Factors Affecting Regional Per-Capita Carbon Emissions in China Based on an LMDI Factor Decomposition Model

被引:17
作者
Dong, Feng [1 ]
Long, Ruyin [1 ]
Chen, Hong [1 ]
Li, Xiaohui [2 ]
Yang, Qingliang [1 ]
机构
[1] China Univ Min & Technol, Sch Management, Xuzhou, Jiangsu, Peoples R China
[2] Yantai Univ, Sch Foreign Languages, Yantai, Shandong, Peoples R China
来源
PLOS ONE | 2013年 / 8卷 / 12期
基金
中国博士后科学基金;
关键词
INPUT-OUTPUT-ANALYSIS; DIOXIDE EMISSIONS; CO2; EMISSIONS; ENVIRONMENTAL EFFICIENCY; ENERGY INTENSITY; DRIVERS; TRADE; OECD;
D O I
10.1371/journal.pone.0080888
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
China is considered to be the main carbon producer in the world. The per-capita carbon emissions indicator is an important measure of the regional carbon emissions situation. This study used the LMDI factor decomposition model-panel co-integration test two-step method to analyze the factors that affect per-capita carbon emissions. The main results are as follows. (1) During 1997, Eastern China, Central China, and Western China ranked first, second, and third in the per-capita carbon emissions, while in 2009 the pecking order changed to Eastern China, Western China, and Central China. (2) According to the LMDI decomposition results, the key driver boosting the per-capita carbon emissions in the three economic regions of China between 1997 and 2009 was economic development, and the energy efficiency was much greater than the energy structure after considering their effect on restraining increased per-capita carbon emissions. (3) Based on the decomposition, the factors that affected per-capita carbon emissions in the panel co-integration test showed that Central China had the best energy structure elasticity in its regional per-capita carbon emissions. Thus, Central China was ranked first for energy efficiency elasticity, while Western China was ranked first for economic development elasticity.
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页数:10
相关论文
共 49 条
[1]   Accounting frameworks for tracking energy efficiency trends [J].
Ang, B. W. ;
Mu, A. R. ;
Zhou, P. .
ENERGY ECONOMICS, 2010, 32 (05) :1209-1219
[2]   Decomposition analysis for policymaking in energy: which is the preferred method? [J].
Ang, BW .
ENERGY POLICY, 2004, 32 (09) :1131-1139
[3]   Factorizing changes in energy and environmental indicators through decomposition [J].
Ang, BW ;
Zhang, FQ ;
Choi, KH .
ENERGY, 1998, 23 (06) :489-495
[4]   Is the energy intensity a less useful indicator than the carbon factor in the study of climate change? [J].
Ang, BW .
ENERGY POLICY, 1999, 27 (15) :943-946
[5]  
[Anonymous], 2006, IPCC GUIDELINES NATL
[6]   The energy intensity in Lithuania during 1995-2009: A LMDI approach [J].
Balezentis, Alvydas ;
Balezentis, Tomas ;
Streimikiene, Dalia .
ENERGY POLICY, 2011, 39 (11) :7322-7334
[7]  
Chuzhi H., 2008, CHINA POPUL RESOUR E, V18, P38, DOI [DOI 10.1016/S1872-583X(09)60006-1, 10.1016/S1872-583X(09)60006-1]
[8]  
CNBSa ( China's National Bureau of Statistics, 2007, CHIN EN STAT YB 1997
[9]  
CNBSa ( China's National Bureau of Statistics), 2008, CHIN EN STAT YB 1997
[10]  
CNBSa ( China's National Bureau of Statistics), 2000, CHIN EN STAT YB 1997