Environmental, Social, and Governance (ESG) has been used as a metric to measure the negative impacts and enhance positive outcomes of companies in areas such as the environment, society, and governance. Recently, investors have increasingly recognized the significance of ESG criteria in their investment choices, leading businesses to integrate ESG principles into their operations and strategies. The Multi-Lingual ESG Issue Identification (ML-ESG) shared task encompasses the classification of news documents into 35 distinct ESG issue labels. In this study, we explored multiple strategies harnessing BERT language models to achieve accurate classification of news documents across these labels. Our analysis revealed that the RoBERTa classifier emerged as one of the most successful approaches, securing the second-place position for the English test dataset, and sharing the fifth-place position for the French test dataset. Furthermore, our SVM-based binary model tailored for the Chinese language exhibited exceptional performance, earning the second-place rank on the test dataset.

翻译：环境、社会与治理（ESG）已成为衡量企业在环境、社会及治理等领域负面影响的减轻与正面效果提升的指标。近年来，投资者日益认识到ESG标准在投资决策中的重要性，促使企业将ESG原则融入其运营与战略之中。多语言ESG议题识别（ML-ESG）共享任务涵盖了将新闻文档分类至35个不同ESG议题标签的过程。在本研究中，我们探索了多种利用BERT语言模型实现新闻文档在这些标签间精准分类的策略。我们的分析表明，RoBERTa分类器是最成功的方法之一，在英语测试数据集中位列第二，并在法语测试数据集中并列第五。此外，我们专为中文定制的基于支持向量机（SVM）的二分类模型展现出卓越性能，在测试数据集中获得第二名。