Testing a Multilevel Model to Identify Geographies of Support for Wind Development in the United States

Abstract

To combat climate change, the U.S. electricity sector must transition from the current system, mostly comprised of fossil-based fuels, to a carbon-free system. A transition of this magnitude requires collaboration with local communities and a deep understanding of geographies of support that contextualize conditions conducive to community-supported renewable energy development. A framework proposed by Boudet (2019) presents a multidimensional model of support with four high-level variable categories that influence public responses to new and renewable energy technologies: technology, people, place, and process. Although grounded in previous research, the framework has yet to be tested in tandem and interactions between variables have yet to be explored in the context of utility-scale renewable energy development. This dissertation fills this gap and helps practitioners better understand community interests in wind development by testing a novel multilevel model predicting support for wind energy using Boudet’s technology, people, and place categorization scheme and a large set of publicly-available, national datasets that assess variables at varying geospatial levels.When all categories of Boudet’s framework were modeled in tandem, perceptions about the environmental and economic impacts of wind turbines (i.e., the technology category), including perceived job impacts, climate impacts, and property value impacts, significantly predicted support for wind development. Climate change acceptance also significantly predicted support for wind development, but demographic components of the people category did not. Disadvantaged community status was examined as part of the place category because the transition to carbon-free energy affords the opportunity to address legacy environmental injustices in those communities. However, the place category did not significantly predict support for wind development, with the possible exception of county-level rural designation. In addition to performing the first test of Boudet’s framework in the context of utility-scale wind development, the current work examined geographies of support for wind development in the form of interactions across variable categories, which have not previously been explored in the context of wind development. The belief that turbines create jobs is more strongly related to support for development among those who assert that climate change is not happening than among those who assert that climate change is caused by a combination of human activities and natural patterns. Trends were also uncovered that indicate the potential existence of additional geographies and warrant further investigation. Representative sampling of liberal or progressive individuals and oversampling of individuals from highly rural areas and those with less than high school educational attainment will provide more precise estimates of the potential moderating effect of rural designation on three variables—the belief that turbines create jobs, political views, and educational attainment. Exploration into the impact of the age of historical energy installations on wind support also revealed no significant results but an interesting trend—more recent installations tended to be associated with lower support for wind development, which can be explored further in future research that focuses on data collected in areas with wind installations. This work can inform first steps in the wind energy siting process by highlighting when and why certain geographies tend to be supportive of wind development. By identifying geographies of support, the current work will help practitioners better align development interests with community interests. Although research shows that those supportive of wind development outnumber those in opposition, working with communities to address the concerns of those opposed to development, even if in the minority, is an important component of energy justice. Moreover, predicting where this opposition is less likely to occur will aid in the pursuit of successful, equitable energy transitions.