Contrastive analyses of the influence factors of interprovincial carbon emission induced by industry energy in China

被引:15
作者
Zhou, Xing [1 ]
Zhou, Meihua [1 ]
Zhang, Ming [1 ]
机构
[1] China Univ Min & Technol, Sch Management, Xuzhou 221116, Peoples R China
关键词
Carbon emission; Influence factor; Hierarchical clustering; Region; STRUCTURAL DECOMPOSITION ANALYSIS; CO2; EMISSIONS; DIOXIDE EMISSIONS; ECONOMIC-GROWTH; CONSUMPTION; OUTPUT; QUANTIFICATION; INDICATORS; INTENSITY; IMPACTS;
D O I
10.1007/s11069-015-2096-9
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
As a major contributor of carbon emission in the world, China should focus on the balance between the universality of development and regional heterogeneity of carbon discharge during the transformation toward low-carbon economy. To reveal the differences among interprovincial industry energy's carbon emissions, some relevant data of carbon emissions in 29 provinces and municipalities during the period of 1996-2012 are selected in this study. Based on the Logarithmic Mean Divisia index decomposition model and hierarchical clustering method, the 29 provinces were clustered in turn by four time intervals according to some indicators, including economic intensity, energy intensity, industry structure, energy structure, demographic effect, and carbon density influence. Research results show that during 1996-2000, economic intensity has a strong positive driving effect on carbon emissions in such eastern provinces as Shanghai, Zhejiang, and Jiangsu, and other seven inland provinces, such as Hunan and Hubei. Demographic effects have strong pulling effects on carbon emissions in municipalities and eastern coastal provinces, and they also exert strong negative effects on carbon emissions in Anhui, Guangxi, Guizhou, and Sichuan in the first three time intervals. During the four periods, highly energy-efficient provinces are Jiangsu, Guangdong, Sichuan, Shandong, Hubei, etc., whose carbon emissions are significantly inhibited by their energy intensity, whereas inefficient provinces are concentrated in western regions, like Guangxi, Hainan, Gansu, Qinghai, Ningxia, Xinjiang, etc.
引用
收藏
页码:1405 / 1433
页数:29
相关论文
共 64 条
[1]   Monitoring changes in economy-wide energy efficiency: From energy-GDP ratio to composite efficiency index [J].
Ang, BW .
ENERGY POLICY, 2006, 34 (05) :574-582
[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]   A survey of index decomposition analysis in energy and environmental studies [J].
Ang, BW ;
Zhang, FQ .
ENERGY, 2000, 25 (12) :1149-1176
[5]  
[Anonymous], 2006, GREENH GAS INV IPCC
[6]   The benchmarks of carbon emissions and policy implications for China's cities: Case of Nanjing [J].
Bi, Jun ;
Zhang, Rongrong ;
Wang, Haikun ;
Liu, Miaomiao ;
Wu, Yi .
ENERGY POLICY, 2011, 39 (09) :4785-4794
[7]   Structural decomposition of industrial CO2 emission in Taiwan:: An input-output approach [J].
Chang, YF ;
Lin, SJ .
ENERGY POLICY, 1998, 26 (01) :5-12
[8]   A spatio-temporal decomposition analysis of energy-related CO2 emission growth in China [J].
Chen, Liang ;
Yang, Zhifeng .
JOURNAL OF CLEANER PRODUCTION, 2015, 103 :49-60
[9]   The costs of mitigating carbon emissions in China: findings from China MARKAL-MACRO modeling [J].
Chen, WY .
ENERGY POLICY, 2005, 33 (07) :885-896
[10]   Decomposition and allocation of energy-related carbon dioxide emission allowance over provinces of China [J].
Chen, Yanan ;
Lin, Sheng .
NATURAL HAZARDS, 2015, 76 (03) :1893-1909