My research aims at developing a framework that helps underserved communities in consideration of diversity, inclusion and equity to access the clean energy technologies in the situation of the rapid transition to the new energy system.
Undesirable challenges and events such as natural disasters and climate change have enhanced deploying sustainable and resilient energy infrastructures. It is because reliability of energy supply has proven to be important especially during emergency situations when, for example, medical services are in high demand. Lack of electricity affects more on respiratory patients that mortality and respiratory hospital admissions increased significantly during the blackout (S. Lin et al. 2011). Clean energy technologies (e.g., solar modules and electric vehicle) have been introduced and utilized all over the world. For instance, solar module installations (PV) have increased on residential, commercial buildings and utility-scale farms encouraged by the clean energy policies and incentives such as federal tax credits. This transition to the new energy system could lead to undesirable impacts on some communities as shown in the case of telecommunication (Caperton et al. 2013). Committed leadership to implement a new policy in regard to the transition is required to avoid the uneven distribution of the service.
While numerous studies have been performed on various aspects of the policies designed to support adoption of the new technologies, there is still a dearth of studies aimed at investigating the impact of such policies on the social equity. Two unanswered questions have emerged: (1) were there certain communities inadvertently left out from incentive opportunities? and (2) do those current policies help the social equity?
To answer these questions, the present study will perform a spatial analysis of the distribution of solar panel installed-buildings (residential solar hereafter) and EV charger installed-buildings in terms of housing and socioeconomic characteristics based on census track in Seattle. In particular, this study aims to explore any patterns of residential (single family and multifamily) in terms of PV by examining spatial clustering patterns, associations among variables through several data sources. The examined data entails the socioeconomic and housing characteristics based on the American Community Survey of the census.
Hotspot analysis and geographically weighted regression (GWR) would be employed in this study in order to identify the variables with respect to solar installations and the areas where solar installations are more sensitive to these variables. Factor and cluster analyses would reveal that how housing stability (homeownership), economic status, and inequality are correlated with the solar installation. In addition, time series of solar installations could be analyzed to find spatiotemporal patterns.
On going..