A Comparative Study of the Effect of Different Carbon-Reduction Policies on Outsourcing Remanufacturing
Bibliographic record
Abstract
To facilitate the green transformation of enterprises and realize low-carbon development, governments have adopted the policies of carbon emission constraint and carbon trade to promote enterprises' low-carbon production. Although the two policies aim to reduce carbon emissions, they have different effects on enterprises' production. Meanwhile, the development of remanufacturing caters to the low-carbon economy. Therefore, this article establishes the game models between an original equipment manufacturer (OEM) and a remanufacturer under carbon-emission-constraint and carbon-trade policies, analyzing the production decisions of enterprises under different policies to compare the influence of the two policies on outsourcing remanufacturing. The main conclusions of the article are as follows: (1) Both carbon-emission-constraint and carbon-trade policies increase the unit retail price of remanufactured and new products, reducing the new products sales volume. However, the sales volume of remanufactured products only decreases if the discount rate is less than the rate of carbon emissions of the two products. (2) The upper limit of carbon emissions can affect the unit outsourcing cost. The unit cost of outsourcing under the carbon-emission-constraint policy is only higher when the upper limit of carbon emissions is less than a certain threshold, and the discount rate is larger than the proportion of carbon emissions for both products; otherwise, the unit outsourcing cost under the carbon-trade policy is higher. (3) Both policies lessen the total environmental implication. When the upper limit of carbon emissions is less than a particular threshold, the environmental effect of the two manufacturers under the carbon-emission-constraint policy is smaller; otherwise, the environmental impact is smaller under the carbon-trade policy.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".