Facilitating or Inhibiting: A Study on the Impact of Artificial Intelligence on Corporate Greenwashing

As a significant driving force behind the latest wave of technological innovation, artificial intelligence profoundly influences corporate greenwashing while advancing the digital and intelligent transformation of enterprises. This paper empirically examines the impact of AI technology on corporate greenwashing and its mechanisms of action using text analysis and word frequency statistics. This study considers the frequency of references to AI in the annual reports of enterprises and the ESG scores of these enterprises as samples. The research findings indicate that the application of AI technology can effectively curb the occurrence of greenwashing behavior. The mechanisms of influence suggest that green innovation plays a partial mediating role in the relationship between AI and corporate greenwashing, while imitation pressure and financial pressure enhance the inhibitory effect of AI technology on this behavior. Further analysis reveals that the inhibitory effect of AI on corporate greenwashing is particularly pronounced in non-state-owned enterprises, large-scale enterprises, and enterprises within high-pollution industries. This paper not only enhances the existing literature on how AI can promote enterprise greening but also offers valuable insights into how governments and enterprises can mitigate corporate greenwashing behavior.

Authors

Institutions

Publication Details

Journal
Sustainability
Published
2025-03-02
DOI
https://doi.org/10.3390/su17052154
Citations
8
Primary Topic
Impact of AI and Big Data on Business and Society
Type
article
Field-Weighted Citation Impact
16.85
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Facilitating or Inhibiting: A Study on the Impact of Artificial Intelligence on Corporate Greenwashing

De‐Li Shi, Xueying Tian
8 citations
Sustainability
Impact of AI and Big Data on Business and Society
16.85
article

Facilitating or Inhibiting: A Study on the Impact of Artificial Intelligence on Corporate Greenwashing

De‐Li Shi, Xueying Tian
article en
8 citations

Abstract

As a significant driving force behind the latest wave of technological innovation, artificial intelligence profoundly influences corporate greenwashing while advancing the digital and intelligent transformation of enterprises. This paper empirically examines the impact of AI technology on corporate greenwashing and its mechanisms of action using text analysis and word frequency statistics. This study considers the frequency of references to AI in the annual reports of enterprises and the ESG scores of these enterprises as samples. The research findings indicate that the application of AI technology can effectively curb the occurrence of greenwashing behavior. The mechanisms of influence suggest that green innovation plays a partial mediating role in the relationship between AI and corporate greenwashing, while imitation pressure and financial pressure enhance the inhibitory effect of AI technology on this behavior. Further analysis reveals that the inhibitory effect of AI on corporate greenwashing is particularly pronounced in non-state-owned enterprises, large-scale enterprises, and enterprises within high-pollution industries. This paper not only enhances the existing literature on how AI can promote enterprise greening but also offers valuable insights into how governments and enterprises can mitigate corporate greenwashing behavior.

SustainabilityVol. 17(5)
Shanghai University of Engineering Science (CN)
Openalex Percentile: Top 1%
Impact of AI and Big Data on Business and Society
16.85
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.