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  • 1. Fried, Harrison Theorizing conditions and incentives that lead actors to develop resilient management strategies in complex environmental governance settings

    Master of Science, The Ohio State University, 2021, Environment and Natural Resources

    Modern environmental problems pose unique management challenges since they are usually interdependent in myriad, complex ways. Climate change is the ultimate example of a problem that forces environmental managers to confront highly interdependent challenges, such as invasive species, rising temperatures, and habitat loss. A growing area of interest in understanding complex, polycentric governance systems has been to analyze the engagement of stakeholders in policy issues and the participation of stakeholders in policy forums. In this thesis, I focus on climate change adaptation governance in Ohio, USA as a model study system to evaluate conditions and incentives that drive actors to manage for interdependent issues or to participate in forums in ways that are collectively beneficial. To answer questions about actor management strategies in complex, polycentric governance arrangements, I analyze climate change governance as a three-mode network of interrelations among actors, forums, and policy issues related to climate change adaption in Ohio. I draw upon the Ecology of Games Theory (EGT) and an Institutional Fitness framework to formulate hypotheses that uncover the conditions, incentive structures, and attributes that prompt actors to engage with issues and participate in forums in ways that promote adaptive capacity. Chapter 2 tests whether actors are likely to simultaneously manage environmental policy issues that are highly interdependent (such as nutrient management and water quality, which are connected through the process of eutrophication). Then, Chapter 3 tests for how different types of theorized closure structures (i.e., unique situations of actor benefits) – lead actors to participate in decision-making forums. To tackle the questions at hand, both chapters utilize Exponential Random Graph Models (ERGMs), which is a tool for inferential network analysis. The results indicate that actors are more likely to manage for pairs of interdependent polic (open full item for complete abstract)

    Committee: Ramiro Berardo Ph.D. (Advisor); Matthew Hamilton Ph.D. (Advisor); Jeremy Brooks Ph.D. (Committee Member) Subjects: Environmental Management; Environmental Studies; Natural Resource Management