; the commitments did not lead to an effective process that universities can follow and implement changes accordingly for improvement [126]. Roorda (2001) argued that the declarations are vague and do not measure the performance of universities at the operational level [122-124]. To solve this issue, the 33 2010 Environmental Performance Index suggested a broader tool to measure performances through the use of indicators [127]. Indicators are the primary tools used to cross-institutional comparison (i.e., against benchmarking) for many reporting systems. Martin (2005) stressed the need to develop techniques that will help in advancing sustainable development in universities by using indicators and suggested that Ecological Footprint (EF) could be a helpful approach for universities to report their performances [128]. The EF, developed by Wackernagel and Rees in 1996, is an aggregate of the different indicators defined by the area of land or number of students [129]. Many universities used this tool to assess their sustainability in terms of the acquired land, energy, and utility requirements, as well as expected GHG emissions [130]. The tool’s features enable it to have flexible modeling options and thus ideal for communication. However, the tool has some limitations, such as lack of standardization, no provision to include parameters like costs, lack of meaningful information to stakeholders which can be used in decision making process [124]. However, the EF is a helpful tool to communicate performances in terms of the environmental impact of a single unit, either based on land area or student number [131]. The issues with declarations raised questions regarding effective methods to measure and quantify the success (i.e., meeting the requirements in the declarations) and to compare cross-institutional performances of different dimensions of sustainability in a scoring scheme. Moreover, Grindsted (2011) argued that these declarations impacted the discussion in three distinctive ways. The declarations i) geared the discussion from a moral and ethical perspective; ii) developed a platform for legislation (e.g., the development of the Kyoto protocol via The International Association of Universities) [105]; 34 and iii) facilitate means to performance benchmarking among HEIs [106]. Therefore, declarations had an impact on universities in terms of three aspects. Firstly, it helped shape an argument of the role of a university in sustainable development [132,133]; secondly, because of HEIs’ role in shaping policies, these declarations are believed to have helped shape national legislation around resource utilization and the adverse impacts on universities [134], and thirdly, by paving the road towards the establishment of tools that help rank, assess, and communicate the progress of sustainability in an HEI context [17,106]. Collectively, the declarations helped raise the awareness needed and initiate the important discussion around means to measure, assess monitor, track, and benchmark universities. Grindsted argued that declarations could impact universities in three ways: i) an emerging sense to the universities’ role and function in terms of sustainable development, ii) the possibility of an international emerging legislation, and iii) competition among universities to drive intrinsic pressure so HEIs can perform better [106]. Furthermore, to achieve the last objective, inter-university benchmarking is needed to rank and classify universities. Table 3.2: Declaration addressing sustainability in higher education institutions adapted from Caeiro et al. (2013). No Year Declaration 1 1972 The Stockholm Declaration on the Human Environment UNEP 2 1977 Tbilisi Declaration UNESCO 3 1988 The Manga Charta of European Universities EUA 4 1990 University Presidents for a Sustainable Future: The Talloires Declaration ULSF 5 1991 The Halifax Declaration (International Institute for Sustainable Development) 6 1992 Agenda 21 Report of the United Nations Conference on Environment and Development UNCED 7 1993 Ninth International Association of Universities Round Table: The Kyoto Declaration [87] 8 1993 Association of Commonwealth Universities “fifteenth Quinquennial Conference: Swansea Declaration [87] 9 1994 CRE Copernicus Charter 10 1997 International Conference on Environment and Society –Education and Public Awareness for Sustainability: Declaration of Thessaloniki UNESCO 35 11 1998 World Declaration on Higher Education for the twenty-first century: Vision and action UNESCO 12 2000 Earth Charter 13 2001 Luneburg Declaration UNESCO 14 2002 Ubuntu Declaration UN 15 2002 Johannesburg Plan of Implementation World Summit on Sustainable Development WSSD 16 2005-2014 The UN Decade Education for Sustainable Development UNESCO 17 2005 Graz Declaration on Committing Universities to Sustainable Development 18 2006 Declaration on the Responsibility of Higher Education for a Democratic Culture Citizenship, Human Rights and Sustainability 19 2008 G8 University Summit Sapporo Sustainability Declaration 20 2009 Abuja Declaration on Sustainable Development in Africa [110] 21 2009 Tokyo Declaration of HOPE ASSU 22 2009 Turin Declaration on Education and Research for Sustainable and Responsible Development Italy 23 2009 World Conference on Higher Education UNESCO 24 2010 G8 University Summit 25 2011 Copernicus Charta 2.0 26 2012 Peoples Sustainability Treaty on Higher Education 27 2012 UN Higher Education Sustainability Initiative with Rio + 20 28 2015 Higher Education Sustainability initiative: Climate change action for sustainable development Declarations put pressure on HEIs, directing them towards continuous monitoring of their energy consumption patterns for sustainable development. For example, the University of British Columbia (UBC) has gone through many initiatives in its pursuit to achieve a sustainable campus [5]. UBC signed the Talloires Declaration in 1990 and built its first green building in 1996. In 1997, UBC was ranked first among the Canadian universities to adopt a sustainable development policy and opened its sustainability office in 1998. It was also the first to launch the ECOTREK (an energy and water intervention program) in Canada and publish a comprehensive campus-wide strategy in 2006. Two declarations have been studied: the Halifax Declaration and the Talloires Declaration, and findings show that both were ineffective in fostering HEIs to create positive environmental changes [87,135]. 36 Sustainability Reporting within HEIs Sustainability Reporting (SR) is the formal act of communicating the social, environmental, and financial performances of an organization [136], intending to reach the meet the demands of the industry (i.e., growth) without causing negative impacts on the environment. This definition embodies the generalized theme of sustainable development (SD) developed by the Word Commission on Environment and Development as one that “meets the needs of the present without compromising the ability of future generations to meet their own needs [137]”. Reporting sustainable development has become a trend among many industries and HEIs. Reporting dates back to 1953 when corporate social responsibilities (CSR) was first introduced by Professor Howard Bowen [138]. CSR is commonly referred to as “a concept whereby companies integrate social and environmental concerns in their business operations and their interaction with their stakeholders voluntarily” [139]. The definition refers to reporting the three-bottom line performance (people, planet, and profit), which is also referred to as the “social responsibility of a business encompasses the economic, legal, ethical, and discretionary expectations that society has of organizations at a given point of time” [140,141]. Due to the interdisciplinary nature of the goals, it is reported that this has received criticism in the early stages [142]. For example, Elkington reflects on the concerns that the dimensionality of SD are interrelated and impact each other in different ways [143,144]. Moreover, evidence of increased research in reporting from 1970-2005 is evident [145]. Reporting did not translate into actionable reports until the 1970s when financial statements were associated with corporate social reporting. Environmental reports 37 emerged a decade later, at the end of the 1980s [146]. Social and environmental reporting was reported by the end of the 1990s [147,148]. Combining the three-bottom line concept (environment, social and economic) leads to the emergence of modern-day sustainability reporting (SR). To report sustainability Lozano (2006) asserts that there are approaches for an institution; Accounts (data is collected and used in a unit, e.g., energy usage), Narrative Assessment (this adds to the previous by adding graphs), and an Indicator-based (there are holistic systems created around indices to reflect an entire systems performance) [149]. Table 3.3, modified from [149], lists the main differences between the three approaches of reporting methods and their relative attributes. Table 3.3: Differences between reporting methods. Approach Accounts Narrative assessments Indicator-based assessments Transparency Low Medium High Consistency High Low High Participation Low High Medium Decision Making Medium Medium High 3.3.1 Global Reporting Initiative: A Generalized Approach In the early stages of SR, many governments and businesses worldwide used the Global Reporting Initiative (GRI) guidelines to communicate critical sustainability issues among stakeholders and governments. The GRI defines SR as “the practice of measuring, disclosing, and being accountable to internal and external stakeholders for organizational performance towards the goal of sustainable development”[150]. The GRI is an indicator-based reporting framework used for all types of organizations regardless of their size, sector, or location [151]. By reporting the triple bottom line (economic, environmental, social) performances based on a set of objectives and key performance indicators. The 38 institution then grades its progress towards self-imposed goals or targets. Afterward, each criterion is given one of four statuses; Not achieved, Behind, On track, and achieved. The GRI consists of three main categories: environmental, social, and economic (i.e., triple bottom line). Each category has a set of indicators and objectives that are required to be self-reported by the institutions. For example, in the Carbon & Energy Management objective, an institution states its goal (based on a specified baseline), objective, and target for that specific area. It assesses where it stands based on its overall goal. After all, categories are graded, a graphical presentation of what has been achieved, what is lacking, and what needs improvement is presented. This is a helpful guide for presentation and for focusing management on critical areas [150]. The GRI is not used for ranking performances (benchmarking) in its classical definition because it does not; provide a combined overall score, thus does not enable cross-institutional comparison. Instead, the GRI may be considered as an internal tool for high-level communication and strategy creation. The tool is adhered to by different organizations and primarily used for tracking performances within an entity. Through publishing the GRI reports help create internal pressure through resource allocation in an organization to address key performances internally. However, by lacking to provide a mechanism for ranking the overall institutions' performance and comparing it with the performances of similar institutions, very little evidence is reported on how GRI creates peer pressure within the industry or sector. Due to the interdisciplinary (holistic) nature of sustainability goals (e.g., reduce and conserve flows of energy) within all institutions, this allowed the GRI -a comprehensive communication tool- to be a prominent and comprehensive framework at the time [88]. 39 However, the tool is reported to have intrinsic limitations when used in an HEI context. For example, the GRI is designed for a business-oriented organization, the type of indicators used to assess the performances are ones associated with production and service organization. Finally, due to its broad range of institutions, it does not cover sector-specific goals. For example, in HEIs it does not integrate core HEI attributes of teaching, outreach, partnership, and stewardship [148]. To overcome these challenges, Lozano et al. (2006) [149] modified the GRI to enable it to become more compatible with HEIs and generated the Graphical Assessment of Sustainability in Universities framework (GASU). Later in 2010, Lozano analyzed the performance of 12 HEIs based on the GASU framework and concluded that HEIs are still in the early stages of SR compared to other industries (in terms of the number of participants and the scope of reports), emphasizing the need for HEIs to collaborate more. Finally, HEIs need to share more information [152]. GASU was the first deductive framework that shifted the attention from a generalized framework to a specific-oriented framework for a specific sector: the higher education industry. To date, there are 77 HEIs reported through the GRI initiative, 3 of which are Canadian. HEIs published over 180 reports in the GRI database [151,153]. 3.3.2 Reporting in HEIs: A Customized Approach Until the early 2000s, HEIs began to individually and collaboratively design a specific sector/industry reporting system that encompasses the intrinsic characteristics, roles, and aspirations of HEIs. In 2006 ten global intensive research universities signed a memorandum of understanding to promote sustainability in their universities and benchmark the best practices. The alliance gave birth to the International Alliance of Research Universities (IARU) to reduce the adverse impacts of universities on the 40 environment. The IARU established the Campus Sustainability Program that measures the performances of the member universities in several areas of concern, including carbon reduction, water management, recycling and waste, and other areas [154]. In 2005 some of the prominent leaders in higher education associations in the United States founded the Higher Education Associations Sustainability Consortium (HEASC), intending to communicate, learn from each other, and advance sustainability education. The Advancement of Sustainability in Higher Education AASHE was established from the HEASC recommendations. A year later, in 2006, the HEASC pursued other HEIs to design a sector-specific rating system which gave birth to the industry’s most prominent reporting system, the Sustainability Tracking, Assessment & Rating System (STARS) [155]. STARS started as a reporting system for US-based HEIs. Over time the reporting system began to incorporate HEIs from all over the world. STARS Launched its first version in 2010 with 149 HEIs registered to use the reporting system. There are nearly 1000 registered institutions and over 600 reports reporting HEIs from at least 40 different countries [156]. Figure 3.4 shows the number of HEIs reporting their performances per year. In this graph, some universities used more than one version of the STARS reporting technical manuals to assess their performance over time. For example, the University of British Columbia used the reporting system in 2011 and 2015. Thus, double counting for the same institution may be included in the overall number of reports. Table 3.4 shows the expanding number of reports and participation of HEIs in each version of the STARS reports. 41 Figure 3.4: Reports of HEIs in the STARS database per year. Table 3.4: HEIs participants per STARTS version report per year. STARS Technical Manual[157] HEIs participants Year STARS 1.0 149 2010 STARS 1.1 69 2011 STARS 1.2 158 2012 STARS 2.0 141 2013 STARS 2.1 401 2016 There is an agglomeration of tools used to communicate sustainable development in HEIs. Table 3.5 lists some of the reported customized systems used in the context of higher education institutions. Some of these tools have been discontinued. For example, the Sustainability Reporting Card suspended in 2012 [107,118,155,158,159]. Table 3 5: Sustainability reporting systems. S/No. System S/No. System 1. Sustainable Assessment Questionnaire 10. Adaptable Model for Assessing Sustainability in Higher Education 2. Graphical Assessment of Sustainability in Universities 11. Sustainability Tracking, Assessment, and Rating 3. Sustainable University Model 12. Green Matric- UI’s GreenMetric University Sustainability Ranking 4. University Environmental Management System 13. Good Company’s Sustainable Pathway Toolkit 5. Assessment Instrument for Sustainability in Higher Education 14. Auditing Instrument for Sustainability in Higher Education 0501001502002503003504004502010 2011 2012 2013 2016Number of HEIsYear42 6. Benchmarking Indicator Questions- Alternative University Appraisal 15. Campus Sustainability Assessment Framework 7. Unit-Based Sustainability Assessment Tool 16. Sustainability Competency and Opportunity Rating and Evaluation 8. The Green Plan 17. College Sustainability Reporting Card 9. Sustainable Campus Assessment System Shi & Lia (2013) analyzed three of the prominent reporting systems: STARS, The American Colleges and Universities Presidents Climate Commitments (ACUPCC), and the Green Reporting Card (GRC). The study focused on assessing the level of comprehensiveness by analyzing the number of criteria. Their analysis is summarized in Figure 3.5 [155]. Illustrating that STARS has the most holistic coverage of sustainability in comparison with ACUPCC and GRC. This is why STARS is considered the most prominent holistic reporting system [17]. A summary of the analyzed systems is provided in Table 3.6. Moreover, Shi & Lia (2013) argued that an industry-standardized framework designed explicitly for HEIs would engage more HEIs in topics related to SD on their campuses. Furthermore, the objective of sustainability in HEIs is to cover dimensions of operation and pedagogical spheres. In addition to the industry-related pressure that will be exerted on HEIs pushing them to engage and improve their sustainability performances [155] The GRC was discontinued in 2012. Illustrating it here is due to maintaining the chronological order of the review, supporting the generalist/holistic view of SR tools at the time, and showing the overlapping similarities with STARS. 43 Figure 3.5: Reporting system comparison. Table 3.6: Reporting systems comparison from adapted from Shi et al. (2013). STARS ACUPCC GRC (Discontinued) Established 2006 2006 2005 Launched 2010 2007 2007 No of Participants* 401 677 322 Benefit *Accreditation *cross-institutional benchmarking *Most Holistic System *Focused solely on GHG reductions. *Mandatory Commitments *Had a grading system. Weaknesses *Complicated *Consumes time *Heavily focused on GHG *Lacks a mechanism to underly building classification *Discontinued Emission Data (8) 24% (29) 85% (5) 15% Water Management (6) 86% (0) 0% (4)57% HEIs report their SD status for cross-comparison with their peers using four prominent systems: the STARS, the Campus Sustainability Assessment Framework (CSAF), and the Sustainability Assessment Questionnaire (SAQ). Recently, Alghamdi et al. (2017) [146] listed 12 assessment tools employed in HEIs based on relevance, usability, accessibility, and holistic coverage. These tools push the boundaries beyond the conventional declarations listed in Table 3.2 modified from [155]. In addition to STARS and SAQ, they included Benchmarking Indicators Questions (BIQ-AUI), Graphic Assessment of Sustainability in University (GASU), The Green Plan (Green Plan), 0%10%20%30%40%50%60%70%80%90%100%STARS ACUPCC GRC44 Adaptable Model for Assessing Sustainability in Higher Education (AMAS), Sustainable University Model (SUM), Green Matric (GM), University Environmental Management System (UEMS), Sustainable Campus Assessment System (SCAS), Assessment Instrument for Sustainability in Higher Education (AISHE), and Unit-based Sustainability Assessment Tool (USAT). These assessment tools are considered to be the most important progress toward the SD of HEIs [45]. STARS is a voluntary and self-reporting system that evaluates the performance of HEIs and ranks them based on five main categories: engagement, planning and administration, academics, innovation, and operations. Each of these categories has subcategories consisting of several indicators. Details on categories, subcategories, indicators, and the assessment procedure are given in Appendix A [9]. Figures 3.6 (a–c) show sized-based performance results reported by STARS. In Figure 3.6 (a), the level of participation is similar across all sizes of HEIs. Figure 3.6 (b) shows that none of the large-sized HEIs were ranked less than Silver. Conversely, in Figure 3.6 (d), 6% of the small- and medium-sized HEIs obtained a Bronze ranking. We used data from the STARS database to compare the overall sustainability performance scores with the WEC scores. Figure 3.7 shows that the normalized scores were higher than the individual WEC normalized average scores for all sizes of HEIs (i.e., 12 small-sized, 10 medium-sized, and 12 large-sized). None of the HEIs was able to secure the Platinum rating (refer to Appendix B1). 45 Figure 3.6: Summary of STARS size-based evaluation: a) distribution of reporting universities, b) performance of large-sized universities, c) performance of small- and medium-sized universities. Figure 3.7: STARS scores vs. WEC scores averaged for 12 small-sized, 10 medium-sized, and 12 large-sized HEIs. According to Sayed et al. (2013), HEIs report their SD status for cross-comparison with their peers in the education sector, using four prominent systems, including STARS, CSAF, SAQ, and College of Sustainability Report Card (CSRC) [160]. Recently, Alghamdi et al. (2017) stated that “although there are many assessment tools and hence there has been a noticeable progress, this progress is not clearly measurable.” Some of the systems require extensive data for analysis; for example, Tenley et al. (2008) used the 00.20.40.60.81STARS Score GHG Energy WaterSustainability performance score Small Medium Large46 CSA framework for sustainability assessment with 175 indicators, sub-grouped into environmental, social, equity and economics categories. They also stated that different units of indicators also cause difficulties in prioritizing improvement actions. As mentioned above, STARS uses a more comprehensive approach by covering five performance categories using 73 indicators. In the operations category, 11 indicators (22.7% of total weight) under five subcategories were related to WEC flows in HEIs, i.e., Air and climate: two indicators, Buildings: two indicators, Energy: two indicators, Waste: three indicators, and Water: two indicators [9]. Here, indicators under Waste were considered to appreciate the ideas of ‘waste-to-energy and ‘waste minimization’ for SD. Aggregating all the indicators and categorizing the HEIs as Gold, Silver, and Bronze are helpful for overall sustainability ranking. Nevertheless, existing tools with their holistic approach cannot be directly used for the sustainability evaluation of engineered systems in HEIs. Hence, technical-level decision making requires to i) evaluate WEC flows for engineered systems at the university level for inter-university benchmarking (at a regional or national level) and to set future targets for continuous sustainability performance improvement, and ii) classify HEIs’ infrastructure systems, particularly building, based on their types and sizes to evaluate their WEC flows for implementing improvement actions at intra-university level. Present work addresses the first step towards sustainable development of HEIs. STARS is currently the most generalist/holistic tool available to rank and benchmark universities. The system has been used in the industry for nearly ten years. STARS measures performances of universities in 5 sectors: Academics, Engagement, Operations, Planning & Administration, and Innovation & Leadership. The number of 47 indicators required to be reported in the STARS exceeds 240. Reporting this amount of data is a resource-intensive exercise m. [159]. Furthermore, reporting these parameters is done internally, with each HEIs reporting the required fields. This may be attributed to the low number of participants compared to the total overall number of HEIs globally. Other reported limitations of STARS are that it is a self-reporting thus not verifiable, does not take factors like seasonal weather patterns into account, and does not provide specialization data such as type of universities or provide means to classify universities based on their size. The reported limitations of the ACUPCC are; Self-reporting not verifiable, heavily weighted on a specific area like reducing GHG (without operational validity, i.e., does not operationally classify the buildings), no overall scoring mechanism [155]. Furthermore, finally, the limitation of the Green Report Card; Discontinued in 2012: because the results found out that many HEIs lacked in energy efficiency upgrades [155], Most HEIs remain conservative and slow to incorporate and institutionalize. Two branches of research attempted to explain the lack of commitments from HEIs to engage in SR: An instrumental argument and an intrinsic argument. Lozano et al. 2013 suggested that barriers to sustainability reporting in HEIs include three drivers; internal (i.e., resource and cost-saving, profits, productivity), connecting (i.e., sustainability reports), and external (i.e., taxation, stakeholder expectations, social legitimacy, competitors benchmarking) [161]. Blanco-Portela et al. (2017) applied a Qualitative Content Analysis on 51 research articles to classify the barriers of change in HEIs into; Internal structure of the institution, external factors, stakeholders, institutional framework, and resources. Furthermore, it concluded that for HEIs to move towards sustainability, they need to overcome the operating dynamics that have been traditionally used. 48 Suggesting that the human factor (behavioral of an individual) is the primary obstacle to change in HEIs [162]. Disterheft et al. (2015) pointed to the lack of stakeholder involvement as the main reason for the barriers to reporting [163]. Some researchers pointed out that research and teaching are not aligned with stakeholders' demands, the internal reward structure of faculty members, and the lack of an entrepreneurial culture in universities focusing on information disclosure. It is argued that the lack of SR in universities is in connection with the absence of external stakeholder engagement processes [164]. Meanwhile, authors have documented that the complexity of the internal structure of universities is a potential barrier to change for sustainability [165]. Other studies noted that the lack of a proper institutional framework could hamper progress toward sustainability in universities [166]. The lack of financial and human resources has been commonly mentioned as an important obstacle to overcome [164,167]. Finally, several authors found different external factors, such as lack of pressure from society, which strongly influence the commitment to sustainability in universities [168,169]. Two plausible barriers to reporting sustainability in HEIs may stem from the subjective nature of the concept of sustainability [155] and due to the overstretched and ambitious goals institutions set for themselves. It is also reported that HEIs find it challenging to balance details between the scope of the work and the objective of tasks [149]. Most generalized sustainability assessment frameworks suffer from double counting [155]. Double counting: occurs when the chosen criteria are at least partly redundant, and critical criteria are not included respectively [170]. In addition to giving several weights for 49 a criterion more than once [171-174]. For example, the achievements in energy efficiency are related to green buildings and GHG emissions [155]. In contrast, HEIs are in the early stages of publishing sustainability reports. This may be attributed to the lack of empirical evidence that supports the integration of sustainability reporting in HEIs and the process in which the reporting systems are formed [175]. Others argue that no one reporting system may cover every aspect [175]. Barriers to fully integrating sustainability reporting in HEIs may be due to human factors such as resistance, involvement, communication, organizational culture, empowerment [176] and could also be attributed to stakeholders' involvement [177]. It is still debated whether HEIs are taking the lead in engaging sustainability in their communities or are moving towards the topic in a reactionary manner due to community pressure [175]. To pursue sustainable development in HEIs, both the top-down and bottom-up management approaches have been reported in the literature. Lozano [149,178] emphasized the importance of leadership and vision from higher management to the lower hierarchical levels in achieving sustainability targets, e.g., emissions reductions. Many others have argued that a bottom-up approach is necessary for raising awareness and addressing the behavioral aspects of resource consumption from the users upwards [18,103,113,149,178,179]. As experts establish the evaluation criteria of the top-down approach [180], it seems to be more suitable for the technical-level sustainability assessment of HEIs. Sustainability assessment is a challenging task due to its inter-disciplinary nature [181,182] Gasparatoes et al. (2008) carried out a critical review on sustainability assessment tools and methodologies and concluded that none of the metrics reviewed 50 seem to be able to assess progress towards sustainability in a holistic manner [53]. Among the tools reviewed: the biophysical models and indicator-based reporting systems include the exergy (vagueness in reference state), Emergy (human judgment preference), ecological footprint (weak analytical tool), and SI have underlying uncertainties [53,183]. Since no one indicator can deliver the state of sustainability, these indicators are aggregated to deliver a heavily influenced by weight and judgment rank of sustainability [184]. Selecting Indicators Performance indicators were suggested as a broader tool to assess sustainability performance Hammond et al., (1995); [128]. Rees and Wachernagal (1996) [129] developed an ecological footprint tool to evaluate the performance per unit area of land or number of students. Although the tool lacks standardization, limited flexibility to include cost-related parameters, and insufficient information for decision-making [124], it was found useful to communicate performance in terms of environmental impact based on the unit area or per student [131]. Such a basis provides the opportunity for benchmarking the systems with different sources of water and energy. In this thesis, Water, Energy, and Carbon emissions will be used to assess the environmental performance. Indicators are modes of reality; they allow us to relate a simple measure to complex phenomena like GHG emissions; they also provide trends for an alarming environmental problem, and finally allow policy makers and decision-makers to assess the performance of current practices [185,186]. Indicators can be defined as “a parameter of value derived from other parameters, which provides information about the achievements of an activity, 51 processes or an organization with a significance extending beyond that directly associated with the calculated value of the parameter itself [187]. In the context of universities, the indicators should measure progress towards sustainability, where each indicator should have a goal, measure, and target to assess the progress over time. A detailed review was conducted for sustainability assessment of residential neighborhoods, including leadership in energy and environmental design – neighborhood (LEED-ND), SCR, building research establishment environmental assessment method (BREEAM), CASBEE, and EPI. Three hundred sixty-four indicators are grouped under environmental, social, and economic categories (i.e., triple bottom line or TBL). A summary of sustainability indicators is presented below in Figure 3.8. Figure 3.8: Number of indicators for residential neighborhoods. According to [160], the most widely used environmental sustainability assessment frameworks in the USA and Canada are the Sustainability Assessment Questionnaire (SAQ), Campus Sustainability Assessment Framework (CSAF), the College of 00213624522433571291459500 20 40 60 80 100 120EPICASBEEBREEAMSCRLEED-NDEconomic Environmnet Social52 Sustainability Report Card (CSRC), and the Sustainability Tracking Assessment and Rating System (STARS). It can be seen in Figure 3.9 that STARS has the highest number of relevant environmental sustainability indicators as compared to other rating systems. In STARS, only 16% of the sustainability indices are related to water-energy-carbon (WEC), corresponding to 12% of total weights. STARS developed a list of relevant categories, and other reporting systems are provided in Annexure A. Keeping in view the current sustainability performance of universities in Canada, there is a need to develop a more detailed, albeit pragmatic, and straightforward information system for SMU. Figure 3.9: Number and distribution of environmental sustainability indicators. STARS marginally considers some economic indicators, focusing on environmental, educational, social, and governing indicators. Economic indicators reflecting the eco-efficiency of interventions (i.e., intervention impacts) need to be included to fully understand and comprehensively study sustainability from a holistic viewpoint [17,28]. A critical review has been conducted to illustrate the multidisciplinary nature of the proposed research. This section will cover sustainability, performance indicators, inter- and intra-0510152025303540SAQ CSAF CSRC STARSNumber of IndicatorsEnergy Water GHG53 university benchmarking, continuous performance improvement, lifecycle thinking, and multi-criteria decision analysis in the context of university campuses. Similar to any other built infrastructure, urban water operations discharge effluents, generate solid waste (bio-solids), pose hazards like stormwater flooding, and release GHG emissions. In addition, energy is also required in all stages of water management: water abstraction, water treatment, distribution, use, wastewater collection, wastewater treatment, disposal, or reuse. Water and wastewater operations have high expenditures in terms of energy cost [188]. Moreover, energy use directly contributes to GHG emissions. Energy use can be reduced significantly by using efficient water appliances, water conservation, and adequate water and wastewater management. These strategies can be combined with renewable energy use, including wastewater energy recovery and onsite solar and wind energy generation for achieving net-zero energy and carbon emissions [189]. The Canadian universities' performance on the sustainability front varies considerably and is mainly driven by mandatory reporting (e.g., the GHG reporting card in BC). These standardized report cards do not fully cover the extended operations of the universities. More critically, the operational reporting systems do not provide an actionable plan to address internal (e.g., increasing population, local demand on utilities) and external (e.g., climate change uncertainties, taxation, and regulatory framework) issues. Furthermore, they do not lay out a clear method for enhancing their processes to achieve their sustainable development goals. They do not provide an actionable plan to address internal (e.g., increasing population, local demand on utilities) and external (e.g., climate change uncertainties, taxation, and regulatory framework) influences. Thus, they cannot 54 provide a clear method for enhancing universities’ sustainability from an operational perspective. Effective reporting systems are needed to guide HEIs to achieve their aspired goals of sustainable development. Moreover, institutions considering “scaling” their performances use indices to reflect on how development is achieved [190]. Many of the reporting systems used by universities are indicator-based reporting systems. The scorecard universities produce on a yearly basis is heavily reliant on an indicator-based reporting system. However, indicators may give a false representation of the actual performance if not aggregated methodologically. Many researchers have argued that specific indicators cannot be adopted in a different field or across areas of multiple dimensions of sustainability [185]. Thus, selecting indicators has to be based on a process, meaningful, definable, and measurable method. Furthermore, indicators should consist of a goal, measure, and a proposed target that defines the objective of using the particular indicator. It is, thus, imperative to determine the type and scope of PI used in HEIs based on the function they address. In order to achieve the sustainability objectives, a university needs to maximize the output of research, community engagement, well-being, and teaching while reducing the cost of operations and potential adverse effects on the environment. Benchmarking Benchmarking was initially used to define terrain reference points in the topographical and geological analysis [191,192]. Webster’s dictionary defines benchmarking as “a systematic process for evaluating and comparing services, products, and work processes in order to facilitate improvement or strategic advantage” [193]. The term implies data collection, process identification, data analysis, communication of the data, and providing 55 meaning to decision-makers. Benchmarking is used in longitude of studies and is also referred to as a self/generic improvement tool [194], the evaluation of services and processes [195], best in class and cross institutional-comparison [196], a management tool to diagnose problems, vivid tools to identify areas of strength, improve quality and costs associated with processes, ongoing systematic processes, diagnostic tool, solutions [197] and it's been linked to the attributes of innovation [195]. Benchmarking was first used in Xerox to aid the company in securing market competitiveness by eliminating redundant tasks and comparing internal performance with that of the market leaders [197]. The term benchmarking is used interchangeably with an energy rating, energy performance, and labeling in the building sector. This confused stakeholders and business leaders. Energy rating is commonly referred to as “ a method for assessing predicted energy use under standard conditions and its potential for improvement” [198], for example, the EnergyStar program and the Leadership in Energy and Environmental Design LEED building rating system. While labeling was used in home appliances and HVAC units to promote energy savings [191]. Benchmarking is sometimes referred to a baselining Federspiel et al. (2002) distinguished baselining from benchmarking with respect to the time the analysis is performed and to the group of data analyzed (e.g., single building performance overtime is baselining, comparing a building performance to other buildings is benchmarking) [199]. In an aim to help improve building energy efficiencies, many cities have laws to mandate large buildings to display the energy performance (i.e., rating) for their buildings [14]. 56 The momentum gained prominence in the early 1990s due to a set of international events that upheld oil flows in the early 1990s; nations began considering means to reduce energy consumption by increasing energy efficiency while maintaining the quality of livelihood and comfort in the buildings. This paved the way for regulatory bodies to adhere to energy certification policies for buildings to increase efficiency and reduce adverse effects on the environment. To manage energy efficiency, regulatory bodies used three routes to increase building efficiencies, namely: regulations, auditing, and certification. These regulations emerged the energy codes that set the minimum requirement of buildings to attain specific performances as in the EU building envelope regulation and similarly in other nations in the world. The momentum also led to the creation of energy usage intensity as units of measurement by the American energy-intensity indicators and the European energy performance indicators. [191]. As the data is continuously collected, a new set of problems arises from a need to compare the performances of buildings based on similarities in scope, size, and climatic conditions. The act of measuring and reporting the performances of these groups (i.e., cluster) of buildings with the most similarities in terms of features and or characteristics to identify a set of best, average, and low performers is referred to today as benchmarking [191,192,200]. Benchmarking can assist decision-makers by identifying gaps/improvements in the performances, saving resources, exemplifying processes, sharing lessons, determining best practices in the industry, optimizing regulatory compliance, and is a well-accepted engineering practice [193]. The benchmarking process requires three stages: planning (to underly the objectives and scope), analysis (aiming at identifying performance gaps), and integration (to continuously and systematically implement the findings) [201]. A 57 significant database should be compiled, performance information, and an analysis method to compare the performances [192]. For energy benchmarking, three general benchmarking methods depend on the nature and source of the data available: white-box, black-box, and grey-box. The first is data generated from simulations, the seconds are referred to when statistical approaches are used, and the final is a combination of the first two [93,202]. A list of benchmarking studies conducted on educational buildings can be found in [36]. Buildings are benchmarked in two main ways. A top-down approach and a bottom-up approach. Depending on the type of data and the level of information available. A top-down approach would derive building data based on the total overall performance, for example, the total EUI [203]. A bottom-up approach would build up energy performance in each level, i.e., HVAC and Lighting [204]. Due to the large set of data collected, limitations may arise from misleading information that may not reflect the measuring unit. For example, buildings that report water irrigation as part of the educational building water usage compare the values with identical buildings that do not report water usage for irrigation as part of the building usage. This may cause unexplained variation in the data when conducting a benchmarking practice because the water used for irrigation is heavily affected by climatic regions and the landscape area. This is why it is preferred to carry the exercise on a multi-dimensional feature instead of reporting one parameter alone [192]. Finally, Benchmarking is not a means to an end. It does not in itself lead to an improvement; however, the practical implementation of benchmarking helps identify the key attributes of the functions being benchmarked. Which intern helps identify best-in-58 class performers. Moreover, benchmarking may have a misleading limitation by itself because benchmarking compares buildings to best-in-class performers. They do not take into considerations actions to show how savings may be achieved or how energy savings potential may be achieved [199]. Thus, the dimensionality of indicators used to assess the performances must be carefully selected. The performance mechanism framework should be linked to areas that address these limitations. The benchmarking exercise can provide the necessary information to the technical staff to take further actions to conserve and improve efficiencies and costs. Natural Resource Canada lists the benefits and barriers to benchmarking in Canada in table 3.7 [205]. Table 3.7: Benefits and barriers to benchmarking. Benefits Barriers Provides objective & reliable information Limited integration of benchmarking Increases awareness in the organization Lack of management support Identifies best practices Data collection challenges Identifies reference point Lack of understanding of the analysis Develop solutions Lack of resources in terms of money or time Improves energy savings Federspiel et al. (2002) applied a model-based benchmarking methodology on an academic building in the University of California Berkeley campus and was able to predict actual energy usage from the ground up and predict the total building energy consumption. This study helped identify aspects when devising a laboratory-intensive building at a university [199]. In contrast, benchmarking processes should collect a large set of data, determine best in class to use as a reference point or a guideline, and finally conduct the comparison [191,192,206,207]. Benchmarking in the context of HEIs is perceived to be the “process by which organizations determine the best practice in the industry” [208]. Benchmarking is an essential tool for broader communication, accessibility, and comprehensibility at all levels 59 of stakeholders to develop a meaningful system for assessment [209]. There is a need in Canadian universities for general benchmarking to establish targets for environmental sustainability. The benchmarking process i) provides the opportunity for sharing of assessment results, ii) identifies the best processes or methods in practice, and iii) enables the comparison of different sustainability development initiatives among universities [88]. [197] define the benchmarking in the university context by posing following the questions: • How well is the campus doing as compared to others? • How are different buildings on the same campus progressing with time? • How can one campus progress without changing its unique features? and • How can a university be better than the best in its mission? Benchmarking systems may vary in breadth, scope, goal, and impact. They become narrower when designed to achieve a particular goal [160]. There are many benchmarking systems used to report HEI performances in the literature in the context of higher education. These systems cover a wide array of sustainability pillars (social, economic, and environmental). The Federation of Canadian Municipalities (FCM) and National Research Council (NRC), Canada, described benchmarking as “the mapping of one’s process and subsequent comparison of your process with those of other companies with exemplary performance in a similar process” [210]. Elmuti, D & Kathawala (1997) [208] listed four different types of benchmarking: internal benchmarking, competitive benchmarking, functional/industry 60 benchmarking, and process/generic benchmarking. Table 3.8 describes the benefit of each type of benchmarking. Table 3.8: Types of benchmarking adapted from Elmuti & Kathawala (1997). Benchmarking type Description Benefit Internal An internal type of benchmarking used for internal processes within an institution. Its ability to identify the best internal process and having the ability to transfer them to other portions of the organization. Competitive An external type of benchmarking used to compete with external institutions. Its ability to monitor, track and assess performances across an industry. Functional / industry An external type of benchmarking performed against industry leaders or the best functional operations in a certain institution. Type and scope of data are easier to retain. Process / generic An external benchmarking method focuses on the best-in-class institution/process with the exception that this type can be used across dissimilar organizations. It is used to define potential improvements by comparing processes/functions in an institution to other institutions not necessarily with a similar scope of work. Generally, there are two themes of management approaches to benchmarking, a top-down and a bottom-up approach depending on how the data was derived. A comprehensive review has been published on both approaches [203,204]. Many other studies on benchmarking have also been reported in the literature. For example, Schofiled (1998) published a review on HEI benchmarking approaches in Canada, Australia, the UK, Europe, and the commonwealth university management club to address different aspects, especially related to educational performance. Chung (2006) proposed an empirical model to benchmark energy efficiency in public buildings [211]. Similarly, another study compared four benchmarking reporting tools, namely the SAQ, CSAF, CSRC, and STARS, in order to assess the social, economic, and environmental performance of the University of Saskatchewan [160]. 61 3.5.1 Inter-University Benchmarking Inter-benchmarking is commonly used in the literature to assess and compare the performances of different utilities within the same industry [212]. Benchmarking based on a selected set of performance indicators is referred to as Inter-benchmarking. It has been used to compare the performance of different water utilities [213,214], road safety [215], and environmental assessment tools [216]. The findings of the Inter-University benchmarking allow researchers to determine the best in class and worst in class performers among the compared institutions. The findings help communicate the performances among the institutions and allow for knowledge sharing, which in turn helps the industry grow through internal and external pressures. Shriberg (2004) first discussed the need to establish a “universal” sustainability assessment tool for the HEIs [209]. Inter-university benchmarking refers to a functional/industry benchmarking type; the goal is to compare the performances of one institution with its peers. The benefit of using this approach is to create a positive pressure on the universities to improve their performance with respect to each other. Common systems in Canada are the STARS reporting system; STARS ranks the universities as platinum, gold, silver, bronze, and reported, where the first one is the best performing, and the last does not pass the minimum criteria requirements [54]. One of the limitations of this benchmarking is that errors heavily depend on the model's accuracy. Hawkins et al. (2012) used the top-down management approach to develop benchmarking models for heating and electricity performances in UK universities [217]. Hong et al. (2014) further investigated these models and improved model accuracies [218]. 62 3.5.2 Intra-University Benchmarking Intra-university benchmarking refers to internal/process benchmarking used within HEIs to compare performances of similar assets (buildings) within a university. Intra-benchmarking takes the generalized top-down approach towards one that is deeply embedded in the specific characteristics of the buildings. Therefore, buildings must be classified in terms of operation of use or function to use this method. The benefit of this method is that it underlays the underperforming assets, addresses more parameters due to the availability of more data, and finally, the method can yield accurate projections. The bottom-up approach is based on a theoretical analysis of the assets. Many studies have been conducted on building levels to benchmark different operational flows and performances (e.g., energy flows, GHG emissions, and other improvement areas of improvement within HEIs) [197,203,204,211,219]. There are two widely used methods, namely, the physical building method and the aggregated end-use method. The first method deals with analyzing each sub-system in a building (e.g., external envelops thermal characteristics, building air permeability, type and efficiency of the HVAC system, lighting, and other services within a building). This is achieved through a series of mathematical equations and simulations to identify underperforming assets/ services in the building. It can be used to establish baseline benchmarking for these processes. The second method aggregates end-user data, building characteristics, and energy flows to identify the system's performance, which can be used to establish a system-level benchmark. It also identifies the root cause for the discrepancy between measured performance and design intent systematically. This method requires more resources. Details of these methods are provided in [204]. 63 Classification and Clustering of Academic Buildings Studies estimate that nearly 40% of the world’s total primary energy consumption and the associated GHG emissions are released by the buildings sector alone [206,220]. Studies also indicate that nearly 94% of the energy’s lifecycle impact of a building is used in its operational phase [13]. Managing buildings in their operational phases is a pivotal element towards reducing adverse impacts on the environment and mitigating the risks associated with their operations. Buildings are complex and heterogenous entities that vary in scope, function, climatic region, population, and the purpose for which they are built. Studies group buildings in terms of sub-groups (i.e., domestic and non-domestic buildings) and different scales (i.e., metropolitan, national, provincial) [221]. Buildings are classified in terms of several factors such as type, occupancy, area, function, climate, and other subgroup factors. Classification aims to compare the similarities that buildings exhibit in WEC, for example, and benchmark the buildings accordingly (i.e., type, function). For Example, the Boma Best in Canada defines buildings in terms of the area. If 75% of the building's area is offices, then it falls in the office buildings [222]. A more comprehensive approach would classify buildings in addition to their type, for instance, to a sub-level of comparative metrics, for example, by climatic region, or province, or the year of construction like the Canadian’s NRC surveys of buildings [77]. In other regions of the world, buildings are classified in terms of occupancy rate into domestic and non-domestic buildings [203,204]. The Australian Building Greenhouse Rating Scheme assesses building performances based on historical data and applies classification to buildings with similar classes and sub-classes [223]. Generally, these 64 tools rely heavily on statistical methods and a pre-defined set of classes and are referred to as supervised algorithms. The main reason for these extended depths of classification is aim to reduce variability in the data collected. Institutional buildings date back to the 15th century, yet only recently emerged in the form of educational buildings; these buildings are reported to consume more than 19% of all the energy in the USA [93,224]. The main variable that distinguishes the academic building from other buildings is that they have a higher occupancy rate than other buildings. Energy costs represent a high percentage of the operating costs in HEIs. Therefore, these buildings are required to run efficiently to increase livelihood and reduce impacts of contaminants, concentrations of formaldehyde, and VOCs suspended particles (Santa 2007). Educational buildings exhibit different performances in terms of EUI. Table 3.9 lists several studies that examined EUI performances in European cities and countries [93]. Table 3.9: EUI in educational buildings. Country Heating EUI in kWh/m2 Source Ireland 96 [225] Slovenia 192 [226] UK 157 [48,93] Serbia 3-4 times higher compared to the EU [227] Greece 31-84 [228,229] Torino 100 [230] Classifying buildings refers to a set of tools used to rate the indoor quality, energy consumption, and environmental parameters of a set of buildings and subsequently class the buildings into similar groups for comparison and policy making. Furthermore, classification is the methodology of class determination among buildings [200]. Classifying is the act of assigning a set of data to a predefined category [231]. 65 Many studies use classifications as means to understand energy consumption patterns in universities. For example, Khoshbakht et al. (2018) classified 80 HEI buildings in Griffith University in Australia into six classes: office, administration, library, research, teaching, and mixed buildings. These classifications were set based on the significant activity of the building, for instance. If a building allocated 40% of its area to laboratories, then the building will fall in the research classification [36]. Chihib et al. (2020) classified 33 buildings of the University of Almeria into six classes as well: research, administration, teaching, library, sports facilities, and restaurants and compared their performances over eight years using climate variables as the independent variable and other variables like the occupancy [95]. Tan. H. et al. (2012) analyzed the university of Tongji in China. They broke down the energy consumption for student dorms, research buildings, others, offices, libraries, and classrooms, with the dorms consuming 29% and classes around 2% of the total university consumption [232,233]. Both studies highlighted that laboratory-intensive buildings consumed higher amounts of energy than their peers, with a noticeable variability in the data. The findings are supported with Mills et al. (1997) findings where he estimated that laboratory buildings are 4-5 times higher energy-intensive than commercial and institutional (i.e., non-laboratory) buildings [234]. Other studies confirmed similar findings [199]. These buildings are highly energy-intensive due to the inefficient HVAC design, which regulates the amount of air flow in these buildings. [235]. This is due to the HVAC design requiring the air-change rate to flow higher than that of traditional buildings. Furthermore, medicine, science, and engineering buildings are highly material-intensive buildings and thus need higher energy than buildings 66 associated with less energy intesive activities like lecture, administrative, and library buildings [236]. Classifying buildings is a step to provide meaningful variance among buildings. It can help decision-makers by providing specific solutions to specific problems. Classifying buildings can also help determine the most and worst type of buildings based on their intrinsic characteristics. For example, the variability point can be addressed in a study that showed that 43% of the buildings were grouped as mixed buildings, i.e., not classified buildings [36]. This is a large and significant number of buildings in a study. It has also been reported that classifying buildings based on sub-groups such as weather conditions and the area of the buildings may be insufficient due to the insignificant number of data and because insufficient buildings may be rated as efficient ones, due to the fact that there is no public data that benchmark building performances based on the subgroup attribute for academic buildings, i.e., HVAC surveys [199]. Recent findings suggest that singularity in classification (i.e., type of building) when analyzing building energy performance is not sufficient on its own; instead, a multitude of factors (i.e., sub-classes) must be accommodated to explain/limit the variability in classification [192]. To overcome limitations in single or multitude of classes in the classification approaches. Methods that consider statistically significant approaches are reported. Among the most prominent are the clustering approaches [192]. University buildings are diverse and cover a wide array of uses. Buildings range from single-use buildings (i.e., administration buildings, residential) to multiple buildings (combination of class, office, lab). These buildings provide different services and require 67 different designs and resource allocations in energy and water. Highly intensive buildings like Hospitals and Laboratory buildings need more energy per area than other types of buildings (i.e., libraries or offices). There is no formal way to classify academic buildings with respect to their energy characteristics. These types of buildings (excluding residential) are often sub-classed in buildings' commercial and institutional sectors. The commercial and institutional buildings cover a larger array of data (i.e., malls, shopping centers, offices, heritage buildings, and so on). Moreover, reference points to these buildings might not reflect the nature of these buildings. Due to these underlying differences and limitations, clustering is often used to help supervise the selection of these buildings and group them into a similar group based on the used performance metrics. Occupancy studies investigated the energy consumption in residential buildings and offices in depth. An additional layer of complexity in assessing and understanding energy patterns in HEIs is due to the uncertainty in occupancy variation. Because occupancy behavior is essential in understanding the energy and water flows in buildings, finding it could be challenging. It is reported that HEIs buildings have a whole building occupancy flux rate in just one hour. Furthermore, it is estimated that 92% of institutional building users are visitors and around 8% of people have a permanent space in that building [93]. The nature of these behavioral [36] studies is stochastic in nature, making them challenging to predict [36,93,237]. Clustering analysis is used to model energy consumption for two reasons; its accuracy and the generated stable clusters can better understand the underlying groups. Clustering 68 models are highly sensitive to the variables and the algorithm of choice, resulting in the misleading interpretation of both the model and the overall goal [221]. Hsu (2015) compared two clustering methods, the latent class regression, and a cluster validation method, to determine the stability of the clusters on a large dataset of residential buildings in New York City [221]. Clustering enables buildings with the greatest similarities in terms of flows and similar characteristics that influence the flows to be grouped in the same cluster [192]. This reduces the variability when benchmarking the clusters and helps model the classification of academic buildings per cluster. Clustering is considered the most helpful method among different data mining techniques because it recognizes the patterns in the underlying datasets [238]. In addition, clustering helps find structure in a set of unclassified data, such as unclassified buildings [239]. This is done by defining similarities between these buildings in terms of the flows of energy, water, and their GHG emissions [240]. The centroid point in a cluster has been used in benchmarking by assessing how far points are from the centroid of a cluster a building performance can be assessed. A clustering approach usually follows these steps: selection of the data, which include cleaning the data sets from outliers and normalizing them, selection of cluster algorithm, the K-mean cluster is one of the most prominent used in the literature, and the adaptation step and finally the results and interpretations. [192]. Clustering is a type of unsupervised learning approach. It is also used in pattern recognition by identifying similarities and dissimilarities. Clustering has been studied extensively in many disciples in the last 40 years [241]. Clustering is the mathematical procedure to identify natural groupings of objects in ways that allow a cluster to group 69 objects with the closest similarities in a defined cluster. Intelligent fuzzy clustering techniques allow for classifying buildings in certain clusters and provide information on how to group the performances of WEC in a multidimensional space and define the degree of membership of each object in a cluster [239]. The distance between two points in clustering is measured, referred to as the Euclidean distance. Even though machine Learning Statistical benchmarking methods are commonly used in the literature to benchmark and predict energy consumption patterns, examples are [242-245]. There is yet a consensus on the best approach to statistically represent and perform the benchmarking method on [14]. Santamouris et al. (2007) compared the Equal Frequency Rating (EFR) to a Fuzzy Clustering Technique and found errors due to normalization inaccuracies in the EFR which resulted in a non-balanced rating of buildings because of the impacts of the building. Small buildings may alter the overall score in both ways. However, classification techniques offer a more robust classification and overcome many of the shortfalls of the EFR. The reason to use a specified tool and its ability to derive meaning from the data and solve the variability issue with other methods is due to an instrumental argument. This method is a highly technical one used primarily for the operational phase monitoring and improvement. It is not to neglect the value or holistic approaches but to provide a technical manual to apply to a technical problem that is highly linked to improving aspects such as WEC flows. Holistic general tools are used for intrinsic value/argument policy making, community outreach. Research and communication, not operation and Specialization tools are instrumental value/argument used primarily for operation, usage and 70 performance tracking, management (depends on whom we are communicating to communication). It can go the other way around depending on the consumer. Figure 3.10 shows the steps highlighted in this review; it provides a historical framework on how the discussion of a sustainable campus is derived. The overview covered the decelerations, which helped shape primary questions and increase attention. This was followed by reporting as an activity to monitor progress in the triple bottom line performances; within these environmental aspects, this chapter proposes a continuous performance improvement to benchmark different asset classes within universities. Figure 3.10: The evolution of SD in HEIs. Trend Analysis It is reported that nearly 2/3rd of GHG emissions in Canadian universities are a result of energy usage [246]. Buildings, in general, are believed to be responsible for more than one-third of the GHG emissions and 32% of the total energy consumption [2,14], With buildings being the largest assets in HEIs. A typical building will emit the largest portion of its total life cycle emissions during its operational phase alone [11-13]. The building sector consumed 20-40% of the energy in developed countries in the EU and USA, and DeclarationsDefinition Sustainability ReportingCommunicationBenchmarkingTrackingClassification & ClusteringTechnical Performance ManagementQuality ControlSustainable CampusGoalAnalysis 71 this sector used more energy than the industry or transportation sectors [206]. These buildings have a substantial role in the climate change effect worldwide. To reduce the anthropogenic effects that contribute to climate change. One hundred eighty-seven nations agreed in the United Nations Paris Agreement to set ambitious goals to reduce their emissions, thus limiting a 1.5oC increase above the pre-industrial levels of the global temperature by 2030 [247,248]. Canada is among those nations that ratified the agreement. In response, Canada issued the Pan-Canadian Framework, a plan to enable Canada to meet the commitments signed in the Paris agreement. Among many agreement goals, Canada is committed to reducing its 2030 emissions to 30% below the 2005 baseline level [10]. Canada emitted nearly 730 Mt CO2E in 2005, and the last reported value in 2017 was 716 Mt CO2E 74% of those emissions are in response to energy transport and stationary combustion sources. Table 3.10 shows Canada’s reported GHG emissions per given year [249]. In BC, it is reported that 79% of the GHG emissions are due to energy generation alone in 2012 [250]. Table 3.10: Canada emission trend. Year 2005 2012 2013 2014 2015 2016 2017 GHG (Mt) 730 711 722 723 722 708 716 GHG/Capita (tCO2 E/Capita) 22.7 20.3 20.4 20.2 19.9 19.4 N/A Energy Mt CO2E 595 574 584 588 585 572 N/A Buildings, electricity, and heating consume a vast amount of energy compared to Canada's total emission and energy usage. A recent study on 34 HEIs in Canada estimated that the combined energy used in these institutions is approximately 20 GJ [17]. These institutions emit 1,312,392 MtCO2e of anthropogenic gases both directly and 72 indirectly (i.e., Scope 1&2). In the united states, the 2010 average annual GHG emissions from all institutional buildings (colleges and universities) are 7.67 tCO2e/FTE [17,251]. In 2007 BC government introduced bill 44 that all public sectors become carbon neutral stated that by 2020 BC, GHG emissions would be at least 33% less than the levels of the baseline emissions of 2007 and more progressively also mentioned that by 2050 emissions should be 80% at least lower than the 2007 baseline [252]. These required mandates served as goals and targets for different buildings to attain. Within Canada, provinces, and institutions alike have set similar goals to reduce their GHG emissions and make their contribution to the Paris accord that was ratified by Canada. The province of British Columbia (BC), along with several provinces (Alberta, Ontario, and Quebec) in Canada, began to pursue carbon taxing, which is a central theme of the Pan-Canadian Framework to meet those obligations [10]. It is reported that this carbon tax would influence consumer behavior by fixing the prices of services and products based on their GHG content. This is believed to reduce GHG emissions by influencing commodities and services with lower GHG content (i.e., solar panels, wind turbines, etc.), thus reducing the overall GHG content. In BC, a carbon tax was implemented in 2008. The tax rate has been $30 per tonnes of CO2e since 2012. In 2017 the provincial government announced that the tax rate will be benchmarked in respect to the nationwide proposed benchmark. This will raise the tax by $5 per tonnes of CO2e each year from 2018 till 2022. This is on par with the nationwide proposal to increase carbon taxes by $50 per tonne of CO2e in 2022 [253]. Moreover, as price may be an option to attempt to alter the economic balances, it should be implemented on all phases of the production of that service. For 73 example, in BC, the carbon tax taxes affect the downstream and ignore upstream emissions associated with their production and transportation [120]. The emission in BC is roughly 80% emitted by buildings and their operations. The public sector is divided into five sectors: core government, crown, health, school, and HEIs. The performance in these five varied. For eight years, the core government achieved the most improvements in reducing GHG by around 31%, followed by schools, HEIs, and the leas performing were the crown and health sector reporting increases, not reductions as shown in Figure 3.11 [79]. The total reported values of GHG for the HEIs sector are reported in Figure 3.12. Figure 3.11: BC Public sector GHG performance from 2010-2018. -31.35%0.40%2.08%-14.19%-7.93% -7.94%-0.35-0.3-0.25-0.2-0.15-0.1-0.0500.05Core Government Crown Health Schools HEIs Total2010-201874 Figure 3.12: GHG for HEIs in BC. BC emitted 56,207 MtCO2e in 1990, in 2005 the number rose 17% to 65,791 MtCO2e. The latest reported emissions were 64,462 MtCO2e. Figure 3.13 shows a trendline analysis carried on the reported BC provincial emissions from 1990-2017. BC emission increased by nearly 15% from 1990 to 2017. By considering the linear growth rate into perspective, BC is expected to emit 68,231 MtCO2e, increasing nearly 6% of the last reported emissions. This is a far stretch from the Paris accord for reduction GHG, which required parties to reduce 30% of their baseline values. 150,959161,727 158,378 154,186174,955141,985 138,022148,324138,990020,00040,00060,00080,000100,000120,000140,000160,000180,0002010 2011 2012 2013 2014 2015 2016 2017 2018TCO2eHEIs in British Columbia75 (a) 76 (b) Figure 3.13: The province of BC GHG emission trend analysis (a) Trend line, (b) Residual plots. The trend is estimated to follow equation (3.1), and the results per year with errors are listed in Table 3.11. ( ) 59873 203.9*Y t t= + Equation 3.1 Table 3.11: Estimated GHG trend results for the province of BC. Year GHG MtCO2E t Trend Error 1990 56207.76 1 60077.13 -3869.36 1991 55046.82 2 60280.99 -5234.17 1992 53839.8 3 60484.85 -6645.06 1993 56328.06 4 60688.72 -4360.66 1994 58991.59 5 60892.58 -1900.99 1995 62477.79 6 61096.45 1381.339 1996 64123.73 7 61300.31 2823.416 1997 62962.74 8 61504.18 1458.559 1998 64058.33 9 61708.04 2350.29 1999 65720.64 10 61911.91 3808.728 2000 66738.52 11 62115.77 4622.746 2001 68345.94 12 62319.64 6026.301 2002 65423.61 13 62523.5 2900.105 2003 66447.83 14 62727.37 3720.46 2004 68015.76 15 62931.23 5084.525 2005 65791.73 16 63135.1 2656.635 2006 64134.33 17 63338.96 795.365 2007 64762.75 18 63542.83 1219.927 2008 64937.4 19 63746.69 1190.703 2009 61792.46 20 63950.56 -2158.09 2010 61440.91 21 64154.42 -2713.51 2011 61902.68 22 64358.29 -2455.61 2012 63008.12 23 64562.15 -1554.03 2013 63701.56 24 64766.02 -1064.45 2014 62971.09 25 64969.88 -1998.79 2015 61918.87 26 65173.75 -3254.87 2016 63667.24 27 65377.61 -1710.37 2017 64462.34 28 65581.47 -1119.13 2018 65785.3 29 2019 65989.2 30 2020 66193.1 31 2021 66396.9 32 2022 66600.8 33 2023 66804.7 34 2024 67008.5 35 2025 67212.4 36 2026 67416.3 37 2027 67620.1 38 2028 67824 39 2029 68027.9 40 2030 68231.7 41 77 These figures underly that even in the most progressive provinces in Canada. Achieved a reduction is an ambitious goal and thus needs a better understanding of institutions and business sectors' deep characteristics. HEIs are not better than the national trend or the provincial trends. HEIs have sought to report their environmental performances in sustainability reporting tools, in addition to commitments carried by many HEIs declarations and memorandums discussed in section 3.1. Reporting is gaining prominence among universities. The STARS database reports that 401 institutions reported using the STARS 2.0 reporting tool, which increased 170% in nearly six years [156]. Universities have shown different performances depending on the region they are positioned within Canada. This is due to their extensive dependencies on the electricity grid that feeds the province. For example, the UBC in BC reduced its emissions by 29%, slightly off its initial target of 33% reduction by 2015 [82,254]. In other demographics in Canada, like in Alberta, some HEIs have their emissions increased. This is due to the type of fuel used to generate electricity for universities. (i.e., BC derives most of its energy from hydro dams and natural gas, Alberta relies heavily on fossil fuels) Even though they have pledged plans and actions to reduce them and are highly ranked in the STARS reporting system. For example, the University of Alberta reported an increase of nearly 34% in emissions, as shown in Table 3.12. Table 3.12: Comparison between rating reports and performance in GHG reductions. HEI Reporting Years Baseline Last Reporting Percent Growth STARS Ranking [84] UBC (Vancouver) 12 61,090 42,786 -29.96 Gold Dalhousie University 9 106,178 87,056 -18 Gold University of Calgary 2 239,955 182,112 -24.11 Gold University of Alberta 2 212,190 285,815 34.7 Gold McGill University 7 61,553 56,004 -9.01 Gold 78 Saskatchewan University 5 152,717 154,063 0.88 Silver University of Toronto (St. George) 8 132,308 123,593 -6.59 N/A The trend analysis for UBC emissions is shown in Figure 3.14. UBC set goals for a 33% reduction by 2015, 67% by 2020, and a neutral campus by 2050. UBC achieved 28% reductions by 2015. By applying trend analysis on UBC-reported emissions. UBC is expected to emit 34,747.2 tCO2e, which would reduce almost 43% of the 2007 baseline value. The planned reduction was to reach a value of nearly 20,160, as shown in Table 3.13. 79 (a) 80 (b) Figure 3.14: GHG trend analysis for the UBC, (a) Trend line analysis, (b) Residual plots. Table 3.13: UBC trend analysis results. GHG Emissions (Scope 1 & 2) Year Reported Target Predicted 2007 61,090 2008 62,470 2009 60,580 2010 58,353 2011 63,803 2012 60,715 2013 52,832 2014 47,814 2015 42,846 40,930 2016 40,536 2017 42,786 2018 39,556.4 2019 37,151.8 2020 20,160 34,747.2 UBC is a pioneer among universities in tackling climate change in the operations and teaching realms and is perhaps among the best performing universities that have reported slight difficulties in achieving their targets. Among 438 universities pledged to reduce GHG reduction and reach carbon neutrality by a defining year. Table 3.14 shows that universities are stretching their goals to reach a neutral campus even beyond 2050. Table 3.14: Second Nature carbon neutrality. Neutrality Date No. of HEIs Before 2030 127 Beyond 2030 272 Beyond 2040 217 Beyond 2050 52 Even though universities have fallen behind their commitments to reduce their footprint on the ecosystem, institutions need to use a more specialized tool to assess, report and manage their performances. Universities must adopt new methods to help them allocate the limited resources to reflect better results towards their self-imposed targets and goals. 81 Decision Making Multi-criteria decision analysis (MCDA) evaluates a problem by giving an order of preference for multiple alternatives based on several criteria that may have different units (Hermann et al., 2007). MCDA is used to compare and rank different alternatives and evaluate their performance according to the established criteria. MCDA can efficiently deal with different criteria having their dimensions; however, the subjectivity of weighing the criteria sometimes becomes a controversial step in MCDA applications. There are different compensatory and non-compensatory MCDA methods used based on whether the benefits of one attribute (or criteria) can be traded for disadvantages of another or not [255]. As the information on the attributes will be available from Life Cycle Analysis (LCA) and the trade-offs among the attribute values will be possible, appropriate compensatory methods will be used in this research, e.g., weighted sum method (WSM), analytical hierarchy process (AHP), Elimination, and Choice Translating Reality (ELECTRE), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), etc. Different types of uncertainties associated with data limitations, equipment or monitoring errors, and vagueness in expert opinion, fuzzy logic can be integrated with the selected MCDA method. Wang et al. (2009) carried out a detailed review on MCDA to aid in sustainable energy decision-making. They found that investment costs and CO2 emissions are the top-level indicators, and Analytical Hierarchical Process (AHP) is the most commonly used MCDA method applied in sustainability assessment studies [256]. 82 Hermann et al. (2007) developed a framework integrating performance indicators, LCA, and MCDA to evaluate the overall environmental impacts of a project. They implemented this framework for a case study of the pulp production industry in Thailand. This framework can be modified to assess the performance of HEIs and incorporate uncertainty analysis, as shown in Figure 3.15. With the implementation of this integrated framework, project-specific performance indicators (PI) can be selected. For instance, indicators presented in Appendix A are primarily used to report and secure universities' desired sustainability ranking by STARS. The PIs related to WEC can be selected, and suitable data variables can evaluate these indicators. The performance of different project components (e.g., infrastructure, operations, quality of service, etc.) will be evaluated using these indicators. These components can be subsequently classified for a detailed lifecycle approach. Figure 3.15: Integrated decision framework modified after Harmann et al. (2007). This integrated framework has not been investigated for the sustainability assessment of HEIs. In order to improve the decision-making process towards achieving sustainability in HEIs, there is a need to integrate PIs with uncertainty analysis, MCDA, and CPI methodology. This framework will identify suitable interventions and retrofitting technologies that guarantee sustained improvement in the realm of the sustainability performance of universities. Existing performance indicators Missing data Data collection & conversion Classification Uncertainty PI MCDA valuation step 83 Uncertainty Analysis There are two types of uncertainties in reporting sustainability assessment; aleatory and epistemic uncertainty [257]. The aleatory uncertainty is the one associated with incomplete data and is addressed using probability theory (i.e., probabilistic techniques). The other type of uncertainty is due to the vagueness that is a result of using linguistic scoring systems [258]. Such as those that contain rankings like Gold, Silver, or LEED-certified, and so on. These uncertainties can be addressed by using fuzzy logic techniques [259]. Fuzzy techniques have been applied in assessing sustainability. Alghamdi et al. (2020) assessed the performances of 71 academic buildings in two campuses in Canada using fuzzy clustering analysis [28]. Santamouris (2007) used fuzzy clustering techniques to address the uncertainty related to classification in energy benchmarks in schools in Greece [239]. Chung (2006) used fuzzy linear regression to classify and benchmark commercial buildings [211]. Haider et al. (2018) used fuzzy synthetic evaluation to assess sustainability in a small neighborhood [259]. A combination of probabilistic and fuzzy synthetic evaluation has been reported in risk assessment studies [257,260,261]. Alghamdi et al. (2021) assessed the spatiotemporal variability of WEC flows of HEIs and used an integrated method to assess two types of uncertainties commonly overlooked in benchmarking techniques. 84 Inter-University Benchmarking Part of the content in this chapter has been published in Sustainability (2019) as a research article titled “Inter-University Sustainability Benchmarking for Canadian Higher Education Institutions: Water, Energy, and Carbon Flows for Technical-Level Decision-Making” [17]. Background The commercial and institutional (C&I) sector has the largest increases in energy and greenhouse gas (GHG) emissions among all sectors in Canada. However, this sector was reported to have the least improvements in terms of energy intensity [7]. The education sub-sector is one of the major GHG emitters among 11 sub-sectors, accounting for 13% of the total secondary source emissions [22]. Educational services are essential activities in the education sub-sector that emit significant GHGs [20]; Canadian HEIs consume around two-thirds of the total energy fed to the educational sector [20]. HEI buildings were 60% more energy-intensive than office buildings and twice as intensive as manufacturing buildings [262,263]. Moreover, the energy use intensity of educational buildings in different academic institutes varies significantly. Universities were reported to consume 1.79 GJ/m2, followed by colleges (1.17 GJ/m2) and primary and secondary schools (0.82 GJ/m2). The Canadian C&I sector average was 1.28 GJ/m2 in 2009 [20]. HEIs are a pivotal point for any government seeking to curb GHG emissions in their country. The WEC flows of Canadian HEIs are still unclear. An inter-university benchmarking framework is proposed to fill the gap. The infrastructure (engineered) systems are the pivotal units responsible for most energy and water consumption. They may have been built, retrofitted, or replaced at different 85 times using different materials and technologies. Consequently, infrastructures have varying efficiencies, designs, building envelopes, and environmental impacts. For technical-level decision-making for improving the sustainability of engineered systems, HEIs need to be benchmarked based on their water, energy, and carbon flows. These flows are believed to be the most impactful in terms of embedded energy and carbon footprints [264]. Sustainability reporting was gaining momentum in Canadian HEIs with respect to its standing a decade ago. However, the reports generally lag in terms of limited scopes, especially environmental impacts and their ability to transform the information to decision-makers. Furthermore, current reports have limited value and are potentially misleading as a tool to inform sustainability-oriented decisions “effectiveness of these efforts is being communicated to stakeholders remain unexplored” [265]. Factors that influence university energy usage include climate, occupancy, size, and type of buildings within the institutions. Universities that host large laboratory buildings are expected to use higher energy than those not [266]. 17 Canadian university presidents and vice presidents were surveyed to determine the most challenging aspects they will face in the next ten years. Nine Items were identified, three of which were related to the sustainability of buildings [267]. Furthermore, a study investigating barriers to energy efficiency in 15 small, medium, and large Canadian HEIs was conducted. A set of 21 possible barriers to the implementation of energy-efficient projects was surveyed. Results highlighted that, on average small and medium-size HEIs face more barriers than do large universities, specifically in the “department not accountable for energy costs” whereas, SMU scored a 4.1 on average while larger HEIs 86 scored on average 2. This was likely a result of a limited ability to track energy use for SMU [263]. Large-sized HEIs consume more water, energy and generate higher GHG emissions as compared with small-and-medium-sized HEIs. However, they provide larger economies of scale than their smaller counterparts when it comes to GHG emissions/number of full-time equivalent (FTE) students. For example, large-sized HEIs can adapt to a wide variety of sustainable solutions and technologies that small campuses cannot afford, e.g., biofuel generation as an alternate energy source can generate a huge amount of waste. Small-sized HEIs face challenges such as the affordability of hiring skilled staff, whereas it is relatively easier to make decisions and implement strategies in smaller organizations [51]. A methodology is greatly needed for the environmental assessment of HEIs of different sizes. The size of HEIs can be classified by the number of FTE students because there is strong evidence that research student FTE is highly correlated to the energy consumption at UK universities (R = 0.91) [48]. Diverse GHG emission trends have been observed in Canadian HEIs; for instance, the University of British Columbia, Vancouver campus (UBCV) emits nearly 38,000 MtCO2e (~ 0.76 MtCO2e/FTE) in 2018 [268-270]. The University of Alberta (UoA), which is operated in cold weather conditions most of the time, emits nearly 300,000 t CO2e (~10.06 t CO2e/FTE student) 2012/2013 [83]. The latest emission reports show a slight improvement [271]. Emissions are influenced by climate, source of energy, and regulation. The energy sources for operations at UBCV and UoA are mainly hydropower-based and fossil fuel-based electricity. In contrast, the University of Alberta relies heavily on the fossil fuel electricity grid. Therefore, the type of energy sources and heating and 87 cooling requirements (i.e., climatic inputs) play an essential role in the operation lifetime of a building. A university campus consists of different infrastructure systems that consume vast water and energy quantities throughout their life cycles. Figure 4.1 shows that most of the energy over the entire lifespan of a building is consumed during the operational phase [13]. During the operational phase, buildings are believed to generate 50% to 80% of the total emissions in the entire lifespan [11-13]. Managing the environmental impacts and optimizing water and energy consumption are critical steps in pursuing objectives like zero-energy, green academic buildings, and sustainable campuses. Figure 4.1: Primary energy use in a buildings life. In Canada, region-based goals have been set by the local authorities for HEIs to achieve specific GHG emission reduction targets. For example, based on the 2007 baseline emission levels, UBCV set goals for a 33% reduction by 2015, 67% by 2020, and 100% by 2050 [5]. However, UBC achieved only a 28% reduction in its GHG emission until 2015 [82,254]. The result reveals an urgent need for more aggressive efforts towards Material Production & Transportation2.20%Operational 94.31%Water services3.30%Demolition0.20%88 sustainability, even in highly motivated HEIs like UBC. According to a Ministry of Environment and Climate Change Canada (ECCC) report on GHG emissions from large facilities, the top 21 GHG emitting HEIs are listed in Table 3.1 [272]. HEIs have set targets and goals on an individual basis through declarations [42,87,103,106,118]. However, many of these goals have not been achieved to meet the intended targets to date [60,113,273]. The purpose of WEC flows benchmarking is to identify a set of points of reference so that a reported event may be measured over time [274]. To establish benchmarks for WEC flows in HEIs, there are several challenges to overcome: 1) due to sustainability’s interdependent nature, measuring sustainability may be a complex challenge [159,275]; 2) there is no universally accepted process to classify universities on the basis of the number of FTE students, Mariorano and Savan (2013) classified 15 Canadian universities into small, medium and large size institutions without referring to a method of classification or on which basis the classification was established [263]; 3) lack of standardized methods to assign weights to different parameters when assessing sustainability holistically [275]; 4) due to the extensive data requirements for sustainability assessment methods, providing meaningful methods to aid technical-level decision making for continuous performance improvement and to aid universities in making long-term sustainability commitments and goals [263] and finally 5) lack of data to complete the benchmarking. Data limitations in terms of water, energy, and carbon parameters are reported for each institution. Regression analysis could be a useful technique to predict the water, energy, and carbon (WEC) performance of HEIs based on available data. Few instances, these cases were missing and could not be found; thus, a regression analysis 89 is used to overcome this limitation. Regression analysis is commonly used to interpret the missing data, it also establishes power functions and shows the correlation between dependent and independent variables. Chung et al. (2006) applied multiple regression analysis to develop a relationship between Energy Usage Intensities (EUI) and various explanatory factors for 30 commercial buildings in Hong Kong. Sun et al (2006) used regression analysis on 95 buildings to benchmark their energy performances. In the past, Birtles & Grigg (1997) studied the energy efficiency of buildings and benchmarked them via regression modeling. The present chapter develops a framework for inter-university WEC flows benchmarking. The developed framework was applied to evaluate the WEC flows in 34 Canadian HEIs with different sizes. The HEIs occupy around 13 million m2 of building area, which accommodate 617,325 FTE students. The combined energy usage in these HEIs was approximately 18.7 MMBTU (Million British Thermal Units) / year. In the inter-university benchmarking framework, a statistical analysis was performed first to fit a relationship between the response variable (GHG emissions) and other parameters such as climate, energy, water, area, and the number of occupants; Secondly, a normalizing method is proposed for the different parameters with different units such as WEC, the number of students, and floor area, i.e. (normalization and a weighting method are proposed to rank the criteria); and, finally, a mathematical model was developed to rank the HEIs based on their overall WEC flows impact. The findings provided a platform for identifying the underperforming HEIs and comparing them with the excelling HEIs to highlight the most critical factors affecting the ranking score. The existing approaches discussed in the following section aggregate the 90 performance of all the functions to rank HEIs based on their overall sustainability. The outcomes mask the facts about the utilization of natural resources and environmental impacts in an HEI. Hence, a sustainability assessment of HEIs in terms of WEC flows is essential for technical-level decision making about improving their performance on a continuous and long-term basis (e.g., greener energy source, efficient plumbing fixtures, and lighting systems, recycling, and building retrofits). Methods and Procedure In an attempt to aid Canadian HEIs in meeting the set of sustainable development targets (i.e., GHG reduction goals), a framework is proposed in Figure 4.2 to classify, benchmark, rank, and weight the WEC factors in Canadian HEIs. To achieve sustainability in HEIs, these institutions need processes and methods for assessment and comparison to determine their performances with regard to each other. 91 Figure 4.2: Inter-University Benchmarking Framework. 4.2.1 Data Collection The methodology adopted to assess the performance of WEC flows in HEIs is shown in Figure 4.2. Data are collected from multiple sources. The WEC flows are obtained from the HEIs sustainability reporting, government reports (for GHG reporting provinces), and the STARS database. These included the water flows, energy flows, and GHG as shown in Table 4.1, additional data that are reported to influence the WEC parameters such as climatic factors, namely the heating degree days (HDD), cooling degree day (CDD), area of buildings, and enrollment data are collected from the STARS database and the relevant climate region reports [276]. HDD and CDD indicate the heating and cooling requirements Development of utility functions for WEC flows Sized-based classification of HEIs Regression analysis Model Selection Prediction Model Normalization of factors affecting WEC flows Development of modified coefficient (Cm) Comparison with STARS results and ranking of HEIs Data collection from HEIs 92 of a building. HDD is a measure of the number of degrees and days that the outside air temperature is lower than a specific base temperature that necessitates the heating system's operation. However, the air conditioning system would start if the outside air temperature rises above the base temperature. Many of these parameters are widely used in energy performance assessment and have been extensively covered in the literature [277] in addition to factors like the behavioral aspects of consumption [211]. A statistical summary of the data is presented in Table 4.2. 93 Table 4.1: Higher education institutions data. Higher Education Institutions Location Code Avg. C° HDD CDD FTE Area of buildings (m2) GHG (MtCO2 e) Energy (MMBTU) Water (Gallons) Royal Roads University Victoria, BC S1 10.00 3,418 189 971 39,912 1,272 34,968 6,535,919 Loyalist College Belleville, ON S2 8.10 4,214 300 1,925 73,079 2,484 52,195 16,492,356 Selkirk College Castlegar, BC S3 8.90 3,667 322 2,289 46,357 1,021 35,055 7,253,960 Fleming College Peterborough, ON S4 6.20 3,812 160 5,792 82,790 4,278 46,370 10,327,014 Polytechnique Montreal Montreal, QC S5 6.15 2,514 164 6,575 121,488 3,590 150,644 32,992,976 University of Winnipeg Winnipeg, MB S6 2.98 6,466 160 7,061 118,928 2,330 124,443 17,067,760 Okanagan College Kelowna, BC S7 8.10 3,418 189 7,655 77,983 1,688 47,689 17,022,397 Sheridan Institute of Technology and Advanced Learning Oakville, ON S8 8.10 4,187 337 8,134 180,129 9,070 207,893 32,583,767 Lakehead University Thunder Bay, ON S9 3.20 5,271 111 8,313 119,619 8,381 184,031 50,686,612 Thompson Rivers University Kamloops, BC S10 9.30 3,601 501 8,315 108,027 3,966 109,667 33,564,007 University of Ontario Institute of Technology Oshawa, ON S11 8.10 4,448 177 8,746 122,777 5,358 110,634 6,326,920 Camosun College Victoria, BC S12 10.00 2,569 139 9,793 87,643 1,371 48,391 14,919,200 Niagara College Canada Welland, ON M1 8.80 4,152 332 10,159 103,953 5,458 126,362 22,867,790 Nova Scotia Community College Dartmouth, NS M2 7.50 7,406 339 10,420 257,194 20,198 166,503 21,774,381 University of Regina Regina, SK M3 3.10 11,296 226 11,550 231,643 15,021 334,570 72,860,421 MacEwan University Edmonton, AB M4 4.18 6,677 68 11,838 241,804 35,632 267,244 30,305,554 Northern Alberta Institute of Technology Edmonton, AB M5 4.18 5,507 176 12,479 297,356 54,128 366,520 39,147,392 Fanshawe college London, ON M6 7.90 4,233 349 13,553 181,160 5,752 128,990 39,285,555 Dalhousie University Halifax, NS M7 7.50 4,049 161 15,794 420,89 88,097 65,374 197,980,185 Mohawk College Hamilton, ON M8 7.92 3,338 361 15,882 127,884 4,919 813,581 14,178,907 University of Saskatchewan Saskatoon, SK M9 3.30 9,441 468 18,082 623,041 152,453 1,443,822 317,337,763 University of Victoria Victoria, BC M10 10.00 2,528 76 19,068 399,590 10,534 351,261 146,493,594 Simon Fraser University Burnaby, BC L1 9.63 3,054 2 24,774 425,574 16,751 519,548 155,083,259 University of Manitoba Winnipeg, MB L2 2.98 5,825 254 26,621 477,252 113,944 815,213 133,223,285 Concordia University Montreal, QC L3 6.15 4,526 296 27,960 544,189 9,660 532,330 99,498,500 University of Calgary Calgary, AB L4 4.40 7,842 390 28,251 1,010,644 185,479 2,109,485 348,360,277 Humber College Toronto, ON L5 9.38 5,824 632 28,575 253,360 8,914 232,893 40,139,000 Western University London, ON L6 7.90 3,864 468 29,050 766,865 76,492 1,013,471 293,172,067 University of Alberta Edmonton, AB L7 4.18 8,406 464 29,740 1,562,124 285,815 2,277,665 411,861,534 McGill University Montreal, QC L8 6.15 6,921 756 31,755 791,139 43,249 1,159,682 457,545,994 Universite Laval Quebec, QC L9 4.20 9,574 218 37,055 715,522 26,841 1,805,661 209,791,404 George Brown College Toronto, ON L10 9.38 3,286 563 39,517 155,788 6,614 173,114 36,784,637 University of British Columbia Vancouver, BC L11 10.40 5,093 95 43,509 1,434,512 52,350 1,812,111 806,266,185 York University Toronto, ON L12 9.38 3,458 605 56,123 810,884 40,410 1,053,006 226,024,521 *Colored cells are the figures calculated through the prediction model and are not reported values.94 Table 4.2: Statistical summary for the collected data. Input Units N Min Max Mean SD Q1 Q3 Climate °C 34 2.980 10.400 6.989 2.458 4.195 9.320 HDD - 34 2515 11296 5114 2210 3448 6519 CDD - 34 2.0 756.0 295.7 179.6 160.8 409.1 FTE - 34 971 56123 18157 13273 8268 28332 Area m2 34 39913 1562125 382680 385382 116203 563903 GHG Mt CO2e 32 1021 285815 38339 64047 4044 50075 Energy MMBTU 34 34968 2277665 550600 652756 110392 864778 Water Gallons 31 6326921 806266185 128404562 180177714 21774381 209791404 STARS, a self-reporting system, is a voluntary reporting system where universities can correct a mistakenly incorrect input value. For example, the Sheridan Institute of Technology reported its mistakes regarding GHG emission inventory [278]. There is no mechanism for preventing incorrect inputs; hence, some abnormalities in the data have been noticed. For instance, Dalhousie University reported a value of 84.91 MT CO2e GHG emissions (i.e., OP1 in Appendix A) [279]; however, this value is significantly lower than the average value of other universities, i.e., 40,004 MT CO2e. On rechecking this value with the reported field in the university’s sustainability report, it was found to be 88,097 MT CO2e [280]. The majority of the data for this study was collected from STARS. Additional data was obtained from the sustainability reporting cards published by each university and the ECCC’s report on the GHG emissions of large facilities in Canada, i.e., GHG scope 1 and 2 [72]. The World Resource Institute/World Business Council for Sustainable Development (WRI/WBCSD) classified GHG emissions into three groups. Scope 1 represents direct emissions resulting from onsite or within an organization, e.g., boilers, combustion, and mobile source combustion; Scope 2 consists of indirect sources from 95 outside an organization, e.g., purchased electricity; Scope 3 includes other indirect sources, e.g., employee and student commuting and business travel [251]. 4.2.2 Classification There is no universally accepted process to classify universities based on the number of FTE students. For example, Wann et al. [281] classified universities as small, medium, and large with numbers of FTE students less than 6,589, 6,590–18,631, and greater than 18,631, respectively. Herremans and Allwright [282] surveyed North American HEIs and classified them as small, medium, and large with FTE students less than 10,000, 10,000–20,000, and greater than 20,000, respectively as per Disterheft et al. (2012) classification [179]. The later classification seems more rational with FTE students in the thousands and is used in the present chapter and this thesis whenever reference is made to the size of a university. 4.2.3 Prediction Module During the data collection, it was observed that various databases define parameters differently. For instance, while collecting GHG values (i.e., scope 1 and 2) from different reports, the scope of GHG and what was considered an offset were distinguished to make sure the comparison is uniform. Due to the missing parameters in this analysis, two approaches have been taken to solve this issue; the first consisted of a closest-to-range-average, which is an average of the closest reporting climatic region. For example, data regarding parameters such as HDD and CDD were not available for some HEIs participated in STARS ranking, such as the parameters to the corresponding HEIs S1, S7, and M6, as shown in Table 4.1. These values could not be accessed in the local climate databases either. The approach is taken to overcome this limitation, the averages 96 of all HEIs serving in the same province (climatic region) were used to determine the missing factors (e.g., the average of all HEIs in Ontario was used to find the missing factor information for universities in Ontario); Secondly two regression analyses are conducted to predict two missing GHG data for Lakehead University and The University of Regina S9 & M3. Another regression analysis is conducted to predict the three missing water values for HEIs: Selkirk College (S3), Camosun College (S12), and Concordia University (L3). 4.2.4 Normalization of Factors Affecting WEC Flows GHG emissions in HEIs are a combination of different factors (utilities); these utilities include energy & water consumption, the FTE, and floor area. These utilities are measured in different units. In order to conduct inter-university benchmarking of HEIs performances, addressing the different units through normalization of the utilities is the first step. The degree to which each factor affects GHG varies and depends on many variables, such as the occupant’s behaviors and other variables not addressed in this research. Therefore, the utility of the overall GHG emissions for all the HEIs is estimated as a function of these different utilities (i.e., water, energy, etc.). This will allow for the sustainability scores of WEC flows to be compared with other used assessment methods such as the STARS reported metrics. All the HEIs were ranked based on their sustainability performance. Details of the methodology steps are presented in Figure 4.2 and are described in the following subsections. The primary assumption is that universities seek to reduce their GHG emissions, knowing that GHG is a function of several factors such as energy, water consumption, the number of FTE students, and floor area; and these factors will be considered as utilities in the 97 following sections. The inputs are measured in different units and thus are replaced with their utilities. Thus, utility is defined as the parameters’ usages of energy, FTE, floor area, water consumption, etc. For sustainable operations, the adverse effects associated with these inputs need to be reduced to minimize GHG emissions. In this research, different universities are compared based on the total GHG emitted from these input utilities. At the same time, the total GHG emissions are also assessed in terms of the total utility of a building. Thus, universities are labeled as 𝑈𝑖, where ⅈ= (1, …𝑁), and the total GHG emitted is denoted by ?̃?[𝑈]𝑖. The symbols used to define the utilities are derived from the Taylor Series variables, which Faust and Baranzini (2014) previously used to study the economic performance of 141 water utilities [283]. Van Calker et al. [284] used utility functions to assess the economic, social, and ecological sustainability of dairy farms. In order to obtain a rational functional relationship of GHG on such factors, every parameter needs to be normalized for a rational comparison. So, the normalized utility of GHG (?̃?[𝑈]𝑖) emitted by a university 𝑈𝑖 as can be defined as: ?̃?[𝑈]𝑖 =𝐺[𝑈]𝑖?̅?[𝑈] , ?̅?[𝑈] = ∑ 𝐺[𝑈]𝑖𝑁𝑖=1 Equation 4.1 In general, the total amount of GHG emitted is a function of many other parameters like the total energy consumed, the total water consumed, the total area, and the FTE, etc. This set of parameters (i.e., WEC, FTE, Area, Climate) for a university 𝑈𝑖 as 𝑄[𝑈]𝑗𝑖, j = (1,…,n), is the total utility for the corresponding parameter. If 𝐸[𝑈]𝑖 = 𝑄[𝑈]1𝑖 denotes the total energy consumed, 𝑊[𝑈]𝑖 = 𝑄[𝑈]2𝑖 , may donate the total water consumed, 𝐴[𝑈]𝑖 = 𝑄[𝑈]3𝑖 , corresponds to the total area building area, 𝑆[𝑈]𝑖 = 𝑄[𝑈]1𝑖 represents the number of FTE in the building etc. These quantities can, 98 in turn, depend on other parameters, such as climatic conditions, building envelope, and age of infrastructure. All of these factors can be normalized to obtain a rational functional dependence of emitted GHG on these factors as: ?̃?[𝑈]𝑖 =𝐸[𝑈]𝑖?̅?[𝑈] , ?̅?[𝑈] = ∑ 𝐸[𝑈]𝑖𝑁𝑖=1 Equation 4.2 ?̃?[𝑈]𝑖 =𝑊[𝑈]𝑖?̅?[𝑈] , ?̅?[𝑈] = ∑ 𝑊[𝑈]𝑖𝑁𝑖=1 Equation 4.3 ?̃?[𝑈]𝑖 =𝐴[𝑈]𝑖?̅?[𝑈] , ?̅?[𝑈] = ∑ 𝐴[𝑈]𝑖𝑁𝑖=1 Equation 4.4 ?̃?[𝑈]𝑖 =𝑆[𝑈]𝑖𝑆̅[𝑈] , 𝑆̅[𝑈] = ∑ 𝑆[𝑈]𝑖𝑁𝑖=1 Equation 4.5 where, ?̃?[𝑈]𝑖 is the normalized energy consumed, ?̃?[𝑈]𝑖 is the normalized water consumption, ?̃?[𝑈]𝑖 represents the normalized area heated, and the normalized number of FTE in the building can be defined as ?̃?[𝑈]𝑖. Therefore, the overall GHG emissions can be considered a function of all these different quantities and can be calculated as: ?̃?(𝑈)𝑖 = 𝑓[?̃?(𝑈)𝑖 , ?̃?(𝑈)𝑖, ?̃?(𝑈)𝑖 , ?̃?(𝑈)𝑖] Equation 4.6 In an HEI, total energy consumed during a year is a function of both HDD and CDD corresponding to winter and summer seasons, respectively. Therefore, in energy calculations, consideration must be given to both seasons. In this regard, ?̃?1𝑖 needs to be estimated as utilities obtained from 𝐻𝐷𝐷ⅈ, and ?̃?2𝑖 as the utilities obtained from 𝐶𝐷𝐷ⅈ. These can be defined as: 99 ?̃?1𝑖 =𝐻𝐷𝐷ⅈ?̅?𝐷𝐷ⅈ , ?̅?𝐷𝐷ⅈ = ∑ 𝐻𝐷𝐷ⅈ𝑁𝑖=1 Equation 4.7 ?̃?2𝑖 =𝐶𝐷𝐷ⅈ𝐶̅𝐷𝐷ⅈ , 𝐶̅𝐷𝐷ⅈ = ∑ 𝐶𝐷𝐷ⅈ𝑁𝑖=1 Equation 4.8 Above utilities are included to normalize the effect of temperature variations because heating and cooling can increase the energy consumption in cold and hot climates, respectively. However, higher-order corrections are neglected; only linear functional dependence of these utilities is considered as a first approximation: ?̃?(𝑈)𝑖 = 𝐶𝑖 [?̃?(𝑈)𝑖 + ?̃?(𝑈)𝑖 + ?̃?(𝑈)𝑖 + ?̃?(𝑈)𝑖], Equation 4.9 Now we can assume a general functional form for this function and write ?̃?(𝑈)𝑖 = 𝐶01𝑖𝑓1𝑖 ?̃? [𝑈]𝑖 + 𝐶02𝑖𝑓2𝑖 ?̃?[𝑈]𝑖 + 𝐶03𝑖𝑓3𝑖?̃?[𝑈]𝑖+ 𝐶04𝑖𝑓4𝑖?̃?[𝑈]𝑖 + [𝐶11𝑖𝑓1𝑖 ?̃? [𝑈]𝑖 + 𝐶12𝑖𝑓2𝑖 ?̃?[𝑈]𝑖 + 𝐶13𝑖𝑓3𝑖?̃?[𝑈]𝑖+ 𝐶14𝑖𝑓4𝑖?̃?[𝑈]𝑖]2+ ⋯ Equation 4.10 Where 𝑓𝑗𝑖 With j = (1,...,4) are coefficients that measure the dependence of these quantities on other parameters like relative humidity, climatic conditions, temperature, etc. In this chapter, we will consider 𝑓1𝑖 =1 ?̃?1𝑖?̃?2𝑖 , and 𝑓2𝑖 = 𝑓3𝑖 = 𝑓4𝑖 = 1 . Here we have defined ?̃?1𝑖 , as the utility obtained from heating degree day 𝐻𝐷𝐷𝑖, and ?̃?2𝑖 is the utility obtained from cooling degree days 𝐶𝐷𝐷𝑖 And they are given by equations 4.7 & 4.8. These climate utilities are introduced to normalize the effect of temperature variation. This is due to the variability of heating and cooling during summer and winter, affecting energy consumption accordingly. Thus, it is reasonable to normalize the total energy 100 consumption by these climate utilities. By neglecting the higher-order corrections and only keep the linear functional dependence of these quantities as a first approximation. ( ) [ ( ) ( ) ( ) ( ) ]i i i i i iG U C E U W U A U S U= + + + Equation 4.11 Now equation 4.11 can also be written as: ?̃?[𝑈]𝑖 = 𝐶𝑖[|?̃?[𝑈]𝑖 ?̃?1𝑖?̃?2𝑖|⁄ + |?̃?[𝑈]𝑖| + |?̃?[𝑈]𝑖| + |?̃?[𝑈]𝑖|], Equation 4.12 Where, 𝑟1𝑖 =𝑐𝑜2𝑖𝑐01𝑖 , 𝑟2𝑖 =𝑐𝑜3𝑖𝑐01𝑖 , 𝑟1𝑖 =𝑐𝑜4𝑖𝑐01𝑖 Equation 4.13 The coefficient jir with (1,...,3)j = measures the relative utility of these quantities as compared to the normalized energy consumption. For instance, it may be difficult to obtain one quantity in a particular area compared to an area with no information provided. In this model, for simplicity, 1jir = . The 𝐶𝑖 is a coefficient that will be used to measure the relative utility of these parameters when compared with FTE, floor area, and water usage. For example, water and energy cannot be compared due to different units used in a single HEI. Therefore, the comparison of the utility for each parameter may be more appropriate. The reason is that all the quantities need to be converted into a consistent unit to incorporate different quantities into the benchmarking process. 4.2.5 Ranking of HEIs based on WEC Flows The coefficients 𝐶𝑖, with ⅈ = (1, … 𝑁), measure the relative utility of these quantities (i.e., WEC) as compared to the normalized energy consumption. These quantities may vary for different regions. For example, it might be more difficult to obtain water in an arid environment) as compared to Canada, or energy is easy to generate in a region with extensive natural resources such as oil. A simpler form of the model can be written as: 101 𝐶?̇? = ?̃?[𝑈]𝑖|?̃?[𝑈]𝑖?̃?1𝑖?̃?2𝑖| + |?̃?[𝑈]𝑖| + |?̃?[𝑈]𝑖| + |?̃?[𝑈]𝑖| = ?̃?[𝑈]𝑖?̅?[𝑈]ⅈ Equation 4.14 If 𝐶1 > 𝐶2 and ?̅?[𝑈]1 = ?̅?[𝑈]2 , then ?̃?[𝑈]1 > ?̃?[𝑈]2 , indicating that university 𝑈1 will generate more GHG emissions for the same average utility than university 𝑈2. This may be attributed to the use of cleaner or less emissions-intensive energy sources. Because a lower value of Ci indicates better environmental performance of the corresponding university, the final sustainability index (SI) is calculated as: 𝑆𝐼 = 1 − 𝐶𝑖 Equation 4.15 Results and Discussion 4.3.1 Regression Models In order to find the two regression equations that predict GHG and Water usage for 5 HEIs (S3, S9, S12, M3, L3), the normality of the data is investigated using the Anderson-Darling Test in Minitab. Most parameters are not normally distributed except for the CDD parameter, as shown in Table 4.3. The distribution of the reported parameters is shown in the following Figure 4.3. Table 4.3: Anderson-Darling Normality Test and the Johnson Transformation. Parameter P-Value Normality > 0.05 Johnson Transformation P-Value Normality C <0.005 Not normal 0.263 Normal HDD <0.005 Not normal 0.703 Normal CDD 0.108 Normal FTE <0.005 Not normal 0.438 Normal Area <0.005 Not normal 0.814 Normal GHG <0.005 Not normal 0.960 Normal Energy <0.005 Not normal 0.839 Normal Water <0.005 Not normal 0.378 Normal The following figures show each parameter's normality graph, Johnson transformation, and finally, the summary report. 102 (a) The climate in degree Celsius. 103 (b) Heating degree day. (c) Cooling degree day. 104 (d) Full-time equivalent. 105 (e) Area of the buildings in m2. (f) Scope 1 & scope 2 GHG emissions in MTCO2e. 106 (g) Water consumption in gallons. 107 (h) Energy consumption in a million British thermal units. Figure 4.3: Johnson Transformation for all the selected parameters. There are three climate factors that are collected for the analysis. Average climate, HDD, and CDD. Two of them are not normally distributed and have been transformed by using the Johnson transformation. CDD was already normally distributed, as shown in Table 4.4 and Figure 4.3. Therefore, to limit magnifying the effects of climate on the analysis, only the average climate will be used as a climatic parameter. Table 4.4 shows the transformed values. Table 4.4: Johnson Transformation for the selected parameters. Code JT_Climate JT_FTE JT_Energy JT_GHG JT_Water S1 1.5678 -2.490 -2.3400 -1.8768 -2.0353 S2 0.3200 -2.067 -1.4332 -1.1008 -0.8965 S3 0.6738 -1.940 -2.3274 -2.5200 S4 -0.3083 -1.136 -1.6014 -0.7010 -1.3104 S5 -0.3235 -1.011 -0.5284 -0.8211 -0.4235 S6 -1.5030 -0.939 -0.6595 -1.1551 -0.8705 S7 0.3200 -0.856 -1.5582 -1.4716 -0.8725 108 S8 0.3200 -0.793 -0.3176 -0.2326 -0.4313 S9 -1.3821 -0.770 -0.3963 -0.1609 S10 0.9059 -0.770 -0.7501 -0.7522 -0.4127 S11 0.3200 -0.716 -0.7437 -0.5544 -2.1315 S12 1.5678 -0.593 -1.5366 -1.7479 M1 0.6232 -0.553 -0.6488 -0.5426 -0.6621 M2 0.1034 -0.525 -0.4618 0.2327 -0.6956 M3 -1.4353 -0.408 -0.0148 0.0551 M4 -0.9600 -0.380 -0.1576 0.5640 -0.4772 M5 -0.9600 -0.319 0.0435 0.8182 -0.3174 M6 0.2446 -0.221 -0.6344 -0.5096 -0.3152 M7 0.1034 -0.033 0.5949 1.1396 0.6669 M8 0.2520 -0.026 -1.1817 -0.6092 -1.0164 M9 -1.3315 0.142 1.1437 1.5773 0.9975 M10 1.5678 0.212 0.0163 -0.1444 0.4755 L1 1.1544 0.587 0.2723 0.1248 0.5109 L2 -1.5030 0.699 0.5965 1.3299 0.4169 L3 -0.3235 0.778 0.2888 -0.1953 L4 -0.8806 0.795 1.9130 1.7778 1.0703 L5 0.9600 0.814 -0.2450 -0.2429 -0.3021 L6 0.2446 0.841 0.7766 1.0424 0.9382 L7 -0.9600 0.881 2.3400 2.5200 1.2107 L8 -0.3235 0.996 0.9019 0.6801 1.3074 L9 -0.9526 1.293 1.4915 0.3975 0.7051 L10 0.9600 1.431 -0.4362 -0.4229 -0.3557 L11 2.7857 1.659 1.4986 0.7974 2.1468 L12 0.9600 2.490 0.8109 0.6391 0.7550 This research develops the following regression model using the selected normalized parameters obtained from the data collection and the Johnson transformation. The goal of the regression model is to find three missing water values and two missing GHG values. 𝑀𝑈 = 𝑎 + 𝑏1𝑥1 + ⋯ + 𝑏𝑛𝑥𝑛 + 𝜀 Equation 4.16 MU is the metabolic usage for GHG (GU) in MtCO2e and Water (WU) in Gallons; a is the intercept, b1 is the coefficient for parameter 1, b2 is the coefficient for parameter 2, …, bn is the coefficient for the nth parameter. x1 to xn are the corresponding values for each component. To find an equation that can predict water values, the parameters of climate, FTE, Area, GHG, and Energy are all used to assess the best model. The summary report for the parameter selection, residual plots, regression plot, the confidence intervals, and the 109 report scorecard, residual plots, and the intersection reports for all regressions are shown in appendix C. Water consumption regression model details are selected between the explanatory variables that show the least multicollinearity and are selected based on the best fit based on the R-square (R2) of 83.19% and VIF of 1.05 as shown in Figure 4.4. The linear regression coefficients are listed in Table 4.5. Table 4.5: Linear regression coefficient. Water Use (WU) Coefficients P-Value VIF Intercept a -0.0368 0.626 JT_Climate b1 0.1731 0.025 1.05 JT_Energy b2 0.8452 0.000 1.05 Figure 4.4: Residual plots. The regression equation for the water is shown in equation 4.17 0.0368 0.1731* 0.8452*WU C EU= − + + Equation 4.17 110 Where WU is the water usage measured in gallons, C is the climate measured in Celsius, and the EU is the energy measure in MMBTU. By applying the regression equation to the parameters of each corresponding HEIs in order to predict the missing water values for these three HEIs, Selkirk College S3, Camosun College S12, and Concordia University L3 are as follows, as shown in Table 4-6. The conversion from the Johnson transformation was calculated using the Johnson transformation for the water parameter listed in equation 4.18 and shown previously in Figure 4.3. The calculation is completed using WolframAlpha computational intelligence [285]. 𝐽𝑇𝑊𝑖 = 1.39004 + 0.0.512001 ∗ 𝐿𝑛((𝑥 − 5314616)/(98894034 − 𝑥)) Equation 4.18 Table 4.6: Water consumption results. HEI JT_W Water in Gallons S3 -1.8873 7,253,960 S12 -1.0641 14,919,200 L3 0.1513 99,498,500 Similarly, to find an equation that can predict the missing GHG values, the climate, FTE, Area, water, and energy parameters are all used to assess the best model. The summary report for the parameter selection, interaction report, residual plots, regression plot, and the report summary card in Appendix C. GHG consumption is found to have the best model of explanatory variable energy. Other variables are not selected due to high multicollinearity, and the best fit is based on the R-squared (R2) of 84.84%. The linear regression coefficients are listed in Table 4.7. Table 4.7: Linear regression coefficients. GHG emission coefficients P-Value VIF Intercept a 0.0751 0.355 JT_Energy b1 0.8950 0.000 1 111 The regression equation for GHG is shown in equation 4.19 𝐺𝐻𝐺𝑈 = 0.0751 + 0.8950 ∗ EU Equation 4.19 By applying equation 4.19 to the parameters of each corresponding HEIs in order to predict the missing GHG values for these two following HEIs, Lakehead University S9 & the University of Regina M3 are as follows, as shown in Table 4.8. The Johnson transformation conversion was calculated using the Johnson transformation for the GHG parameter in equation 4.20. The calculation is completed using WolframAlpha computational intelligence [285]. JTGHG = 1.70160 + 0.520174 × Ln((X – 918.443) / (344890 - X)) Equation 4.20 Table 4.8: GHG emission results. HEI JT_W GHG in MtCO2E S9 -0.2796 7,254 M3 0.0618 15,021 The only climatic variable used is the Johnson transformation of the average climate in degrees Celsius. Other climatic variables such as HDD and CDD others were not used to avoid magnifying the effect of climate on the overall prediction model. Adding other factors such as water consumption, climatic factors, and others do not necessarily lead to a better prediction model due to high multicollinearity in the data (i.e., it does not produce statistical significance). The area was the best predictor for the GHG emission, followed by the climate and water usage. Results shown in Table 4.8 represent the best fit based on the R-squared (R2) coefficients found, i.e., 84.84% and shown in Figure 4.5. 112 Figure 4.5: Parameter selection 4.3.2 Ranking Based on relative utility coefficient Ci, all 34 Canadian HEIs are benchmarked (ranked). Using the methodology described above. As an example, reported data of UBCV is presented in Table 4.9. The first step is to normalize all the values by the mean using equations 4.2-4.5. A normalized example for a university is shown in Table 4.9. The new utility (i.e., normalized energy) is calculated with equations 4.7 & 4.8, and the value found is 10.2. The total utility value is calculated using equations 4.12, and the value calculated is 22.63. The value of the coefficient was calculated by using equation 4.14, and the value is 0.06. The sustainability index is established for all the HEIs using equation 4.15. The final sustainability rank for the 34 HEIs is presented in Figure 4.6. The figure shows that the sustainability rank for all the small-sized HEIs underperformed, with sustainability 113 ranks lower than the average. The figure shows that the ranking of all small-sized HEIs rank above the threshold, making all small universities better than the overall average score for all the universities. 67% of the large-sized HEIs are higher above the average (0.812), while 50% of the medium-sized HEIs underperformed. Finally, 33% of the large-size universities are underperformed as their SIs are lower than the average. Figure 4.6: Sustainability ranking for all HEIs in Canada. The benchmark represents the overall average value. Table 4.9: Data and results of the University of British Columbia (UBCV). HEI Avg. Temperature HDD CDD FTE Area GHG Energy Water UBCV 10.4 5,093 95.8 43,509 15,440,970 52,350 1,182,111 806,266,185 HEIs mean 6.99 5,114.4 295.7 18,157 382,680 37,339 550,599 128,404,562 Normalized UBCV 1.49 1 0.32 2.39 3.75 1.37 3.29 6.28 In Figure 4.8, the model data was normalized for simplicity with equal values across all the parameters for each HEI, assuming that they all had equal effects. The variability of the reported data may impact the university’s performance; for example, the data was not collected in a cut-off year; instead, the most recent reports were taken into consideration. 0.00.10.20.30.40.50.60.70.80.91.0L1S12M10S7S5L3S6L10L11S3M6L5M8L9S10S1S9M1S11S2L12S4L8S8M3L6M4M2M7M5L4L2M9L7Sustainability Indx RankHEIsSI Average Score114 Some of the latest reports published by the universities were 2-5 years old. As per the average values of all the reporting, HEIs have shown in Figure 4.6, the ten least performing universities are L7, M9, L2, L4, M5, M7, M2, M4, L6, and M3. Sized-based classification of the HEIs better justifies the relationship between the flows of energy and GHG. A strong coefficient of determination (i.e., R2 = 94.09%) between energy consumed and associated GHG emissions were observed for small-and-medium-sized HEIs in Figure 4.7. These results of the coefficient of determination became weaker when all HEIs were considered, where the R2 value for all the HEIs, including small, medium, and large, was found to be 62.88%. Figure 4.7: Relationship between energy consumed and GHG emissions for small-and-medium-sized HEIs in Canada. The HEIs with relatively poor performance regarding the sources of fuels are presented in Table 4.10. Performance of the HEIs in terms of energy conservation can vary depending on the modes of energy conservation, efficiencies of different processes, and GHG = 0.1095(energy consumed) - 6092.3R² = 0.9409-200000200004000060000800001000001200001400001600001800000 200000 400000 600000 800000 1000000 1200000 1400000 1600000GHG emissions MTCO2eEnergy consumed in MMBtu115 use of GHG intensive fuels, e.g., coal, among other fossil fuels. For instance, in the case of the University of Regina (M3), 100% of the electricity is being produced by the use of coal. Nova Scotia Community College (M2) meets 53% of its electricity requirement by the use of coal, and the MacEwan University (M4) produces 40.6% of electricity through coal. The University of Calgary (L4), the University of Alberta (L7), and the University of Saskatchewan (M9). The latest energy report indicates that Alberta’s electricity generation is fueled by 51% coal [286]. However, precise information could not be obtained about these universities’ inner grid combinations (e.g., gas vs. electricity). The province of Saskatchewan generates approximately 34% of electricity from coal [287]. Secondly, inefficient energy usage in old buildings (e.g., in terms of the building envelope and its thermal characteristics) was found to be a reason for the low performance of HEIs in Table 4.10. For example, Nova Scotia Community College (M2) performed better than Dalhousie University (M7); these two HEIs are located in the same city and rely on the same electricity grid. However, due to improved energy efficiency and the usage of renewable energy sources that result in an improved inner grid distribution (more reliance on renewable energy sources, e.g., hydro or wind), an overall performance enhancement was noticed in Nova Scotia Community College. The definitions of Scope 1 and Scope 2 emissions are given in section 4.2.1. Table 4.10: Ten bottommost underperforming HEIs with their sources of fuels. No. Higher Education Institution (HEI) Source of fuel Scope 1 Source of fuel Scope 2 L4 University of Calgary 84% natural gas, 0.22% propane %73.8 coal, %20.61 natural gas, renewables % 5.39, 0.18% other M3 University of Regina natural gas (%100) Coal (%100) L6 Western University %98 natural gas, %2 electricity 40.85% hydro, %55.67 nuclear, %3.49 natural gas, biomass, coal M4 MacEwan University %100 natural gas %3 biomass, %40.6 coal, %6.4 hydro, 41.3% natural gas, %7.9 wind, 0.8 (other sources) 116 M2 Nova Scotia Community College %70 fuel oil, %8 other sources, %22 natural gas %2 biomass, %53 coal, %14 hydro, %14 natural gas, %12 wind, %4 other sources M7 Dalhousie University %8 biomass, %84 natural gas, %3 electricity, %5 fuel oil %2 biomass, % 63 coal, %7 hydro, %12 natural gas, %9 wind, %7 other sources L7 University of Alberta 54% natural gas, 45.4% purchased electricity 53% coal, 38% natural gas, %3 hydro, 1% wood biomass, 5% renewables M5 Northern Alberta Institute of Technology - - L2 University of Manitoba %99.9 natural gas, 0.05% fuel oil %95 hydro, %5 natural gas M9 University of Saskatchewan %99.96 natural gas, 0.04% fuel oil %37 Coal, %20 hydro, %37 natural gas, %0.02% solar, %5 wind, %0.08 other sources Figure 4.8 (a,b,c) presents the aggregated sustainability scores for WEC flows in 34 HEIs. This includes weighted values for each category and the average score of each category per university size (i.e., for 34 reporting universities). The values are normalized by dividing the score each institution receives in the WEC categories by the total grading scheme STARS assigns. Universities are then listed per the classification size proposed in this research to provide a sense of similarity to compare the performance of each university in Canada. Figure 4.8 (a) illustrates that regardless of size, the performance of all HEIs is almost the same in terms of GHG emissions; however, some of the large-sized HEIs clearly underperformed, i.e., L2, L3, L7, and L12. In energy consumption, small-sized HEIs on average outperformed large- and medium-sized HEIs, as shown in Figure 4.8 (b & c). However, small-sized HEIs underperform based on the water scores in the STARS. Furthermore, the performance of medium- and large-sized HEIs was almost the same in terms of water consumption. Possible reasons for these results are i) Small HEIs manage a smaller number of FTE students, smaller infrastructure, and relatively less-complex processes than those of medium and large-sized HEIs, ii) emissions in large-sized HEIs are normalized over a 117 large number of students (i.e., CO2/FTE) and may result in a higher sustainability ranking. Large universities accommodate more students per water-consuming activity, such as laboratories, and washrooms with larger economies of scale, etc. GHG generation per square foot of the area in large-sized HEIs is less than that of small-and-medium size HEIs. Regression analysis was performed between the floor area of buildings in the HEIs and their corresponding water consumption, energy consumption, and GHG emissions. The analysis revealed that the ‘power function’ best fits the relationship between the variables with high R2 values. Empirical constants for all three relationships are presented in Table 4.11. The results presented in Table 4.11 show a strong coefficient of determination between floor area of HEIs buildings with the flow of WEC. The regression details are provided in appendix C. Table 4.11 shows that energy consumption is highly correlated with the floor area compared to GHG emissions and water consumption. Table 4.11: Empirical constants for power functions between floor area of HEIs buildings and WEC flows. ‘Floor area of the building (thousand ft2)’ Y = a Xb X = Water consumption (million gallons) X = Energy consumption (MMBtu) X = GHG emissions (million ton CO2 equivalent) a b R2 a b R2 a b R2 0.1319 0.8322 0.82.62 0.1272 0.7954 0.9331 0.0.0678 0.7571 0.8062 118 Figure 4.8: STARS ranking of Canadian HEIs by size. A comparison between the STARS ranking and the ranks established in the present study for all sizes of HEIs is presented in Figure 4.10. The figure shows that of the 10 worst-performing HEIs in Figure 4.7, the performing HEIs need to i) implement the SD declarations by embedding sustainability in the fabric of the organization structure, 119 planning and setting realistic goals, and ii) use of renewable sources of fuels. The sustainability ranks presented in Figure 4.9, the sustainability ranks of four HEIs (M3, M4, M7, and L4) were approximately equal or higher than the STARS ratings. The sustainability rank presented in Figure 4.6 was calculated based on comparative assessment. Hence a higher SR does not ensure that there is no need for further improvement. To attain long-term sustainability goals, HEIs should adopt a continuous performance improvement approach. Figure 4.9: Comparison between STARS overall ranking and sustainability ranks based on WEC flows. HEIs have sought to implement rating systems in their buildings as an attempt to reduce energy consumption and subsequently reduce GHG emissions. However, research has shown that many of these building rating systems could not achieve their desired goals [60-62]. Agdas et al. (2015) studied the EUI of 10 LEED-certified buildings and compared them to non-LEED-certified buildings; they found that the EUI LEED-certified buildings are higher than that of non-LEED buildings. Existing systems do not identify the causes 00.20.40.60.81Sustainability rank (SR) / STARS ratingSustainability Rank (present study) STARS Rating120 of poor performance and the actions required to improve the sustainability performance of universities in the future. There is a need for an integrated system that can lead HEIs towards continuous improvement in terms of sustainability while maintaining cost-effectiveness and simplicity. There is also a need to shift the focus from corrective controls to proactive actions [288]. Energy baseline targets (the reported values for the year 2007) were self-imposed by the universities to conserve or reduce energy consumption. Figure 4.10 presents a comparison between the performance of HEIs and the baseline energy targets using STARS data. Around 58% of the large-sized universities succeeded in reducing energy consumption below the baseline. While around 70% of medium-sized and 75% of small-sized HEIs performed better than the baseline value. The reasons for non-compliance can be unrealistic targeting, increased student enrollment, or the absence of any interventions. Overall, approximately 43% of Canadian HEIs fall behind their baseline values [54]. Public-funded HEIs had an average EUI of 293 kWh/m2 in FY 2001, gradually decreasing to 287 kWh/m2 by FY 2006. This EUI level is still substantially below the best practice benchmark of 162 kWh/m2 [289]. The GHG emissions follow similar patterns [55]. 121 Figure 4.10: Energy baseline targets and performance of Canadian HEIs based on STARS reporting. HEIs still rely heavily on local electricity providers to strive to a net-zero stage. This requires an understanding of the inner components of buildings and how the performance can be improved. Imposing taxes on HEIs intensive fuels may result in a positive impact in terms of GHG reductions at HEIs. In an attempt to achieve high sustainability standards, specific targets are required to be met. Assessing the state of engineered systems in Canadian HEIs in Figure 4.10 requires more information for technical level decision-making, e.g., solar coefficient, split of energy used and purchased by sources, age of buildings to determine the efficiency, solar power, street lighting, etc. Energy use can be optimized by using efficient water appliances, adopting water conservation strategies, and effective conservation practices. These strategies can be combined with renewable energy use, including wastewater energy recovery and onsite solar and wind energy generation for achieving net-zero energy and carbon emissions [189]. 05000001000000150000020000002500000S1 S2 S3 S4 S5 S6 S7 S8 S9S10S11S12M1M2M3M4M5M6M7M8M9 M…L1 L2 L3 L4 L5 L6 L7 L8 L9L10L11L12Energy in MMBtuPerformance Baseline122 HEIs require proper planning approaches to reduce both the short-term and long-term environmental impacts during their operations. Comprehensive data are needed for practical assessment and prudent decision-making for selecting rational interventions, i.e., day-to-day maintenance and major long-term improvements. These interventions have their own socioeconomic and environmental implications over the lifecycle of a building. Hence, an integrated technological-human effort is required for energy and water conservation, GHG emission reductions, and ecosystem protection in a cost-effective manner. HEIs must address mounting issues due to the increase in the number of FTE students and associated energy demand, costs associated with increases in fuel prices, aging of buildings and associated reductions inefficiencies, uncertainties due to emerging regulations aimed at curbing carbon emissions, and ultimately climate change and its impact on seasonal variation. These factors, coupled with the limited available resources to improve these assets, will significantly impact the institution's ability to forecast future trends, goals, and performances. So, the HEIs must consistently address these changing factors and their impacts while attaining sustainability goals. A benchmark can be established using a representative sample of similar-sized universities having comparable characteristics, e.g., number of students, types, and age of buildings, etc. Due to temporal and geographical factors, such as seasonal and climatic variations and differences in provincial/state regulations, the benchmark can be applicable to a specific region. For continuous performance improvement (CPI), future benchmarks can also be established based on improving WEC projections from interventions, changing regulations, technological advances, and students’ expectations. 123 The goal of the CPI process is to improve upon the original benchmark for a factor or group of factors and cluster individual HEIs (or buildings in case of inter-university application) closer to the benchmark value. The present research provides an insight into the current sustainability status of Canadian HEIs. Establishing sustainability ranks based on WEC flows will be useful for technical-level decision-making. Instead of relying on existing rating systems that cover a wider spectrum of sustainability, technical level decisions can practically improve the exiting global and regional GHG scenarios and will be a step towards water and energy conservation. Limitations of this chapter include: i) the results are influenced heavily by missing parameters that were not included in the data collection, ii) the uncertainty associated with the random nature of the data was not considered, iii) the use of the closest approximation to calculate carbon equivalency may generate uncertainty in the results; iv) the impacts of occupants’ behavior were not studied; v) the interdependent nature of the interactions between water, energy, and carbon which were not investigated; and finally, vi) these flows were not normalized by areas of buildings and HEIs are classified by the number of FTE. The findings provided a platform for identifying the underperforming HEIs and comparing them with the excelling HEIs to highlight the most important factors affecting the ranking score. The existing approaches discussed in the following section aggregate the performance of all the functions to rank HEIs based on their overall sustainability. The outcomes mask the actual performance of the utilization of natural resources and environmental impacts in an HEI. Hence, a sustainability assessment of HEIs in terms of 124 WEC flows is essential for technical-level decision making about improving their performance on a continuous and long-term basis (e.g., greener energy source, efficient plumbing fixtures, and lighting systems, recycling, and building retrofits). Summary In Canada, HEIs consume a high percentage of (i.e., 60%) of the total energy allocated to the educational sector, which is equivalent to ~ 430,000 households in the country. Accordingly, such institutions have been recognized as one of the major contributors to the total nations’ emissions. Buildings in HEIs have been constructed at different times depending on increasing enrolments and the establishment of new educational programs. Consequently, they have varying process efficiencies, features, and environmental impacts. Existing sustainability ranking systems include a wide range of sustainability indicators and do not measure actual performance in terms of natural resources consumptions and generation of GHG emissions based on their energy utilization, water consumption, and production of associated GHG emissions. Instead of relying on existing global sustainability ranks, decision-makers can select and practically implement actions for the long-term sustainability of HEIs, e.g., development of resource conservation strategies, building retrofits. The inter-university benchmarking methodology developed in this research for sustainability assessment of Canadian HEIs is based on their WEC flows. The sizes of the HEIs were considered for rational benchmarking of 34 HEIs. The proposed methodology also addresses the difficulties encountered during benchmarking due to the use of different reporting systems by the HEIs. Regression analysis was used to address 125 the issue of missing data for a given climatic region. GHG emissions in HEIs are a function of different factors (utilities), including energy, water consumption, FTE, and floor area, which are measured in different units and thus are normalized in this research. It is recommended that all the universities should follow a score-based ranking system that normalized WEC flows for a rational comparison in the future. Regression analysis established power function, with a strong coefficient of determinations (i.e., R2 values higher than 80%), between the floor area of building in the HEIs and their corresponding WEC flows. Canadian HEIs are ranked based on the sustainability performances of their engineered systems keeping in view the differences in climate regions, size of the campus, number of occupants, and the WEC flows. The results revealed that small-sized HEIs outperformed in terms of absolute values of WEC flows than the large-sized HEIs, while the performance of 50% of medium-sized HEIs was found to be less than the average for the total universities. The reasons for such results could be: i) small HEIs deal with less number of students, smaller infrastructure, and relatively less complex physical processes, ii) emissions in large-sized HEIs are normalized over a large number of students (i.e., CO2/FTE), iii) larger economies-of-scale, i.e., each water-consuming facility serve a high number of students in large-sized HEIs, and iv) energy consumption is directly related to the size of a HEIs while medium-sized HEIs, serving less number of students. The results suggest the need for more in-depth investigations to improve the sustainability of different buildings’ operations through effective operational level decision-making (e.g., efficient retrofits, particularly for older institutions) for all sizes of HEIs. Future research is 126 expected to focus on assessing detailed environmental impacts during the entire life cycle of HEIs, including all the construction, operation, and demolition phases. 127 Intra-University Benchmarking The main content of this chapter has been published in Sustainability (2020) as an article titled “Benchmarking of water, energy, and carbon flows in academic buildings: A fuzzy clustering approach”[28]. Background The findings in Chapter 4 call for a more detailed investigation on an intra-university level to understand the effects that affect higher education institutions (HEI) and for technical level decision-makers to know the resource utilization patterns of the buildings and operations. This chapter is completed by benchmarking the asset classes (i.e., buildings) within an HEI. In Canada, HEIs are responsible for a significant portion of energy consumption and anthropogenic greenhouse gases (GHG) emissions. Improving the environmental performance of HEIs is an important step to achieve nationwide impact reduction. Academic buildings are among the largest infrastructure units in HEIs. Therefore, it is crucial to improve the environmental performance of academic buildings during their operations. Identifying critical academic buildings posing high impacts calls for methodologies that can assess the environmental performance of buildings with respect to water and energy consumption and GHG emissions. This chapter proposes a fuzzy clustering approach (FCA) to classify academic buildings in an HEI and benchmark their environmental performance in terms of water, energy, and carbon flows (WEC). FCA allows for the extraction of similar information within different clusters. To account for the uncertainties in partitioning, the fuzzy c-means algorithm is employed to classify the buildings based on water, energy, and carbon flow indicators. The application of the 128 developed methodology is demonstrated by a case study of 71 academic buildings on two campuses at the University of British Columbia (UBC). The assessed buildings are grouped into three clusters representing different levels of performance with different degrees of membership. The environmental performance of each cluster is then benchmarked. Based on the results, the environmental performances of academic buildings are determined, and the building clusters associated with low environmental performances are identified for potential improvements. The subsequent benchmark will allow HEIs to compare the impacts of academic building operations and set realistic targets for impact reduction. Methods and Procedure The method used in this chapter is explained in detail in the following sections, followed by a case study. 5.2.1 Framework Development The proposed fuzzy clustering-based framework is outlined in Figure 5.1. The framework provides a local classification of different buildings within a university according to their similarities in resource consumption and GHG emissions. Based on the classification results, decision-makers can benchmark the environmental performance of individual buildings among others within the same class. The first step is to select performance indicators needed to be benchmarked. In the case of this study, energy and water consumption data were collected, and carbon emission was derived from the energy consumption data collected on both UBC campuses. These parameters relevant to water, energy, and carbon are referred to as metabolic flows (MF). The MF is used as performance indicators because they are the most important aspects influencing the 129 environmental impacts within the context of HEIs. The second step is to normalize MF data using a common factor, such as climatic factors, area, and the number of users. After normalization, MF data can be converted to several performance indices, such as water usage intensity (WUI), energy usage intensity (EUI), and carbon emission intensity (CEI). The derived indices are used as inputs to the fuzzy clustering analysis using the fuzzy c-means algorithm. The optimal number of clusters is determined using the elbow method. This method is used to evaluate the reduction of within-group variance that is brought by increasing the number of clusters. The reduction of within-group variation as a function of the increase of cluster number is graphically represented as an “elbow,” where the “elbow point” is often selected as the optimal number of clusters [192,290]. After the fuzzy clustering analysis, each building assessed will be assigned a degree of membership (DOM) to the clusters formed. The DOM is the measure of how strong the data points are associated with a cluster, and the centroid is the center of gravity for that cluster [291]. The general environmental performance of each cluster, in terms of WUI, EUI, and CEI, will be evaluated and ranked. A weighting method may be needed if the performance indices do not follow the same direction. Finally, an environmental performance index is generated for each building based on its DOMs to different clusters using a weighted aggregation method. The environmental performance of individual buildings can be ranked according to their environmental performance indices. 130 Figure 5.1: Fuzzy clustering-based environmental performance benchmarking of academic buildings. 5.2.2 Case Study The University of British Columbia is investigated as a case study. UBC is one of the largest and highly ranked universities in Canada and is home to 64,798 students and a Data Collection Normalizing Weighting Fuzzy Clustering Analysis Selecting number of clusters Fuzzy C-mean Algorithm Generate Centroid: Degree of Membership for each cluster DOM 1 DOM 2 DOM 3 Environmental Performance Index Ranking Benchmarking WUI EUI CUI 131 total of 16,891 faculty and staff as of 2019 [270]. UBC consists of two campuses: The Vancouver campus (UBCV) and the Okanagan campus (UBCO) in two different yet close geographical regions. The first is in Vancouver, where is a moderate oceanic climate, while the other campus in Kelowna is roughly 280 km inland towards the east. Kelowna is characterized as a subcontinental hemiboreal climate that is generally drier than Vancouver, with warmer summers and colder winters. The annual precipitation in Vancouver is about 3.25 times more than Kelowna on average [292]. There are over 160 buildings on both campuses, including classrooms, laboratories, libraries, administration, residential, and recreational buildings. Due to the lack of continuous data recording for more than half of the buildings, only 71 buildings are included in the intra-university benchmarking. The university uses energy from several sources to operate buildings, such as electricity supplied by the service providers, natural gas, and biomass-derived energy. Close to 95% of BC’s electricity is generated from renewable sources, with hydro being the most dominant and important source [293]. There are other small energy sources, such as geothermal and biodiesel, and limited quantities of solar energy panels on the residential buildings. UBCV reports energy by source for general usage as electricity and for heating separately, in addition to water usage in the Skyspark portal [294]. The portal provides the energy and water consumption data for most of the buildings in days, weeks, months, and years. In addition to the types of buildings (e.g., percentage of classrooms, laboratories, libraries, and offices), the buildings' areas are also recorded in the Skyspark portal. The distributions of heating energy by sources used in this study are presented in Table 5.1. Renewable gas is the natural gas derived from organic waste on campus. 132 Table 5.1: Distribution of energy source for building heating in the UBCV. Energy source Percent Renewable Gas 10 Biomass 25 Natural Gas 65 From UBCO, 11 buildings are included in this research. The sustainability office on campus provided information on source-wise energy distribution. Electricity from the main grid and natural gas are two primary energy sources at UBCO. The geothermal plant consumes natural gas to increase the efficiencies of heating and cooling. To derive the carbon emissions for each building, it is important to know the energy by source for calculating the carbon equivalent of each building. Energy supplying facilities such as the central heating plants for both campuses were not included in this study due to their energy-exporting nature. Based on the available data, a total of 71 buildings at both campuses were evaluated. Water and energy consumption data from 1st April 2018 to 31st March 2019 were obtained for benchmarking. Carbon equivalents were calculated using the method recommended in the BC Best Practices Methodology for Quantifying GHG Emissions [295]. The sources of emission factors were calculated based on the information of emission factors provided by the utility companies [296]. Table 5.2 summarizes the emission factors for different energy sources to estimate carbon emission. Table 5.3 shows the reported energy and water consumptions and the calculated GHG emissions for Alumni Centre in UBCV as an example. By using the BC Best Practices Methodology for Quantifying GHG Emissions, the CO2e emissions were found to be 38.4 kg for Alumni Centre for both heating energy and electricity used. 133 Table 5.2: Carbon emission factor by energy source. Source kg CO2e/GJ UBCV kg CO2e/kWh UBCO kg CO2e/kWh Renewable Gas 0.29 0.0010 0.0010 Biomass 0 0.0000176 0.0000176 Natural Gas 49.87 0.1795 0.1795 Electricity 0 0.0107 0.0026 Table 5.3: Metabolic flow data for the building Alumni Centre, UBC. Source Value Electricity (kWh) 713,572 Heating (kWh) 229,117 Water (m3) 3,968 Natural gas (m3) 2,025 GHG (kg, CO2e) 38 The assumption is that the overall goal is to reduce the MFs in the buildings without compromising the operational efficiencies; however, the overall goal for this chapter is to propose a new methodology for conducting intra-university benchmarking on asset classes (i.e., buildings). Part of the MFs is related to water usage; the usage of water throughout a building’s lifecycle uses energy and emits GHGs. The upstream emissions associated with energy or water (production, transportation, and recycling) are neglected in this study because they do not fall within a university’s control or sphere of influence. The WEC data of all investigated buildings are summarized in Table 5.4. Normalization of the WEC data was carried out by using the building floor area as the normalizing factor. The fuzzy clustering analysis was conducted following the same process reported in Hu et al. (2019) [297]. The resultant WUI, EUI, and CEI after normalization have values on a scale ranging from 0 to 1. The fuzzy c-means algorithm (FCA) was used to group the index values based on their performance on the three factors. One of the FCA characteristics is that it offers each data point in a dataset a DOM to every cluster formed, indicating that each data point belongs to different clusters with a different level of 134 association. The DOM is a unique feature that distinguishes FCA from other crisp clustering algorithms, such as C-means clustering and hierarchical clustering. This feature offers FCA great flexibility in benchmarking of buildings because it can address fuzzy uncertainties (i.e., the concept of partial truth) and is suitable for grouping data points with weakly defined boundaries [298]. For a dataset ( )= 1 2x , ,..., nx x x comprising n data points, the fuzzy c-means algorithm classifies the data points into predefined pclusters based on measured similarities among the data points. Each cluster has a center ( [1, ])je j p , and the Euclidean distance ijd between a data point ix and je can be calculated as: = −ij i jd x e Equation 5.1 In this chapter, each building was considered as a data point defined by three-dimensional values ( , , )a b c of the metabolic flows (i.e., WUI, EUI, and CEI). Thus, the Euclidean distance between the data point ( , , )i i i ix a b c and ( , , )j j j je a b c in three-dimensional space was calculated as: = − + − + −2 2 2( ) ( ) ( )ij i j i j i jd a a b b c c Equation 5.2 At the beginning of fuzzy c-means, random centers (usually with a value of zero) are selected for the clusters. Based on the derived ijd , a DOM ( ( )ix ) can be calculated as a measure of the similarity between a data point xi and the thj cluster: 135 −−==2/( 1)2/( 1)1(1 / )( )(1 / )mijj i pmikkdxd Equation 5.3 Where m is a fuzzification parameter to determine the degree of fuzziness between different clusters. A higher value of m will lead to higher fuzziness between clusters. Commonly m takes values between 1.25 and 2 [291,299]. In this study, m value was set at 2. The parameter ikd is the Euclidean distance between ix and the center of the thk cluster. The new centers of clusters can be calculated as: =[ ( )][ ( )]mj i iij mj iix xex Equation 5.4 Based on the new , ( )j j ie x will be updated. The iteration will continue until the minimum objective function J is achieved: = ==  21 1[ ( )] ;pnmj i iji jJ x d p n Equation 5.5 A total of three clusters were formed based on the available factors. The FCA process was carried out using the statistical computing software R TM (version 1.0.136). Based on the value ranges of the three factors, the benchmark characteristics of each cluster can be interpreted. After the FCA, three DOMs   1 2 3( , , ) can be generated for each value to show the degrees of similarity between the benchmark characteristics and the number of clusters. An environmental performance index (EPI) can be calculated for each value based on the DOMs using Equation (5.6): 136 𝐸𝑃𝐼 = ∑ 𝜇𝑗 𝑤𝑗𝑝𝑗=1 Equation 5.6 where jw is the specific quality-ordered weight of the cluster j , and p is the total number of clusters. The values of jw were determined by the characteristics of different clusters. For example, jw values (i.e., the DOMs) can be assigned to the number of clusters ordered from the lowest to the highest benchmark, respectively. The values of jw were assigned subjectively, and they can be modified to accommodate different levels of the benchmark [300]. To determine the useful number of clusters, an elbow analysis was conducted to calculate the within-cluster sum of squares or validity index (VI) as a function of the number of clusters. VI is calculated using the equation adapted from [192]: = = == = − = −    1/221 1 1,..., ,...,1 1,..., ,... ( )( 1)ijnk d q ji j qk d iji jx xVIn Equation 5.7 Where, jx is the mean of data values of j dimension and ijn is the number of data values of j dimension that belongs to cluster i . Results and Discussion 5.3.1 Benchmarking of Water, Energy, and Carbon Flows Box plots of the range of MF are shown in Figure 5.2. The mean values are also identified within the boxplots. All variables are not normally distributed, where the distribution of CEI is more skewed than those of WUI and EUI. Thus, assuming the mean to be the reference 137 point (benchmark), misleading information could be generated about the distribution of the data. Figure 5.2: Box-plots of the metabolic flows ranges. Using the mean as the benchmark for each normalized flow. A cumulative distribution function (CDF) was applied in this approach, and the 50th and 75th percentile ranges were used as the benchmarks. This will allow the nature of the distribution in the data to be better reflected and subsequently give more meaning to the benchmarks. Table 5.4 shows the suggested benchmarks, the 50th and 75th percentiles, and the number of buildings above and below the benchmark levels. Buildings above the 75th percentiles are considered the buildings with low environmental performance, and buildings below the 138 50th percentile are considered as the high-environmental performance buildings. A graphical presentation for each normalized MF is presented in Figure 5.3 (a–c). By assuming the 75th percentile as a benchmark, all points (buildings) above would be identified with low environmental performance and require management attention. Table 5.4: Cumulative distribution of functional benchmarking. Parameter Percentile Value No. of Buildings Above No. of Buildings Below WUI 50th Percentile 0.84584 35 35 75th Percentile 1.262188 18 53 EUI 50th Percentile 235.05355 35 35 75th Percentile 315.3586 18 53 CEI 50th Percentile 0.01029 35 35 75th Percentile 0.02273 18 35 139 (a) (b) 00.511.522.533.544.5V24O5V83V133V70O7V19V94V42V113V16O11V105V37V84V57V86O26V17V31V48V73V51V39WUIWUI 50th percentile 75th percentile0100200300400500600700800900V9O19V83O21O5V113V134V21V133V130V64V70V73V2V105O11V51O7V69V19V25V109V17V35EUIEUI 50th percentile 75th percentile140 (c) Figure 5.3: The 50th and 75th percentile benchmarks for (a) water usage intensity (WUI), (b) energy usage intensity (EUI), (c) carbon usage intensity (CEI). 5.3.2 Fuzzy Clustering The relevant outputs of the FCA are listed in Table 5.5. This table shows the cluster numbers, three DOMs, and the percentages of each building in terms of class, library, office, and laboratory. For example, the alumni building consists of predominantly an office area and is therefore considered an office building. 00.0050.010.0150.020.0250.030.0350.040.045V9V75V28O7V73V19V71O21O26V134V32O18V133V130O3V45V26V76V48O5V35V86V17V25CEICEI 50th percentile 75th percentile(C)141 Table 5.5: A detailed fuzzy clustering analysis results. S/N Name Code Cluster WUI EUI CEI DOM1 DOM2 DOM3 Area m2 Class Lab Library Office Office Library Lab Class 1 Alumni Centre V6 1 0.97 235.05 0.01 0.66 0.33 0.02 4,106 0 0 0 0.18 Office 2 AMS Nest V2 1 0.93 259.53 0.02 0.86 0.13 0.01 22,933 0 0 0 0.07 Office 3 ASC O3 1 0.88 316.82 0.01 1.00 0.00 0.00 7,801 0.11 0.38 0 0.12 Lab 4 Brimacombe-QMI V19 1 0.40 375.39 0.00 0.89 0.07 0.04 13,781 0 0.28 0 0.05 Lab 5 CCM V25 1 0.24 427.85 0.04 0.75 0.12 0.13 10,367 0 0.52 0 0.06 Lab 6 CEME V26 1 1.00 270.76 0.02 0.92 0.07 0.01 9,361 0.06 0.29 0 0.32 7 Centre for Brain Health V31 1 1.43 245.79 0.00 0.76 0.23 0.01 15,441 0 0.14 0 0.2 Office 8 Chem Bio V34 1 0.30 379.72 0.02 0.89 0.07 0.04 14,030 0.06 0.33 0 0.15 Lab 9 Chem East V36 1 0.85 348.95 0.03 0.96 0.03 0.01 3,573 0.07 0.49 0 0.03 Lab 10 CICSR V27 1 0.49 262.79 0.01 0.88 0.11 0.01 10,097 0 0.47 0 0.16 Lab 11 EME O6 1 0.76 288.11 0.00 0.98 0.02 0.00 16,520 0.11 0.28 0 0.22 12 EOS V45 1 4.44 374.11 0.02 0.90 0.06 0.03 10,799 0.01 0.55 0 0.15 Lab 13 ESB V46 1 0.98 272.83 0.01 0.93 0.06 0.01 17,755 0.06 0.22 0 0.25 14 Fipke O7 1 0.35 308.63 0.00 1.00 0.00 0.00 6,725 0.17 0.34 0 0.12 Lab 15 FNH V48 1 1.53 397.79 0.02 0.84 0.09 0.07 5,962 0.08 0.26 0 0.17 Lab 16 Forest Sci V50 1 1.54 476.18 0.03 0.58 0.14 0.28 22,459 0.07 0.31 0 0.17 Lab 17 Frank Forward V51 1 2.30 287.50 0.02 0.98 0.02 0.00 7,880 0.06 0.29 0 0.22 Lab 18 Henry Angus V62 1 0.93 262.63 0.01 0.88 0.11 0.01 16,922 0.11 0.01 0 0.42 Office 19 ICICS V65 1 0.68 294.79 0.01 0.99 0.01 0.00 10,583 0 0.28 0 0.3 Office 20 J.B. MacDonald V69 1 0.69 332.43 0.03 0.98 0.01 0.00 7,328 0.02 0.2 0 0.28 Office 21 Jack Bell V70 1 0.30 221.14 0.00 0.51 0.48 0.02 2,712 0.14 0.05 0.38 0 Lib 22 Klinck V72 1 0.73 236.09 0.01 0.67 0.32 0.02 10,720 0.11 0.02 0 0.33 Office 23 Koerner Library V73 1 1.79 235.58 0.00 0.66 0.32 0.02 7,303 0 0 0.29 0.12 Lib 24 Life Sci V76 1 1.02 493.52 0.02 0.51 0.13 0.36 52,177 0.04 0.39 0 0.13 Lab 25 Longhouse V78 1 0.79 278.62 0.02 0.96 0.04 0.00 2,352 0 0 0.09 0.34 Office 26 NSDC-UBC V86 1 1.01 224.54 0.03 0.55 0.44 0.02 3,714 0 0 0 0 27 Pond East V94 1 0.45 313.90 0.04 1.00 0.00 0.00 11,080 0 0.18 0 0.05 Lab 28 RHS O11 1 0.79 278.58 0.02 0.96 0.04 0.00 5,021 0.17 0.19 0 0.23 29 Strangway V105 1 0.86 267.52 0.01 0.91 0.08 0.01 12,403 0 0 0 0.25 Office 30 University Centre V117 1 0.88 279.16 0.02 0.96 0.04 0.00 3,944 0.11 0 0 0.19 Office 31 AERL V1 2 0.33 142.44 0.01 0.01 0.99 0.00 5,368 0.06 0.05 0 0.46 Office 32 Allard Hall V5 2 0.11 138.36 0.00 0.00 1.00 0.00 14,909 0.08 0 0.2 0.2 33 Asian Centre V9 2 0.42 59.48 0.00 0.07 0.92 0.01 4,926 0 0.03 0.42 0.14 Lib 34 Buchanan A,B,C V21 2 1.09 147.11 0.01 0.01 0.99 0.00 10,936 0.31 0.04 0 0.18 Class 35 Buchanan D,E V22 2 1.28 89.76 0.00 0.03 0.96 0.00 7,134 0.28 0.01 0 0.25 Class 36 C.K. Choi V24 2 0.06 124.68 0.01 0.00 1.00 0.00 2,912 0.01 0.03 0 0.42 Office 37 Cassiar O18 2 1.56 80.19 0.01 0.05 0.95 0.01 3,951 0 0 0 0 38 CCS O5 2 0.20 111.31 0.03 0.01 0.99 0.00 4,797 0 0.44 0 0.23 Lab 39 Chan Centre V32 2 0.30 162.56 0.01 0.05 0.95 0.00 11,440 0 0 0 0.02 Office 40 CIRS V28 2 0.30 104.95 0.00 0.02 0.98 0.00 5,454 0.08 0.04 0 0.27 Office 41 Cunningham V42 2 0.51 153.17 0.01 0.02 0.98 0.00 4,901 0 0 0 0.07 Office 42 Geography V57 2 0.98 160.57 0.00 0.04 0.96 0.00 5,525 0.17 0.22 0 0.35 43 I.K. Barber V64 2 0.36 177.77 0.01 0.11 0.88 0.01 27,316 0.05 0.01 0.43 0.12 Lib 44 Kalamalka O19 2 1.24 79.77 0.01 0.05 0.95 0.01 4,835 0 0 0 0 45 Kenny V71 2 0.21 131.32 0.01 0.00 1.00 0.00 9,613 0.02 0.31 0 0.16 Lab 142 46 Lasserre V75 2 1.42 88.64 0.00 0.03 0.96 0.00 4,710 0.16 0.23 0 0.25 47 Liu V77 2 0.26 73.22 0.00 0.05 0.94 0.01 1,729 0 0 0.53 0 Lib 48 Mathematics V83 2 0.22 87.09 0.00 0.04 0.96 0.00 6,140 0.15 0 0 0.28 Office 49 MWO O10 2 0.52 123.90 0.04 0.00 1.00 0.00 1,681 0 0 0 0 50 Neville Scarfe V88 2 0.56 146.85 0.01 0.01 0.99 0.00 19,382 0.12 0.02 0.15 0.19 51 Nicola O21 2 1.48 91.53 0.01 0.03 0.97 0.00 5,667 0 0 0 0 52 Orchard Commons V90 2 1.83 179.87 0.01 0.13 0.86 0.01 43,194 0.04 0 0 0.03 Class 53 Pond North V95 2 1.38 153.86 0.01 0.02 0.98 0.00 27,922 0.02 0.01 0 0.05 Office 54 Pond West V96 2 0.50 177.47 0.01 0.11 0.88 0.01 18,779 0 0.03 0 0 Lab 55 Purcell O22 2 0.98 81.13 0.01 0.04 0.95 0.01 6,208 0 0 0 0 56 Sing Tao V103 2 0.14 189.10 0.01 0.19 0.80 0.01 1,571 0.09 0.15 0 0.24 57 SPPH V100 2 0.35 173.27 0.01 0.09 0.90 0.01 8,442 0.05 0.06 0 0.48 Office 58 Totem Infill V113 2 0.64 130.24 0.00 0.00 1.00 0.00 15,756 0 0 0 0 59 USB V116 2 0.41 96.07 0.00 0.03 0.97 0.00 11,598 0 0 0 0.2 Office 60 Valhalla O26 2 1.11 78.84 0.01 0.05 0.95 0.01 4,797 0 0 0 0 61 Wesbrook Building V130 2 3.22 170.72 0.01 0.08 0.92 0.01 10,272 0.06 0.29 0 0.08 Lab 62 Woodward IRC V133 2 0.29 159.68 0.01 0.04 0.96 0.00 12,049 0.08 0.01 0 0.15 Office 63 Woodward Library V134 2 2.09 139.34 0.01 0.00 1.00 0.00 7,777 0 0 0.43 0 Lib 64 Aquatic Centre V8 3 3.99 889.49 0.04 0.08 0.05 0.87 8,041 0 0 0 0 65 Bio Sci West V16 3 0.70 609.39 0.02 0.11 0.04 0.85 8,021 0.04 0.36 0 0.1 Lab 66 Biomed V17 3 1.33 624.05 0.04 0.07 0.03 0.90 4,407 0 0.47 0 0.1 Lab 67 Chem Centre V35 3 1.52 847.20 0.03 0.06 0.03 0.91 7,274 0.06 0.37 0 0.16 Lab 68 Chem North V37 3 0.91 680.79 0.04 0.01 0.00 0.99 2,739 0 0.53 0 0.09 Lab 69 Chem South V39 3 4.16 536.41 0.04 0.34 0.11 0.55 5,373 0.1 0.42 0 0.09 Lab 70 Michael Smith V84 3 0.94 818.22 0.03 0.04 0.02 0.94 8,477 0.03 0.41 0 0.15 Lab 71 Tennis Centre—Old V109 3 0.20 515.76 0.03 0.42 0.12 0.46 2,409 0 0 0 0.01 Office *Lib: Library, Lab: Laboratory 143 Academic buildings were considered a subgroup of buildings as per the commercial and institutional building surveys in Canada. This raises two limitations when interpreting similar CDF benchmarking results. First, they are not found as a separate entity in any national reports because of their specific nature. Instead, all academic buildings are usually assumed to be commercial and institutional buildings. Second, it is difficult to conclude which set of buildings has satisfactory environmental performance compared to the overall population if the performance of the entire population is not satisfactory [199]. Comparing the best performers to a known high-environmental performance building may help determine whether the performance of the best performers is satisfactory or not. A clustering approach is used to resolve these two limitations. The suitable number of clusters is derived from Equation (5.7), and the variation of the within-cluster sum of squares (WCSS) as a function of the number of clusters is shown in Figure 5.4. The sum of squares is the sum of the squared distance between each member of the cluster and its centroid. As can be seen in Figure 5.4, three clusters are selected to optimally represent this case study. The cluster centers are shown in Table 5.6. The lower value of the index, the better performance of the cluster. Based on the centroids for the MF, cluster 2 is identified as the group with the highest environmental performance, followed by cluster 1 and cluster 3. Table 5.6: Cluster centroids. Cluster WUI EUI CEI 1 1.008317 308.9435 0.016878 2 0.796999 130.3005 0.008255 3 1.603258 714.9797 0.034582 144 Figure 5.4: within-cluster sum of square (WCSS) plot to select the number of clusters. The detailed fuzzy clustering analysis results are shown in Figure 5.5. As shown in Table 5.4, the list of buildings is identified with their corresponding cluster number. Buildings with high environmental performance (cluster 2), buildings with low performance (cluster 3), while the moderately-performed buildings (cluster 1). Some buildings have information about the areas for specific functions, namely, classroom, laboratory, library, and office. For example, the given data from the portal shows that the Alumni Building has 18% of the area used as offices, 0% for libraries, 0% for laboratories, and 0% for classrooms. Similar information for the other buildings is also provided in Table 5.4. A building may serve multiple functions, e.g., one building may contain a library, classrooms, and administration offices. Therefore, to negate the intertwined effect of multiple areas within a building, a building is assumed to be a specific type of building (i.e., lab building) based on the function that most of the area is used for. For example, the Asian Centre building has 0% of the area for classrooms, 3% of the area for 145 laboratories, 42% of the area for a library, and 14% of the area for offices. Because most of its area serves as a library, it is considered as a library building. Figure 5.5 presents a plot of three clusters; each point in the figure represents a building, and its proximity to the centroids of each cluster is presented by the distance. Figure 5.5: The layout of the three clusters. To examine the variability in the data and set the benchmarks for the data in total and each cluster, a statistical summary of the obtained data is presented in Table 5.7. Cluster 1 has 30 buildings, cluster 2 has 33 buildings, and cluster 3 has eight buildings. The variability (IQR: Inter Quartile Range) in each cluster is a more accurate presentation than the overall buildings in combination. For example, the mean WUI values for clusters 1, 2, and 3 are 1.01, 0.8, and 1.7, respectively, and the value for all of the buildings is 0.9918. This shows that the cluster analysis generates different water usage assessment results for different clusters, and the different results cannot be revealed by the average water use-value generated by assessing all buildings together. Cluster 3 has a larger data variance in terms of the three indices due to the larger number of outliers. 146 Table 5.7: Statistical summary of the cluster results. Variable Total Mean Standard Error Mean Standard Deviation Variance Sum Minimum Q1 Median Q3 Maximum IQR* WUI_C1 30 1.01 0.15 0.8 0.63 30.3 0.2 0.6 0.87 1.02 4.4 0.38 EUI_C1 30 308.2 13.1 71.9 5170.9 9246 221.1 261.9 283.3 355.2 493.5 93.4 CEI_C1 30 0.02 0.002 0.01 0.0001 0.5 0.002 0.01 0.017 0.02 0.043 0.014 WUI_C2 33 0.8 0.12 0.7 0.5 26.3 0.06 0.292 0.5 1.3 3.22 0.97 EUI_C2 33 127.4 6.58 37.8 1429.6 4204 59.5 89.20 131.3 160.1 189.1 70.92 CEI_C2 33 0.008 0.001 0.007 0.00005 0.3 0.0006 0.00198 0.008 0.01 0.035 0.008 WUI_C3 8 1.7 0.5 1.5 2.3 13.7 0.2 0.754 1.1 3.4 4.16 2.62 EUI_C3 8 690.2 51 144.3 20833 5521 515.8 554.7 652.4 840.0 889.5 285.3 CEI_C3 8 0.03 0.003 0.007 0.00005 0.28 0.03 0.03 0.03 0.04 0.04 0.01 *IQR: Inter quartile range The fuzzy clustering approach classifies buildings with similar WEC performance into the same group. It is found that buildings with a large portion of their areas allocated for laboratories are among the worst-performing buildings in the university. Table 5.8 summarizes the area allocation per function and the averages per cluster with respect to the flows studied. The results show that the buildings in cluster 3 are predominantly laboratory-intensive, with a higher percentage of the area dedicated to the laboratories than cluster 1. While cluster 2 was found to be the cluster with the highest environmental performance and the lowest area of laboratories per building on average. Table 5.8: Benchmarking results. Cluster No. of Buildings Average Area Avg WUI Avg EUI Avg CEI Office % Library % Laboratory % Classroom % C1 30 11,528 1.01 308.21 0.02 33.33 6.67 43.33 0 C2 33 10,051 0.8 127.4 0.01 30.3 12.12 12.12 9.08 C3 8 5,843 1.72 690.16 0.04 12.5 0 75 0 Laboratory-intensive buildings are reported to be the assets with the highest environmental impact in a university due to the high energy consumption [36,95]. Some research has attributed the high energy consumption to inefficient HVAC systems, which are either outdated or provide flows more than needed as a precautionary measure [235]. The results of the present study also show that laboratory-intensive buildings are responsible for high water consumption and carbon emissions. 147 The national grid highly influences the universities grid in the jurisdiction. Figure 5.6 illustrates the GHG growth per province and the number of universities in that province. By collecting the data for 36 Canadian HEIs in the STARS database, the baseline GHG values were set for Canadian HEIs. The growth rate was calculated, aggregated by province, and plotted in the graph with the number of HEIs per province. Only one HEI obtained the platinum ranking (i.e., the most sustainable rank in STARS), while 16 HEIs obtained the gold, 17 HEIs obtained the silver, and two HEIs obtained the bronze rating from the STARS reporting system. Provinces that use renewables as their major source of energy (i.e., BC hydro projects and QB) have shown better performance in terms of GHG reductions, while AB and SK highly depended on fossil fuels as their main source of energy, thus, have shown little improvements from their baseline values. This could be attributed to the offset programs available in provinces like BC as well. For example, the University of Calgary and the University of Alberta are located in the same province (i.e., Alberta) and use similar electric grid systems. The former managed to reduce GHG emissions by 24%, while the latter had a 34% increase in emissions. 148 Figure 5.6: GHG growth rate per province and the number of HEIs per province. Finally, using holistic reporting systems, such as STARS, to communicate the overall sustainability may not yield the desirable momentum needed for universities to reach the desired goal of a sustainable campus. For instance, both universities (the University of Calgary and the University of Alberta) are ranked gold in the STARS reporting system. This is not to neglect the importance of holistic reporting but to provide a mechanism within the holistic reporting systems for infrastructure management improvements. Furthermore, most reporting systems fail to capture this or reflect this into their weighing structure. Appendix (D1) illustrates some of the academic buildings studied in this phase, and the red blocks are responsible for 50% of electricity and around 17% of gas consumption at UBCO. For example, The engineering, management, and education (EME) building consist of lecture rooms and laboratories (i.e., asset class 1); it solely consumes more than 18% of the total energy consumed by the investigated buildings. The energy demand is expected to increase due to the extension of laboratories with more electromechanical 149 equipment in the future. Similarly, other academic buildings in this classification share the same characteristics. This data is available for the building as a whole. Energy consumption for specific operations, such as lighting, lecturing, laboratories, etc., will be investigated in detail in this research. Summary Holistic reporting systems, communicating the overall sustainability performance, may result in the same (high) performance for different universities based on meeting their overall socio-economic sustainability goals. While for technical level decision-making to practically optimize the WEC flows in HEIs, the environmental performance of individual academic buildings needs to be benchmarked. The fuzzy clustering method proposed can help the classification of buildings with respect to their WEC flows. Buildings with many laboratories were found the least sustainable among different types of buildings. In addition, the clustering approach is shown to be an effective way to benchmark the sustainability performances of HEI buildings regarding WEC flows. The proposed framework was applied to benchmark 71 academic buildings in two different campuses of UBC. The academic buildings were grouped into three clusters based on the reported MF in terms of energy and water consumption, and carbon emissions. Cluster 2 (33 buildings) is the group of buildings with the best environmental performance, followed by cluster 1 (30 buildings), and eight buildings associated with the lowest environmental performance are grouped into cluster 3. The average area of buildings per cluster is 11,528 m2 for cluster 1, 10,051 m2 for cluster 2, and 5,843 m2 for cluster 3. The average WUI and EUI per cluster are 1.01 m3/m2 and 308.21 kWh/m2 for cluster 1, 0.8 m3/m2, and 127.4 kWh/m2 for cluster 2, and 1.72 m3/m2 and 690.16 kWh/m2 150 for cluster 3, respectively. By comparing the results, the average EUI of buildings in cluster 3 is roughly four times higher than that of buildings in cluster 2 and nearly 120% more than that of cluster 1. By grouping academic buildings into three clusters and identifying clusters performers and least performers (laboratory buildings), this study also identified the inner characteristics of academic buildings. The clustering analysis results showed that the environmental performances of predominant laboratory buildings are generally low, and this is in line with the results discovered in other studies. Benchmarking academic buildings in HEIs is facing two main challenges. The first challenge is the lack of an available national academic building database that is required to compare and determine a set of best practices in academic buildings; the second challenge is relevant to the conventional benchmarking methods that may yield misleading benchmarking results. By determining the environmental performance of academic buildings, the proposed fuzzy clustering-based framework allows efficient resource allocation for buildings that are identified with low environmental performance. There are several limitations to the proposed benchmarking methodology. The carbon emission factors for converting electricity consumption in two campuses were derived from the BC Best Practices Methodology for Quantifying GHG. The factor for the city of Kelowna, where UBCO is located, was 0.719 kg CO2e/GJ as of 2016, while the factor for the city of Vancouver was 2.964 kg CO2e/GJ as of 2016. However, the values of carbon emission factors vary significantly from year to year. This could result in variations in the benchmarking results for the same buildings in different years. 151 Moreover, the benchmarking results cannot provide detailed solutions to help HEIs improve the aspects associated with low performance. Future research can apply a more aggressive data collection program to report detailed energy and water use behavior in the buildings which are identified as poorly performed in the benchmarking. Based on the collected big data, system dynamic modeling and optimization can help improve the buildings' performance. The developed methodology represents a new approach to track, assess, and aid retrofitting and/or decision making that best allocates the resources available to achieve low-impact infrastructure management in HEIs. By identifying a set of building performance, decision-makers can manage their resources more efficiently for further investigations and planning of interventions. Moreover, by identifying critical buildings, further information may be collected per floor or functional systems within a building. The flexible nature of the proposed framework allows the decision-makers to include further information for developing a more detailed decision support tool. The clustering results may also be used to help set attainable goals and plan future environmental commitments accordingly. This chapter applies the framework at the building level (top-down benchmarking) based on the available data and the type of study conducted [294]. However, the framework can be generalized further to include a bottom-up approach of benchmarking by aggregating data of building components (e.g., HVAC components and lighting type). By including this sort of data for each building level, a better understanding of these characteristics will be gained, and better information will be provided for the management to act upon (i.e., by determining inefficient components). The bottom-up approach is shown in Figure 5.7. As 152 more data becomes available (through simulations and or measurements), better information can be gained, which will help improve the quality of the data and subsequently improve the assessment results by pinpointing inefficiencies in a building and subsequently benchmark building based on their envelope, thermal efficiencies, and so on. To find ways to improve and continuously assess the status of these buildings. Figure 5.7: Scalability of the framework. Benchmarking Level 2Benchmarking Level 1BenchmarkingTriple Bottom LineSustainability Reporting Green CampusEnvironmentalEnergyEnergy sourceHVAC LightingGHG EmissionWater Transportation WasteSocial Economical153 Spatiotemporal Variability of WEC Flows in HEIs The main content of this chapter has been accepted in Environments 2021, as an article titled “Investigating Spatiotemporal Variability of Water, Energy, and Carbon Flows: A Probabilistic Fuzzy Synthetic Evaluation Framework for Higher Education Institutions [183]. Background The findings in chapter 5 call on an investigative approach to address the uncertainties associated with data collection and uncertainties associated with the linguistic “ranks” of sustainability reporting. Rigorous data collection is performed on 23 higher education institution (HEI) buildings in a single campus, and 58 continuous months of data is collected in terms of water and energy consumption and the associated greenhouse gas (GHG) emissions released. Sustainability assessment is a challenging task due to its inter-disciplinary nature [181,182] Gasparatoes et al. (2008) carried out a critical review on sustainability assessment tools and methodologies and concluded that none of the metrics reviewed seem to be able to assess progress towards sustainability in a holistic manner [53]. Among the tools reviewed: the biophysical models and indicator-based reporting systems include the exergy (vagueness in reference state), Emergy (human judgment preference), ecological footprint (weak analytical tool), and SI have underlying uncertainties [53]. Since no one indicator can deliver the state of sustainability, these indicators are aggregated to deliver a heavily influenced by weight and judgment rank of sustainability [184]. Building rating tools such as Leadership in Energy and Environmental Design (LEED) certifications are among the tools that carry significant uncertainties. Agdas et al. (2015) 154 assessed the energy performance of 10 LEED-certified academic buildings and 14 non-LEED buildings at the University of Florida. They concluded no statistically significant differences between the two types of buildings. The energy usage intensity (EUI) is slightly higher in LEED classified buildings than those not certified. Reporting is needed to communicate sustainability, and to do this; it needs to address the uncertainties associated with the ranks, judgment, and data limitations, to give better understanding and enhance engagement with the stakeholders, to avoid indiscriminate decisions taken as a result of these assessments tools [301]. Therefore, to address the uncertainties, data collection needs to be completed on a rigorous basis, the quality and scope of data need to be homogenous. In many instances, water impact is neglected due to lack of data, lack of monitoring techniques, and or the scope of the study. Water is reported to go through the following phases: Raw water conveyance, water treatment, water distribution, water use, wastewater collection, wastewater treatment, and biosolids transportation [302]. Negating these large impacts may result in a misleading level of sustainability an institution aspires to become. For instance, Chhipi-Shrestha et al. (2017) conducted a full-life cycle assessment of water from the conveyance to the biosolids transportation. And concluded that raw water conveyance, water treatment, and water distribution happen before the impacts of water use are calculated [264]. The total energy consumed by these three processes is 4,172 MWh, producing 6893 ML of water. Therefore, the total energy for each cubic meter of water delivered is 0.6053 kWh/m3. This study was conducted in the Okanagan region of British Columbia, where the university investigated is located. 155 Furthermore, carbon sequestration happens naturally on campuses with substantial vegetation. Nevertheless, the calculation of the carbon sinks on campuses is often overlooked due to their perceived minimal impact. To investigate the impacts of carbon sequestration on campus, a calculation method is proposed to quantify the carbon sequestration that happens on campus and compare that with the amount of carbon footprint the irrigation water consumes during different production phases. Finally, to address the uncertainty, a spatiotemporal technique is applied to cover a wide range of human judgments. An integrated probabilistic-fuzzy framework is developed to help universities address the uncertainty associated with reporting water, energy, and carbon (WEC) flows within a campus. The probabilistic assessment using Monte Carlo Simulations effectively addressed the aleatory uncertainties due to the randomness in the variations of the recorded WEC usages, while the fuzzy synthetic evaluation addressed the epistemic uncertainties due to vagueness in the linguistic variables associated with WEC benchmarks. The developed framework was applied to operational, academic, and residential buildings at the University of British Columbia (Okanagan Campus). Three scenarios were analyzed, allocating the partial preference to water, or energy, or carbon. Furthermore, nine temporal seasons were generated to assess the variability due to occupancy and climate changes. Finally, the aggregation was completed for the assessed buildings. The study revealed that climatic and type of buildings significantly affect the overall performance of a university. This study will help the sustainability centers and divisions in HEIs assess the spatiotemporal variability of WEC flows and effectively address the uncertainties to cover a wide range of human judgment. 156 Method and Procedure The method used in this chapter is explained in detail in the following sections, followed by a case study 6.2.1 Framework Figure 6.1 presents the developed probabilistic-fuzzy synthetic evaluation framework (PFSEF) to assess the performance of the building in terms of WEC flows. The framework consists of three modules: Module 1: the selection and calculation of indicators, Module 2: probabilistic assessment, and Module 3: fuzzy synthetic evaluation assessment. Performance indicators are used to assess water use, energy use, and carbon emissions. The GHG emissions were calculated using the carbon equivalency of each source of energy. The biological carbon sequestration (negative emission) on campus was also estimated for the irrigation activities. The normalized WEC flows per building area were then calculated. The probabilistic assessment addresses the aleatory uncertainties with the WEC data collection. The outcomes were then processed in a fuzzy synthetic system to accommodate the uncertainties in linguistic variables commonly used in benchmarking. A scenario analysis addressed the preferences to WEC. The fuzzy system consists of a fuzzy set and a fuzzy membership function. The analytical hierarchal process (AHP) accounts for the preference over the three indicators under three scenarios. Finally, the indicators were aggregated to determine the sustainability rank of the buildings in the University of British Columbia (Okanagan Campus). 157 Figure 6.1: Probabilistic Fuzzy Synthetic Framework. 6.2.2 Evaluation of Water, Energy, and Carbon Emissions The first step is the selection of performance indicators. In this research, reported water use and energy use were collected from the university. Carbon emissions were estimated using the carbon emission factors collected from the energy provider of the campus. Identification of Sustainability indicators Probabilistic Assessment Fuzzy Synthetic Evaluation Prioritization and Ranking Fuzzification Aggregation Defuzzification EUI WUI CEI Probability distribution Monte Carlo Simulation Weighting: Analytical Hierarchy Process Scenario 1 Scenario 2 Scenario 3 158 These flows were then normalized by area as a common factor for comparability. This generates the performance indicators used in this study: WUI, EUI, and CEI. A proposed methodology is provided for this calculation for parameters that were not reported on a building level, such as water utilization and carbon emissions. Water utilization data of the entire campus from April 2016 to January 2021 are provided by the university. Two approaches were taken to estimate each building’s consumption of water. In the first approach, a water-to-area ratio was calculated. In contrast, in the second approach, water consumption per building was estimated using the Bonneville Environmental business water calculator, a web page interface calculator. This calculator helps the business owners to estimate the closest approximation to water utilization, based on type and area, in their buildings, e.g., schools, office buildings, and health services, which is based on the type and area of the building [303] The results yield the closest approximation to the actual reported water values are used. Water undergoes several processes depending on the nature of the water source, and typically in BC, the processes are extraction, treatment, distribution, use, and disposal. Energy is used in every step of these processes, and, simultaneously, emissions are emitted in every phase of these processes. Water, in its nature, may be a source of energy. For instance, depending on water's elevation, it may be a source of kinetic energy, such as in hydroelectricity dams. Furthermore, due to the thermophysical properties of water, water may be used as a thermally bonding agent in the transfer of energy, such as in geothermal plants. Thus, water may be directly involved and indirectly involved in generating electricity and, consequently, emitting GHG emissions. 159 A recent study published in 2017 studied the interaction between the different water processes for the city of Penticton. The study analyzed all the stages of the energy-water nexus using a dynamic system model generated by [264]. The results of this study will be used to further assess the water-energy nexus due to the similarity in water processes and the similar climate conditions between Kelowna and Penticton. Where water conveyance uses 2782 MWh and emits 48 tCO2e to convey 7100 million liters of water, similarly water treatment consumes 1372 MWh and emits 94.5 tCO2e of carbon equivalence for the treatment of 7100 million liters of water. For this study Raw Water Conveyance, Water Treatment, and Water distribution are used to assess the energy for water on the campus and consequently calculate the associated GHG emissions. Using the Water-Energy-Nexus nexus model established by [302]. The total energy required to deliver this water is calculated by. Pr ( )( )nocesses ii iEnergyEnergyWaterNexusWater= Equation 6.1 Energy data is obtained from the energy facilities of the university from April 2016 to January 2021 as monthly data per source of energy. The amounts of energy generated from different sources are also provided. However, the energy used by water is missing, and to find this energy, this chapter will adopt the method proposed by Chhipi-Shrestha et al. (2017) since this study is carried out in closest approximation to Kelowna (i.e., where the campus is located) in terms of energy sources and water sources. Furthermore, both cities are located within the same geographic and climate factors. The ratios used by Chhipi-Shrestha (2017) will be used and derived from equation (6.1) will be applied to estimate embodied energy (energy footprint) of necessary irrigation water. 160 By using the water-energy-carbon nexus model established by [302], the total energy required to deliver the volume of water is calculated: Equation 6.2 Where the is the embodied energy of supplied drinking water in kWh/m3 and is the volume of water used in m3. This generates the amount of energy needed for that process in kWh. From this energy, the amount of carbon equivalent was calculated as given in the following section. *irrigationW Area IR= Equation 6.3 Area irrigation is irrigated area in hectares, and IR is the irrigation rate in L/ha/da, i.e., 977 L/m2/year for the Okanagan [304]. The emissions covered in this study are Scope 1 and Scope 2 GHG emissions. Scope 1 emissions include the direct emissions released from the universities, such as combusting the natural gas on-site to produce heat. Scope 2 emissions are the indirect emissions released by the electricity provider during electricity production. The distinction is usually referred to as the amount the university controls. Emissions are reported for the entire campus, including fleet transportation, emissions from electricity, and so on. The carbon emission factors are obtained from the BC Best Practices Methodology for Quantifying GHG Emissions [295]. This method has been used extensively to estimate emission factors for various sources of energy generation [17,28]. Since the UBCO uses natural gas and the local electricity grid as the energy sources, the GHG emission per building is calculated: *water water VolumeEnergy EE Water=waterEEVolumeWater161 𝐶𝐸 = 𝐶𝐸𝐹 × 𝐸𝑈 Equation 6.4 Where CE is the carbon emission in CO2e, CEF is the carbon emission factor for that source of energy CO2e/kWh, and EU is the energy use in kWh. The carbon sequestration is estimated based on the previous similar research [305,306] as follows: Equation 6.5 Where sC is the total carbon sequestration it is measured in kg CO2/m2/year, sSOC is total the total landscape sequestration SOC it is measured in kg CO2/m2/year, is the total landscaping area in m2 [305], stC and ssC are total carbon sequestration by the trees and shrubs in kg CO2/m2 or by the tree, and tN & sN are numbers of trees and shrubs, respectively [302]. The net carbon emission landscaping in a neighborhood can be estimated as 𝐶𝑠 = 𝐶𝐸 − ∑(𝐶𝑠)ⅈ𝑛ⅈ=1 Equation 6.6 Where is net carbon emission (kg CO2/year), is carbon emission, and it is measured for each energy source in (CO2e), is total carbon sequestration by individual landscaping (kg CO2/m2/year), and n is a number of all landscaping with water supplied [302]. The area was calculated approximately using google earth. 6.2.3 Probabilistic Assessment Probabilistic methods are used to address uncertainties that result from the randomness and stochastic nature of the data. These types of uncertainties are inherently common in data reporting [258,307]. Probabilistic uncertainties are a result of data selection, for * *s s i st t ss sC SOC A C N C N=  + +iAnC ECsC162 instance, using the average usage intensities on a yearly or monthly basis. Uncertainties may result from other factors, such as human behavior and occupancy uncertainties, which all may affect the readings [36]. Monte Carlo simulation (MCS) is a common technique used to address model-parameter uncertainties. MCS assumes models are random, and it relies on computational representation in the provided data. The overall model can be generated in a probability-density function if “Y” is assumed to be a random variable, with probabilities should be less or equal to “y” for every unknown “Y” and is illustrated by [258] Equation 6.7 and if Y is continuous, then Equation 6.8 MCS was performed by assigning a probabilistic distribution for the flows corresponding to percentile values used as inputs in the following fuzzy synthetic evaluation. The 90th percentile of the distribution was used to perform the Monte Carlo simulation using 50,000 iterations of @RiskTM 8.1 (Palisade Corporation). A cumulative probability distribution is acquired to determine the corresponding percentiles of each indicator. Monte Carlo simulations propagate the distribution hundreds of times to account for random uncertainty in the data [308]. The outputs of the simulation can be used to determine the fuzzy classes. 6.2.4 Fuzzy-based Assessment Fuzzy-based techniques address the uncertainty that is caused by the vagueness and imprecise judgment in human insights [290]. A fuzzy set consists of a fuzzy number and a membership function. The assessment is done in the following steps: ( ) ( )F y P Y y= ( ) ( ) /f y dF y dy=163 A fuzzy set is presented as Equation 6.9 where A(x) is the fuzzy set of X, and X is the universal set of variable x, and 𝜇𝐴𝑥 ranges between the normalized value of 0 and 1. A smaller 𝜇𝐴𝑥 indicates a less association between 𝑥 and 𝐴. Furthermore, fuzzy sets can be illustrated in many shapes, and a common shape used is the triangular membership (a,m,b), as shown in Figure 6.2. The 𝜇𝐴𝑥 of 𝑥(𝑥 ∈ [𝑎, 𝑏]) Is the membership function calculated using equation (6.10). The fuzzy membership functions are developed to numerically transform linguistic variables. This can be achieved by using five linguistic variables: Very low (VL), Low (L), Medium (M), High (H), Very high (VH), as shown in Figure 6.35. The outcomes of the probabilistic assessment are mapped into the membership function to extract fuzzy critical levels. Assuming that the red line in Figure 6.3 represents the probability of with a normalized value of 0.13, then the memberships to both “VL” and “L” levels are 0.5. ( ) {( , ), }, : [0,1]x xA AA x x x X X =  →1OEUI Equation 6.10 xA =0;;;0;x ax aa x mm ab xm x bb mx b −   −−   − 164 Figure 6.2. Triangular fuzzy membership functions. Figure 6.3. Fuzzification and Fuzzy memberships. The AHP is commonly used in planning and multicriteria decision-making, addressing both inductive and deductive reasoning to reach a synthesis [309]. AHP follows a hierarchical structure that generally consists of a well-defined goal, followed by criteria, and ends with an alternative or multiple levels of sub-criteria; AHP is commonly used in decision making due to its ability to deal with complex problems in simple pairwise comparison judgments, which are then used to develop the overall priorities for ranking 01Membership μa m bXxA00.20.40.60.810 0.2 0.4 0.6 0.8 1Membership μNormalized WECVL L M H VH165 the alternatives ability to select the best set of numbers of the evaluated alternatives with respect to multiple criteria [310]. The method is based on the pairwise relative importance and could be obtained by several methods: such as the geometric mean [260], least square method, or the characteristic root method [311]. The steps needed to apply AHP to generate preferred weights [311,312]: 1. Decompose a complex problem into a hierarchy of goals, criteria, and alternatives. 2. Measurement methodology is used to create pair-wise comparison priorities among the sub-criteria and alternatives by creating a Xthen deriving the geometric mean for each criterion. 3. Measurement theory to establish the relative importance of a parameter. This is calculated using. Equation 6.11 4. To verify the consistency, the following steps are taken: a. Calculate the maximum eigenvalue b. Derive the consistency index CI and consistency ratio CR Equation 6.12 Equation 6.13 n m11 where , 1,...,ziji zjijiqw i j zzq== =maxmax( )( 1)nCIn −=−CICRRI=166 Where RI is a given random index generated and is listed in Table 6.1 [313]. If CR>0.1, then it is inconsistent; the larger the CI implies that the judgment taken by a decision-maker is more inconsistent [313,314]. To assess pairwise comparison Saaty (1980) developed a nine-point intensity scale (i.e., degree of preference) of importance between any pairs of criteria. The nine-point intensity scale and their internment points may be referred to Saaty (1980) and are listed in table 6.2 [309]. The preference between the elements is conducted through a focus group, expert opinions, or several different and opposing scenarios. The AHP is commonly used in fuzzy synthetic evaluation to establish a set of preference weights based on the relative importance of each attribute using pair-wise comparison. The set of preference weights is normalized to a sum of 1. This weighting method has been used to is commonly used in the literature [260,261,300]. Equation 6.14 Assume an importance matrix is established where each element expresses the importance of the attributes with respect to . These preferences should be assigned based on expert opinions [260]. Equation 6.15 The weights can be obtained by taking the geometric mean of the weights vector and the normalization of the matrix. 11( ,...., ), where 1nn jjW w w w== =A mnAm n11 11nm mna aAa a  =    167 Equation 6.15 Table 6.1: Random Indexes by Raharjo et al. (2006). Matrix Order 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 RI 0.00 0.00 0.58 0.9 1.12 1.24 1.32 1.41 1.45 1.49 1.51 1.48 1.56 1.57 1.59 Table 6.2: Nine-point intensity scale developed by Saaty (1980). Absolute Intensity of importance Definition Explanation 1 Equal importance Two activities contribute equally to the objective. 3 Moderate importance Experience and judgement slightly favor one activity over the other. 5 Strong importance Experience judgment strongly favor one activity over the other. 7 Very strong importance An activity is favored very strongly over another: its dominance demonstrated in practice. 9 Extreme importance The evidence favoring one activity over another is of the highest possible order of affirmation. 2,4,6,8 Intermediate values When compromise is needed. The step aggregates all the scopes with their respective weights and ranks them. Because the three criteria used are WEC, and they are not opposing, meaning that the more consumed of any parameter will result in a “worst” sustainability, therefore the higher the number is, the less sustainable it will be. Equation 6.16 The final step is defuzzification. There are many methods listed in the literature to defuzzify. The max method in equation 6.17 will be used as means for defuzzification [315]. The score ranges will be between 0 and 1. The higher the numbers are, the worst the sustainability is. Equation 6.17 ...i ni w w=( ) ( ) ( ) ( ) ( )( ) ( ) ( ) ( ) ( )( ) ( ) ( ) ( ) ( )VL L M H VHwui i wui i wui i wui i wui iVL L M H VHi wui eui cui eui i eui i eui i eui i eui iVL L M H VHcui i cui i cui i cui i cui iB w w w            = * [ , , , , ]VL L M H VHx Max     =168 6.2.5 Case Study UBCO investigated in this case study is a branch of the University of British Columbia, located in the interior region of British Columbia. The campus has grown significantly over recent years since 2005; The university started with 12 buildings and an area of 105 Hectares in 2005. The campus has 105 buildings within an overall area of 209 Hectares in 2019 [316]. The growth in student enrollment has significantly increased over the years. Figure 6.4 shows the growth in enrollment since 2015. The university operating budget also increased from $39 million in 2005/2006 to $175 million in 2019/2020. The campus is home to 10,708 Full-time equivalents (FTE); 49% of the students are in arts & sciences, 18% in applied sciences, 12% in health & social development, 11% in creative and critical studies, 7% in management, and 3% in education. There are 46 buildings on the campus with an operating budget of $175 million in the fiscal year 2019/2020 [316]. Based on the campus FTE the campus may be considered a medium-size HEI [17]. Figure 6.4: Student enrollment in the University of British Columbia (UBCO) in the past five years. 169 The campus is located in a hemiboreal climate with long cold winters and warm summer [292]. Figure 6.5 shows the heating and cooling degree days (HDD and CDD) from 2013-2020. The average HDD is 3680.66 and subsequently 201.58 for the CDD [317]. The HDD and CDD measure the number of days where the outside temperature is below or above a set point temperature. It indicates the heating and cooling loads for a building [17,317]. Figure 6.5. Heating degree days and cooling degree days. This study investigates 23 buildings on the campus; two buildings are used for operational services and will be classified as operational buildings, while the rest includes 12 academic buildings and nine residential buildings. The annotation for the buildings and their relative parameters is listed in Appendix (E). Results and Discussion The results are organized into three titles: data collection, probabilistic assessment, and finally the fuzzy-based assessment. 6.3.1 Water Use, Energy Use, and Carbon Emission Status Water reporting is on a campus level, on type of buildings: whether they are academic or residential per month. It is reported that the entire campus consumed a total of 797,968.8 170 m3 during April 2016-January 2021, with an average water use per year of 163,898.95 m3. Figure 6.6 shows the average annual water use of different types of buildings. This study uses raw water conveyance (from the extraction of water to treatment), water treatment, and water distribution to assess the energy for water use on campus and, consequently, calculate the associated GHG emissions. Due to the lack of data, the embodied energy (upstream energy) of municipal water supply (potable water) is assumed to be the same as the nearby city (Penticton), which is 0.6053 kWh/m3 [302]. Figure 6.7 presents the total volumes of water used over the years. Water consumption increases during the months of May-Aug due to irrigation. The water used for irrigation is included with each building's water consumption. Irrigation values alone are not metered. A recent commissioning report estimated that the campus uses an average amount of 55,781m3 of irrigation water. This can also be calculated by using equation (6.3). 129,600171,493141,628188,525153,95055%54%53%52%52%45%46%47%48%48%020,00040,00060,00080,000100,000120,000140,000160,000180,000200,0002016 2017 2018 2019 2020Water in m3Total Adacemic ResidentialFigure 6.6. Total campus water usage. 171 Figure 6.7. The trend of water consumption. Water consumptions on campus have not been reported per building usage. To overcome this limitation, two approaches have been considered to estimate the water usage per building. First, a ratio-to-area approach, where the total reported campus water was divided into each building depending on each building's area. For instance, the CHP (O1) building has an area of 528 m2 which is equivalent to 0.37% of the total buildings assessed in this study, and therefore the corresponding water values are given in Appendix E. An average of 6 years was considered. The second approach used the Bonneville Environmental business water calculator which is based on building type and the area of the building [303]. Both the ratio-to-area and the BEF values are also reported in Appendix E. There is a significant difference between the two methods. For example, the mean of the ratio-to-area method is 6,498 m3, whereas the mean for the BEF is 4,570 m3, and the standard deviation of the first method is 3,661 and 2,575, the standard error of the mean is 763 for the first method and 537 for the latter. Figure 6.8 shows the total water calculated by the first method is 149,444 m3 and by the second 105,104 m3. The first method was found closer to the actual average reported 020000400006000080000100000120000Jan Feb Mar April May June July Aug Sept Oct Nov DecWater Consumption in m3Year 2016 Year 2017 Year 2018 Year 2019 Year 2020172 water consumption of 163,898.95 m3. Water is then normalized by area, and the statistical summary and graph are illustrated in Appendix E. Figure 6.8. Ratio-to-area and BEF box-plots. A Monthly energy utilization data (kWh) for each building was obtained from the university for the period between April 2016 and January 2021. The energy is being supplied from two sources, electricity and natural gas. The first source is connected with the electricity grid of FortisBC, which is predominantly powered by hydroelectricity [296]. Within the campus, there are two operational buildings: a central heating plant and a geothermal plant, both use natural gas to supply energy to each building. The central heating plant serves buildings A1, A2, A7, A8, A9, A11 through a low-temperature district energy system (LDES). The geothermal plant serves academic buildings A1, A2, A3, A4, A5, A6, A7, A8, A10, A11, A12 through a medium temperature district energy system (MDES). The geothermal plant converts ambient temperature water to cold (7°C) and hot water (45°C). Also, the geothermal plant was built to regulate surplus heat when the outside temperature was lower than -2°C and heating demand at outside temperatures of 15°C 173 and higher. By recapturing the excess waste heat of buildings, the plant is able to achieve reductions in overall emissions [318]. The monthly energy per building is normalized by the area of that building. A box plot is provided for energy comparison between the buildings in Figure 6.9, which includes monthly utilization from April 2016-January 2021. A statistical summary for the normalized energy usage intensity is provided in Appendix E. It is noted that, for instance, the average monthly EUI of operational buildings is 52.99 kWh/m2 and the monthly average for academic buildings is 30.21 kWh/m2, and finally, 11.45 kWh/m2 for the residential buildings. It is important to note that these figures represent a monthly average of the EUI. It is also noted that the energy used for the operational buildings is extensively higher than that of other buildings due to the nature of these two buildings, where they consume vast amounts of energy to deliver heat, natural gas, and electricity to the other buildings. 174 Figure 6.9. Box-plots of EUIs calculated for different buildings on campus. UBCO is mandated by law to submit a yearly GHG emission inventory report. The emissions in this report include scope 1 and scope 2 GHG emissions and any offset-able emissions. These emissions include emissions from the energy used in buildings, fleet transportation, paper, and fugitives. A historic layout of the emission from UBCO is illustrated in Figure 6.10 [319]. 175 Figure 6.10. Historic GHG emissions on the campus. Each building's carbon emissions are calculated based on BC's best practices methodology, as stated. The natural gas and electricity emissions factors in Kelowna are 0.1795 kg CO2e/kWh for natural gas and 0.0026 kg CO2e/kWh [295]. To illustrate an example of how the emissions are calculated, the Administration buildings (A1) used in April 2016 226,933.9 kWh of energy from electricity and natural gas. Natural gas accounts for 78,678.7 kWh, and electricity accounts for 148,255.1 kWh, therefore by applying equation (6.4), the overall emissions released by the building in April 2016 are estimated to be 14.5 tCO2e. Subsequently, the remaining emission for each building is calculated. After that, the total emissions are divided by each building's area to attain the carbon usage intensity per month. Figure 6.11 shows the boxplots for each building. 05001,0001,5002,0002,5003,0003,5002010 2011 2012 2013 2014 2015 2016 2017 2018 2019GHG Emissions (tCO2e)Year176 Figure 6.11. Box-plots of the calculated CEI in the UBCO buildings. A detailed statistical summary of the CEI is also calculated in Appendix E. The average CEI for the operational buildings is 0.19 tCO2e/m2, for academic buildings is 1.85 tCO2e/m2 and finally for the residential buildings 0.61 tCO2e/m2. Academic buildings, on average, produce 70% of the campus's total GHG emissions. Like water usage, GHG emissions are reported on the campus level using the normalized average outputs in the literature. The total energy needed to convey, treat, and distribute the estimated irrigation water of 55,781 m3 on campus is estimated to be 33,764 kWh/year. Since the water facilities use the local electrical grid as an energy source, the total carbon emitted due to irrigation is 87.79 kgCO2e/year using the carbon equivalency and by using equation (6.4). 177 The energy from water usage in the first three stages proposed by [264] includes Raw water conveyance, water treatment, and water distribution use a total of 4,172 MWh of energy to produce a water value of 6893 ML or 6.893 Mm3 of water. Resulting in 0.6053 kWh/m3 of energy needed to meet the demand in the city of Penticton in the Okanagan Valley in BC. Therefore, to calculate the amount of energy used by the irrigation water on campus, the model in the graph will be used to assess the energy and consequently the amount of carbon equivalent emitted. The total water consumption for the UBCO includes the water used in academic buildings, residential buildings, and the water used for irrigation. The entire campus consumed 797,969 m3 from April 2016-January 2021 by using the ratio of 0.6053 ratios generated from equation (6.2) from the [302], the total energy required is 483,010.6 kWh. Since the water utility uses the electricity grid as the main energy source, the corresponding GHG emissions are 1,255.83 kgCO2e. Figure 6.12 illustrates the box-plot graph of the energy and water nexus in the campus from April 2016 to January 2021. Carbon is also sequestrated naturally through the growth of trees, vegetation, and shrubs. To calculate the total carbon sequestration from in the campus as shown in equation (6.5). The parameters in the equation are listed in Table 6.3, which are modified from [305]. The total sequestration is around 13.879 tCO2e/year. The tree density in Kelowna is assumed to be 150 stems/ha [320]. UBCO releases a vast amount of carbon, making the sequestration carry a minimal effect, if any, on the total performance. Table 6.3: Carbon-Sequestration parameters and results. Parameter Description Unit Turf Shrub Trees sSOC Net sequestration kg CO2e/m2/ year 0.0254 0.05853 3.4 kgCO2e/ tree/ year 178 1A Landscape Area m2 238,331 39,055 89,318.52 ,t sN N Number of trees 1,340 ,st ssC C Net carbon sequestration Kg/CO2e 6,054 3,270 4,556 sC Total carbon sequestration kg CO2e/year 13,879 6.3.2 Probabilistic Assessment The probabilistic distribution was generated using Monte Carlo simulation (MCS) with 50,000 iterations using @RiskTM 8.1 student version [321]. The probabilistic distributions of the three parameters are listed in Appendix E. The distribution of EUI is Gama, for CEI is Lognorm and for WUI is Uniform. The probabilistic assessment addressed uncertainties related to the randomness of data. The 5%, 25%, 50%, 75%, and 95% percentiles are used to generate the fuzzy numbers. For example, for the EUI, the 5% is 4.293 a log-normal is taken, and the corresponding 5% is 0.6328, similarly for CEI, it is 0.0278, and the Log for it is -1.5560, and for WUI it is 0.0841, and the log is -1.07534 as shown in Table 6.4. These generated values were obtained from the MC simulation to address the random uncertainties. 179 Table 6.4: Fuzzy classes and their corresponding percentiles Percentile Fuzzy Class Log EUI Log CEI Log WUI 5% VL 0.6328 -1.5560 -1.0753 25% L 1.0569 -0.8665 -1.0479 50% M 1.3089 -0.3788 -1.0158 75% H 1.5249 0.1123 -0.9860 95% VH 1.7835 0.8203 -0.9635 Figure 6.13: Probabilistic Assessment distribution for the normalized WEC Figure 6.12 Water-Energy-Carbon nexus of the supplied water at campus (a) water quantity, (b) energy from water usage, (c) carbon emission from the energy needed to use water at the campus level and its corresponding energy use. 180 6.3.3 Fuzzy-based assessment The fuzzy classes are then mapped onto their corresponding fuzzy sets, as shown in Figure 6.14, which will enable to generation of the corresponding membership for each building. AHP is used to assign weights for water, energy, and carbon emissions considering three scenarios, namely water, energy, and carbon preferences. Under the water preference scenario, Water is of strong importance (i.e., 5 compared to the energy and Water is very strong (i.e., 7) compared to CUI. Table 6.5 shows the weights and consistency ratios. For the final two scenarios, because it is 2 parameters, so the consistency ratio is 0. Water is excluded because water values are not reported per building level, and in this study, a close approximation is used to estimate water (i.e., based on ratio). Therefore, water value will hold a single class under any scenario and will not explain the variability in the buildings. In all AHP scenarios, the CR is less than 0.1 making the weights consistent. Buildings will be benchmarked spatially and temporally. These seasons are selected at a time when occupancy in the university is at its peak during the winter season and low in the summer due to the limited enrollment in the summer programs. Winter is assumed to be from October till the end of April of the next year because the corresponding HDD and CDD figures shown in Figure 6.5 become dominant, while the summer averages are taken from May till the end of September of each year. Table 6.6 presents the seasons selected for this study and their duration. 181 Figure 6.14. Fuzzy sets for the metabolic flows. Table 6.5. AHP weights and the scenarios. Scenario 1: Water preference weight Criteria WUI EUI CUI Weights CI RI CR WUI 1 5 7 0.7235 -0.97806 0.58 -1.68631 EUI 1/5 1 3 0.1932 CUI 1/7 1/3 1 0.0833 Scenario 2: Energy preference weight Criteria WUI EUI CUI Weights CI RI CR WUI 1 1/3 5 0.2828 -0.97806 0.58 -1.68631 EUI 3 1 7 0.6434 CUI 1/5 1/7 1 0.0738 Scenario 3: Carbon preference weight Criteria WUI EUI CUI Weights CI RI CR WUI 1 1/3 5 0.0986 -0.88347 0.58 -1.52323 EUI 3 1 1/9 0.1716 CUI 5 9 1 0.7298 Scenario S4, S6, S8, S10, S12, S14, S16, S18, S20 Energy preference weight Criteria EUI CUI Weights EUI 1 5 0.833 00.10.20.30.40.50.60.70.80.910.6328 0.8328 1.0328 1.2328 1.4328 1.6328Membership μEUI (log scale)VL L M H VH00.10.20.30.40.50.60.70.80.91-1.5560 -1.0560 -0.5560 -0.0560 0.4440Membership μCEI (log scale)VL L M H VH00.10.20.30.40.50.60.70.80.91-1.0753 -1.0553 -1.0353 -1.0153 -0.9953 -0.9753Membership μWUI (log scale)VL L M H VH182 CUI 1/5 1 0.167 Scenario S5, S7, S9, S11, S13, S15, S19, S21 Carbon preference weight Criteria EUI CUI Weights EUI 1 7 0.125 CUI 1/7 1 0.875 Table 6.6. Temporal classification of the seasons. No Season begin Season Ends Code 1 May 2016 September 2016 Summer 1 2 October 2016 April 2017 Winter 1 3 May 2017 Septermber 2017 Summer 2 4 October 2017 April 2018 Winter 2 5 May 2018 September 2018 Summer 3 6 October 2018 April 2019 Winter 3 7 May 2019 September 2019 Summer 4 8 October 2019 April 2020 Winter 4 9 May 2020 September 2020 Summer 5 The aggregation is obtained by using Equation (6.16). Table 6.7 presents the defuzzifying results for all the three preference scenarios, including water preference, energy preference, and carbon preference. Table 6.7 includes all the collected data from April 2016 till the end of January 2021. This proposed type of benchmarking classifies buildings into 5 classes (VL, L, M, H, VH). It can be noted that Scenario 1 classifies all the buildings in the M class. This is due to the use of ratio-based calculation in the water in section 6.2.2.1 and due to the highly emphasized weight on the water in this scenario. Scenario 2: 65% of the buildings fall in the VL, L, and M class, and 35% fall in the H, VH class in an energy preference scenario. Meaning that 35% of the buildings fall behind in terms of performance. Moreover, finally, in scenario 3, 43% of the buildings fall in the VL, L, and M class, while 57% fall in the H and VH class. Meaning that 57% of the buildings highly impact the HEIs goal towards carbon reductions in a carbon scenario. To better understand the variability, a temporal analysis is completed by assuming 9 seasons are proposed based on seasonality and occupancy load to understand their impact on the 183 overall analysis. These seasons are associated with noticeably high HDD and or CDD. These 9 seasons are shown in Table 6.8. Table 6.7. Spatiotemporal fuzzy classes of all the data. Scenario S1 S2 S3 Scenario S1 S2 S3 Scenario Preference W E C Scenario Preference W E C O1 M VH M A11 M VH VH O2 M M L A12 M H H A1 M H H R1 M L VL A2 M M H R2 M L VL A3 M H H R3 M L H A4 M M H R4 M L M A5 M H M R5 M L H A6 M H H R6 M L M A7 M M H R7 M L M A8 M M M R8 M M H A9 M M H R9 M L M A10 M H H - - - - Table 6.8 shows the defuzzification results using Equation (6.17) for the energy preference scenarios proposed in table 6.5. Table 6.9 shows the carbon preference scenarios based on the same seasonality proposed. It can be noted that buildings tend to underperform in the winter season due to the increased use of heating which is often supplied via fossil fuels. A graphical presentation of the percentiles of each building in each class is presented in Figure 6.15(a) for the energy preference scenarios and for the carbon preference scenarios in Figure 6.15(b). It can be seen that the percentage building in each class under each season. It can be noted that in Figure 6.15, during the summer, on average, 32% of the buildings fall in the VL class. While in the winter season, none of the buildings are classified in the VL class. Table 6.8: Seasonal variation results Energy preference Scenarios. Season / Building S- 1 W- 1 S - 2 W - 2 S - 3 W - 3 S - 4 W - 4 S - 5 Scenario S4 S6 S8 S10 S12 S14 S16 S18 S20 O1 VH VH VH VH VH VH VH VH VH O2 M H M H M H M H M A1 M H M H M H M H M A2 L H L M L M L M L 184 A3 H VH H VH H VH H VH H A4 L H L H L H L H L A5 M H M H H H H H M A6 M H M H M H H H H A7 M H M H M H M H L A8 M H M H M H L M L A9 M H L H M H M H M A10 M H M VH M H M H M A11 M VH M VH M VH M VH M A12 M VH M VH M VH H H M R1 L M VL M L M VL L VL R2 VL M VL L VL L VL L VL R3 VL M VL M VL M VL L VL R4 VL M VL L VL L VL L VL R5 VL M VL M VL M VL M VL R6 VL L L L L M VL L VL R7 VL L L L L L L L VL R8 L H VL M VL M VL H VL R9 VL M VL L VL L VL L VL Table 6.9: Seasonal variation results Carbon preference scenarios Season / Building S- 1 W- 1 S- 2 W- 2 S- 3 W- 3 S- 4 W- 4 S- 5 Scenario S5 S7 S9 S11 S13 S15 S17 S19 S21 O1 M L M L M L M L M O2 VL L VL L VL L VL L VL A1 M VH M H M H M H M A2 L H L H VL H L H VL A3 H VH H VH M VH M H H A4 L VH L VH L VH M VH L A5 M H M H L H L M L A6 L H L H L H L H L A7 M H M H M H M H L A8 L H L H VL H VL H VL A9 H VH H VH H VH H VH H A10 M H M VH M H M H M A11 L VH M VH L VH L VH L A12 M VH M VH M VH H H M R1 VL VL VL VL VL VL VL VL VL R2 VL VL VL VL VL VL VL VL VL R3 M H M H M H M H L R4 M H M H L H L H L R5 M H M H M H M H L R6 L H L M L H L H L R7 M M M M M H M M M R8 M VH L M L M L H L R9 M H L H M H L H L 185 (a) (b) Figure 6.15: Temporal preference scenarios (a) Energy, (b) Carbon. Figure 6.16 illustrates the two scenarios that considered energy preference and carbon preference weights for the entire data from April 2016 till January 2021. It can be seen that in the energy preference weighted scenario, none of the buildings fall in the VL class, and the majority of the L class buildings are residential buildings. While in the carbon preference weighted scenario, some of the residential buildings lie in the VL and L classes due to their dependency on electricity generated by hydro sources in BC. Therefore, these buildings have low carbon footprints. These buildings are considered slightly impactful in 0%20%40%60%80%100%Summer 1 Winter 1 Summer 2 Winter 2 Summer 3 Winter 3 Summer 4 Winter 4 Summer 5 ENERGYVL L M H VH0%20%40%60%80%100%Summer 1 Winter 1 Summer 2 Winter 2 Summer 3 Winter 3 Summer 4 Winter 4 Summer 5 CARBONVL L M H VH186 the energy scenarios, indicating scope for improvements in the overall energy consumption in all buildings (also see Table 6.8 and Table 6.9). Figure 6.16: Energy and Carbon preference scenarios classification per building. Spatiotemporal benchmarking results classify the buildings into 5 distinctive classes. To understand the performance at the building level, crisp defuzzification ranks the buildings based on the proposed WEC scenarios. Figure 6.17 shows the building type in the three primary scenarios. It can be seen that academic buildings underperform residential buildings in all scenarios. (a) (b) 02468101214VL L M H VHNumber of BuildingsFuzzy ClassENERGY CARBON0 0230 08762217121V L L M H V HS1S2S3(a)0 0140 06621391V L L M H V HS1S2S3187 (c) Figure 6.17: Results of scenario analysis, (a) Building per proposed class, (b) Academic buildings per class, (c) Residential buildings per class. 6.3.4 Ranking of buildings To rank individual buildings, crisp numbers were generated by assigning arbitrary weights to each membership, as proposed by Sadiq et al. (2004) [260]. Ideally, this would be set based on the goals, aspirations, and capabilities of each university. By using the order weighted aggregation [322], the assigned weights are (0.1, 0.15, 0.2, 0.25, 0.3). A risk index was developed for each building using Equation (6.18). Equation 6.18 Where RI is the risk index is an effective measure of quantifying the risk from a particular building with respect to the weights of importance in each fuzzy class. Table 6.10 shows the rank of the buildings based on water, energy, and carbon preferences of the buildings from most sustainable to the least. Ideally, these weights would be set by a group of experts in the HEIs with a proper understanding of their goals, limitation, budgets, and aspirations. Table 6.10. The three scenario ranks. Rank S1 Rank S2 Rank S3 R2 0.181 R2 0.156 R2 0.117 R1 0.184 R7 0.164 R1 0.123 0 090 0812043V L L M H V HS1S2S31 2 3 4 5VL L M H VHRI          = + + + +188 R7 0.190 R6 0.164 O2 0.156 R6 0.190 R4 0.165 R6 0.197 R4 0.191 R9 0.165 R7 0.198 R9 0.191 R1 0.166 R4 0.203 R3 0.193 R3 0.169 R9 0.205 R5 0.196 R5 0.177 O1 0.206 R8 0.201 R8 0.194 R3 0.214 O2 0.201 A2 0.198 A8 0.219 A8 0.203 A8 0.203 A5 0.222 A2 0.203 A4 0.210 R5 0.224 A4 0.207 A7 0.212 R8 0.230 A7 0.207 O2 0.217 A2 0.236 A5 0.209 A9 0.219 A6 0.240 A9 0.210 A5 0.226 A7 0.242 A1 0.213 A1 0.233 A4 0.249 A6 0.214 A6 0.236 A1 0.249 A10 0.215 A10 0.238 A10 0.251 A12 0.217 A12 0.242 A9 0.257 O1 0.218 A3 0.249 A12 0.259 A3 0.219 A11 0.254 A3 0.263 A11 0.221 O1 0.263 A11 0.274 Occupancy ratios are one of the main limitations of this study since student enrollment at a building is difficult to quantify. Thus, conclusions regarding occupancy influence on WEC consumption may not be derived. Furthermore, HEIs usually do not report the amount of water used per building, and even when this is done, it usually includes irrigation and out-of-building scope activities. Therefore, the extent of water performance on buildings and their impact on building types may not be derived. HEIs need to aggressively pursue data collection to detect failures in the building or deteriorating appliances and address them before they are left unattended, impacting the university’s overall efficiency and performance. Summary Currently, HEIs do not have a technical benchmarking tool capable of undressing the two common types of uncertainties associated with benchmarking tools. This may be a result 189 of two factors. Firstly, it can result from assigning impartial weights to other attributes of sustainability, namely social and economic aspects [28]. This is achieved through weight judgment uncertainties, as in the case by assigning a higher weight to other indicators in their reporting system, which is conveyed in a linguistic score associated with uncertainties. Secondly, this could be due to the nature of holistic systems' inability to address specific areas in their reporting systems [301]. This is not to undermine the importance of holistic systems in assessing multi-dimensional tasks such as sustainability. On the contrary, this is to give attention to the set of considerations set in these options and to shed light on the need for examining the uncertainty inherent in these reporting systems. In addition, to a need to highlight more attention towards a reductionist approach to sustainability [53]. The proposed benchmarking method with both aspects, the spatial and temporal benchmarking approaches, are shown to illustrate how these two types of benchmarking systems can address the uncertainties and their ability to underpin underlaying causes that affect HEIs performance. To improve on benchmarking and communicating performance. 21 temporal scenarios are proposed to cover a wide range of judgments in human perception, which can give a better understanding of the individual underperformer and the set of themes (i.e., climatic factors) that highly affect the university performance. Figure 6.17 (a-c) shows the classification of each building by type in terms of the 5 classes. It can be noted that academic buildings hold a larger effect on the overall performance within the university compared to residential buildings. By classifying the buildings in terms of the type of buildings (i.e., academic or residential) academic (which include operational buildings), and residential buildings as a separate 190 class. In academic buildings in Scenario 2, 43% of the buildings fall in the (VL, L, M) class, and 57% fall in the (H, VH) class. Similarly, for Scenario 3, 29% of the academic buildings are in the (VL, L, M) class, and 71% are falling behind in the carbon scenario. Residential buildings are less impactful since all the residential buildings fall in the (VL, L, M) class with 0 buildings in the VL class, 8 buildings in the L class, and 1 building in the M class. And in the 3rd scenario, 67% of residential buildings are better performers, and 33% have a considerable impact on the carbon scenario. In the energy preference scenario, 2 buildings are noted to have VH, which are operational and academic buildings. These two buildings have high EUI, where O1 monthly average EUI is 77.17 kWh/m2 and A11 reports 44.85 kWh/m2. Building A11 is reporting a VH class in both scenarios, which means this building is among the least performers in terms of energy and carbon scenarios. This chapter showed that heating requirements might be the main contributor to the energy and carbon impacts, and addressing these high-intensity areas in the buildings is one of the challenges for universities seeking to minimize their impact on the environment. Finally, this chapter illustrated a proposal to the calculation method based on system dynamic modeling of water-energy-carbon nexus and the carbon sequestration in HEIs. It showed that due to the intensive nature of academic buildings, biological sequestration might not be a viable option for universities to pursue, especially in regions where water resources are heavily dependent on fossil fuels. 191 Conclusions and Recommendations In Canada, HEIs consume 60% of the total energy allocated to the educational sector and have been recognized as one of the major contributors to the nation’s total GHG emissions. A review of the historical background of declarations revealed that the emphasis was on introduction, integration, and implementing the concept of sustainability within the HEIs. Beyond increasing the awareness of sustainability, little measurable evidence is shown on the impact of declarations. Nevertheless, declarations led to the need to develop reporting systems in HEIs even though these reporting systems covered a wide range of sustainable topics in HEIs. Infrastructure systems in HEIs have been constructed over a long time period as a result of increasing student enrolments and the establishment of new study programs. Consequently, they have varying energy and water use efficiencies, features, and environmental impacts. Existing sustainability ranking systems include a wide range of sustainability indicators that do not measure actual performance in natural resource consumption and GHG emissions. Technical-level decision-makers need to the resource use efficiencies of infrastructures, particularly buildings, and their operations. This can be accomplished by comparing HEIs based on their energy use, water consumption, and associated GHG emissions. Instead of relying on existing global sustainability performance, decision-makers can select and practically implement actions for the long-term sustainability of HEIs, e.g., development of resource conservation and building retrofits strategies. 192 Summary and Conclusions A summary of the specific chapters of this thesis and the main conclusions are listed below. • Chapter 3: A detailed literature review of sustainability-related declarations, reporting systems, indicators, benchmarking, and classification methods used in HEIs were reviewed, and the limitations of existing systems and methods were discussed. The findings of this chapter were used to identify the research gap and suitable methodology for this research. • Chapter 4: An inter-university benchmarking methodology was developed to benchmark 34 HEIs in terms of WEC flows. A method was proposed to classify HEIs into small, medium, and large-sized groups based on FTE. The results indicated that the energy and GHG emissions of small- and medium-sized HEIs were strongly correlated with a correlation coefficient of 0.97, whereas the correlation coefficient for all HEIs is 0.79. The study results revealed that 67% of large-sized HEIs outperformed the average performance of all the HEIs, while 50% of the medium-sized HEIs were under the average performance. Small universities performed better than medium-and large-sized HEIs with respect to WEC flows. Furthermore, this chapter identified the main reasons for the low performance of the 10 least sustainable HEIs. • Chapter 5: This chapter proposed an FCA-based method for intra-university performance benchmarking of academic buildings in an HEI in terms of WEC flows. FCA allows for the extraction of similar information within different clusters. To account for the uncertainties in partitioning, the fuzzy c-means algorithm was 193 employed to classify buildings based on WEC flows. The application of the developed method was demonstrated by a case study of 71 academic buildings in two UBC campuses. The buildings were clustered into 3 clusters with different degrees of membership. The results showed that buildings with a lot of laboratories were frequently found in the least sustainable cluster. • Chapter 6: It is important to consider the uncertainties originating from data variation and human judgment in sustainability benchmarking. For this purpose, a spatiotemporal probabilistic fuzzy synthetic evaluation framework was proposed to address aleatory and fuzzy uncertainties. Data related to water, energy, and carbon flows of 23 buildings over 58 months were collected at a UBC campus. The study revealed that the type of buildings significantly affects the overall performance of a university. Furthermore, 32% of the buildings were grouped into the most sustainable class during the summer, while in the winter season, none of the buildings were classified as most sustainable, indicating that heating greatly affected the sustainability performance of academic buildings. Furthermore, the assessment considered the upstream water-energy-carbon nexus in the irrigation of greeneries on campus. The findings indicated that greeneries had an insignificant carbon sequestration effect at the campus level. Originality and Contributions This research has the following contributions to the existing body of knowledge: • A new inter-university benchmarking method has been proposed to reduce the variability in current benchmarking techniques. The proposed method consists of three steps; i) using a defined set of indicators to reduce the ambiguity-associated 194 judgment, prioritizing, and underrepresentation. ii) use the proposed classification technique to promote customized solutions to customized problems, and iii) use the normalized utility functions, resulting in the creation of uniformity among the comparable entities. The proposed method is an attempt to assist HEIs and policy makers by understanding the intrinsic features that affect the performance of small, medium, and large-sized HEIs. This will aid the improvement process by identifying solutions to each class of HEIs. • An intra-university benchmarking methodology was proposed and developed. The findings help HEIs communicate their findings in three ways; firstly, the least performers are identified, the intrinsic characteristics and features associated with the least performing buildings are also identified (i.e., laboratory dominated buildings). Second, the findings are an attempt to aid technical managers with the means and tools to communicate the impacts of academic building in HEIs. Finally, the proposed framework reduces the variability of data used in benchmarking techniques, which will allow for a normalized comparison among similar types of buildings that share the same intrinsic characteristics. • A probabilistic fuzzy synthetic evaluation framework to account for two types of uncertainties: Aleatory and linguistic uncertainties that are commonly overlooked in benchmarking techniques. These uncertainties originated from data variation and human judgments, respectively, are often overlooked in performance benchmarking of academic buildings. The probabilistic fuzzy synthetic evaluation method considers the two types of uncertainties in performance benchmarking of academic buildings for the first time. The results outlined the significant impact of 195 academic buildings, energy-intensive operations (i.e., heating) and finally included the upstream impact of the utilization of water in the aggregation sector at HEIs by addressing the uncertainty in benchmarking. Limitations The following challenges and limitations were identified concerning the present study: • Data limitation: In this study, the GHG emissions and water consumption for five HEIs were not available; water consumption per building was also not available; some HEIs did not report climatic factors. Reasonable assumptions and approximations were made when the data were not available. Furthermore, the HEIs data available were not always reported in the same years. The last reports of some universities were published in 2005, and others were reported in 2016. The results, therefore, may be influenced by abnormalities in weather or other impacts not included in this study. Adjustments were made to normalize the effect of climate through the use of climate utility factors. However, other facts such as efficiencies, age, and other parameters are not included in this study. • Occupants’ behavior is believed to be an important factor affecting energy consumption in academic buildings; however, due to privacy concerns high variability, the number of occupants per building was not studied, and therefore, normalization was completed by area. Furthermore, the values of FTE may be affected by different guidelines of different HEIs, and thus the normalization of FTE may result in uncertainties in the assessment results. However, literature data were used to reduce the uncertainty. 196 • Technical-Reductionist model: This study includes a reductionist model of sustainability. This is not the opposite of holistic sustainability; however, social and economic aspects of sustainability should be investigated for a holistic assessment. Furthermore, this research did not consider other aspects that may contribute to some HEIs performances, such as the significance of pedagogical impacts. It may be the case that underperforming universities are associated with higher output in terms of research for example. Or the efficiencies, size of campuses, and age of buildings may be the factors influencing the overall score. • Fuzzy logic was used to address the fuzzy uncertainty. However, the weights were arbitrarily determined for the defuzzification process. The weight-associated uncertainty could be minimized by consulting experts in the field. Future Research Future efforts are needed to advance the study of sustainability of HEIs through the following channels: • Data limitation: HEIs are encouraged to collect more data regarding the WEC flows. Further research may address areas that have not been touched in this research, such as occupants’ behaviors. This could be a very challenging task due to a large number of occupants and their highly variable behaviors. Providing data on occupants’ behaviors may help to identify subtle energy and water use patterns in relatively more sustainable buildings. • Future research is needed to identify the most important facts affecting the performances between small, medium, and large-sized HEIs. 197 • Scope: The scope of benchmarking can be expanded to include building envelope, the performance of electric loads, the types of interventions used, solar gains, shading effects on the buildings. A more comprehensive performance benchmarking would provide a more holistic picture of HEIs regarding various aspects of sustainability, such as green building development, use of renewable energy, and water footprint minimization. • Building level analysis: Interventions for increasing energy and water use efficiencies within individual buildings are worth investigating. 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In Proceedings of 2007 IEEE International Conference on Computational Cybernetics, 19-21 Oct. 2007; pp. 241-244. 223 Appendices Appendix A STARS Table A1: groups/subgroups and points awarding system of the STARS reporting system obtained from the STARS technical manual Category/Group Subgroups Indicators Points Academics (AC) Curriculum (40 points) AC1 Academic Courses AC2 Learning Outcomes AC3 Undergraduate Program AC4 Graduate Program AC5 Immersive Experience AC6 Sustainability Literacy Assessment AC7 Incentives for Developing Courses AC8 Campus as a Living Laboratory 14 8 3 3 2 4 2 4 Research (18 points) AC9 Academic Research AC10 Support for Research AC11 Access to Research 12 4 2 Engagement (EN) Campus Engagement (21 points) EN1 Student Educators Program EN2 Student Orientation EN3 Student Life EN4 Outreach Materials and Publications EN5 Outreach Campaign EN6 Assessing Sustainability culture EN7 Employee Educators Program EN8 Employee Orientation EN9 Staff Professional Development 4 2 2 2 4 1 3 1 2 Public Engagement (20 points) EN10 Community Partnerships EN11 Inter-Campus Collaboration EN12 Continuing Education EN13 Community Service EN14 Participation in Public Policy EN15 Trademark Licensing 3 3 5 5 2 2 Operations (OP) Air & Climate (11 points) OP1 Greenhouse Gas Emissions OP2 Outdoor Air Quality 10 1 Buildings (8 points) OP3 Building Operations and Maintenance OP4 Building Design and Construction 5 3 Energy (10 points) OP5 Building Energy Consumption OP6 Clean and Renewable Energy 6 4 Food & Dining (8 points) OP7 Food and Beverage Purchasing OP8 Sustainable Dining 6 2 Grounds (3-4 points) OP9 Landscape Management OP10 Biodiversity 2 1-2 Purchasing (6 points) OP11 Sustainable Procurement OP12 Electronics Purchasing OP13 Cleaning and Janitorial Purchasing OP14 Office Paper Purchasing 3 1 1 1 Transportation (7 points) OP15 Campus Fleet OP16 Student Commute Modal Split OP17 Employee Commute Modal Split OP18 Support for Sustainable Transportation 1 2 2 2 Waste (10 points) OP19 Waste Minimization and Diversion OP20 Construction and Demolition Waste Diversion OP21 Hazardous Waste Management 8 1 1 Water (6-8 points) OP22 Water Use OP23 Rainwater Management 4-6 2 Planning & Administration (PA) Coordination & Planning (8 points) PA1 Sustainability Coordination PA2 Sustainability Planning PA3 Participatory Governance 1 4 3 Diversity & Affordability (10 points) PA4 Diversity and Equity Coordination PA5 Assessing Diversity and Equity PA6 Support for Underrepresented Groups PA7 Affordability and Access 2 1 3 4 Investment & Finance PA8 Committee on Investor Responsibility 2 224 (7 points) PA9 Sustainable Investment PA10 Investment Disclosure 4 1 Well-being & Work (7 points) PA11 Employee Compensation PA12 Assessing Employee Satisfaction PA13 Wellness Program PA14 Workplace Health and Safety 3 1 1 2 Innovation & Leadership (IN) (4 bonus points available) Exemplary Practice (Catalog of credits available) Innovation (4 credits available) 0.5 each 1 each 225 Appendix B Data from the STARS database Table B1: Data collected for Canadian HEIs in the STARS database HEI Code Province Rank Baseline YR Baseline Value Performance Year Performance Value Growth MacEwan College AB1 Alberta Silver 13/14 30,754 16/17 28,068 –9% MRU AB2 Alberta Silver 14/15 29,295 16/17 34,393 17% NAIT AB3 Alberta Silver 07/08 68,190 13/14 54,129 –21% University of Alberta AB4 Alberta Gold 05/06 212,190 15/16 285,815 35% University of Calgary AB5 Alberta Gold 08/09 239,955 17/18 182,112 –24% Camosun College BC1 BC Silver 2010 1,826.26 2014 1,371 –25% Okanagan College BC2 BC Silver 2015 1,686 2018 1,236 –27% Royal Roads University BC3 BC Gold 2010 1,460 2017 1,016 –30% Selkirk College BC4 BC Silver 2008 1,698 2018 949 –44% Simon Fraser University BC5 BC Gold 2007 18,934 2017 15,235 –20% TRU BC6 BC Platinum 2016 3,359 2018 3,715 11% UBC BC7 BC Gold 2007 60,100 2017 42,786 –29% UNBC BC8 BC Silver 2010 5,688 2018 7,199 27% University of Victoria BC9 BC Gold 2010 15,545 2018 11,603 –25% University of Manitoba MB1 MB Gold 90/91 38,442 16/17 35,304 –8% University of Winnipeg MB2 MB Silver 09/10 3,883 17/18 2,860 –26% UNBF NB1 NB Silver 07/08 39,070 15/16 30,947 –21% Dalhousie University NS1 NS Gold 09/10 106,178 16/17 87,056 –18% Carleton University ON1 ON Silver 2012 26,729 2015 26,203 –2% Durham College ON2 ON Silver 2012 5,600 2013 5,369 –4% Fanshawe College ON3 ON Gold 2005 5,367 2017 5,200 –3% Fleming College ON4 ON Gold 12/13 4,614 17/18 4,506 –2% George Brown College ON5 ON Silver 06/07 3,703 12/13 4,192 13% Loyalist College ON6 ON Bronze 2013 2,488 2014 2,485 –0.1% Mohawk College ON7 ON Gold 2007 8,521 2017 3,235 –62% Sheridan College ON8 ON Silver 10/11 8,136 16/17 7,307 –10% St Lawrence College ON9 ON Bronze 2010 4,079 2017 3,373 –17% University of Ottawa ON10 ON Silver 05/06 37,317 2016 27,919 –25% University of waterloo ON11 ON Silver 2010 38,711 2017 39,495 2% Western University ON12 ON Gold 2009 84,418 2016 55,524 –34% Wilfrid Laurier University ON13 ON Gold 2009 10,876 2018 9,902 –9% Concordia University QC1 QC Gold 10/11 10,362 14/15 9,665 –7% HEC Montreal QC2 QC Silver 05/06 1,910 2016 1,105.57 –42% McGill University QC3 QC Gold 02/03 57,590 2014 43,249 –25% Polytechnique Montreal QC4 QC Gold 04/05 3,597 16/17 3,153 –12% University of Saskatchewan SK1 SK Silver 05/06 151,542 15/16 152,454 0.6% Table B2: The data collected for the HEIs in Canada from the STATS database HEI Size FTE Energy MMBtu Gross area Emissions in metric ton waters in gallons UBC L1 43,509.00 1,812,111.00 15,440,970.00 52,350.00 806,266,185.00 University of Ottawa L2 42,027.00 609,678.55 63,523,299.56 28,911.00 133,404,508.89 Universite Laval L3 40,515.00 913,132.00 7,277,156.00 26,638.00 173,616,520.00 George brown college L4 39,517.00 173,114.00 1,676,888.80 6,614.00 36,784,637.38 University of Alberta L5 29,740.00 2,277,664.80 13,994,757.40 299,127.00 473,111,012.00 Western University L6 29,050.00 1,013,471.07 8,254,478.00 76,492.00 293,172,067.00 University of Calgary L7 27,722.00 1,846,317.86 10,351,247.83 239,953.00 308,739,496.55 University of Manitoba L8 26,621.00 815,213.00 5,137,104.00 NA 133,223,285.00 Simon Fraser University L9 26,501.00 519,548.70 4,580,845.37 16,751.13 155,083,259.37 Humber College L10 24,774.00 216,337.00 2,371,853.00 23,492.00 47,994,000.00 Sheridan institutre of technology and advanced learning L11 24,401.00 207,893.00 1,938,902.00 9,070.30 32,583,767.00 Wilfrid Laurier University M1 17,937.00 91,700.51 2,726,014.00 11,748.00 37,045,639.40 University of Sask. M2 18,649.00 1,524,399.00 6,910,894.81 150,358.30 232,533,658.00 University of victoria M3 17,669.00 465,544.56 3,736,960.38 14,020.29 160,911,158.00 226 Dalhousie University M4 15,794.00 813,581.00 4,530,493.00 84.91 197,980,184.94 Fanshawe College M5 13,553.00 128,990.00 1,950,000.00 5,752.00 39,285,555.00 Northern Alberta Institute of Technology M6 12,479.00 366,519.62 3,200,724.65 54,128.58 39,147,392.21 MacEwan University M7 11,838.15 267,244.00 2,602,767.36 35,632.00 30,305,553.63 University of Regina M8 11,550.00 334,570.00 2,493,393.00 NA 72,860,421.00 Camosun college S1 9,792.90 48,391.00 943,690.97 1,371.32 NA Nova Scotia community College S2 9,614.00 182,156.00 2,508,461.00 26,168.00 28,294,896.00 Thompson River University S3 8,315.18 109,667.30 1,162,794.09 3,966.42 33,564,007.70 Lakehead University S4 8,313.00 184,031.47 1,287,572.51 14,064.13 50,686,612.37 Okanagan College S5 7,655.00 47,689.00 839,412.00 1,463.00 16,027,523.00 UBCO Okanagan S6 7,358.75 85,847.26 43,505,966.00 university of Winnipeg S7 7,512.00 124,443.14 1,280,141.10 2,330.00 17,067,760.04 Fleming College S8 6,001.00 97,378.00 793,204.00 NA 42,757.00 Royal Roads University S9 971.00 34,967.63 429,618.24 1,272.00 6,535,919.30 Loyalist College S10 1,925.26 52,195.17 786,626.00 2,484.60 16,492,356.96 Niagara College of Applied Arts and Technology S11 NA 17,563,446.00 NA 5,549.38 NA 227 Appendix C Regression Parameters Figure C1: Parameter selection 228 Figure C2: Residual plot for the WU regression Figure C3: Regression plot for the WU regression 229 Figure C4: Confidence interval for the WU regression Figure C5: GHG parameter selection 230 Figure C6: Interaction report for the CEI regression Figure C7: Residual plot for the CEI regression 231 Figure C8: Regression plot for the CEI regression Figure C9: Residual plots for the area of buildings and water consumption. 232 Table C1: Regression coefficient for the area of buildings and water consumption Term Coef SE Coef 95% CI T-Value P-Value VIF Constant 0.1319 0.0684 (-0.0080, 0.2718) 1.93 0.064 JT_Water 0.8322 0.0709 (0.6872, 0.9772) 11.74 0.000 1.00 Table C2: Model summary for the area of buildings and water consumption S R-sq R-sq(adj) PRESS R-sq(pred) AICc BIC 0.379623 82.62% 82.02% 5.26843 78.09% 32.74 36.16 Figure C10: Residual plots for the area of buildings and energy consumption Table C3: Regression coefficient for the area of buildings and energy consumption Term Coef SE Coef 95% CI T-Value P-Value VIF Constant 0.1272 0.0423 (0.0410, 0.2134) 3.01 0.005 JT_Energy 0.7954 0.0376 (0.7187, 0.8721) 21.13 0.000 1.00 Table C4: Model summary for the area of buildings and energy consumption S R-sq R-sq(adj) PRESS R-sq(pred) AICc BIC 0.244347 93.31% 93.10% 2.21993 92.23% 5.40 9.18 233 Figure C11: Residual plots for the area of buildings and GHG emissions. Table C5: Regression coefficient for the area of buildings and GHG emissions Term Coef SE Coef 95% CI T-Value P-Value VIF Constant 0.0678 0.0756 (-0.0866, 0.2222) 0.90 0.377 JT_GHG 0.7571 0.0678 (0.6187, 0.8955) 11.17 0.000 1.00 Table C6: Model summary for the area of buildings and GHG emissions S R-sq R-sq(adj) PRESS R-sq(pred) AICc BIC 0.427035 80.62% 79.98% 6.19311 78.07% 41.15 44.69 234 Appendix D Buildings at UBCO Figure D1: UBCO building usage of natural gas and electricity 235 Appendix E Spatiotemporal parameter Table E1: Buildings investigated Building Annotation Annotation Classification Area in m2 Central heating plant CHP O1 Operational building 528 Geothermal plant GEO O2 Operational building 454 Administration ADM A1 Offices 5,792 Arts ARTS A2 Academic building 9,667 Arts and Science II ASC A3 Academic building 7,801 Creative and Critical Studies CCS A4 Academic building 4,797 Engineering, Management and Education EME A5 Academic building 16,520 Charles E. Fipke Centre for Innovation Research FIPKE A6 Academic building 6,725 Gym GYM A7 Recreation 4,929 Library LIB A8 Academic building 6,179 Upper Campus Health MWO A9 Academic building 1,681 Reichwald Health Sciences Centre RHS A10 Academic building 5,021 Sciences SCI A11 Academic building 8,952 University Centre Building UNC A12 Academic building 7,238 Cascade lower CASU R1 Residential building 8,144 Cascade Upper CASL R2 Residential building 4,669 Cassiar CASS R3 Residential building 3,951 Kalamalka KAL R4 Residential building 4,835 Monashee MON R5 Residential building 7,684 Nicola NIC R6 Residential building 5,667 Purcell PUR R7 Residential building 6,208 Similkameen SIM R8 Residential building 3,528 Valhalla VAL R9 Residential building 4,797 Table E2: Water calculation based on the area-ratio-method Building Area Percentage 2016 2017 2018 2019 2020 Avg BEF O1 528.0 0.37% 479.6 634.6 524.1 697.7 569.7 581.2 408.7 O2 454.0 0.32% 412.4 545.8 450.7 600.0 489.9 499.8 351.5 A1 5,792.0 4.06% 5,261.6 6,962.4 5,749.8 7,653.8 6,250.1 6,375.5 4,484 A2 9,667.0 6.78% 8,781.6 11,620.3 9,596.6 12,774.4 10,431.6 10,640.9 7,483 A3 7,801.0 5.47% 7,086.5 9,377.2 7,744.2 10,308.5 8,417.9 8,586.9 6,039 A4 4,797.0 3.36% 4,357.6 5,766.2 4,762.0 6,338.9 5,176.3 5,280.2 3,713 A5 16,520.0 11.58% 15,007.0 19,858.0 16,399.7 21,830.2 17,826.6 18,184.3 12,789 A6 6,725.0 4.71% 6,109.0 8,083.8 6,676.0 8,886.6 7,256.8 7,402.5 5,206 A7 4,929.0 3.45% 4,477.5 5,924.9 4,893.1 6,513.3 5,318.8 5,425.5 3,816 A8 6,179.0 4.33% 5,613.1 7,427.5 6,134.0 8,165.1 6,667.7 6,801.5 4,784 A9 1,681.0 1.18% 1,527.0 2,020.6 1,668.7 2,221.3 1,813.9 1,850.3 1,301 A10 5,021.0 3.52% 4,561.2 6,035.6 4,984.5 6,635.0 5,418.1 5,526.9 3,887 A11 8,952.0 6.27% 8,132.0 10,760.7 8,886.7 11,829.4 9,659.9 9,853.8 6,930 A12 7,238.0 5.07% 6,575.1 8,700.5 7,185.3 9,564.5 7,810.4 7,967.1 5,603 R1 8,144.0 5.71% 7,398.1 9,789.5 8,084.6 10,761.7 8,788.1 8,964.4 6,305 R2 4,669.0 3.27% 4,241.4 5,612.4 4,635.0 6,169.8 5,038.3 5,139.4 3,615 R3 3,951.0 2.77% 3,589.1 4,749.3 3,922.2 5,221.0 4,263.5 4,349.0 3,059 R4 4,835.0 3.39% 4,392.1 5,811.9 4,799.7 6,389.1 5,217.3 5,322.0 3,743 R5 7,684.0 5.39% 6,980.2 9,236.6 7,628.0 10,153.9 8,291.7 8,458.1 5,949 R6 5,667.0 3.97% 5,148.0 6,812.0 5,625.7 7,488.6 6,115.2 6,237.9 4,387 R7 6,208.0 4.35% 5,639.4 7,462.3 6,162.7 8,203.4 6,698.9 6,833.4 4,806 R8 3,528.0 2.47% 3,204.9 4,240.8 3,502.3 4,662.0 3,807.0 3,883.4 2,731 R9 4,797.0 3.36% 4,357.6 5,766.2 4,762.0 6,338.9 5,176.3 5,280.2 3,714 Table E3: Statistical summary of the calculated water values Variable Total Count Mean SE Mean Standard Deviation Minimum Q1 Median Q3 Maximum IQR Skewness Any building 58 0.09644 0.00128 0.00974 0.08272 0.08992 0.10017 0.10093 0.11011 0.01101 -0.05 236 Figure E1: Normalized water box plot Table E4: Statistical summary of the reported EUI consumption Variable Total Count Mean SE Mean Standard Deviation Minimum Q1 Median Q3 Maximum IQR Skewness O1 58 77.17 1.98 15.08 10.90 75.76 75.76 75.76 111.75 0.00 -1.03 O2 58 28.82 1.50 11.46 14.62 18.74 22.87 40.03 50.98 21.30 0.52 A1 58 31.60 0.98 7.43 16.61 26.05 29.28 38.02 51.13 11.97 0.55 A2 58 18.46 1.25 9.52 5.70 11.12 15.18 25.55 45.23 14.43 0.93 A3 58 41.87 1.98 15.07 25.44 29.17 37.52 53.21 83.43 24.04 0.94 A4 58 21.95 1.81 13.79 7.24 10.06 18.28 31.97 60.15 21.92 0.85 A5 58 29.65 0.77 5.83 21.41 25.67 27.86 31.55 50.10 5.88 1.54 A6 58 33.93 1.38 10.47 19.48 25.33 28.92 42.62 54.55 17.30 0.65 A7 58 23.12 1.17 8.95 7.63 15.50 21.71 30.62 40.40 15.12 0.37 A8 58 20.76 0.89 6.81 9.59 16.12 18.39 25.41 36.58 9.29 0.64 A9 58 25.29 1.44 10.97 11.17 16.39 22.31 34.48 61.24 18.08 0.93 A10 58 34.54 1.75 13.32 14.24 24.16 28.70 44.15 75.48 19.99 0.96 A11 58 44.85 3.48 26.47 18.24 21.60 36.74 62.69 113.57 41.09 0.94 A12 58 36.52 1.82 13.83 13.61 24.34 33.71 47.30 68.70 22.96 0.47 R1 58 12.41 0.77 5.87 3.96 7.39 11.33 17.33 26.38 9.94 0.48 R2 58 10.05 0.71 5.40 2.38 5.44 8.93 14.13 22.24 8.70 0.40 R3 58 10.89 0.83 6.36 1.88 5.45 9.65 15.72 27.65 10.28 0.51 R4 58 10.15 0.69 5.28 2.71 5.22 9.80 14.98 21.42 9.76 0.28 R5 58 12.58 0.99 7.52 0.91 6.35 11.33 17.77 28.26 11.42 0.48 R6 58 9.96 0.64 4.87 3.66 5.77 8.35 13.33 23.29 7.57 0.72 R7 58 9.78 0.47 3.58 2.22 6.64 9.06 12.77 17.40 6.13 0.19 R8 58 17.13 1.81 13.79 3.41 5.33 13.99 22.19 64.32 16.86 1.42 R9 58 10.14 0.71 5.38 2.33 5.17 9.63 14.68 21.60 9.51 0.37 Table E5: Statistical summary for the CEI Variable Total Count Mean SE Mean Standard Deviation Minimum Q1 Median Q3 Maximum IQR Skewness O1 58 0.30 0.03 0.20 0.13 0.20 0.20 0.37 1.07 0.17 2.04 O2 58 0.07 0.00 0.03 0.04 0.05 0.06 0.10 0.13 0.06 0.52 A1 58 1.62 0.17 1.25 0.12 0.51 1.41 2.59 4.67 2.08 0.48 A2 58 1.38 0.21 1.59 0.02 0.03 0.69 2.54 5.75 2.51 1.12 A3 58 2.47 0.27 2.03 0.48 0.87 1.79 3.75 8.60 2.88 1.31 A4 58 2.11 0.31 2.36 0.02 0.05 1.03 3.74 8.99 3.69 1.09 A5 58 0.67 0.11 0.82 0.06 0.08 0.39 0.81 3.99 0.73 2.10 A6 58 1.11 0.17 1.27 0.05 0.07 0.47 1.77 4.09 1.70 1.00 A7 58 1.52 0.18 1.35 0.07 0.22 1.17 2.60 4.57 2.38 0.71 A8 58 0.76 0.12 0.92 0.03 0.04 0.33 1.51 3.71 1.46 1.25 A9 58 2.75 0.25 1.91 0.12 1.12 2.41 4.19 8.93 3.07 0.83 A10 58 1.69 0.24 1.83 0.18 0.32 0.90 2.73 9.83 2.41 1.98 A11 58 3.84 0.60 4.57 0.05 0.07 1.99 6.64 16.48 6.57 1.22 A12 58 2.32 0.24 1.84 0.17 0.59 1.95 3.58 6.92 2.99 0.66 237 R1 58 0.03 0.00 0.02 0.01 0.02 0.03 0.05 0.07 0.03 0.48 R2 58 0.03 0.00 0.01 0.01 0.01 0.02 0.04 0.06 0.02 0.40 R3 58 0.86 0.09 0.68 0.08 0.27 0.79 1.28 3.18 1.01 1.06 R4 58 0.62 0.06 0.43 0.05 0.21 0.60 0.99 1.50 0.77 0.18 R5 58 1.10 0.13 0.95 0.00 0.34 0.60 1.89 3.16 1.55 0.84 R6 58 0.51 0.04 0.34 0.09 0.19 0.43 0.79 1.41 0.60 0.54 R7 58 0.53 0.03 0.22 0.16 0.35 0.55 0.71 1.08 0.35 0.12 R8 58 1.16 0.24 1.86 0.04 0.13 0.45 1.04 8.62 0.91 2.46 R9 58 0.66 0.06 0.45 0.07 0.21 0.70 0.96 1.57 0.75 0.36 Table E6: Probabilistic results and its statistical summary Cell Energy Energy Carbon Carbon WUI WUI Minimum 0.856 0.846 0.0019 0.0016 0.0827 0.0827 Maximum 197.09 544.52 0.1101 0.1101 Mean 24.85 24.853 1.71 1.711 0.0964 0.0964 90% CI ± 0.135 ± 0.0469 ± 5.825E-005 Mode 10.9 10.805 0.0257 0.0263 0.0839 N/A Median 20.37 20.367 0.418 0.418 0.0964 0.0964 Std Dev 18.36 18.364 6.38 6.814 0.0079 0.0079 Skewness 1.5304 1.5299 26.915 75.2509 0 0 Kurtosis 6.5141 6.511 1497.9003 91707.2631 1.8 1.8 Values 50000 50000 50000 Errors 0 0 0 Filtered 0 0 0 Left X 4.3 4.3 0 0 0.0841 0.0841 Left P 0.05 0.05 0.05 0.05 0.05 0.05 Right X 63.7 63.7 7 7 0.1088 0.1088 Right P 0.958 0.958 0.95 0.95 0.95 0.95 Dif. X 59.41 59.408 6.58 6.581 0.0247 0.0247 Dif. P 0.908 0.908 0.9 0.9 0.9 0.9 1.0% 2.12 2.117 0.0099 0.01 0.083 0.083 2.5% 3.07 3.073 0.017 0.017 0.0834 0.0834 5.0% 4.29 4.293 0.0278 0.0278 0.0841 0.0841 10.0% 6.29 6.286 0.0499 0.0499 0.0854 0.0854 20.0% 9.74 9.741 0.103 0.103 0.0882 0.0882 25.0% 11.4 11.401 0.136 0.136 0.0896 0.0896 30.0% 13.07 13.071 0.174 0.174 0.0909 0.0909 35.0% 14.78 14.779 0.219 0.219 0.0923 0.0923 40.0% 16.55 16.547 0.273 0.273 0.0937 0.0937 45.0% 18.4 18.401 0.338 0.338 0.095 0.0950 50.0% 20.37 20.367 0.418 0.418 0.0964 0.0964 55.0% 22.48 22.478 0.516 0.516 0.0978 0.0978 60.0% 24.77 24.775 0.639 0.639 0.0992 0.0992 65.0% 27.31 27.315 0.797 0.797 0.1005 0.1005 70.0% 30.18 30.178 1.01 1.006 0.1019 0.1019 75.0% 33.49 33.489 1.29 1.295 0.1033 0.1033 80.0% 37.45 37.451 1.71 1.714 0.1046 0.1046 90.0% 49.31 49.316 3.59 3.59 0.1074 0.1074 95.0% 60.74 60.743 6.61 6.611 0.1088 0.1088 97.5% 71.88 71.889 11.22 11.227 0.1094 0.1094 99.0% 86.31 86.332 20.78 20.784 0.1099 0.1099 Table E7: Statistical summary for the EUI EUI Summer 1 Winter 1 Summer 2 Winter 2 Summer 3 Winter 3 Summer 4 Winter 4 Summer 5 O1 95.27 75.76 90.11 75.76 84.11 75.76 81.28 75.76 49.60 O2 18.93 36.95 19.50 36.58 18.63 36.87 19.37 38.67 17.07 A1 26.28 44.26 26.49 35.55 26.49 34.71 26.39 32.84 21.29 A2 11.31 27.64 11.26 24.48 11.60 25.07 11.22 23.89 7.14 A3 29.02 58.46 30.51 52.23 28.04 49.73 27.25 46.90 30.95 A4 9.56 35.25 9.91 30.69 9.95 32.06 10.08 28.50 8.75 A5 27.16 34.65 26.59 31.11 26.19 31.73 26.90 30.86 25.07 A6 25.55 39.63 22.17 42.94 23.92 39.95 26.34 39.68 27.61 A7 17.18 28.47 18.11 26.97 15.14 31.15 16.55 30.21 10.48 238 A8 17.00 25.51 16.90 29.65 17.34 26.50 15.58 20.77 11.13 A9 20.06 32.33 14.55 37.03 16.56 34.88 15.19 28.90 17.34 A10 24.39 42.32 23.52 44.49 23.66 42.21 23.55 40.48 22.90 A11 21.99 71.28 21.96 69.21 20.95 55.24 20.21 56.67 25.07 A12 24.62 51.55 21.10 48.41 25.46 47.27 27.23 41.83 21.91 R1 8.34 18.29 7.65 17.95 7.73 17.11 6.21 15.17 5.42 R2 4.28 16.64 6.24 14.98 6.00 14.57 5.80 12.30 3.19 R3 5.29 18.62 6.54 16.36 6.32 15.35 5.17 14.76 2.73 R4 5.56 16.10 6.38 14.38 5.82 14.53 5.68 14.02 3.31 R5 6.44 16.88 6.43 18.97 6.98 19.42 6.12 19.91 2.81 R6 5.83 13.41 6.65 13.58 6.85 15.32 6.05 13.05 4.17 R7 6.31 12.86 6.86 12.77 6.96 13.19 7.31 11.91 5.31 R8 8.62 37.67 5.34 18.96 5.35 19.27 5.58 32.47 4.99 R9 5.88 16.19 6.02 14.21 5.73 14.61 5.59 14.08 3.67 Table E8: CEI results CUI Summer 1 Winter 1 Summer 2 Winter 2 Summer 3 Winter 3 Summer 4 Winter 4 Summer 5 O1 0.36 0.20 0.44 0.20 0.49 0.20 0.53 0.20 0.36 O2 0.05 0.10 0.05 0.10 0.05 0.10 0.05 0.10 0.04 A1 0.42 3.45 0.46 2.37 0.49 2.27 0.54 2.22 0.30 A2 0.08 2.76 0.14 2.27 0.05 2.35 0.09 2.31 0.06 A3 0.84 4.95 1.03 3.71 0.68 3.09 0.66 2.88 1.09 A4 0.08 4.09 0.09 3.44 0.07 3.87 0.28 3.22 0.20 A5 0.41 1.70 0.25 0.93 0.07 0.86 0.07 0.63 0.07 A6 0.09 1.98 0.07 2.31 0.06 1.44 0.08 1.48 0.08 A7 0.36 2.32 0.46 2.14 0.31 2.61 0.45 2.62 0.23 A8 0.13 1.34 0.07 1.33 0.05 1.60 0.05 1.07 0.04 A9 1.68 4.28 0.77 4.84 1.08 4.24 0.90 3.47 1.29 A10 0.43 3.00 0.39 3.53 0.36 2.60 0.26 1.65 0.25 A11 0.07 8.68 0.26 8.36 0.11 5.34 0.13 5.77 0.21 A12 0.56 4.71 0.51 3.71 0.57 3.46 0.80 2.90 0.71 R1 0.02 0.05 0.02 0.05 0.02 0.04 0.02 0.04 0.01 R2 0.01 0.04 0.02 0.04 0.02 0.04 0.02 0.03 0.01 R3 0.32 1.86 0.35 1.38 0.32 1.19 0.29 1.25 0.13 R4 0.25 1.04 0.26 0.94 0.21 1.04 0.23 1.02 0.13 R5 0.46 1.58 0.38 1.84 0.42 1.92 0.37 2.20 0.12 R6 0.22 0.75 0.22 0.73 0.25 0.97 0.24 0.79 0.17 R7 0.35 0.73 0.32 0.71 0.39 0.78 0.41 0.62 0.31 R8 0.49 4.46 0.21 0.50 0.13 0.48 0.13 2.99 0.15 R9 0.32 1.15 0.22 0.84 0.29 1.02 0.21 1.07 0.23 239 Figure E2: EUI results Figure E3: CEI results 0100200300400500600700800900Summer1Winter 1 Summer2Winter 2 Summer3Winter 3 Summer4Winter 4 Summer5EUI (kWh/m2)SeasonsO1 O2 A1 A2 A3 A4 A5 A6 A7 A8 A9 A10A11 A12 R1 R2 R3 R4 R5 R6 R7 R8 R90.0010.0020.0030.0040.0050.0060.00Summer1Winter1Summer2Winter2Summer3Winter3Summer4Winter4Summer5CUI (KtCO2E)SeasonsO1 O2 A1 A2 A3 A4 A5 A6 A7 A8 A9 A10A11 A12 R1 R2 R3 R4 R5 R6 R7 R8 R9240 Table E9: Conditional distribution by class Scenario/ Class S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16 S17 S18 S19 S20 S21 VL 0% 0% 9% 30% 13% 0% 9% 30% 13% 0% 9% 26% 22% 0% 9% 35% 17% 0% 9% 39% 22% L 0% 35% 4% 17% 26% 9% 9% 22% 30% 22% 9% 22% 30% 17% 9% 17% 35% 30% 9% 17% 48% M 100% 30% 30% 43% 52% 26% 4% 39% 48% 22% 13% 39% 43% 26% 4% 26% 39% 13% 9% 30% 22% H 0% 26% 52% 4% 9% 48% 48% 4% 9% 35% 43% 9% 4% 39% 57% 17% 9% 43% 61% 9% 9% VH 0% 9% 4% 4% 0% 17% 30% 4% 0% 22% 26% 4% 0% 17% 22% 4% 0% 13% 13% 4% 0%
Thesis/Dissertation
eng
Civil Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Sadiq, Rehan
Hewage, Kasun
Performance assessment and benchmarking of higher education institutions and academic buildings in Canada : a sustainability perspective
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