Turning tobacco Extended Producer Responsibility (EPR) into Extended Producer Liability (EPL): critical safeguards for the UN Plastics Treaty
Bibliographic record
Abstract
Extended Producer Responsibility (EPR) is recognised as a valuable tool for environmental management of products' end-of-life impacts; it was featured in all the draft negotiating text for the future UN Plastics Treaty. However, when applied to the tobacco industry, its implementation faces significant challenges due to the industry's historical manipulation of health policies. This study explores the inherent contradictions in using EPR schemes for tobacco products, which are designed to make producers 'stewards' for the life cycle of their products, including end-of-life impacts. The study highlights the potential for tobacco companies to exploit these schemes to weaken health regulations and greenwash their public image. By examining frameworks like the European Union Single-Use Plastics Directive alongside the UN Plastics Treaty negotiations, the study stresses the need for stringent safeguards to ensure EPR schemes do not serve as tools for greenwashing but support health and environmental objectives. The study also proposes enhanced regulatory measures, such as redefining EPR for tobacco as 'Extended Producer Liability' and integrating it with WHO Framework Convention on Tobacco Control guidelines.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.037 | 0.043 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".