ESG-washing detection in corporate sustainability reports

Lagasio, Valentina

ESG-washing detection in corporate sustainability reports

International Review of Financial Analysis, 2024, vol. 96, issue PB

Abstract: This study introduces the ESG-washing Severity Index (ESGSI) for quantitatively assessing discrepancies between portrayed and actual sustainability practices in corporate disclosures. Using advanced Natural Language Processing (NLP) techniques, we analyze sustainability reports from 749 listed companies, integrating sentiment analysis with the frequency of sustainability terms to calculate the ESGSI. Our findings reveal significant variation in ESG-washing practices across industries and geographical regions. The ESGSI serves as a critical tool for stakeholders and policymakers, highlighting the need for stricter sustainability reporting standards and more effective regulatory frameworks to combat ESG-washing. This study contributes to the growing body of literature on corporate sustainability and provides practical implications for investors, corporate managers, and policymakers in evaluating and improving ESG practices and disclosures.

Keywords: Greenwashing; ESG-washing; Natural language processing; Transparency; ESG disclosure; ESG; Sustainability (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1057521924006744
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:finana:v:96:y:2024:i:pb:s1057521924006744

DOI: 10.1016/j.irfa.2024.103742

Access Statistics for this article

International Review of Financial Analysis is currently edited by B.M. Lucey

More articles in International Review of Financial Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().