近年來，永續議題引起了社會的廣泛關注，環境保護、社會責任以及公司治理(ESG)成為評估企業表現的有效工具之一，愈來愈多的投資人將企業每年公開的ESG訊息視為投資決策的參考依據，倘若企業能夠在ESG永續報告書中獲得較好的評級，這將增強投資人對企業未來的信任，並促進更好的融資機會。然而，目前國際上存在著多種評估ESG表現的模型，但缺乏以企業需求為出發點，提供企業改善ESG績效表現方向指引的模型。此外，儘管當前的相關法規和全球報告皆顯示企業提升ESG評級沒有特定的規律，但可通過了解卓越企業的ESG表現狀況，進而推敲企業自身的改善空間。因此，本研究選取已發行過永續報告書之企業為研究對象，並基於類神經網路方法建構一套評估績效與預測改善方向之系統。該系統包含績效模型與改善模型，前者用於「預測企業ESG之績效表現」，以幫助企業明白其於市場中的定位；而後者則是「預測企業改善方向」，通過學習等級上升資料，為企業提供可遵循之改善指引。本研究使用了Refinitiv Eikon機構所蒐集之ESG資料庫，並選擇了「工業」行業作為模型的輸入數據集。在過程中本研究應用SMOTE模組來處理改善模型資料不平衡之問題，且更進一步透過熱力圖及SHAP套件檢視每項指標的影響力，亦探討了ESG表現優異企業之支出情形，以說明指標的重要性。最終研究結果顯示，績效與改善兩模型之預測能力皆在多個評估指標中擁有突出表現，這意味著企業可以通過本研究提出的模型獲得準確的績效等第評估和可信的改善方向，而由一實際案例驗證了改善模型之有效性，表明了本研究的方法能夠為企業在實施ESG績效改善行動時提供一劑可靠的改善方針。

The importance of sustainability issues is increasing, and the Environmental, Social, and Governance (ESG) pillars have emerged as an effective tool for evaluating the performance of businesses. As investors begin to consider ESG reports as the investment references, achieving higher ESG ratings in the report can enhance investor trust and create a positive impression of enterprise's future prospects. Hence, improving ESG performance can lead to higher firm valuation and investment returns. However, the existing regulations and world-wide reports have revealed that there is no unique rules or procedures for an enterprise to enhance their ESG ratings. Moreover, previous literature has not provided clear guidelines on improving corporate ESG performance. For enterprises to achieve a higher rating, they need to know what are the status of the leading ESG enterprises and where they need to improve. In this study, we propose a corporate ESG performance improvement system, which includes two models of performance evaluation and improvement. The performance evaluation model is used to predict the rating of corporate ESG performance, and the improvement model is used to identify the directions and the issues still need to be improved. In addition, we apply random forest (RF) to build models to learn corporate historical data features in the“Industrial”category, using the SMOTE module to deal with data imbalance in the improvement model. Finally, we use the heatmap and SHAP package to examine the influence of each indicator and then illustrate the importance of indicators by discussing the expenditures of companies with outstanding ESG performance. The results show that this study's performance and improvement model have achieved outstanding capability in multiple evaluation metrics, which means that enterprises can obtain accurate performance prediction and effective improvement directions through this system.

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