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Session Chair: Bo Bian, University of British Columbia Discussant: Alejandro Lopez-Lira, University of Florida
Location:9B301 (3rd basement floor, International Hall)
Presentations
Greenwashing: Measurement and Implications
Qiyang He1, Ben Marshall2, Justin Hung Nguyen3, Nhut H. Nguyen4, Buhui Qiu1, Nuttawat Visaltanachoti2
1University of Sydney Business School, Australia; 2Massey University; 3Edith Cowan University; 4Auckland University of Technology
This study leverages earnings conference call transcripts and the FinBERT machine learning model to measure greenwashing (GW) intensity across a broad sample of U.S. public firms from 2005 to 2021. We document an economy-wide increase in GW intensity following the 2015 Paris Agreement, with a significant rise in GW among fossil fuel and stranded asset industries. Higher GW intensity is linked to more future environmental incidents, EPA enforcement actions, and higher carbon emissions, but not to increased green innovation. GW is associated with lower cumulative abnormal stock returns post-earnings calls and poorer future operating performance, especially in firms with greater information asymmetry and weaker institutional monitoring. GW firms receive higher future environmental ratings, face lower forced CEO turnover, exhibit reduced CEO pay-for-performance sensitivity, and are more likely to link CEO pay to corporate environmental performance. Additionally, these firms show reduced risk-taking behaviors. Our findings suggest an agency motivation for GW, where managers engage in GW to enhance their job security and compensation at the expense of shareholders.