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Identifying the driving forces of national and regional CO2 emissions in China: Based on temporal and spatial decomposition analysis models

Author

Listed:
  • Li, Hao
  • Zhao, Yuhuan
  • Qiao, Xiaoyong
  • Liu, Ya
  • Cao, Ye
  • Li, Yue
  • Wang, Song
  • Zhang, Zhonghua
  • Zhang, Yongfeng
  • Weng, Jianfeng

Abstract

This study explores the driving forces of the changes of national and regional CO2 emissions using temporal decomposition analysis model, and investigates the driving forces of the differences of CO2 emissions between China's 30 regions and the national average using spatial decomposition analysis model. The changes or the differences in national and regional CO2 emissions during 2000–2014 are decomposed into nine underlying determinants. Temporal decomposition results show that economic scale effect is the dominant driving force leading to the increases in both national and regional CO2 emissions, while energy intensity effect is the main contributor to the reduction of CO2 emissions. Contribution of various variables to CO2 emissions between eastern region and central region are roughly same. Spatial decomposition results demonstrate that the differences of CO2 emissions among China's 30 regions are expanding increasingly. Economic scale effect is main driving force responsible for the difference in CO2 emissions among regions, and energy intensity effect, energy structure effect and industrial structure effect are also important factors which result in the increasing differences in regional CO2 emissions. In addition, resource-based and less developed regions have greater potential in the reduction of CO2 emissions. Understanding CO2 emissions and the driving forces of various regions is critical for developing regional mitigation strategies in China.

Suggested Citation

  • Li, Hao & Zhao, Yuhuan & Qiao, Xiaoyong & Liu, Ya & Cao, Ye & Li, Yue & Wang, Song & Zhang, Zhonghua & Zhang, Yongfeng & Weng, Jianfeng, 2017. "Identifying the driving forces of national and regional CO2 emissions in China: Based on temporal and spatial decomposition analysis models," Energy Economics, Elsevier, vol. 68(C), pages 522-538.
  • Handle: RePEc:eee:eneeco:v:68:y:2017:i:c:p:522-538
    DOI: 10.1016/j.eneco.2017.10.024
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    Keywords

    Driving forces; CO2 emissions; Temporal decomposition analysis; Spatial decomposition analysis; China;
    All these keywords.

    JEL classification:

    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • C67 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Input-Output Models
    • P28 - Political Economy and Comparative Economic Systems - - Socialist and Transition Economies - - - Natural Resources; Environment
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming
    • Q56 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environment and Development; Environment and Trade; Sustainability; Environmental Accounts and Accounting; Environmental Equity; Population Growth

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