Exploring the regional characteristics of inter-provincial CO2 emissions in China:An improved fuzzy clustering analysis based on particle swarm optimization

Shiwei Yu, Yi-Ming Wei, Jing-Li Fan, Xian Zhang and Ke Wang

No 22, CEEP-BIT Working Papers from Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology

Abstract: The better to explore the regional characteristics of inter-provincial CO2 emissions and the rational distribution of the reduction of emission intensity reduction in China, this paper proposes an improved PSO-FCM clustering algorithm. This method can obtain the optimal cluster number and membership grade values by utilizing the global capacity of Particle Swarm Optimization (PSO) on Fuzzy C-means (FCM). The clustering results of CO2 emissions indicate that the 30 provinces of China are divided into five clusters and each has its own significant characteristics. Compared with other clustering methods, the results of PSO-FCM are more explanatory. The most important indicators affecting regional emission characteristics are CO2 emission intensity and per capita emissions, whereas CO2 emission per unit of energy is not obvious in clustering. Furthermore, some policy recommendations on setting emission reduction targets according to the emission characteristics of different clusters are made.

Keywords: Fuzzy C-means cluster; carbon emission; characteristics; mitigation policy (search for similar items in EconPapers)
JEL-codes: C61 Q54 (search for similar items in EconPapers)
Pages: 23 pages
Date: 2011-11
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Journal Article: Exploring the regional characteristics of inter-provincial CO2 emissions in China: An improved fuzzy clustering analysis based on particle swarm optimization (2012)
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