Assessing The Impact Of ESG Factors On Sustainable Finance In India: A Random Forest Regression Approach

Abstract

In contemporary economies, environmental, social, and governance (ESG) disclosures have become important factors in determining both financial performance and sustainable development. This study investigates how ESG disclosures affect Indian companies' financial results and how they contribute to long-term sustainability in financial development. This study uses a conceptual analysis to pinpoint the main elements that affect ESG reporting, including risk management, corporate governance, investor sentiment, and regu`latory compliance. In order to promote sustainable growth, this paper offers insights into how Indian businesses can strategically match their ESG initiatives with their financial objectives. The study also identifies future research directions and policy implications in the rapidly changing fields of corporate sustainability and ESG. Business Firm 1(BUSINESS FIRM 1), Business Firm 2, Business Firm 3, Business Firm 4, and Business Firm 5 are the main companies in this study that use Random Forest Regression to examine how ESG disclosures affect their financial performance. In order to evaluate their impact on profitability, market valuation, and cost of capital, the study looks at important ESG initiatives like carbon neutrality, board governance, corporate social responsibility (CSR) programs, and sustainability integration.The use of machine learning methods, specifically Random Forest Regression, offers important new information about the non-linear connections between financial performance and ESG factors. By providing policy recommendations for companies and regulators to enhance ESG reporting standards and promote sustainable financial growth in India, this study adds to the expanding body of research on ESG analytics.