Progress and gaps in climate change adaptation in coastal cities across the globe
Bibliographic record
Abstract
Coastal cities are at the frontlines of climate change impacts, resulting in an urgent need for substantial adaptation. To understand whether, and to what extent, cities are on track to prepare for climate risks, this paper systematically assesses the academic literature to evaluate evidence on climate change adaptation in 199 coastal cities worldwide. Results show that adaptation in coastal cities is rather slow, of narrow scope and not transformative. Adaptation measures are predominantly designed based on past and current—rather than future—patterns in hazards, exposure and vulnerability. City governments, particularly in high-income countries, are more likely to implement institutional and infrastructural responses, whereas coastal cities in lower-middle-income countries often rely on households to implement behavioral adaptation. There is comparatively little published knowledge on coastal urban adaptation in low- and middle-income countries, and regarding particular adaptation types such as ecosystem-based adaptation. These insights make an important contribution for tracking adaptation progress globally and help to identify entry points for improving adaptation of coastal cities in the future. This study performs a systematic review of empirical evidence for climate change adaptation in coastal cities around the world. It found that reported adaptation is mostly slow, narrow, and not transformative as coastal cities predominantly focus their adaptation on past and current challenges, and not future scenarios of risk.
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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 | 0.017 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".