Seaport adaptation to climate change-related disasters: terminal operator market structure and inter- and intra-port coopetition
Bibliographic record
Abstract
With the prevalence of global terminal operators in port operation, the market structure of terminal operator companies (TOCs) becomes more important in shaping intra- and inter-port competition and cooperation (i.e., coopetition). The port adaptation investment to climate change-related disaster might also be affected by such TOC intra- and inter-port coopetition. This paper examines analytically how the TOC market structure could affect ports’ adaptation investment. More specifically, it considers two landlord-type ports within a region that compete with each other. The two ports are subject to uncertain disaster threats and have an asymmetric number of TOCs. The analytical and numerical results suggest that more TOCs at the own port and the competing port have opposite impacts on the port's adaptation investment. An inter-port TOC joint venture would decrease the adaptation at both ports. Moreover, the TOC market structure is found to moderate the effect of disaster uncertainty on port adaptation. That is, TOC intra- and inter-port coopetition can strengthen or weaken ports’ sensitivity to disaster occurrence uncertainty. Finally, the regional welfare is found to increase monotonely with the two ports’ total adaptation. It is suggested that the regulators encourage new TOC entries while restricting inter-port TOC joint ventures. The cases with heterogeneous disaster uncertainties at the two ports are also examined.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".