Advanced TRST01 ESG Scoring Model with Beta Based Financial Metrics and Machine Learning Techniques

Abstract

In the current corporate world, assessing a company’s sustainability performance is very important for investors, stakeholders, and policymakers. The TRST01’s ESG (Environmental. Social and Governance) Scoring Model introduces an innovative approach integrating beta-based financial metrics with advanced machine learning techniques to comprehensively evaluate ESG credentials. This study demonstrates the development and application of the TRST01’s ESG scoring model, which leverages data from the most reputable sources such as MSCI and S&P Global to ensure its reliability and accuracy. The model’s unique methodology involves calculating country-specific beta values to normalize carbon emission data, thereby providing a standardized metric for meaningful comparisons across countries. Further, ESG scores are adjusted using both country and company beta values to reflect specific risk exposures, enhancing the precision and relevance of the assessments. The model ensures robust input data quality, by taking Market capital, Scope 1, Scope 2, industry wis