Firms increasingly face pressure to demonstrate their environmental sustainability, which sets strong incentives for these firms to engange in greenwashing by misleading stakeholders about their environmental impact. While prior research has extensively examined greenwashing through overt false claims or decoupling communication from action, this study sheds light on an overlooked dimension of greenwashing that we term greenwashing by omission. We define this concept as the strategic exclusion of information about environmental violations in corporate disclosures to appear greener. Using a comprehensive dataset of 648,164 environmental violations in the United States from 2000 to 2024, we develop and employ a fine-tuned natural language processing (NLP) model to detect greenwashing by omission in 10-K filings and earnings call transcripts of S&P 500 firms. Our analysis reveals that 34% of 10-K filings omit environmental violations entirely, with omissions even more prevalent in earnings calls (90.6%). Our findings highlight that despite increased steakholder pressure and regulatory scrutiny, greenwashing by omission persists. Our contributions are twofold. First we conceptualize and empirically validate greenwashing by omission which enriches the discourse on corporate greenwashing. Second we develop a novel and scalable methodology to detect greenwashing by omission using NLP, paving the way for future research and policy interventions to promote transparency in corporate sustainability reporting.
