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Decomposition of intensity of energy-related CO2 emission in Chinese provinces using the LMDI method

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  • Zhang, Wei
  • Li, Ke
  • Zhou, Dequn
  • Zhang, Wenrui
  • Gao, Hui

Abstract

Uncovering the driving factors of CO2 emission intensity declining is important for China. This paper improves the logarithmic mean Divisia index technique, which includes energy density and energy consumption intensity, to explore the driving factors of carbon emission intensity (CI) in 29 Chinese provinces from 1995–2012. The main results are: (1) energy consumption intensity plays a more important role than carbon emission density (CD) for a rapid decrease in CI during the research period, so a much room is left for a significant CD reduction through carbon emission reduction technology, energy structural reduction, and energy consumption proportional reduction. (2) The decrease in energy consumption technology and energy structure in secondary industries contributes the most reduction in energy consumption intensity. (3)The energy consumption proportions of secondary and tertiary industries are the two most important drivers to decrease CD. (4) During the research period, the energy consumption proportions of secondary industries result in the most decrease in CD, whereas the energy consumption proportions of tertiary industries cause the most increase in CD.

Suggested Citation

  • Zhang, Wei & Li, Ke & Zhou, Dequn & Zhang, Wenrui & Gao, Hui, 2016. "Decomposition of intensity of energy-related CO2 emission in Chinese provinces using the LMDI method," Energy Policy, Elsevier, vol. 92(C), pages 369-381.
    • Handle: RePEc:eee:enepol:v:92:y:2016:i:c:p:369-381
    DOI: 10.1016/j.enpol.2016.02.026
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    as
    1. 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.
    2. Wang, Zhaohua & Zhang, Bin & Liu, Tongfan, 2016. "Empirical analysis on the factors influencing national and regional carbon intensity in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 34-42.
    3. 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.
    4. Su, Bin & Ang, B.W., 2015. "Multiplicative decomposition of aggregate carbon intensity change using input–output analysis," Applied Energy, Elsevier, vol. 154(C), pages 13-20.
    5. Joachim Schleich & Wolfgang Eichhammer & Ulla Boede & Frank Gagelmann & Eberhard Jochem & Barbara Schlomann & Hans-Joachim Ziesing, 2001. "Greenhouse gas reductions in Germany-lucky strike or hard work?," Climate Policy, Taylor & Francis Journals, vol. 1(3), pages 363-380, September.
    6. Diakoulaki, D. & Mandaraka, M., 2007. "Decomposition analysis for assessing the progress in decoupling industrial growth from CO2 emissions in the EU manufacturing sector," Energy Economics, Elsevier, vol. 29(4), pages 636-664, July.
    7. Bhattacharyya, Subhes C. & Ussanarassamee, Arjaree, 2004. "Decomposition of energy and CO2 intensities of Thai industry between 1981 and 2000," Energy Economics, Elsevier, vol. 26(5), pages 765-781, September.
    8. Zhang, F. Q. & Ang, B. W., 2001. "Methodological issues in cross-country/region decomposition of energy and environment indicators," Energy Economics, Elsevier, vol. 23(2), pages 179-190, March.
    9. Cao, Jing & Karplus, Valerie J., 2014. "Firm-level determinants of energy and carbon intensity in China," Energy Policy, Elsevier, vol. 75(C), pages 167-178.
    10. Geng, Yong & Zhao, Hongyan & Liu, Zhu & Xue, Bing & Fujita, Tsuyoshi & Xi, Fengming, 2013. "Exploring driving factors of energy-related CO2 emissions in Chinese provinces: A case of Liaoning," Energy Policy, Elsevier, vol. 60(C), pages 820-826.
    11. Kuishuang Feng & Yim Ling Siu & Dabo Guan & Klaus Hubacek, 2012. "Analyzing Drivers of Regional Carbon Dioxide Emissions for China," Journal of Industrial Ecology, Yale University, vol. 16(4), pages 600-611, August.
    12. Liu, Lan-Cui & Fan, Ying & Wu, Gang & Wei, Yi-Ming, 2007. "Using LMDI method to analyze the change of China's industrial CO2 emissions from final fuel use: An empirical analysis," Energy Policy, Elsevier, vol. 35(11), pages 5892-5900, November.
    13. Chang, Kai & Chang, Hao, 2016. "Cutting CO2 intensity targets of interprovincial emissions trading in China," Applied Energy, Elsevier, vol. 163(C), pages 211-221.
    14. Wang, Yafei & Zhao, Hongyan & Li, Liying & Liu, Zhu & Liang, Sai, 2013. "Carbon dioxide emission drivers for a typical metropolis using input–output structural decomposition analysis," Energy Policy, Elsevier, vol. 58(C), pages 312-318.
    15. B. W. Ang & Ki-Hong Choi, 1997. "Decomposition of Aggregate Energy and Gas Emission Intensities for Industry: A Refined Divisia Index Method," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 59-73.
    16. Wood, Richard, 2009. "Structural decomposition analysis of Australia's greenhouse gas emissions," Energy Policy, Elsevier, vol. 37(11), pages 4943-4948, November.
    17. Hatzigeorgiou, Emmanouil & Polatidis, Heracles & Haralambopoulos, Dias, 2008. "CO2 emissions in Greece for 1990–2002: A decomposition analysis and comparison of results using the Arithmetic Mean Divisia Index and Logarithmic Mean Divisia Index techniques," Energy, Elsevier, vol. 33(3), pages 492-499.
    18. Davis, W. Bart & Sanstad, Alan H. & Koomey, Jonathan G., 2003. "Contributions of weather and fuel mix to recent declines in US energy and carbon intensity," Energy Economics, Elsevier, vol. 25(4), pages 375-396, July.
    19. Shrestha, Ram M. & Timilsina, Govinda R., 1996. "Factors affecting CO2 intensities of power sector in Asia: A Divisia decomposition analysis," Energy Economics, Elsevier, vol. 18(4), pages 283-293, October.
    20. 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.
    21. Fernández González, P. & Landajo, M. & Presno, M.J., 2014. "Multilevel LMDI decomposition of changes in aggregate energy consumption. A cross country analysis in the EU-27," Energy Policy, Elsevier, vol. 68(C), pages 576-584.
    22. Cahill, Caiman J. & Ó Gallachóir, Brian P., 2010. "Monitoring energy efficiency trends in European industry: Which top-down method should be used?," Energy Policy, Elsevier, vol. 38(11), pages 6910-6918, November.
    23. Xu, Ming & Li, Ran & Crittenden, John C. & Chen, Yongsheng, 2011. "CO2 emissions embodied in China's exports from 2002 to 2008: A structural decomposition analysis," Energy Policy, Elsevier, vol. 39(11), pages 7381-7388.
    24. Zhao, Min & Tan, Lirong & Zhang, Weiguo & Ji, Minhe & Liu, Yuan & Yu, Lizhong, 2010. "Decomposing the influencing factors of industrial carbon emissions in Shanghai using the LMDI method," Energy, Elsevier, vol. 35(6), pages 2505-2510.
    25. Dhakal, Shobhakar, 2009. "Urban energy use and carbon emissions from cities in China and policy implications," Energy Policy, Elsevier, vol. 37(11), pages 4208-4219, November.
    26. Liu, Zhu & Liang, Sai & Geng, Yong & Xue, Bing & Xi, Fengming & Pan, Ying & Zhang, Tianzhu & Fujita, Tsuyoshi, 2012. "Features, trajectories and driving forces for energy-related GHG emissions from Chinese mega cites: The case of Beijing, Tianjin, Shanghai and Chongqing," Energy, Elsevier, vol. 37(1), pages 245-254.
    27. Zhang, Youguo, 2009. "Structural decomposition analysis of sources of decarbonizing economic development in China; 1992-2006," Ecological Economics, Elsevier, vol. 68(8-9), pages 2399-2405, June.
    28. Ma, Chunbo & Stern, David I., 2008. "Biomass and China's carbon emissions: A missing piece of carbon decomposition," Energy Policy, Elsevier, vol. 36(7), pages 2517-2526, July.
    29. Rutger Hoekstra & Jeroen van den Bergh, 2002. "Structural Decomposition Analysis of Physical Flows in the Economy," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 23(3), pages 357-378, November.
    30. Robaina Alves, Margarita & Moutinho, Victor, 2013. "Decomposition analysis and Innovative Accounting Approach for energy-related CO2 (carbon dioxide) emissions intensity over 1996–2009 in Portugal," Energy, Elsevier, vol. 57(C), pages 775-787.
    31. Wang, Hongsheng & Wang, Yunxia & Wang, Haikun & Liu, Miaomiao & Zhang, Yanxia & Zhang, Rongrong & Yang, Jie & Bi, Jun, 2014. "Mitigating greenhouse gas emissions from China's cities: Case study of Suzhou," Energy Policy, Elsevier, vol. 68(C), pages 482-489.
    32. Sheinbaum, Claudia & Ozawa, Leticia & Castillo, Daniel, 2010. "Using logarithmic mean Divisia index to analyze changes in energy use and carbon dioxide emissions in Mexico's iron and steel industry," Energy Economics, Elsevier, vol. 32(6), pages 1337-1344, November.
    33. Zhang, Ming & Mu, Hailin & Ning, Yadong & Song, Yongchen, 2009. "Decomposition of energy-related CO2 emission over 1991-2006 in China," Ecological Economics, Elsevier, vol. 68(7), pages 2122-2128, May.
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