  This paper presents a comprehensive analysis of the social and environmental
determinants of solar photovoltaic (PV) deployment rates in Colorado, USA.
Using 652,795 satellite imagery and computer vision frameworks based on a
convolutional neural network, we estimated the proportion of households with
solar PV systems and the roof areas covered by solar panels. At the census
block group level, 7% of Coloradan households have a rooftop PV system, and
2.5% of roof areas in Colorado are covered by solar panels as of 2021. Our
machine learning models predict solar PV deployment based on 43 natural and
social characteristics of neighborhoods. Using four algorithms (Random Forest,
CATBoost, LightGBM, XGBoost), we find that the share of Democratic party votes,
hail risks, strong wind risks, median home value, and solar PV permitting
timelines are the most important predictors of solar PV count per household. In
addition to the size of the houses, PV-to-roof area ratio is highly dependent
on solar PV permitting timelines, proportion of renters and multifamily
housing, and winter weather risks. We also find racial and ethnic disparities
in rooftop solar deployment. The average marginal effects of median household
income on solar deployment are lower in communities with a greater proportion
of African American and Hispanic residents and are higher in communities with a
greater proportion of White and Asian residents. In the ongoing energy
transition, knowing the key predictors of solar deployment can better inform
business and policy decision making for more efficient and equitable grid
infrastructure investment and distributed energy resource management.
