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Driving Effect of Decoupling Provincial Industrial Economic Growth and Industrial Carbon Emissions in China

Author

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  • Jingfen Hua

    (Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
    Research Center of Urban Carbon Neutrality, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China)

  • Junli Gao

    (Shenzhen Academy of Environmental Sciences, Shenzhen 518000, China)

  • Ke Chen

    (Finance and Statistics College, Hunan University, Changsha 410006, China)

  • Jiaqi Li

    (Longcheng Street Office, Longgang District, Shenzhen 518100, China)

Abstract

China is facing the dual challenges of fostering economic growth and mounting an effective response to climate change, so it is vital to continue promoting industrial carbon emission reduction. This paper uses panel data from 1998 to 2019 to measure the industrial carbon emissions of 30 provinces in China. The Tapio decoupling and IPAT (Impact = Population × Affluence × Technology)-based decoupling models are used to analyze each province’s velocity and quantity decoupling index for industrial carbon emissions. The fixed effect model analyzes the influencing factors for carbon decoupling. The results show that the industrial carbon emissions of various provinces in China are increasing yearly, but there are significant differences among provinces. The carbon decoupling of the industrial economy in most provinces is weak, and the quantitative decoupling index is better than the velocity decoupling index. The cleanliness of energy, balance, and labor productivity significantly affect the velocity decoupling index. The cleanliness of energy, the industry’s structure, and the population significantly affect the quantity decoupling index. Based on empirical results, the study puts forward some policies to promote the efficient carbon decoupling of the industrial economy.

Suggested Citation

  • Jingfen Hua & Junli Gao & Ke Chen & Jiaqi Li, 2022. "Driving Effect of Decoupling Provincial Industrial Economic Growth and Industrial Carbon Emissions in China," IJERPH, MDPI, vol. 20(1), pages 1-20, December.
    • Handle: RePEc:gam:jijerp:v:20:y:2022:i:1:p:145-:d:1011505
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    1. de Freitas, Luciano Charlita & Kaneko, Shinji, 2011. "Decomposition of CO2 emissions change from energy consumption in Brazil: Challenges and policy implications," Energy Policy, Elsevier, vol. 39(3), pages 1495-1504, March.
    2. Wang, Zhaojing & Jiang, Qingzhe & Dong, Kangyin & Mubarik, Muhammad Shujaat & Dong, Xiucheng, 2020. "Decomposition of the US CO2 emissions and its mitigation potential: An aggregate and sectoral analysis," Energy Policy, Elsevier, vol. 147(C).
    3. John C. Driscoll & Aart C. Kraay, 1998. "Consistent Covariance Matrix Estimation With Spatially Dependent Panel Data," The Review of Economics and Statistics, MIT Press, vol. 80(4), pages 549-560, November.
    4. Wen, Hong-xing & Chen, Zhe & Yang, Qian & Liu, Jin-yi & Nie, Pu-yan, 2022. "Driving forces and mitigating strategies of CO2 emissions in China: A decomposition analysis based on 38 industrial sub-sectors," Energy, Elsevier, vol. 245(C).
    5. Liu, Lan-Cui & Fan, Ying & Wu, Gang & Wei, Yi-Ming, 2007. "Using LMDI method to analyze the change of China's industrial CO2 emissions from final fuel use: An empirical analysis," Energy Policy, Elsevier, vol. 35(11), pages 5892-5900, November.
    6. Zhao, Min & Tan, Lirong & Zhang, Weiguo & Ji, Minhe & Liu, Yuan & Yu, Lizhong, 2010. "Decomposing the influencing factors of industrial carbon emissions in Shanghai using the LMDI method," Energy, Elsevier, vol. 35(6), pages 2505-2510.
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    1. Le Jing & Bin Zhou & Zhenliang Liao, 2024. "Decoupling Analysis of Economic Growth and Carbon Emissions in Xinjiang Based on Tapio and Logarithmic Mean Divisia Index Models," Sustainability, MDPI, vol. 16(18), pages 1-17, September.

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