Implications of climate change for shipping: Ports and supply chains
Bibliographic record
Abstract
Ports are an important economic actor—at local, national, and international scales—that have been identified as being vulnerable to future changes to the climate. This paper details the findings from an international review of state‐of‐the‐art knowledge concerning climate risks, and adaptation responses, for ports and their supply chains. Evidence from both academic and gray literature indicates that there has already been major damage and disruption to ports across the world from climate‐related hazards and that such impacts are projected to increase in the years and decades to come. Findings indicate that while a substantial—and growing—body of scientific evidence on coastal risks and potential adaptation options is acting as a stimulus for port authorities to explicitly consider the risks for their assets and operations, only a notable few have actually made the next step toward implementing adaptation strategies. This paper concludes by putting forward constructive recommendations for the sector and suggestions for research to address remaining knowledge gaps. It emphasizes a call for collaboration between the research and practice communities, as well as the need to engage a broad range of stakeholders in the adaptation planning process. This article is categorized under: Assessing Impacts of Climate Change > Evaluating Future Impacts of Climate Change
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".