Coastal Hazard Mitigation and Adaptation in North American Coastal Settlements
Bibliographic record
Abstract
Frequency and intensity of coastal hazards driven by climate and land change have been increasing in many parts of the world, including in North America, where 40% of the population lives near the coast. While many settlements are mandated to have hazard mitigation plans, the extent to which these plans address emerging vulnerabilities of these settlements to coastal hazards is unclear. To assess the main factors affecting coastal hazard mitigation and adaptation in North American coastal cities, we carried out a systematic review of the coastal hazard mitigation literature to assess how mitigating natural hazards has been studied in the literature. We identified 67 papers that specifically studied how and why hazard mitigation planning aided or hindered mitigation in 140 locations vulnerable to coastal hazards. Case studies situated in the United States account for nearly 90% of all case studies in this review. The themes of institutional capacity, implications for stakeholders and infrastructure, and attitudes toward resettlement are threaded through the 67 selected articles. We grouped papers in our review according to the type of coastal hazards they considered, namely, Sea Level Rise, Environmental Change, Extreme Weather, and Tsunami. While no case study location was studied in all four categories, 31 settlements across the United States and Canada were studied in the context of sea level rise, environmental change, and extreme weather, and one state – Alaska – was studied in the context of the three categories of sea level rise, environmental change, and tsunami. Of the locations in this review, Alaska, Louisiana, and Florida are the states most studied among state-wide case studies, while the most studied settlements are Kivalina, Alaska, and New York City, New York. The literature indicates that better interagency and scientific communication with the public as well as improved governance frameworks for adaptation and mitigation, including resettlement, can serve to minimize stakeholders’ climate science mistrust and to promote the rise of local leaders for local mitigation strategies and greater environmental equity in both urban and rural coastal communities.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".