Assessing CO2 emissions in China's iron and steel industry: A nonparametric additive regression approach

Bin Xu and Boqiang Lin ()

Renewable and Sustainable Energy Reviews, 2017, vol. 72, issue C, 325-337

Abstract: The greenhouse effect caused by CO2 emissions leads to global warming, glacial melting, sea–level rise and frequent outbreaks of extreme weather, which seriously threatens the safety of human life. China is currently the biggest emitter of carbon dioxide in the world, and the iron and steel industry is the largest contributor to China's CO2 emissions. Therefore, investigating the main driving forces of the growth in CO2 emissions in the iron and steel industry is very necessary and urgent. Interestingly, ignoring the nonlinear relationships between economic variables, most of the existing studies use linear methods to examine the industry's CO2 emissions. Based on 30 provincial panel data from 2000 to 2013, this study uses the nonparametric additive regression model to examine the key driving forces of CO2 emissions in China's iron and steel industry. The estimation results show that the nonlinear effect of economic growth on CO2 emissions is consistent with the Environmental Kuznets Curve (EKC) hypothesis. Energy efficiency improvement follows an inverted “U–shaped” pattern in relation to CO2 emissions because of the difference in R&D funding and R&D personnel investments at different times. An inverted “U–shaped” effect of energy structure is due to the optimization of energy consumption. However, the impact of urbanization exhibits a positive “U–shaped” pattern since fixed asset investment and private car population are much larger at the later stage. Hence, the differential nonlinear effects of these driving forces at different times should be taken into consideration, when discussing reduction of CO2 emissions in China's iron and steel industry.

Keywords: The iron and steel industry; CO2 emissions; Nonparametric additive regression models (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (25)

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DOI: 10.1016/j.rser.2017.01.009

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