Modal shift from road haulage to short sea shipping: a systematic literature review and research directions
Bibliographic record
Abstract
Modal shift from road haulage to short sea shipping (SSS) has been advocated by authorities and researchers for more than two decades. This paper provides a review of literature on modal shift and pinpoints paths for future research on topics in six categories: (1) factors influencing SSS competitiveness, (2) the policy-oriented perspective, (3) environmental legislation, (4) SSS performance, (5) port characteristics, and (6) the multi-agent perspective. In particular, we propose first, in evaluating the performance of SSS versus road haulage in different trade corridors, three performance-related dimensions – the economic dimension (e.g. external costs), the environmental dimension, and the dimension of service quality – should be considered. Second, researchers should use rich, real-world, numerical data and operational research techniques to identify the relative importance of individual drivers and barriers for a modal shift from road haulage to SSS. Third proposed direction is related to assessing which groups of actors certain policies should target. In doing so, researchers should extend their policy-related focus beyond the European Union, which has long encompassed the major geopolitical scope of research on the modal shift. Fourth, to moderate the adverse impact of environmental legislation on SSS, strategic solutions need to be identified. Fifth, we also suggest that the influence of contingencies, particularly port strikes and cyberattacks, on SSS operations and approaches for managing them should be investigated. Sixth, the economic and financial advantages of coordination and alliance for each transport chain agent need to be evaluated.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.013 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".