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The relative importance of ESG pillars: A two‐step machine learning and analytical framework.

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Title: The relative importance of ESG pillars: A two‐step machine learning and analytical framework.
Authors: Mashayekhi, Bita1 (AUTHOR), Asiaei, Kaveh2 (AUTHOR) kaveh.asiaei@monash.edu, Rezaee, Zabihollah3 (AUTHOR), Jahangard, Amin4 (AUTHOR), Samavat, Milad1 (AUTHOR), Homayoun, Saeid5 (AUTHOR)
Source: Sustainable Development. Oct2024, Vol. 32 Issue 5, p5404-5420. 17p.
Subject Terms: *Environmental, social, & governance factors, *Corporate sustainability, Social responsibility of business, Cluster analysis (Statistics), Investors
Company/Entity: Thomson Reuters Corp.
Abstract: This study aims to contribute to the ongoing and inconclusive debates regarding the relative significance of environmental, social, and governance (ESG) sustainability key performance indicators and their correlation with overall sustainability performance. We present a unique two‐step analytical framework to leverage Thomson Reuters' ESG Score (ASSET4) database to determine the most impactful ESG pillars and their subcomponents at both the firm and industry levels. This framework integrates the Method based on the Removal Effects of Criteria (MEREC) with K‐means cluster analysis. Through the MEREC‐K‐means framework, we determine the two most noteworthy ESG pillars within various industries, subsequently clustering them to form peer groups for comprehensive comparative analysis. We find that while the social and economic pillars are the two fundamental pillars of ESG performance in all industries in general, this prioritization sometimes differs from industry to industry. This research makes theoretical and practical contributions by introducing a novel dimensionality reduction technique in ESG pillars, offering valuable insights for strategic resource allocation in corporate social responsibility (CSR) and sustainability initiatives. The implications of our findings extend broadly to investors, policymakers, and practitioners, empowering them to make more informed decisions. [ABSTRACT FROM AUTHOR]
Copyright of Sustainable Development is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: This study aims to contribute to the ongoing and inconclusive debates regarding the relative significance of environmental, social, and governance (ESG) sustainability key performance indicators and their correlation with overall sustainability performance. We present a unique two‐step analytical framework to leverage Thomson Reuters' ESG Score (ASSET4) database to determine the most impactful ESG pillars and their subcomponents at both the firm and industry levels. This framework integrates the Method based on the Removal Effects of Criteria (MEREC) with K‐means cluster analysis. Through the MEREC‐K‐means framework, we determine the two most noteworthy ESG pillars within various industries, subsequently clustering them to form peer groups for comprehensive comparative analysis. We find that while the social and economic pillars are the two fundamental pillars of ESG performance in all industries in general, this prioritization sometimes differs from industry to industry. This research makes theoretical and practical contributions by introducing a novel dimensionality reduction technique in ESG pillars, offering valuable insights for strategic resource allocation in corporate social responsibility (CSR) and sustainability initiatives. The implications of our findings extend broadly to investors, policymakers, and practitioners, empowering them to make more informed decisions. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Sustainable Development is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – SubjectFull: Cluster analysis (Statistics)
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      – TitleFull: The relative importance of ESG pillars: A two‐step machine learning and analytical framework.
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              Text: Oct2024
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