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A Big Data-Driven Approach for Early Warning of Enterprise Emissions Alignment with Carbon Neutrality Targets: A Case Study of Guangxi Province

Author

Listed:
  • Chunli Zhou

    (Guangxi Power Grid Co., Ltd., Nanning 530023, China)

  • Huizhen Tang

    (Guangxi Power Grid Co., Ltd., Nanning 530023, China)

  • Wenfeng Zhang

    (School of Applied Economics, Renmin University of China, Beijing 100872, China)

  • Jiayi Qiao

    (School of Applied Economics, Renmin University of China, Beijing 100872, China)

  • Qideng Luo

    (Guangxi Power Grid Co., Ltd., Nanning 530023, China)

Abstract

Achieving the target of carbon neutrality has been an important approach for China to mitigate global climate change. Enterprises are major carbon emitters, and a well-designed early warning system is needed to ensure that their emissions align with carbon neutrality goals. Therefore, this study utilized electricity big data to construct an early warning model for enterprise carbon emissions based on carbon quota allocation. Taking key carbon-emitting enterprises in Guangxi as a case study, we aim to provide insights to support China’s dual carbon goals. Firstly, we established the Carbon Quota Allocation System, enabling carbon quota allocation at the enterprise levels. Secondly, we developed the Enterprise Carbon Neutrality Index, facilitating dynamic warnings for carbon emissions among enterprises. The main conclusions are as follows: (1) In 2020, Guangdong received the highest carbon quota of 606 million tons, representing 5.72% of the national total, while Guangxi only received 2.63 billion tons. (2) Only 39.34% of enterprises in Guangxi are able to meet the carbon neutrality target, indicating significant emission reduction pressure faced by enterprises in the region. (3) Over 90% of enterprises in Guangxi receive Commendation and Encouragement warning levels, suggesting that enterprises in Guangxi are demonstrating a promising trend in emission reduction efforts.

Suggested Citation

  • Chunli Zhou & Huizhen Tang & Wenfeng Zhang & Jiayi Qiao & Qideng Luo, 2024. "A Big Data-Driven Approach for Early Warning of Enterprise Emissions Alignment with Carbon Neutrality Targets: A Case Study of Guangxi Province," Energies, MDPI, vol. 17(11), pages 1-16, May.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:11:p:2508-:d:1400213
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    References listed on IDEAS

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    1. Zhang, Yue-Jun & Hao, Jun-Fang & Song, Juan, 2016. "The CO2 emission efficiency, reduction potential and spatial clustering in China’s industry: Evidence from the regional level," Applied Energy, Elsevier, vol. 174(C), pages 213-223.
    2. Cong, Ren & Lo, Alex Y., 2017. "Emission trading and carbon market performance in Shenzhen, China," Applied Energy, Elsevier, vol. 193(C), pages 414-425.
    3. Battese, G E & Coelli, T J, 1995. "A Model for Technical Inefficiency Effects in a Stochastic Frontier Production Function for Panel Data," Empirical Economics, Springer, vol. 20(2), pages 325-332.
    4. Shan, Yuli & Liu, Jianghua & Liu, Zhu & Xu, Xinwanghao & Shao, Shuai & Wang, Peng & Guan, Dabo, 2016. "New provincial CO2 emission inventories in China based on apparent energy consumption data and updated emission factors," Applied Energy, Elsevier, vol. 184(C), pages 742-750.
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