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Exploring the changes of energy-related carbon intensity in China: an extended Divisia index decomposition

Author

Listed:
  • Juan Wang

    (Tianjin University)

  • Tao Zhao

    (Tianjin University)

  • Xianshuo Xu

    (Tianjin University)

  • Xiaohu Zhang

    (Nanjing University of Aeronautics and Astronautic)

Abstract

To gain a better understanding of the changes in carbon intensity of China, this study firstly adopted logarithmic mean Divisia index decomposition analysis to decompose the carbon intensity into three driving factors, including emission coefficient effect, energy intensity effect and industrial structure effect. Then, the analysis was furtherly conducted to study the contributions of four economic sectors to the percent change in carbon intensity through each influencing factor by attribution analysis. The results illustrated that the carbon intensity dropped by 46.21 % from 1996 to 2012 mainly caused by the decrease in energy intensity, of which the industrial sector, transportation sector and commercial & service sector were the dominant contributors. The emission coefficient effect and industrial structure effect were equally important in terms of increasing carbon intensity, which principally due to the industrial sector and commercial & service sector. In addition, the energy efficiency of agricultural sector should be furtherly improved, and it is imperative to optimize the energy mix and industrial structure of the industrial sector and commercial & service sector. Therefore, more differentiated policies are urgently required to be implemented in different economic sectors to mitigate the China’s carbon intensity.

Suggested Citation

  • Juan Wang & Tao Zhao & Xianshuo Xu & Xiaohu Zhang, 2016. "Exploring the changes of energy-related carbon intensity in China: an extended Divisia index decomposition," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 83(1), pages 501-521, August.
    • Handle: RePEc:spr:nathaz:v:83:y:2016:i:1:d:10.1007_s11069-016-2326-9
    DOI: 10.1007/s11069-016-2326-9
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    Cited by:

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    3. Xiao, Hao & Sun, Ke-Juan & Bi, Hui-Min & Meng, Bo, 2021. "Attribution of changes in an intensity index," Energy, Elsevier, vol. 216(C).
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    5. Juan Wang & Tao Zhao & Xiaohu Zhang, 2017. "Changes in carbon intensity of China’s energy-intensive industries: a combined decomposition and attribution analysis," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 88(3), pages 1655-1675, September.
    6. Jia-Yin Yin & Yun-Fei Cao & Bao-Jun Tang, 2019. "Fairness of China’s provincial energy environment efficiency evaluation: empirical analysis using a three-stage data envelopment analysis model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 95(1), pages 343-362, January.
    7. Wenyan Wang & Juan Wang & Fang Guo, 2018. "Carbon Dioxide (CO 2 ) Emission Reduction Potential in East and South Coastal China: Scenario Analysis Based on STIRPAT," Sustainability, MDPI, vol. 10(6), pages 1-18, June.

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