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

Nature Energy volume 8, pages 251–263 (2023)Cite this article

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Abstract

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.

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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.

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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 www.isss.pku.edu.cn/cfps. 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 http://www.stats.gov.cn/tjsj/. 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 http://data.sheshiyuanyi.com/WeatherData/. 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.

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Acknowledgements

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).

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Author notes

  1. These authors contributed equally: Qiang Wang and Jie Fan.

Authors and Affiliations

  1. Key Laboratory of Humid Subtropical Eco-geographical Process (Ministry of Education), College of Geographical Sciences, Fujian Normal University, Fuzhou, China

    Qiang Wang & Bowei Wu

  2. Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China

    Jie Fan & Kan Zhou

  3. Department of Geography and Resource Management, Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Kong, China

    Mei-Po Kwan

  4. Department of Human Geography and Spatial Planning, Utrecht University, Utrecht, The Netherlands

    Mei-Po Kwan

  5. College of Urban and Environmental Sciences, Peking University, Beijing, China

    Guofeng Shen

  6. Finance and Economics College, Jimei University, Xiamen, China

    Na Li

  7. Sierra Nevada Research Institute, University of California, Merced, CA, USA

    Jian Lin

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Contributions

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.

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

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

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Source data for Supplementary Figs. 2–7.

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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). https://doi.org/10.1038/s41560-023-01193-z

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