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Investigating drivers of CO2 emission in China’s heavy industry: A quantile regression analysis

Bin Xu and Boqiang Lin ()

Energy, 2020, vol. 206, issue C

Abstract: High energy-consuming heavy industry is one of the main sources of China’s carbon dioxide (CO2) emissions. Based on 2005–2017 panel data of China’s 30 provinces, this paper uses a quantile regression model to investigate CO2 emissions in the heavy industry. The empirical results show that economic growth exerts a stronger influence on the heavy industry’s CO2 emissions in the 25th-50th quantile provinces, due to the difference in the fixed asset investment and heavy industrial output. The impact of urbanization on CO2 emissions in the 10th-25th quantile provinces is lower than that in other quantile provinces because these provinces have the least number of college graduates. Energy efficiency has a smaller impact on CO2 emissions in the upper 90th quantile province, owing to the difference in R&D personnel investment and the number of patents granted. Similarly, environmental regulations have minimal impact on CO2 emissions in the upper 90th quantile province, since the growth rate of industrial pollution treatment investment in these provinces is the lowest. However, the impact of energy consumption structure on CO2 emissions in the 10th-25th and 25th-50th quantile provinces is the highest, because of the provincial differences in coal consumption.

Keywords: CO2 emissions; The heavy industry; Quantile regression approach (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1016/

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