ESG-KIBERT: A new paradigm in ESG evaluation using NLP and industry-specific customization

Abstract

This study presents a significant advancement in Environmental, Social, Governance (ESG) evaluation by addressing critical gaps in transparency, consistency, and industry-specific relevance. The ESG-Keyword integrated bidirectional encoder representations from transformers (ESG-KIBERT) model, developed using advanced natural language processing (NLP) techniques, enhances ESG classification performance and sets a new standard for automated ESG analysis. With robust performance metrics, it supports reliable and consistent assessments across industries. Additionally, incorporating Sustainability Accounting Standards Board's materiality map offers a customized evaluation framework that accounts for industry-specific factors affecting corporate sustainability. Furthermore, the integration of sentiment analysis enriches ESG evaluations by capturing market and investor perceptions, contributing to a more transparent assessment. This study offers a comprehensive, standardized ESG evaluation framework that improves both the methodological rigor and practical utility of corporate sustainability assessments, enabling more informed decision-making for companies, investors and policymakers.