标题:Club Convergence and Factors of Per Capita Transportation Carbon Emissions in China
作者:Bai, Caiquan; Mao, Yuehua; Gong, Yuan; Feng, Chen
作者机构:[Bai, Caiquan] Shandong Univ, Ctr Econ Res, Jinan 250100, Shandong, Peoples R China.; [Mao, Yuehua] Univ Int Business & Econ, Sch Int Trade & Econ, 更多
通讯作者:Feng, C
通讯作者地址:[Feng, C]Shanghai Univ Finance & Econ, Sch Publ Econ & Adm, Shanghai 200433, Peoples R China.
来源:SUSTAINABILITY
出版年:2019
卷:11
期:2
DOI:10.3390/su11020539
关键词:transportation carbon emissions; club convergence; log t regression; test; Ordered Logit model
摘要:China is the largest carbon dioxide emitter in the world, and reducing China's transportation carbon emissions is of great significance for the world. Using the Chinese provincial data from 2005-2015, this article analyzes the convergence characteristics of per capita transportation carbon emissions in China. It employs the log t regression test method and the club clustering algorithm developed by Phillips and Sul (2007) to separate the provinces and municipalities in China into three convergence clubs with different transportation carbon emission levels and one divergent group. Among them, the divergent group consisted of Beijing and Liaoning; the high carbon emission club consisted of Shanghai and Inner Mongolia; the low carbon emission club consisted of Jiangxi, Henan, Shandong, Hebei, and Sichuan; the medium carbon emission club consisted of the remaining 21 provinces and municipalities. On this basis, this article adopts the Ordered Logit model to explore factors influencing the formation of the convergence clubs. The regression results showed that the per capita transportation carbon emissions in the provinces with a high energy intensity of the transportation sector, a high urbanization level, or a high fixed assets investment intensity of the transportation sector tended to converge into the high carbon emission club.
收录类别:SCOPUS;SCIE;SSCI
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060286951&doi=10.3390%2fsu11020539&partnerID=40&md5=28e97641a3eed085e8aea5ec248b7d2c
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