EXPLAINING ESG-DRIVEN FINANCIAL AND SUSTAINABILITY PERFORMANCE IN INDIAN CORPORATIONS USING SHAP AND LIME-BASED INTERPRETABILITY APPROACH

Abstract

In recent years, Environmental, Social, and Governance (ESG) factors have gained significant attention in shaping corporate financial performance and sustainability strategies. This study investigates the impact of ESG initiatives on the financial and sustainability performance of Indian corporations using a machine learning-based interpretability approach. Specifically, we employ SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) to analyze and interpret the influence of ESG components on corporate performance metrics. By leveraging these explainable AI techniques, we provide insights into how ESG factors drive profitability, risk management, and long-term value creation. This study investigates the impact of ESG initiatives on the financial and sustainability performance of leading Indian corporations, specifically Tata Consultancy Services (TCS), Infosys, Reliance Industries, Mahindra & Mahindra, and ITC Limited. The findings highlight the critical ESG determinants that contribute to sustainable financial growth, offering valuable implications for policymakers, investors, and corporate decision-makers. This research bridges the gap between ESG investment strategies and their quantifiable impact, enhancing transparency in corporate sustainability practices within the Indian business landscape.