Greening China's supply chains: A coordinated policy approach
Bibliographic record
Abstract
China's commitment to fighting air pollution has propelled the greening of supply chains to the forefront of its long-term strategy. This study proposes a coordinated policy approach to greening supply chains. It investigates how strategic interactions between the government, manufacturers, and retailers can be shaped by various policy mixes (involving subsidies, environmental tax, and carbon trading) to promote sustainable practices in supply chains. Employing a non-cooperative evolutionary game model, simulations reveal that retailers' active green promotion can facilitate bottom-up green technology innovation among manufacturers. While individual policies often yield limited or heterogeneous impacts, coordinated policies demonstrate much better potential for stimulating green innovation. However, achieving policy success is contingent on addressing critical vulnerabilities, including regulatory gaps, greenwashing, and illicit emissions activities. Ultimately, effective execution of green policies depends on not only robust design but also competence and integrity of the actors charged with implementation. The findings suggest that a combination of retailer engagement, policy complementarity, and dynamic policy adaptation is crucial for successful green transformation of supply chains.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".