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A carbon risk prediction model for Chinese heavy-polluting industrial enterprises based on support vector machine

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  • Zhou, Zhifang
  • Xiao, Tian
  • Chen, Xiaohong
  • Wang, Chang

Abstract

Chinese heavy-polluting industrial enterprises, especially petrochemical or chemical industry, labeled low carbon efficiency and high emission load, are facing the tremendous pressure of emission reduction under the background of global shortage of energy supply and constrain of carbon emission. However, due to the limited amount of theoretic and practical research in this field, problems like lacking prediction indicators or models, and the quantified standard of carbon risk remain unsolved. In this paper, the connotation of carbon risk and an assessment index system for Chinese heavy-polluting industrial enterprises (eg. coal enterprise, petrochemical enterprises, chemical enterprises et al.) based on support vector machine are presented. By using several heavy-polluting industrial enterprises’ related data, SVM model is trained to predict the carbon risk level of a specific enterprise, which allows the enterprise to identify and manage its carbon risks. The result shows that this method can predict enterprise’s carbon risk level in an efficient, accurate way with high practical application and generalization value.

Suggested Citation

  • Zhou, Zhifang & Xiao, Tian & Chen, Xiaohong & Wang, Chang, 2016. "A carbon risk prediction model for Chinese heavy-polluting industrial enterprises based on support vector machine," Chaos, Solitons & Fractals, Elsevier, vol. 89(C), pages 304-315.
    • Handle: RePEc:eee:chsofr:v:89:y:2016:i:c:p:304-315
    DOI: 10.1016/j.chaos.2015.12.001
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    2. Liang Shen & Fei Lin & T. C. E. Cheng, 2022. "Low-Carbon Transition Models of High Carbon Supply Chains under the Mixed Carbon Cap-and-Trade and Carbon Tax Policy in the Carbon Neutrality Era," IJERPH, MDPI, vol. 19(18), pages 1-21, September.
    3. Alexander Kramer & Fernando Morgado‐Dias, 2020. "Artificial intelligence in process control applications and energy saving: a review and outlook," Greenhouse Gases: Science and Technology, Blackwell Publishing, vol. 10(6), pages 1133-1150, December.
    4. Song, Yazhi & Liu, Tiansen & Li, Yin & Zhu, Yue & Ye, Bin, 2022. "Paths and policy adjustments for improving carbon-market liquidity in China," Energy Economics, Elsevier, vol. 115(C).
    5. Rania Hentati-Kaffel & Alessandro Ravina, 2020. "The Impact of Low-Carbon Policy on Stock Returns," Post-Print hal-03045804, HAL.
    6. Sun, Xiaojun & Lei, Yalin, 2021. "Research on financial early warning of mining listed companies based on BP neural network model," Resources Policy, Elsevier, vol. 73(C).
    7. Rania Hentati-Kaffel & Alessandro Ravina, 2020. "The Impact of Low-Carbon Policy on Stock Returns," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-03045804, HAL.
    8. Ping Liu & Mengchu Xie & Jing Bian & Huishan Li & Liangliang Song, 2020. "A Hybrid PSO–SVM Model Based on Safety Risk Prediction for the Design Process in Metro Station Construction," IJERPH, MDPI, vol. 17(5), pages 1-24, March.

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