Insight into bilateral efforts in green supply chain driven by manufacturers: A new dimension of coordination mechanisms
Bibliographic record
Abstract
Under the rapid development of the global economy, environmental pollution has intensified significantly. The evolving external environment presents both opportunities and challenges to traditional supply chains. As an innovative management philosophy and practical approach, the green supply chain has rapidly become an integral component of corporate sustainable development strategies. However, the transition from traditional supply chain to green supply chain necessitates effective collaboration and coordination among supply chain members. Contractual coordination has therefore emerged as an effective methodology to enhance operational efficiency and profitability across supply chain participants. To investigate the impact of various coordination mechanisms on the optimal decision-making of green supply chains, we construct a Stackelberg game model involving bilateral green investments by both manufacturer and retailer within a two-echelon green supply chain system. Considering the bilateral green efforts from both manufacturer and retailer, we comparatively analyze game equilibrium solutions under three scenarios: non-coordination, cost-sharing contract coordination, and two-part contract coordination. Specifically，we examine how these coordination mechanisms influence pricing strategies, green investment levels, and profit distributions within the supply chain network. Finally, the results are validated and illustrated using numerical simulation. It is discovered that 1) the cost-sharing contract cannot simultaneously increase the Pareto improvement of producers' and retailers' revenues; 2) the cost-sharing contract cannot increase the social utility and additive greenness of products; however, it can improve the marketing effort; and 3) When retailers maintain an optimistic stance toward manufacturers' green initiatives, the two-part tariff contract enables concurrent Pareto improvements in both parties' profits while simultaneously enhancing product greenness, marketing efforts, and social welfare, thereby achieving efficient supply chain coordination.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".