Exploring rooftop solar photovoltaics deployment and energy injustice in the US through a data-driven approach

Abstract

Abstract The dramatic expansion in solar photovoltaics (PV) represents an opportunity to accelerate the energy transition by building clean energy assets into all aspects of our infrastructure. As solar costs fall, a data-intensive process of technology integration becomes critical to make solar, and ultimately both energy storage and energy efficiency, the default options for everyone, not simply the affluent. Deep learning studies that leverage large volumes of data to develop and provide insights into adoption patterns of solar technologies can enable better informed decisions and policies that promote clean energy and an equally widespread transition to build social equity. Getting there, however, necessitates the use of multiple data sources and a variety of statistical tools. In this chapter we summarize the efforts we, the authors, have taken to elucidate disparities in solar PV adoption in the United States. We start by highlighting the importance and progress of solar PV deployment in recent years; we then focus on the evaluation of different tools that can enable a detailed understanding of solar PV deployment at the neighborhood level; lastly, after identifying some the most suitable tools to be used at large scale with sufficient spatial resolution, we proceed to assess the disparity in rooftop PV adoption in the United States.