Coastal communities are increasingly experiencing climate change–induced coastal disasters and chronic flooding and erosion. Decision makers and the public alike are struggling to reconcile the lack of ‘‘fit’’ between a rapidly changing environment and relatively rigid governance structures. In efforts to bridge this environment-governance gap in Tillamook County, Oregon, stakeholders formed a knowledge-to-action network (KTAN). The KTAN examined alternative future coastal policy and climate scenarios through extensive stakeholder engagement and the spatially explicit agent-based modeling framework Envision. The KTAN’s results were further evaluated through a two-step mixed methods approach. First, KTAN-identified metrics were quantitatively assessed and compared under present-day vs. alternative policy scenarios. Second, the feasibility of implementing these policy scenarios was qualitatively evaluated through a review of governmental regulations and semi-structured interviews. The findings show that alternative policy scenarios ranged from significantly beneficial to extremely harmful to coastal buildings and beach accessibility in comparison to present-day policies, and they were relatively feasible to almost impossible to implement. Beneficial policies that lower impacts of flooding and erosion clearly diverge from the existing regulatory environment, which inhibits their implementation. In response, leadership and cross-sector cooperation and coordination can help to overcome mixed interests and motivations, and increase information exchange between and within the public and government organizations. The combination of stakeholder engagement, an alternative futures modeling framework, and the robust quantitative and qualitative evaluation of policy scenarios provides a powerful model for coastal communities hoping to adapt to climate change along any coastline.
Authors: Lipiec, Eva; Ruggiero, Peter; Mills, Alexis; Serafin, Katherine A.; Bolte, John; Corcoran, Patrick; Stevenson, John; Zanocco, Chad; Lach, Denise