Game-theoretic modeling of sustainable intermodal freight transportation: Optimal pricing and energy efficiency strategies under government intervention and fuzzy uncertainty
Bibliographic record
Abstract
Sustainable freight transportation plays a pivotal role in addressing pressing environmental challenges while simultaneously fostering socio-economic development. Governmental entities worldwide are increasingly implementing strategic policy interventions to enhance the sustainability of freight transportation systems. A comprehensive understanding of the complex interactions and dynamics between these policy measures and transportation operations is essential for developing effective sustainable transportation strategies. This study aims to explore the impact of government intervention on pricing strategies, and energy-saving level determination in the transportation sector under conditions of fuzzy uncertainty. While the government looks into three distinct strategies, each with two decision variables, transportation enterprises are considering two alternate scenarios for decision-making. It means that twelve distinct scenarios are being considered by the government. Our analyses reveal that: (1) The government's goals of maximizing social welfare and energy saving cannot be aligned with the enterprises' goals of maximizing profits, regardless of whether decision-making is decentralized or centralized. (2) The carbon cap-and-trade mechanism emerges as the most effective strategy for governmental regulation, whereas transportation enterprises demonstrate optimal responsiveness to subsidy-based policy interventions. (3) Centralized decision-making by transportation enterprises yields superior outcomes across multiple dimensions, including LSSC profitability, social welfare enhancement, and energy conservation efficiency, when contrasted with decentralized decision-making paradigms. (4) The implementation of a carbon cap-and-trade policy by the government, combined with increased investments in environmental awareness and centralized decision-making by transportation enterprises, significantly advances both profit objectives and energy-saving targets.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".