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Characterizing sustainable forest management at the local-level in British Columbia, Canada Gough, Angeline 2009

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CHARACTERIZING SUSTAINABLE FOREST MANAGEMENT AT THE LOCAL-LEVEL IN BRITISH COLUMBIA, CANADA by Angeline Gough B.Sc., University of British Columbia, 2003 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in The Faculty of Graduate Studies (Forestry) THE UNIVERITY OF BRITISH COLUMBIA (Vancouver) April 2009 © Angeline Gough, 2009 Abstract In this study, sustainable forest management (SFM) indicators employed by 13 different forest management entities in British Columbia (BC) have been analyzed according to their use and associated monitoring costs. The analysis revealed significant gaps in the monitoring of many social and economic indicators amongst forestry companies, largely due to a perception that these indicators were not the companies’ responsibilities to monitor. In addition, some widely recognized values, such as the maintenance of soil and water quality, were also not being monitored. This can be attributed, at least in part, to the high cost of monitoring these indicators. Amongst Aboriginal entities, they were also major gaps in the monitoring, often reflecting a lack of capacity or a disagreement with the context in which the indicators were framed. Further exploration of the data through Q-type factor analysis revealed that there are five distinct organizational perspectives on stewardship amongst the 13 case studies. These perspectives are predominantly delineated by causal mechanism related to access and control of resources. These results represent important policy signals for government when attempting to craft a cohesive SFM policy across a broad scale of diverse landscapes and cultures and provide a basis for exploring the relationship between institutional failures and adaptation in SFM. A generic ‘SFM system’ was devised for BC using the theory of Panarchy as an underlying conceptual framework. In this system, the constraints and incentives on SFM monitoring derived from the analysis of the 13 case studies are connected to institutional failures in the SFM system. Relationships between failure and adaption are explored in order to make policy suggestions for better incorporation of a diversity of operational SFM definitions into higher-level SFM implementation and monitoring in BC. 11 “Not everything that counts can be counted, and not everything that can be counted counts” — Albert Einstein 111 Table of Contents Abstract.ii Table of Contents iv List of Tables vi List of Figures vii Acknowledgements viii Dedication ix Co-authorship statement x 1 Introduction 1 Sustainable forest management in British Columbia 1 Fundamental requirements of sustainable forest management 3 History of sustainable forest management 4 Themes and objectives of the research 8 References 12 2 Local-level applied characterizations of the national criteria and indicator framework for sustainable forest management in British Columbia 14 Introduction 14 Methods 20 Results 27 Discussion 37 Ecological indicators 37 Economic indicators 40 Social indicators 41 Conclusions 49 References 51 3 Organizational perspectives on stewardship for sustainable forest management. 54 Introduction 54 Methods 63 Results 70 Analysis of post-hoc testing and factor scores 76 Discussion 85 iv Organizational stewardship perspectives 85 Implications for policy 94 Conclusions 99 References 101 4 Institutional failures in the implementation of sustainable forest management in British Columbia 104 Introduction 104 Methods 121 Results and discussion 122 Narrow use of the SFM concept 122 Tenure issues 125 Uncertainty around Aboriginal land and treaty rights 126 Data management obstacles 129 Lack of incentive to value ecosystem services 132 Conclusions 135 References 138 5 Conclusions 142 References 148 Appendix A— Complete list of indicators 149 Appendix B — Post-hoc (Tukey) test results 166 Appendix C — Supplementary materials for Chapter 3 170 Review of methodology for Q-type factor analysis versus Q-methodology 170 References 172 Correlation matrix of the cases 174 Appendix B — BREB Approval Certificate 175 v List of Tables Table 2.1 Canadian Council of Forest Ministers (CCFM) criteria with case study elements and the number of indicators per case study element 21 Table 2.2 Interview schedule 24 Table 2.3 Graphical rating scale with descriptions and numerical equivalents 25 Table 2.4 F-test and t-test results for the overall sample 30 Table 2.5 F-test and t-test results for the forest company sub-sample 32 Table 2.6 F-test and t-test results for the Aboriginal entity sub-sample 33 Table 3.1 Combined scoring and interpretations for factor analysis data matrix 65 Table 3.2 Standard factor loadings with corresponding overlapping variance and interpretation 66 Table 3.3 Canadian Council of Forest Ministers (CCFM) criteria and case study elements 69 Table 3.4 Eigenvalues of the Correlation Matrix 71 Table 3.5 Factor loadings for each run of the model with increasing nfactor 72 Table 3.6 Final Communality estimates for each case study 73 Table 3.7 Variance explained by each factor 73 vi List of Figures Figure 2.1 Comparison of averaged perceived incremental cost estimates (cost) and percent positive response (use) by element over (a) the total sample; (b) Aboriginal entities; (c) forest companies; 28 Figure 2.2 Comparison of cost and use between forest companies and Aboriginal entities (a) use comparison and (b) cost comparison 35 Figure 2.3 Adjusted data sourcing- proportion each of internal, external, and mixed data sourcing adjusted for the proportion use (%) for each element for (a) forest companies and (b) Aboriginal entities 36 Figure 3.1 Factor groupings over the nfactor runs, with labels for each factor 75 Figure 4.1 The adaptive cycle 108 Figure 4.2 Diagram of a generic SFM system 111 Figure 4.3 Revolt and Remember 113 Figure 4.4 Characteristic institutions at different phases of the adaptive cycle 114 vii Acknowledgements I would like to thank the Forest Sciences Program for funding this research over the past two years. I would also like to thank my supervisor, Dr. John L. Times and my committee members Dr. Robert Kozak and Dr. Ronald Trosper for providing guidance, advice, and support for my research. I am indebted to them for their contributions of knowledge and time to my research and writing process and for always making themselves available to share ideas and answer questions. Particularly, I would like to thank Dr. limes for his support and trust in my work and ideas over years. He has always given me the space to explore my research however I best see fit; while his frank and insightful comments and criticisms have helped me shape research ideas that sometimes felt too big and unruly to ever put to paper. I would also like to thank Dr. Kozak for lending his expertise to my data analysis and for encouraging me in my work when I needed cheering. I also owe Dr. Trosper a debt of gratitude for his insights into methodologies and Aboriginal issues. I have worked and studied as a member of the SFM Research Group since 2005 and have made valuable and abiding friendships with the researchers there. I would like to thank them all for their intelligence, good humor, and inspiration. Notably, I thank Dr. Craig Nitschke for being my informal fourth committee member, Denise Allen for lending her expertise and experience to my work, Alyson McHugh for her collaboration early on and her steadfast encouragement, and Dr. Anne-Helene Mathey for her great ideas and conversation. I would also like to thank my amazing network of friends for offering their encouragement. Finally, I owe my parents, Frank and Diane Gough, a huge debt of gratitude for their support while pursuing my degree and their patience and love throughout this process. viii Dedication I would like to dedicate this thesis to the case study participants who took time out of their busy schedules for an interview process that was always enlightening for me, but perhaps a touch arduous sometimes for them. This thesis would not have been possible without your input. ix Co-authorship statement As the author of this thesis, I am responsible for the identification and design of the study and the complete conduct of the research, including all field work and data analysis. I was aided in preparing the manuscript for Chapter 2 by Denise Allen, who contributed to the introduction and to the social indicator section of the discussion. I was also aided in preparing the manuscript for Chapter 4 by Dr. John L. Innes, who provided early feedback and contributions to the description of institutional failures and the discussion of forest policy in British Columbia. x I Introduction Sustainable forest management in British Columbia British Columbia (BC), Canada’s westernmost province, is 93 million hectares in size, 60.6 million of which is forested (Canadian Forest Service (CFS), 2000). Of this forested area, 95% is provincial crown land that is publicly owned and governed by provincial forest policy. 44.6 million hectares of forest in BC are voluntarily certified to sustainable forest management standards (BC Market Outreach Network, 2007). Forest planning is governed by a hierarchical planning structure consisting of a provincial strategy, regional plans, and sub-regional plans (Karjala and Dewhurst, 2003). Government objectives for forestry stem from the Forest and Range Practices Act (FRPA) and regulations grandfathered in from the Forest Practices Code (FPC), as well as higher-level plans such as Strategic Land and Resource Plans, which have been completed for approximately 85% of the province (Ministry of Forests and Range, 2006). Lower-level plans such as forest stewardship plans and sustainable forest management plans (landscape level planning) and access management plans (operational level planning) are guided by government objectives and higher-level plans. The province is divided into 32 administrative units for land and resource planning and management. BC does not have a provincial-level criteria and indicator (C&I) framework for monitoring the sustainability of forest management practices. The province developed the results-based Forest and Range Practices Act (FRPA) which sets objectives for 11 public values, which then have indicators that are used to evaluate effectiveness of FRPA through the Forest Resources Evaluation Program (FREP) (Hickey and Innes, 2008). 1 Nevertheless, there is a demand for a province-wide framework to provide a holistic conceptualization of SFM, structure monitoring, analysis, and reporting in the province, foster more consistency between local-level initiatives and national and international reporting, and provide policy guidance to decision-makers (Hickey and Innes, 2008). Currently, the national set of criteria and indicators in Canada, the Canadian Council of Forest Ministers (CCFM) core set of criteria and indicators, informs SFM monitoring at the local-level directly or through certification standards such as the Canadian Standards Association (CSA). Generally, goals and objectives for SFM in British Columbia are locally-generated through public involvement in forest management decision-making that is driven by industry and government. These ‘bottom up’ processes are important in defining appropriate context for SFM, especially regarding the monitoring of social, cultural and economic indicators. Just as ecosystems differ between landscapes, social and cultural boundaries delineate different human community systems as well. These require sustainability criteria that are effective at measuring their particular contexts. However, the presence of many parallel indicator selection processes complicates the expression of the national core set and creates confusion around how sustainable forestry can be defined at the provincial level in BC. As well, competing certification schemes such as the Forest Stewardship Council (FSC Regional Standards for BC) and the Canadian Standards Association (CSA Sustainable Forest Management Program) are sources of variability in defining actions necessary to ensure sustainability in forest practices, furthering complicating SFM monitoring and definition in BC. At the provincial-level, forest policy under the Forest and Range Practices Act (FRPA) includes monitoring that is different again to SFM monitoring at both the local-level, through organizations such as forest companies and Aboriginal 2 entities, and the national-level through the CCFM. It appears that BC has a wealth of different ecological and human communities and these communities modify the definition of SFM so that it is relevant to their particular contexts. In light of this situation, it is difficult to imagine how an overarching provincial C&I suite could effectively address SFM in the province. The national core set of criteria and indicators must be linked the local-level SFM initiatives through provincial regulation and governance, but we lack the means of doing this effectively. As the governing body responsible for forest management, the BC provincial government should confirni the equivalence of sustainability monitoring for each of the SFM initiatives under their jurisdiction, in accordance with their commitments to the national definition of SFM for Canada enshrined in the CCFM core set. As well, the government must be able to assess sustainability in forest practices on lands under their jurisdictions. These are considerable tasks. The first, and perhaps largest, barrier to achieving them is to devise a means of defining sustainable forest management in a way that is both flexible and adaptive to local-level variability on the one hand and yet, on the other hand, be meaningful and concrete enough to be the basis for tangible policies and actions. Fundamental requirements of sustainable forest management Nelson et al. (2003) provide some characteristics of SFM that may be considered fundamental requirements. SFM is considered a highly political process, where competing values may differ considerably but must be aligned in order to promote behavior that is sustainable for present and future generations. Therefore, it must be transparent and embedded in institutional arrangements (market and government) that are 3 flexible and can respond issues of context and scale appropriately. Furthermore, it should incorporate learning and adaptation as integral parts of institutional design and be robust to changes by providing for innovation and resilience in the forest industry and in management practices. The breadth of objectives under consideration in SFM means that SFM policies must also promote and accommodate the acquisition and validation of information. Since SFM necessitates decision-making (such as multi-criteria evaluation), it is heavily reliant on information collection and management processes. Finally, the spatial and temporal dimensions of SFM are vast (Varma et al., 2000). Spatially, SFM relates to management across many scales. Ecologically speaking, it spans the monitoring of microscopic interactions in the soil up to connectivity between landscapes and global ecological processes such as the carbon cycle. Socially, it includes sustainability assessments at the international, national, sub-national, local and even individual levels. Temporally, SFM planning spans long time horizons, with highly uncertain outcomes, and yet requires time-efficient planning processes that do not incur prohibitively high transaction and information costs and have some assurances of success. These requirements are closely related to those of sustainable development, from which concept of sustainable forest management developed. History of sustainable forest management It is widely accepted that the concept of sustainable forest management (SFM) arose from the notion of sustainable development (Kotwal et al., 2008). The roots of sustainable development are well known and gained momentum from such early publications as Limits to Growth (Meadows et al., 1972) and Only One Earth (Ward and Dubos, 1972). Fifteen years after the first world conference on the environment in Stockholm in 1972, this discourse culminated in the release of Our Common Future (the Brundtland Report) 4 (WCED, 1987), in which the World Commission on Environment and Development (WCED) articulated their vision for a sustainable future. From sustainable development, sustainable forest management developed as a concept in which the health and well-being of the people and the forest ecosystem together determine the progress of management practices towards sustainability (Varma et aL, 2000). In 1992, the United Nations Conference on Environment and Development (UNCED) produced the ‘Forest Principles’, which were non-legally binding authoritative statements of principles for consensus on conservation, sustainable development, and management of forests all over the world. The UNCED defined sustainable forest management as a concept in which we recognize and seek to maintain a wide array of ecological, as well as economic and social forest functions, both locally and globally (UNCED, 1992). This definition and principles had a subsequent impact on the creation and activities of organizations such as the UN Forum on Forests, the International Tropical Timber Organization, the Helsinki Process (now known as the Ministerial Conference for the Protection of Forests in Europe), and the Montreal Process (MacDonald and Lane, 2004). These programs have also brought clarity to operational definitions of SFM by providing consistent guidance that SFM should be inclusive of the conservation of biological diversity, maintenance of forest health and productivity, the role of forests in watersheds and global ecological cycles, and the maximization of long-term multiple social and economic benefits of forest use (McDonald and Lane, 2004). The Montreal Process, the program most relevant to Canada, speaks to the principles of sustainable forestry in temperate and boreal forests of non-European countries and employs a framework of 7 criteria and 65 indicators created in 1994 that aimed to define and monitor sustainable forest management. This evolving narrative captured in the criteria and indicator (C&I) framework of the Montreal Process 5 has laid the basis for what is widely accepted as the sustainable management of Canada’s forests (Gough et al., 2008). At the national level in Canada, the transition to SFM as a new paradigm and the implementation of C&I frameworks in forest management began before the Montreal Process, with the creation of the CCFM in 1985 and Canada’s first National Forest Strategy (NFS) in 1987. The initial NFS was clearly focused on maximizing timber yields (Charron, 2005). While this emphasis reflected dominant Canadian concerns at the time, it was out of step with the larger global discourse on sustainable development outlined in the Brundtland Report (WCED, 1987). In 1992, the CCFM created the Canada Forest Accord (CFA), a revised NFS (National Forest Strategy, 1992-1998) and a corresponding indicator selection process for creating a suite of criteria and indicators of SFM (Charron, 2005). The CCFM released its first C&I suite in 1995. This suite was an adaptation of the Montreal Process that had been tailored to fit the particular political, economic, social, and cultural conditions and institutions related to forestry in Canada. This suite is meant to, “define, measure and report on the forest values Canadians want to sustain and enhance,” including ecological, economic, and social values spread over 6 criteria’ (CCFM, 1997). Assurances that forestry practices in Canada maintain these values are gauged by the assessment of 46 indicators2associated with each criterion (CCFM, 2006). This national core set of C&I frames the definition of SFM for indicator suites at the sub-national, regional, and local-levels in Canada, although the process of refining the definition and operationalizing SFM is left to the provincial jurisdictions. A criterion is a category of conditions or processes designed to assess SFM (McDonald and Lane, 2004). 2 An indicator is a quantitative or qualitative measure of a theme or aspect of the criterion that shows performance and trends against baselines, targets, and/or thresholds (McDonald and Lane, 2004). 6 Almost from the beginning, the claim that the CCFM core set of criteria and indicators represented all Canadians was called into question. The CCFM (1997) states that their core set of C&I suggests an “implicit definition of the conservation and sustainable management of forest ecosystems at the country level”. Ideally, this C&I suite should inform the monitoring of sustainable forestry at the provincial and local levels in order to create continuity between the internationally agreed upon definition of SFM and the actual application of these principles on local landscapes. In reality, the applied characterizations, or locally constructed C&I monitoring frameworks (local-level definitions of SFM), are not be congruent with the national approach, leading to a multiplicity of SFM definitions. Generic criteria and indicators generated through ‘top- down’ approaches (international agreements informing national and then sub-national policy) may be inappropriate for guiding forest management planning and decision- making at the local-level because the information generated through monitoring national C&I is not specific enough to address local forest management issues (Karjala and Dewhurst, 2003). Furthermore, the CCFM faced criticism over rejecting a proposal from Canada’s Aboriginal community to include a distinct Aboriginal Criterion (Criterion 7) specifically addressing ‘Respect and Provision for Aboriginal and Treaty Rights’ (Bombay, 1995) in the 1997 version of the CCFM core set of criteria and indicators (CCFM, 1997). The current 2005 version of criteria and indicators contains six criteria, 11 elements and 46 indicators and continues to address Aboriginal and treaty rights within Criterion 6 (Society’s Responsibility) (CCFM, 2006). The ability of the CCFM framework to adequately monitor social and cultural aspects of SFM, particularly those of Aboriginal Peoples, remains a major question. Questions of responsibility, relevancy, scale, discourse, and capacity come into play when considering 7 how social and economic indicators in general, and Aboriginal indicators in particular, can be appropriately included in SFM monitoring. For example, in practice, the extent to which forest managers in Canada at any level are responsible for the resilience of Aboriginal and rural communities is constantly under debate (Gough et al., 2008). Themes and objectives of the research This research is divided into three sections. The first section uses basic statistics to explore data from 13 case studies of SFM indicator monitoring in BC. The goal is to explore the data for patterns between the variables of indicator use and cost, and to observe and describe differences in monitoring between (a) the national core-set of indicators and local-level applied characterizations and (b) between forest companies and Aboriginal entities. The second section uses more sophisticated statistical techniques to delve deeper into the data and describe typologies of organizational stewardship perspectives from the case studies through patterns in monitoring behavior. The goal is to provide richer descriptions of the barriers to SFM implementation faced by a variety of organizations. The third section takes the results from the preceding analyses and places them in the context of a hypothetical system of management for sustainable forestry that reflects a paradigm of sustainability based on the Panarchy theory. The goal is to describe the relationship between failure and adaptation at the institution level, as well as the relationship between institutional failures and organizational barriers to SFM implementation described in the preceding analyses. Section 1. Local-level applied characterizations of the national criteria and indicator framework for sustainable forest management in British Columbia 8 Objective la. Compare the CCFM national suite ofcriteria and indicators with the local applied characterizations ofSFM and describe the differences observed in terms gaps in SFM monitoring at the local-level. A comparison of the national core set of criteria and indicators for SFM with local applied characterizations of SFM is completed in Chapter 2 in order to test if the definitions of sustainable forest management on the national and local levels are incongruent. Any incongruence observed is described in terms of gaps in monitoring at the local-level and reasons for these gaps are explored. Objective lb. Compareforest companies’ and Aboriginal entities’ local applied characterizations ofSFM through analysis ofindicator use, cost and data sourcing and describe the differences observed. Reasons why these groups emphasize or exclude certain aspects of the CCFM criteria and indicators in their definitions of SFM are explored through issues of relevancy, scale, responsibility, discourse and capacity, particularly as they apply to the use of social and economic indicators. Section 2. Organizational perspectives on stewardship for sustainable forest management Objective 2. Testfor differences in SFM monitoring betweenforest companies and Aboriginal entities using Q-typefactor analysis and to describe the results in terms of typologies oforganizational stewardship perspectives in the case studies. In order to understand the mechanisms of how and why different organizations are defming and expressing SFM, an exploration of organizational stewardship perspectives is completed in Chapter 3. Multivariate statistics are employed to test if 1) that forest 9 companies and Aboriginal entities have distinct organizational stewardship perspectives and 2) that these groups that formed through the analysis represented stewardship perspectives whose underlying causal mechanisms would be similar to those discussed in Chapter 2. Section 3. Institutional failures in the implementation of sustainable forest management in British Columbia Objective 3a. Describe this hypothetical management systemfor sustainableforestry and the relationship between institutionalfailure and adaptation. The results from the analyses in Chapters 2 and 3 represent important policy signals for the BC provincial government when attempting to craft a cohesive SFM policy across a broad scale of diverse landscapes and cultures. It is put forward in Chapter 4 that SFM policy formation requires that we understand and conceptualize SFM as a complex and dynamic human-natural system of management that represents a new paradigm of sustainability and not a modification of present forest practices in the paradigm of sustained yield. The theory of Panarchy is described as a means of understanding SFM as a management system, including mechanisms of system change through institutional adaptation and failure. Objective 3b. Integrate the results ofthe analyses in the preceding chapters and explore the role ofinstitutionalfailures in British Columbia. The core messages from the results of Chapters 2 and 3 are presented in Chapter 4 as barriers to SFM implementation at the organizational level of the SFM system. These barriers are related to institutional failures in the theorized SFM system of management. Adaptations are discussed in terms of integration of new types of knowledge and 10 information, formal policy interventions, capacity building, and simply maintaining awareness of failures and vulnerabilities in the SFM system of management and the ecological, social, and economic systems which we attempt to manage. In summary, the study begins with a simple comparison of national to local-level monitoring practices and of two broad groups of SFM practitioners who have different frames of reference for their forestry practices: forest companies and Aboriginal entities. It then takes a step back and asks what these frames of reference may be, and attempts to state them as organizational perspectives on sustainable forest stewardship. The study continues with an exploration of the causal mechanisms behind stewardship perspectives in the case studies and how these relate to forest policy in BC. Finally, the study attempts to place the results of these inquiries into a new framework for understanding SFM as a system of management. This chapter aims to bring the lessons learned from the analysis into the framework of the ‘SFM system’ as a series of nested complex dynamic systems experiencing the adaptive cycle — where institutional failure and adaptation work contrary to each other in a system governed by novelty, innovation, flexibility, and change. 11 References Bombay, H., Smith, P. and D. Wright. 1995 An Aboriginal criterion for sustainable forest management. Ottawa, ON: National Aboriginal Forestry Association. British Columbia Market Outreach Network. 2007. Third-Party Forest Certification in British Columbia. BC Forest Facts. http://www.bcforestinformation.com!publications/documents/FSA-064-E.pdf (accessed April 9, 2009). Canadian Council of Forest Ministers (CCFM). 1997. Criteria and Indicators of Sustainable Forest Management in Canada Technical Report 1997. Ottawa, ON: Canadian Forest Service. http://www.ccfrn.org/ci/rprt2005/English1toc.htm (accessed March 25, 2009). Canadian Council of Forest Ministers (CCFM). 2006. Criteria and Indicators of Sustainable Forest Management in Canada, National Status 2005. Ottawa, ON: Canadian Forest Service. http://www.ccfimorg/ci/rprt200S/Englishltoc.htm (accessed January 15, 2008). Canadian Forest Service (CFS). 2000. The State of Canada’s Forests. Ottawa, ON: Canadian Forest Service. Charron, M. 2005. Sustainable Forest Management in Canada: clear policy- questionable practice. Library of Parliament, Parliamentary Information and Research Service. Ottawa, ON: Science and Technology Division. http://www.parl.gc.calinformationllibrary/PRBpubs/prbo5 13 -e.htm (accessed 9 January 2008). Gough, A.G., Innes, J.L. and S.D. Allen. 2008. Development of Common Indicators of Sustainable Forest Management. Ecological Indicators 8(5): 425—430. Hickey, G.M. and J.L. Innes. 2008. Indicators for demonstrating sustainable forest management in British Columbia, Canada: An international review. Ecological Indicators 8: 13 1—140. Karjala, M.K. and S.M. Dewhurst. 2003. Including aboriginal issues in forest planning: a case study in central interior British Columbia, Canada. Landscape and Urban Planning 64: 1—17. Kotwal, P.C., Omprakash, M.D. Gairola, S. and D. Dugaya. 2008. Ecological Indicators: Imperative to sustainable forest management 8: 104—107. Meadows, D.H., Meadows, D.L., Randers, J. and W.W. Behrens III. 1972. Limits to Growth. New York: Universe Books. 12 Ministry of Forests and Range. 2006. The State of British Columbia’s Forests 2006. http://www.for.gov.bc.ca/hfj/sof/2006/pdf/sof.pdf (accessed April 9, 2009). Nelson, H, Vertinsky, I, Luckert, M.K., Ross, M. and Wilson, B. 2003. Designing institutions for sustainable forest management. In: Towards Sustainable Forest Management of the Boreal Forest, eds Burton, P.J., Messier, C., Smith, D.W. and W.L. Adamowicz. Ottawa, ON: Natural Resources Council Research Press. United Nations Conference on Environment and Development (UNCED). 1992. Agenda 21: the Rio Declaration on Environment and Development, the State of Forest Principles, the United Nations Framework Convention on Climate Change and the United Nations Convention on Biological Diversity. UNCED, Rio de Janeiro, June 3- 14, 1992. United Nations Commission on Environment and Development Secretariat, Geneva. Varma, V.K, Ferguson, I. and I. Wild. 2000. Decision support system for the sustainable forest management. Forest Ecology and Management 128: 49—55. Ward, B. and R. Dubos. 1972. Only One Earth: The Care and Maintenance of a Small Planet. New York: W.W. Norton. World Commission on Environment and Development (WCED). 1987. Our Common Future. New York: Oxford University Press. 13 2 Local-level applied characterizations of the national criteria and indicator framework for sustainable forest management in British Columbia3 Introduction In this study, we compare the use and costs of indicators across a diverse group of thirteen forest companies and Aboriginal entities in British Columbia (BC) in order to explore how a nationally defined set of criteria and indicators (C&I) is characterized at the local-level amongst different actors. We also explore some of the possible constraints and incentives that exist for monitoring and compare the responses of our forest company and Aboriginal participants in this regard. The definition of sustainable forest management (SFM) is closely bound to what we measure and monitor, both as national public policy and as local-level forest management. By comparing these thirteen SFM indicator case studies to the national C&I suite in Canada and by examining the differences between the monitoring behavior of the case studies as forest company and Aboriginal entity sub-samples, we can gain a better idea of the mechanisms which govern the characterization of SFM and inevitably define it. At the local-level, indicator sets are highly variable: they are tailored to local priorities and needs and cannot be readily compared. However, national, state-wide or provincial core sets of criteria and indicators play the unique function of broadly outlining one definition of SFM for a wide variety of social, economic and ecological situations. Taking indicators from a variety of local-level SFM initiatives and organizing them into A version of this chapter will be submitted for publication. Gough, A.D., Allen, S.D. and J.L. Innes. 2009. Local-level applied characterizations of the national criteria and indicator framework for sustainable forest management in British Columbia. 14 the hierarchy of an overarching set of criteria makes it is possible to survey locally diverse SFM initiatives using an applicable core set as a common ground for comparison. As no provincial core set exists for BC, the Canadian Council of Forest Ministers (CCFM) criteria and indicator suite is the common denominator in the study. The seven forest company and six Aboriginal entity case studies were selected with the objective of studying a variety of forest practices and perspectives in forest management. Indicator use was surveyed in each case study as a proxy measure of the applied characterization of SFM. The perceived costs of monitoring, estimated for each indicator, and the patterns of data sourcing for each sub-sample were also analyzed. Analysis of these variables revealed gaps in SFM monitoring and allowed for the exploration of issues related to discourse, scale, relevancy, organizational capacity and responsibility, particularly in the use or exclusion of social and economic indicators of SFM. Discourse Sustainability has become an internationally accepted key word for a political discourse committed to quality of life, the conservation of natural resources and a sense of obligation for the well-being of future generations. Having introduced concern for social justice and political participation to environmental issues, sustainability might best be characterized as a contested discursive field which allows for the articulation of political and economic differences (Becker et al., 1999). From this perspective, the different meanings attributed to sustainability in forest management indicate tensions between competing socio-political projects with different rationalities and different political implications (Becker et al., 1999). 15 The role of political discourse in these matters should not be understated; especially where the legitimacy of shared public preferences is shaped by a divisive mixture of cultural worldviews. In their discussion of the dynamics of political discourse in seeking sustainability, Pritchard Jr. and Sanderson (2002) recognize three archetypal discourses that relate to political decision-making: administrative rationalism, pluralism, and communitarianism. Administrative rationalism embodies efficient problem solving: knowable public interests can be served through hierarchical bureaucracies using ‘command-and-control’ strategies. Pluralism (or pluralistic democracy) also expects that public interest is knowable but relies on the legitimacy of voting to determine the balance of competing interests. Communitarian discourse, on the other hand, is focused on balanced consideration and discussion. It subjugates competing interests in favor of consensus and creating community preferences. The presumption with this discourse is that communities have a more intimate relationship to their resources and, given the authority, will steward them more successfully. Scale and relevancy In exploring the dynamics of these three discourses, it is important to remember that choice of forum for political discussion is a political act in itself (Pritchard Jr. and Sanderson, 2002). Communitarian discourses work well at the local-level but come into conflict with more formal political processes for decision-making at higher levels, where pluralism and administrative rationality are more efficient means of making decisions (Pritchard Jr. and Sanderson, 2002). However, the reliance ofboth pluralist and administrative-rationalist discourses on more prescriptive analyses such as optimization can be too simplistic given the complex socio-economic and ecological interactions that we attempt to manage. In SFM, this means that the scale of the indicator measurement 16 can determine which discourse will be most successful in framing the decision-making process. Those who understand this may attempt to move the process to a level where they have the comparative advantage (Pritchard Jr. and Sanderson, 2002). Thus, it is important to ensure that the process of indicator selection, at any level, is equitable. One way to explore equality in decision-making processes such as this is to evaluate the extent to which the discourse is democratic or bureaucratic. As discourse framing the indicator suite skews towards the formal, technical, and globalizing language of management agencies, reconciling the resultant definition of SFM with the place-specific knowledge and perspectives of both rural communities and Aboriginal peoples becomes more challenging (Scott, 1998). Meaningful participation by stakeholders, the public and Aboriginal peoples can balance out the discrepancies between alternative discourses and lead to more equitable decision-making. This is vital in the translation of national core sets of indicators into local-level applications because in representative core sets, such as the CCFM suite, whoever controls the discourse also decides what gets measured and — as the saying goes — we manage what we measure. If measurement occurs at the local- level (where democratic discourses are most effective) then it is possible for us to also measure what we value. Communities could wield significant power over how a national core set gets translated to a local-level indicator suite, or the ‘applied characterization’ of the national core set. With the prospect ofpower dynamics fluctuating with scale comes the question of the relevancy of the national C&I framework for different organizations monitoring SFM. The applied characterization of the CCFM suite for forest company and Aboriginal entities at the local-level is a statement about the relevancy of the top-down framework of 17 monitoring. Patterns of indicator selection by these case studies reflect relevancy issues and can highlight discrepancies both between forest company and aboriginal entities and between these local-level initiatives and the national suite. Capacity The applied characterization of SFM at the local-level is controlled through management and access to information. Information and decision-making must be democratic, transparent and accountable for society as a whole to make the essential choices and trade-offs on the path to sustainability (Gray, 2002). Across Canada, forest inventories are the primary source of information for forest management planning and decision- making (CCFM, 2006) and the degree of public access to forest inventory information is an important indicator of the value that the government places on informing the public. However, there is a concern that forest inventory data collected by forest companies could be categorized as proprietary information, and therefore not fully available (CCFM, 2006). For example, in British Columbia the public is obliged to pay fees and may have to pursue data sharing agreements in order to access crown and private forest inventory information. Rural and Aboriginal communities do not have the same access to information, infrastructure or the financial and human resources necessary for organizational change enjoyed by the central (management and bureaucratic) powers (Greskiw, 2006; Michel et al., 2002). With the burgeoning information requirements (and concomitant cost) of monitoring SFM typically outpacing monitoring budgets, most organizations are focused on identifying data and monitoring priorities, reducing uncertainty and optimizing problem-solving — an approach consistent with the discourse of administrative rationality (Pritchard Jr. and Sanderson, 2002). However, though the focus of C&I frameworks and the associated monitoring goals are not very compatible 18 with inclusive and holistic approaches to building consensus and conserving moral meanings in the realm of information management, they are consistent with pluralist values such as balancing competing interests. Responsibility Responsibility around stewardship and sustainable forest management is matter of perspective. For some, there is an ingrained and unique sense of responsibility to the landscape, for others it is a more institutionalized allegiance to formal organization or community structure. In the social and economic aspects of SFM, the perspective on responsibility can have a lot of weight in the indicator selection process. Even if an indicator is framed in a suitable discourse, at the right scale and addressing a relevant issue and even if the organization had the capacity to measure the indicator, they may still feel that it is not their responsibility to do so. Responsibility is central to any discussion about social and economic indicators. For example, is it really the responsibility of forest companies to monitor social capital in a community, even if the company is the major employer? What about comparing forest-based community well-being across a company’s area of operations? Is the government, the company or the community most responsible for ascertaining and maintaining this information flow? It is clearly important to understand and assign proper roles amongst stakeholders involved in the monitoring. Furthermore, access to sensitive information may be impossible for a private firm, especially in regards to Aboriginal conununities. In such cases, forest companies should not be held responsible for monitoring social indicators, but they may stiIlbe held accountable for failing to include social information in their forest stewardship decisions. In such a scenario, roles must be defined so that decisions about forest use can be informed in the most advantageous way possible. 19 Methods The study focused on practitioners of sustainable forestry in British Columbia, Canada. Since British Columbia has no provincial criteria and indicator framework, the Canadian Council of Forest Ministers’ (CCFM) core set of criteria, elements, and indicators, at the national-level, was used to frame the structured interview-based process. There are six criteria and 12 elements in the CCFM indicator suite. Elements, which are hierarchical components of the CCFM below criteria and above indicators, were retained for organizational and analytical purposes in this study. However, since not all criteria in the CCFM have elements associated with them, elements were added to the hierarchy that are not found in the CCFM suite (see Table 2.1). There are 46 indicators in the CCFM suite and these indicators also helped shape the hierarchy of the study’s indicator list. In addition, indicators from 70 local-level indicator initiatives drawn from around the world were incorporated into the indicator list (this process is described in Hickey and limes, 2005). This ensured that the core set was inclusive of local-level variations. Further indicators were added from research done by McHugh et al. (2005). The final list of indicators used for the interviews therefore included 386 indicators applicable to forest management in BC organized under the CCFM criteria and element hierarchy (see Table 2.1) (for a complete list of indicators, please see Appendix A). Thus, the CCFM acts as an organizing method as well as a source of indicators, with many more indicators added in to capture local-level variability and to add in themes not included in the CCFM (including extended Aboriginal indicators and social capital indicators, among others). 20 Table 2.1 Canadian Council of Forest Ministers (CCFM) criteria with case study elements and the number of indicators per case study element CCFM Criteria Case study Description # of element indicators I Biological diversity 1.1 Ecological diversity 14 1.2 Species diversity 37 1.3 Genetic diversity 13 2 Ecosystem condition 2.1 & 2.2 Sustainability of harvest of timber and 25 and productivity non-timber forest products 2.3 & 2.4 Natural and human-induced 23 disturbances 2.5 Forest regeneration 8 3 Soil and water 3.1 Soil 26 3.2 Impact of harvesting on riparian areas 29 3.3 Water 18 4 Role of forests in 4 Carbon sink/source, impacts of forest 13 global ecological ecosystems on climate change, forest cycles product carbon procurement, forest product sector contributions to C02 emissions. 5 Economic and social 5.1 Economic benefits 23 benefits 5.2 Distribution of benefits 9 5.3 Sustainability of benefits 26 6 Society’s 6.1 Provision for duly established Aboriginal 21 responsibility and treaty rights 6.2 Aboriginal traditional land use and 18 forest-based ecological knowledge 6.3 Forest community well-being and 34 resilience 6.4 Fair and effective decision-making 25 6.5 Informed decision-making 21 Social Volunteerism, community participation, 6 Capital trust Notes: Descriptions follow the titles of the elements of the CCFM except for CCFM criteria C.2 and C.3, which do not have elements in the original CCFM indicator suite but have been split into elements for this study. Elements 2.1 & 2.2 and 2.3 & 2.4 are combined because of their short lengths. There is only one element for C.4 because of its short length. 21 Location and sampling The case studies represent a purposive sample of forest companies and Aboriginal entities located throughout British Columbia (BC) and were selected for their theoretical salience to the scope of the study and because they had knowledge of and experience with the research topic. However, care was taken to see that although purposive, the sampling was representative of various types of forest stewardship and ecosystems present in the province. The thirteen participants were selected for a broad distribution of geographic areas and their commitment to sustainable forestry. Case study participants’ plans include three Timber Supply Area (TSA)4 SFM Plans, two FSC-certified operations, a Tree Farm License (TFL)5 SFM Plan, a TFL watershed-based management plan, a collaborative forest management initiative focused on ecosystem-based management, a non-renewable salvage license in a TSA with an SFM Plan, and four stewardship management plans. The thirteen case studies were made up of seven forest companies (FCs) and six Aboriginal entities (AEs). Specifics, including names, for each case study are intentionally left anonymous in compliance with the research protocol agreement. While some Aboriginal entities in the study held small forest tenures, all were in some phase of developing stewardship plans for their traditional territories. These plans delineate sensitive cultural areas, critical species habitat, and the range of contemporary and traditional uses and activities on the land base (including forestry). These plans focus on how to ensure jurisdiction and capacity for managing and monitoring territorial ‘ Timber Supply Areas are “areas of the province created by the Ministry of Forests for the purpose of analysis, planning, and management of timber resources. Boundaries have been determined on the basis of present and expected population centers, transportation networks, manufacturing facilities, and existing administrative boundaries” (Ministry of Forest and Range, 1997) Tree Farm Licenses are “privately managed sustained yield units in which the Crown adds forest land to the company’s private holdings (if any) sufficient to provide a continuous supply of wood for an existing or planned mill” (Ministry of Forest and Range, 1997) 22 lands. Although this study used the CCFM criteria and indicators of SFM (2003) as an organizing framework for both indicators and analysis, it was recognized that different groups within the province are using planning tools (e.g., watershed-based and ecosystem-based management planning and Aboriginal stewardship planning) which are within the scope of SFM monitoring but do not follow a conventional criteria and indicator hierarchy. These plans still use indicators and focus on similar, if not the same, goals for sustainable forestry. The strategies and action detailed in these plans are designed to achieve sustainable forestry goals and contribute to good monitoring practices. The researcher-administered surveys were conducted one-on-one and in person with a forestry expert from each of the participating organizations. Survey questions were designed to assess the current and/or intended use, perceived incremental cost and data sources for each of the 387 indicators included in the study (see Table 2.2). Participants first answered (y/n) regarding the use of the indicator. Participants gave a ‘yes’ response for indicators where they had the data and could monitor, even if they were not at a stage of development where monitoring had been fully implemented. Conversely, they used a ‘no’ response to indicate situations where they did not have the data or would not consider it for SFM, even if they had the data to monitor the indicator. If the participant answered positively, the interviewer inquired if data for monitoring the indicator was collected internally, sourced externally or a mix of the two. This is provided a basic metric for measuring each organization’s capacity to source data themselves, versus the reliance in their monitoring system on external data sources. However, it did not provide an explanation as to why one indicator may have been chosen over another, which would allow for a deeper understanding of internal capacity. Participants were given the 23 opportunity to comment on their data sourcing to give these replies more depth and many took the time to elucidate their situations more fully. They could give explanations for their ‘no’ replies if they felt it was important to explain why they were not using an indicator or group of indicators. This information provided insight into the indicator selection process and has been used throughout the study to provide richer descriptions of the data. Table 2.2 Interview schedule Interview questions 1 Do you use (or do you have information that would readily allow you to use) this indicator? a. If ‘yes’: Are you sourcing your data internally, externally, or as a mix of the two? b. If ‘no’, can you tell me why not? 2 Please estimate an incremental cost for the indicator using the graphical rating scale 3 Do you have any comments or suggestions for this section? Participants were then asked to rate these incremental costs on a graphical rating scale (see Table 2.3) from minimal (e.g., a GIS6 query) through doable (e.g., compiling data into a new database), expensive (e.g., collecting more information), very expensive (e.g., requiring a new research project), to cost-prohibitive (unable to measure due to cost). The graphical scale and incremental nature of the cost estimates represent the participants’ perceptions of incremental cost (versus absolute values). This qualitative approach was used to eliminate the potential complications of differential cost scales between smaller and larger organizations. By asking participants to estimate the affordability of the extra cost associated with an indicator, based on their current SFM monitoring activities and budget, perceived incremental cost is also a potential proxy indicator of organizational capacity. 6 Geographical Information System 24 Table 2.3 Graphical rating scale with descriptions and numerical equivalents Graph. depicUor... Description NiLHericàl EquivaI 0 No extra cost 0 $ Minimal cost I $$ “Do-able” cost 2 $$$ Expensive 3 $$$$ Very expensive 4 $$$$+ Cost prohibitive 5 Open-ended qualitative comments were also solicited and participants identified other indicators currently in use that did not otherwise appear in the indicator list. Both comments and novel indicators provided by the participants were then used to inform the analysis of trends in the data. Statistical analysis The thirteen case studies comprised two sub-samples: seven forest companies (hereafter referred to as FC) and six Aboriginal entities (AE). Indicators were grouped into 19 elements for the purpose of reporting results and analyzing patterns (see Table 2.1). The elements used for this analysis correspond predominantly to elements in the CCFM hierarchy with some minor adaptations (see Table 2.1). First, the CCFM criteria that do not have elements were assigned elements related to the themes within each criterion for the purpose of this study. Secondly, the social capital indicators — which have no directly corresponding CCFM element — are reported separately. To assess the level of use on an elemental basis, the proportion of positive responses from both FCs and AEs was calculated for each indicator and then averaged over the element. To assess the average cost estimate on an elemental basis, the responses given on the graphical rating scale were transposed into numerical equivalents (see Table 2.3) for use 25 in statistical analysis. The average of the cost estimates was then taken for the indicators in each element. The relationship between the level of use and perceived incremental cost was then analyzed to investigate both overall trends for the total sample and patterns within the FC and AE sub-samples. F-tests and t-tests were completed for each element for the overall sample and for each element of each sub-sample to test the difference between two small, independent populations: n-cost and y-cost (cL = 0.05). The nominal use data (yes/no/not applicable) was used to group the cost estimate data so that the cost estimates associated with ‘no’ responses to the use question were classified as the n-cost group. The cost estimates associated with ‘yes’ responses were classified as the y-cost group. Indicators with ‘not applicable’ responses were removed from the analysis because they did not have cost estimates. F-tests tested the two groups for equality of variances. One-tailed t-tests determined if the n-cost mean (Pt,) was significantly greater than the y-cost mean The hypothesis statements are as follows: Ho: LJn-Py 0 Hi: IJnjJy> 0 During the interviews, relevant data sources were noted for all indicators receiving a positive response. Data sources were categorized as either internal (data collected or mainly compiled by the participating organization: e.g., community statistics) or external (any information sourced outside of the organization which is not significantly altered from the originally transmitted format: e.g., forest cover data). Data collected in compliance with government legislation were classified as externally sourced. The level of internal data sourcing (calculated as the percent of the total data sourcing classified as ‘internal’) was used to inform the gap analysis because it gave an indication of the desire 26 and ability of each case study to take on their own monitoring. The level of internal data was standardized to account for the level of use so that it could be compared between the sub-samples (adjusted % internal). Results Relationship between cost and use Figure 2.1 a illustrates the broad relationship between indicator use and perceived incremental cost for the total sample (FC and AE combined). The data are suggestive of a relationship between cost and use, especially in the ecological indicators where they seem to fluctuate in an inverse manner. However, more complicated influences over monitoring may be at work with the economic and the social indicators where this relationship between cost and use seemingly breaks down (loses its inverse appearance). When the results are divided into the two sub-samples (forest companies and Aboriginal entities) and examined across the 20 elements, a similar pattern is also apparent (see Figure 2.lb and Figure 2.lc). 27 Figure 2.1 Comparison of averaged perceived incremental cost estimates (cost) and percent positive response (use) by element over (a) the total sample; (b) Aboriginal entities; (c) forest companies; a) ___________ • Use (Overall) —cost (OveraII) 100.0 90.0 80.0 70.0 60.0 50.0 0 40.0 30.0 20.0 10.0 0.0 b) _ • Use(AE) Cost (AE)] 0 0 D ‘ - 2-Y ft’ b’ b t. Element c • Element 28 In the t-tests using the total sample, the n-cost mean is significantly higher than the y-cost mean (pr, - Jy >0) for 14 out of 19 elements (w0.05) (see Table 2.4 for values). Out of these 14 tests where the null hypothesis is rejected, 9 ecological elements (out of a possible 10), 3 economic elements (out of 3), and two social elements (out of 6) are represented. In these instances, the n-cost mean is higher than the y-cost mean, suggesting that when respondents are not using an indicator, they estimate it to cost more to monitor. Conversely, if they are using an indicator, they estimate it to cost less. This is indicative of a relationship between cost and use for these elements in the overall analysis. • Use (FC) 100.0 90.0 80.0 70.0 60.0 50.0 0, D 40.0 30.0 20.0 10.0 0.0 5.00 4.50 4.00 3.50 3.00 2.50 0) 0, 2.00 0 U 1.50 1.00 0.50 c:D . 9, 0c> Element 0.00 29 Table 2.4 F-test and t-test results for the overall sample Element Variance p-value Decision Conclusion (F-test) (aO.O5) Q-test) 1.1 = 1.61 1.82 0.0891 Accept H0 1.2 2.77 1.92 <0.001 Reject H0 pn - jJ >0 1.3 = 3.29 1.91 <0.00 1 Reject H0 p - P >0 2.1 & 2.2 2.34 1.82 <0.001 Reject H0 pr, - p, >0 2.3 & 2.4 2.32 1.48 <0.001 Reject H0 p1, - p >0 2.5 2.26 1.16 <0.001 RejectH0 3.1 4.14 1.94 <0.001 Reject H0 p,, - p,, >0 3.2 3.62 2.02 <0.001 Reject H0 p, - p,, >0 3.3 = 4.01 2.39 <0.001 Reject H0 p - p, >0 4 = 3.34 1.88 <0.001 Reject H0 pr, - p >0 5.1 = 2.68 2.07 <0.001 Reject H0 p1 - p, >0 5.2 2.03 1.16 <0.001 Reject H0 p, - p,, >0 5.3 1.95 1.57 0.0012 Reject H0 p - p, >0 6.1 2.04 1.82 0.1122 Accept H0 Pr 6.2 2.11 1.5 <0.001 Reject H0 p1 - p, >0 6.3 = 1.37 1.48 0.0956 Accept H0 p - p, = 0 6.4 1.92 1.16 <0.001 RejectH0 6.5 1.45 1.25 0.0776 Accept H0 - IJy = 0 Social = 1.32 1.5 0.1647 Accept H0 p, - p,, = 0 capital The elements where there is no significant difference (p’, - p, = 0) are Element 1.1 (Ecological diversity), Element 6.1 (Provision for duly established Aboriginal and treaty rights), Element 6.3 (Forest community well-being and resilience), Element 6.5 (Informed decision-making) and Social Capital (see Table 2.4). As the means are not significantly different, there is no indication of a relationship between cost and use on these elements. Social indicators are highly represented in this group, suggesting that if cost and use are unrelated, other factors may be affecting how social indicators are used 30 and valued. Element 1.1 stands out as the only example of an ecological element where there is no significant difference between the n-cost and y-cost. In the forest company sub-sample (see Table 2.5), the pattern was the same as in the overall analysis but here Social Capital could not be tested because the y-cost population was too small. The social indicators were highly represented among the tests where there was no significant difference. Again, Element 1.1 stood out as the only ecological element. Element 5.3, arguably the most socio-economic aspect of the economic indicators, was also among the tests where the null hypothesis was accepted. The implication is that the tests for the ecological and economic indicators show a pattern that suggests a cost/use relationship. However, tests on social indicators do not support this pattern, which implies that there may be other factors affecting the use and costs of social indicators. 31 T-tests for the Aboriginal entity sub-sample show major differences from the above trend. The n-cost mean is significantly higher than the y-cost mean for 16 out of 19 elements (ct=O.05) (see Table 2.6). The elements where the means are not significantly different include Element 1.1 and Social Capital (same as the FC sub-sample and the overall analysis) but also include Element 1.3 (Genetic diversity). The null hypothesis is rejected for all elements in Criterion 6 except social capital (see Table 2.6) for the social indicators in the AE sub-sample. This is quite different from the FC sub-sample, which accepted the null hypothesis for most of the elements of the social portion of the 1.1 Table 2.5 F-test and t-test results for the forest company sub-sample ‘r ____ ____ _____ &r °‘ 1.29 1.48 0.0995 Accept H0 IJn-IJy=O 1.2 2.57 1.43 >0.001 Reject H0 pr, - p,, >0 1.3 = 3.31 1.5 >0.001 Reject H0 - p, >0 2.1 & 2.2 2.27 1.60 >0.001 Reject H0 - jj,, >0 2.3 & 2.4 1.79 0.99 >0.001 Reject H0 p1, - p, >0 2.5 1.93 0.78 >0.001 Reject H0 - p, >0 3.1 = 3.86 1.40 >0.001 Reject H0 - p,, >0 3.2 2.98 1.65 >0.001 Reject H0 pr, - p, >0 3.3 = 4.01 2.14 >0.001 Reject H0 pr, - p, >0 4 2.70 0.92 >0.001 Reject H0 Pn - IJy >0 5.1 = 2.36 1.70 0.0177 Reject H0 p, - y >0 5.2 1.56 0.85 0.0182 Reject H0 p1, - p, >0 5.3 = 1.34 1.13 0.0905 Accept H0 pr,. 6.1 1.41 1.16 0.0908 Accept H0 pr,p=o 6.2 1.93 0.86 >0.001 Reject H0 pr, - Py >0 6.3 = 1.19 1.21 0.4287 Accept H0 p - p, = 0 6.4 1.27 0.82 >0.001 Reject H0 pr, - p,, >0 6.5 0.98 1.08 0.2364 Accept H0 p,, - p, = 0 Social n/a 1.18 2 n/a n/a n/a capital 32 indicators, and suggests different behavior between the two sub-samples for social monitoring in SFM. Table 2.6 F-test and t-test results for the Aboriginal entity sub-sample ELetflnt Variance p ii P-valIe Decision Conclusion l!4e,t) oO 05) (t-test) 1.1 = 2.21 2.15 0.4188 Accept H0 1.2 3.03 2.42 >0.001 Reject H0 - p >0 1.3 = 3.27 2.67 0.0839 Accept H0 - = 0 2.1 & 2.2 = 2.46 2.04 0.0075 Reject H0 p,, - p, >0 2.3 & 2.4 2.98 2 >0.001 Reject H0 p, - p, >0 2.5 = 2.5 1.77 0.0100 Reject H0 ji - p,, >0 3.1 4.47 2.48 >0.001 Reject H0 IjrI - p >0 3.2 = 4.29 2.54 >0.001 Reject H0 p,, - p >0 3.3 = 4 2.73 >0.001 Reject H0 jJ - p,, >0 4 = 4.10 3 0.0035 Reject H0 Fin - p >0 5.1 3.18 2.26 >0.001 Reject H0 p - ji, >0 5.2 2.47 1 .51 0.0047 Reject H0 p,., - p,, >0 5.3 2.75 2.01 >0.001 Reject H0 pr, - p, >0 6.1 3.45 2.23 >0.001 Reject H0 p,, - p, >0 6.2 = 2.68 2.14 0.0117 Reject H0 p - p, >0 6.3 = 1.80 1.56 0.0293 Reject H0 Fin - >0 6.4 2.56 1.68 >0.001 Reject H0 pr, - p, >0 6.5 2.30 1.42 >0.001 Reject H0 p - y >0 Social 1.61 1.39 0.1199 Accept H0 Fin - Jy = 0 capital Overall differences in use and perceived incremental cost estimates Comparative analysis of the costs perceived by FCs and AEs for measurement and monitoring reveals a distinct trend. Aboriginal Entities always estimated higher incremental costs for monitoring indicators than FCs (see Figure 2.2b). For example, in Element 5.3 (Sustainability of [economic and social] benefits), the average cost estimate 33 by AEs was 2.42 in comparison to the FC average estimate of 1.26. The proportion of use, however, was comparable: 42.6% for AE and 37.0% for FC (see Figure 2.2a). For Criterion 4 the results are even more striking. AEs estimated an average incremental cost of 3.94; much higher than the FC average cost estimate of 2.44 (see Figure 2.2b). In this case, the proportions of participants in each sub-sample using the indicators was the same (AE: 14.1% and FC: 14.3%). This pattern of similar use but higher AE cost estimates suggests that the AE experience higher monitoring costs but that their level of monitoring is similar. Data sourcing Overall, data sourcing is most prominent in Elements 1.1 and 1.2, all of the elements in Criterion 2 and Element 5.2. It is least prominent for Element 1.3, Criterion 4 and the elements of Criterion 3 (see Figure 2.3). Notably, 100% of the data sources cited for social capital for both sub-samples were internal. As the level of use between the FC and AE sub-samples was so similar (see Figure 2.2a), a comparison between their data sources, once adjusted for use, is pertinent. Sourcing data internally was most prevalent for AEs in Criterion 6, particularly in Elements 6.1 and 6.2 (see Figure 2.3b) which is not unexpected, considering that these are the Aboriginal indicators. It was lowest in Elements 1.3 and 2.5 and generally much lower for the ecological and economic indicators than for the social ones. FC internal data sourcing was high for Element 2.5 (Forest regeneration) and Element 6.4 (Fair and effective decision-making) (see Figure 2.3a). This makes sense, considering that the former is a legislative requirement and the latter involves public participation and compliance. 34 Figure 2.2 Comparison of cost and use between forest companies and Aboriginal entities (a) use comparison and (b) cost comparison a) • *Use (AE) b) ,‘ ,‘ ‘ f q,) q C, Element • Cost(F ——Cost(AE) çb c C,,?‘ ‘• ‘V Element c0 35 Figure 2.3 Adjusted data sourcing- proportion each of internal, external, and mixed data sourcing adjusted for the proportion use (%) for each element for (a) forest companies and (b) Aboriginal entities a) b) 0 0 0 0 0 C 0 z 0 0 0 0 0 0 0 0 0 0) C 0 0 0 0 0 Element Element 100 ft ‘‘ n)% ,5) c0 ç F 36 Discussion The evidence in this study supports a negative relationship between cost and use and it is likely that cost is discouraging use. Although the analysis does not prove causality or rule out the idea that use may be discouraging cost, it seems feasible that these forest practitioners are avoiding costly indicators and this would be a likely explanation for why some indicators are not being used. Assuming that a negative relationship between cost and use is expected, it is interesting to examine elements for which the cost/use relationship is not negative. Where these instances occur, other factors are affecting use and cost. Most of these instances include situations of low cost estimate and low use, suggesting that factors other than cost are inhibiting use or vice versa. The relevancy of the indicators, capacity to monitor, perceptions of responsibility to monitor, and the mode of discourse used in the framing of criteria and indicators may be playing a more dominant role in these elements. There are also instances of elements having doable to expensive cost estimates while still experiencing high use. In these situations where monitoring is occurring despite cost, factors such as legislative requirements, data availability, and SFM monitoring ‘buy-in’ could be playing a role. Ecological indicators Ecological indicators generally showed a trend where high costs corresponded with low use and vice versa but there are some interesting outliers. Interpretation of the t-tests for Element 1.1, where there was not a significant difference between the n-cost and y-cost means, does not suggest a relationship between cost and use. The data suggest that there is sufficient buy-in around the inclusion of ecosystem diversity indicators in SFM monitoring that most forest practitioners will monitor some form of these indicators as 37 part of their planning despite incurring high monitoring costs. As well, affordable and accessible information exists for monitoring ecological and species diversity in the province7. Finally, scientific research is supporting progress in monitoring biological diversity (Kneeshaw et al., 2000; Bunnell et al., 2004; Pearse and Venier, 2004; Bunnell, 2003; see also Whittaker et al., 2005). This is a unique scenario in this research. It emphasizes that biological diversity, and particularly ecological diversity, is a broadly accepted social value that has an established application in forest management (Kotwal et al., 2008; Stevenson and Webb, 2003). In contrast, all of the AE and FC participants reported limited monitoring of genetic diversity and cited capacity issues such as high monitoring costs, insufficient research around these indicators and lack of access to technology and skilled personnel as reasons not to monitor. AEs also complained about the relevancy of these indicators, which are geared towards large-scale commercial forest operations. Although Criteria 3 (Soil and water) and Criteria 4 (Role of forests in global ecological cycles) supported a negative relationship between cost and use, there were some elements with large gaps between the means of n-costs and y-costs, with y-costs being much cheaper. This may reflect a capacity issue that is expressed through cost— where things that seem too expensive to undertake are estimated to be very expensive to cost prohibitive. Comments on the weak implementation of monitoring for Criterion 3 suggested that participants considered these indicators expensive to monitor (especially Examples of data sources used to monitor indicators in this element include: Forest cover inventory data from the British Columbia Ministry of Forests and Range Terrestrial Ecosystem Mapping (TEM) Predictive Ecosystem Mapping (PEM) Committee on the Status of Endangered Wildlife in Canada (COSEWIC) The Conservation Data Centre (CDC) 38 those requiring data on the historic range of variation) and requiring a lot of field time, access to technology and skilled personnel. In Criterion 4, this trend is also evident. Based on comments from FCs such as, “Data not yet available”, “under development”, “Where would this be tracked?” and “needs significant research”, indicators associated with carbon and climate change are not well developed for the forest industry in BC. For Aboriginal entities, the higher cost estimates but similar use patterns suggest that this lack of capacity is even more acute. Based on an analysis of the data sources, it is apparent that the forest companies are a source of much of the ecological data on which the Aboriginal entities rely. In Criterion 4, Aboriginal cost estimates may be higher because forest companies are not developing the expertise and information for those indicators that AEs can then capitalize on. Without the expertise developed by the industry, Aboriginal foresters have to rely on using precious community resources to do this work or wait until developments become more broadly available through the government. The opposite occurs with information on culturally important species and non-timber forest products (NTFPs), where Aboriginal entities have vested interests and work to build capacity to collect this information and forest companies may come to rely on Aboriginal entities for data. Where ‘high cost, low use’ patterns exist, high cost estimates for the elements in Criteria 3 and 4 indicate lack of good data and low capacity to undertake expensive and time consuming measurements requiring highly skilled personnel. Unfortunately, this also means that important aspects of SFM — namely soil, water and carbon — are insufficiently measured. Governments could provide incentives for such monitoring (and lower the associated costs for FCs and AEs) by creating baseline data, building capacity through 39 investment in technology and training in communities, and developing ongoing educational opportunities. In Canada, the Carbon Budget Model of the Canadian Forest Sector is an important federal government program that has created an operational-scale carbon accounting tool for the forest industry (Natural Resources Canada, 2006). In addition, market-based mechanisms such as the value added to timber through certification or the promise of fostering alternative revenue streams from forests through valuation exercises for ecosystem services (such as the provision of drinking water and the water regulation services of forested watersheds) may provide incentive for higher quality monitoring and information gathering. Economic indicators Elements 5.1 and 5.2 represent tangible benefits from forestry and the distribution of benefits both locally and across British Columbia. Indicators in Element 5.1 were not well used by any of the case studies. In Element 5.2, more indicators were being used, but there were also more indicators in this section for which information is readily available externally. Comments from these elements suggest that there is no strong motivation to monitor economic benefits and their distribution and that cost and proprietary information were obstacles where motivations do exist. Comments such as: “information is available but not monitored for SFM”, “could compile information but it’s expensive to do so”, “information is proprietary”, “no baseline data exists and data that does exist is not accessible” and “depending on level of detail it could be a full-time job [monitoring these indicators]. We could decrease cost but then it would be anecdotal information and it wouldn’t be very accurate”. 40 Forest companies on average reported low use and low costs for Element 5.3 which suggests that cost was not a major disincentive for monitoring the sustainability of benefits from the forest. From the interview comments, this situation could be attributed to relevancy, scale, and data availability issues. FC and AE participants commented on incorrectly scaled indicators and poor terms of reference. In particular, one AE respondent commented that “terms of reference for water quality and restoration need to be culturally defined for First Nations”. One FC respondent commented that: “if the information is out there then there is a minimal cost to filter it out but we won’t collect it ourselves”. Other FCs commented that the information for cultural and social indicators in this particular element would either be available from government or not be the company’s responsibility. Aboriginal cultural information was cited as more expensive by all FC respondents because it is proprietary information. Furthermore, it is conceivable that the concept of sustainability in regards to this element is not well defined for forest companies and may contribute to a lack of interest or willingness to pursue it. Social indicators Overall, the negative cost/use relationship seems to break down the most where the indicators become more social in nature and their relationship to conventional forest management becomes less clear. AEs dominate these more social indicators, both in use (except for Element 6.4) and in sourcing data internally, despite perceiving higher costs than forest companies across all elements. The AE case studies seem to have focused their monitoring activities on areas where external data are available and they have an associated vested interest or community 41 mandate, whereas FCs have prioritized monitoring activities around either legislative or certification requirements or areas where they have a vested interest in monitoring. There may be a tendency to think that as Aboriginal groups develop forestry businesses their vested interests will align more and more with what we would expect from private forestry companies. However, the governance of Aboriginal forestry companies is different because the conmiunities themselves form registered companies and most, if not all, community members are shareholders. In addition, the boards of the companies are comprised of a mix of traditional leaders, elected officials, and elders (Mayers and Vermeulen, 2002). As such, Aboriginal forestry interests tend to be oriented towards seeking to uphold the social, cultural, ecological and ethical values of the community (Mayers and Vermeulen 2002), which each Aboriginal entity defines uniquely. In contrast, forest companies’ interests are predominantly focused on maximizing returns to private stakeholders. Frequent comments from FC participants — including “not the company’s responsibility”, “ask First Nations or government”, “not part of the SFM process” and “hard to get information” — highlight the reluctance of forest companies to expand beyond their vested interests and also points to a lack of clarity regarding the legal obligations of FCs towards Aboriginal land-use and treaty rights. For example, in Element 6.1, many participants from both sub-samples noted that the legalities of treaty negotiation processes (in conjunction with unclear definitions of consultation and traditional land use) complicate cooperation between companies and Aboriginal groups. Some FCs only considered the indicators under Elements 6.1 — 6.2 valuable for SFM monitoring in cases where Aboriginal land claims were already settled. However, despite the high cost estimates by AEs, the prevailing social importance of asserting Aboriginal rights is an incentive for AEs to monitor indicators of Element 6.1 (Aboriginal and Treaty 42 Rights). For FCs, on the other hand, the use of these indicators seems to depend on the kind of working relationship they have with First Nations. In Element 6.2, access to traditional ecological knowledge (TEK) may be limited for some forest companies because they do not have a working relationship with First Nations through which this information can be confidently shared. Ability to gather TEK and map traditional use may be hindered for AEs by their capacity (both in cost, time, and skills) to do so, even with promised support from the provincial government. However, the use of traditional knowledge in forest management seems to have broad buy-in from the study participants despite these problems. Government could play a role here in brokering the exchange of data and information to make the inclusion of TEK and traditional use mapping more widespread without violating privacy or undermining the ownership of information. However, a challenging pre-requisite for successful transmission and appropriate treatment of sensitive and proprietary information is building trusting relationships between all parties involved. In Element 6.3 (Forest community well-being and resilience) the levels of use between the two sub-samples were similar despite cost estimates from AEs that were three times those of FCs. This suggests that FCs face large disincentives for implementing monitoring of Element 6.3. While there were significant differences between the means on the t-tests for AEs, the FC sub-sample had low costs for both n-cost and y-cost means. The perception that indicators in this element were not obligatory for SFM monitoring was common among the FC sub-sample. FCs also commented on the appropriateness of private firms monitoring sensitive social indicators such as “sense of place” or “access/use of social services” (for example) even if this information was available from 43 government and did not have to be collected internally. Element 6.3 is a good example of where social indicators for SFM lack sufficient buy-in. Even in the AE sub-sample, the monitoring for these indicators is not necessarily motivated by the desire to create a comprehensive SFM indicator suite. These indicators are usually monitored on behalf of the community or Nation and the information is readily available for a variety of reporting purposes. Comments from FC participants also reflected an attitude that the indicators in Element 6.3 lay outside of their responsibilities as forest managers. For the 34 indicators in Element 6.3, a majority of the qualitative comments from FCs were variations on: “No, this information may be available from government” and “not the company’s responsibility”. However, the AE case studies represent their respective First Nations (in contrast to a private company) and as such their responsibilities usually include the well being of their communities. Clearly, the AEs in the study were more diligent in terms of monitoring social indicators. This is not to say that FCs do not take their responsibilities in the communities in which they work into consideration. Rather, the results suggest that there is still no clear determination around the responsibilities of FCs operating in BC to monitor social and Aboriginal indicators. There is also no incentive for FCs to include these indicators in their monitoring practices except for the incentive provided by certification systems that include social and economic indicators in their standards. For example, the Forest Stewardship Council (FSC) is inclusive of Aboriginal themes (Principle 2.2, FSC-BC Regional Standards, 2005) and the Canadian Standards Association (CSA) standards include requirements for public participation (CAN/CSA Z809 - Canada’s National Standard for Sustainable Forest Management, 2003). The 44 extent to which certification systems are having a positive impact on the incorporation of social and economic indicators into SFM monitoring is an area of future research. For FCs, Element 6.4 (Fair and effective decision-making) was the opposite of Element 6.3. The indicators were considered appropriate and relevant and were widely monitored. The difference is that this element deals predominantly with issues of compliance, legislation, guidelines, and best management practices regarding SFM and with public participation in forest management planning. These are seen by forest companies as legitimate social indicators for a comprehensive SFM Plan. They were not considered expensive to do, but were highly touted in SFM Plans (and in some plans public participation represented the only visible effort by the company to go beyond legislative requirements for forest practices). Generally, Aboriginal entities did not widely monitor public participation indicators. AEs felt they were grouped with the public and, in some cases, monitored some of the indicators in a ‘watch dog’ role to make sure that forest companies were keeping promises. More commonly, they saw themselves as separate from the public participation process and found many of the public participation indicators irrelevant. In a few instances, they were obliged to conduct public participation exercises and engage with other Aboriginal groups as part of their planning processes; in which case they still felt the public participation indicators related more to large forest companies than to small-scale forest operations. In terms of compliance, Aboriginal entities on the whole were much cooler to the concept of adhering to rules and regulations that were ill-suited to their goals on the landscape and generally were more likely to use these indicators in instances where they held tenure and engaged in some commercial forestry as per their legislative and/or certification requirements. 45 In Element 6.5, AEs reported their lowest use of indicators under Criterion 6 and comments from AE participants included “not applicable”, “too difficult to get a percentage”, “out of our league”, “too detailed” and “difficult to do”. Indicators under this element may therefore be geared more towards the expectations of industrial forest companies’ around informed decision-making. While including indicators that address guidelines, deadlines, research, extension, funding, compliance, ‘and availability and cost of data, Element 6.5 does not incorporate indicators that may better address Aboriginal concerns around decision-making. Indicators such as “adequate knowledge is available (incorporation of traditional and local knowledge)”, “transparency of process (communities must have full disclosure of information)”, “adequate capacity to undertake the process (need for trained and educated personnel)”, and “establishment of guiding principles for decision-making (establishment of trust, accountability, mutual respect collaborative spirit, and fairness)” (Sherry et al., 2005) were not included in the element. The concept of ‘informed decision-making’ that is the focus of Element 6.5 is a very subjective idea and, depending on the management paradigm that one adheres to, the indicators that would be appropriate could change dramatically. This is also the case for social capital. These social indicators are best defined within the context in which they are going to be used and at an appropriate level for management (Gough et al., 2008). The scale that these indicators may be appropriate at could be quite fine, creating problems for broad implementation across even a regional landscape unit such as a Timber Supply Area (TSA) or a forest district. As framed by the indicators included in Elements 6.4—6.5, the concepts of “fair and effective” or “informed” decision-making — where fair, effective and informed decision 46 making is achieved simply by assuring compliance with standards, adequate training and public participation — are more reflective of a discourse of administrative rationality. The absence of fuzzy concepts of social capital in Elements 6.4—6.5 further suggests that these indicators are imperfect tools for contending with moral meanings, trust and endogenous preferences. The focus on efficient measurement is more likely to overlook communitarian discourses than other democratic discourses, such as pluralism. Pritchard Jr. and Sanderson (2002) state that pluralism does not include the concept of endogenous preferences — rather, there is an assumption that (predetermined) values exist ‘out there’ and merely need to be uncovered and served (similar to administrative rationalism, where the optimal policy exists ‘out there’ and is discoverable by experts). Notably, the social capital element minimizes this assumption: the discourse is predominantly communitarian. Although FCs reported a small amount of external data sourcing under this element they were not using any of the social capital indicators, suggesting that FCs perceived such measures to be extraneous to their SFM monitoring responsibilities. The issues related to relevancy, responsibility, discourse, and capacity that are highlighted in this research are indicative of a larger trend in SFM monitoring: sustainability is still framed in a largely reductionist and bureaucratic discourse that has, for many years, highlight timber as the only valuable forest use. This attitude permeates many of the findings in this study and highlights the difference between the current status of sustainable forestry and the direction SFM must go in order to achieve the goal of sustainability in a more comprehensive sense. Forest managers of all stripes need to achieve a better understanding of how their roles will change from a sustained-yield forest management paradigm to a sustainable forest management paradigm. Clearly, vested interests are currently influencing SFM monitoring priorities and this is unlikely to 47 change. There is no real incentive for forest companies to undertake more comprehensive monitoring of the social elements unless there is a broader shift in the management paradigm away from sustaining timber yields and towards more inclusivity for other forest values (Stevenson and Webb, 2003). In democratic discourses, such as pluralism or communitarianism, including indicators of Aboriginal rights and community well-being in SFM monitoring makes sense, aligned with the pluralistic ideal of balancing competing interests and the communitarian belief in upholding the public good (Pritchard Jr. and Sanderson, 2002). When these ideals translate into indicators, they are often associated with measure the relationship between different (human, natural) components of sustainable forest management. However, from a regulatory or management institution perspective, criteria and indicator monitoring is a tool for efficiently validating the individual sustainability of the components of a given forest operation and not the relationships between those components. For example, forest companies did not use social capital indicators, whereas Aboriginal entities did. These indicators are in line with communitarian and even pluralistic ideals of trust, tolerance, integration and volunteerism- all related to relationship and not to a reductionist view of components. In another example, Stevenson and Webb (2003) argue that human- ecological information that is considered relevant in a reductionist approach is measured in a simplistic way, such as monitoring human impacts on managed lands and resources. This approach ignores a measurement of the ability of humans to sustain ecological integrity and resilience by monitoring ecosystem composition, structure, and function, which would require managers to consider complex relationships. Instead, quantitative indicators based on objective measurements that leave limited room for interpretation (aside from monitoring inaccuracy) and readily produce answers consistent with the 48 management paradigm of reductionist western science are more highly prioritized. For example, the State of British Columbia’s Forests (2006) states that, “The credibility of reporting depends on the use of the best science-based information” but make no mentions of other forms of knowledge such as traditional ecological knowledge and local knowledge (Ministry of Forests and Range, 2006). In accepting a more democratic discourse for forest management we allow the definition of sustainable forestry to truly encapsulate the human dimensions of this new paradigm. We must then go further and also examine the underlying structure of the C&I monitoring framework; especially its ability to fit into adaptive (co-)management and alternative forms of management and decision-making than those that currently dominate forest governance. Conclusions Choices around the implementation and monitoring of C&I of SFM reflect the different priorities and discourses of management (or regulatory) and social institutions. Forest companies still apply a legislative requirement to filter their data collection activities (targeting information to help demonstrate compliance with regulation). In BC, tensions resulting from unresolved issues of Aboriginal rights and title further strain the relationships between the resource industry and the Aboriginal community (Michel et al., 2002). Forest companies perceive the responsibility and authority for monitoring and resolution of issues around Aboriginal and treaty rights to lie with government and there is an urgent need for clarity of roles on this matter in the context of sustainable forest management. Similarly, the monitoring priorities and activities of AEs reflect the reality that participation in SFM monitoring is typically only one of many responsibilities facing social institutions in Aboriginal communities (Marshall, 1998). While Aboriginal entities 49 need improved information for planning the sustainable management of their lands and resources into the future, versatile data with a wide range of applications — such as treaty and compensation negotiations, community economic development plans and preservation of contextualized traditional ecological knowledge — are obvious priorities (Michel et al., 2002). The dynamics of these competing discourses suggest that while the dominant culture and macro-functions of aggregate political-economic entities such as regulatory institutions (government bureaucracies) drive the management priorities of corporations (forest companies), they remain distanced from the communitarian focus of social institutions such as Aboriginal and rural communities (Abel and Stepp, 2003; Greskiw and limes, 2008; Pritchard Jr. and Sanderson, 2002). There is an important difference between business-centered environmental reporting and life-centered reporting for sustainability and the nature of regulatory initiatives can sway the balance (Gray, 1994). If, as the adage says, “we manage what we measure”, these results highlight some important inconsistencies with the CCFM hierarchy that currently remain outside the realm of monitoring — and therefore SFM — in the BC context. 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Toronto: Knopf. 53 3 Organizational perspectives on stewardship for sustainable forest management8 Introduction In striving to understand why organizations monitor some aspects of sustainable forest management (SFM) more than others, we must have some concept of the underlying organizational perspectives concerning forest stewardship. Stewardship herein refers to the careful and responsible management of something entrusted to one’s care. A perspective on stewardship is then what one considers to be a satisfactory level of ‘care’ and what one considers to be one’s responsibilities for management. Obviously, various factors will influence one’s stewardship perspective regarding sustainable forest management. Any perspective is multidimensional and the social relationships, economic flows and ecological impacts associated with stewardship through SFM are components in complex, dynamic and non-linear socio-economic and ecological systems that are coupled in policy and management. In the abstract, the forest represents a point where these human — natural systems and dimensions intersect; where qualitative and quantitative, objective and subjective are woven together to form tight-knit, ephemeral, but ultimately resilient, patterns. Thus, the analysis of perspectives is only a snapshot in time over a range of detailed cases. The goal of such an analysis is not to infer stable causal relationships between certain perspectives and actions, but to explore the causal mechanisms that are active in a set of case studies in order to inform policy decisions. More specifically, by using monitoring practices to observe typologies of organizational 8 A version of this chapter will be submitted for publication. Gough, A.D. 2009. Organizational perspectives on stewardship for sustainable forest management. 54 perspectives, policy-makers may find suggestions and insights for new ways of implementing meaningful and relevant criteria and indicator (C&I) monitoring systems at the local level. Stewardship perspectives and policy in British Columbia Rangan and Lane (2001) state that perspectives regarding resource use delineate boundaries within which policy interventions can occur. In a contemporary democratic nation where there is ‘public’ or ‘crown’ ownership over the majority of natural resources, such as is the case in Canada, policy making and policy implementation are the means by which the state exercises authority and control via the allocation of rights of access and use of resources (Rangan and Lane, 2001). As social actors articulate their needs and their claims to these rights, they express their competing stewardship perspectives which, in turn, delineate the boundaries of policy interventions by using discursive strategies that can sway the allocation of resources in favor of a particular level of exclusive access and control (Rangan and Lane, 2001). Thus, organizational perspectives on stewardship are simultaneously influenced by policy formation via state control of resources and influential in policy intervention via social actors’ discursive strategies. This process is also affected by pre-existing legal parameters, macro economic pressures, and the attributes of the production systems related to the resources, which influence the extent to which resource extraction and management can be altered by policy interventions (Rangan and Lane, 2001). Consequently, rights allocation (through government policy) and rights assertion (through the discursive strategies of competing interests) influence each other to the extent allowed by the rigidity of the overarching institutional-economic system in which they operate. For B.C., timber rights 55 allocations and Aboriginal land and treaty rights are important to understanding the relationship between organizational stewardship perspectives and forest policy in this framework (although forest companies assert their rights, usually through tenure reform). Timber rights allocations Most forest land in B.C. is under provincial government ownership. Timber rights allocated for these public, or Crown, forests by the province in B.C. are time-bound, tenurial, and usufructuary. There are many types of tenure in the province, but they can be broadly divided into area-based and volume-based tenure. This analysis includes the two most common types of these tenures— Tree Farm Licenses (TFL) (area-based) and Timber Supply Area (TSA) tenure (volume-based) ‘. TFL tenure holders carry the greatest management responsibilities on Crown land of any of the tenures in the province. They have nearly exclusive rights to harvest an allowable annual cut’° (AAC) of timber from a defined area of Crown land for up to 25 years. Their responsibilities include protection, planning (operational and 5-yr management plans), road building, reforestation, basic silviculture, and maintaining resource inventories (Cortex Consultants, 2001). In comparison, TSA tenure holders have the right to harvest an annual volume of timber for up to 20 years but do not have exclusive rights to timber on a defined area of land and are therefore less beholden (Cortex Consultants, 2001). They carry the responsibilities for protection, road building, reforestation, and operational planning but do not have to maintain forest inventories (Cortex Consultants, 2001). For a complete list of tenures in BC, please see: A Quick Reference: British Columbia’s Timber Tenure System (Cortex Consultants 2001) 10 AAC apportionment is the distribution of the allowable annual cut for a timber supply area among timber tenures in British Columbia by the minister for forests and range, in accordance with Section 10 of the BC provincial government’s Forest Act. (BC Ministry of Forests and Range, 2008b) 56 Furthermore, pursuing certification standards adds responsibilities to both types of tenure, including (depending on the certification standard) the provisions for conservation values, ecosystem representation, old growth retention targets, riparian strategies, rare and endangered ecosystems, visual constraints, consultation with Aboriginal entities and the public, production of 5-year SFM Plans, and yearly audits. These tenure and certification responsibilities constitute the majority of a company’s legislative requirements and shape the access and control of timber resources in the province. Aboriginal land and treaty rights assertions The political tensions brought about by competing perspectives force the state to be an arbiter of rights as well as an allocator (Rangan and Lane, 2001). In the province, the role of arbiter is complicated by the fact that the government benefits greatly from the rents and taxes garnered through the commercial forestry tenure system (Ministry of Forests and Range, 2008a). The province does not enjoy the confidence of many Aboriginal groups, who assert that participating in land-use and forest planning processes is tantamount to acknowledging the provincial government’s authority to manage resources over areas where there are Aboriginal land claims (Karj ala and Dewhurst, 2003). Rangan and Lane (2001) outline how, beyond the historical injustices wrought on Aboriginal peoples, contemporary systems of recourse and reclamation of land and use rights can be fraught with problems for Aboriginal claimants. For example, native peoples in Australia who are attempting to gain title and use rights to land and resources must provide copious amounts of legal proof of continuous residence in their traditional territories, despite the fact that many were forced from their lands either physically or through economic necessity as a result of colonization. A similar situation faces 57 Aboriginal peoples in B.C., which is the only province that has not settled land claims with most of its Aboriginal population (Karjala and Dewhurst, 2003). Rangan and Lane (2001) assert that as a result of situations such as these, the critical issue for indigenous groups’ centers on the extent to which they actually gain access to, and control over resources in their traditional territories. As a result, Aboriginal entities are concerned with A) getting their claims officially recognized through court cases and treaty negotiations; B) actively participating and influencing the processes that shape policy and management of resources; and C) using a distinct discursive strategy to persuade management in favor of their perspective on stewardship and, by extension, assert their land and use rights. The most common interaction between tenure rights allocations and Aboriginal land and use rights assertions in the commercial forest industry in B.C. is the referrals process. When the provincial government in B.C. wants to lease, license or sell Crown land, it must consult with Aboriginal entities that may be affected by such actions and this is usually attempted through letters of referral regarding any residential, commercial, industrial, agricultural, tourism, recreation and investigative land-uses. This obligation to consult comes from the case law that has emerged over the past decade regarding Aboriginal rights and title where the duty “. . . flows from the Crown’s assumption of sovereignty over lands and resources formerly held by the Aboriginal group any time the Crown [provincial government] makes a strategic level policy decision regarding land and resource use, or when it is called up on to make a specific decision that may affect the use of lands and resources within the traditional territory of an Aboriginal people.” (Haida Nation v. Government of British Columbia and Weyerhaeuser (SCC 73), 2004). 58 Nevertheless, Aboriginal entities are compelled to participate in the referrals process because they see crown lands covered by their land and treaty claims being alienated from their treaty negotiation processes as others gain access and control over disputed resources (First Nations Land and Referrals Forum (FNLRF) Final Report, 2007). Tn addition, by not participating in the referrals process, Aboriginal entities may negatively impact their chances of success when challenging a court decision (Flahr, 2002). However, in the face of increasing pressures on Crown Land for development, Aboriginal entities are being inundated with referral letters — most of which require short turn around times for response ( 45 days). The process of responding to referrals is difficult for most Aboriginal entities because they lack sufficient technical staff and resources to integrate information, research the issue, consult with their own communities, and respond to the project proponents and the government in the timeframe demanded of them (FNLRF Final Report, 2007). Furthermore, there is no provision for financial compensation for the costs incurred when Aboriginal entities respond to referrals, exacerbating their inability to mobilize. Finally, referrals are a necessary part of facilitating Aboriginal empowerment through input in land-use planning and decision- making (Weber, 2008) but referrals generally address operational level issues, not strategic level planning. Thus, the referrals process does not provide the kind of claims recognition, active participation, and opportunity to influence the policy process that Aboriginal entities are seeking. Thus, allocations of tenure and Aboriginal treaty rights assertions may be important determinants of the stewardship perspective that an organization develops. As policy interventions, the discursive strategies embedded in organizational stewardship 59 perspectives are meant to sway resource allocation and exclusivity of access and control in favor of particular vested interests. As tensions between competing perspectives increase, there is evidence that Aboriginal entities have very different perspectives from those of forest companies and that these perspectives may be marginalized in SFM in its present form, as defined by the Canadian Council of Forest Ministers (CCFM) core set of criteria and indicators (C&I) (National Aboriginal Forestry Association (NAFA), 2005). This research focuses on thirteen case studies from British Columbia (B.C.), Canada that represent various types of forest stewardship and sustainable forest management. A representative from each organization was asked to rank 386 indicators of sustainable forest management applicable to B.C. according to the organization’s use of the indicator (used/not used) and perceived cost of monitoring it. From these data, scores were created for each indicator according to use and cost and these were subsequently analyzed to group the organizations. The use and cost data sheds light on what indicators were being selected or rejected and what role cost plays in the decision to use the indicator. This is very important to understanding stewardship perspectives, as it immediately highlights monitoring priorities which can be connected back to forest stewardship practices. By grouping organizations with similar patterns of cost and use, the analysis also highlights broader trends that can provide policy insights and new roles for government in implementing SFM. Multivariate analysis of SFM stewardship perspectives Observing perspectives on stewardship is needed but remains challenging. Multivariate analysis is useful in opening portals into the data from which one can gain insight and 60 richer descriptions, but caution must always be exercised as the analysis is limited by the scope and scale of the research and the type of data obtained. In this study, Q-type factor analysis is explored as a tool for providing deeper insights into the use and cost estimate data presented in Chapter 2. Q-type factor analysis is the transpose of R-type, or normal, factor analysis. Normally, factor analysis is used to reduce a large number of correlated variables (n) to a smaller number of generally uncorrelated factors (in the case of an orthogonal rotation) (Miller, 1978). The goal is to discover which variables form cohesive subsets that reflect underlying processes that contribute to correlations among the variables (Tabachnick and Fidell, 2001). In Q-type factor analysis, observations, or ‘cases’ (m) are grouped rather than the variables (n) (Banks and Gregg, 1965). The goal is then re-focused on forming cohesive subsets, or factors, of the observations where the factors reflect underlying processes related to the similarities and differences of the sample population based on the variables. Q-type factors analysis is also quite similar to cluster analysis, for example both can handle many variables, but factors formed by orthogonal rotations are easier to interpret based on their score patterns (or loadings) than closely related clusters (Miller, 1978). The study follows Lin (1998) in the understanding of interpretivist and positivist approaches to qualitative research. Lin (1998) discerns between identifying causal relationships, which are positivist and are present in the data with some degree of probability, and causal mechanisms, which explains how particular variables interact. This study used a purposive sampling procedure (as opposed to a random sample) so the Q-type factor analysis does not provide statistically generalizable results (Nijnik et al., 2008; Swedeen, 2006). Instead, Q-type factor analysis herein is interpretivist, meaning 61 the categories that are reconstructed from the data are embedded in the context of the case studies. This analysis allows for interpretations of causal mechanisms and detailed explanations of how the cases interact, in order to understand what the general concept of sustainable forest management means in specific operations. However, without including causal relationships, it caimot be determined how widespread the existence of similar cases may be. While lacking a positivist methodology, the use of parallel case studies provides some basis for hypothesis testing because case studies can yield generalizations inductively (Weimer, 1999). Thus, while some statements could possibly be made more generally, care is taken in not overstepping the boundaries of what the factor analysis model can say. As a tool for interpretation, the Q-type factor analysis used in this study is similar to Q methodology, but the latter is specialized for a specific use in the study of subjectivity: the sample population can only be comprised of individuals who are giving their perspectives on a particular concept or issue (Brown, 1997; Cross, 2005). Q methodology assumes that the R and Q transposes of the data matrix constitutes two separate matrices; one that is “objective” (R), in the sense that an outsider is testing a relationship, and one that is subjective (Q) and is only concerned with tests (n) that are measured or scaled by the individuals (m) in the study (Brown, 1997). Q-type factor analysis makes no such distinction between the R and Q matrices (Burt, 1937). Q methodology and Q-type factor analysis are both organized to establish patterns within and across the sample, but Q-methodology is not applicable to this study because it deals with the individual’s internal frame of reference and highlights individual subjectivity (Dasgupta, 2005). The scale of this research is at the level of the organization; therefore, 62 it is inherent to the goals herein that the perspectives wrought through this process be implicated through data about the organization, not the individual. Data, such as the types of indicators used in monitoring SFM and the expert opinions on the incremental costs associated with indicator use, can measure organization-level outputs that are based on the organization’s perspective on stewardship. Grouping the organizations by their responses to the cost and use questions then provides information on the similarities and differences across the sample, and may surprise the researcher by revealing distinctions between groups that are commonly assumed to be in agreement regarding forest stewardship. Conversely, it can reveal similarities between groups that are assumed to lack common ground. The differences between Q-type factor analysis and Q methodology mean that for this research, the direct analysis of perspective is abandoned in favor of an indirect approach that incorporates the outputs of organizations’ decisions (which indicators to monitor, where to put money into monitoring) as measures for perspectives on stewardship. Methods Study location and description For a description of the study and the data collection, please see Chapter 2. Q-type factor analysis and post hoc tests Since variables are used as tests to identifr major discontinuities between groups of observations (Williams and Lambert, 1961), the factor analysis matrix was transposed so that the observations were in columns and the variables were in rows. In this study, the observations are the case studies, or cases, and the variables are the 386 indicators Of 63 SFM that are applicable to B.C., organized around the hierarchical structure of the CCFM core set of criteria and indicators for Canada (for a complete list of indicators, please see Appendix A) There are 13 cases in total and these were selected purposively to represent a variety of types of forest management and forest ecosystems in the province. The practicalities of recruitment meant that the sample was non-random, as participation was also based on the presence of an SFM Plan (or equivalent stewardship monitoring) and discussions with organizations. The sample did not contain community forest agreements”, Integrated Forest Practices Agreements 12, or cases explicit to certain ecozones13within the province. However, the resulting sample was broadly reflective of the diversity of interests in the province related to monitoring for SFM. Specifics, including names, for each case study are intentionally left anonymous in compliance with the research protocol agreement. The use and cost data was re-coded using a combined scale of -6 to +6 where the use (yes, no, nla) was combined with the cost (numerically defined as 0—5) were combined for total scores between +6 and -6 (see Table 3.1). Any “nla” responses for use and cost were coded as 0. The final matrix of data included the 386 indicators along the vertical axis and the case studies, coded as FC 1, FC 2... FC 7 and AE 1, AE 2 ... AE 6 along the horizontal axis (FC= forest company and AE= Aboriginal entity). A Community Forest Agreement (CFA) is “a competitively or directly awarded form of tenure that issues an exclusive right to a First Nation, municipality, or regional district to harvest an allowable annual cut in a specific area. This agreement may also include the right to harvest, manage, and charge fees for botanical forest products and other products” (BC Ministry of Forests and Range, 2008b) 12 An Innovative Forest Practices Agreement (IFPA) is an initiative that “enables government to enter into agreements with forest licensees to test innovative forestry practices through a variety of pilot projects.” (BC Ministry of Forests and Range, 2008b) 13 An ecozone is an “area of the earth’s surface representing large and very generalized ecological units characterized by interacting abiotic (non-living) and biotic (living) factors.” (BC Ministry of Forests and Range, 2008b) 64 Table 3.1 Combined scoring and interpretations for factor analysis data matrix Use Cost Combined Interpretation score Yes 0 6 Monitoring at no extra cost Yes 1 5 Monitoring at minimal cost Yes 2 4 Monitoring at ‘do-able’ costs Yes 3 3 Monitoring at expensive costs Yes 4 2 Monitoring at very expensive costs Yes 5 1 Monitoring but cost prohibitive n/a n/a 0 Not applicable No 0 -1 Not monitoring but no extra cost No 1 -2 Not monitoring but minimal cost No 2 -3 Not monitoring but ‘do-able’ costs No 3 -4 Not monitoring and expensive costs No 4 -5 Not monitoring and very expensive costs No 5 -6 Not monitoring and cost prohibitive Statistical Analysis Software (SAS) was used to perform the inverse factor analysis. A Principal Components Analysis (PCA) was initially run, as is standard for SAS factor analysis procedure, and produced the initial unrotated factor pattern. Kaiser-Guttman, cumulative variance and Broken-stick rules, as well as a scree plot, were employed to determine roughly how many number of factors to retain after running the preliminary principle components analysis (PCA) (Jackson, 1993; Dasgupta, 2005; McKeown and Thomas, 1988). Two orthogonal rotations (varimax and equamax) were used on the data. The rotation that gave the best results (i.e. obtained factors that could be interpreted clearly) was retained. Although there are an infinite number of potential rotations in factor analysis, the most commonly used rotation for Q-methodology is varimax (Dasgupta, 2005). In preliminary runs of the analysis however, equamax provided equal or better results, leading to its inclusion in the final analysis. Oblique varimax and equamax rotations were also tested in preliminary runs but oblique rotations were not 65 included in the final analysis because they allow for correlations between the factors, which can make the results more difficult to interpret (Tabachnick and Fidell, 2001). Factor loadings, total common variance, final communality estimates, variance explained by each factor, and the fit of the data with results from prior analysis and interview data were all used to assess which rotation gave the best results. The rotated factor pattern was then analyzed for the correlations between each of the retained factors and the 13 cases, in order to determine to which factor each case belonged. A +/-10.51 level was used to determine an important correlation. This level was chosen because it is sufficiently high to capture strong positive or negative correlations (Comrey and Lee, 1992), but low enough to potentially capture all of the organizational observations in the factors. Values of +/-l0.45 were also noted in the factors as weaker correlations. Table 3.2 shows a scale of correlations, their overlapping variance, and the interpretation of the score (Comrey and Lee, 1992). This scale illustrates an effective way of assessing correlations. For the purpose of this study, the lowest rank (0.32 or poor correlation) was not acceptable, as weaker correlations confuse the labeling of the factors, which is central to the purpose of the Q-type factor analysis procedure. Table 3.2 Standard factor loadings with corresponding overlapping variance and interpretation 0.55 30% Good 0.45 20% Fair 0.32 10% Poor Source: Comrey and Lee (1992) Labeling each factor required in-depth analysis of the model output. Three techniques aided in the final labels: assessment of marker and complex cases, analysis of variance and post-hoc testing of the factors using the original scores, and analysis of distinctive 0.63 40% Very aood 66 factor scores. In combination, these techniques were useful in identifying, verifying and describing trends in the data that distinguish each factor. First, the factor analysis was run several times, with the nfactor increased each time. The nfactor is the number of factors retained in the model and is set by the researcher according to various heuristics. The maximum nfactor was 13, or the number of cases in the study. Although heuristics were used to determine how many factors to retain, it was also important to examine how the groupings changed as the cumulative variance increased. As the number of factors retained was increased by the researcher, the pattern of groupings was examined to see if stable groupings formed and to see which case studies remained unstable or stood out from the others. Simple cases are cases that load on only one factor and have a zero loading on at least one other factor (Tabachnick and Fidell, 2001). The most strongly correlated simple cases, usually 0.71 or higher (Comrey and Lee, 1992), are pure cases, or markers (see Table 3.2). Markers load on a factor regardless of extraction or rotation technique and are useful in defining the nature of the factor (Tabachnick and Fidell 2001). Based on this, cases which were simple and were markers for factors were observed and were used to help label factors. Complex cases were also identified to help interpret the factor groupings. Complex cases are cases that load on more than one factor (usually weakly), may not have a zero loading on any of the factors, and have a spread across factors that makes them difficult to analyze because the factors are more ambiguous. For example, cases with similar levels of complexity may ‘catch’ on each other in factors that have little to do within underlying processes meaning that they will group together because of their complexity and not because they 67 relate to the same factOr (Tabachnick and Fidell 2001). Based on this process, preliminary labels were created for each factor. The second means of discerning factor labels was to test the factor groupings on the raw data. This was done using a one-way analysis of variance (ANOVA) and post-hoc (Tukey) testing. In order to confirm the preliminary factor labels, and to reveal the differences between the factors based on the original data, the data were divided into 19 elements by themes within the CCFM core set of criteria and indicators (see Table 3.3) and a one-way analysis of variance (ANOVA) was used to test the differences between each factor group for each element (a= 0.05). A post-hoc Tukey test was then employed to examine the differences and similarities between the factor groupings. Where the post-hoc tests confirm relationships or statistically significant differences, factor scores were analyzed to describe the differences and attribute defining characteristics to each factor. In Q-type factor analysis, factor scores are derived as follows: each case is weighted proportionally according to its involvement in each factor — the more involved the case, the higher the weight. Once calculated, the weight for each case is multiplied by the raw data for each indicator and summed across the cases to achieve a composite factor score or Z-score. This score is said to reflect what the raw score for the factor might be if it were measured directly. However, factor groups in Q type factor analysis are not new organizations, and their scores cannot be treated as raw scores would. They are rich in description about the factor, but results derived from them must be used cautiously, especially when compared to the ANOVA and post-hoc results, which grouped the cases according to the factor groupings but did not weight the raw data. 68 Table 3.3 Canadian Council of Forest Ministers (CCFM) criteria and case study elements CCFM Criteria Case study Description . element I Biological diversity 1.1 Ecological diversity 1.2 Species diversity 1.3 Genetic diversity 2 Ecosystem condition 2.1 & 2.2 Sustainability of harvest of timber and non-timber and productivity forest products 2.3 & 2.4 Natural and human-induced disturbances 2.5 Forest regeneration 3 Soil and water 3.1 Soil 3.2 Impact of harvesting on riparian areas 3.3 Water 4 Role of forests in 4 Carbon sink/source, impacts of forest global ecological ecosystems on climate change, forest product cycles carbon procurement, forest product sector contributions to C02 emissions. 5 Economic and social 5.1 Economic benefits benefits 5.2 Distribution of benefits 5.3 Sustainability of benefits 6 Society’s 6.1 Provision for duly established Aboriginal and responsibility treaty rights 6.2 Aboriginal traditional land use and forest-based ecological knowledge 6.3 Forest community well-being and resilience 6.4 Fair and effective decision-making 6.5 Informed decision-making Social Volu nteerism, community participation, trust Capital Notes: Desciiptions follow the titles of the elements of the CCFM except for CCFM criteria C.2, C.3, and C.4, which do not have elements in the original CCFM indicator suite but have been split into elements for this study. Elements 2.1 & 2.2 and 2.3 & 2.4 are combined because of their short lengths. There is only one element for C.4 because of its short length. 69 Factor scores are standardized to a mean of zero, a variance of one, and a spread of +1-3. As such, most points (67%) will fall between +1-1 and scores on the tails of the normalized distribution (> +1- 2) are distinctive on the factor (Rummel, 1970). The factor array and these distinctive scores are used to help label the factor as an ideal type or group of observations (Dasgupta, 2005; Rummel, 1970). Theses scores are useful in identifying patterns of data that are contribute to the uniqueness of the factor, especially where they occur in clusters specific to a topic or theme in the indicator set. Furthermore, factor scores above or below +1- 2 represent the 5% of scores that are distinctive for that factor. These distinctive scores have a defining influence when distinguishing between the groups (Rummel, 1970). Results nfactor level selection Using the Kaiser-Guttman rule of keeping factors with eigenvalues greater than unity (A=1 .0), it was determined that the first three factors should be kept (see Table 3.4). The Broken Stick rule only allowed one factor, while the scree plot allowed three. However, to include a cumulative variance of 80%, eight factors would have to be kept. After examining these heuristics, the model was run seven times with a range of 2—8 factors allowed. From 2 to 5 factors, the factor groupings increased in complexity and description without becoming singular and were retained for analysis. From 6 to 8 factors, unique cases began to emerge which, although interesting, undermined the goal of examining similarities between the case studies. Finally, 5 factors (nfactor=5), with a 70 cumulative variance of 65%, were chosen as the best number of groups to examine in depth. Table 3.4 Eigenvalues of the Correlation Matrix (Total = 13; Average = 1) # Eigenvalu, Difference Proportion Cumulative 1 4.55957925 3.31012188 0.3507 0.3507 2 1.24945737 0.16807293 0.0961 0.4468 3 1.08138444 0.21186118 0.0832 0.5300 4 0.86952326 0.07848341 0.0669 0.5969 5 0.79103985 0.056281 74 0.0608 0.6578 6 0.73475810 0.06271344 0.0565 0.7143 7 0.67204467 0.06908765 0.0517 0.7660 8 0.60295702 0.02230188 0.0464 0.8124 9 0.58065514 0.04686511 0.0447 0.8570 10 0.58065514 0.03383025 0.0411 0.8981 11 0.49995978 0.05859742 0.0385 0.9365 12 0.44136236 0.05787363 0.0340 0.9705 13 0.38348873 0.0295 1.0000 Rotation The equamax rotation was chosen as the best rotation of the original (unrotated) PCA output. This orthogonal rotation preserves the independence of the factors while maximizing the sum of the variances of the factor loadings (Abdi, 2003). This rotation gave the best results because there were very few overlaps between factors on the observations and there was a good spread between 0 and +1- 1 across all 5 factors. As well, 12 out of 13 case studies grouped at the +/-10.51 level (see Table 3.5). Given that the cumulative variance explained by the 5 factors was only 65% of the total variance in the data, it is not surprising that some organizational observations would not be significant at this level. When the +/-10.451 level was used, all case studies were significantly correlated with one or more factors. 71 Table 3.5 Factor loadings for each run of the model with increasing nfactor. Marker cases are in bold. Complex cases are in italics Faotorl Factor 2 Factor 3 Factor 4 Factor 5 nfactor 2 FC 1 (0.58) FC 3 (0.59) FC 2 (0.65) AE 1 (0.49) FC 4 (0.69) AE 2 (0.51) FC 5 (0.70) AE 3 (0.50) FC 6 (0.68) AE5 (0.74) FC 7 (0.72) AE6 (0.58) AE 1 (0.47) AE 2 (0.46) AE 4 (0.46) nfactor 3 FC 1 (0.53) FC 3 (0.63) AE2 (0.52) FC 2 (0.62) AE 1 (0.43) AE 3 (0.85) FC 4 (0.69) AE 5 (0.72) AE 4 (0.64) FC 5 (0.67) AE 6 (0.71) FC 6 (0.64) FC 7 (0.71) nfactor 4 FC 2 (0.62) FC 3 (0.52) AE2 (0.51) FC 1 (0.75) FC 4 (0.69) AE 5 (0.72) AE 3 (0.82) FC 3 (0.62) FC 5 (0.57) AE 6 (0.70) AE 4 (0.67) FC 7 (0.53) FC 6 (0.62) FC 7 (0.51) AE 1 (0.46) nfactor 5 FC 2 (0.64) FC 1 (0.73) AE 1 (0.67) AE 2 (0.47) FC 3 (0.86) FC 4 (0.46) FC 4 (0.49) AE5 (0.79) AE 3 (0.87) AE 6 (0.46) FC 5 (0.56) FC 7 (0.79) AE6 (0.51) AE 4 (0.58) FC 6 (0.70) AE 4 (0.56) The final communality estimates, which measure the variance accounted for by each case (Tabachnick and Fidell, 2001), were all between 0.5—0.8 and summed to a total communal variance of 8.55 out of a possible variance of 13 (each case has a total variance of 1, leaving approximately 4.45 in unique variance not explained by the model) (see Table 3.6). The lowest communality estimate corresponded to the case that correlated the weakest on any factor (AE 3). AE 3 was also a consistently complex case (complex on each of the 4 runs from nfactor 2-5). The equamax rotation also gave the most even spread of variance across each factor (see Table 3.7). Factor 1 still accounted for the most variance (2.03), but the other factors were close enough to the variance on 72 Factor 1 so that Factor 1 did not have exceedingly more explanatory power in the model (see Table 3.7). Table 3.6 Final Communality estimates for each case study Observation Estimate FC 1 0.73231 452 FC 2 0.6281 6253 FC 3 0.77939809 FC4 0.61634222 FC 5 0.6241 4814 FC6 0.61262249 FC 7 0.66896257 AE 1 0.65486541 AE 2 0.52086323 AE3 0.78519687 AE4 0.66678373 AE5 0.72315895 AE 6 0.5381 6541 Total common variance 8.550984 Unique variance 4.449016 Table 3.7 Variance explained by each factor Factor Variance explained 1 2.0334302 2 1.7965646 3 1.7303530 4 1.5253371 5 1.4652993 Patterns in the factor groupings Table 3.4 shows the cases and their loadings for each factor over each run from nfactor 2—5. In the first run (nfactor=2), the cases roughly split between forest company and Aboriginal entity sub-samples. The FC group also contained weak correlations for 73 Aboriginal Entities that have forestry components. The AE group included the only case of Ecosystem-based management (EBM) (FC 3). The EBM group correlated with either the forest company groups or the aboriginal entity groups, depending on how many factors were allowed in the run. In the nfactor=5 run, the EBM case became unique (with a weak correlation with AE 6). As the nfactor increased, stable groups formed for the AE sub-sample. AE 2, AE 3, and AE 4 formed a group while AE 5 and AE 6 (and later, AE 1) formed a second group. These stable groups had consistent marker cases, even as the nfactor increased. The marker for the AE 234 group was AE 3. The marker for the AE 156 group was AE 5. The FC group defined in the nfactor=2 run stayed consistent in the nfactor=3 run. In the nfactor=4 run, the FCs broke into two groups, one dominated by FC 1 and FC 7 and the other comprised of the remaining FC cases (minus FC 3, the EBM case). The markers on the FC groups were less consistent than those of the AE groups (factor 1- FC 6; factor 2- FC 7). Factor labels Figure 3.1 illustrates the nfactor groupings for all runs and provides labels for each factor. After examining the similarities between cases in the factor, it was apparent that tenure and geographic location, as well as type of organization (forest company, Aboriginal entity or integrated forest operation) were important in distinguishing the groups. The major difference between the stable AE factors (factors 3 and 4) that formed was related to geography; with the north-south axis playing a much more important role than east 74 west or a comparison of interior versus coastal AEs. As such, these factors were labeled ‘Northern Aboriginal Entities’, or NAE, and ‘Southern Aboriginal Entities’, or SAE. Figure 3.1 Factor groupings over the nfactor runs, with labels for each factor orest companies (weaker correlation Aboriginal for Aboriginal entities & entities with well- EBM-based developed integrated 1c0:t 2 forest mgmt I The FCs in factors 1 and 2 were delineated by tenure and certification. The difference was between the forest companies in factor 1, which have tree farm licenses (TFLs) and/or Forest Stewardship Certification (FSC), and those in factor 2, which have Timber Supply Area (TSA) tenures. Importantly, the only Aboriginal entity in the case studies to have achieved FSC certification was also correlated with factor 1. Factor 1 was thus labeled TFL/FSC and factor 2 was labeled TSA. Factor 5 was labeled EBM, after the most significant case in that factor, FC 3. AE6 was had a fair positive correlation (based FCs with longer-term management planning 4 FCs with shorter-term management planning 2 orthern AEs & EBM-based integrated forest mgmt Southern Interior and coastal Aboriginal entities 3 I 5 3 75 on interpretation in Table 3.2) with Factor 5 as well, which confounded the labeling slightly, as AE 6 and FC 3 correlated throughout the factor analysis runs. However, based on its high degree of similarity with the other AE cases in factor 3, AE 6 was left in with the NAE group instead of being put with the EBM case. Analysis of post-hoc testing and factor scores Criterion 1 — Conservation ofBiological Diversity The TFL/FSC and TSA groups were statistically different on the first two elements of Criterion 1 (see Appendix B, Elements 1.1 and 1.2). When examining the scores generated for these groups by the model, there were clusters of factor scores that were high for the TFL/FSC group for fine-filter indicators of species diversity, stand-level biodiversity, and coarse-woody debris, while factor scores for invasive species and noxious weeds were low. There were also distinctive low factor scores for indicators of genetic diversity for the TFL/FSC group. In contrast, the TSA group had clusters of factor scores that were low for fine-filter indicators of species diversity and stand-level biodiversity, while indicators for invasive species and noxious weeds scored highly. In addition, the TSA group had high scores for the same genetic diversity indicators that had a distinctive low score for TFL/FSC. TSA also had a distinctive low score regarding the indicator “Status of sensitive ecosystems with reduced ranges”. However, on Element 1.3 (Genetic diversity), the ANOVA did not pick up any significant differences between the groups using the raw data and there were no significant comparisons in the post-hoc test (see Appendix B, Element 1.3). 76 Based on the ANOVA results, the SAE, NAE, and EBM groups were not significantly different from each other on any of the elements in Criterion 1 (see Appendix B, Elements 1.1, 1.2, & 1.3). However, the factor scores generated by the model did show some interesting differences that are corroborated by interview comments. There were high factor scores for the NAE group on Criterion 1 related to indicators that monitor cumulative effects and permanent conversion to non-forest or intensive forestry-related land uses. There were also high scores for species diversity indicators that were focused on selected rare and endangered species and critical species, but not for indicators that used the term “species at risk”. For SAE, there were unusually high scores for very few indicators in Criteria 1 but all of these indicators related to connectivity, including riparian connectivity and connectivity between protected areas. For the EBM group, the factor scores were unusually high for many of the indicators and only unusually low for the fine-filter species diversity indicators, where the post-hoc tests showed that it was statistically the same as both the TSA and SAE groups. Criterion 2 — Ecosystem condition andproductivity In Elements 2.1 & 2.2 (Sustainability ofharvest of timber and non-timber forest products), TFL/FSC group had the highest mean while the TSA group had the lowest. The two were significantly different in comparison, while the EBM, SAE, and NAE groups were not different from each other (see Appendix B, Elements 2.1 & 2.2). From the factor scores, it seems that the major differences between the TFL/FSC and the TSA groups were related to tenure. For example, indicators for mean annual increment (MAI) — used in forest inventorying — had high factor scores for the TFL/FSC factor and low scores for the TSA factor. The TFL/FSC group also had a distinctively low score for the 77 indicator related to annual harvest of non-timber forest products (NTFPs) and low scores for both the TFL/FSC and TSA groups for indicators of wild salmon and fish populations. In contrast, the NAE and SAE factors both had high factor scores for wild salmon and fish populations. In Elements 2.3 & 2.4 (Natural and human-induced disturbances), the NAE and SAE groups were statistically different in comparison (see Appendix B, Elements 2.3 & 2.4). The factor scores showed that the SAE factor had low factor scores for indicators of natural and human-induced disturbances and a distinctive low score for indicators of regeneration and change in the composition and structure of ecosystems. Meanwhile, the NAE group’s factor scores on the same indicators were predominantly high, with a distinctive score on ‘conditions of residual forest’. This happened to be the only distinctive high factor score for the NAE group out often distinctive scores. The TFL/FSC and TSA factors are also significantly different in comparison (see Appendix B, Elements 2.3 & 2.4). The TSA group had high scores for indicators of human-induced disturbances (related to harvest systems and clear-cut sizes) and indicators of human actions that modify natural disturbance (e.g. condition of the residual forest). They also had high scores for indicators of forest health mitigation including a distinctive high score for “percent harvest in high beetle [Mountain Pine Beetle, Dendroctonusponderosaej risk stands”. The TFL/FSC group had low scores for human actions that modify natural disturbance but high scores for indicators regarding landslides and fire as human-induced disturbance. In Element 2.5 (Forest regeneration), the TFL/FSC and TSA groups were not significantly different and both had high mean scores (see Appendix B, Element 2.5). Here, again, the TSA group had high factor scores, 78 including four distinctive scores for indicators regarding success, compliance, and species composition of regeneration. Criterion 3 — Soil and Water The ANOVA results reflected an overall lack of use of the indicators in Criterion 3 (see Appendix B, Elements 3.1, 3.2 and 3.3). All of the means for all the groups on all three elements are negative except the TFL/FSC group on Element 3.2 (Impact of harvesting on riparian areas). The TFL/FSC group was also unique on both Element 3.2 and 3.3 (Water). The order of the groups from lowest to highest mean was the same on each of the elements in Criterion 3: the EBM group was consistently on the negative end, while the TFL/FSC group was on the other extreme. These two had significantly different means throughout the criterion. The factor scores for the EBM group across the criterion were low, with one distinctive low score in Element 3.2 (indicator: sedimentation of fish habitat). Interview comments showed that the respondent felt the soil and water indicators were too fine-filter for the size of the area under EBM and the complexity of the management structure; although in the future there would be efforts to undertake fine- filter monitoring. On the other hand, the TFL/FSC group had scores that were high on the criterion and suggest a fine-filter approach to monitoring soil and water, especially for 3.2. Although this group also had a pattern of distinctive high scores in Criterion 3 on the cheaper indicators and those which are already legislative requirements for forestry in B.C., suggesting that cost is a limiting factor on monitoring. The TSA and SAE groups were not significantly different on any elements and also not significantly different from NAE on Elements 3.1 (Soil) and 3.3 (Water) (see Appendix B, Elements 3.1 & 3.3). However, similar responses here did not indicate similar 79 perspectives on stewardship. For the TSA group, scores were high for only those indicators related to rate of compliance, including distinctive high scores for compliance with soil and riparian standards, and low for almost all fine-filter indicators for soil. On the riparian indicators, those indicators related to disruption of riparian habitat during harvest were high, as would be expected if a forest company is meeting their legislative requirements. For the NAE and SAE groups, there were various reasons for their responses in Criterion 3. The few scores that were high for either group were related to soil erosion, landslides and protective functions, which may be indicative of an emphasis on water quality (for fish, for drinking water) as a priority for monitoring. Distinctive low scores were noted for NAE on indicators of channel form and windthrow in riparian areas; both of which use language that assume monitoring for compliance with legislative requirements. Low scores for these indicators may be because none of the NAE cases are conducting commercial logging operations. Criterion 4— Role offorests in global ecological cycles The TSA and TFL/FSC groups were not significantly different for Criterion 4, but the TFL/FSC factor was significantly different from the other three groups (see Appendix B, Criterion 4). The EBM factor again had the lowest mean. None of the groups had a mean that was above zero, indicating that the raw scores reflect low use and high costs for the indicators. The factor scores for all of the groups reflected the predominance of negative responses. EBM, NAE and SAE all had factor scores below zero, some unusually low, especially on those indicators which address BC forest sector carbon emissions. TFL/FSC and TSA had few positive factor scores, mostly related to 80 information that they would already collect, such as removals through fire and harvesting (a distinctive high score for TFL/FSC) and fuel consumption. Criterion 5— Economic and social benefits The TFL/FSC group consistently had positive mean scores in the elements of Criterion 5 while the EBM group always had the lowest mean (see Appendix B, Elements 5.1, 5.2 & 5.3). TFL/FSC and SAE were not significantly different in Elements 5.1 (Economic benefits) or 5.2 (Distribution of benefits) and were not similar to each other or to any of the other indicators on Element 5.3 (Sustainability of benefits). The EBM and NAE groups had means that are consistently negative, while the TSA group was positive only on Element 5.2. The NAE group showed a distinctive low score in Element 5.2 on indicators “distribution of financial benefits from the timber products industry” and “values of contracts issued by demographic class”. In Element 5.3, NAE was also distinctly low for “return on capital employed” and had low scores for indicators related to productivity, market share, sales to Asia, and delivered wood costs. NAE also had a distinctive low score for “water consumption” on Element 5.3. The TSA group had a distinctive high score for the indicator related to mapping cultural values but on the other indicators in the element they had predominantly negative scores. Criteria 6— Society ‘s responsibility In Element 6.1 (Provision for duly established Aboriginal and treaty rights), the SAE factor had the highest mean (see Appendix B, Element 6.1). It is interesting to note that the NAE and SAE factors were significantly different on this element, although neither had factor score patterns that highlighted potential differences. The NAE group had a 81 lower mean and, although it was positive, it reflects higher cost estimates for monitoring than the SAE group. The TFL/FSC group was not significantly different from SAE on this element. They also did not have revealing factor scores, but their cost estimates were much closer to the SAE group. The EBM and TSA groups had the lowest means and were not significantly different from NAE. In Element 6.2 (Aboriginal traditional land use and forest-based ecological knowledge), NAE and SAE were not significantly different (see Appendix B, Element 6.2). All of the indicators related to meeting legal obligations with respect to aboriginal and treaty rights had high scores for the SAE group. SAE also had a distinctive high score for “absence of unsolved disputes on legal, tenure, and use rights”. The EBM group was unrelated to any other groups in Element 6.2 and had a much lower mean, although the factor scores did not suggest any unusually low scores to account for this. The TSA group had distinctive high scores for indicators in Elements 6.1 and 6.2 that were related to monitoring referrals and providing education and training. These indicators were the only ones in a cluster of related indicators that didn’t have negative values for TSA, suggesting that referrals and training opportunities are the predominant means by which cases in the TSA group monitor their work with Aboriginal entities in their forest management units. Element 6.3 (Forest community well-being and resilience) had a very distinct pattern where all of the means on the groups were negative except SAE, which was positive (see Fig.3, Element 6.3). The SAE group was not related to any other factors. This pattern is similar on the Social Capital Element; where the SAE group is significantly different 82 from the other groups and is the only one with a mean positive score (see Appendix B, Social Capital Element). Factor scores for the SAE group supported the ANOVA results — SAE was the only group where the factor scores generated by the model were almost all positive (30 on 35 indicators) and had distinctive high factor scores for indicators of well being, health, employment, and gender equity. The NAE group had fewer positive factor scores than the SAE, but had some clusters of high scores related to indicators of employment, entrepreneurship, and education. The TSA group had a negative mean score on Element 6.3 (see Appendix B, Element 6.3). TSA cases were interested in monitoring their interaction with forest-based communities through indicators such as “annual harvest compared to local log consumption that is provided”; where they had a distinctive high score. However, the TSA group also showed clusters of low factor scores for indicators related to social capital and resilience such as ‘personal identity with community (sense of place)’, ‘social capital infrastructure’, ‘Percent ofpeople achieving minimum of Gr.12’, and ‘mortality rate’. The EBM group showed a similar pattern — they had a negative mean score (see Appendix B, Element 6.3) and predominantly low scores across the element despite having a distinctive high score for “distribution of expenditures locally”. The TSA, TFL/FSC and SAE groups were related on Element 6.4 (Fair and effective decision-making) and had high positive means in the post-hoc tests (see Appendix B, Element 6.4). The bulk of the indicators in this element addressed public (non Aboriginal) participation; which is an important component of commercial forestry SFM Plans and SFM certification schemes. All of the cases in the TSA group had Canadian Standards Association (CSA) certification, which includes public participation 83 requirements. TSA!FSC and TSA had high positive factor scores on this element. The TSA group was especially distinguished here, having 8 indicators related to public participation that had distinctive high scores. The EBM and NAE groups were related and had means that were negative. Although they are related, they had the opposite factor scores on an indicator related to SFM governance and compliance — EBM was distinctly high, while NAE was distinctly low. They were also significantly different from the SAE, TSA, TFL/FSC groups. The NAE and SAE groups were significantly different and again, the factor scores generated by the model for the SAE group were predominantly positive, while the NAE group had many more negative scores. In Element 6.5 (Informed decision-making), the EBM, NAE and TSA groups were all related and had negative means while the TFL/FSC and SAE groups were related and had the same positive means (see Appendix B, Element 6.5). Although the two groups were significantly different, the TSA and SAE groups both had a distinctive high score for “Percent forest management commitments completed on time resulting from consultations regarding non-timber features and interests by licensee”. This indicator could have a public or Aboriginal focus, or be a combination of the two. Comments from the interviews reveal that the indicator was construed differently by each group. The SAE group was monitoring either their own commitments within their community, their certification or legislative requirements, or the commitments that forest companies made to them. The TSA cases, on the other hand, were focused on monitoring their commitments with the public and/or Aboriginal groups. 84 Discussion Organizational stewardship perspectives (1) Forest stewardship focused on pure duties over areas where the company has exclusive access and control The TFL/FSC group is readily defined by its emphasis on ecological indicators, attention to fine-filter indicators and cost-effectiveness approach to indicator selection. The TFL licensees and the FSC-certified licensees both have long-term exclusive rights to a clearly defined and mapped forest area and this characteristic is very distinctive for this group. Crown land within TFLs is regarded as the most secure form of private rights over Crown forest in the province (Zhang and Pearse, 1997). Timber companies consistently press for greater security in their tenure in order to facilitate long-term planning and investments (Zhang and Pearse, 1997). Thus, fine-filter monitoring, especially regarding indicators of ecological diversity, species diversity and natural disturbance, is suggestive of more exclusivity in the tenure arrangement because detailed long-term monitoring would not be feasible if there was inadequate control by the licensee over the land base. There is also an emphasis in this group on cost-effective monitoring, especially around genetic diversity, soil, water, and carbon indicators — where monitoring is perceived as being more expensive and intensive. In these areas, especially genetic diversity, there are fewer prescriptive government regulations, so the decisions regarding whether or not to monitor these themes and how to go about it is left up to the licensees. Predominantly, the result is that the cheapest and easiest indicators are selected. 85 The TFL/FSC group strongly rejects monitoring of social and economic indicators that go beyond the activities of the organizations or their staff. This should be interpreted as a perspective that social and economic indicators that are beyond the scope of the company are the responsibility of the government and not a rejection of the value of monitoring social and economic aspects of SFM. This perspective is similar to what Davies and Hodge (2007) refer to as ‘pure’ and ‘mitigated’ duties. Pure duties are associated with elements of the environment where there is a baseline level of protection and a set of regulations controlling their use (Davies and Hodge 2007). Arguably, pure duties also have an implicit or explicit social agreement around the responsibility to conserve. The monitoring of ecological indicators in SFM, especially biological diversity, constitutes a pure duty; although there is evidence here in that there is not full agreement around monitoring other ecological indicators related to genetic diversity, soil, water, and carbon. Mitigated duties are associated with elements where use is not explicitly governed through regulation; there is no baseline level of protection and no social agreement over who, if anyone, should practice conservation. Mitigated duties depend on economic circumstances and it is up to the individual to decide on a course of action (Davies and Hodge 2007). In the context of this research, mitigated duties include all of the social and economic indicators. Using this approach, it becomes clear that the TFL/FSC group perceives their stewardship as confined primarily to those pure duties related to ecological monitoring. Even then cost is still a major driver, as suggested by the treatment of genetic diversity, soil, water, and carbon indicators suggests. Comments from the cases in the TFL/FSC group support the comparison of pure versus mitigated 86 duties. Responses such as “not the company’s responsibility” and “government should be responsible” were common in the socio-economic indicators, especially in Criterion 6. (2) Forest stewardship focused on compliance and performance over areas where the company does not have exclusive access and control The TSA factor is characterized by volume-based tenures, emphasis on compliance monitoring, reliance on referrals as a means of working with Aboriginal groups, and strong support for communication and public participation in forest management planning. This group had the highest number of distinctive factors (21) and the most positive factors (20). Quantitative indicators related to compliance with legal requirements or internally defined targets (for feedback to management) dominate the positive distinctive indicators. This type of compliance monitoring is always done and it costs little (if anything) to extend the reporting to SFM plans. The emphasis on performance targets fosters adaptive management but is generally self-referential — the monitoring does not necessarily provide information that can be used in forest inventories or provide a picture of the state of the forest. Instead, most of the information collected reflects goals set within the organization that may not be meaningful outside that context. In addition, the indicators and targets selected rely heavily on the assumption that standards and best management practices create conditions that reflect sustainable forestry. Unfortunately, this assumption leaves the compliance approach open to weak legislation and policy, both governmental and internal. The lack of exclusive rights to a defined forest area influences what the TSA group defines as pure duties. Mainly, they are not obliged to maintain a forest inventory and 87 they do not have the incentive to monitor long-term changes to a landscape that they do not have exclusive control over. Thus, what constitutes pure duties is still ecological in nature, but not as extensive as with the TFL/FSC group. The mitigated duties of the TSA group are social and economic in nature, but there is definitely a decision to focus on public participation as the key mitigated duty as well as a source of pride for the companies in their SFM plans. Public participation and communication are hallmarks of the TSA group and a number of distinct indicators were clustered around these themes. However, these indicators are either simple quantitative metrics that are not useful beyond comparisons to internally defined numerical targets aimed at increasing the number of participants or check marks based on the presence or absence of a plan that addresses the topic of the indicator. Lauber and Knuth (1997) argue that fair and effective decision-making, which is the overarching element related to public participation, goes beyond simply providing individuals with an opportunity to attend a process. Merely increasing participation in a decision-making process does not always increase satisfaction or the perception of fairness (Leung and Li, 1990) and indicators crafted to measure public participation in such a way provide no information beyond the performance of a licensee to satisfy certification standards. Again, this reflects the emphasis on compliance and performance monitoring — the data collected cannot convey the success of public participation against anything but the internal definition of success set by the organization through their targets. Overall, although compliance and performance indicators are internally very useful, it is difficult to ascertain what effect compliance monitoring has in addressing sustainability. 88 Finally, there is a strong emphasis in the TSA group on referrals as a means of working with Aboriginal groups. These indicators are mostly count indicators, such as number of referrals where comments from the Aboriginal entity were integrated into forest management plans. Relying on the referrals process as a meaningful indication of Aboriginal involvement is fraught with problems, as the referrals process is not widely accepted by Aboriginal entities as meaningful consultation (Karjala and Dewhurst, 2003) and suggests that a feature of the TSA group is a dependence on indicators that are too simplistic when trying to characterize relationships with Aboriginal entities. Other indicators that were defining of the TSA groups’ approach to working with Aboriginal entities include education/training opportunities, log provisions to Aboriginal communities, mapping of cultural values, and public participation opportunities. The TSA stewardship perspective on Aboriginal entities seems to be to treat them as a stakeholder. Although public participation is a strongly represented area for TSA cases, evidence suggests that treating Aboriginal entities as stakeholders on an equal footing with other forest users underestimates their importance in forest management and could undermine attempts at sustainable forest management (Stevenson and Webb, 2003). (3) Forest stewardship that has a specific cultural context and focuses on extrospective SFM monitoring Negative distinctions dominate the NAE group’s factor scores. Post-hoc tests show that they are almost never related to the SAE group and have many unique characteristics. First, they often reject indicators that are not framed in an Aboriginal context, especially those that are related to compliance with legal requirements and/or take a narrow definition of SFM. They also focus their monitoring on what other resource users are 89 doing in their traditional territories rather than their own impacts. For example, the cumulative impacts of forestry, mining, oil and gas on a landscape could easily dwarf the impacts of small-scale resource or sustenance use by Aboriginal entities. Indeed, the NAE cases do not have commercial forestry components’4and are involved in monitoring through stewardship plans that are aimed at taking stock of the impact of resource extraction activities on their Aboriginal land and use rights. The results of this study suggest that much of the information for ecological monitoring, apart from non- timber forest products and wild salmon and fish stocks, comes primarily from forest inventory data from forest companies and government. Beyond these external sources, they face a lack of capacity to monitor, especially fine-filter indicators, and usually estimate costs for these indicators as very expensive or cost-prohibitive; especially in Criteria 3 and 4 (soil, water, and carbon indicators). The NAE group has a distinctive interest in the relationship between natural disturbances and the condition of the forest under their land management regimes. They have unique definitions for what is considered damage and restoration in their ecosystems. For example, one respondent retold the story ofhow the elders in the community would not accept the government’s confirmation that the river in their traditional territory had been fully restored. The elders insisted that the river needed much more time to recover from pollution and would not touch it despite assurances and scientific testing. This attitude towards damage and restoration leads to a broad rejection on behalf of the NAE cases of indicators that assume a commercial forest operator’s perspective around stewardship of natural and human-induced disturbance. The negative response of the NAE group to ‘4Although there may be members of the Aboriginal group that are involved in forestry, there is no commercial forest company owned and operated by the nation. 90 compliance indicators is also related to their focus on their own context when defining management. Their ecological monitoring emphasizes cumulative effects and infringements on Aboriginal rights and title. They are also more likely to monitor indicators related to conversions of forest land and losses of culturally valuable species as opposed to species at risk. In fact, all AE respondents in the interview process routinely asked for clarification on the kinds of species that could be included for species diversity monitoring, with some cases indicating that they would prefer to monitor species of economic and cultural important over species that were classified as threatened, rare, or endangered by either the Committee on the Status of Endangered Wildlife in Canada (COSEWIC) or the Conservation Data Centre (CDC). The contextual nature of the NAE group’s approach to monitoring is also reflected by their economic indicators. Most of the economic indicators are not framed in an appropriate Aboriginal context and scale. For example, NAE will not monitor economic indicators that are at the provincial scale, such as “B.C.’ s share in all forest products markets”. They also will not monitor indicators where the topic is right but the issue is wrong. For example, the group is distinctly negative on water consumption, but comments from the interviews reflect a desire to monitor water. However, they wish to look at quality and security of water supplies, not quantity. Statements from the interviews include, “the terms of reference for water quality and restoration would have to be culturally defined” and suggested substitute indicators such as, “Number of domestic supply watersheds in First Nation communities that have been designated as community watersheds”. 91 (4) Forest stewardship that has a specific cultural context and is focused on introspective SFM monitoring The SAE group has a vision of forest stewardship that is inclusive of social aspects of SFM and they have the capacity and incentive to monitor these indicators within their specific cultural contexts. SAE organizations are also involved in commercial forestry and predominantly respond to the interview questions introspectively, where they focus on monitoring themselves and their effects on the landscape in their traditional territories. This is the opposite of the NAE factor, which was extrospective, or focused on monitoring other organizations’ activities in their traditional territories. The difference is very dramatic in Element 2.3/2.4, where indicators related to ‘human actions that could modify natural disturbance’ had distinctively negative factor scores for the SAE group and distinctively positive scores for the NAE group. The SAE group was also introspective about many elements of the social criterion, including health, employment, and gender equality, but their perspective was more mixed on public participation indicators, where they construed indicators as either reflecting on their engagement strategies (and may have answered ‘no’ to many of the indicators because of this) or responding extrospectively by reflecting on their participation experiences with forest companies. Both the NAE and SAE groups overwhelmingly rejected detailed ecological indicators that were framed in terms of large commercial tenure holders and would require a lot of money to monitor. Although the two groups responded similarly, the reasons for these responses differed according to monitoring perspective. For example, if the Aboriginal entity is introspective (e.g. the SAE group) on these types of indicators, they will respond 92 negatively and estimate high costs for monitoring. If they are extrospective (e.g. the NAE group), they will respond positively to these indicators if the information has been collected by forest companies and they can access it freely and easily. Unfortunately, it is clear from the forest company responses that expensive indicators that are not legislative requirements are not usually monitored, so this information is not available. Therefore, Aboriginal entities that are monitoring extrospectively instead respond negatively and estimate high costs. Thus, the responses of the NAE and SAE groups are similar. (5) Forest stewardship perspective focused on ecosystem-based management over a large area of land and where monitoring is inclusive of multiple tenure holders The EBM group is very highly distinctive on ecological indicators. They reflect an emphasis on ecological indicators as ‘pure’ elements — a stewardship perspective shared with the TFL/FSC group. The EBM group had 10 distinctive factor scores (out of a total of 15) for indicators relating ecological processes including protected areas, connectivity, outstanding natural features, old growth management, stand-level biodiversity, impacts from forestry on species ranges, forest interior conditions, wildlife tree patches, riparian connectivity, and genetic diversity of regeneration. It is a testament to the ecological focus that there was such a high number of distinctive scores for Criterion 1 (Conservation of biological diversity) and points to an emphasis on management of biological legacies in the “matrix forest” (not just in parks), where matrix refers to “the largest or dominant part of a landscape mosaic. In the context of planning in British Columbia, the matrix is the managed portion of the landscape where forest harvesting or 93 other extractive resource use may occur. It is the area outside of protected areas and reserves” (Coast Information Team, 2004). This also provides evidence that high-level planning is a major driver in ecosystem-based management, especially the influence of special management zones designated through land and resource management plans (LRMPs). Challenges of fine-filter monitoring also differentiate EBM from the TFL/FSC group — the EBM group faces more barriers to this kind of monitoring because of the large land area encompassed by the case and the issues of compiling different kinds of data from multiple stakeholders. Implications for policy Stewardship perspectives provide the ethical background against which the legitimacy of government policy can be judged (Davies and Hodge, 2007). However, the discourse of each perspective has markedly different effects on the acceptability of policy from group to group and area to area (within the themes of the criteria for SFM) (Barry and Proops, 1999). Thus, identifying a legitimate expression of sustainable forest management is not an easy task for policy-makers because there is no one expression that will satisfy all perspectives. To explore how policy affects different forest users in different ways, the following discussion is separated into three parts: (1) Influence of access and control to forest lands, (2) Meaningful incorporation of Aboriginal contexts, and (3) New roles for government. Influence ofaccess and control Access and control regarding forest lands is regulated by the provincial government through the tenure system. In this study, the stewardship perspectives held by 94 commercial forest licensees are likely formed according to the tenure that the licensee holds (although this cause and effect relationship could not be empirically tested, the correlation is evident). For example, area-based tenure has a major effect on stewardship perspectives in the TFL/FSC group because this type of tenure allows for greater control over forest resources, but it comes with greater monitoring responsibilities (e.g. maintaining a forest inventory). On the other hand, the TSA cases lack exclusive control over forest resources under volume-based tenure arrangements and this shifts their focus to cost-effective and self-referential monitoring centered on compliance and performance. Consequently, this creates a reliance on government to get the policy right. In both of these examples the government, as the creator of tenure and forest policy, wields considerable power over how forest companies think about forest stewardship. Davies and Hodge (2007) refer to the government regulation as being an important ‘signaling device’ — where a set of values that influence stewardship is communicated via regulation. An important question in this relationship relates to causality — does the regulation impact stewardship or does stewardship influence regulation? Although it is beyond the scope of this study to answer this empirically, the results of the study suggest that it is likely that regulation and stewardship affect each other. If so, it would imply that there is tight relationship between stewardship perspectives of commercial forest licensees and those of the government. Aboriginal context The emphasis in the AE cases on cultural context for monitoring and management of forest resources is a major driver of stewardship perspectives. Whether monitoring introspectively or extrospectively, the AE cases shared an underlying message that their 95 particular cultural context is paramount to their forest management practices. Their attention to context extends from a predominantly communitarian discourse, their history on the land, and from their unique situation in modem Canadian society. Furthermore, they illuminate the contextual nature of social and economic indicators of SFM and stress the need for new ways of looking at the inclusions of these types of indicators in SFM. They also emphasize alternative modes of management based on holism and traditional ecological knowledge. This emphasis may be, in part, a reaction to the narrow definition of Aboriginal interests in SFM. In Canada, the Canadian Council of Forest Ministers (CCFM) indicators for Aboriginal concerns are limited to issues of consultation, Aboriginal land ownership, and traditional land use studies (CCFM 2003). Although the CCFM appears to address Aboriginal themes, the focus on cultural heritage and consultation forestalls active and meaningful involvement of Aboriginal entities in the forest industry by caricaturing Aboriginal entities as preservationists and ignoring their economic aspirations regarding the resources in their traditional territories. Their cultural context is then merely a footnote in SFM monitoring and not a legitimate alternative forest stewardship perspective. Without mention of the specific issues facing Aboriginal entities in the forest industry or the paradigmatic shifts necessary to address Aboriginal forest stewardship priorities, the CCFM effectively blocks active Aboriginal participation in decision-making around sustainable forestry. Rangan and Lane (2001) use a similar situation in Australia to illustrate this point. By limiting the role of indigenous peoples in the decision-making process around Regional Forestry Agreements (RFA) in Australia to the identification of traditional and spiritual values, the government effectively maintains the dominant bureaucratic discourse. In addition, they protect policy processes from 96 accusations of excluding indigenous groups and prevent inclusion of other concerns related to social well-being expressed by indigenous groups. The government also succeeds in limiting the notion of culture to indigenous peoples, while excluding the values and priorities stemming from the ambient cultures of regional and local communities (Rangan and Land, 2001). This final point is especially important because the ambient cultures of forest-based communities are often deeply related to resource extraction activities and are pitted against the caricature of the Aboriginal entity as preservationist and anti-development. Thus, by hampering the expression of ambient cultures and limiting the expression of Aboriginal cultures to their traditional land uses, the government achieves symbolic recognition of community and Aboriginal contexts, while ensuring actual marginalization of both. This fosters animosity, especially when Aboriginal entities are considered to be stakeholders on equal footing with other forest users when they desire negotiation on a government to government level (Stevenson and Webb, 2003; Karjala and Dewhurst, 2003). Furthermore, communities and Aboriginal entities comprise a significant pooi of new entrants into the forest industry in BC. It is not clear that the stewardship perspectives they bring to forestry will be congruent with the definition and scope of SFM that currently prevails. Decentralized forest governance could become a policy theme that both forest-based communities and Aboriginal entities will push for in order to retain a local, and more meaningful, context for decision-making and forest management. In the face of these issues, governments pursuing criteria and indicator monitoring could benefit from greater flexibility amongst their culturally-defined and scale-sensitive indicators. This does not mean that standardized indicators should be abandoned or 97 excluded, as governments must also find ways to aggregate and report on SFM practices at broader levels. But standardized indicators should not be adopted at the cost of more locally meaningful measures. This would lead to a set of C&I that are not particularly relevant to anyone and that contribute very little to adaptive management and decision- making. New Roles for Government The results for the TFL/FSC group highlighted the concept that socio-economic indicators represent elements that are mitigated by circumstances instead of pure elements that can be regulated similarly across different organizations and regions. This suggests that there are many legitimate ways to consider socio-economic aspects of SFM. Therefore, despite very different ideas about which social and economic indicators shOuld be measured and how to measure them, there is a basic agreement between the groups that these indicators must be measured in the context in which the organizations operate. It is then a question of who is responsible for monitoring social and economic aspects of SFM and to whom they should be reporting. AEs may argue that they should be given the support to do socio-economic monitoring themselves while forest companies may insist that government fill this role. Given this situation, government may either be directly responsible for monitoring or responsible for building the capacity to monitor. Furthermore, when the role of local communities is considered, government may be wise to utilize the experiences of Aboriginal communities’ monitoring programs to craft a province-wide community monitoring capacity-building program. Linking SFM monitoring to community atlases such as the Kamloops—South Thompson Sustainable 98 Community Atlas’5may also help satisf’ the socio-economic data requirements for criteria and indicator monitoring. Forest companies could then liaise directly with community groups in order to complete their socio-economic monitoring for their SFM Plans. In this way, they avoid handling sensitive socio-economic information or taking on responsibilities that they do not feel they ought to have. This creates an ‘honest broker’ role for government — where they are the interlocutor between community monitoring and forest company SFM reporting. In this role, government could also act as a data quality controller and create guidelines for crafting locally applicable indicators that still allow for some aggregation to the provincial level. As well, they could issue guidelines that control compilation of community information across regions for use in high-level planning. Conclusions The organizational stewardship perspectives elicited through this research highlight some important themes in the study of sustainable forest management. The importance of access and control, via tenure rights allocations and Aboriginal land and treaty rights assertions, on stewardship perspectives is evident from the divisions between the groups. The TFL/FSC groups and TSA groups had monitoring practices that closely resembled their legislative requirements and certification standards. These requirements and standards, in turn, are dictated by the kind of tenure held by the licensee; creating a tight relationship between stewardship perspectives of forest companies and those of the government. This was also evident in what forest companies perceived to be their pure duties and what they felt could be mitigated according to vested interests and economic 15 http://www.kam1oopsat1as.com/index.htm1 99 position. Social and economic indicators were revealed to be less important that ecological indicators such as those pertaining to the conservation of biological diversity. This highlights an important point for inclusion of social and economic indicators, and indicators regarding soil, water, and carbon to a lesser degree, that where there is social agreement around the inclusion of a theme in sustainable forest management, there may be a better chance that it will be picked up in monitoring schemes more comprehensively. There is a role for policy in creating better incentives to monitor social and economic indicators by fostering wider acceptance of social and economic themes in SFM. However, the analysis of the case studies also reinforced the idea that context is vital to defining social and economic indicators. The appropriate level of monitoring is most often the local-level, so policy should focus on orchestrating local-level monitoring that is relevant and appropriate to the particular context while still being able to provide information to higher level planning and assessment. As Aboriginal entities and communities pursue forest tenures and alternate ways to conducting forest management, definitions of SFM will become increasingly contextualized. Proper planning for these new entrants in the forest industry includes ensuring that government policy creates proper incentives for active involvement and recognizes the legitimacy of alternative stewardship perspectives. 100 References Abdi, H. 2003. Factor Rotations in Factor Analysis. In Lewis-Beck, M., Bryman, A. and T. Futing (Eds.) Encyclopedia of Social Sciences Research Methods. Thousand Oaks (CA): Sage. Barry, J. and J. Proops. 1999. Seeking sustainability discourses with Q methodology. Ecological Economics 28: 337—345. 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Pearse. 1997. The influence of the form of tenure on reforestation in British Columbia. Forest Ecology and Management 98: 239—250. 103 4 Institutional failures in the implementation of sustainable forest management in British Columbia16 Introduction In this study, it is argued that implementing sustainable forest management (SFM) requires that we accept a systems perspective in forest management planning and decision-making; that what we endeavor to manipulate when we manage forests is a series of nested socio-ecological systems that are complex, dynamic and non-linear in nature. This concept is embodied in the theory of the adaptive cycle, or Panarchy theory (Holling, 2001). Panarchy theory supplies a framework for a hypothesized three-tiered management system for sustainable forestry based on the adaptive cycle. This system does not describe the coupled ecological social and economic systems that sustainable forestry seeks to manage. Instead, it describes the management system itself. This ‘SFM system’ accounts for the components and relationships between systems of broad societal drivers and embedded institutions of SFM, lower-level drivers and embedded institutions of SFM, and organizations implementing SFM at the local-level. It has the flexibility to allow for a multiplicity of contextualized SFM definitions to co-exist without undermining the basic orientation of the SFM system or competing with each other. The SFM system is described generically and provides a basis for the hypothesis that there is a negative feedback relationship between adaptation and failure at the institutional level in forest management where institutional failures stimulate adaptations and in every adaptation there is a potential for new failures. In the SFM system, organizations are ‘6A version of this chapter will be submitted for publication. Gough, A.D. and J.L. Innes. 2009. Institutional failures in the implementation of sustainable forest management in British Columbia. 104 influenced by institutional failures and the effects are manifested as SFM implementation issues at the organization level. This ‘SFM system’ hypothesis is applied to SFM implementation in British Columbia, Canada. The SFM implementation issues addressed are derived from prior analyses of thirteen case studies of SFM monitoring in BC, in which causal mechanisms for SFM monitoring behavior and underlying organizational perspectives on forest stewardship were explored (see Chapters 2 and 3). The results of these analyses are grouped into five categories of issues: narrow use of the SFM concept, tenure issues, uncertainty around Aboriginal land and treaty rights, data management obstacles, and lack of incentive to value ecosystem services. In this paper, these issues are linked to market, government policy, institutional design, and forest paradigm failures and the failures are discussed in terms of their effect on the SFM system. The contextualization of sustainable forest management The definition of sustainable forest management and the characteristics of sustainably managed forests are evolving amidst the ongoing debate over the meaning of SFM amongst forest management practitioners, policy-makers, and scientists (Mendoza and Prabhu, 2003). Wang (2004), in comparing sustainable forest management (SFM) to conventional forest management, states that SFM is interdisciplinary, heterogeneous, less hierarchical, and more socially accountable; taken together, SFM has become the key term used to describe the shift from forestry focused on sustaining yields (timber-centric) to a paradigm of sustainable forestry. This is a good basis on which to make relative claims regarding sustainable forest management, but an absolute definition of the term 105 still remains elusive. SFM has been variously referred to as a “voodoo science” (Prabhu et al., 2001), “a wicked problem, a messy situation” (McCool and Stankey, 2001) and an “eminently malleable concept” (Colfer et a!., 2001). Arguably, scientists who study SFM may see these attributes because the concept itself has no absolute definition. SFM represents a response to the needs and demands expressed by society; therefore, it will change as society progresses and becomes increasingly contextualized (Wang, 2004). Furthermore, if we accept that constituents of SFM are context-based and that human beings’ perceptions are not static, then we must deduce that what it means to utilize forest resources is constantly changing (Wang, 2004). Thus, experiences of SFM are inherently contextualized and characteristics of SFM are constantly in a state of flux. There is a need for a management system that can reflect these characteristics. In the absence of an absolute definition, the greatest threat to the concept of SFM is its own implementation because, in acting on policy, we need to create a tangible action and direction from a relative concept. Thus, the implementation of SFM is vulnerable to the emergence of a confusing and even chaotic situation where no one is quite sure what the processes are going yield. In the shift from conventional forest management, major structural and conceptual changes are necessary but must be accomplished under great uncertainty. Therefore, without a clear idea of where policy is headed under SFM, the transition can be a very risky venture. In order to navigate this situation, one must be very aware of the vulnerabilities inherent to the system that they are perturbing. This requires a conceptualization SFM where we have a crude but essentially realistic understanding of the total system and its essential subsystems (Bossel, 2001). 106 Panarchy theory and the adaptive cycle A good candidate for a conceptual understanding of the total system of natural, human, human-natural, and socio-ecological systems engendered in sustainable forest management is the Panarchy theory put forth by Holling and Gunderson (2002) (see Figure 4.1). Panarchy describes a hierarchical structure in which systems of nature and humans, in various combinations, are interlinked in never-ending adaptive cycles of growth, accumulation, restructuring, and renewal (Holling, 2001). These systems are nested sets that range from miniscule to massive on temporal and spatial scales and each of these nested systems has an adaptive cycle (Holling, 2001). Using this concept to understand how managers perturb the systems that they work in, one can evaluate indicators related to system viability (of each nested system and of their contributions to other systems) and identify points along the adaptive cycle at which the system is capable of accepting positive change and where it is vulnerable to perturbation (Bossel, 2001; Holling, 2001). The overarching goal is then to leverage these points of strength and vulnerability to foster forest resilience and sustainability. There are four phases to the adaptive cycle: r: This is the growth or exploitation phase defined by rapid growth, low connectivity, colonization, innovation, availability of capital (resources), and high resilience. k: This is the conservation or accumulation phase and it is defined by slowing growth, increasing connectivity and rigidity, a ‘locking-up’ of capital (resources), and lower resilience. Regulation and institutionalization of the system is strong. : This is the omega, release, or restructuring phase and it is defined by rapid change and creative destruction. Capital that was tightly bound in the k phase is now 107 released and rigid structures are destroyed or changed. Resilience and connectivity is low and regulation is weak. ci: This is the alpha, renewal or reorganization phase. It is defined by inherently unpredictable circumstances. A repository of capital is available and new and unexpected combinations and associations are possible. There is high resilience and high potential but low connectedness and weak regulation of the system. This is the point where it is determined if the system will repeat a similar cycles, collapse, or a new system will emerge that is distinguished by its novelty (Gunderson et al., 2002). Figure 4.1 The adaptive cycle Adapted from Holling and Gunderson (2002) Gunderson et al. (2002) explain that the adaptive cycle is “essentially a tautology of birth, growth, maturation, death and renewal that must apply to any living system and perhaps 108 to non-living ones as well” (p.317). The authors acknowledge that from this perspective there is almost a universal applicability about this model that may undermine its usefulness in specific explanations. But the key to understanding how this is a good model of adaptive cycles and of systemic change, as it is pointed out, is the importance of the a. phase (renewal! reorganization), where the potential for adaptation, failure, and success are available to all. It is the “fly wheel of the whole system” (Gunderson et al., 2002). It is here that manipulations by wise managers may make the difference between a bifurcation point where we tip into a new (and potentially less agreeable) system, a similar iteration of the same system but with less acceptable circumstances, or a system that builds on the capital released and corrects for the failures that we learned from in the last cycle. This level of awareness requires that we have a good theoretical framework for understanding the adaptive cycle. Three key criteria explained by Holling (2001) regarding a good theoretical framework for understanding complex systems include: simplicity (simple as possible, but not too simple), dynamic and prescriptive monitoring (linking monitoring in the present and past with policies and actions that evaluate different futures), and embracing uncertainty and unpredictability. This final criterion is a key stepping off point for this discussion, as often the true gravitas of this statement is not well understood or reflected in policy actions. It is common knowledge that there is always unpredictability and uncertainty in planning and decision-making but it is less well understood that failure is inevitable in dynamic systems. Failure refers to the vulnerabilities that Holling (2001) highlights to be aware of in management. Failures are like cracks in the concrete foundations that we pour — we are never going to pour a perfect foundation but by becoming aware of the cracks and planning for their expression 109 in the system, we can be managers who are not surprised by our failures; instead, we are ready with actions and processes to address the issues as they arise — keeping in mind the ultimate goal of maintaining the resilience of the system and not just one desirable system state where we aim to achieve maximum benefit. The SFM system: a management system for sustainable forestry The SFM system has a higher and a lower order structure. The higher order structure is the nesting hierarchy of the system in which the lower order is embedded (Kim, 2005). The lower order thus takes its character from the higher order structure but contributes the observed pattern of relationships that decides the form of the system (Kim, 2005). These are akin to what Holling (2001) refers to as the controlling processes of the system (which control and maintain self-organization) and the subsidiary variables that exercise influence in the system but exist at the whim of the controlling processes. Wang (2004) identifies two strata of process in SFM: ‘general level’ or ‘engines of change’ and the operational level. These ‘engines of change’ are defined as (1) local resource production, (2) local historical events and (3) broad societal trends. These higher order structures are not above the flux and contextualization inherent to the system — although they remain drivers, their definition will change from one jurisdiction to another. At the operational level, Wang (2004) identifies management objectives, means of attaining goals, and criteria for assessing outcomes as questions of interest to decision makers. Arguably, these could be construed as the lower order structure or subsidiary variables in the SFM system. 110 Fusing these conceptual frameworks, the SFM system is depicted spatially in Figure 4.2. The general, or higher-order, structure is overarching and contains a small number of controlling processes such as ecological and climatic processes, local! regional! national history, the broad societal trends that affect the system and the means of production that fuel the economy. Institutions are embedded in the higher and lower structures. In this framework, an institution is described as a set of rules and values that constrain and!or foster actions via cognitive, normative and regulative frames that endow social behavior with particular meanings (Kim, 2005). As a component of a system, the institution’s mode of interaction with other components is a function of the structural framework in Figure 4.2 Diagram of a generic SFM system -, CD C) CD which it operates. The institutions that are embedded in the higher-order structure are the national and sub-national government involved, all market processes that influence local production, and Aboriginal government, which are distinguishable from national!sub national governments by their distinct world view, use of other forms of knowledge (such 111 as traditional ecological knowledge) and special legal status (Stevenson and Webb, 2003). The major institutional arrangements that flow from this level are market regulatory systems, government regulatory systems, and Aboriginal governance systems. The lower-order structure or operational level includes the subsidiary variables that are of interest when practicing sustainable forestry. These variables are the objectives, plans and strategies, and criteria for assessment and monitoring. The embedded institutions that are affected by these variables, as well as the system and institutions at higher levels are local government (regional and sub-regional), local community norms and values and local Aboriginal community norms and values (in essence, local Aboriginal governance) (see Figure 4.2). The higher-order and lower-order structures and their concomitant institutions represent a series of nested systems that are decreasing in scale from provincial to local. The next level of structure as the scale decreases is the organization. Organizations, in the SFM system, are groups of individuals who are bounded by a common purpose within a broader institutional construction (Kim, 2005). An organization has a common objective, a distinct pattern of interactions, and features certain conditions of membership. Organizations are affected by all the systems in which they are nested. Compared to the slow variables represented in the general and operational levels, organizations represent fast variables of novelty and innovation and therefore change more quickly than the systems above them. Importantly, organizations have the ability to affect both the operational and general levels when these systems are in their phases (release! reorganization), indicated by the red arrows in Figure 4.2 (Gunderson et al, 2002). Organizations can affect higher order structures, and the institutions embedded in them, when the effects of organizational-level processes are 112 scaled up to the lower and higher-order structures. These effects rapidly push, through positive feedbacks of temporal processes intersecting with broader level connections, higher level systems into the phase in response to the crisis. Gunderson et a!. (2002) refer to these as ‘revolt’ processes (see Figure 4.3). Revolt processes can be counteracted by processes from higher order systems that cascade their effects down, engaging resources from broader levels to solve local or regional issues that are having broad effects. In this way, the system is said to ‘remember’ or come around from the phase started by a local crisis through the a phase (renewal/reorganization) to the beginning of the r phase (growth/exploitation) again (see Figure 4.3). The higher-order systems keep the cascading crisis from affecting the entire SFM system. Thus, the revolt/ remember process is an important relationship between systems that promotes overall resilience. Figure 4.3 Revolt and Remember: influences across levels of nested systems. Organizations revolt and affect larger systems while larger systems control by ‘remembering’ or bring the systems beneath them back to the r phase (exploitation) from the 2 (release) and ct (reorganization) phases (adapted from Evans (2008). Higher order structures and embedded institutions Lower order structures and embedded institutions Organizations 113 Institutional change in the SFM system through the adaptive cycle Kim (2005) uses the concept of nested systems to describe institutional change through the adaptive process. Since change is irreversible in a complex, dynamic system (it will never be restored perfectly to a previous state) and brings about diverse outcomes, institutions cannot always adapt in step with the structure in which they are embedded. Figure 4.4 Characteristic institutions at different phases of the adaptive cycle- different institutional arrangements dominant different phases of the cycle (adapted from Gunderson et al., 2002). * Specialized sects refer to “small groups that are often organized around a charismatic leader with a strong singular ideological purpose. Power emerges only occasionally when their ferocious allegiance to internal rules and purpose intersects with the vulnerability of a mature and rigid bureaucracy” (Douglas, 1978 and Thompson, 1983 in Gunderson et al., 2002, p.120). Thus, different kinds of institutions come to dominate different phases of the adaptive cycle (see Figure 4.4). Components of institutions are often designed to maintain the tuLonaI hierarchies. monopolism. social rigiditY WEAK CoMct, STRONG 114 stability of the institution over time or to change in slow, incremental steps. However, these steps may not be quick enough or in the right direction to keep pace with the structural changes that are driving the system (Kim, 2005), leading to the rise of different institutions that are more conducive to the characteristics of the next phase of the adaptive cycle. Kim (2005) points out that “normative and cognitive constraints imposed by institutional membership prohibit the members from recognizing problems and dysfunctions as such.” Even incremental remedies, if not creating movement in the right direction, have the potential to exacerbate the problem or be nothing more than ‘band aids’ that hide the problem and increase its potential to go viral. Thus, as institutions start to operate under the increasing demands for change, they can be befuddled by their own rigidity (their structure, hierarchy and sophisticated rules and norms) and cannot see where they need to adapt; creating a potential for failures that can result in unpredictable and transformative changes that threaten the institution (Kim, 2005) and the subordinate organizations in the SFM system. If these changes are severe enough, the revolt/remember interaction may be triggered, threatening the entire system. This is a major way that institutional failures can trigger surprising and systemic change. This phenomenon occurs most acutely in the k phase (conservation/accumulation) of the adaptive cycle — where tight organization and hierarchical control by institutional hierarchies, monopolies, and social rigidity precludes alternatives and creates a ‘brittleness’ that makes the system vulnerable to external events and triggers (see Figure 4.4). As said before, these vulnerabilities are institutional failures. If the failure is minor enough, the institution may be able to maintain control by invoking a shifting set of rules and other mechanisms to induce alternatives and generate novelty (Gunderson et al., 115 2002). However, if the failure is large enough or the inertia in the institution great enough, the institution will be unable to adapt in time to prevent crisis. Gunderson et al. (2002) site an incapability to produce novel solutions or create policies to solve chronic resource problems as two reasons why an institution may succumb to failure. Both of these reasons relate to the inertia of institutions. For example, in high moments of inertia, there is an emphasis on maintaining the status quo by using ambiguities and uncertainties around resource issues to reinforce the idea of not acting until extant polices are proven incorrect and outcomes of alternatives have known quantities (Walters, 1997). What is not considered is that these actions subvert the intention of policy as an organic process that changes through time and creates the inevitability of only realizing policy failures once the failure has already occurred. At this point, it is too late to come up with novel solutions and policy interventions. Therefore, identification, diligent awareness and anticipation of potential failures are vital to reducing the impact of failures on the institution and, by extension, key to adaptive management in the SFM system. In the SFM system (see Figure 4.2), failures apply to institutions, but affect organizations in their capacity to implement SFM. Institutional failures can be overcome through adaptation and policy intervention. However, the adaptation of management and policy can itself create new failures; creating a negative feedback cycle where failures and adaptation play into each other. A dynamic and prescriptive monitoring program is needed to condense vital information into a compact set of reliable system signals that can help forest managers identify potential failures, monitor them, and make plans for adaptation or policy intervention. Bossel (2001) states that comprehensive indicator set that can monitor system viability, performance and sustainability is urgently needed. 116 Knowledge in the SFM system profoundly affects how forest management is understood and practiced (Wang, 2004). It stems from the information that managers and decision- makers collect and compile about the natural, human and human-natural systems that they work in. Currently, the mechanisms for the flow of knowledge into decision-making around sustainable forest management are criteria and indicator frameworks. These frameworks, with their focus on measuring the component parts of ecological, economic and social aspects of sustainability, present a reductionist view of the desired outcomes for the use of forests. As well, C&I suites, such as the Montreal Process, are diagnostic in their definitions of the type of management system needed to achieve these outcomes — they do not identify systems and components or provide guidance on structural attributes (McDonald and Lane, 2004). By adapting the C&I framework so that it uses the SFM system as a conceptual basis for decision-making, adaptive management, and indicator selection, a monitoring framework could be created that is designed for monitoring the viability of the management system. This framework would be inclusive of monitoring for failures in both (1) the ecological, social, and ecological systems that are encompassed by sustainable forest management and (2) the policy! management! monitoring system utilized in the process of implementing SFM. Institutional failures in the SFM system SFM implementation failures include market, government policy, institutional design and forest paradigm failures that happen at the institutional level. These evolve from situations where the actions of an institution, usually the state, cause disincentives to pursue SFM through direct forest policies, or through extra-sectoral policies (Richards, 2000). 117 Forest paradigmfailures exist when there is a lack of consensus on the practical meaning of SFM so that, while there may be broad support for the general concept of SFM, there may be much disagreement over how to define and implement it. Nelson et al. (2003) refer to a forest paradigm failure as “reaching social consensus about forestry which is inconsistent with SFM”. Policy intervention is made easier when there is a broad social understanding of forestry that is consistent with SFM. Nelson et al. (2003) cite three pre conditions for this consensus: (1) public and professional awareness of the role of forests and their vulnerability to human exploitation, (2) the emergence of post-materialistic values (e.g. a greater emphasis on self-expression, quality of life and belonging (Inglehart and Appel, 1989)), and (3) a commitment to informed decision-making with sound scientific knowledge (including an effective monitoring program) and effective transmission from principles to actions. Failure to meet these pre-conditions constitutes forest paradigm failure. However, these pre-conditions can be encouraged through government policies that create market incentives for organizations and individuals to internalize SFM in their decision-making processes (Nelson et al, 2003). Arguably, they could also be encouraged by shifting monitoring and education efforts from a focus on information about the valued resource components to knowledge about valued human ecosystem relationships (Stevenson and Webb, 2003), where the public and stakeholders view SFM from a systems perspective. This would be more congruent with the theory of the adaptive cycle. Marketfailures occur when markets are absent, distorted or malfunctioning (e.g. Crook and Clapp, 1998; Haener and Luckert, 1998). Richards (2000), for example, has argued that SFM is rarely a viable financial proposition, whereas forest exploitation and 118 deforestation remain highly profitable activities, such that deforestation is a clear instance of market failure (Cropper and Griffiths, 1994). Under such circumstances, many forest goods and services are undervalued or not valued at al (Bishop, 1998). Sources of market failure include externalities, missing markets, and market imperfections (Acheson, 2000). For example, Bulte et al. (2000) argue that the forest area in the Atlantic zone of Costa Rica is sub-optimally large and that economic welfare would be increased by further forest conversion. Market forces will always encourage the search for short-term profits over long-term objectives because markets pay no attention to environmental and social considerations such as irreversibilities (Richards, 2000). These are the externalities that disrupt the perfect competition identified in neo-classical economics (e.g. Debreu, 1951). This creates uncertainty about the future that, in turn, emphasizes short-term objectives. Richards (2000) contends that this market failure must be resolved through policy intervention that regulates the freedom to pursue short-term gains. Such externalities are inevitable when property rights are insecure or absent. Institutional designfailures develop when institutions (e.g., governments, companies, certification groups) are not properly coordinated, organized, or do not exist to meet the demands of SFM (Richards, 2000). Coordination and organizational issues are usually more apparent at provincial and national scales because these institutional structures are more rigid and are unable to respond to changing demands in a timely fashion. This rigidity makes accommodating new demands on forest management presented by SFM, including the competing interests of stakeholders (including the public) and Aboriginal entities much more difficult. This situation can lead to what Nelson et al. (2003) calls 119 “capacity collapse” — where existing institutions break down in the face of these pressures and cannot respond effectively. This is congruent with the institutional rigidity described in the k phase (conservation/accumulation) of the adaptive cycle. Government policyfailures stem from the government’s lack of commitment, as the ultimate manager of public forests, to practice the SFM paradigm. The classic view of institutional failure by government was expounded by Hardin (1968) as the “tragedy of the commons”. Hardin argued that only government action can avoid the over- exploitation of commonly held natural resources. But commitments in principle are not always readily translated into policy interventions when they could accrue a lot of costly responsibilities for government institutions or compete with other government objectives (Nelson et al., 2003). This has resulted in calls (e.g., Cheung, 1970; Johnson, 1972) for the transfer of property rights from a government agency unable or reluctant to fulfill its responsibilities to private institutions which, it is assumed, would exercise greater care over the development of the resource. However, Berkes et al. (1989) have argued that public ownership, particularly by communities, does not necessarily lead to resource degradation. Government policy failures are affected by institutional arrangements, specifically when government policies and actions are inappropriate (e.g. with a focus on short-term political gains rather than the longer-term impacts of a decision) (Nelson et al., 2003) or create disincentives for industry to properly and/or comprehensively implement SFM (Richards, 2000). The working theory is that the SFM system in British Columbia will always suffer from a combination of market, forest paradigm, institutional design and government policy failures as a result of the relationship between failure and adaptation. Managers are not 120 often aware of these failures and how they relate to the barriers and constraints that organizations in the SFM system face when attempting to implement sustainable forestry. The SFM system described in this paper provides a frameworkfor exploring the relationship between failure and adaptation through five key issues in SFM implementation: narrow use of the SFM concept, tenure issues, uncertainty around Aboriginal land and treaty rights, data management obstacles, and lack of incentive to value ecosystem services. Methods Thirteen organizations implementing sustainable forest practices were interviewed to determine their use of indicators commonly associated with sustainable forest management. The participants were asked to assess the extent to which they were using a number of indicators, based on the criteria and elements adopted by the Canadian Council of Forest Ministers (CCFM, 2003). A full list of the indicators is provided by McHugh et al. (2005) (see Appendix A). Initial analysis of the data revealed SFM monitoring issues related to discourse, capacity, responsibility, relevancy, and scale (see Chapter 2 for details). Further, more in-depth analysis of the data through Q-type factor analysis indentified five types of organizational stewardship perspectives in the case studies and explored the underlying causal relationships that distinguished each perspective (see Chapter 3 for details). The results of these two studies were condensed in this paper into five broad issues related to SFM implementation and monitoring: narrow use of the SFM concept, tenure issues, uncertainty around Aboriginal land and treaty rights, data management obstacles, and lack of incentive to value ecosystem services. These issues were then explored for their relationship to institutional failures and identified as (1) 121 creating barriers to successful SFM implementation by organizations and (2) reducing the ability of organizations to adapt to changes necessary for SFM implementation. Finally, each broad issue was related to the SFM system and policy adaptations and interventions, including the inclusion of new indicators to strengthen criteria and indicator monitoring, were suggested. Results and discussion Narrow use of the SFM concept The results of the exploration of organizational stewardship perspectives in Chapter 3 illustrate that there are monitoring duties that organizations feel are fundamental and those that they feel can be mitigated by economic circumstances (pure versus mitigated duties). Amongst forest companies, on average, those ‘pure’ monitoring duties include ecological indicators related to ecosystem and species diversity, forest ecosystem health, natural disturbances, and some soil and water indicators (primarily regarding compliance). Conversely, those duties that could be mitigated by economic circumstance include the social and economic indicators as well as those indicators related to ecosystem services not directly involved in the provisioning of timber resources. Aboriginal entities in the case studies, on average, have more pure social and economic duties and less pure ecological duties. Although all of the indicator themes need to be included in any comprehensive indicator suite, the reality is that those duties that are seen as being mitigated are given far less weight or are absent from the monitoring framework. Underlying this issue is a forest paradigm failure related to the awareness of the fundamental importance of social, economic, and ecosystem service monitoring in 122 sustainable forestry. The presence of this type of failure in the SFM system suggests that the paradigm of ‘sustained yield’ or timber-centric forestry that has traditionally dominated forest companies is still predominant and that sustainable forest policy has simply been modified to allow for conservation of biodiversity (Stevenson and Webb, 2003) without provision for integration of social and economic indicators that would challenge the dominant view of forestry. For example, in British Columbia, the Forest and Range Practices Act encourages the protection of values such as biodiversity, soils, watersheds, wildlife, fish, and riparian areas, provided that it is done “without unduly reducing the supply of timber from British Columbia’s forests” [BC Reg 14/04 2(1)]. The actual percent reduction allowed for protection of these values is not stated in the legislation, but is commonly assessed to be between 1-6% of the timber supply (Wood and Flahr, 2004). By attaching this rider to values other than timber, the government has curbed the ability to manage forests for multiple values, a key tenet of sustainable forest management. The net effect has been a step backwards in the progression towards the goal of SFM. The situation is not unique, and seems also to have occurred in Maine (Osborne, 1974; Acheson, 2000) and elsewhere. It reflects the mistakes that can occur when government takes control of a resource without adequate counter-balances (Scott, 1998). When institutional rigidity sets in and governments are the central regulatory institution (such is the case in B.C. where most forests are publicly held by the Crown) and inadequate counter-balances exist, the potential for surprise crisis in the SFM system increases. Kim (2005) states that failures in institutions that are central to the system can negatively affect the entire system. Thus, government policy failures in SFM systems with predominantly publicly- 123 owned forests are the most threatening form of institutional failure in SFM implementation. Stevenson and Webb (2003) advocate for enhancing the present reductionist management model based predominantly on information about resource components with management based on the knowledge surrounding resource relationships, or human activities and their relationships with or connections to the natural world (Stevenson and Webb, 2003). Including resource relationships in the management model would also make the monitoring and decision-making processes more harmonized with traditional ecological knowledge (TEK); where TEK holders and traditional land users provide wisdom and knowledge related to valued ecosystem relationships. This includes human-forest interactions over long time horizons (Stevenson and Webb, 2003), such as the relationship between humans and fire in fostering forest resilience. Combining indicators focused on resource components with those that measure resource relationships would benefit the transformation of C&I frameworks from reductionist to systemic monitoring. For example, Bossel (2001) suggests that indicators be included that describe both the performance of a system and its contribution to the performance of other systems. For example, in the SFM system, this may mean having an indicator such as “Number of changes made to operational plans based on public participation” to measure performance and “Impact of public participation exercises on public perception of forests” to measure effects on other systems. 124 Tenure issues The industry case studies gave a clear impression that they do not consider it their responsibility to monitor most social indicators. It is unclear whether or not forest companies should be expected to monitor social indicators (such as mental health or community social infrastructure) or if this is the responsibility of governments and/or communities. The murky boundaries of responsibility delegated to industry, government and communities continues to impede the development of social indicators as valid measures of SFM. In British Columbia, where the crown owns 95% of forest lands, there has been a failure to clearly define those aspects of SFM that companies are responsible for and those aspects that government is responsible for. Different types of tenure come with different levels of responsibility. Broadly speaking, tenures with exclusive rights to a clearly defined operational area come with more stewardship responsibilities than tenures with exclusive rights to a volume of timber from a timber supply area. Although the two types of tenure may offer similar harvesting opportunities, there is difference in the level of access and control. This difference has a large influence over the organizational stewardship perspectives of tenure holders. Management practices are heavily guided by the level of exclusivity granted to the licensee — both in the management practices that the licensee is responsible for and the kind of certification that the licensee will pursue. For example, Principle 2 of the Forest Stewardship Council (FSC) standards for British Columbia states that “clear long-term tenure and use rights to the land” are prerequisites for certification (FSC-BC Regional Standards, 2005). This would seem to preclude any volume-based (Timber Supply Area or TSA) tenure from pursuing FSC certification (unless special agreements are made 125 between the Crown and the TSA tenure holder). The Canadian Standards Association (CSA), on the other hand, does not include this prerequisite. As a result, most of the TSA tenure case studies had CSA certification. Both of these factors heavily influence monitoring and change the relationship between the licensee, the government, and affected Aboriginal entities across the landscape. The complexity of the tenure system and its overarching effect on SFM implementation make it a source of government policy failure that reduces the ability of organizations to adapt to the demands of SFM comprehensively. Once identified, the government policy failures related to tenure could be addressed by making all tenure area-based and enhancing community and co-management tenure relationships. This would clarify responsibilities for stewardship, create incentive to pursue more prescriptive monitoring, and create appropriate partnerships for monitoring social aspects of SFM. Inevitably, there will be flaws in these tenure policies as well, so the added influence of a third party certification system may act as a counterbalancing force against the dominant government institutions to keep the regulatory framework flexible and introduce novelty in face of new failures and challenges. Uncertainty around Aboriginal land and treaty rights Many of the issues complicating the monitoring of Aboriginal rights and title, land use, consultation processes, and economic opportunities are related to treaty negotiations and settlement processes. This situation has implications for constraining data sharing, but its greatest effect is on the ability of Aboriginal groups to comfortably control the future of their traditional lands. Without some assurance that time and expense to build capacity, 126 plan for the future and implement SFM will produce results that will not be jeopardized, the benefits that could be derived from participating in SFM initiatives remain unclear to Aboriginal groups. Thus, incentives to participate SFM initiatives are weak. Lack of participation by Aboriginal groups can result in the inability of forest companies to address Aboriginal issues when implementing SFM. Certainty surrounding Aboriginal land-use and treaty rights is a pillar of the Forest Stewardship Council’s requirements for sustainable forest management certification, as outlined in Principle 2.2 of the FSC-BC Regional Standards (2005). In theory, the lack of certainty surrounding this issue in British Columbia should preclude any FSC certifications in the province (except in the case of the Nisga’a Territories and the area covered by Treaty 8), but in an effort to establish FSC certification, this requirement has been set aside, at least in part. Government policy has failed to create the right incentives for SFM stewardship and certification under different forms of tenure — the setting aside of Aboriginal treaty negotiations in granting FSC certification sends a message that forest operations on traditional lands are valid without free and informed consent from local Aboriginal communities with legal, customary tenure or use rights. Furthermore, Aboriginal groups that are entrenched in the treaty negotiations processes who agree to any activity on lands under dispute risk seeming complacent or in tacit agreement with forest operations that are not effectively under their control or completed in meaningful consultation with Aboriginal values. For example, over much of British Columbia regional planning has been implemented through integrated land management plans. However, the planning process has been boycotted in some cases by Aboriginal communities who believe that participation, even in the form of attending meetings 127 silently, could be construed as acquiescing to the plans, thereby compromising their position in subsequent treaty negotiations (Karjala and Dewhurst, 2003). This situation further exacerbates the sometimes tenuous relationship between forest companies and Aboriginal entities and adds to the climate of uncertainty. There is a forest paradigm failure when Aboriginal entities are viewed as simply another stakeholder in sustainable forest management and their special legal status is ignored. Stevenson and Webb (2003) argue that when this is the case, SFM is simply not achievable. There is also a policy failure in the government’s intransigence to accommodate the rights and interests of Aboriginal entities, which may be one of the greatest barriers to SFM in Canada (Stevenson and Webb, 2003). Removing this barrier is a difficult task. A first step would be the acknowledgement of an Aboriginal criterion within the CCFM, which would lead to broader acceptance of Aboriginal Peoples as more than just another stakeholder in SFM, thereby reducing the potential for forest paradigm failures and increasing the pressure on legislators to acknowledge Aboriginal land and treaty rights. In 1995, the National Aboriginal Forestry Association (NAFA) criticized C&I development in Canada by contending that Aboriginal peoples should have their own criterion, on the basis that their issues were unique and required special representation: “Aboriginal and treaty rights are a criterion for sustainability, not indicators of sustainability” (National Aboriginal Forestry Association, 1995). The CCFM rejected a seventh ‘Aboriginal criterion’ on the premise that it could be prejudicial to future negotiations between Aboriginal groups and the provinces (National Aboriginal Forestry Association, 1995). However, the Canadian National Forest Strategy suggests, under Theme 3, “Rights and participation of aboriginal peoples”, that “a shared 128 understanding of Aboriginal and treaty rights, how they can be accommodated in forest management and how this affects roles and responsibilities, is essential in order to achieve the clarity and relative stability sought by all parties in the forest sector” (National Forest Strategy, 2006). They go on to list several ways in which achieving SFM for First Nations would be unique: it would require more appropriate policy frameworks, more capital and capacity in place in Aboriginal communities, more participation from women and youth, and more inclusion of traditional approaches to land use in the forest management planning and decision-making process (National Forest Strategy, 2006). Data management obstacles Data management issues in the province create barriers to effective SFM implementation. These issues are related to data reliability, compatibility and the lack of data regarding the cumulative effects of forestry, mining, oil and gas extraction on forest ecosystems. All Aboriginal participants in the case studies noted that the reliability of forest cover data can vary considerably from region to region and company to company. This is an acute issue for Aboriginal groups who rely on the provincial government to provide forest cover data for their stewardship, forest planning and traditional-use mapping exercises. Three of the Aboriginal participants expressed deep concern about data reliability in their area, and the concomitant effect on their ability to monitor indicators. Although one Aboriginal participant reported that the forest cover data for their area (augmented by predictive ecosystem mapping (PEM) and terrestrial ecosystem mapping (TEM)) was very good, they also felt that this was an exception, rather than the rule. 129 Forest company participants did not comment on reliability of forest cover data for any indicators. Although the provincial government houses much of the forest data that Aboriginal entities in this study were sourcing and provided it to them free of charge, the collection, organization and reliability of the data varies considerably from region to region. Many participants in the case studies reported indicators for which they had only partial information. Depending on the indicator, the compilation of information at a regional level in British Columbia might be the responsibility of the participant or might require cooperation with other agencies, industries, and organizations that must in turn be willing to share such information. For example, if clear monitoring data on forest regeneration means compiling information from the forest, mining, and oil and gas industries, costs for monitoring could easily become cost prohibitive for organizations. The indicator, “Impact of mining and oil and gas reclamation on meeting forest industry regeneration objectives” was suggested in the by a case study respondent as a meaningful indicator, noting that it was the responsibility of government and industry to cooperate on monitoring the cumulative environmental effects in the region and produce these data. Incompatible data sources further aggravate data compilation issues especially in large regions where multiple licensees and stakeholders undertake data collection activities. Organizational compatibility is therefore an important component of standardizing those indicators which need to be aggregated from regional to provincial levels. Forest companies in the case study interviews reported that acquiring traditional use data from First Nations was not always possible, especially where Aboriginal groups were not participating in the SFM process. Data sharing agreements, such as referrals, were 130 suggested as a potential means of resolving proprietary impasses. However, participants from both groups also cautioned that issues such as appropriation of Traditional Ecological Knowledge (TEK), information sensitive to legal cases and treaty negotiation processes, and the possibility of undermining the forest companies’ competitive edge, could all slow or stop data sharing endeavors. Holling (2001) states that a dynamic and prescriptive monitoring approach (linking monitoring in the present and past with policies and actions that evaluate different futures) is a key aspect of a good theoretical framework for understanding complex systems. However, the forest paradigm failure related to an incomplete adoption of sustainable forest management negatively influences the creation of this type of monitoring. Natural resource agencies that still operate under the assumptions of sustained yield can prevent the adoption of adaptive management by suppressing the signals for management that there are failures in the system (Walters, 1997). In the SFM system, their discursive strategies are to maintain the frame of reference that fits their management practices by influencing policy formation to maintain the status quo. As stated before, a reliable set of signals for management is vital information for the system (Bossel, 2001). Trosper (2005) refers to the paradigm, or cultural system configuration, of ‘sustained yield’ as a necessary contradiction — where the first idea, ‘sustained’, calls forth the second idea ‘yield’, and the two are incongruent but cannot be divided. Proponents of necessary contradictions work (consciously or sub-consciously) to conceal the incongruity in the configuration. They do so because they are dedicated to a pathology of resource management that enforces command and control measures to support the dominant paradigm that they have already invested in heavily (Holling and 131 Meffe, 1986). In doing so, they restrict the signals that reveal the contradiction by suppressing monitoring data and other information. This information is essential to understanding the SFM system and without it the system becomes vulnerable to all types of failures. Lack of incentive to value ecosystem services Analysis in previous studies (see Chapters 2 and 3) revealed that genetic diversity, soil, water, non-timber forest products (NTFPs) and carbon monitoring are under-utilized in C&I monitoring and not explicitly recognized for their ecosystem services. Lack of proper incentives to value forest-based ecosystem services results from market and government policy failures to integrate ecosystem services into SFM practices. As well, social gains from forested landscapes are not always included in economic models because land use values typically have a much broader spatial and temporal distribution than the distribution of costs (Nijnik et al., 2008). Many of the services provided by the forests of British Columbia can be described as public goods, in the sense of Olson (1965). Such goods are frequently associated with market failures because of the lack of incentive for individuals to pay for them, even though these services are essential to the resilience and continued function of forests. Viewed through the lens of the adaptive cycle, factors such as short-term objectives and externalities contribute to the surprise collapse of resources because institutions do not identify the market failures and make regulatory adjustments to minimize externalities. There is potential for more and better market and policy-based incentives for the inclusion of ecosystem services in monitoring for SFM. These market and policy failures 132 create barriers to proper SFM implementation at the organizational level. One way of mitigating this barrier is to create better indicators for measuring the quantities and values of forest-based ecosystem services and increase the quality and type of data available for ecosystem service valuation exercises. Various criteria and indicator suites attempt to fashion indicators to address this and examples are provided below, where appropriate. Although the values associated with the supporting and regulating services of soil and water are not well understood, there are a number of ecological, social, and economic drivers that depend on clean water and healthy soils. The Canadian Council of Forest Ministers (CCFM) core set of criteria and indicators, which frames the definition of SFM for indicator suites at the sub-national, regional, and local-levels in Canada, does not include any indicators which deal directly with the protection of ecosystem services and very few that deal with their value. At the provincial level, there is no mandate to value soil and water services and no indicators related to monitoring either conservation or valuation. Regarding monitoring, one example of an indicator for a regulating service of soil and water could be 4.lb of the Australian State of the Forest Report (2008), “Area of forest land managed primarily for protective functions”. This is a good example of integrating forest management with the protection of ecosystem services, whereas soil and water indicators in the CCFM focus primarily on soil health and water quality for increased future timber yields. Furthermore, as a supporting service, an indicator such as, “Water quality of forest streams”, indicator D.a of the Forestry Program for Oregon (Oregon Department of Forestry (ODF), 2007), compiles complex information into a single measure expressing the degree of impairment of a body of water. This impairment could then be valued according to the cost of restoration. 133 Provisioning services such as game, fish, and non-timber forest products (NTFPs) are important parts of the diets and economies of small towns in British Columbia, especially in First Nations communities. Overall, the market for NTFPs is growing and now contributes about $1 billion to the Canadian economy per year (CCFM, 2006). The CCFM measures NTFPs in terms of their contribution to GDP, which is flawed in that GDP either ignores the ecosystem degradation that can result from NTFP harvest or, worse, treats the depletion of resources as an asset. Again, there is potential for better incentives and the inclusion of ecosystem services in SFM monitoring and implementation would be helpful. From the analysis of interview data, the monitoring of game, fish and NTFPs (such as mushrooms) was not accomplished by forest company organizations, who stated that monitoring these services would be costly, time consuming, and otherwise outside of their mandates. Indicators such as, “Contribution of NTFPs and forest-based services to the gross domestic product” (Indicator 5.1.4 of the CCFM C&I suite) (CCFM, 2006) reflect an intention to include this monitoring in SFM but couch the measurements in terms of gross domestic product (GDP). If GDP is used, it should include adjustments for the environmental and societal costs associated with extraction. This would correct for the decrease in the value of the ecosystem service which results from the impact of present extraction on the service’s ability to provide in the future. For example, Ontario’s State of the Forest Report (2001) uses ‘green GDP’ in their economic assessments. Human-induced climate change from increasing greenhouse gases can be partly addressed by measuring the capacity of BC’s forests to sequester carbon from the atmosphere. The CCFM has four indicators that address carbon stocks and storage 134 capacity but does not include an indicator for valuing carbon sequestration. An obvious method is to use carbon emissions trading prices. However, since most tree-planting in BC is for reforestation and not afforestation, the additionality in these projects is usually negligible and the amount of carbon for trade is very low. This is problematic as the underlying sequestration service is valuable regardless of the tonnes of carbon available for emissions trading. To ameliorate the problems associated with the emissions trading price method, a two-pronged approach could be utilized. First, one could value carbon sequestration in terms of the carbon offsets from intensive (plantation) forestry and emissions credits from the substitution of a non-renewable energy source with a renewable one. One carbon value indicator for this is 5.c of the Montreal Process, “Avoided fossil fuel carbon emissions by using forest biomass for energy” (Montreal Process, 2006). Secondly, since carbon sinks are publicly owned on crown land in BC, one could use the financial benefits to protect and enhance natural carbon sinks in forests and peat lands that do not have market prices because their carbon is not ‘new’ (i.e. additional). Conclusions If we accept that the definition of sustainable forest management is inherently contextualized then it follows that the management system used to govern it should be flexible, adaptive, and provides for the unhindered flow ofmany types of knowledge (e.g. scientific, local, expert and traditional ecological knowledge). It should create a management climate where a multiplicity of contextualized SFM definitions can co-exist without undermining the basic orientation of the SFM system or competing with each other (e.g. thought discursive strategies). In this study, the SFM system is hypothesized 135 to satisfy these requirements. It is based on the theory of Panarchy, where hierarchies of nested systems interact through their adaptive cycles. In the SFM system, criteria and indicators must be adapted to reflect the demands for information and monitoring that can provide managers with a systemic understanding needed to anticipate failures and plan for adaptations. However, there are institutions that use discursive strategies to maintain the old paradigm of sustained yield as the dominant frame of reference for forest management. Consciously or sub-consciously, these institutions create barriers to SFM implementation by suppressing monitoring and sharing of information and knowledge and by governing resource access and control without acknowledging that institutional failures in rights allocations (through issues regarding forest tenures) and rights assertions (through uncertainty around Aboriginal treaty and use rights) are hindering comprehensive SFM monitoring and contributing to narrow, incomplete definitions of the concept. This is especially poignant for social indicators and indicators such as ecosystem services that are related to multiple use forestry. Market failures related to monitoring and valuation of ecosystem services and forest paradigm failures related to social consensus around what are pure versus mitigated duties also contribute to narrow definitions of SFM that lack a complete conceptual understanding. Without a conceptual understanding of the total system, including the relationship between failure and adaptation, the concept of SFM may be no more than a series of discursive strategies struggling to define the new paradigm of forestry in a way that best suits their vested interests. However, if forestry continues to trend away from the traditional concept of a mono functional forest and the focus on timber values is replaced by the multiple land use 136 values of sustainable forest management, the role of good institutions that foster public participation, novelty, and innovation to control tenure, management, and production of public goods from the forest will become increasingly influential (Nijnik Ct al., 2008). Institutions must learn by doing and gain experience in order to be increasingly competent in identifying and ameliorating the inevitable failures and pit falls of the SFM system they wish to operate within and influence. 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Canadian Public Policy 30(4): 38 1—399. 141 5 Conclusions There is no single definition of sustainable forest management that will suit every context, stakeholder, and society: the concept means different things to different people (Stevenson and Webb, 2003; McDonald and Lane, 2004). Even the basic idea that SFM is a mandate for ecologically sound, socially acceptable and economically efficient approach to forestry (Wang and Wilson, 2007) is difficult to visualize in an absolute way when it is embedded in specific societal and cultural contexts. Factors such as ‘socially acceptable’ take on contextualized meanings are laden with expectation. In addition, SFM is an overarching term that captures the unfolding paradigm shift in contemporary forest management (Wang, 2004; McDonald and Lane, 2004). This shift is from a timber-centric paradigm of sustained yield to an ecosystem-centric paradigm of sustainable forest management and from management by exclusion to management by inclusion (Kant, 2003). As such, we must take the time to characterize how SFM is defined at all levels of management, but especially at the local-level, and to try to understand what the requirements for management are in the new paradigm of sustainability. It is postulated in this thesis that SFM requires a new perspective, or rather, the inclusion of a myriad of perspectives that can be understood in an overarching framework that incorporates learning and adaptation. In Chapter 2, the study focused on comparisons between national and local-level indicators and between forest company and Aboriginal entities’ SFM monitoring behaviors. Monitoring practices reflect vested interests and management priorities that make up competing discourses between the national core set and local-level monitoring 142 and between different frames of reference of forest company and Aboriginal entities. The relationship between cost and use was tested and it was interpreted that cost discouraged use. This relationship was found to have explanatory power over indicator selection in the data. However, areas where it was not as likely that cost discouraged use presented monitoring gaps and other situations where there could be different causal mechanisms. The study explored issues of relevancy, scale of indicators, responsibility, discourse and capacity as factors that could influence how organizations monitor SFM. An important observation was that social and economic indicators were not as well used as the ecological indicators. This study has applications in policy research, particularly to the implementation of sustainability monitoring schemes at the local-level. However, the study employed descriptive statistics and simple t-tests to interpret the data. The results were decent descriptions of what may be happening in the data, but they lacked depth and understanding to describe why these mechanisms exist and how they affect SFM implementation. They also lacked richer descriptions of the similarities and differences between the case studies. The assumption that forest companies and Aboriginal entities have different frames of reference was not fully tested in Chapter 2, although the results suggested that this assumption is valid. Finally, the question of inclusion of social and economic indicators warranted more research that could explain why these themes of SFM are not as well used. The analysis in Chapter 3 was designed to address some of these issues. Using Q-type factor analysis to group the case studies, it was hypothesized that forest companies and Aboriginal entities would group separately based on their monitoring behavior. It was 143 also hypothesized that the groups that formed through the analysis represented organizational stewardship perspectives whose underlying causal mechanisms would be similar tothose discussed in Chapter 2. The results confirmed that Aboriginal entities and forest companies do not group together based on their monitoring behavior (with some exceptions) and that causal mechanism for similarities and differences could be interpreted. The interpretation yielded similar factors to those in Chapter 2 but allowed for richer descriptions of why organizations measure some aspects of SFM more comprehensively. The results are applicable to the provincial government’s efforts to foster the inclusion of social and economic indicators in sustainable forest management, particularly in understanding how policy affects SFM monitoring. The study focused on causal mechanisms that explain the similarities and differences in the case studies. However, the methodology did not include an exploration of the causal relationships, or the systematic conjunction of two factors in the Q-type factor analysis (Lin, 1998). This is an area for further research. Exploring causal relationships includes the creation of archetypes of organizational perspectives and entails another round of analysis. In this second round, new cases, randomly selected, would be required in order to test whether the typologies uncovered in the Q-type factor analysis are replicable or not. The result would be a relative distribution of the stewardship perspectives that is generalizable across the province. It would be valuable to policy makers to know what the distribution of organizational perspectives is, including the prevalence of each typology, when crafting policy. In addition, the study dealt entirely with practical/technical matters related to use of indicators and the perception of cost of monitoring. This is useful information when 144 considering organizational typologies delineated by monitoring practices, but further research, perhaps in the form of a Q-method study, is needed to describe fundamental or paradigmatic values more thoroughly and subjectively. Taken together, the conclusions from these analyses suggest that the definition of sustainable forest management is context-dependent, but that an overarching conceptual understanding of SFM is required in order to allow the multiplicity of SFM definitions to co-exist without undermining the basic orientation of SFM or competing with each other. Chapter 4 hypothesizes what such a conceptual understanding of SFM could look like. A management system for SFM is devised using a hierarchy of nested systems that interact through their adaptive cycles. The hierarchy of structure, embedded institutions, and relationships between systems are described. The need for new knowledge and understanding is highlighted in the SFM system, and changes to criteria and indicator monitoring frameworks are discussed to fulfill new monitoring requirements. The relationship between failure and adaptation in the SFM system is theorized where failure motivates institutions to adapt during crisis and new adaptations have potential for new failures. Institutional change in the SFM system, either through crisis (from failure) or adaptation (through novel approaches and policy interventions), is discussed using this concept. Institutional failures are outlined. The analysis in Chapter 3 involves linking the issues summarized from Chapters 2 and 3 to institutional failures in the SFM system, using the forest industry in British Columbia, Canada, as an example. Chapter 4 outlines five issues faced by organizations implementing SFM in British Columbia and links each of these to institutional failures and the SFM system. This analysis takes the current knowledge about sustainable forest management and combines 145 it with ideas and concepts from systems theory, ecology, management operations, political science, complexity and chaos theory. The overarching idea of the adaptive cycle is not a new concept to the study of management, but its application here is a novel approach. The application of the Panarchy theory in management for sustainable forestry has been discussed before (Wang, 2004; Wang and Wilson, 2007) and Chapter 4 borrows from many sources (Holling, 2001; Wang, 2004; Kim, 2005; Bossel, 2001) to bring the concept together. However, there are still many assumptions that need to be tested in the hypothesized SFM system before it would be suitable to apply to a real management scenario. Future research, including a large-scale adaptive management study regarding sustainable management of forest resources would be necessary to make assumptions explicit and test their validity. In conclusion, this thesis brings many new ideas to the field of sustainable forest management. As exploratory work, the patterns and mechanism uncovered here have the potential to inform future research and policy in very meaningful ways. First, the research fosters greater recognition of Aboriginal contexts for sustainable forestry and the role of democratic discourse in selection and monitoring social indicators. It outlines areas of low capacity and other barriers to Aboriginal participation in sustainable forest management. It also provides important research into the causal mechanisms between SFM monitoring by organizations and rights allocations such as forest tenure. Research into tenure reform, incentives to pursue certification and the role of decentralization in sustainable resource management and monitoring could be informed by this work. Policy interventions are suggested throughout the thesis, including new roles for government in 146 brokering data management and sharing across different organizations. Finally, the hypothesized SFM system is a stepping off point for potentially lucrative research into management design for sustainable forestry, including rethinking the way that we assess sustainability through criteria and indicator frameworks. Criteria and indicators are presented in this thesis as “a good idea that has lost its way” (Poore, 2003), suffering from a narrow frame of reference, a linear conceptualization, and hindered by the continued practice of sustained yield forestry. C&I is still a very useful tool for defining the parameters of sustainable forest management and is a good idea worth pursuing. By exposing the issues related to SFM monitoring, revealing causal mechanisms, and re positioning C&I in a new SFM system, the results of this thesis may help re-imagine C&I monitoring in British Columbia and beyond. 147 References Bossel, H. 2001. Assessing viability and sustainability: a systems-based approach for deriving comprehensive indicator sets. Conservation Ecology 5(2): 12. Holling, C.S. 2001. Understanding the Complexity of Economic, Ecological, and Social Systems. Ecosystems 4: 390—405. Kant, 5. 2003. Extending the boundaries of forest economics. Forest Policy and Economics 5: 39—56. Kim, S.C. 2005. Nested institutions and the retardation of the adaptive process. Systems Research and Behavioral Science 22: 483—495. Lin, A.C. 1998. Bridging Positivist and Interpretivist Approaches to Qualitative Methods. Policy Studies Journal 26(1): 162—180. McDonald, G.T. and M.B. Lane. 2004. Converging global indicators for sustainable forest management. Forest Policy and Economics 6: 63—70. Poore, D. 2003. Changing landscapes. London, UK: Earthscan. Stevenson, M.G. and J. Webb. 2003. Just another stakeholder? First Nations and sustainable forest management in Canada’s boreal forest. In: Towards Sustainable Management of the Boreal Forest, eds Burton, P.J., Messier, C., Smith, D.W. and W.L. Adamowicz. Ottawa, ON: NRC Research Press, pp. 65—112. Walter, C.J. 1997. Challenges in adaptive management of riparian and coastal ecosystems. Conservation Ecology 1(2): 1. http://www.consecol.org!voll/iss2/artl/ (accessed March 24, 2009). Wang, S. 2004. One hundred faces of sustainable forest management. Forest Policy and Economics 6: 205—213. Wang S. and B. Wilson. 2007. Pluralism in the economics of sustainable forest management. Forest Policy and Economics 9: 743—750. 148 Appendix A— Complete list of indicators Percentage of tree species by age class, by site quality, by Landscape Unit (LU) by Biogeoclimatic Ecosystem Classification (BEC) zone Connectivity between areas with similar habitat types (tree species, age class, etc.) (Connectivity/fragmentation indices) Habitat supply for indicator species Structural stage distribution by BEC by site series over time (Seral stage distribution by LU by BEC by licensee, Area of old growth by BEC zone) Percentage of non-forest communities area (e.g. wetlands, All Terrain Vehicle tracks, Non productive Brush) by LU by licensee Area of forest community types with significantly reduced area 1.1.2 Area of forest, by type and age class, soil types, and geomorphological feature types in protected areas Area of forest, by type, age class, and BEC zone in protected areas Range of sizes and average size of protected areas for each forest type Percentage of protected areas connected by biological corridors Outstanding or unique biological, zoological, geological, and paleontological features in protected areas Total forest cover in relation to are of forest outside of protected areas Area of forest under management in relation to area of forest in protected areas Element 1 2 Species diversity 1.2.1 The status of forest-associated species at risk Change in the status of threatened and vulnerable species or indicator species Criterion 1 Element 11 Ecological diversity 1.1.1 Area of forest, by type and age class, in each ecozone Number of forest-dependent species classified as vulnerable, threatened, or endangered within the Forest Management Area (FMA) 149 Number, type, and severity of threats to species at risk (cumulative risk index) Percentage of original range occupied by selected rare, threatened, or endangered species Areas of high, medium, and low habitat by species over time 1.2.2 Population levels of selected forest-associated species Population growth rates Changes in the number and percentage of threatened species in relation to total number of forest species Change in relative abundance Rate of change in community species assemblages over time Populations of critical species 1.2.3. Distribution of selected forest-associated species Area without roads by key habitat type over time Number of forest-dependent species that occupy a small portion of their former range Percentage of area of mature forest within LU and biogeoclimatic variants Area of forest permanently converted to non-forest land use Area of old growth by BEC zone Area (ha) Old Growth Management Areas (OGMAs) by site series Interior core area of stands, after sharp edges are buffered Forest interior conditions Areas recruited for future old growth 6. Distribution of selected habitat elements in Timber Harvesting Land Base (THLB) by LU by BEC by licensee over time Percentage of stems in large live tree diameter class Percentage of area retained in Wildlife Tree Patches Dead and dying trees: volume (m3/ha) of dead potential Snags per hectare Area (ha) of interior forest 150 Interior to edge ratio Volume (m3/ha) Coarse Woody Debris by size class by site series Riparian connectivity corridors Percentage of harvested cutblocks more than 5 more than 5 ha that have wildlife trees or tree patches in operational plans 1.2.4 Number of invasive, exotic forest-associated species Location and dispersal of introduced species Degree of disturbance to native species caused by invasive species Percentage of noxious and uncontrolled weeds in grass seed mixture applied Percentage of introduced species in THLB by LU Element 1.3 Genetic diversity 1.3.1 Genetic diversity of reforestation seedlots Size of parent population having produced regeneration Change in the amount of certified seed-producing stands Areas of natural and man-made forests 1.3.2 Number of in situ and ex situ conservation efforts for commercial and endangered tree species within each ecozone Changes in genetic diversity of species undergoing selective pressures Change in the amount of gene protection forests Amount of genetic variation within and between populations of representative forest-dwelling species Changes in population, genetic diversity and structure, and gene flow for selected species Status of sensitive ecosystems with reduced ranges Criterion 2: Ecosystem condition and productivity Element 2.1/2.2 Sustainability of harvest of timber and non-timber forest products 2.1 Total growing stock of both merchantable and non-merchantable tree species on forest land Percentage of and extent of cover types and maturity classes 151 Timber Harvesting Land Base (THLB) area over time (hectares and percentage of gross and productive) Area (ha) of land managed intensively Area classified Non-Commercial Brush Area in problem forest (ha) by type Percentage of reduction due to permanent access feature Mean Annual Increment (MAI) by forest type and age class MA! by Biogeoclimatic Ecosystem Classification (BEC) by licensee Area leading tree species and site quality over time Weighted average basal area by Analysis Unit (AU) by Landscape Unit (LU) over time Weighted average MA! byAU by LU over time Rate of change in total biomass Species distribution of growing stock (THLB and non-contributing areas) by Natural Disturbance Type (NDT) and by period 5.3.2 Annual harvests of non-timber forest products relative to the levels of harvest deemed to be sustainable Carrying capacity of the system for economically important species Loss of the THLB to roads, seismic lines, well sites, and other developments Wild salmon and fish populations Change in numbers of fish by life stage, by species Habitat quality 2.2 Additions and deletions of forest area, by cause Strictly protected forest reserves Forest protected by special management regime Area (ha) removed due to inoperability Area (ha) reclassified Element 2.3/2.4 Natural and human-induced disturbances 152 2.3 Area of forest disturbed by fire, insects, pests, disease, and timber harvest 2.4 Area of forest with impaired function due to drought, ozone, and acid rain Area and type of natural disturbances Area and severity of insect infestations Extent of forest area under noxious weeds, pests, and diseases of epidemic proportions Extreme weather and storm damage Forest fire number, area, frequency, and shape (and history?) Area and type of human-induced disturbance Percentage of harvest, by harvest system and by silvicuitural system method Frequency distribution of clearcut sizes Area of operationally induced windthrow Area of slides originating in harvested areas or roads Game and grazing damage Area of operationally caused fire damage Human actions that could modify natural disturbance Conditions of residual forest Regeneration and change in the composition and structure of ecosystems Ratio of area reforested to area harvested or lost to fire and pests Fire detection and suppression success Percentage of harvest, by harvest system and by silvicultural system by licensee Percentage of total harvest (m3) comprised to salvage Percentage of forested land over time and hectares harvested to mitigate forest health concerns Percentage of harvest occurring in high beetle risk stands 2.5 Proportion of timber harvest area successfully regenerated Area out of compliance with free-to-grow objectives Means of regeneration and desired species composition (Genetically improved stock of ecologically suitable species including non-native) 153 Forest health (Areas (ha) identified with epidemic levels of forest health agents such as bark beetles, budworm, etc.) Silviculture (Areas (ha) treated by treatment type by ilcensee including commercial thinning, fertilization, and pesticides) Landscape patterns Species distribution of growing stock (THLB and non-contributing areas) by NDT Percentage of area declared as mixed-species regeneration Criterion 3 Soil and water Element 3.1 Soil 3.1 Rate of compliance with locally applicable soil disturbance standards Changes in soil fertility, structure, and function in harvested areas Decomposition rates Soil nutrient levels Sensitive soils Terrain class Ecomycorrhizal fungi Number of landslides (no./km2) Inoperable areas Amount (km) of road where protective road measures are carried out to minimize soil erosion Percentage of annual harvest area with soil loss due to establishment of permanent access Area and percentage of rangeland with significant change in extent of bare ground Area and percentage of forest land with significant soil erosion due to forestry Sheet and nh erosion Wind erosion Classic gully erosion Streambank erosion Area and percentage of forest land with significantly diminished soil organic matter and/or changes in other soil chemical properties Change in soil acidification Degree of Cation Exchange Capacity (CEC), i.e. saturation Soil micro/macro fauna Organic matter content (time trend) Permeability rate Shrink-swell potential On-site damage 154 Stability Element 3.2 Impact of harvesting on riparian areas 3.2 Rate of compliance with locally applicable road construction, stream crossing, and riparian zone management standards Change in watershed characteristics over time Peak flow index (includes Equivalent Clearcut Area (ECA) calculation) Road density for entire sub-basin (km/km2) Number of stream crossings (no.1km) Stream Crossing Quality Index (size of the sediment source, soil texture of the source, slope gradient of the source, age of the source. Level of road use) Roads on unstable slopes (km/km2) Disruption of aquatic habitat Coarse woody debris in stream channel Sedimentation of fish habitat Trampling, rubbing, or browsing Failed culvert by culvert type by licensee Condition of canopy in riparian Channel form within treatment area versus channel for upstream Sinuosity Width/depth ratio Gradient Pool/riffle ratio Channel stability Condition of adjacent vegetation (ie. root structure) 51, S2, S3, S4, S5, S6 stream riparian reserve zone widths Windthrow in riparian Condition of soil in riparian areas (exposed soils, compaction, bank shearing, rills, gullies, or evidence of excessive soil movement) Percentage of water bodies in forest areas (e.g. stream km, lake ha) with significant variance of biological diversity from the historic range of variability Distribution and abundance of aquatic fauna Type and level of algal growth Trophic state Fish community assemblage Macroinvertebrate assemblage 155 Element 3.3 Water 3.3 Proportion of watersheds with substantial stand-replacing distribution in the last 20 years Percentage of stream kms in forested catchments in which stream flow and timing has significantly deviated from the historic range of variation Area of stream affected by the timber harvesting and road construction Flow hydrology Stream flows (timing, magnitude, late summer flows, low winter flows, freshet flows, peak flows) Area and percentage of forest land managed primarily for protective functions, e.g. watersheds, flood protection, riparian zones Area and percentage of forest land managed as Riparian Reserve Zone or Riparian Management Zone by appropriate stream, lake or wetland classification Percentage of water bodies in forest areas (e.g. stream km, lake ha) with significant variation form the historic range of variability in pH, dissolved oxygen... Sediment levels Fine organic debris (FOD) Stream temperature Groundwater sources important to instream flows Turbidity Contaminants Forest-related pesticides/herbicides/fungicides in surface water and the percentage that exceeds water quality standards Chemical water quality index Biological Water Quallty Index Significant discharges to water by type of effluent or waste (pulp mills, etc.) Criterion 4 Role of forests in global ecological cycles 4.1.1 Net change in forest ecosystem carbon Tree biomass volumes Non-tree biomass volumes Soil carbon pools Removals (fire and harvesting) 4.1.2 Forest ecosystem carbon storage by forest type and age class Available carbon credits in British Columbia’s forest sector 156 4.1.3 Net change in forest products carbon Report separate subtotals for C02, CH4, N20, HFCs, PFCs, SF6 in tonnes and tonnes of C02 Fuel consumption (per m3 of product) Use and emissions of ozone- depleting substances (in tonnes of chloroflurocarbon-1 I (CFC 11) equivalents) 4.1.4 Forest sector carbon emissions Life cycle analysis of forest products Criterion 5: Economic and social benefits from the timbr Element 5.1 Economic benefits 5.1.1 Contribution of timber products to the GDP 5.1.2 Value of secondary manufacturing of timber products per volume harvested 5.1.3 Production, consumption, import and export of timber products 5.1.4 Contribution of non-timber products to the GDP 5.1.5 Value of unmarketed non-timber forest products 5.1.6 Production, consumption, import, and exports of non-timber forest products 5.1.7 Contribution of forest-based services to the GDP 5.1.8 Value of unmarketed forest-based services Investment as percent of GDP Ratio of stum page charge to wood product prices Sawmill Lumber Recovery Factor, Chip Recovery Factor, and shipment of mini-chips Timber price trend The value of forage harvested from rangeland by livestock Outfitting revenue Wildlife harvested Fish harvested Volume by type of Non-Timber Forest Product (NTFP) (m3, kg) Records of assessment of the productive capacity for existing non-wood products 157 Areas suitable for recreation expansion through inventory Nature and quality of benefits deriving from forest management Contribution of the tourism sector to area and provincial economy Number of recreational user days Element 5.2 Distribution of benefits 5.2.1 Forest area by timber tenure 5.2.2 Distribution of financial benefits from the timber products industry 5.2.3 Revenue generated by Aboriginal businesses in timber products industry Existing tenures (forest tenures and other types of tenure) Opportunities for allocation of community-based tenures Value of contracts issued by demographic class Number of tenures offered to First Nations Level of expenditure on research and development, and education Stumpage paid Element 5.3 Sustainability of benefits 5.3.1 Annual harvest of timber relative to the level deemed to be sustainable 5.3.3 Return on capital employed 5.3.4 Productivity index Buyer identification by product BC’s share in all forest products markets Percentage of increase in wood product sales in Taiwan, China, and Korea Extension and use of new and improved technologies Economic sustainabil ity (delivered wood costs C$/m 3) High-use rates of local wood processing capacity 158 Value of investment, including investment in forest growing, forest health and management, planted forests, wood processing, recreation, and tourism 5.3.5 Employment 5.3.6 Average income in major employment categories Aboriginal employment Total person days and jobs per cubic meter Total payroll and benefits by country/region Employment diversity 5.3.7 Area of forest land managed primarily for the protection of domestic water supply Water consumption Cost-effective delivery of drinking water Expenditures (monetary and in-kind) to restoration activities Watersheds that support water licenses Area and percentage of forest land managed for general recreation and tourism, in relation to the total area of forest land Number and type of facilities available for general recreation and tourism, in relation to population and forest area Road density index within recreation zone Cost of maintenance activities in recreation tourism zone Area and percentage of forest land managed in relation to the total area of forest land to protect the range of cultural, social, and spiritual needs and values Sites and features of cultural significance are identified, mapped, discussed with interested local people and authorities, and efforts made to protect them Criterion 6 Society s responsibility Element 6.1 Provision for duly established Aboriginal and treaty rights 6.1.1 Extent of Aboriginal involvement in the development of policies, legislation, and agreements related to forest management Percentage of forestry joint ventures by demographic class Percentage of forest licenses by demographic class 159 Local representative in provincial or federal government First Nations information sharing and referrals program Research partnerships Level of First Nations satisfaction with involvement in development policies, legislation, and agreements related to forest management Level of First Nations participation and/or consultation Level of incorporation of First Nations traditional roles and systems into forest management plans 6.1.2 Extent to which forest planning and management processes consider and meet legal obligations with respect to duly established Aboriginal and treaty rights Recognizes and respects the legal and customary rights of First Nations over their lands, territories and resources Areas where treaty or Aboriginal rights are being practiced Area available for subsistence purposes Area available for continued cultural use Area available for continued resource use Extent of incorporation of First Nations knowledge in cultural inventories Absence of unsolved disputes on legal, tenure, or use rights lncidences of non-compliance with treaty settlements and Interim Measures Agreements 6.1.3 Area of forest land owned by Aboriginal peoples Documentation of property and use rights Areas of British Columbia with treaties versus area of British Columbia under treaty claims Element 6 2 Aboriginal traditional land use and forest-based ecological knowledge 6.2.1 Number of traditional land use studies and the extent to which they are incorporated in forest management plans All uses of traditional knowledge are documented Area of First Nations traditional use sites by type 160 Number of people affected by off-site impacts, without compensation First Nations information sharing and referrals program (percentage of cutblocks by band where agreement is reached around the management) Number of surveys conducted versus number of surveys requested (Number who requested a Cultural Heritage Resources Survey (CHRS) contract versus the number who have one) 6.2.2. Aboriginal income derived from traditional ecological knowledge (TEK) Number of traditional land users and income earned from traditional land use (Other person- days employment to First Nations and/or joint venture) Number of bands that have requested a CHRS contract vs the number who have one (Number of sites developed for tourism) Number of Aboriginal communities that have a significant forestry component Degree of satisfaction with contract development process (First Nations sector to gather the data) Extent of Aboriginal participation in forest-based economic opportunities Joint ventures with First Nations Creation of co-managed (forests) Contract total paid to First Nations Bands Sponsorship of local events, scholarships, sports teams, etc. Education and training programs Number of training hours Element 6.3 Forest community well-being and resilience 6.3.1 Economic diversity index of forest-based communities Distribution of expenditures locally Size of labour pool Civic participation Index of social structure quality Number of households with forest-based employment (full- or part-time) Annual harvest compared to local log consumption that is provided Migration history, likelihood of future migration Social capital infrastructure Rates of entrepreneurship 161 Personal identification with community, sense of place Population mental health rate Infant mortality rate Mortality rate Life expectancy Cancer Low birth weights 6.3.2 Education attainment levels in forest-based communities Percentage of people achieving minimum Grade 12 Contract total paid to local enterprise 6.3.3 Employment rate in forest-based communities Gender related indices in forestry (Gender-related Development Index in Human Development Reports of the United Nations Development Program Person-days employment to First Nations Composition of senior management and corporate governance bodies Accident rates Standard injury, lost day, and absentee rates and numbers of work-related fatalities 6.3.4 Incidence of low income in forest-based communities Business and property values Average household income Composition of income Poverty rate (percent of income spent on food) Crime rates Access/use of social services Income distribution Element 6 4 Fair and effective decision-making Instances of significant non-compliance with FRPA Area of forest under SFM Plans Public and private funding for research, educational, and extension programs Compatibility with other countries in measuring, monitoring, and reporting on indicators 162 Participation in planning 6.4.1 Proportion of participants who are satisfied with public involvement processes in forest management in Canada Level of public/shareholder comments Number of communications (operational) by interest group, by type of communication, and by licensee Percentage of comments receiving response by type by licensee Response by licensees to public comments/participation Local communities and organizations directly affected by forestry activities given opportunities to participate in forest management planning Publicizes operational activities and objectives Forest management plans are made public with respect for confidentiality Number of participation opportunities by the different types of opportunity (e.g. public meetings) Diversity of participation opportunities (number of participation opportunities by opportunity type) 6.4.2 Rate of compliance with sustainable forest management laws, regulations, and best management practices Incidents of and fines for non-compliance with all applicable international declarations/conventions/treaties and national, sub-national, regional... Number of opportunities for First Nations involvement (The number of working relationships with applicable First Nations) Extent to which mitigative action is undertaken when ecosystems, culturally important areas, and traditional resources are damaged Proactive consultation process for significant activities such as proposed timber harvesting Number of public comments received and percentage of those that result in changes to operational plans Evidence that community feedback was considered in management planning Number of SFM-related research projects initiated and/or completed by type Research dollars spent in Define Forest Area (DFA) by licensee Applied social and natural science research which addresses issues of local and regional significance 163 Element 6.5 Informed decision-making 6.5.1 Coverage, attributes, frequency, and statistical reliability of forest inventories Existence of a repeated forest inventory at the scale of the province 6.5.2 Availability of forest inventory information to the public Extension- extent to which the public is informed of information availability Cost of acquiring data or level of access fee for forest inventory information 6.5.3 Investment in forest research, timber products industry research and development, and education 6.5.4 Number of new or updated forest management guidelines and standards related to ecological issues (should also address socio-economic issues) Percent apprenticeships and training programs by demographic class Average hours of training per year per employee by category of employee Dollars invested in projects Dollars spent on forest education programs Certification implementation committee Extension- Number of documents posted on web site Extension- Number of hits on and downloads from web site Extension- Number of workshops and field trips Number of communities with co-management responsibilities Percentage of forest management commitments completed on time resulting from consultations regarding non-timber features and interests by licensee Percentage of known non-timber features and interests where licensee has consulted and/or incorporated non-timber management/activities Level of funding for Forest Practices Board Number of Registered Professional Foresters (RPF5) “disbarred” Number of complaints to Forest Practices Board (FPB) versus number addressed 164 Social Capital Element Contributing time and money to charities and nonprofit organizations (volunteerism) Membership in organizations Participation in community sustainability initiatives Intra-community trust Holistic forest management Racial discrimination 165 Appendix B — Post-hoc (Tukey) test results The following figures show the relationships between the groups (ANOVA and post-hoc Tukey test), based on the raw data from the cases. Overlapping lines denote groups that do not have significantly different means on the ANOVA test. The number in brackets is the mean combined score (out of possible range of-6 to +6). For interpretation of mean scores, please see Table 3.1. Criterion 1— Biological diversity Element 1.1— Ecological diversity Element 1.2— Sp cies diversity Element 1.3— Genetic diversity SAE (-3.27) NAE (-2.47) TSA (-1.97) TFLIFSC (-1.07) EBM (-0.60) TSA (-1.55) EBM (-0.55) SAE (-0.59) NAE (0.58) TFLIFSC (2.21) Criterion 2— Ecosystem condition and productivity Elements 2.1 & 2.2’— Sustainability of harvest of timber and non-timber forest products TSA (-0.81) EBM (-0.08) SAE (0.75) NAE (2.03) TFLIFSC (2.73) 17 Grouped together because of their small size TSA (0.29) SAE (1.52) EBM (2.29) NAE (3.00) TFL/FSC (3.83) 166 Elements 2.3 & 2.418_ Natural and human induced disturbances EBM (-2.30) SAE (-0.91) TSA (1.61) NAE (2.93) TFLIFSC (3.38) Element 2.5— Forest regeneration EBM (-3.13) SAE (-0.17) NAE (1.83) TSA (3.33) TFL!FSC (4.79) Criterion 3— Soil and water Element 3.1— Soil EBM (-4.46) NAE (-3.28) TSA (-2.60) SAE (-2.17) TFLIFSC (-1.24) Element 3.2— Impact of harvesting on riparian areas EBM (-4.76) NAE (-3.64) TSA (-1.16) SAE (-1.08) TFLIFSC (1.37) Element 3.3— Water EBM (-5.61) TSA(-3.19) NAF (-3.09) SAE (-2.52) TFLIFSC (-0.50) Grouped together because of their small size 167 Criterion 419_ Role of forests in global ecological cycles EBM (-4.92) NAE (-4.26) SAE (-3.49) TSA (-2.72) TFLIFSC (-1.26) Criterion 5— Economic and social benefits Element 5.1— Economic benefits EBM (-4.14) TSA (-2.61) NAE (-2.48) TFL/FSC (-0.98) SAE (-0.03) Element 5.2— Distribution of benefits EBM (-1.78) NAE (-0.04) TSA (1.96) SAE (3.41) TFL!FSC (4.37) Element 5.3— Sustainability of benefits EBM (-1.67) NAE (-1.48) TSA (-1.12) SAE (0.77) TFLIFSC (2.75) 19 There are small number of indicators in this criterion, so it is analyzed as one element 168 Criterion 6— Society’s responsibility Element 6.1— Provision for duly established Aboriginal and treaty rights Element 6.2— Aboriginal traditional land use and forest-based ecological knowledge EBM (-3.94) TSA (-0.26) SAE (0.93) NAE (2.04) TFLIFSC (2.33) TSA (-1.62) TFL!FSC (-0.50) NAE (-0.12) Element 6.4— Fair and effective decision-making EBM (-1.89) NAE (-1.59) SAE (2.68) TSA (2.96) TFLIFSC (3.59) )rmed decision-ma kingL — NAE (-0.48) TSA (-0.24) TFLIFSC (2.10) EBM (-1.19) TSA (0.02) NAE (0.71) TFL/FSC (2.21) SAE (2.48) EBM (-2.18) Element 6.3— Forest community well-being and resilience SAE(2.23) Element 6.5— Inf EBM (-0.86) SAE (2.10) Social Capital element TFL!FSC (-1.94) EBM (-1.83) TSA (1.17) NAE (-0.33) SAE (2.28) 169 Appendix C — Supplementary materials for Chapter 3 Review of methodology for Q-type factor analysis versus Q methodology The process of Q-methodology has some advantages over Q-type factor analysis, which is a simple transpose of the normal R-type factor analysis. Q-methodology is intensive rather than extensive so the small sample sizes allowed in the analysis can provide richer and more reliable information than survey results (although, depending on the sampling technique, the results may not be extrapolated to larger populations) (Swedeen, 2006). A variety of studies utilize Q-methodology or Q-type factor analysis to study issues of sustainability, natural resource use, and organizational typologies (Dasgupta, 2005; Williams and Lambert, 1961; Steelman and Maguire, 1999; Cheng and Mattor, 2006; Banks and Gregg, 1965; Miller and Friesen, 1977; Barry and Proops, 1999; Davies and Hodge, 2007). It has also been applied to areas of conflict within environmental policy and issues that are socially contested (such as forest practices in the Pacific Northwest of the United States) (Swedeen, 2006; Dasgupta, 2005). It is advocated in ecological economics as a tool for designing socially acceptable (and thus more successful) policy solutions to ecological sustainability problems (Swedeen, 2006). These studies have been consulted in the creation of the methodology herein and efforts have been made to emulate the Q-method technique where appropriate. First, Q-methodology focuses on analyzing the variability within a well-selected sample in order to permit an in-depth portrayal of typologies of perspectives in a given situation 170 (Dasgupta, 2005). In this study this is also the focus, although techniques for extracting organizational perspectives are different. Second, the set of indicators from which the indicators for this study were selected is similar in inception and function to a ‘concourse’ or area of discourse in Q-methodology. The indicator list was established through a rigorous survey, review, and analysis of indicators from around the world (Hickey and Innes, 2005) and pre-tested within the Sustainable Forest Management Research Group at the University of British Columbia. This is known in Q-methodology as a ‘ready-made’ Q-sample (Dasgupta, 2005). This process yielded a matrix of 3000 potential indicators for this research, which is far too large for a meaningful Q exercise (McKeown and Thomas, 1988). Second, the distilled set of indicators (corresponding roughly to the final list of statements used in Q-method surveys) were selected systematically through expert working groups and an extensive review of the literature appropriate to British Columbia (Hickey and Innes, 2005; McHugh et al., 2005). Feedback from published articles, fora, and online resources generated from this process led to the inclusion of social capital and Aboriginal rights and title indicators not present in the initial matrix (McHugh et al., 2005; Gough et al., 2008). In Q-methodology, the final set of statements would be ranked on a scale of agreement by survey participants in a forced distribution (Swedeen, 2006), creating individual Q-sorts that could then be examined via factor analysis (Barry and Proops, 1999). This emphasis on personal subjectivity and richer descriptions are hallmarks of Q-methodology and the concept of the self-referential subject- where the subject defines the discourses and categories themselves rather than the researcher- distinguishes it further from traditional survey methods (Swedeen, 2006). However, this step was incongruent with the research because 171 the goal of the research was to analyze organizational perspectives and not individual ones. Instead, structured interviews that garnered data on cost and use of indicators were preferred for comparing organizations. In this step, the methods used in organizational studies to group firms into ideal types (represented by factors) using selected characteristics are adopted in lieu of Q-sorts (Miller and Friesen, 1977). References Banks. A.S. and P.M. Gregg. 1965. Grouping Political Systems: Q-Factor Analysis of A Cross-Polity Survey. The American Behavioral Scientist. Barry, J. and J. Proops. 1999. Seeking sustainability discourses with Q methodology. Ecological Economics 28: 337—345. Cheng, A.S. and K.M. Mattor. 2006. Why Won’t They Come? Stakeholder Perspectives on Collaborative National Forest Planning by Participation Level. Environmental Management 38: 545—561. Dasgupta, P. 2005. Q-Methodology for mapping stakeholder perceptions in participatory forest management. Annex B3 of the Final Technical Report of project R8280. Delhi: Institute of Economic Growth. 44 pp. Davies, B.B. and I.D. Hodge. 2007. Exploring environmental perspectives in lowland agriculture: A Q methodology study in East Anglia, UK. Ecological Economics 61: 323—333. Gough, A.G., Innes, J.L. and S.D. Allen. 2008. Development of Common Indicators of Sustainable Forest Management. Ecological Indicators 8(5): 425—430. Hickey, G.M. and limes, J.L. 2005. Scientific review and gap analysis of sustainable forest management criteria and indicators initiatives. FORREX Series 17. Kamloops, BC: FORREX. McHugh, A., Gough, A. and J.L. limes. 2005. Indicators of Sustainable Forest Management: Review of Potential Indicators. University of British Columbia, Faculty of Forestry, Sustainable Forest Management Lab. Unpublished report. McKeown, B., and D. Thomas. 1988. Q Methodology. Sage University Paper Series on Quantitative Applications in the Social Sciences 07-066. Beverly Hills, CA: Sage. 172 Miller, D. and P.H. Friesen. 1977. Strategy-making in context: ten empirical archetypes. Journal of Management Studies, October: 253—280. Steelman, T.A. and L.A. Maguire. 1999. Understanding Participant Perspectives: Q Methodology in National Forest Management. Journal of Policy Analysis and Management 18(3): 361—388. Swedeen, P. 2005. Post-normal science in practice: A Q study of the potential for sustainable forestry in Washington State, USA. Ecological Economics 57: 190—208. Williams, W.T. and J.M. Lambert. 1961. Multivariate Methods in Plant Ecology: III. Inverse Association-Analysis. The Journal of Ecology 49(3): 7 17—729. 173 Correlation matrix of the case studies FC1 FC2 FC3 FC4 FC5 FC6 FC7 AE1 AE2 AE3 AE4 AE5 AE6 FCI 1.00 0.39 0.31 0.39 0.42 0.29 0.39 0.32 0.36 0.24 0.22 0.24 0.18 FC2 0.39 1.00 0.27 0.44 0.55 0.51 0.29 0.39 0.41 0.15 0.32 0.31 0.31 FC3 0.31 0.27 1.00 0.19 0.37 0.23 0.11 0.24 0.31 0.16 0.15 0.30 0.29 FC4 0.39 0.44 0.19 1.00 0.49 0.38 0.34 0.40 0.34 0.06 0.30 0.26 0.21 FC5 042 055 037 049 100 046 033 041 038 017 032 023 024 FC6 029 051 023 038 046 100 035 034 038 017 030 019 020 FC7 0.39 0.29 0.11 0.34 0.33 0.35 1.00 0.27 0.25 0.09 0.21 0.04 0.11 AEI 0.32 0.39 0.24 0.40 0.41 0.34 0.27 1.00 0.40 0.23 0.33 0.40 0.29 AE2 0.36 0.41 0.31 0.34 0.38 0.38 0.25 0.40 1.00 0.31 0.36 0.30 0.27 AE3 0.24 0.15 0.16 0.06 0.17 0.17 0.09 0.23 0.31 1.00 0.29 0.22 0.09 AE4 0.22 0.32 0.15 0.30 0.32 0.30 0.21 0.33 0.36 0.29 1.00 0.15 0.12 AE5 0.24 0.31 0.30 0.26 0.23 0.19 0.04 0.40 0.30 0.22 0.15 1.00 0.30 AE6 0.18 0.31 0.29 0.21 0.24 0.20 0.11 0.29 0.27 0.09 0.12 0.30 1.00 174 Appendix D — BREB Approval Certificate _____ The University of British Columbia Office of Research Services Behavioural Research Ethics Board Suite 102, 6190 Agronomy Road, Vancouver, B.C. V6TIZ3 CERTIFICATE OF APPROVAL - MINIMAL RISK PRINCIPAL INVESTIGATOR: UBC BREB NUMBER: UBC/Forestry/Forest ResourcesIohn L. Innes Mgt H08-00672 INSTITUTION(S) WHERE RESEARCH WILL BE CARRIED OUT: Institution I Site N/A N/A Other locations where the research will be conducted: In the field, usually in subject’s office. O-lNVESTIGATOR(S): \ngeline D. Gough SPONSORING AGENCIES: British Columbia Ministry of Forests PROJECT TITLE: Improvement of social, economic and ecological indicators of sustainable forest management and :ools for their integration CERTIFICATE EXPIRY DATE: August 15, 2009 uBc] DOCUMENTS INCLUDED IN THIS APPROVAL: DATE APPROVED: August 15, 2008 Document Name Version I Date Protocol: Forest Science Program Proposal for SFM indicators Y071 307 May 1, 2006 Consent Forms: Consent Form for SFM Indicators 2 July 29, 2008 Questionnaire. Questionnaire Cover Letter. Tests: Interview Schedule for SFM Indicators 1 July 29, 2008 Letter of Initial Contact: Letter of Initial Contact for SFM Indicators 2 July 29, 2008 Other: The website www.sfmindicators.org is part of the research project, but will not be used in the interview process. rhe application for ethical review and the document(s) listed above have been reviewed and the procedures were found to be acceptable on ethical grounds for research involving human subjects. 175 Approval is issued on behalf of the Behavioural Research Ethics Board and signed electronically by one of the following: Dr. M. Judith Lynam, Chair Dr. Ken Craig, Chair Dr. Jim Rupert, Associate Chair Dr. Laurie Ford, Associate Chair Dr. Daniel Saihani, Associate Chair Dr. Anita Ho. Associate Chair 176


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