An innovative performance assessment approach of ESG-(environment, social and governance) related risk management

公司治理 业务 过程管理 风险管理 风险治理 环境资源管理 环境科学 财务
作者
Meheresh Masanpally,Titas Bhattacharjee,Talib E. Butt
出处
期刊:Journal of Accounting Literature [Elsevier BV]
卷期号:: 1-28
标识
DOI:10.1108/jal-12-2024-0392
摘要

Purpose This study addresses the challenges posed by interwoven complexities that expose businesses to ESG (environmental, social and governance) risks. Specifically, the study aims to investigate and provide mechanisms to effectively use COSO’s (Committee of Sponsoring Organizations of the Treadway Commission) guidance in managing ESG-related risks. Design/methodology/approach This study adopts a “combined approach,” integrating reductionistic risk management (COSO’s ERM) and holistic sustainability perspectives (India’s BRSR–Business Responsibility and Sustainability Report). It developed a worldwide transferable ESG risk management mechanism based on COSO’s and WBCSD’s ERM guidance, GRI (Global Reporting Initiative) and Integrated Reporting standards. It then applied to BRSR to create a country-specific performance assessment tool for ESG-related risk management. This tool, which categorizes 55 risk-relevant disclosures into ESG dimensions, has a four-point scale developed through sustainability-related content analysis. The study is validated with machine learning algorithms and experts’ opinions to enhance the robustness of the findings. The tool is operationalized on three Indian companies. Findings The study’s findings reveal a gap in existing mechanisms for supporting the effective use of ERM in managing ESG-related risks. The two-folded mechanism demonstrates its applicability globally while offering a tailored solution for India through the BRSR system. Application of the developed tool to three real-world use cases showcases its practical effectiveness in addressing and mitigating ESG risks. Originality/value This study offers a novel approach to assessing ESG-related risks. The two-folded mechanism adds originality to existing literature. Additionally, validating the “holistic and reductionistic” approach with machine learning algorithms enhances the originality and innovation of the study, providing a valuable resource for diverse stakeholders involved in decision-making related to ESG risks.

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