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Using a new generalized LMDI (logarithmic mean Divisia index) method to analyze China's energy consumption

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  • Wang, Wenwen
  • Liu, Xiao
  • Zhang, Ming
  • Song, Xuefeng

Abstract

As one of the largest energy consumer in the world, China has been facing great pressure to guarantee energy supply security. Investigating the driving forces dominating China energy consumption levels and their evolution may help to institute energy saving policy. Nowadays, the LMDI (logarithmic mean Divisia index) method has become a popular tool to find the nature of the factors that influence the changes in energy consumption. But the LMDI method does not study such factors as fixed asset investment and labor. Combined C-D production function and LMDI method, a new LMDI method is generalized, and that method can be utilized to study many factors. Finally, the new generalized LMDI method is utilized to analyze the driving factors dominating China's energy consumption over the period 1991–2011. Since 1992, China has become a net energy importer. Energy intensity effect played the dominant role in decreasing energy consumption during the study period. However, the investment effect and labor effect were the critical factors in the growth of energy consumption.

Suggested Citation

  • Wang, Wenwen & Liu, Xiao & Zhang, Ming & Song, Xuefeng, 2014. "Using a new generalized LMDI (logarithmic mean Divisia index) method to analyze China's energy consumption," Energy, Elsevier, vol. 67(C), pages 617-622.
    • Handle: RePEc:eee:energy:v:67:y:2014:i:c:p:617-622
    DOI: 10.1016/j.energy.2013.12.064
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    1. Hua Liao & Ce Wang & Zhi-Shuang Zhu & Xiao-Wei Ma, 2012. "Structural decomposition analysis on energy intensity changes at regional level," CEEP-BIT Working Papers 40, Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology.
    2. G. Boyd & J. F. McDonald & M. Ross & D. A. Hansont, 1987. "Separating the Changing Composition of U.S. Manufacturing Production from Energy Efficiency Improvements: A Divisia Index Approach," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 77-96.
    3. Zhao, Xiaoli & Li, Na & Ma, Chunbo, 2012. "Residential energy consumption in urban China: A decomposition analysis," Energy Policy, Elsevier, vol. 41(C), pages 644-653.
    4. Ang, BW, 1994. "Decomposition of industrial energy consumption : The energy intensity approach," Energy Economics, Elsevier, vol. 16(3), pages 163-174, July.
    5. Ang, B.W. & Liu, F.L., 2001. "A new energy decomposition method: perfect in decomposition and consistent in aggregation," Energy, Elsevier, vol. 26(6), pages 537-548.
    6. Chung, William & Kam, M.S. & Ip, C.Y., 2011. "A study of residential energy use in Hong Kong by decomposition analysis, 1990–2007," Applied Energy, Elsevier, vol. 88(12), pages 5180-5187.
    7. Jenne, C. A. & Cattell, R. K., 1983. "Structural change and energy efficiency in industry," Energy Economics, Elsevier, vol. 5(2), pages 114-123, April.
    8. Wang, Wenchao & Mu, Hailin & Kang, Xudong & Song, Rongchen & Ning, Yadong, 2010. "Changes in industrial electricity consumption in china from 1998 to 2007," Energy Policy, Elsevier, vol. 38(7), pages 3684-3690, July.
    9. 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.
    10. Ang, B.W & Zhang, F.Q & Choi, Ki-Hong, 1998. "Factorizing changes in energy and environmental indicators through decomposition," Energy, Elsevier, vol. 23(6), pages 489-495.
    11. 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.
    12. Ma, Chunbo & Stern, David I., 2008. "China's changing energy intensity trend: A decomposition analysis," Energy Economics, Elsevier, vol. 30(3), pages 1037-1053, May.
    13. 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.
    14. Sun, J.W., 1998. "Accounting for energy use in China, 1980–94," Energy, Elsevier, vol. 23(10), pages 835-849.
    15. 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.
    16. 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.
    17. Zhang, Ming & Mu, Hailin & Ning, Yadong, 2009. "Accounting for energy-related CO2 emission in China, 1991-2006," Energy Policy, Elsevier, vol. 37(3), pages 767-773, March.
    18. Ang, B.W. & Liu, Na, 2007. "Handling zero values in the logarithmic mean Divisia index decomposition approach," Energy Policy, Elsevier, vol. 35(1), pages 238-246, January.
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