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Inequalities in carbon intensity in China: A multi-scalar and multi-mechanism analysis

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  • Wang, Shaojian
  • Wang, Jieyu
  • Fang, Chuanglin
  • Feng, Kuishuang

Abstract

This study estimates disparities in carbon intensity in China using a multi-scalar and multi-mechanism analysis. In order to avoid the inconsistency between regional, provincial, and city-level data, city-level CO2 emissions from energy consumption in China were estimated through Defense Meteorological Satellite Program/Operational Linescan System nighttime light imagery. Our results reveal a trend of decreasing inequality in carbon intensity in China, and the study also found that the contribution made by eastern China to that inequality decreased continuously, while the share of inequality in western China increased consistently during the study period. Spatial Markov chains were also applied to identify the spatiotemporal dynamics of inequalities in Chinese cities. The results show that there is a strong effect of the emission status of neighboring cities’ on a city’s emission dynamics and the effect of self-reinforcing agglomeration was significant. Based on a multi-level model, the study further revealed that the disparity in China’s carbon intensity levels was sensitive to the regional hierarchy across a variety of mechanisms acting as potential influencing factors. We found that technological progress and population density have a potential to mitigate the intensities driven by economic development, trade openness, road density, secondary industry proportion, and investment intensity. Through the present study, we argue that the policies targeting emissions mitigation in China have been restrained due to a lack of effective restraint in relation to the influencing factors that have promoted emissions levels, while mitigation factors have not been adequately exploited.

Suggested Citation

  • Wang, Shaojian & Wang, Jieyu & Fang, Chuanglin & Feng, Kuishuang, 2019. "Inequalities in carbon intensity in China: A multi-scalar and multi-mechanism analysis," Applied Energy, Elsevier, vol. 254(C).
    • Handle: RePEc:eee:appene:v:254:y:2019:i:c:s0306261919314072
    DOI: 10.1016/j.apenergy.2019.113720
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