Skip to main content

Thank you for visiting 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.

Examining energy inequality under the rapid residential energy transition in China through household surveys


Since 2013, China has initiated a rapid energy transition that replaces traditional solid fuels with modern clean energy. Despite the tremendous success of the energy transition, its impacts on household energy costs and associated energy inequality remain largely unexplored. Here we use data from a large nationwide household survey to investigate these trends. We find that about two-fifths (43.0%) of surveyed households switched from traditional solid fuels to clean energy during 2013–2017. However, 56.1% to ~61.0% of them were from extremely poor or poor households, causing deep concern for increasing household energy burden. Accordingly, the share of surveyed households in energy poverty increased from 30.1% to 34.2%. Despite the declining inequality in energy cost, a growing inequality in energy burden was revealed during 2013–2017. Our results demonstrate that the energy burden on rural households increased due to the dramatic rise in the cost of clean energy, while urban households tend to spend a lower and decreased proportion of their income on energy.

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

Access options

Rent or buy this article

Prices vary by article type



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

Fig. 1: Residential energy transitions from 2011 to 2017.
Fig. 2: Distribution patterns of households with residential energy transition by income groups.
Fig. 3: Lorenz curves and Gini indexes for energy cost by household income levels and energy types.
Fig. 4: Household energy cost and its growth rate by urban–rural divide and associated inequality.
Fig. 5: Distributions in the median household energy burden by urban–rural household income groups and regions.
Fig. 6: Performance in the random forest regression model.

Data availability

The household-level data supporting the findings of this study are openly available from the Institute of Social Science Survey at Peking University at The dataset contains household identifiers, location, energy costs by fuel types, basic sociodemographic information of all family members and family economic conditions, all of which are derived and generated by the authors. The household identification variable allows us to track the households in follow-up surveys. Sociodemographic data at the province level are collected from the China Census Bureau via API at The daily average temperature data were obtained from 665 meteorological observation stations in 2013, 648 meteorological observation stations in 2015 and 563 meteorological observation stations in 2017, which can be downloaded from Requests for all primary data will be reviewed and made available upon reasonable request. Source data are provided with this paper.

Code availability

Requests for the code developed and annotated in Stata (Version 15) and R (Version 4.0.2) to process and analyse the primary data will be reviewed and made available upon reasonable request.


  1. Notice of the General Office of the State Council on Issuing the Air Pollution Prevention and Control Action Plan (State Council of the People’s Republic of China, 2013); http://

  2. Lei, Y. et al. Primary anthropogenic aerosol emission trends for China, 1990–2005. Atmos. Chem. Phys. 11, 931–954 (2011).

    Article  Google Scholar 

  3. Zhu, X. et al. Stacked use and transition trends of rural household energy in mainland China. Environ. Sci. Technol. 53, 521–529 (2019).

    Article  Google Scholar 

  4. Lelieveld, J. et al. The contribution of outdoor air pollution sources to premature mortality on a global scale. Nature 525, 367–371 (2015).

    Article  Google Scholar 

  5. Zhang, Q. et al. Drivers of improved PM2.5 air quality in China from 2013 to 2017. Proc. Natl Acad. Sci. USA 116, 24463–24469 (2019).

    Article  Google Scholar 

  6. Meng, W. et al. Energy and air pollution benefits of household fuel policies in northern China. Proc. Natl Acad. Sci. USA 116, 16773–16780 (2019).

    Article  Google Scholar 

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

    Article  Google Scholar 

  8. Graff, M. & Carley, S. COVID-19 assistance needs to target energy insecurity. Nat. Energy 5, 352–354 (2020).

    Article  Google Scholar 

  9. Chapman, A., Fujii, H. & Managi, S. Multinational life satisfaction, perceived inequality and energy affordability. Nat. Sustain. 2, 508–514 (2019).

    Article  Google Scholar 

  10. Tao, S. et al. Quantifying the rural residential energy transition in China from 1992 to 2012 through a representative national survey. Nat. Energy 3, 567–573 (2018).

    Article  Google Scholar 

  11. Wu, S., Zheng, X. & Wei, C. Measurement of inequality using household energy consumption data in rural China. Nat. Energy 2, 795–803 (2017).

    Article  Google Scholar 

  12. Carter, E. et al. Household transitions to clean energy in a multiprovincial cohort study in China. Nat. Sustain. 3, 42–50 (2020).

    Article  Google Scholar 

  13. Clean Winter Heating Plan in Northern China (2017–2021) (National Development and Reform Commission, 2017).

  14. Xie, Y. & Zhou, X. Income inequality in today’s China. Proc. Natl Acad. Sci. USA 111, 6928–6933 (2014).

    Article  Google Scholar 

  15. Drehobl, A., Ross, L. & Ayala, R. How High are Household Energy Burdens? (American Council for an Energy-Efficient Economy, 2020);

  16. Wang, Q., Kwan, M. P., Fan, J. & Lin, J. Racial disparities in energy poverty in the United States. Renew. Sustain. Energy Rev. 137, 110620 (2021).

    Article  Google Scholar 

  17. Hernandez, D. et al. Energy burden and the need for integrated low-income housing and energy policy. Poverty Public Policy 2, 5–25 (2010).

    Article  Google Scholar 

  18. Liddell, C. et al. Measuring and monitoring fuel poverty in the UK: national and regional perspectives. Energy Policy 49, 27–32 (2012).

    Article  Google Scholar 

  19. Lin, B. et al. Does off-farm work reduce energy poverty? Evidence from rural China. Sustain. Prod. Consumption 27, 1822–1829 (2021).

    Article  Google Scholar 

  20. Datta, G. & Meerman, J. Household income or household income per capita in welfare comparisons. Rev. Income Wealth 26, 401–418 (1980).

    Article  Google Scholar 

  21. Blanchflower, D. G. Is happiness U-shaped everywhere? Age and subjective well-being in 145 countries. J. Popul. Econ. 34, 575–624 (2021).

    Article  Google Scholar 

  22. Estiri, H. & Zagheni, E. Age matters: ageing and household energy demand in the United States. Energy Res. Soc. Sci. 55, 62–70 (2019).

    Article  Google Scholar 

  23. O’neill, B. C. & Chen, B. S. Demographic determinants of household energy use in the United States. Popul. Dev. Rev. 28, 53–88 (2002).

    Google Scholar 

  24. Lu, Y. et al. Forty years of reform and opening up: China’s progress toward a sustainable path. Sci. Adv. 5, eaau9413 (2019).

    Article  Google Scholar 

  25. Zhao, H. et al. Inequality of household consumption and air pollution-related deaths in china. Nat. Commun. 10, 4337 (2019).

    Article  Google Scholar 

  26. Xie, Y. & Lu, P. The sampling design of the China Family Panel Studies (CFPS). Chin. J. Sociol. 1, 471–484 (2015).

    Article  Google Scholar 

  27. Xie, Y. et al. China Family Panel Studies User’s Manual 3rd edn (Social Science Academic Press, 2017).

  28. Sachs, J. D. et al. Six transformations to achieve the sustainable development goals. Nat. Sustain. 2, 805–814 (2019).

    Article  Google Scholar 

  29. Fuso Nerini, F. et al. Mapping synergies and trade-offs between energy and the sustainable development goals. Nat. Energy 3, 10–15 (2017).

    Article  Google Scholar 

  30. Davidson, D. J. Exnovating for a renewable energy transition. Nat. Energy 4, 254–256 (2019).

    Article  Google Scholar 

  31. Chen, P. et al. The heterogeneous role of energy policies in the energy transition of Asia-Pacific emerging economies. Nat. Energy 7, 588–596 (2022).

    Article  Google Scholar 

  32. Clean Winter Heating Plan in Northern China (2017–2021) (National Development and Reform Commission, 2017).

  33. Hebei Provincial Clean Winter Heating Plan in 2018 (Work Leading Group Office of City Coal to Electricity and Coal to Gas, 2018);

  34. Hebei Provincial Measures for the Administration of Subsidy Funds to the Comprehensive Air Quality Management (Department of Finance of Hebei Province & Department of Ecology and Environment of Hebei Province, 2017).

  35. Shrestha, P. et al. In-use emissions and usage trend of pellet heating stoves in rural Yangxin, Shandong Province. Environ. Pollut. 280, 116955 (2021).

    Article  Google Scholar 

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

    Article  Google Scholar 

  37. Broto, V. C. et al. A research agenda for a people-centred approach to energy access in the urbanizing Global South. Nat. Energy 2, 776–779 (2017).

    Article  Google Scholar 

  38. Malakar, Y. & Day, R. Differences in firewood users’ and LPG users’ perceived relationships between cooking fuels and women’s multidimensional well-being in rural India. Nat. Energy 5, 1022–1031 (2020).

    Article  Google Scholar 

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

    Article  Google Scholar 

  40. Zhang, H. et al. Solar photovoltaic interventions have reduced rural poverty in China. Nat. Commun. 11, 1969 (2020).

    Article  Google Scholar 

  41. Wang, J. & Du, Y ‘Sunshine Passbook’ warms people’s hearts, energy poverty alleviation creates a bright road to prosperity. People’s Daily Online (19 March, 2020);

  42. Lorenz, M. O. Methods for measuring the concentration of wealth. J. Am. Stat. Assoc. 9, 209–219 (1905).

    Google Scholar 

  43. Gini, C. Variabilità e Mutuabilità: Contributo Allo Studio Delle Distribuzioni e Delle Relazioni Statistiche (Cuppini, 1912).

  44. Yang, D. T. Urban-biased policies and rising income inequality in China. Amer. Econ. Rev. 89, 306–310 (1999).

    Article  Google Scholar 

  45. Sovacool, B. K. & Dworkin, M. H. Energy justice: conceptual insights and practical applications. Appl. Energy 142, 435–444 (2015).

    Article  Google Scholar 

  46. Brown, M. A. et al. Low-income energy affordability in an era of U.S. energy abundance. Prog. Energy 2, 042003 (2020).

    Article  Google Scholar 

  47. Fumo, N. & Biswas, M. A. R. Regression analysis for prediction of residential energy consumption. Renew. Sust. Energ. Rev. 47, 332–343 (2015).

    Article  Google Scholar 

  48. Dergiades, T. et al. Energy consumption and economic growth: parametric and non-parametric causality testing for the case of Greece. Energy Econ. 36, 686–697 (2013).

    Article  Google Scholar 

  49. Kong, Y. & Yu, T. A deep neural network model using random forest to extract feature representation for gene expression data classification. Sci. Rep. 8, 1–9 (2018).

    Article  MathSciNet  Google Scholar 

Download references


We thank the study participants and field staff involved in the CFPS.This study was funded by the National Natural Science Foundation of China (41971159, 41922057, 42077328 and 41971164), and the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA23020101).

Author information

Authors and Affiliations



Q.W. and J.F. conceived the initial framework. Q.W., J.F. and J.L. drafted the manuscript. Q.W., K.Z., J.L. and B.W. were involved in data collection and cleaning. Q.W. and N.L. performed the modelling, wrote the codes and carried out the analysis. J.F., J.L., M.-P.K. and Q.W. led the writing of the paper, with all other authors contributing to the writing, revisions and editing.

Corresponding authors

Correspondence to Qiang Wang, Jie Fan or Jian Lin.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Energy thanks Yue Dou and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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 Figs. 1–6, Tables 1–10 and Notes 1–5.

Reporting Summary

Supplementary Data 1

Source data for Supplementary Figs. 2–7.

Source data

Source Data Figs. 1–6

Source data for Figs. 1–6.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, Q., Fan, J., Kwan, MP. et al. Examining energy inequality under the rapid residential energy transition in China through household surveys. Nat Energy 8, 251–263 (2023).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

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