Evolutionary game analysis of carbon emission reduction in transportation infrastructure construction under dual regulation
Bibliographic record
Abstract
Under the dual regulation of government intervention and environmental constraints, user demand, low-carbon awareness, and the willingness of construction enterprise to reduce carbon emissions are crucial to the realization of low-carbon transportation infrastructure construction. Basing on prospect theory, this paper constructs an evolutionary game model for the government, construction enterprise, and user, explores the stabilization of the three-game players' strategy choices, and discusses the influence of key parameters on the behavior evolution of relevant players through simulation analysis. The results indicate that: the regulatory cost when the government adopts incentives or no incentives is an important factor affecting the strategy choice. The enhancement of incentives by the government can promote the construction of enterprise and the selection of low-carbon transportation infrastructure by user. However, when the benefit of government credibility outweighs the incremental cost of household subsidies, carbon subsidies, and regulation, the government changes its strategy and no longer adopts encouraging strategy. Improving benefits of build low-carbon transportation infrastructure reducing incremental costs, setting a ladder carbon tax rate, increasing carbon tax rate for traditional construction, and decreasing for low-carbon construction is an effective approach to stimulate enterprise to construct low-carbon transportation infrastructure. The user's choice of low-carbon transportation infrastructure strategy is positively correlated with the subsidy received and is less affected by environmental benefits. And the construction enterprise's strategy choice hasn't been influenced by user, which is related to the public service characteristics of transportation infrastructure.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".