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Carbon Footprint Prediction of Thermal Power Industry under the Dual-Carbon Target: A Case Study of Zhejiang Province, China

Author

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  • Cheng Zhang

    (School of Law, Fuzhou University, Fuzhou 350116, China)

  • Xiong Zou

    (School of Law, Fuzhou University, Fuzhou 350116, China)

  • Chuan Lin

    (School of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China)

Abstract

China is the world’s primary energy consumer. In order to address global warming, China has proposed a strategic goal of “reaching peak carbon and carbon neutrality”, which is related to a balance between human and natural life and has vital strategic significance for accelerating the construction of a sustainable society and achieving high-quality development. The energy sector is the main battlefield upon which the country will strive to achieve the “double carbon” goal, and power systems take the hierarchical first place in the current carbon emissions structure in China. Thermal power enterprises are facing severe challenges, such as low-carbon development, transformation, and upgrading. Therefore, it is crucial to study the thermal power industry’s carbon footprint. A scenario prediction method for estimating the carbon footprint of the thermal power industry in Zhejiang Province based on stacking integrated learning—i.e., the STIRPAT model—is proposed in this study. Using this model, to identify the main influencing factors, one can take the coefficient of determination ( R 2 ) and mean absolute percentage error (MAPE) as evaluation indicators, building a fusion advantage model to predict the carbon footprint. Four carbon peak action scenarios are set up to determine the thermal power industry’s carbon peak in 2021–2035, taking Zhejiang Province as an example. The findings indicate that the proposed method can accurately predict the carbon footprint of the thermal power industry, with the prediction coefficient ( R 2 ) being higher than 0.98 and the error (MAPE) being lower than 0.01. The carbon emission peaks of the thermal power industry under different carbon peak action scenarios are calculated, verifying that Zhejiang Province can reach the goal of a carbon peak; however, the low-carbon development model is too extreme and needs to be revised in combination with more reasonable improvement methods. Therefore, Zhejiang Province must be restructured industrially, the construction of high-tech industries must be encouraged, the energy consumption structure must be optimized, energy efficiency must be boosted, and energy use must be reduced. Relevant research offers a theoretical foundation and benchmark for China’s thermal power industry to promote industrial restructuring and low-carbon transformation by means of comprehensive governance.

Suggested Citation

  • Cheng Zhang & Xiong Zou & Chuan Lin, 2023. "Carbon Footprint Prediction of Thermal Power Industry under the Dual-Carbon Target: A Case Study of Zhejiang Province, China," Sustainability, MDPI, vol. 15(4), pages 1-20, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:3280-:d:1064747
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    References listed on IDEAS

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    1. Ma, Qiang & Murshed, Muntasir & Khan, Zeeshan, 2021. "The nexuses between energy investments, technological innovations, emission taxes, and carbon emissions in China," Energy Policy, Elsevier, vol. 155(C).
    2. Huafang Huang & Xiaomao Wu & Xianfu Cheng, 2021. "The Prediction of Carbon Emission Information in Yangtze River Economic Zone by Deep Learning," Land, MDPI, vol. 10(12), pages 1-23, December.
    3. Zhou, Wenhao & Zeng, Bo & Wang, Jianzhou & Luo, Xiaoshuang & Liu, Xianzhou, 2021. "Forecasting Chinese carbon emissions using a novel grey rolling prediction model," Chaos, Solitons & Fractals, Elsevier, vol. 147(C).
    4. Wu, Rong & Wang, Jieyu & Wang, Shaojian & Feng, Kuishuang, 2021. "The drivers of declining CO2 emissions trends in developed nations using an extended STIRPAT model: A historical and prospective analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    5. Wu, Linfei & Sun, Liwen & Qi, Peixiao & Ren, Xiangwei & Sun, Xiaoting, 2021. "Energy endowment, industrial structure upgrading, and CO2 emissions in China: Revisiting resource curse in the context of carbon emissions," Resources Policy, Elsevier, vol. 74(C).
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