Club Convergence and Factors of Per Capita Transportation Carbon Emissions in China

Caiquan Bai, Yuehua Mao, Yuan Gong and Chen Feng ()
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Caiquan Bai: The Center for Economic Research, Shandong University, Jinan 250100, China
Yuehua Mao: School of International Trade and Economics, University of International Business and Economics, Beijing 100029, China
Yuan Gong: School of Economics and Management, Nanchang University, Nanchang 330031, China

Sustainability, 2019, vol. 11, issue 2, 1-13

Abstract: 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.

Keywords: transportation carbon emissions; club convergence; log t regression test; Ordered Logit model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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