Household Solar Analysis for Policymakers: Evidence from U.S. Data.

There is a vast literature on household solar-panel uptake but there are mixed results for many explanatory variables such as income, education, age, and race. This creates a major challenge for policymakers, who devise solar-panel policies that relate to variables such as income. This study uses logit, probit, and linear probability models, along with the matching method of entropy balancing. We use household data from the 2019 American Housing Survey. Results using entropy balancing suggest that high housing values and older respondent age are key factors promoting solar-panel uptake. Income has some positive impacts, although detailed analysis tends to show insignificance. Education and race variables have insignificant coefficients when controlling for key variables. This paper could provide a basis for future policy approaches, such as means testing based on asset thresholds rather than income thresholds. [ABSTRACT FROM AUTHOR]