ESGify: Automated Classification of Environmental, Social, and Corporate Governance Risks

Abstract

The growing recognition of environmental, social, and governance (ESG) factors in financial decision-making has spurred the need for effective and comprehensive ESG risk assessment tools. In this study, we introduce an open-source Natural Language Processing (NLP) model, “ESGify”^1,2, based on MPNet-base architecture and aimed to classify texts within the frames of ESG risks. We also present a hierarchical and detailed methodology for ESG risk classification, leveraging the expertise of ESG professionals and global best practices. Anchored by a manually annotated multilabel dataset of 2000 news articles and domain adaptation with texts of sustainability reports, ESGify is developed to automate ESG risk classification following the established methodology. We compare augmentation techniques based on back translation and Large Language Models (LLMs) to improve the model quality and achieve 0.5 F1-weighted model quality in the dataset with 47 classes. This result outperforms ChatGPT 3.5 with a simple prompt. The model weights and documentation is hosted on Github https://github.com/sb-ai-lab/ESGify under the Apache 2.0 license.