Joint Decision-Making and the Coordination of a Sustainable Supply Chain in the Context of Carbon Tax Regulation and Fairness Concerns
Bibliographic record
Abstract
Carbon tax regulation and consumers' low-carbon preference act as incentives for firms to abate emissions. Manufacturers can improve product sustainability and retailers can strengthen the promotion of low-carbon products as part of such abatement. Current incomplete rationality also affects product sustainability and low-carbon promotion level. In this context, we consider a supply chain with a manufacturer and a retailer and investigate the impacts of the manufacturer's and the retailer's fairness concerns on their production sustainability level, low-carbon promotion level and profitability. We also explore the coordination contract. The results show that the manufacturer's and the retailer's fairness concerns decrease their product sustainability and low-carbon promotion level, together with the profits of the system and the manufacturer. With regard to the retailer's fairness concern, the product sustainability level and the manufacturer's profit are lower; moreover, the low-carbon promotion level and the profits of the supply chain and the retailer are higher. A revenue-sharing contract can coordinate the supply chain perfectly; however, members' fairness concerns increase the difficulty of coordination. Finally, the numerical results reveal that carbon tax regulation can encourage the manufacturer to enhance the product sustainability level. Further, the impacts on the low-carbon promotion level and firms' profitability are related to the cost coefficients of product sustainability.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| 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".