The Evolving Landscape of Big Data Analytics and ESG Materiality Mapping

Gopal, Sucharita; Pitts, Joshua; Inampudi, Kalyani; Xu, Yingqiang; Cook, Graham

doi:10.3905/jesg.2021.1.034

Cited by 3 publications

(1 citation statement)

References 24 publications

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“…Academic investigation of materiality reflects the diverse aspects of this important concept [10]. In literature, various materiality analyses have been performed, both at an academic level [1], [11][12][13] and in a business-oriented framework [14][15][16]. Materiality can affect the accuracy and inclusiveness of ESG scores and ratings [17], while enhancing the ambiguity around rating divergence among the different rating agencies and systems.…”

Section: Introductionmentioning

confidence: 99%

A novel ESG Materiality methodology combining criterion level and sector-based approaches.

Kakogiannis¹,

Doukas

Chrysanthopoulos³

et al. 2023

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As the ESG principles are rapidly becoming critical in the decision-making process of investment undertaking, companies must deal with ESG reporting either to fulfil obligatory requirements according to EU legislation or, to stay competitive and in line with the investing trends. In the market, several ESG rating methodologies exist consisting of numerous criteria and indicators that companies must consider in their reporting. Nevertheless, neither all ESG rating frameworks consider the same criteria, nor do all criteria have the same materiality weighting when estimating the final ESG rating. Thus, the establishment of a standardized and normalized ESG criteria materiality framework is considered crucial. This will enable the fine-tuning and calibration of the ESG evaluation, with materiality values that reflect accordingly the significance of the most important criteria. In addition, this methodology will enhance the comparability of the results of the different companies’ evaluations while creating a harmonized framework. The present manuscript introduces an integrated methodology for the estimation of the ESG materiality factors putting emphasis on the most frequent criteria of the main economic sectors. The methodology analyses data from several sources, including academic papers and methodologies, companies’ reports and globally established rating frameworks. The proposed approach results in the estimation of the materiality values for each criterion of a specific ESG rating scorecard, as well as introduces an overview of the materiality issues for each economic sector.

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Section: Introductionmentioning

confidence: 99%

A novel ESG Materiality methodology combining criterion level and sector-based approaches.

Kakogiannis¹,

Doukas

Chrysanthopoulos³

et al. 2023

View full text Add to dashboard Cite

show abstract

Environmental, social, and governance (ESG) and artificial intelligence in finance: State-of-the-art and research takeaways

Lim

2024

Artif Intell Rev

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The rapidly growing research landscape in finance, encompassing environmental, social, and governance (ESG) topics and associated Artificial Intelligence (AI) applications, presents challenges for both new researchers and seasoned practitioners. This study aims to systematically map the research area, identify knowledge gaps, and examine potential research areas for researchers and practitioners. The investigation focuses on three primary research questions: the main research themes concerning ESG and AI in finance, the evolution of research intensity and interest in these areas, and the application and evolution of AI techniques specifically in research studies within the ESG and AI in finance domain. Eight archetypical research domains were identified: (i) Trading and Investment, (ii) ESG Disclosure, Measurement and Governance, (iii) Firm Governance, (iv) Financial Markets and Instruments, (v) Risk Management, (vi) Forecasting and Valuation, (vii) Data, and (viii) Responsible Use of AI. Distinctive AI techniques were found to be employed across these archetypes. The study contributes to consolidating knowledge on the intersection of ESG, AI, and finance, offering an ontological inquiry and key takeaways for practitioners and researchers. Important insights include the popularity and crowding of the Trading and Investment domain, the growth potential of the Data archetype, and the high potential of Responsible Use of AI, despite its low publication count. By understanding the nuances of different research archetypes, researchers and practitioners can better navigate this complex landscape and contribute to a more sustainable and responsible financial sector.

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Environmental, Social, and Governance (ESG) and Artificial Intelligence in Finance: State-of-the-Art and Research Takeaways

Lim

2023

Preprint

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The rapidly growing research landscape in finance, encompassing environmental, social, and governance (ESG) topics and associated Artificial Intelligence (AI) applications, presents challenges for both new researchers and seasoned practitioners. This study aims to systematically map the research area, identify knowledge gaps, and examine potential research areas for researchers and practitioners. The investigation centers around three research questions: key research themes for ESG and AI in finance, research intensity and interest evolution, and the use and progression of AI techniques within these themes. Eight archetypical research domains were identified: (i) Trading and Investment, (ii) ESG Disclosure, Measurement and Governance, (iii) Firm Governance, (iv) Financial Markets and Instruments, (v) Risk Management, (vi) Forecasting and Valuation, (vii) Data, and (viii) Responsible Use of AI. Distinctive AI techniques were found to be employed across these archetypes. The study contributes to consolidating knowledge on the intersection of ESG, AI, and finance, offering an ontological inquiry and key takeaways for practitioners and researchers. Important insights include the popularity and crowding of the Trading and Investment domain, the growth potential of the Data archetype, and the high potential of Responsible Use of AI, despite its low publication count. By understanding the nuances of different research archetypes, researchers and practitioners can better navigate this complex landscape and contribute to a more sustainable and responsible financial sector.

show abstract

The Evolving Landscape of Big Data Analytics and ESG Materiality Mapping

Cited by 3 publications

References 24 publications

A novel ESG Materiality methodology combining criterion level and sector-based approaches.

A novel ESG Materiality methodology combining criterion level and sector-based approaches.

Environmental, social, and governance (ESG) and artificial intelligence in finance: State-of-the-art and research takeaways

Environmental, Social, and Governance (ESG) and Artificial Intelligence in Finance: State-of-the-Art and Research Takeaways

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