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

Zhifang Zhou, Tian Xiao, Xiaohong Chen and Chang Wang

Chaos, Solitons & Fractals, 2016, vol. 89, issue C, 304-315

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.

Keywords: Heavy-polluting Industrial Enterprises; Carbon Risk Prediction; SVM Model Carbon Risk Assessment Indicators (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:89:y:2016:i:c:p:304-315

DOI: 10.1016/j.chaos.2015.12.001

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