The governance analysis has focused on two main themes during the reporting period. First, we have generated a network data set representing the complex governance systems in the San Francisco Bay Area. The data was derived from a comprehensive inventory of ongoing climate adaptation projects, which was mined to identify the organizational partners. The data was assembled into a two-mode network, with organizations connected to specific adaptation projects. We are currently analyzing the resulting network to understand the evolution of new governance institutions for climate adaptation, using co-PI Lubell's "ecology of games" framework as the theoretical basis. Abstracts for this paper have been submitted to several conferences for 2017. We are also planning on using a part of the network data to integrate with the hydrodynamic and transportation analyses of interdependency among local government actors.
Second, we have engaged in an intensive qualitative study of governance in the Bay Area that has included semi-structured interviews of over 40 key stakeholders, as well as focus groups planned in Fall 2016. The main purpose of the focus group was to identify the key governance challenges facing climate adaptation and sea-level rise in the Bay Area, and also to provide the qualitative insights needed to design the broader stakeholder survey expected to be delivered in 2017. The main governance challenges include institutions multi-level cooperation, regional planning, funding, political leadership, integrated permitting, best available science, and community engagement.
The qualitative interviews also revealed an important new direction for the research that provides additional opportunities for integrated analysis. In particular, the SF Bay Area recently passed a local parcel tax initiative called Measure AA, which assess at $12 per parcel tax to pay for wetland restoration and climate adaptation. This is a first-of-its-kind local initiative and provides the opportunity for a spatially explicit analysis of citizen voting preferences. NSF funding has been used to contract with Qualtrics to provide a household sample at the zip-code level, which can be matched with sea-level rise predictions to see what types of people are more likely to respond to sea-level rise risk through their voting decisions.