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How to Effectively Control Energy Consumption Growth in China’s 29 Provinces: A Paradigm of Multi-Regional Analysis Based on EAALMDI Method

Author

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  • Yunlong Zhao

    (State Key Laboratory of Power Systems, Department of Energy and Power Engineering, Tsinghua-BP Clean Energy Research and Education Centre, Tsinghua University, Beijing 100084, China
    Tsinghua-Rio Tinto Joint Research Centre for Resources, Energy and Sustainable Development, International Joint Laboratory on Low Carbon Clean Energy Innovation, Laboratory for Low Carbon Energy, Tsinghua University, Beijing 100084, China)

  • Geng Kong

    (State Key Laboratory of Power Systems, Department of Energy and Power Engineering, Tsinghua-BP Clean Energy Research and Education Centre, Tsinghua University, Beijing 100084, China
    Tsinghua-Rio Tinto Joint Research Centre for Resources, Energy and Sustainable Development, International Joint Laboratory on Low Carbon Clean Energy Innovation, Laboratory for Low Carbon Energy, Tsinghua University, Beijing 100084, China)

  • Chin Hao Chong

    (State Key Laboratory of Power Systems, Department of Energy and Power Engineering, Tsinghua-BP Clean Energy Research and Education Centre, Tsinghua University, Beijing 100084, China
    Tsinghua-Rio Tinto Joint Research Centre for Resources, Energy and Sustainable Development, International Joint Laboratory on Low Carbon Clean Energy Innovation, Laboratory for Low Carbon Energy, Tsinghua University, Beijing 100084, China)

  • Linwei Ma

    (State Key Laboratory of Power Systems, Department of Energy and Power Engineering, Tsinghua-BP Clean Energy Research and Education Centre, Tsinghua University, Beijing 100084, China
    Tsinghua-Rio Tinto Joint Research Centre for Resources, Energy and Sustainable Development, International Joint Laboratory on Low Carbon Clean Energy Innovation, Laboratory for Low Carbon Energy, Tsinghua University, Beijing 100084, China)

  • Zheng Li

    (State Key Laboratory of Power Systems, Department of Energy and Power Engineering, Tsinghua-BP Clean Energy Research and Education Centre, Tsinghua University, Beijing 100084, China
    Tsinghua-Rio Tinto Joint Research Centre for Resources, Energy and Sustainable Development, International Joint Laboratory on Low Carbon Clean Energy Innovation, Laboratory for Low Carbon Energy, Tsinghua University, Beijing 100084, China)

  • Weidou Ni

    (State Key Laboratory of Power Systems, Department of Energy and Power Engineering, Tsinghua-BP Clean Energy Research and Education Centre, Tsinghua University, Beijing 100084, China
    Tsinghua-Rio Tinto Joint Research Centre for Resources, Energy and Sustainable Development, International Joint Laboratory on Low Carbon Clean Energy Innovation, Laboratory for Low Carbon Energy, Tsinghua University, Beijing 100084, China)

Abstract

Controlling energy consumption to reduce greenhouse gas emissions has become a global consensus in response to the challenge of climate change. Most studies have focused on energy consumption control in a single region; however, high-resolution analysis of energy consumption and personalized energy policy-making, for multiple regions with differentiated development, have become a complicated challenge. Using the logarithmic mean Divisia index I (LMDI) decomposition method based on energy allocation analysis (EAA), this paper aims to establish a standard paradigm for a high-resolution analysis of multi-regional energy consumption and provide suggestions for energy policy-making, taking 29 provinces of China as the sample. The process involved three steps: (1) determination of regional priorities of energy consumption control by EAA, (2) revealing regional disparity among the driving forces of energy consumption growth by LMDI, and (3) deriving policy implications by comparing the obtained results with existing policies. The results indicated that 29 provinces can be divided into four groups, with different priorities of energy consumption control according to the patterns of coal flows. Most provinces have increasing levels of energy consumption, driven by increasing per capita GDP and improving living standards, while its growth is restrained by decreasing end-use energy intensity, improving energy supply efficiency, and optimization of industrial structures. However, some provinces are not following these trends to the same degree. This indicates that policy-makers must pay more attention to the different driving mechanisms of energy consumption growth among provinces.

Suggested Citation

  • Yunlong Zhao & Geng Kong & Chin Hao Chong & Linwei Ma & Zheng Li & Weidou Ni, 2021. "How to Effectively Control Energy Consumption Growth in China’s 29 Provinces: A Paradigm of Multi-Regional Analysis Based on EAALMDI Method," Sustainability, MDPI, vol. 13(3), pages 1-26, January.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:3:p:1093-:d:484516
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    References listed on IDEAS

    as
    1. Du, Kerui & Lin, Boqiang, 2015. "Understanding the rapid growth of China's energy consumption: A comprehensive decomposition framework," Energy, Elsevier, vol. 90(P1), pages 570-577.
    2. Ang, B.W., 2015. "LMDI decomposition approach: A guide for implementation," Energy Policy, Elsevier, vol. 86(C), pages 233-238.
    3. Chong, ChinHao & Ma, Linwei & Li, Zheng & Ni, Weidou & Song, Shizhong, 2015. "Logarithmic mean Divisia index (LMDI) decomposition of coal consumption in China based on the energy allocation diagram of coal flows," Energy, Elsevier, vol. 85(C), pages 366-378.
    4. White, David J. & Hubacek, Klaus & Feng, Kuishuang & Sun, Laixiang & Meng, Bo, 2018. "The Water-Energy-Food Nexus in East Asia: A tele-connected value chain analysis using inter-regional input-output analysis," Applied Energy, Elsevier, vol. 210(C), pages 550-567.
    5. Xiao, Hongwei & Ma, Zhongyu & Mi, Zhifu & Kelsey, John & Zheng, Jiali & Yin, Weihua & Yan, Min, 2018. "Spatio-temporal simulation of energy consumption in China's provinces based on satellite night-time light data," Applied Energy, Elsevier, vol. 231(C), pages 1070-1078.
    6. Ma, Linwei & Allwood, Julian M. & Cullen, Jonathan M. & Li, Zheng, 2012. "The use of energy in China: Tracing the flow of energy from primary source to demand drivers," Energy, Elsevier, vol. 40(1), pages 174-188.
    7. Zhang, Ming & Li, Huanan & Zhou, Min & Mu, Hailin, 2011. "Decomposition analysis of energy consumption in Chinese transportation sector," Applied Energy, Elsevier, vol. 88(6), pages 2279-2285, June.
    8. Hoekstra, Rutger & van den Bergh, Jeroen C. J. M., 2003. "Comparing structural decomposition analysis and index," Energy Economics, Elsevier, vol. 25(1), pages 39-64, January.
    9. Sun, Xudong & Li, Jiashuo & Qiao, Han & Zhang, Bo, 2017. "Energy implications of China's regional development: New insights from multi-regional input-output analysis," Applied Energy, Elsevier, vol. 196(C), pages 118-131.
    10. Si, Shuyang & Lyu, Mingjie & Lin Lawell, C.-Y. Cynthia & Chen, Song, 2018. "The effects of energy-related policies on energy consumption in China," Energy Economics, Elsevier, vol. 76(C), pages 202-227.
    11. Chong, ChinHao & Liu, Pei & Ma, Linwei & Li, Zheng & Ni, Weidou & Li, Xu & Song, Shizhong, 2017. "LMDI decomposition of energy consumption in Guangdong Province, China, based on an energy allocation diagram," Energy, Elsevier, vol. 133(C), pages 525-544.
    12. Zha, Donglan & Zhou, Dequn & Ding, Ning, 2009. "The contribution degree of sub-sectors to structure effect and intensity effects on industry energy intensity in China from 1993 to 2003," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(4), pages 895-902, May.
    13. Guo, Shan & Li, Yilin & Hu, Yunhao & Xue, Fan & Chen, Bin & Chen, Zhan-Ming, 2020. "Embodied energy in service industry in global cities: A study of six Asian cities," Land Use Policy, Elsevier, vol. 91(C).
    14. Wang, Qiang & Li, Rongrong, 2016. "Drivers for energy consumption: A comparative analysis of China and India," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 954-962.
    15. Wang, Yanqiu & Zhu, Zhiwei & Zhu, Zhaoge & Liu, Zhenbin, 2019. "Analysis of China's energy consumption changing using the Mean Rate of Change Index and the logarithmic mean divisia index," Energy, Elsevier, vol. 167(C), pages 275-282.
    16. Ang, B. W., 2004. "Decomposition analysis for policymaking in energy:: which is the preferred method?," Energy Policy, Elsevier, vol. 32(9), pages 1131-1139, June.
    17. Xingpeng Chen & Guokui Wang & Xiaojia Guo & Jinxiu Fu, 2016. "An Analysis Based on SD Model for Energy-Related CO 2 Mitigation in the Chinese Household Sector," Energies, MDPI, vol. 9(12), pages 1-18, December.
    18. Valentín Molina-Moreno & Pedro Núñez-Cacho Utrilla & Francisco J. Cortés-García & Antonio Peña-García, 2018. "The Use of Led Technology and Biomass to Power Public Lighting in a Local Context: The Case of Baeza (Spain)," Energies, MDPI, vol. 11(7), pages 1-12, July.
    19. Li, Y.L. & Chen, B. & Chen, G.Q., 2020. "Carbon network embodied in international trade: Global structural evolution and its policy implications," Energy Policy, Elsevier, vol. 139(C).
    20. Ren, Shenggang & Fu, Xiang & Chen, XiaoHong, 2012. "Regional variation of energy-related industrial CO2 emissions mitigation in China," China Economic Review, Elsevier, vol. 23(4), pages 1134-1145.
    21. Sheng, Pengfei & Guo, Xiaohui, 2018. "Energy consumption associated with urbanization in China: Efficient- and inefficient-use," Energy, Elsevier, vol. 165(PB), pages 118-125.
    22. Ang, B. W., 2005. "The LMDI approach to decomposition analysis: a practical guide," Energy Policy, Elsevier, vol. 33(7), pages 867-871, May.
    23. Chong, Chin Hao & Tan, Wei Xin & Ting, Zhao Jia & Liu, Pei & Ma, Linwei & Li, Zheng & Ni, Weidou, 2019. "The driving factors of energy-related CO2 emission growth in Malaysia: The LMDI decomposition method based on energy allocation analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).
    24. Lin, Boqiang & Ouyang, Xiaoling, 2014. "Analysis of energy-related CO2 (carbon dioxide) emissions and reduction potential in the Chinese non-metallic mineral products industry," Energy, Elsevier, vol. 68(C), pages 688-697.
    25. Bin Su & B. W. Ang, 2012. "Structural Decomposition Analysis Applied To Energy And Emissions: Aggregation Issues," Economic Systems Research, Taylor & Francis Journals, vol. 24(3), pages 299-317, March.
    26. Lan, Jun & Malik, Arunima & Lenzen, Manfred & McBain, Darian & Kanemoto, Keiichiro, 2016. "A structural decomposition analysis of global energy footprints," Applied Energy, Elsevier, vol. 163(C), pages 436-451.
    27. Akkemik, K. Ali & Göksal, Koray & Li, Jia, 2012. "Energy consumption and income in Chinese provinces: Heterogeneous panel causality analysis," Applied Energy, Elsevier, vol. 99(C), pages 445-454.
    28. Xu, Jin-Hua & Fleiter, Tobias & Eichhammer, Wolfgang & Fan, Ying, 2012. "Energy consumption and CO2 emissions in China's cement industry: A perspective from LMDI decomposition analysis," Energy Policy, Elsevier, vol. 50(C), pages 821-832.
    29. Su, Bin & Ang, B.W., 2012. "Structural decomposition analysis applied to energy and emissions: Some methodological developments," Energy Economics, Elsevier, vol. 34(1), pages 177-188.
    30. Pokharel, Shaligram, 2007. "An econometric analysis of energy consumption in Nepal," Energy Policy, Elsevier, vol. 35(1), pages 350-361, January.
    31. Lima, Fátima & Nunes, Manuel Lopes & Cunha, Jorge & Lucena, André F.P., 2017. "Driving forces for aggregate energy consumption: A cross-country approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P2), pages 1033-1050.
    32. Cullen, Jonathan M. & Allwood, Julian M., 2010. "The efficient use of energy: Tracing the global flow of energy from fuel to service," Energy Policy, Elsevier, vol. 38(1), pages 75-81, January.
    33. Chen, B. & Li, J.S. & Wu, X.F. & Han, M.Y. & Zeng, L. & Li, Z. & Chen, G.Q., 2018. "Global energy flows embodied in international trade: A combination of environmentally extended input–output analysis and complex network analysis," Applied Energy, Elsevier, vol. 210(C), pages 98-107.
    34. Ang, B.W. & Zhang, F.Q., 2000. "A survey of index decomposition analysis in energy and environmental studies," Energy, Elsevier, vol. 25(12), pages 1149-1176.
    35. Boqiang Lin, & Wang, Miao, 2019. "Possibilities of decoupling for China’s energy consumption from economic growth: A temporal-spatial analysis," Energy, Elsevier, vol. 185(C), pages 951-960.
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