Optimal Decision-making for Green Supply Chain Based on Overconfidence under the Carbon Emission Constraint
Bibliographic record
Abstract
In light of the combination of overconfident manufacturerrational retailergreen-preferring consumers, this paper establishes a Stackelberg game model under the carbon emission constraint, obtains the optimal green and emission reduction strategy and optimal pricing strategy in case of decentralized decision-making using the backward induction method, and further analyzes the impacts of the manufacturers' overconfidence and the consumers' green preference on the optimal decision and profit of the supply chain.According to the results of the study, under certain conditions, the low-carbon supply chain will no longer be "lowcarbon" and the carbon tax policy will be ineffective; over-confident manufacturers will reduce the investment in carbon emission reduction while increasing the wholesale price of unit products; rational retailers may expand the market demands for products at the expense of some of its profit margins; the profits of the supply chain system and its members are all negatively correlated with the manufacturer's overconfidence level, but positively correlated with the consumers' green preference level.Finally, the model is proved to be effective through example analysis, showing that it can provide some reference for relevant supply chain enterprises when they are making decisions on emission reduction investment.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".