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

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• 作者：Shiwei Yu^a ; ^b ; ^c ; ^{ysw81993@sina.com} ; Yi-Ming Wei^b ; ^c ; Jingli Fan^b ; ^c ; Xian Zhang^b ; ^c ; Ke Wang^b ; ^c
• 关键词：Fuzzy C-means cluster ; Carbon emission ; Characteristics ; Mitigation policy
• 刊名：Applied Energy
• 出版年：2012
• 出版时间：April 2012
• 年：2012
• 卷：92
• 期：Complete
• 页码：552-562
• 全文大小：1473 K

文摘

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.