Berkeley Lab study explores measures to increase solar adoption equity

November 9, 2020

Low- and moderate (LMI) income households are less likely than high-income households to adopt rooftop solar photovoltaics in the United States, though policy-makers and others in the industry have increasingly sought out strategies for addressing this inequity.

In a new study published in the journal Nature Energy, Berkeley Lab researchers explore the effects of five policy and business models on “PV adoption income equity”, which the researchers define as the degree to which adopter incomes reflect the incomes of the general population. The researchers find that three of the five studied measures drive more equitable PV adoption: incentives targeted toward LMI households, PV leasing models (including power purchase agreements), and property assessed clean energy (PACE) financing. As illustrated in Figure 1, PV adopters earning less than their county’s median income are more likely to receive LMI incentives, lease PV, and use PACE financing than higher-income adopters. The study finds that LMI incentive recipients, PV lessees, and PACE recipients earn about $47k, $11k, and $9k per year less, respectively, than other PV adopters, on average, when controlling for other factors. The study finds that all three interventions are associated with higher levels of PV adoption among LMI households. The study also explores the impacts of Solarize campaigns and incentives not restricted to LMI customers, but does not find evidence that these measures improve adoption equity.

Figure 1. Share of PV adopters using interventions by income group. The figure depicts the percentage of adopters using LMI incentives (a), leasing (b), and PACE (c) by income bin. Income bins are defined as percentages of county median income. For instance, the bin 25-50% refers to PV adopters that earned between 25% and 50% of their counties’ median income. Note that the y axes are on different scales, given that different shares of adopters use the different interventions.

Figure 1. Share of PV adopters using interventions by income group. The figure depicts the percentage of adopters using LMI incentives (a), leasing (b), and PACE (c) by income bin. Income bins are defined as percentages of county median income. For instance, the bin 25-50% refers to PV adopters that earned between 25% and 50% of their counties’ median income. Note that the y axes are on different scales, given that different shares of adopters use the different interventions.

The researchers find evidence that these results stem from both a deepening and a broadening of solar adoption. First, the interventions increase LMI household adoption rates in areas where other households were already adopting, thus increasing adoption equity in existing markets. Second, the interventions shift PV deployment patterns into under-served low-income areas—particularly in the case of LMI incentives. The authors posit that, by shifting PV deployment into new markets, these interventions could catalyze forces that further increase PV adoption equity. For instance, homeowners in low-income communities that see PV on a neighbor’s rooftop may be more likely to adopt PV themselves, a phenomenon known as “peer effects.”

Overall, the results of the analysis show that policy and business model interventions could increase PV adoption income equity. The study examines only a subset of potential policy interventions and excludes potentially important measures—such as community solar—that do not apply to rooftop PV. Further analysis could demonstrate additional interventions that could increase PV adoption equity with respect to income and other demographic factors such as race and education level.

The study is titled The impact of policies and business models on income equity in rooftop solar adoption. A link to the article published in Nature Energy is available at https://emp.lbl.gov/publications/impact-policies-and-business-models.

The authors will also host a webinar highlighting key findings from this work on December 3, 2020 at 1:00 pm EST. Register for the webinar here: https://register.gotowebinar.com/register/459636952942492941

For questions on the report, feel free to contact Eric O’Shaughnessy at Lawrence Berkeley National Laboratory (720-381-4889, [email protected]) or Galen Barbose ([email protected]).

We appreciate the funding support of the U.S. Department of Energy Solar Energy Technologies Office in making this work possible.

Contacts: