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Machine Learning-Based Carbon Emission Predictions and Customized Reduction Strategies for 30 Chinese Provinces

Author

Listed:
  • Siting Hong

    (College of Mathematics and Computer, Guangdong Ocean University, Zhanjiang 524088, China
    These authors contributed equally to this work.)

  • Ting Fu

    (College of Mathematics and Computer, Guangdong Ocean University, Zhanjiang 524088, China
    These authors contributed equally to this work.)

  • Ming Dai

    (College of Mathematics and Computer, Guangdong Ocean University, Zhanjiang 524088, China)

Abstract

With the intensification of global climate change, the discerning identification of carbon emission drivers and the accurate prediction of carbon emissions have emerged as critical components in addressing this urgent issue. This paper collected carbon emission data from Chinese provinces from 1997 to 2021. Machine learning algorithms were applied to identify province characteristics and determine the influence of provincial development types and their drivers. Analysis indicated that technology and energy consumption had the greatest impact on low-carbon potential provinces (LCPPs), economic growth hub provinces (EGHPs), sustainable growth provinces (SGPs), low-carbon technology-driven provinces (LCTDPs), and high-carbon-dependent provinces (HCDPs). Furthermore, a predictive framework incorporating a grey model (GM) alongside a tree-structured parzen estimator (TPE)-optimized support vector regression (SVR) model was employed to forecast carbon emissions for the forthcoming decade. Findings demonstrated that this approach provided substantial improvements in prediction accuracy. Based on these studies, this paper utilized a combination of SHapley Additive exPlanation (SHAP) and political, economic, social, and technological analysis—strengths, weaknesses, opportunities, and threats (PEST-SWOTs) analysis methods to propose customized carbon emission reduction suggestions for the five types of provincial development, such as promoting low-carbon technology, promoting the transformation of the energy structure, and optimizing the industrial structure.

Suggested Citation

  • Siting Hong & Ting Fu & Ming Dai, 2025. "Machine Learning-Based Carbon Emission Predictions and Customized Reduction Strategies for 30 Chinese Provinces," Sustainability, MDPI, vol. 17(5), pages 1-29, February.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:5:p:1786-:d:1595527
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    References listed on IDEAS

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