Clean Energy Justice: Clean Energy Access and Vulnerable Communities toward Just Energy Transition

Abstract

This dissertation proposes clean energy justice that links energy justice to clean energy access and vulnerable communities in terms of geographic distribution (distributional justice) and community attributes with respect to places, people, and equality (recognition justice). The first study of this dissertation argues that adoption attributes are different by communities and technologies. In particular, I find that rooftop solar adoption is strongly associated with housing variables and communities with lower adoption rates. On the other hand, I find that electric vehicle (EV) charger adoption is additionally and strongly associated with economic variables. Furthermore, communities in Seattle present higher variations in rooftop solar adoption than in EV charger adoption. The second study proposes that energy vulnerability can be characterized by energy resiliency associated with rooftop solar adoption and energy dependency related to energy burden. I find that city-level variations of rooftop solar adoption and energy burden are obvious even after controlling for community attributes. Furthermore, rooftop solar distribution in the Pacific Northwest major cities - Seattle, Bellevue, and Portland, presents significant spatial lag effects while energy burden shows a higher city-level variation. In addition, I identify vulnerable communities in terms of energy resiliency and energy dependency. In the third study, I introduce four energy justice domains in terms of two driving forces - technology development and equitable policies. Based on inequality and inequity associated with distributional and recognition justice, I quantify clean energy access in terms of four indices in three cities. I find that inequality and inequity of rooftop solar distribution and adoption have increased across communities in the cities over time. In conclusion, I discuss implications for future research and advocate for implementing tailored support to local communities based on the identified attributes.