Optimizing ESG reporting : innovating with E-BERT models in nature language processing

Abstract

Developing a quantitative Environmental, Social, and Governance (ESG) rating tool using technology to reduce manpower requirements and ensure the objectivity and authenticity of the ratings is an important research question in the ESG field. The primary objective of this research is to develop an automated system, encapsulated in the Bidirectional Encoder Representation from Transformers for ESG rating (E-BERT) model, that evaluates ESG information with a high degree of objectivity and consistently delivers reliable rating outcomes. The E-BERT model, an advanced natural language processing (NLP)-based tool, is designed to transform ESG reporting by automating the evaluation process. Utilizing the architecture of Google’s BERT, this model offers precise and consistent ESG ratings, focusing on accurately assessing the sustainability contributions of enterprises. This research is initiated due to the absence of a comprehensive ESG dataset and involves constructing a tailored corpus that supports the E-BERT model. E-BERT meets the demand for objective ESG assessments by filtering out irrelevant data and standardizing criteria across various sectors, thereby streamlining the process and minimizing the need for manual intervention. This model not only automates the rating process but also achieves a notable 93% accuracy rate. By enhancing transparency and reducing biases, E-BERT marks a substantial improvement in ESG reporting, offering a robust tool for stakeholders to reliably assess corporate ESG performance. The success of the E-BERT model confirms its potential as a pivotal resource in supporting informed decision-making for sustainable development.