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Decomposition of energy-related CO2 emissions in China: An empirical analysis based on provincial panel data of three sectors

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WOS被引频次:77
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成果类型:
期刊论文
作者:
Wang, Miao;Feng, Chao*
通讯作者:
Feng, Chao
作者机构:
[Wang, Miao; Feng, Chao] Cent S Univ, Sch Business, Changsha 410083, Peoples R China.
[Wang, Miao; Feng, Chao] Cent S Univ, Inst Met Resources Strategy, Changsha 410083, Peoples R China.
通讯机构:
[Feng, Chao] Cent S Univ, Sch Business, Changsha 410083, Peoples R China.
语种:
英文
关键词:
Energy-related CO2 emissions;Driving factors;Centre of gravity;LMDI method
期刊:
Applied Energy
ISSN:
0306-2619
年:
2017
卷:
190
页码:
772-787
文献类别:
WOS:Article;EI:Journal article (JA)
所属学科:
ESI学科类别:工程学;WOS学科类别:Energy & Fuels;Engineering, Chemical
入藏号:
WOS:000395959100064;EI:20170303250689
基金类别:
National Natural Science Foundation of China [71373283, 71373287, 71403298, 71573282, 71633006]; National Social Science Foundation of China [13ZD024, 13ZD169, 14ZDB136]
机构署名:
本校为第一且通讯机构
院系归属:
商学院
摘要:
To grasp the characteristics of CO<inf>2</inf>emissions across provinces in China and to determine changes in the centre of gravity of CO<inf>2</inf>emissions over the 2000&ndash;2014 period, a gravity model is first used to examine the spatial distribution and centre of gravity of energy-related CO<inf>2</inf>emissions. Then, to explore the main factors driving CO<inf>2</inf>emission changes and to uncover feasible ways to reduce CO<inf>2</inf>emissions, this paper decomposes changes in energy-related CO<inf>2</inf>emissions into a population effect (&Delta;C<inf>P</inf>), an economic output effect (&Delta;C<inf>Q</inf>), an industrial structure effect (&Delta;C<inf>S</inf>), an energy intensity effect (&Delta;C<inf>I</inf>), an energy structure effect (&Delta;C<inf>M</inf>) and a carbon dioxide emission coefficient effect (&Delta;C<inf>U</inf>) at both the national and provincial levels based on the Log-Mean Divisia Index (LMDI) method. The results indicate that (1) energy-related CO<inf>2</inf>emissions rose by approximately 5.46 billion tonnes during the 2000&ndash;2014 period, with secondary industry accounting for approximately 80% of total CO<inf>2</inf>emissions. (2) Economic output (Q) was the dominant positive driving factor, and energy intensity (I) was the dominant negative driving factor. The population changes had a weak positive effect on CO<inf>2</inf>emissions, but the industrial structure effect and energy structure effect varied considerably over the years without showing clear trends. (3) Over multiple spatial scales, the contribution ratios of the factors varied significantly across provinces;in general, the positive driving effects outweighed the negative inhibiting effects. Based on these empirical findings, policy recommendations to further reduce CO<inf>2</inf>emissions are provided. The Chinese central and local governments should make full use of the important inhibiting factors, i.e., energy intensity and energy structure, and strive for breakthroughs in secondary sector. &copy;2017
参考文献:
Ang BW, 2007, ENERG POLICY, V35, P1426, DOI 10.1016/j.enpol.2006.04.020
Ang BW, 2016, ENERG POLICY, V94, P56, DOI 10.1016/j.enpol.2016.03.038
Ang BW, 2015, ENERG ECON, V51, P67, DOI 10.1016/j.eneco.2015.06.004
Ang BW, 2015, ENERG ECON, V47, P68, DOI 10.1016/j.eneco.2014.10.011
Ang BW, 2013, ENERG ECON, V40, P1014, DOI 10.1016/j.eneco.2013.05.014

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