No End in Sight? A Greenwash Review and Research Agenda
Bibliographic record
Abstract
Greenwashing is more virulent than ever. A profusion of environmental, social, and governance and net zero commitments are becoming fraught with questionable and misleading claims. At the same time, we are no closer to solving the pressing environmental and social issues of our time. In this review, we seek to examine this shift and summarize changes in greenwash research into three key phases: (a) 1.0 Static Communication; (b) 2.0 Dynamic Management; and (c) 3.0 Narratives about the Future. We analyze current key areas of developing literature and point to numerous open questions for future research. Next, we go beyond much of the published work to examine emerging tactics and lay out a forward-looking agenda for future research. We also propose a model of Corporate Miscommunication, integrating various streams in greenwash research. In doing so, we seek to lay a pathway for greenwashing researchers to finally find that elusive "end" to greenwashing.
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".