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Energy Poverty in China: A Dynamic Analysis Based on a Hybrid Panel Data Decision Model

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  • Bing Wang

    (College of Resources and Safety Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
    Beijing Key Laboratory for Precise Mining of Intergrown Energy and Resources, China University of Mining and Technology (Beijing), Beijing 100083, China
    Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing 100081, China)

  • Hua-Nan Li

    (Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing 100081, China
    School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China)

  • Xiao-Chen Yuan

    (Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing 100081, China
    Donlinks School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China)

  • Zhen-Ming Sun

    (College of Resources and Safety Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China)

Abstract

Renewable energy can help to tackle energy poverty issues of the availability of modern energy services and the sustainability of energy supply. Based on the concept of the Energy Development Index, published by International Energy Agency, this paper builds the clean energy development index and applies the Grey incidence decision method to analyze regional energy poverty issues in China. A model using panel data investigates the influencing factors that are governing energy poverty alleviation and the relationship between energy poverty and social development. The improved index system not only considers the access to modern energy services, but also addresses the cleanliness of energy supply and the transition to clean energy consumption for cooking. The results indicate that due to insufficient clean energy development, China’s Northeast and West regions have experienced increasing energy poverty problems, while energy poverty in the Southwest region has improved considerably because of its renewable energy development. Urbanization, affordability, and renewable energy development can increase access to modern energy services, contributing to energy poverty alleviation. However, the role of rural household consumption levels in energy poverty alleviation should be considered in rural energy policy.

Suggested Citation

  • Bing Wang & Hua-Nan Li & Xiao-Chen Yuan & Zhen-Ming Sun, 2017. "Energy Poverty in China: A Dynamic Analysis Based on a Hybrid Panel Data Decision Model," Energies, MDPI, vol. 10(12), pages 1-14, November.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:12:p:1942-:d:120151
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    4. Wang, Bing & Wang, Qian & Wei, Yi-Ming & Li, Zhi-Ping, 2018. "Role of renewable energy in China’s energy security and climate change mitigation: An index decomposition analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 187-194.
    5. Zhang, Dayong & Li, Jiajia & Han, Phoumin, 2019. "A multidimensional measure of energy poverty in China and its impacts on health: An empirical study based on the China family panel studies," Energy Policy, Elsevier, vol. 131(C), pages 72-81.
    6. Xue, Yan & Hu, Dongmei & Irfan, Muhammad & Wu, Haitao & Hao, Yu, 2023. "Natural resources policy making through finance? The role of green finance on energy resources poverty," Resources Policy, Elsevier, vol. 85(PA).
    7. 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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    10. Gupta, Srishti & Gupta, Eshita & Sarangi, Gopal K., 2020. "Household Energy Poverty Index for India: An analysis of inter-state differences," Energy Policy, Elsevier, vol. 144(C).
    11. Milena N Rajić & Miroslav B Milovanović & Dragan S Antić & Rado M Maksimović & Pedja M Milosavljević & Dragan Lj Pavlović, 2020. "Analyzing energy poverty using intelligent approach," Energy & Environment, , vol. 31(8), pages 1448-1472, December.
    12. Kocak, Emrah & Ulug, Eyup Emre & Oralhan, Burcu, 2023. "The impact of electricity from renewable and non-renewable sources on energy poverty and greenhouse gas emissions (GHGs): Empirical evidence and policy implications," Energy, Elsevier, vol. 272(C).
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    14. Peng Wang & Chunsheng Wang & Yukun Hu & Liz Varga & Wei Wang, 2018. "Power Generation Expansion Optimization Model Considering Multi-Scenario Electricity Demand Constraints: A Case Study of Zhejiang Province, China," Energies, MDPI, vol. 11(6), pages 1-15, June.
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