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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.

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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.


  1. Wolske, K., Stern, P. & Dietz, T. Explaining interest in adopting residential solar photovoltaic systems in the United States: toward an integration of behavioral theories. Energy Res. Soc. Sci. 25, 134–151 (2017).

    Article  Google Scholar 

  2. Borenstein, S. Private net benefits of residential solar PV: the role of electricity tariffs, tax incentives, and rebates. J. Assoc. Environ. Resour. Economists 4, S85–S122 (2017).

    Google Scholar 

  3. Barbose, G., Forrester, S., Darghouth, N. & Hoen, B. Income Trends among U.S. Residential Rooftop Solar Adopters (Lawrence Berkeley National Laboratory, 2020).

  4. Lukanov, B. & Krieger, E. Distributed solar and environmental justice: exploring the demographic and socio-economic trends of residential PV adoption in California. Energy Policy 134, 110935 (2019).

    Article  Google Scholar 

  5. Yu, J., Wang, Z., Majumdar, A. & Rajagopal, R. DeepSolar: a machine learning framework to efficiently construct a solar deployment database in the United States. Joule 2, 2605–2617 (2018).

    Article  Google Scholar 

  6. Graziano, M. & Gillingham, K. Spatial patterns of solar photovoltaic system adoption: the influence of neighbors and the built environment. J. Economic Geogr. 15, 815–839 (2015).

    Article  Google Scholar 

  7. Wolske, K., Gillingham, K. & Schultz, W. Peer influence on household energy behaviours. Nat. Energy 5, 202–212 (2020).

    Article  Google Scholar 

  8. Mond, A. U.S. Residential Solar PV Customer Acquisition 2017 (GTM Research, 2017).

  9. O’Shaughnessy, E., Nemet, G. & Darghouth, N. The geography of solar energy in the United States: market definition, industry structure, and choice in solar PV adoption. Energy Res. Soc. Sci. 38, 1–8 (2018).

    Article  Google Scholar 

  10. Mueller, J. A. & Ronen, A. Bridging the Solar Income Gap (GW Solar Institute, 2015).

  11. Brown, M., Soni, A., Lapsa, M. & Southworth, K. Low-Income Energy Affordability: Conclusions from a Literature Review (Oak Ridge National Laboratory, 2020).

  12. Sunter, D., Castellanos, S. & Kammen, D. Disparities in rooftop photovoltaics deployment in the United States by race and ethnicity. Nat. Sustain. 2, 71–76 (2019).

    Article  Google Scholar 

  13. Carley, S. & Konisky, D. M. The justice and equity implications of the clean energy transition. Nat. Energy 5, 569–577 (2020).

    Article  Google Scholar 

  14. Bednar, D. & Reames, T. Recognition of and response to energy poverty in the United States. Nat. Energy 5, 432–439 (2020).

    Article  Google Scholar 

  15. Sigrin, B. & Mooney, M. Rooftop Solar Technical Potential for Low-to-Moderate Income Households in the United States (National Renewable Energy Laboratory, 2018).

  16. Vaishnav, P., Horner, N. & Azevedo, I. Was it worthwhile? Where have the benefits of rooftop solar photovoltaic generation exceeded the cost? Environ. Res. Lett. 12, 094015 (2017).

    Article  Google Scholar 

  17. Drury, E. et al. The transformation of southern California’s residential photovoltaics market through third-party ownership. Energy Policy 42, 681–690 (2012).

    Article  Google Scholar 

  18. Davidson, C., Steinberg, D. & Margolis, R. Exploring the market for third-party-owned residential photovoltaic systems: insights from lease and power-purchase agreement contract structures and costs in California. Environ. Res. Lett. (2015).

  19. Rai, V. & Sigrin, B. Diffusion of environmentally-friendly energy technologies: buy versus lease differences in residential PV markets. Environ. Res. Lett. (2013).

  20. Rai, V., Reeves, D. C. & Margolis, R. Overcoming barriers and uncertainties in the adoption of residential solar PV. Renew. Energy 89, 498–505 (2016).

    Article  Google Scholar 

  21. Barbose, G. & Darghouth, N. Tracking the Sun: Pricing and Design Trends for Distributed Photovoltaic Systems in the United States 2019 edn (Lawrence Berkeley National Laboratory, 2019).

  22. Borenstein, S. & Davis, L. The distributional effects of U.S. clean energy tax credits. Tax. Policy Econ. 30, 191–234 (2016).

    Article  Google Scholar 

  23. Paulos, B. Bringing the Benefits of Solar Energy to Low-Income Customers (Clean Energy States Alliance, 2017).

  24. O’Shaughnessy, E. Trends in the market structure of US residential solar PV installation, 2000 to 2016: an evolving industry. Prog. Photovolt. Res. Appl. 26, 901–910 (2018).

  25. Deason, J. & Murphy, S. Assessing the PACE of California Residential Solar Deployment (Lawrence Berkeley National Laboratory, 2018).

  26. Kirkpatrick, A. J. & Bennear, L. S. Promoting clean energy investment: an empirical analysis of property assessed clean energy. J. Environ. Econ. Manag. 68, 357–375 (2014).

    Article  Google Scholar 

  27. Ameli, N., Pisu, M. & Kammen, D. Can the US keep the PACE? A natural experiment in accelerating the growth of solar electricity. Appl. Energy 19, 163–169 (2017).

    Article  Google Scholar 

  28. Gillingham, K. & Bollinger, B. Solarize Your Community (Yale Center for Business and the Environment, 2017).

  29. Irvine, L., Sawyer, A. & Grove, J. The Solarize Guidebook: a Community Guide to Collective Purchasing of Residential PV Systems (Energy Trust of Oregon, 2012).

  30. Tidemann, C., Engerer, N., Markham, F., Doran, B. & Pezzey, J. C. Spatial disaggregation clarifies the inequity in distributional outcomes of household solar PV installation. J. Renew. Sustain. Energy (2019).

  31. Callaway, B. & Sant’Anna, P. Difference-in-Differences with Multiple Time Periods (SSRN, 2019).

  32. Bollinger, B., Gillingham, K. T. & Ovaere, M. Field experimental evidence shows that self-interest attracts more sunlight. Proc. Natl Acad. Sci. USA 117, 20503–20510 (2020).

    Article  Google Scholar 

  33. California Solar Initiative—Biennial Evaluation Studies for the Single-Family Affordable Solar Homes (SASH) and Multifamily Affordable Solar Housing (MASH) Low-Income Programs (Navigant Consulting, 2015).

  34. Sigrin, B., Pless, J. & Drury, E. Diffusion into new markets: evolving customer segments in the solar photovoltaics market. Environ. Res. Lett. (2015).

  35. Ho, D., Imai, K., King, G. & Stuart, E. MatchIt: nonparametric preprocessing for parametric causal inference. J. Stat. Softw. 42, 1–28 (2011).

    Article  Google Scholar 

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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.

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Authors and Affiliations



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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The authors declare no competing interests.

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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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