Unmasking greenwashing in ESG disclosure: insights from evolutionary game analysis

Abstract Environmental, Social, and Governance (ESG) disclosure is acknowledged as a compelling initiative to facilitate sustainable business practices. However, greenwashing undermines the credibility of this initiative, presenting a decision dilemma for stakeholders. Involving principal stakeholders (enterprises, investors, rating agencies) in ESG disclosure, this study conducts an evolutionary game analysis to explore the strategic evolution mechanisms. The equilibrium results suggest the potential market dilemma stemming from deceptive enterprises and unreliable rating agencies. Meanwhile, the system can converge to an ideal state without greenwashing. Reaching this state necessitates a market-based approach combined with government regulations, such as the ongoing monitoring of rating agencies to provide truthful and stringent ESG evaluation. Additionally, mandatory enterprise ESG disclosure is a robust measure to curb greenwashing. Investment-returns-based solutions can be considered for investors to augment the rigorous ESG ratings. Managers should understand the impact factors and evolution paths in ESG disclosure and how to deal effectively with greenwashing.

Authors

Institutions

Publication Details

Journal
Annals of Operations Research
Published
2025-03-08
DOI
https://doi.org/10.1007/s10479-025-06538-3
Citations
10
Primary Topic
Corporate Social Responsibility Reporting
Type
article
Field-Weighted Citation Impact
22.26

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Unmasking greenwashing in ESG disclosure: insights from evolutionary game analysis

Kee‐hung Lai, Tingting Tian, Liang Dong, Christina W.Y. Wong et al.
10 citations
Annals of Operations Research
Corporate Social Responsibility Reporting
22.26
article

Unmasking greenwashing in ESG disclosure: insights from evolutionary game analysis

Kee‐hung Lai, Tingting Tian, Liang Dong, Christina W.Y. Wong, Xinyu Hao
article en
10 citations

Abstract

Abstract Environmental, Social, and Governance (ESG) disclosure is acknowledged as a compelling initiative to facilitate sustainable business practices. However, greenwashing undermines the credibility of this initiative, presenting a decision dilemma for stakeholders. Involving principal stakeholders (enterprises, investors, rating agencies) in ESG disclosure, this study conducts an evolutionary game analysis to explore the strategic evolution mechanisms. The equilibrium results suggest the potential market dilemma stemming from deceptive enterprises and unreliable rating agencies. Meanwhile, the system can converge to an ideal state without greenwashing. Reaching this state necessitates a market-based approach combined with government regulations, such as the ongoing monitoring of rating agencies to provide truthful and stringent ESG evaluation. Additionally, mandatory enterprise ESG disclosure is a robust measure to curb greenwashing. Investment-returns-based solutions can be considered for investors to augment the rigorous ESG ratings. Managers should understand the impact factors and evolution paths in ESG disclosure and how to deal effectively with greenwashing.

Annals of Operations ResearchVol. 356(1)
Shanghai International Studies University (CN), Hong Kong Polytechnic University (HK), City University of Hong Kong (HK), City University of Hong Kong, Shenzhen Research Institute (CN), Shanghai University of International Business and Economics (CN)
Hong Kong Polytechnic University
Openalex Percentile: Top 1%
Corporate Social Responsibility Reporting
22.26
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.