Utilising machine learning for corporate social responsibility (CSR) and environmental, social, and governance (ESG) evaluation: Transitioning from committees to climate
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 sustainab...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Sustainable Futures |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666188824001783 |
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| author | Ravichandran K. Subramaniam Shyamala Dhoraisingam Samuel Manjeevan Seera Nafis Alam |
| author_facet | Ravichandran K. Subramaniam Shyamala Dhoraisingam Samuel Manjeevan Seera Nafis Alam |
| author_sort | Ravichandran K. Subramaniam |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-33c3cf84442f40fd8df9dc61be20fba7 |
| institution | Kabale University |
| issn | 2666-1888 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Sustainable Futures |
| spelling | doaj-art-33c3cf84442f40fd8df9dc61be20fba72024-12-18T08:52:39ZengElsevierSustainable Futures2666-18882024-12-018100329Utilising machine learning for corporate social responsibility (CSR) and environmental, social, and governance (ESG) evaluation: Transitioning from committees to climateRavichandran K. Subramaniam0Shyamala Dhoraisingam Samuel1Manjeevan Seera2Nafis Alam3Department of Finance, School of Business, Monash University Malaysia, Malaysia; Corresponding author.Independent Researcher, No 34, Jalan USJ9/3P, Subang Jaya, 47620, Selangor, MalaysiaDepartment of Econometrics & Business Statistics, Monash University Malaysia, MalaysiaSchool of Business, Monash University Malaysia, MalaysiaTransparency 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.http://www.sciencedirect.com/science/article/pii/S2666188824001783CSR committeesESG initiativesCorporate sustainabilityEnvironmental performanceMachine learning |
| spellingShingle | Ravichandran K. Subramaniam Shyamala Dhoraisingam Samuel Manjeevan Seera Nafis Alam Utilising machine learning for corporate social responsibility (CSR) and environmental, social, and governance (ESG) evaluation: Transitioning from committees to climate Sustainable Futures CSR committees ESG initiatives Corporate sustainability Environmental performance Machine learning |
| title | Utilising machine learning for corporate social responsibility (CSR) and environmental, social, and governance (ESG) evaluation: Transitioning from committees to climate |
| title_full | Utilising machine learning for corporate social responsibility (CSR) and environmental, social, and governance (ESG) evaluation: Transitioning from committees to climate |
| title_fullStr | Utilising machine learning for corporate social responsibility (CSR) and environmental, social, and governance (ESG) evaluation: Transitioning from committees to climate |
| title_full_unstemmed | Utilising machine learning for corporate social responsibility (CSR) and environmental, social, and governance (ESG) evaluation: Transitioning from committees to climate |
| title_short | Utilising machine learning for corporate social responsibility (CSR) and environmental, social, and governance (ESG) evaluation: Transitioning from committees to climate |
| title_sort | utilising machine learning for corporate social responsibility csr and environmental social and governance esg evaluation transitioning from committees to climate |
| topic | CSR committees ESG initiatives Corporate sustainability Environmental performance Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2666188824001783 |
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