Abstract Environmental, Social, and Governance (ESG) has been used as a metric to measure the neg ative 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 doc uments across these labels. Our analysis re vealed that the RoBERTa classifier emerged as one of the most successful approaches, secur ing the second-place position for the English test dataset, and sharing the fifth-place posi tion for the French test dataset. Furthermore, our SVM-based binary model tailored for the Chinese language exhibited exceptional perfor mance, earning the second-place rank on the test dataset.