Unlocking Widespread Solar Adoption: Understanding Preferences of Low- to Moderate-Income Households to Create Scalable, Sustainable Models
The U.S. Energy Department is investing $1.35M in a 3-year collaboration between National Renewable Energy Laboratory, Lawrence Berkley National Laboratory, GRID Alternatives, University of Michigan, and University of Chicago to explore preferences of low- to moderate-income (LMI) households to create scalable, sustainable models for unlocking widespread solar adoption. This project is one of 17 Projects in which the U.S. Energy Department is investing $21.4 Million to speed solar adoption and maximize solar benefits for states.
This project focuses on identifying novel, data-driven, and evidence-based strategies that could dramatically scale up solar adoption rates in low- and moderate-income (LMI) communities. The goal is to develop pathways for reaching parity in solar penetration rates across socioeconomic groups. This project serves a core need for developing objective tools and datasets for policymakers and identifying the barriers that have previously limited deployment. The primary focus of this project is to rate the technical solar potential of buildings in LMI communities across the country, develop predictive models to understand previous LMI deployment, and then work with a national nonprofit solar installation group to determine how communication about solar energy usage occurs within LMI communities.
The project team is determining the technical potential for LMI households to adopt solar themselves and the proportion that would need to be served by other models, such as community solar or aggregated net metering. This will be supplemented with a deeper analysis of three representative regions to determine the range of existing institutional and market barriers. The team will also design and implement a market pilot experiment to examine the role that referrals and existing social networks may play in driving interest in solar, in addition to creating a predictive algorithm for determining the propensity of different LMI communities and individuals to adopt solar. The results of the referral experiments will be used to estimate how such programs could impact customer acquisition costs.
This project will help unlock the roughly 30 to 40% of LMI households nationally that have not been marketed to by solar developers. By developing predictive models and conducting analyses of this population, this work will reduce soft costs and, in tandem, could boost access to solar to more people in the U.S. than ever before.
Sponsor: U.S. Department of Energy SunShot Initiative