Understanding Preferences for Coastal Climate Change Adaptation: A Systematic Literature Review
Bibliographic record
Abstract
Lack of public support for coastal adaptation can present significant barriers for implementation. In response, policy makers and academics are seeking strategies to build public support for coastal adaptation, which requires a deeper understanding of peoples’ preferences for coastal adaptation and what motives those preferences. Here, we conduct a systematic literature review to understand preferences for coastal adaptation options and the factors influencing these preferences. Ninety peer-reviewed publications meet the inclusion criteria. The findings revealed that hard protection options were often the most frequently preferred, likely due to a desire to maintain current shoreline, for the protection of recreational spaces and private property, and a perceived effectiveness of hard protection options. Soft protection, including nature-based approaches, accommodation, and no action were the next most preferred options. Finally, retreat options were the least preferred, often due to strong place attachment. We identify twenty-eight factors that could influence preferences, with risk perception, place attachment, and financial considerations occurring most frequently in the literature. In the conclusion, we outline the most significant research gaps identified from our analysis and discuss the implication for adaptation research and practice.
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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 | 0.019 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".