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

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.

[1]  B. W. Ang,et al.  Decomposition analysis for policymaking in energy:: which is the preferred method? , 2004 .

[2]  B. W. Ang,et al.  Handling zero values in the logarithmic mean Divisia index decomposition approach , 2007 .

[3]  B. W. Ang,et al.  Decomposition of industrial energy consumption: The energy intensity approach , 1994 .

[4]  D. Stern,et al.  China's Changing Energy Intensity Trend: A Decomposition Analysis , 2008 .

[5]  Xiaoli Zhao,et al.  Residential energy consumption in urban China: A decomposition analysis , 2012 .

[6]  Xiaowei Ma,et al.  Structural decomposition analysis on energy intensity changes at regional level , 2013 .

[7]  B. W. Ang,et al.  A new energy decomposition method: perfect in decomposition and consistent in aggregation , 2001 .

[8]  Claudia Sheinbaum-Pardo,et al.  Decomposition of energy consumption and CO2 emissions in Mexican manufacturing industries: Trends between 1990 and 2008 , 2012 .

[9]  B. W. Ang,et al.  Structural decomposition analysis applied to energy and emissions: Some methodological developments , 2012 .

[10]  Binay Kumar Ray,et al.  Decomposition of energy consumption and energy intensity in Indian manufacturing industries , 2010 .

[11]  J. Sun,et al.  Accounting for energy use in China, 1980–94 , 1998 .

[12]  Gale A. Boyd,et al.  Separating the Changing Composition of U.S. Manufacturing Production from Energy Efficiency Improvements: A Divisia Index Approach , 1987 .

[13]  R. K. Cattell,et al.  Structural change and energy efficiency in industry , 1983 .

[14]  B. W. Ang,et al.  Decomposition of Aggregate Energy and Gas Emission Intensities for Industry: A Refined Divisia Index Method , 1997 .

[15]  Yi-Ming Wei,et al.  Using LMDI method to analyze the change of China's industrial CO2 emissions from final fuel use: An empirical analysis , 2007 .

[16]  Ming Zhang,et al.  Accounting for energy-related CO2 emission in China, 1991-2006 , 2009 .

[17]  B. W. Ang,et al.  Factorizing changes in energy and environmental indicators through decomposition , 1998 .

[18]  Na Li,et al.  Residential Energy Consumption in Urban China , 2011 .

[19]  Yadong Ning,et al.  Changes in industrial electricity consumption in china from 1998 to 2007 , 2010 .

[20]  B. W. Ang,et al.  STRUCTURAL DECOMPOSITION ANALYSIS APPLIED TO ENERGY AND EMISSIONS: AGGREGATION ISSUES , 2012 .

[21]  Bin Chen,et al.  Using LMDI method to analyze the change of industrial CO2 emission from energy use in Chongqing , 2011 .

[22]  William Chung,et al.  A study of residential energy use in Hong Kong by decomposition analysis, 1990-2007 , 2011 .

[23]  Ming Zhang,et al.  Decomposing the decoupling of energy-related CO2 emissions and economic growth in Jiangsu Province , 2013 .