Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Analysis
  • Published:

Disparities in rooftop photovoltaics deployment in the United States by race and ethnicity

Nature Sustainability volume 2, pages 71–76 (2019)Cite this article

Subjects

Matters Arising to this article was published on 12 June 2023

Abstract

The rooftop solar industry in the United States has experienced dramatic growth—roughly 50% per year since 2012, along with steadily falling prices. Although the opportunities this affords for clean, reliable power are transformative, the benefits might not accrue to all individuals and communities. Combining the location of existing and potential sites for rooftop photovoltaics (PV) from Google’s Project Sunroof and demographic information from the American Community Survey, the relative adoption of rooftop PV is compared across census tracts grouped by racial and ethnic majority. Black- and Hispanic-majority census tracts show on average significantly less rooftop PV installed. This disparity is often attributed to racial and ethnic differences in household income and home ownership. In this study, significant racial disparity remains even after we account for these differences. For the same median household income, black- and Hispanic-majority census tracts have installed less rooftop PV compared with no majority tracts by 69 and 30%, respectively, while white-majority census tracts have installed 21% more. When correcting for home ownership, black- and Hispanic-majority census tracts have installed less rooftop PV compared with no majority tracts by 61 and 45%, respectively, while white-majority census tracts have installed 37% more. The social dispersion effect is also considered. This Analysis reveals the racial and ethnic injustice in rooftop solar participation.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Census tracts analysed in the United States for solar rooftop adoption, median household income, home ownership and racial composition.
Fig. 2: Relationship between household income and rooftop PV installation by race and ethnicity.
Fig. 3: Relationship between home ownership and rooftop PV installation by race and ethnicity.
Fig. 4: Percentages of each census tract with and without existing rooftop photovoltaic installations.
Fig. 5: Relationship between household income and rooftop PV installation after excluding census tracts without existing rooftop PV installations by race and ethnicity.

Similar content being viewed by others

Data availability

The data that support the findings of this study are available from Google Project Sunroof (https://www.google.com/get/sunroof/data-explorer/) and the 2009–2013 5-year ACS27. The computer codes used for this study are available online at https://github.com/DeborahSunter/Rooftop-PV-Deployment-Disparities.

References

  1. Fu, R., Feldman, D., Margolis, R., Woodhouse, M. & Ardani, K. US Solar Photovoltaic System Cost Benchmark: Q1 2017 Technical Report (National Renewable Energy Laboratory, 2017).

  2. Haegel, N. M. et al. Terawatt-scale photovoltaics: trajectories and challenges. Science 356, 141–143 (2017).

    Article  CAS  Google Scholar 

  3. Solar Market Insight Report 2017 Q3 Technical Report (Solar Energy Industries Association, 2017).

  4. Feldman, D. & Bolinger, M. On the Path to SunShot: Emerging Opportunities and Challenges in Financing Solar (National Renewable Energy Laboratory, 2016).

  5. Richter, L.-L. Social Effects in the Diffusion of Solar Photovoltaic Technology in the UK Working Paper (Univ. Cambridge, 2013).

  6. Bollinger, B. & Gillingham, K. Peer effects in the diffusion of solar photovoltaic panels. Market. Sci. 31, 900–912 (2012).

    Article  Google Scholar 

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

    Article  Google Scholar 

  8. Pyper, J. New US residential solar capacity down 17% year-over-year for Q1. Green Tech Media https://www.greentechmedia.com/articles/read/residential-solar-capacity-down-17-year-over-year-for-q1#gs.a=WhwhI (2017).

  9. 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 

  10. Ghaith, A. F., Epplin, F. M. & Scott Frazier, R. Economics of grid-tied household solar panel systems versus grid-only electricity. Renew. Sust. Energy Rev. 76, 407–424 (2017).

    Article  Google Scholar 

  11. Kann, S. & Toth, A. How Wealthy are Residential Solar Customers? Household Income and SolarAdoption in the US (GTM Research, 2017).

  12. Renew300 Federal Renewable Energy Target (US Department of Housing and Urban Development, 2016).

  13. Report on Alternative Approaches to Providing Low and Moderate Income (LMI) Clean Energy Services Technical report (Clean Energy Advisory Council and LMI Clean Energy Initiatives Working Group, 2017).

  14. California Public Utilities Commission. CPUC creates $100 million solar for multifamily affordable housing program. EE Online https://electricenergyonline.com/article/energy/category/solar/142/673274/cpuc-creates-100-million-solar-for-multifamily-affordable-housing-program.html (2017).

  15. Dodson, G., Hutchison, E. & Omoletski, J. New Solar Homes Partnership Guidebook 11th edn CEC-300-2017-054-ED11CMF (California Energy Commission, 2017).

  16. Baker, C. D., Polito, K. E., Beaton, M. A. & Judson, J. Solar Massachusetts Renewable Target (SMART) Final Program Design Technical Report (Massachusetts Department of Energy Resources, 2017).

  17. Low- and Moderate-Income Solar Policy Basics (National Renewable Energy Laboratory, 2017).

  18. Cowell, R. Wind power, landscape and strategic, spatial planning—the construction of ‘acceptable locations’ in Wales. Land Use Policy 27, 222–232 (2010).

    Article  Google Scholar 

  19. Gross, C. Community perspectives of wind energy in Australia: the application of a justice and community fairness framework to increase social acceptance. Energy Policy 35, 2727–2736 (2007).

    Article  Google Scholar 

  20. Jenkins, K., McCauley, D., Heffron, R., Stephan, H. & Rehner, R. Energy justice: a conceptual review. Energy Res. Soc. Sci. 11, 174–182 (2016).

    Article  Google Scholar 

  21. Sovacool, B. K., Heffron, R. J., McCauley, D. & Goldthau, A. Energy decisions reframed as justice and ethical concerns. Nat. Energy 1, 16024 (2016).

    Article  Google Scholar 

  22. Yenneti, K. & Day, R. Procedural (in)justice in the implementation of solar energy: the case of Charanaka solar park, Gujarat, India. Energy Policy 86, 664–673 (2015).

    Article  Google Scholar 

  23. Simpson, G. & Clifton, J. Subsidies for residential solar photovoltaic energy systems in Western Australia: distributional, procedural and outcome justice. Renew. Sust. Energy Rev. 65, 262–273 (2016).

    Article  Google Scholar 

  24. Catney, P. et al. Big society, little justice? Community renewable energy and the politics of localism. Local Environ. 19, 715–730 (2014).

    Article  Google Scholar 

  25. Sengupta, M. et al. The National Solar Radiation Data Base (NSRDB). Renew. Sust. Energy Rev. 89, 51–60 (2018).

    Article  Google Scholar 

  26. Electric Power Monthly with Data for May 2018 Technical Report (US Energy Information Administration, July 2018); https://www.eia.gov/electricity/monthly/

  27. 2015 Planning Database (US Census Bureau, 2015); https://www.census.gov/research/data/planning_database/2015/

  28. Glasmeier, A. K. Living wage calculator (Massachusetts Institute of Technology, 2018); http://livingwage.mit.edu/

  29. Cilluffo, A., Geiger, A. & Fry, R. More US households are renting than at any point in 50 years. Pew Research Center http://www.pewresearch.org/fact-tank/2017/07/19/more-u-s-households-are-renting-than-at-any-point-in-50-years/ (2017).

  30. Inskeep, B., Daniel, K. & Proudlove, A. Solar on Multi-unit Buildings: Policy and Financing Options to Address Split Incentives Technical Report (Solar Outreach Partnership, 2015).

  31. Colgate, S. A., Stanley, E. A., Hyman, J. M., Layne, S. P. & Qualls, C. Risk behavior-based model of the cubic growth of acquired immunodeficiency syndrome in the United States. Proc. Natl Acad. Sci. USA 86, 4793–4797 (1989).

    Article  CAS  Google Scholar 

  32. Taylor, D. E. Green jobs and the potential to diversify the environmental workforce. Utah Environ. Law Rev. 31, 47–77 (2011).

    Google Scholar 

  33. US Solar Industry Diversity Study: Current Trends, Best Practices, and Recommendations Technical Report (The Solar Foundation, 2017).

  34. Taylor, D. E. Blacks and the environment: toward an explanation of the concern and action gap between blacks and whites. Environ. Behav. 21, 175–205 (1989).

    Article  Google Scholar 

  35. CA SB-338 Integrated Resource Plan: Peak Demand (LegiScan, 2017); https://legiscan.com/CA/bill/SB338/2017

  36. Project Sunroof Data Explorer: a Description of Methodology and Inputs Technical Report (Google Project Sunroof, 2017).

  37. Folch, D. C., Arribas-Bel, D., Koschinsky, J. & Spielman, S. E. Spatial variation in the quality of American Community Survey estimates. Demography 53, 1535–1554 (2016).

    Article  Google Scholar 

  38. Poverty Thresholds (US Census Bureau, 2013); https://www.census.gov/data/tables/time-series/demo/income-poverty/historical-poverty-thresholds.html

  39. Gagnon, P., Margolis, R., Melius, J., Phillips, C. & Elmore, R. Rooftop Solar Photovoltaic Technical Potential in the United States: a Detailed Assessment Technical Report (National Renewable Energy Laboratory, 2016).

  40. Seabold, S. & Perktold, J. Statsmodels: economic and statistical modeling with Python. In Proc. 9th Python Sci. Conf. 57–61 (2010).

  41. Efron, B. & Tibshirani, R. Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy. Stat. Sci. 1, 54–75 (1986).

    Article  Google Scholar 

Download references

Acknowledgements

The authors thank Google’s Project Sunroof team for providing valuable data, and S. Hsiang for insightful discussions. D.A.S. gratefully acknowledges support from the Energy Efficiency and Renewable Energy Postdoctoral Research Award from the US Department of Energy and Berkeley Institute for Data Science. S.C. gratefully acknowledges support from the Berkeley Energy and Climate Institute–Instituto Tecnológico de Estudios Superiores de Monterrey Energy Fellowship. D.M.K. acknowledges support from the Karsten Family Foundation and Zaffaroni Family Foundation.

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Tufts University, Medford, MA, USA

    Deborah A. Sunter

  2. Berkeley Institute for Data Science, University of California, Berkeley, CA, USA

    Deborah A. Sunter

  3. Energy and Resources Group, University of California, Berkeley, CA, USA

    Deborah A. Sunter, Sergio Castellanos & Daniel M. Kammen

  4. Renewable and Appropriate Energy Laboratory, University of California, Berkeley, CA, USA

    Deborah A. Sunter, Sergio Castellanos & Daniel M. Kammen

  5. California Institute for Energy and Environment, University of California, Berkeley, CA, USA

    Sergio Castellanos

  6. Escuela de Gobierno y Transformación Pública, Instituto Tecnológico y de Estudios Superiores de Monterrey, San Pedro Garza García, Mexico

    Sergio Castellanos

  7. Goldman School of Public Policy, University of California, Berkeley, CA, USA

    Daniel M. Kammen

Authors
  1. Deborah A. Sunter
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sergio Castellanos
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Daniel M. Kammen
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

D.A.S. and S.C. designed and performed the research, analysed the data and wrote the paper. D.M.K. supervised the research, guided the study and edited the paper.

Corresponding authors

Correspondence to Deborah A. Sunter or Sergio Castellanos.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figures 1–5, Supplementary Tables 1–3.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sunter, D.A., Castellanos, S. & Kammen, D.M. Disparities in rooftop photovoltaics deployment in the United States by race and ethnicity. Nat Sustain 2, 71–76 (2019). https://doi.org/10.1038/s41893-018-0204-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41893-018-0204-z

This article is cited by

Search

Advanced search

Quick links

Nature Briefing Anthropocene

Sign up for the Nature Briefing: Anthropocene newsletter — what matters in anthropocene research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: Anthropocene