What will a greenwashing ban entail for the sport industry?
Bibliographic record
Abstract
Greenwashing is a widespread phenomenon in many industries, including in sport. Legal repercussions of greenwashing are intensifying, however, as evidenced by the European Union (EU) Directive on Green Claims passed in January 2024. The Green Claims Directive (hereafter, The Directive) aims to curb misleading environmental claims and better regulate sustainability certification and product labeling. This commentary explores the anticipated impacts of The Directive on the sports industry, with a particular focus on marketing and communication practices. In response to The Directive, sports organizations in the EU will need to adjust their marketing and communication strategies. This adjustment can improve transparency, yet organizations may resort to developing new terminology, potentially perpetuating greenwashing in alternative forms and challenging the integrity of sport communication. It is not sufficient for organizations to only alter communication practices, as environmental practices must permeate all organizational practices, with metrics and reporting to validate claims and ensure alignment between communication and action. A research agenda across five areas is proposed to address organizational transitions needed for ethical environmental action and communication, to enhance sport’s opportunity to drive climate action.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".