Evolutionary game analysis of vehicle procurement in the courier industry from the perspective of green supply chain
Bibliographic record
Abstract
In the contemporary era, green development has become integral to modern industrial supply chains. Accelerating the green transformation of the supply chain in the express delivery industry poses a significant challenge in China. To address this challenge, we establish a trilateral evolutionary game model that considers the interdependent constraints involving the government, vehicle suppliers, and courier companies. This model aims to explore the optimal stable decisions for each stakeholder and the entire supply chain system. Through numerical simulations, we analyze the impact of key parameters on the stability of strategies and find that there are four Evolutionary Stable Strategies (ESS) in the system. Economic factors play a dual role: income-related factors encourage the adoption of green strategies by stakeholders, whereas cost-related factors extend the time required for stakeholders to transition to green strategies. For sustained production and utilization of new energy vehicles, the government must utilize a balanced system of rewards and penalties effectively. Vehicle suppliers and courier companies should collaborate for mutually beneficial outcomes, jointly fostering the green transformation of the supply chain with a focus on cost reduction and efficiency improvement. This study offers theoretical insights and methodological support for decision-makers in green supply chain management.
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 | 0.004 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".