Navigating the Environmental, Social, and Governance (ESG) landscape: constructing a robust and reliable scoring engine - insights into Data Source Selection, Indicator Determination, Weighting and Aggregation Techniques, and Validation Processes for Comprehensive ESG Scoring Systems

Liu, Yiting; Osterrieder, Jörg Robert; Hadji Misheva, Branka; Koenigstein, Nicole; Baals, Lennart John (2023). Navigating the Environmental, Social, and Governance (ESG) landscape: constructing a robust and reliable scoring engine - insights into Data Source Selection, Indicator Determination, Weighting and Aggregation Techniques, and Validation Processes for Comprehensive ESG Scoring Systems Open Research Europe, 3, p. 119. F1000 Research 10.12688/openreseurope.16278.1

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This white paper explores the construction of a reliable Environmental, Social, and Governance (ESG) scoring engine, with a focus on the importance of data sources and quality, selection of ESG indicators, weighting and aggregation methodologies, and the necessary validation and benchmarking procedures. The current challenges in ESG scoring and the importance of a robust ESG scoring system are addressed, citing its increasing relevance to stakeholders. Furthermore, different data types, namely self-reported data, third-party data, and alternative data, are critically evaluated for their respective merits and limitations. The paper further elucidates the complexities and implications involved in the choice of ESG indicators, illustrating the trade-offs between standardized and customized approaches. Various weighting methodologies including equal weighting, factor weighting, and multi-criteria decision analysis are dissected. The paper culminates in outlining processes for validating the ESG scoring engine, emphasizing the correlation with financial performance, and conducting robustness and sensitivity analyses. Practical examples through case studies exemplify the implementation of the discussed techniques. The white paper aims to provide insights and guidelines for practitioners, academics, and policy makers in designing and implementing robust ESG scoring systems. This ESG white paper explores the interplay between Environmental, Social, and Governance (ESG) factors and green finance. We begin by defining ESG and green finance, exploring their evolution, and discussing their importance in financial markets. The paper emphasises the role of green finance in driving sustainable development. Next, we delve into the ESG scoring landscape. We outline various methodologies, key players in ESG ratings, and present challenges and criticisms of current ESG scoring systems. In the third section, we propose a blueprint for a reliable ESG scoring engine. This includes discussion on various data sources and the selection of ESG indicators, highlighting the role of materiality assessment, and the balance between standardized and customized indicators. We then discuss different methodologies for weighting and aggregating these indicators. The paper concludes with the necessity of validation and benchmarking of ESG scores, particularly correlating them with financial performance and performing robustness and sensitivity analyses.

Item Type:	Journal Article (Original Article)
Division/Institute:	Business School > Institute for Applied Data Science & Finance Business School
Name:	Liu, Yiting0009-0006-9554-8205; Osterrieder, Jörg Robert0000-0003-0189-8636; Hadji Misheva, Branka; Koenigstein, Nicole and Baals, Lennart John
Subjects:	H Social Sciences > HG Finance H Social Sciences > HV Social pathology. Social and public welfare
ISSN:	2732-5121
Publisher:	F1000 Research
Funders:	Organisations 0 not found.; Organisations 0 not found.; [7] Swiss National Science Foundation
Language:	English
Submitter:	Yan Liu
Date Deposited:	28 Nov 2023 08:24
Last Modified:	12 Dec 2023 14:29
Publisher DOI:	10.12688/openreseurope.16278.1
Uncontrolled Keywords:	Environmental, Social, and Governance (ESG); ESG Scoring Engine; ESG Indicator Selection; Weighting Methodologies; Validation
ARBOR DOI:	10.24451/arbor.20516
URI:	https://arbor.bfh.ch/id/eprint/20516