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The impact of policies and business models on income equity in rooftop solar adoption


Low- and moderate-income (LMI) households are less likely to adopt rooftop solar photovoltaics (PVs) than higher-income households in the United States. As the existing literature has shown, this dynamic can decelerate rooftop PV deployment and has potential energy justice implications, in light of the cost-shifting between PV and non-PV households that can occur under typical rate structures and incentive programmes. Here we show that some state policy interventions and business models have expanded PV adoption among LMI households. We find evidence that LMI-specific financial incentives, PV leasing and property-assessed financing have increased the diffusion of PV adoption among LMI households in existing markets and have driven more installations into previously underserved low-income communities. By shifting deployment patterns, we posit that these interventions could catalyse peer effects to increase PV adoption in low-income communities even among households that do not directly benefit from the interventions.

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Fig. 1: Shares of adopters using interventions by income brackets.
Fig. 2: LMI PV adoption rates before and after intervention implementation.
Fig. 3: Average group–time effects on zip-code-level LMI PV penetration rates.
Fig. 4: Predicted and actual number of installs supported by interventions.
Fig. 5: Average group–time effects on LMI PV penetration rates in low-income communities.

Data availability

The solar PV adopter data that support the findings of this study are available from the Lawrence Berkeley National Laboratory (LBNL) TTS dataset, Individual customer street addresses, however, are not included in that public dataset, as such information is considered personally identifiable information and has been collected by LBNL under non-disclosure agreements with individual state agencies and utilities. The modelled income data were obtained under a data licensing agreement with Experian that prohibits LBNL from directly sharing individual records. Summarized versions of the modelled income data are available from the LBNL Solar Demographics Tool, Other versions of these data may be made available upon request from the author R.W. Source data are provided with this paper.

Code availability

Most data cleaning and analysis was performed using R statistical software, including code from the tidyverse, sp, plm, raster, MatchIt and did packages. The adopter income bias regressions were implemented using Stata statistical software. All scripts are available upon request from the authors.


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This work was supported by the US Department of Energy (DOE)’s Office of Energy Efficiency and Renewable Energy (EERE). We especially thank A. Qusaibaty (DOE) for his contributions to this paper. We also thank B. Callaway (University of Mississippi) for gracious support in the development of the group–time models. The views expressed in the article do not necessarily represent the views of the DOE or the US government. The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for US government purposes.

Author information




G.B. and R.W. conceived the study. E.O. developed the analysis and wrote the paper with important contributions from all authors. S.F. and N.D. collected and curated the data and contributed to data analysis.

Corresponding author

Correspondence to Ryan Wiser.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Energy thanks Deborah Sunter and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Tables 1–9, Figs. 1–2 and Notes 1–2.

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Source data

Source Data Fig. 1

Source data for Fig. 1, shares of adopters using interventions by income brackets.

Source Data Fig. 2

Source data for Fig. 2, LMI PV adoption rates before and after intervention implementation.

Source Data Fig. 3

Source data for Fig. 3, average group–time effects on zip-code-level LMI PV penetration rates.

Source Data Fig. 4

Source data for Fig. 4, predicted and actual number of installs supported by interventions.

Source Data Fig. 5

Source data for Fig. 5, average group–time effects on LMI PV penetration rates in low-income communities.

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O’Shaughnessy, E., Barbose, G., Wiser, R. et al. The impact of policies and business models on income equity in rooftop solar adoption. Nat Energy 6, 84–91 (2021).

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