Environmental and Economic Impacts of Mismanaged Plastics and Measures for Mitigation
Bibliographic record
Abstract
The mismanagement of plastic materials has grown to become a mounting global pollution concern that is closely implicated in unsustainable production and consumption paradigms. The ecological, social, and economic impacts of plastic waste mismanagement are currently transboundary in nature and have necessitated numerous methods of government intervention in order to address and mitigate the globalized and multifaceted dilemmas posed by high rates and volumes of plastic waste generation. This review examines the current landscape of a plastics economy which has operated with a linear momentum, employing large quantities of primary resources and disincentivizing the functioning of a robust recycling market for collecting plastic waste and reintegrating it into the consumer market. This contextualizes an increasing plastic pollution crisis that has required global efforts to address and mitigate the ecological risks and socio-economic challenges of mismanaged plastic waste. A timeline of government interventions regarding plastic pollution is described, including numerous international, regional, and local actions to combat plastic waste, and this is followed by an examination of the relevance of the extended producer responsibility principle to improve plastic waste management and obligate industry to assume responsibility in waste collection and recycling.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".