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Decomposition of Electricity Consumption Intensity of High Energy-consuming Industries in Shanxi Province Based on LMDI Method

Received: 18 May 2015    Accepted: 14 June 2015    Published: 15 July 2015
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Abstract

In this paper, the electricity consumption intensity of the high energy-consuming industries in Shanxi province from2007 -2012 is decomposed based on LMDI method, in which the affecting factors involve structure effect and intensity effect. The results show that the structure adjustment of Shanxi province is the main driver which declines the electricity consumption intensity for high energy-consuming industries. For most industries, the structure effect declines the intensity with a high contribution proportion. Meanwhile, the contribution proportions of efficiency effect for most industries are less than the corresponding structure effect. The totla effect of each high energy-consuming industry is shown in table 4, in which the totoal effect of Non-ferrous metal smelting and rolling processing industry is relative higher than other industries, and the effect of Black metal smelting and rolling processing industry is just smaller than it.

Published in International Journal of Energy and Power Engineering (Volume 4, Issue 4-1)

This article belongs to the Special Issue Current Energy Issues in China

DOI 10.11648/j.ijepe.s.2015040401.12
Page(s) 7-11
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

LMDI, Electricity Consumption Intensity, High Energy-consuming Industry, Structure Effect, Efficiency Effect

References
[1] Rose A, Chen C Y. Sources of change in energy use in the US economy, 1972–1982: a structural decomposition analysis[J]. Resources and Energy, 1991, 13(1): 1-21.
[2] Ang B W, Zhang F Q. A survey of index decomposition analysis in energy and environmental studies[J]. Energy, 2000, 25(12): 1149-1176.
[3] Azadeh A, Ghaderi S F, Sohrabkhani S. Annual electricity consumption forecasting by neural network in high energy consuming industrial sectors[J]. Energy Conversion and Management, 2008, 49(8): 2272-2278.
[4] Chan D Y L, Yang K H, Hsu C H, et al. Current situation of energy conservation in high energy-consuming industries in Taiwan[J]. Energy policy, 2007, 35(1): 202-209.
[5] Ang, B.W., Choi, K.H. 1997.Decomposition of aggregate energy and gas emission intensities for industry: a refined Divisia index method. Energy J. 18 (3): 59–73.
[6] Choi, K.H., Ang, B.W.. 2003. Decomposition of aggregate energy intensity changes in two measures: difference and ratio. Energy Economics 25: 615-624.
[7] Chunbo Ma, DavidI.Stern. 2008. China’s changing energy intensity trend: A decomposition analysis. Energy Economics , 30: 1 037-1 053.
[8] Fengdan Shi. 2008. The analysis on cause of the changes of China's industrial energy consumption. Systems Engineering 04: 55-60.
[9] Yuhui Ou, Yifang Liu, Jiangyi Man. 2007.The decomposition of energy consumption growth in china based on LMDI. Economic Management Journal, 07.
[10] Zhengyu Gao, Yi Wang. 2007. Decomposition analysis of changes of Chinese production energy consumption. Statistical Research, 03.
[11] Zhiyong Han, Yiming Wei, Ying Fan. 2004. The research on characteristics of changes of Chinese energy intensity and economic structure. Journal of Applied Statistics and Management, 01.
[12] De Haan M. A structural decomposition analysis of pollution in the Netherlands[J]. Economic Systems Research, 2001, 13(2): 181-196.
[13] Achão C, Schaeffer R. Decomposition analysis of the variations in residential electricity consumption in Brazil for the 1980–2007 period: measuring the activity, intensity and structure effects[J]. Energy policy, 2009, 37(12): 5208-5220.
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  • APA Style

    Huiru Zhao, Nana Li. (2015). Decomposition of Electricity Consumption Intensity of High Energy-consuming Industries in Shanxi Province Based on LMDI Method. International Journal of Energy and Power Engineering, 4(4-1), 7-11. https://doi.org/10.11648/j.ijepe.s.2015040401.12

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    ACS Style

    Huiru Zhao; Nana Li. Decomposition of Electricity Consumption Intensity of High Energy-consuming Industries in Shanxi Province Based on LMDI Method. Int. J. Energy Power Eng. 2015, 4(4-1), 7-11. doi: 10.11648/j.ijepe.s.2015040401.12

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    AMA Style

    Huiru Zhao, Nana Li. Decomposition of Electricity Consumption Intensity of High Energy-consuming Industries in Shanxi Province Based on LMDI Method. Int J Energy Power Eng. 2015;4(4-1):7-11. doi: 10.11648/j.ijepe.s.2015040401.12

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  • @article{10.11648/j.ijepe.s.2015040401.12,
      author = {Huiru Zhao and Nana Li},
      title = {Decomposition of Electricity Consumption Intensity of High Energy-consuming Industries in Shanxi Province Based on LMDI Method},
      journal = {International Journal of Energy and Power Engineering},
      volume = {4},
      number = {4-1},
      pages = {7-11},
      doi = {10.11648/j.ijepe.s.2015040401.12},
      url = {https://doi.org/10.11648/j.ijepe.s.2015040401.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijepe.s.2015040401.12},
      abstract = {In this paper, the electricity consumption intensity of the high energy-consuming industries in Shanxi province from2007 -2012 is decomposed based on LMDI method, in which the affecting factors involve structure effect and intensity effect. The results show that the structure adjustment of Shanxi province is the main driver which declines the electricity consumption intensity for high energy-consuming industries. For most industries, the structure effect declines the intensity with a high contribution proportion. Meanwhile, the contribution proportions of efficiency effect for most industries are less than the corresponding structure effect. The totla effect of each high energy-consuming industry is shown in table 4, in which the totoal effect of Non-ferrous metal smelting and rolling processing industry is relative higher than other industries, and the effect of Black metal smelting and rolling processing industry is just smaller than it.},
     year = {2015}
    }
    

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  • TY  - JOUR
    T1  - Decomposition of Electricity Consumption Intensity of High Energy-consuming Industries in Shanxi Province Based on LMDI Method
    AU  - Huiru Zhao
    AU  - Nana Li
    Y1  - 2015/07/15
    PY  - 2015
    N1  - https://doi.org/10.11648/j.ijepe.s.2015040401.12
    DO  - 10.11648/j.ijepe.s.2015040401.12
    T2  - International Journal of Energy and Power Engineering
    JF  - International Journal of Energy and Power Engineering
    JO  - International Journal of Energy and Power Engineering
    SP  - 7
    EP  - 11
    PB  - Science Publishing Group
    SN  - 2326-960X
    UR  - https://doi.org/10.11648/j.ijepe.s.2015040401.12
    AB  - In this paper, the electricity consumption intensity of the high energy-consuming industries in Shanxi province from2007 -2012 is decomposed based on LMDI method, in which the affecting factors involve structure effect and intensity effect. The results show that the structure adjustment of Shanxi province is the main driver which declines the electricity consumption intensity for high energy-consuming industries. For most industries, the structure effect declines the intensity with a high contribution proportion. Meanwhile, the contribution proportions of efficiency effect for most industries are less than the corresponding structure effect. The totla effect of each high energy-consuming industry is shown in table 4, in which the totoal effect of Non-ferrous metal smelting and rolling processing industry is relative higher than other industries, and the effect of Black metal smelting and rolling processing industry is just smaller than it.
    VL  - 4
    IS  - 4-1
    ER  - 

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Author Information
  • School of Economics and Management, North China Electric Power University, Beijing, China

  • School of Economics and Management, North China Electric Power University, Beijing, China

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