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Assessing CO2 emissions in China’s iron and steel industry: A dynamic vector autoregression model

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  • Xu, Bin
  • Lin, Boqiang

Abstract

Energy saving and carbon dioxide emission reduction in China is attracting increasing attention worldwide. At present, China is in the phase of rapid urbanization and industrialization, which is characterized by rapid growth of energy consumption and carbon dioxide (CO2) emissions. China’s steel industry is highly energy-consuming and pollution-intensive. Between 1980 and 2013, the carbon dioxide emissions in China’s steel industry increased approximately 11 times, with an average annual growth rate of 8%. Identifying the drivers of carbon dioxide emissions in the iron and steel industry is vital for developing effective environmental policies. This study uses Vector Autoregressive model to analyzetheinfluencingfactors of the changes in carbon dioxide emissions in the industry. The results show that energy efficiency plays a dominant role in reducing carbon dioxide emissions.Urbanization also has significant effect on CO2 emissions because of mass urban infrastructure and real estate construction. Economic growthhas more impact on emission reduction than industrialization due to the massive fixed asset investment and industrial energy optimization. These findings are important for the relevant authorities in China in developingappropriateenergy policy and planning for the iron and steel industry.

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  • Xu, Bin & Lin, Boqiang, 2016. "Assessing CO2 emissions in China’s iron and steel industry: A dynamic vector autoregression model," Applied Energy, Elsevier, vol. 161(C), pages 375-386.
  • Handle: RePEc:eee:appene:v:161:y:2016:i:c:p:375-386
    DOI: 10.1016/j.apenergy.2015.10.039
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    Cited by:

    1. Chun Deng & Jie-Fang Dong, 2016. "Coal Consumption Reduction in Shandong Province: A Dynamic Vector Autoregression Model," Sustainability, MDPI, Open Access Journal, vol. 8(9), pages 1-16, August.
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    4. Shao, Shuai & Liu, Jianghua & Geng, Yong & Miao, Zhuang & Yang, Yingchun, 2016. "Uncovering driving factors of carbon emissions from China’s mining sector," Applied Energy, Elsevier, vol. 166(C), pages 220-238.
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    6. Xu, Bin & Lin, Boqiang, 2017. "Assessing CO2 emissions in China's iron and steel industry: A nonparametric additive regression approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 325-337.
    7. Xu, Bin & Lin, Boqiang, 2016. "A quantile regression analysis of China's provincial CO2 emissions: Where does the difference lie?," Energy Policy, Elsevier, vol. 98(C), pages 328-342.
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    16. Sheinbaum-Pardo, Claudia, 2016. "Decomposition analysis from demand services to material production: The case of CO2 emissions from steel produced for automobiles in Mexico," Applied Energy, Elsevier, vol. 174(C), pages 245-255.

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