Authorization or outsourcing? Investigating remanufacturing decisions under carbon trading policies and remanufacturing subsidies considering trade-in programs
Bibliographic record
Abstract
With the introduction of carbon emission policies, subsidy policies, and the promotion of "trade-in" programs worldwide, determining the optimal remanufacturing strategy under various policy environments has become a critical issue. We develop six models to evaluate the effects of three policy combinations—carbon trading alone, carbon trading with consumer subsidies, and carbon trading with remanufacturer subsidies—under authorization and outsourcing remanufacturing strategies. The results show that dual policy of carbon emission trading and government subsidies more effectively promotes remanufacturing than a single carbon trading policy. When consumer subsidies reach a certain threshold, all supply chain members can achieve a win-win outcome, regardless of whether the remanufacturing strategy is authorization or outsourcing. The environmental cost is primarily influenced by carbon emissions from new products. If emissions are high, remanufacturer subsidies should be prioritized; if emissions are lower, consumer subsidies are more effective. Without subsidies, authorization has pricing advantages with low emissions, while outsourcing is more economical with high emissions or under market uncertainty. High carbon trading prices and subsidies increase overall supply chain profits but exhibit diminishing returns as excessive carbon prices increase corporate costs and reduce consumer surplus and social welfare. Moderate subsidies can mitigate these negative effects.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".