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High-Frequency Estimation and Prediction of Carbon Emissions in Chinese Municipalities: A Case Study of 14 Municipalities in Guangxi Province

Author

Listed:
  • Chunli Zhou

    (Guangxi Power Grid Co., Ltd., Nanning 530004, China)

  • Haoyang Ji

    (School of Economics, Peking University, Beijing 100871, China)

  • Bin Liu

    (China Energy Engineering Group Guangxi Electric Power Design Institute Co., Ltd., Nanning 530023, China)

  • Huizhen Tang

    (Guangxi Power Grid Co., Ltd., Nanning 530004, China)

  • Huaying Zhang

    (China Energy Engineering Group Guangxi Electric Power Design Institute Co., Ltd., Nanning 530023, China)

  • Junyi Shi

    (School of Statistics, Beijing Normal University, Beijing 100875, China)

Abstract

In October 2024, the National Development and Reform Commission (NDRC) and other departments released the “Work Plan for Improving the Carbon Emission Statistics and Accounting System”, which explicitly proposed the promotion of municipal-level energy balance tables and the development of carbon emission prediction and early warning models. Currently, China has not yet released municipal-level energy balance tables, making it impossible to directly estimate municipal carbon emissions using the IPCC inventory-based method. This paper draws on the electricity–energy–carbon model at the industry level and conducts high-frequency carbon emission estimation for 14 municipalities in Guangxi as a case study. Based on this, the Prophet model is introduced, incorporating planned electricity consumption data to construct a carbon emission prediction and early warning model, enabling long-term carbon emission forecasting at the municipal level. The results indicate the following: First, among the 14 municipalities in Guangxi, Baise has the highest share of carbon emissions (27%), followed by Liuzhou (13%). In terms of carbon emission intensity, six municipalities exceed the regional average, including Baise, Chongzuo, and Fangchenggang. Second, the total carbon emissions in Guangxi (from energy consumption) are expected to peak by 2030, and all 14 municipalities are expected to achieve peak carbon emissions from energy consumption before 2030.

Suggested Citation

  • Chunli Zhou & Haoyang Ji & Bin Liu & Huizhen Tang & Huaying Zhang & Junyi Shi, 2025. "High-Frequency Estimation and Prediction of Carbon Emissions in Chinese Municipalities: A Case Study of 14 Municipalities in Guangxi Province," Energies, MDPI, vol. 18(6), pages 1-20, March.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:6:p:1382-:d:1609925
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    References listed on IDEAS

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    2. Shan, Yuli & Liu, Zhu & Guan, Dabo, 2016. "CO2 emissions from China’s lime industry," Applied Energy, Elsevier, vol. 166(C), pages 245-252.
    3. Luo, Haizhi & Wang, Chenglong & Li, Cangbai & Meng, Xiangzhao & Yang, Xiaohu & Tan, Qian, 2024. "Multi-scale carbon emission characterization and prediction based on land use and interpretable machine learning model: A case study of the Yangtze River Delta Region, China," Applied Energy, Elsevier, vol. 360(C).
    4. Schulz, Niels B., 2010. "Delving into the carbon footprints of Singapore--comparing direct and indirect greenhouse gas emissions of a small and open economic system," Energy Policy, Elsevier, vol. 38(9), pages 4848-4855, September.
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