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A framework for enterprise assessment of carbon performance using support vector machines

  • Application of soft computing
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Abstract

In recent years, the escalating global concerns surrounding climate change have placed a growing emphasis on achieving dual objectives: reducing carbon emissions and achieving carbon neutrality. Businesses and organizations are under mounting pressure to align their operations with these crucial environmental goals. This paper introduces the concept of an enterprise carbon performance evaluation index system (ECPIS) to strike a balance between economic development and environmental protection and enhance overall enterprise management and development strategies. The ECPIS framework is constructed using machine learning and advanced data mining techniques, particularly support vector machines (SVM). Its core purpose is to provide enterprises with a systematic tool to gauge, analyze, and enhance their carbon performance, addressing dual carbon objectives. ECPIS development hinges on data mining techniques, extracting insights from diverse data sources to construct a comprehensive system that accommodates these dual carbon goals’ intricacies. Its methodology includes data collection, preprocessing, feature selection, and data mining algorithms to unveil vital patterns and relationships within data. It conforms to international standards, establishing a tailored carbon performance index system aligned with China’s national conditions. It validates carbon-related enterprise data and employs data mining’s association rules to uncover pertinent carbon performance information. The results obtained from ECPIS are auspicious, boasting an experiential accuracy rate of 97.5%. This level of accuracy surpasses that achieved by other algorithms like K-Nearest Neighbors (KNN), Random Forest (RF), Decision Trees (DT), Naïve Bayes (NB), and Logistic Regression (LR). ECPIS stands out by considering various factors, including carbon emissions reduction, energy consumption, supply chain efficiency, and financial performance indicators. This multifaceted approach enables enterprises to gain a comprehensive understanding of their carbon performance and identify areas for improvement.

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Funding

This study was funded by 2022 General project in humanities and Social Sciences of Education Department of Henan Province: Research on the Path to Accelerate the Construction of Zhengzhou International Exhibition Capital during the 14th Five-year Plan Period (Project No. 2022-ZZJH-055).

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  1. Zhongyuan University of Technology, Zhengzhou, 450000, Henan, China

    Yijun Shou

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Shou, Y. A framework for enterprise assessment of carbon performance using support vector machines. Soft Comput 28, 641–660 (2024). https://doi.org/10.1007/s00500-023-09406-z

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