Do Peer Effects Impact Residential Solar PV Adoption? Evidence from Solarize Programs
Saturday, November 9, 2019
Plaza Building: Lobby Level, Director's Row J (Sheraton Denver Downtown)
*Names in bold indicate Presenter
The literature on the determinants of residential solar energy investment has identified strong policy mechanisms and solar insolation metrics as key drivers on why and where adoption takes place. However, less is known about the role of other latent factors such as peer effects or social spillovers among adopters. In this paper, we assess the impact of peer effects on solar energy adoption by using Solarize programs as stimuli for driving residential adoption in certain geographic areas. These programs are often preceded by community educational workshops and other activities to mobilize interested adopters in an area, with certain households signing contracts and achieving economies of scale through the group purchasing mechanism. We assess the data on adopters of solar energy from these Solarize campaigns, and then supplement this with subsequent solar energy adopters in these same geographies in the years after the initial program intervention. Since these programs offer incentives (e.g., discounted pricing, easily accessible information on tax incentives and financing, and convenient access to qualified local solar installers) to those who participate, but otherwise has no direct impacts on their neighbors, we aim to provide a valuation of the role of peer effects on solar energy adoption. We scrape county websites listing construction permits for data on residential solar installations in the same Solarize campaign areas, up to three years after the initial programs. Through the concept of information transmission, we find neighbors to be more likely to adopt solar energy if their peer was exogenously induced to adopt. The higher comprehension of how this diffusion process expedites adoption has direct implications for solar energy marketing and multi-phase Solarize programs, such as which neighborhoods or demographics to target in the future based on historic adoption patterns.