The digitization of ESG matrix

Investors and stakeholders have intensified the demand for responsible and sustainable business practices. However, traditional ESG ratings suffer from transparency, comparability, and consistency challenges, limiting stakeholders' ability to make meaningful comparisons and interpretations. Despite efforts by organizations like GRI, CDP, and SASB to standardize ESG reporting frameworks, a universal definition remains elusive. This study proposes an advanced ESG rating technique based on key categories and consolidated indicators. This technique establishes a standardized and reliable approach to measuring ESG performance. Moreover, the study is grounded in sustainability theories which emphasize standardizing ESG metrics. This study proposes the adoption of natural language processing to identify, and auto-populate the consolidated ESG matrix. ML (Machine Learning) algorithms are used to identify and analyze sustainability-related documents and reports, such as annual reports, and sustainability statements. The research findings may interest managers interested in integrating social responsibility into their decision-making process.