Spatial characteristics of CO2 emissions and PM2.5 concentrations in China based on gridded data

Yanmei Li, Yifei Cui, Bofeng Cai, Jingpeng Guo, Tianhai Cheng and Fengjie Zheng

Applied Energy, 2020, vol. 266, issue C, No S0306261920303640

Abstract: Owing to global climate change and increased environmental pollution, China faces the dual responsibility of reducing CO2 emissions and controlling PM2.5 pollution. This study compares the spatial characteristics of PM2.5 concentrations and CO2 emissions using 10 km × 10 km grid data. The increase and decrease of CO2 emissions and PM2.5 concentrations are divided into four quadrants, which indicates four different conditions. Then, spatial autocorrelation method is conducted to analysis the spatial relationships. The empirical results show that (1) In the four quadrants, the increase of CO2 emissions and the decrease of PM2.5 concentrations accounted for the highest proportion (25.9%). (2) The spatial differences in CO2 emissions are large, but the PM2.5 concentrations show strong spatial aggregation. (3) China’s three major urban agglomerations contain more than half of the areas in which both CO2 emissions and PM2.5 concentrations increased, and the Pearl River Delta urban agglomeration exhibits the best synergistic reduction effect. By contrast, the Beijing–Tianjin–Hebei urban agglomeration has the worst synergistic reduction of CO2 emissions and PM2.5 concentrations. (4) At the urban level, as a typical city in the Beijing–Tianjin–Hebei urban agglomeration, Tianjin's overreliance on heavy chemical industries has led to co-increases in its CO2 emissions and PM2.5 concentrations. Shaoxing and Jiangmen, in the Yangtze River Delta and Pearl River Delta urban agglomeration, are among the few cities where CO2 emissions and PM2.5 concentrations have both been reduced. Finally, this paper suggests some policy implications of these findings.

Keywords: Gridded data; Four-quadrant analysis; Spatial autocorrelation; PM2.5 concentrations; CO2 emissions (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (5)

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DOI: 10.1016/j.apenergy.2020.114852

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