Comparative Analysis of Environmental, Economic, and Social Criteria for Plastic Recycling Technology Selection in India, Sri Lanka, Pakistan, and Thailand
Bibliographic record
Abstract
This study examines stakeholder perceptions of environmental, economic, and social indicators for selecting Polyethylene terephthalate recycling technologies in India, Pakistan, Sri Lanka, and Thailand.An online survey was conducted with 154 stakeholders from these countries.The survey data were analyzed using descriptive and inferential statistical methods.The results showed that global warming potential and total energy demand were the most important environmental indicators across all countries.Capital costs and profits from main recycling businesses were of utmost importance economically.Social indicators such as working environment and job creation opportunities were also deemed significant.However, the study found differences across countries.For instance, water consumption and solid waste generation were more important for Pakistan than for Thailand, while acidification potential and photochemical oxidant formation were more important for Thailand.Moreover, electricity cost was more significant in Sri Lanka, while job creation opportunity was more important in India, Pakistan, and Sri Lanka than in Thailand.The findings of this study can inform decisionmaking processes for policymakers and industry leaders in the plastic recycling field, aiding them in the transition toward a circular economy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".