Li Zhenzu, Yu Yuli, Wang Shiyue
School of Business, Zhuhai College of Science and Technology, Zhuhai, 519041, China.
Department of Ocean Science, The Hong Kong University of Science and Technology, Hongkong, 999077, China.
Sci Rep. 2024 Aug 20;14(1):19394. doi: 10.1038/s41598-024-70376-9.
ESG (Environmental, Social and Governance) management practice is an important part of promoting sustainable operation and development of manufacturing enterprises. Currently, traditional evaluation methods have limitations such as low efficiency and lack of objectivity. To improve the efficiency and accuracy of ESG evaluation and promote the optimization of ESG performance in manufacturing enterprises, this article combined data mining and analytic hierarchy process (AHP) to conduct effective research on ESG management practice evaluation in manufacturing enterprises. This article adopted the best priority search strategy to collect and process enterprise ESG data. By using AHP to construct hierarchical and segmented objectives for target problems, a performance evaluation index system for management practices was built based on the evaluation objectives and hierarchical priority order. Finally, based on the performance evaluation of ESG management practices, the K-nearest Neighbor algorithm was applied to analyze historical data of key indicators. According to the weights, various key indicators were re-integrated, achieving practical evaluation and decision support for enterprise ESG management. To verify the effectiveness of data mining and AHP, this article took Z enterprise as the research object and conducted empirical analysis on it. The results showed that in terms of evaluation accuracy, the method proposed in this article achieved the highest evaluation accuracy of 92.51%, 91.16%, and 91.75% in environmental, social, and governance dimension data use case evaluation, respectively. The conclusion indicated that data mining and AHP could improve the accuracy of ESG management practice evaluation in enterprises, provide reliable decision support for enterprise development, and help promote sustainable development of enterprises.
ESG（环境、社会和治理）管理实践是促进制造企业可持续运营与发展的重要组成部分。当前，传统评估方法存在效率低下和缺乏客观性等局限性。为提高ESG评估的效率和准确性，促进制造企业ESG绩效优化，本文结合数据挖掘和层次分析法（AHP）对制造企业ESG管理实践评估进行有效研究。本文采用最佳优先搜索策略收集和处理企业ESG数据。通过运用AHP为目标问题构建分层和分段目标，基于评估目标和层次优先顺序建立了管理实践绩效评估指标体系。最后，基于ESG管理实践的绩效评估，应用K近邻算法分析关键指标的历史数据。根据权重，对各项关键指标进行重新整合，实现对企业ESG管理的实际评估和决策支持。为验证数据挖掘和AHP的有效性，本文以Z企业为研究对象并对其进行实证分析。结果表明，在评估准确性方面，本文提出的方法在环境、社会和治理维度数据用例评估中分别达到了92.51%、91.16%和91.75%的最高评估准确率。结论表明，数据挖掘和AHP能够提高企业ESG管理实践评估的准确性，为企业发展提供可靠的决策支持，并有助于推动企业可持续发展。
