Plastic Waste Mitigation Strategies: A Review of Lessons from Developing Countries
Bibliographic record
Abstract
Global plastics waste is an issue of ever-increasing urgency. Estimates suggest some 79% of plastic waste is dumped into the environment, where it is likely to have devastating effects on ecosystems and human health. Marine plastic pollution is a particularly challenging issue, as plastics take decades to break down, and do so into micro- and nanoparticles that affect marine ecosystems and the food web. The plastics pollution problem is magnified in the Global South, where rising production and consumption coexist with underdeveloped waste treatment systems and large volumes of imported plastic waste. This article examines the reasons for the failure to curb plastic waste in Sub-Saharan Africa (SSA) and South Asia (SA), target regions of the Sustainable Manufacturing and Environmental Pollution (SMEP) program funded to address such issues. The article examines the challenges in shifting manufacturing processes and natural materials substitution for reducing plastics waste. It recommends greater external financial and technical support for waste treatment, stakeholder consensus and awareness-building, regulatory policies that reduce the price and convenience differentials between plastics and substitute materials, and a push towards enforcement of environmental regulations.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".