Conceptualizing and operationalizing human wellbeing for ecosystem assessment and management
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2016-12-01
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© 2016 Elsevier Ltd There is growing interest in assessing the effects of changing environmental conditions and management actions on human wellbeing. A challenge is to translate social science expertise regarding these relationships into terms usable by environmental scientists, policymakers, and managers. Here, we present a comprehensive, structured, and transparent conceptual framework of human wellbeing designed to guide the development of indicators and a complementary social science research agenda for ecosystem-based management. Our framework grew out of an effort to develop social indicators for an integrated ecosystem assessment (IEA) of the California Current large marine ecosystem. Drawing from scholarship in international development, anthropology, geography, and political science, we define human wellbeing as a state of being with others and the environment, which arises when human needs are met, when individuals and communities can act meaningfully to pursue their goals, and when individuals and communities enjoy a satisfactory quality of life. We propose four major social science-based constituents of wellbeing: connections, capabilities, conditions, and cross-cutting domains. The latter includes the domains of equity and justice, security, resilience, and sustainability, which may be assessed through cross-cutting analyses of other constituents. We outline a process for identifying policy-relevant attributes of wellbeing that can guide ecosystem assessments. To operationalize the framework, we provide a detailed table of attributes and a large database of available indicators, which may be used to develop measures suited to a variety of management needs and social goals. Finally, we discuss four guidelines for operationalizing human wellbeing measures in ecosystem assessments, including considerations for context, feasibility, indicators and research, and social difference. Developed for the U.S. west coast, the framework may be adapted for other regions, management needs, and scales with appropriate modifications.
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Breslow, SJ, B Sojka, R Barnea, X Basurto, C Carothers, S Charnley, S Coulthard, N Dolšak, et al. (2016). Conceptualizing and operationalizing human wellbeing for ecosystem assessment and management. Environmental Science and Policy, 66. pp. 250–259. 10.1016/j.envsci.2016.06.023 Retrieved from https://hdl.handle.net/10161/18614.
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Xavier Basurto
I am interested in the fundamental question of how groups (human and non-human) can find ways to self-organize, cooperate, and engage in successful collective action for the benefit of the common good. To do this I strive to understand how the institutions (formal and informal rules and norms) that govern social behavior, interplay with biophysical variables to shape social-ecological systems. What kind of institutions are better able to govern complex-adaptive systems? and how can societies (large and small) develop robust institutions that provide enough flexibility for collective learning and adaptation over the long-term?
My academic and professional training is based on a deep conviction that it is through integrating different disciplinary perspectives and methods that we will be able to find solutions to challenging dilemmas in natural resources management, conservation, and environmental policy. Trained as a marine biologist, I completed a M.S in natural resources studying small-scale fisheries in the Gulf of California, Mexico. Realizing the need to bring social science theories into my work on common-pool resources sustainability, I earned an MPA and a Ph.D. in Management (with a minor in cultural anthropology) from the University of Arizona and under the supervision of Edella Schlager. Following I spent two years working with Elinor Ostrom, 2009 co-winner of the Nobel Prize in Economics, at the Workshop for Political Theory and Policy Analysis of Indiana University. Methodologically, I am familiar with a variety of quantitative and qualitative approaches and formally trained to conduct Qualitative Comparative Analysis (QCA or more recently fsQCA), that allows among other things, systematic comparisons of middle range N sample sizes and address issues of multiple-causality.
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