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Household multidimensional energy poverty and its impacts on physical and mental health

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  • Zhang, Ziyu
  • Shu, Hongting
  • Yi, Hong
  • Wang, Xiaohua

Abstract

This paper aims to measure multidimensional energy poverty at the household level and study its impacts on health status. Based on Chinese General Social Survey (CGSS) data, we evaluated multidimensional energy poverty at the household level using the entropy method. We then examined the effects of energy poverty on health status from both physical and psychological perspectives. The empirical results indicate that multidimensional energy poverty harms both physical and mental health. We further conducted regional and urban–rural comparative analyses. The findings demonstrate that the health of residents in less developed provinces is more severely affected, and multidimensional energy poverty deteriorates the physical health of rural residents but impacts the mental health of urban residents. For developing countries like China, the first priority should be multidimensional energy poverty reduction through modern energy infrastructure system construction and subsidies for energy poor households, and improving the medical security system should be considered as next step. There is also an urgent need of policy measures focusing on mitigating regional and urban–rural disparities at the same time.

Suggested Citation

  • Zhang, Ziyu & Shu, Hongting & Yi, Hong & Wang, Xiaohua, 2021. "Household multidimensional energy poverty and its impacts on physical and mental health," Energy Policy, Elsevier, vol. 156(C).
  • Handle: RePEc:eee:enepol:v:156:y:2021:i:c:s0301421521002512
    DOI: 10.1016/j.enpol.2021.112381
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    7. Richard S. J. Tol, 2023. "Navigating the energy trilemma during geopolitical and environmental crises," Papers 2301.07671, arXiv.org.
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    18. Tiwari, Sunil & Si Mohammed, Kamel & Guesmi, Khaled, 2023. "A way forward to end energy poverty in China: Role of carbon-cutting targets and net-zero commitments," Energy Policy, Elsevier, vol. 180(C).
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    20. Abbas, Khizar & Butt, Khalid Manzoor & Xu, Deyi & Ali, Muhammad & Baz, Khan & Kharl, Sanwal Hussain & Ahmed, Mansoor, 2022. "Measurements and determinants of extreme multidimensional energy poverty using machine learning," Energy, Elsevier, vol. 251(C).
    21. Dalia Streimikiene & Grigorios L. Kyriakopoulos, 2023. "Energy Poverty and Low Carbon Energy Transition," Energies, MDPI, vol. 16(2), pages 1-15, January.
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    23. Siyou Xia & Yu Yang & Xiaoying Qian & Xin Xu, 2022. "Spatiotemporal Interaction and Socioeconomic Determinants of Rural Energy Poverty in China," IJERPH, MDPI, vol. 19(17), pages 1-15, August.

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