Issues in production, recycling and international trade: analysing the Chinese plastic sector using an optimal life cycle (OLC) model
Bibliographic record
Abstract
There have been increasing pressures by governments and NGOs to restrict international trade in secondary material waste in the conviction that imports of these goods are in reality a disguise for waste dumping by the exporting country. Moreover, cheap imports of secondary material waste tend to crowd out the local recovery system leading to a domestic waste disposal problem. Alternatively, proponents of trade argue that a ban on secondary material waste leads to an inefficient use of resources resulting inevitably in higher economic and environmental costs, both in developed and developing countries. In this paper we set out to investigate if free trade in secondary material waste can support economic development and simultaneously reduce environmental degradation in a developing country and the conditions necessary for the trade to be permitted. In this study we focus on the trade in waste plastics in China. A life cycle model is formulated within an optimization framework and solved by non-linear programming methods. Preliminary results suggest that trade in waste plastics is both economically and environmentally advantageous but under a number of stringent conditions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".