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The Solar Influencer Next Door: Predicting Low Income Solar Referrals and Leads

Journal Article · · Energy Research & Social Science
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Increasing the adoption of solar among low-to-moderate income (LMI) households remains an important policy goal because of its promise to simultaneously reduce energy burden and support the just distribution of benefits of renewable energy. However, scaling LMI solar remains challenging due to affordability and access issues. Most existing LMI adoption has occurred under public-funded programs, highlighting the importance of increasing the cost-effectiveness of these programs at scale. We develop a new household-level data set on LMI solar lead acquisition, referrals, and adoption to understand the processes through which LMI solar uptake has occurred in California. Then, we develop models to predict two sub-mechanisms in the solar adoption process: whether an otherwise qualified lead becomes "lost" i.e. non-responsive to outreach and, for existing clients, whether they refer solar to others. For the program analyzed, participants received their solar system at no cost, which deemphasizes economic drivers of solar adoption and could differ from other program experiences. Both models substantially improved the accuracy of prediction relative to a baseline. Overall, we find that peer effects and solar economics are important to predicting referrals, and household demographic factors in lead loss prediction. Finally, we find that referrals are both the highest quality and largest source of LMI solar leads, providing a promising mechanism to expand LMI programs further.

Research Organization:
National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
DOE Contract Number:
AC36-08GO28308
OSTI ID:
1838021
Report Number(s):
NREL/JA-7A40-79687; MainId:35908; UUID:ae3ed503-06f8-4d14-bb72-9d70d3e410a0; MainAdminID:63497
Journal Information:
Energy Research & Social Science, Vol. 86
Country of Publication:
United States
Language:
English

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Related Subjects

ENERGY PLANNING, POLICY, AND ECONOMY,SOLAR ENERGY
customer acquisition costs
deployment models
energy justice
LMI solar
predictive models
Random Forest
solar leads
solar referrals