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Economic growth and household energy footprint inequality in China

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  • Qiaoqiao Zhu
  • Xiaowen Sang
  • Zhengbo Li

Abstract

There are significant differences in energy footprints among individual households. This study uses an environmentally extended input-output approach to estimate the per capita household energy footprint (PCHEF) of 10 different income groups in China’s 30 provinces and analyzes the heterogeneity of household consumption categories, and finally measures the energy equality of households in each province by measuring the energy footprint Gini coefficient (EF-Gini). It is found that the energy footprint of the top 10% income households accounted for about 22% of the national energy footprint in 2017, while the energy footprint of the bottom 40% income households accounted for only 24%. With the growth of China’s economy, energy footprint inequality has declined spatially and temporally. Firstly, wealthier coastal regions have experienced greater convergence in their energy footprint than poorer inland regions. Secondly, China’s household EF-Gini has declined from 0.38 in 2012 to 0.36 in 2017. This study shows that China’s economic growth has not only raised household income levels, but also reduced energy footprint inequality.

Suggested Citation

  • Qiaoqiao Zhu & Xiaowen Sang & Zhengbo Li, 2023. "Economic growth and household energy footprint inequality in China," PLOS ONE, Public Library of Science, vol. 18(3), pages 1-15, March.
    • Handle: RePEc:plo:pone00:0282300
    DOI: 10.1371/journal.pone.0282300
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    References listed on IDEAS

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    1. Eric Scheier & Noah Kittner, 2022. "A measurement strategy to address disparities across household energy burdens," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    2. Nguyen, Canh Phuc & Nasir, Muhammad Ali, 2021. "An inquiry into the nexus between energy poverty and income inequality in the light of global evidence," Energy Economics, Elsevier, vol. 99(C).
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    Cited by:

    1. Jiao He & Yuhang Li & Xiaochuan Xu & Di Wu, 2025. "Energy consumption forecasting for oil and coal in China based on hybrid deep learning," PLOS ONE, Public Library of Science, vol. 20(1), pages 1-21, January.

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