The Green Hush: A foundational model of brownwashing and the impacts of greenwashing
Bibliographic record
Abstract
In the face of both increasing climate risks and growing market opportunities, numerous firms are rushing to trumpet their environmental and social achievements. Many firms are, however, seemingly paradoxically, choosing to minimize or withhold their sustainability accomplishments, an increasingly prominent phenomenon known as brownwashing or greenhushing. We draw on an in-depth qualitative study with over 50 firms in the North American wine industry to offer in situ insights on why and how firms engage in brownwashing. Our findings are a significant departure from prior research that has focused on brownwashing to mitigate negative stakeholder judgments. First, our findings reveal firms frequently engage in brownwashing driven by their own judgments of sustainability, specifically its constraints, branding, and community. Second, our analysis reveals the complex interaction between brownwashing and greenwashing and highlights how rampant greenwashing can cause sustainable firms to withdraw green claims, thus considerably advancing the literature on environmental communications. In short, firms are brownwashing because of rampant greenwashing by others. We then develop a foundational model to integrate previously established mechanisms (avoidance brownwashing) with our newly identified mechanisms (rejection brownwashing). As society seeks to engage firms in transparent reporting of sustainability to address grand challenges, our study offers timely theoretical and practical insights.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".