A carbon risk prediction model for Chinese heavy-polluting industrial enterprises based on support vector machine |
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| Institution: | 1. Business School of Central South University, Changsha Hunan 410083, China;2. Collaborative Innovation Center of Resource-Conserving & Environment-Friendly, Society and Ecological Civilization, Changsha Hunan 410083, China;3. Hunan University of Commerce, Changsha Hunan 410205, China;1. Eastern Mediterranean University, Famagusta, North Cyprus, Turkey;2. Southern Illinois University Edwardsville, Edwardsville, IL, USA;3. Drexel University, Philadelphia, PA, USA;4. IPAG Business School, Paris, France;5. University of Pretoria, Pretoria, South Africa;1. Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China;2. Eco-Environmental Science & Research Institute of Zhejiang Province, Hangzhou 310012, China;3. College of Surveying and Geo-informatics, North China University of Water Resources and Electric Power, Zhengzhou 450000, China;4. ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou 311200, China;5. Department of Land Resource Management, School of Tourism and Urban Management, Jiangxi University of Finance and Economics, Nanchang 330013, China |
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| 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. |
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