Nationwide insights on solar deployment trends and spatial inequalities revealed by vision transformer models

The transition to renewable energy is essential for achieving sustainable development goals and mitigating the impacts of climate change. Solar energy, in particular, has emerged as a viable and cost-effective option, yet its adoption exhibits significant disparities. While existing research has extensively explored equity issues in energy access, a critical gap remains in understanding the broader distributional disparities in residential rooftop solar adoption at local, state, and national scales. This study addresses this gap by quantifying spatial inequalities in rooftop solar adoption, leveraging a novel dataset created using vision transformers to identify rooftop-installed photovoltaic (PV) systems across the United States through 2022. By employing Lorenz curves, an approach traditionally used to analyze income inequality, the distribution of residential rooftop solar at multiple geographic and temporal scales is assessed to uncover areas with disproportionate adoption levels. This analysis identifies key factors that drive these inequalities, including economic conditions, policy frameworks, and demographic characteristics. The findings reveal pronounced inequalities in solar adoption within and between counties and states, despite a doubling of rooftop PV installations nationwide between 2017 and 2022 from 1.47 to 2.95 million systems. While policies and incentives have helped some disadvantaged communities overcome barriers related to lower household income, they have often fallen short of achieving broader spatial equality. The dataset, DeepSolar-3M, has been released as an open resource to support policymakers, researchers, energy analysts, and others in advancing data-driven energy solutions.