Dirty, difficult and dangerous: Establishing a plastics waste upcycling system in Nepal
Bibliographic record
Abstract
In large parts of the global South, conventional methods of treating plastic waste including: recycling, banning of single use plastics, extended producer responsibility systems, and attempts to reuse plastic waste have largely failed to reduce increasing volumes of untreated waste in the face of limited policy resources and capacity. This article explores the potential for creating plastic upcycling markets that would be financially self-sustaining through using plastic waste to develop valuable new products. The methodology is to explore a case study, the Plastic to Ghar project in Nepal, which seeks to incubate new upcycling businesses, with a focus on rural areas that lack proper waste management. The project proves the viability of creating customizable useful products for secondary markets from plastic waste. The lessons center around the need to pay attention to developing sustainable business models and more robust policy support to complement technological solutions. • The growing global issue of plastic waste can be offset by upcycling it into new and useful products. • This article relays the lessons from the Plastic to Ghar upcycling project in Nepal. • The lessons include the centrality of policy support and the challenges of developing sustainable business models.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".