Plastic recycling: A panacea or environmental pollution problem
Bibliographic record
Abstract
Increasing plastic waste is a critical global challenge to ecological and human health requiring focused solutions to reduce omnipresent plastic pollution in the environment. While recycling has been touted as one solution to counter plastic waste and resource utilization, it has been largely ineffective in offsetting the impact of rising global plastic production of more than 400 million metric tonnes annually, due to low global recycling rates of only 9%. Over three decades since implementing plastic resin codes, recycling has favoured thermoplastics, neglecting thermoset plastics. There is a constant need to enhance overall recycling efficiency by exploring advanced methods, as enormous gaps exist in fully unlocking the potential of plastic recycling. We identify critical gaps associated with plastic waste recycling and its potential environmental impacts. We discuss substantial progress in recycling technology, designs-for-recyclability with controlled chemical use, and economic incentives to expand markets for recycled plastics and to curb plastic leakage into the environment. Additionally, we highlight some emerging strategies and legally binding international policy instruments, such as the Global Plastics Treaty that require further development to reduce plastic waste and improve plastic recyclability.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".