Panel estimation for the impact factors on carbon dioxide emissions: A new regional classification perspective in China. (10th January 2021)

• Record Type: Journal Article
• Authors: Chang, Keliang; Du, Zifang; Chen, Guijing; Zhang, Yixin; Sui, Lili
• Is Part Of: Journal of cleaner production. Volume 279(2021)
• Publication Date: 2021-01-10
• Subjects: China -- STIRPAT model -- Factor analysis -- Ward cluster analysis -- Regional classification -- CO2 emissions; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2020.123637
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: Global warming is an urgent issue facing humanity. Excessive emissions of CO2 are the leading cause of global climate change. As economy grows, China's CO2 emissions grows rapidly correspondingly. Since 2006, China has ranked first in terms of total CO2 emissions, results of different contributions of different provinces. In view of the shortcomings in the previous regional classification, this work adopted a new regional classification framework which combines factor analysis and Ward clustering method, and classified 30 provinces into four regions by using panel data from 2002 to 2016. Then, this work investigated the regional differences in terms of the effect of population size, GDP per capita, energy structure, energy intensity, urbanization level and industrialization level on CO2 emissions by STIRPAT model. The outcomes indicated that population size, GDP per capita and energy intensity were the top three factors affecting CO2 emissions for all regions. The impact of population size on carbon emissions is not only determined by the size of the population. Along with the process of urbanization, the role of urbanization on CO2 emissions gradually changes from positive to negative. The factor urbanization had positive impacts in region (Ⅲ and Ⅳ) but negative impacts in region (Ⅰ and Ⅱ). The greatest positive impacts of energy structure can be seen in region IV. Yet, statistically, energy structure shows no significant impact in region Ⅱ. As for industrialization level, it demonstrated negative effect in region Ⅱ but positive effect in the other three regions. From these results, implications for CO2 emission-reduction policies in each region were discussed. Highlights: This work adopted a new regional classification framework and classified 30 provinces into four regions. This work investigated the regional differences in the effects of factors on CO2 emissions by STIRPAT model. Implications for CO2 emission-reduction policies in each region were discussed.