Factor analysis of energy-related carbon emissions: a case study of Beijing

JOURNAL OF CLEANER PRODUCTION

卷:163

Article

Fan, Fengyan^1,2  Lei, Yalin^1,2 

[1] China Univ Geosci, Sch Humanities & Econ Management, Beijing 100083, Peoples R China

[2] Minist Land & Resource, Key Lab Carrying Capac Assessment Resource & Envi, Beijing 100083, Peoples R China

关键词: Carbon emissions;    Beijing;    Generalized Fisher index;    Factor decomposition;    Policy suggestions;   

DOI  :  10.1016/j.jclepro.2015.07.094

来源: Elsevier

【 摘 要 】

Carbon emissions in China have attracted increasing world attention with rapid urbanization of this country. It is critical for the government to identify the key factors causing these emissions and take controlling measures. Consistent results have not been achieved yet although some research has been conducted on the factors leading to emissions. Meanwhile, there is still considerable room to improve the methods of previous research. Index decomposition analysis (IDA) is the main method for quantifying the impact of different factors on carbon emissions. At present, the widely used forms of IDA are primarily the Laspeyres and the Divisia index methods. Compared with the Laspeyres and the majority of the Divisia index methods, the generalized Fisher index (GFI) decomposition method can eliminate the residuals and has better factor decomposition characteristics. This paper chooses Beijing as a typical example and analyzes the factors causing carbon emissions. Based on the extended Kaya identity, we built a multivariate generalized Fisher index decomposition model to measure the impacts of economic growth, population size, energy intensity and energy structure on energy-related carbon emissions from 1995 to 2012 in Beijing. The results show that the sustained growth of economic output in Beijing was the leading factor in carbon emissions. Population size had a stimulating effect on the growth of carbon emissions during this period; the pulling effect increased after 2003 and then decreased slightly after 2011 with a cumulative effect of 165.4%. Energy intensity was the primary factor restraining carbon emissions, and the inhibition effect increased yearly. The continuous optimization of the energy structure had no obvious inhibitory effect on carbon emissions. To control carbon emissions, Beijing should continue to adjust the mode of economic development and appropriately control the population size while improving energy efficiency. (C) 2015 The Authors. Published by Elsevier Ltd.

【 授权许可】

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