Impacts of booming economic growth and urbanization on carbon dioxide emissions in Chinese megalopolises over 1985–2010: an index decomposition analysis
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Given the booming economic growth and urbanization in China, cities have become crucial to sustaining this development and curbing national emissions. Understanding the key drivers underlying the rapid emissions growth is critical to providing local solutions for national climate targets. By using index decomposition analysis, we explore the factors contributing to the carbon dioxide (CO2) emissions in Chinese megalopolises from 1985 to 2010. An additional decomposition analysis of the industry sector is performed because of its dominant contribution to the total emissions. The booming economy and expanding urban areas are the major drivers to the increasing CO2 emissions in Chinese megalopolises over the examined period. The significant improvement in energy intensity is the primary factor for reducing CO2 emissions, the declining trend of which, however, has been suspended or reversed since 2000. The decoupling effect of the adjustments in the economic structure only occurred in three megalopolises, namely, the Yangtze River Delta (YRD), the Beijing-Tianjin-Heibei Megalopolis (BTJ), and the Pearl River Delta (PRD). In comparison, the impacts of urban density and carbon intensity are relatively marginal. The further disaggregated decomposition analysis in the industry sector shows that energy intensity improvements were widely achieved in 36 sub-industries in the PRD. The results also indicate the concentrations of energy-intensive industries in the PRD, posing a major challenge to local governments for a low-carbon economy. As economic growth and urbanization continue, reductions in energy intensity and clean energy therefore warrant much more policy attentions due to their crucial roles in reducing carbon emissions and satisfying the energy demand.
KeywordsCarbon dioxide emissions Driver Index decomposition analysis Chinese megalopolis
This work was supported by the National Natural Science Foundation of China (NSFC) fund projects (Grants No. 41501586), the Natural Science Foundation of Fujian Province of China (Grants No. 2016J05106), and the Fundamental Research Funds for the Central Universities (Grants No. 20720151280). We also acknowledge the financial support from a visiting scholarship from Utrecht University. Finally, we sincerely thanks the editors and five anonymous reviewers for their insightful comments and suggestions.
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