Optimizing pricing for sustainable government-subsidized omnichannel closed-loop supply chains
Bibliographic record
Abstract
In this study, we explore how government subsidies contribute to the adoption of environmentally sustainable practices in supply chains, focusing on pricing, green awareness, marketing, and recycling. We compare decentralized, centralized, and collaborative operational models, both with and without subsidies, and find that the green-cost-sharing collaborative model significantly enhances supply chain profitability. This model is more cost-effective for manufacturers and retailers than decentralized or centralized approaches and achieves higher green performance compared to the decentralized model. It also strengthens recycling efforts and enhances retailers’ multitasking capabilities within a closed-loop network. Furthermore, it delivers stakeholder satisfaction comparable to centralized models while requiring significantly less selling effort, and it outperforms decentralized models in operational efficiency. Additionally, we identify the equilibrium subsidy level that maximizes environmental efficiency across decentralized and collaborative frameworks. This research provides valuable insights for policymakers and strategists, contributing to both academic literature and practical applications. • Developing pricing strategies for sustainable closed-loop supply chains. • Examining centralized, decentralized, and collaborative settings. • Optimizing sustainable economic decisions. • Assessing the role of government subsidies in green initiatives.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| 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".