Authors: Qiyang He (University of Sydney), Buhui Qiu (University of Sydney), Ben Marshall (Massey University), Justin Nguyen (Edith Cowan University), Nhut Nguyen (Auckland University of Technology) and Nuttawat Visaltanachoti (Massey University)
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 2007 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 and 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.
Authors: Quyen Nguyen (University of Otago), Ivan Diaz-Rainey (Griffith University), Adam Kitto (University of New South Wales), Nicholas Pittman (EMMI), Renzhu Zhang (University of Otago)
