Utilising machine learning for corporate social responsibility (CSR) and environmental, social, and governance (ESG) evaluation: Transitioning from committees to climate

被引：0

作者：

Subramaniam, Ravichandran K. ^{[1
]}

Samuel, Shyamala Dhoraisingam ^{[2
]}

Seera, Manjeevan ^{[3
]}

Alam, Nafis ^{[4
]}

机构：

[1] Monash Univ Malaysia, Sch Business, Dept Finance, Subang Jaya, Malaysia

[2] 34 Jalan USJ9-3P, Subang Jaya 47620, Selangor, Malaysia

[3] Monash Univ Malaysia, Dept Econometr & Business Stat, Subang Jaya, Malaysia

[4] Monash Univ Malaysia, Sch Business, Selangor, Malaysia

来源：

SUSTAINABLE FUTURES | 2024年 / 8卷

关键词：

CSR committees; ESG initiatives; Corporate sustainability; Environmental performance; Machine learning;

D O I：

10.1016/j.sftr.2024.100329

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

Transparency and accountability are critical components of corporate sustainability. This study uses machine learning and empirical analysis to examine the influence of corporate social responsibility (CSR) committees and environmental, social, and governance (ESG) initiatives on corporate sustainability. Using 2017-2021 Bloomberg Terminal data, we investigated the environmental footprints, disclosure practices, risk profiles, and ESG fund commitments of Fortune 500 companies. Key findings indicate that CSR committees positively impact environmental performance, with an increase in environmental responsibility over time. Policy implications highlight the necessity for collaboration to prioritize environmental sustainability and address climate risk disclosure auditing within the audit profession.

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页数：10

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