In this Open Access article, published in Sustainability on November 9, 2018, I work with Abraham Tidwell and Steffan Nelson to unpack the nuances of solar adoption in the State of Georgia from the perspective of local governance structures. By constructing a quantitative dataset that is structured with state-level institutional lock-in at the forefront (county) rather than national modes of organization (zip code), we demonstrated that solar adoption in this state is inextricably bound with social factors. Furthermore, we explore how the Gini coefficient–typically used for studies of poverty and wealth in developing countries–can be used to demonstrate disparities in the type and degree of energy technology adoption.
Abstract: Despite a global push in the development and implementation of widespread alternative energy use, significant disparities exist across given nation-states. These disparities reflect both technical and economic factors, as well as the social, political, and ecological gaps between how communities see energy development and national/global policy goals. Known as the “local-national gap”, many nations struggle with fostering meaningful conversations about the role of alternative energy technologies within communities. Mitigation of this problem first requires understanding the distribution of existing alternative energy technologies at the local level of policymaking. To address the limitation of existing adoption trend analysis at the scale of local governance (e.g., county governments), this paper demonstrates a novel method for contextualizing solar technology adoption by using the State of Georgia in the United States as an exemplar. Leveraging existing work on the Gini Coefficient as a metric for measuring energy inequity, we argue these tools can be applied to analyze where gaps exist in ongoing solar adoption trends. As we demonstrate, communities that adopt solar tend to be concentrated in a few counties, indicating existing conversations are limited to a circumscribed set of social networks. This information and the model we demonstrate can enable focused qualitative analyses of existing solar trends, not only among high-adoption areas but within communities where little to no adoption has occurred.