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A Study on the Decoupling Effect and Driving Factors of Industrial Carbon Emissions in the Beibu Gulf City Cluster of China

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  • Peiyu Ma

    (School of Business, Guilin University of Technology, Guilin 541006, China)

  • Hewei Liu

    (School of Business, Guilin University of Technology, Guilin 541006, China)

  • Xingwang Zhang

    (School of Business, Guilin University of Technology, Guilin 541006, China)

Abstract

This study investigates the decoupling relationship between industrial carbon emissions and economic development in the Beibu Gulf City Cluster based on panel data from 2005 to 2022. It also uses the Tapio decoupling model to assess the degree of decoupling and synergy in Guangdong, Guangxi, and Hainan and combines it with the logarithmic mean differential index (LMDI) decomposition model to study the driving factors affecting industrial carbon emissions. The study indicates that the industrial carbon emissions of the Beibu Gulf City Cluster increases from 71.42 MT in 2005 to 108.29 MT in 2022 but peaks in 2020 and changes from weak decoupling to strong decoupling; the synergistic relationship among Guangdong, Guangxi, and Hainan will evolve from poor to favorable. The LMDI decomposition results show that the economic scale and population scale effects increase 157.05 MT and 11.67 MT of carbon emissions in the study period, while the optimization of energy structure and energy intensity reduces 117.26 MT and 19.60 MT of carbon emissions, respectively, and the industrial development of many cities in the Beibu Gulf region gradually decouples economic growth and carbon emissions after 2021. Based on this, this study proposes targeted measures to reduce carbon emissions from industrial production in the Beibu Gulf City Cluster, which is of constructive significance for promoting sustainable industrial development in the region.

Suggested Citation

  • Peiyu Ma & Hewei Liu & Xingwang Zhang, 2025. "A Study on the Decoupling Effect and Driving Factors of Industrial Carbon Emissions in the Beibu Gulf City Cluster of China," Sustainability, MDPI, vol. 17(9), pages 1-17, April.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:9:p:3993-:d:1645243
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    1. Singpai, Bodin & Wu, Desheng Dash, 2021. "An integrative approach for evaluating the environmental economic efficiency," Energy, Elsevier, vol. 215(PB).
    2. Ping Zhou & Hailing Li, 2022. "Carbon Emissions from Manufacturing Sector in Jiangsu Province: Regional Differences and Decomposition of Driving Factors," Sustainability, MDPI, vol. 14(15), pages 1-17, July.
    3. Hanyue Fang & Hongbing Li, 2024. "Analysis of Influencing Factors and Prediction of the Peak Value of Industrial Carbon Emission in the Sichuan-Chongqing Region," Sustainability, MDPI, vol. 16(11), pages 1-24, May.
    4. Pata, Ugur Korkut, 2018. "The influence of coal and noncarbohydrate energy consumption on CO2 emissions: Revisiting the environmental Kuznets curve hypothesis for Turkey," Energy, Elsevier, vol. 160(C), pages 1115-1123.
    5. Wang, H. & Ang, B.W. & Su, Bin, 2017. "Assessing drivers of economy-wide energy use and emissions: IDA versus SDA," Energy Policy, Elsevier, vol. 107(C), pages 585-599.
    6. Shamsuzzaman, Mohammad & Shamsuzzoha, Ahm & Maged, Ahmed & Haridy, Salah & Bashir, Hamdi & Karim, Azharul, 2021. "Effective monitoring of carbon emissions from industrial sector using statistical process control," Applied Energy, Elsevier, vol. 300(C).
    Full references (including those not matched with items on IDEAS)

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