The promotion of new energy refrigerated vehicles: an evolutionary game over complex networks
Bibliographic record
Abstract
The promotion and application of new energy refrigerated vehicles is a significant step toward the low-carbon development of cold chain logistics. This study considers carbon trading policies and corporate low-carbon preferences, constructs a complex network evolutionary game model for the diffusion of new energy refrigerated vehicles, and employs Matlab simulation software to investigate the decision-making interaction mechanism of cold chain logistics enterprises in a scale-free network. The results suggest that reasonable carbon trading prices can provide useful price signals for enterprises to lower carbon emissions, which supports the development of new energy refrigerated vehicles; A carbon quota total that exceeds a specific threshold will be unable to drive enterprises toward energy saving and emission reduction activities effectively; The diffusion effect of new energy refrigerated vehicles improves as the price elasticity coefficient of demand and the cross elasticity coefficient of demand price decrease; The diffusion rate remains constant as the network scale increases, while the time it takes for the network to achieve a stable state increases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".