Evolutionary game analysis of green packaging supply chain cooperative development considering consumer preferences for traceability
Bibliographic record
Abstract
Promoting green packaging production represents a crucial strategy for the packaging industry in its pursuit of sustainable development. This study constructs a three-party evolutionary game model involving suppliers, manufacturers, and brands to examine their strategic decision-making under various scenarios. Simulation and analysis yield three principal findings. First, the system initially begins at (0,0,0) and may transition to a manufacturer-dominated intermediate state—either (1,1,0) or (0,1,1)—before gradually stabilizing at the equilibrium point (1,1,1). Second, supply chain decision-making is influenced by both internal and external factors. Internal factors include penalty mechanisms, carbon trading allocation, and cooperative concessions, whereas external factors comprise consumer preferences for traceability and the environmental attributes of packaging. Specifically, suppliers are primarily driven by internal factors, manufacturers are predominantly influenced by external factors, and brands are impacted by a combination of both. Third, serving as the central node in the supply chain, manufacturers enable upstream and downstream integration through traceable production, refine cooperative concession mechanisms to enhance brand participation, and harness market signals to promote green transformation and co-production among suppliers. Therefore, the effective management of the green packaging supply chain necessitates the establishment and ongoing refinement of a tripartite active cooperation mechanism. Additionally, cultivating consumer preferences for traceability is essential for advancing the long-term sustainable development of the supply chain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".