Assessing the ecological and economic transformation pathways of plastic production system
Bibliographic record
Abstract
Plastic's incredible versatility drives its continuous production growth, contributing to 4.5% of global greenhouse gas (GHG) emissions. With an unsustainable 4% annual production growth rate, plastics' environmental impact is significant. Our study, using climate and economic models, assesses the effects of a voluntary plastic levy imposed on the top 100 resin producers. The results suggest a potential 70% reduction in global plastic production emissions by 2050, lowering emissions from business–as–usual levels to 1.62 Gt CO 2 e. The proposed USD 82.5 billion levy over 25 years could fund recycling initiatives, increasing recycling rates by 73%. To align with the Paris Agreement target of 1.5 °C, plastic production growth would need to drop to approximately 2.9%–3.1% annually, achieving a 25% decrease by 2050. Implementing this levy could significantly enhance recycling and reduce emissions, mitigating climate change. • A voluntary levy imposed on the leading resin producers, who account for over 90% of global production, could reduce plastic emissions by 70% by 2050. • The levy is projected to generate USD 82.5 billion over 25 years, increasing recycling rates by 73%. • To meet the 1.5 °C climate target, plastic production growth must be reduced and sustained at 3.1% annually. • Game theory analysis demonstrates that cooperative behavior can effectively minimize environmental damage while keeping costs low. • Enhanced recycling and circular economy practices are essential to mitigate the impacts of plastic pollution.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".