Self-financing of transport infrastructure for multiple types of transport services and capacities
Bibliographic record
Abstract
We reexamine the self-financing result in the context of multiple types of transport services and capacities. First, we confirm that the standard assumption—that the total cost function is homogeneous of degree one—is sufficient but not necessary for self-financing in the case of a single transport service. The necessary and sufficient condition is that the total cost function exhibits local homogeneity at the welfare-maximizing optimum. This distinction is practically important, as ensuring local homogeneity is more challenging when multiple types of transport services and capacities are involved. Second, we extend the analysis to multiple types of transport services and show that a similar conclusion holds. The result also applies to settings such as airports, where both aeronautical and non-aeronautical services (e.g., retail, parking, and car rentals) are provided, with the latter not necessarily being transport services. Third, we consider cases in which prices are predetermined and potentially distorted, while the policy authority controls only service capacities. Examples include predetermined passenger facility charges, uniform public transport fares, and flat vehicle mileage taxes. Even in these multi-service and multi-capacity settings, self-financing can be maintained despite losses from one service, provided that they are offset by profits from other transport or non-core services. Our findings demonstrate that self-financing can remain consistent with second-best welfare maximization under fixed or distorted prices, expanding its applicability to modern multi-service transport operators and informing policy and infrastructure management.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".