Internalization of port congestion: strategic effect behind shipping line delays and implications for terminal charges and investment
Bibliographic record
Abstract
This paper develops a theoretical model to analyze the congestion internalization of the shipping lines, taking into account the ‘knock on’ effect (i.e. the congestion delay passed on from one port-of-call to the next port-of-call). We find that with the presence of the knock-on effect, liners will operate less in terminals, and an increase of a liner’s operation in one terminal will decrease its operation in the other. If the liners are involved in a Stackelberg competition, whether they operate more or less in a terminal under the knock-on effect depends on the comparison between the marginal congestion costs of terminals. Furthermore, we find that the coordinated profit-maximizing terminal charges are higher than both the socially optimal terminal charges and the independent profit-maximizing terminal charges. When the knock-on effect is small, the independent profit-maximizing terminal charges are set at higher levels than the socially optimal terminal charges; but when the knock-on effect is sufficiently large, this relationship may reverse. Besides, the capacity investment rules are the same for welfare-maximizing terminal operator and coordinated profit-maximizing terminal operator, while independent profit-maximizing terminal operators invest less in capacity. The larger the knock-on effect, the larger this discrepancy.
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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 | 0.008 |
| 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 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".