Coastal Adaptation and Vulnerability Assessment in a Warming Future: A Systematic Review of the Tourism Sector
Bibliographic record
Abstract
The uncertainty behind a warming climate produces significant risk for communities that rely on tourism, but vulnerability assessment can help inform planning for adaptation and mitigation efforts. Since coastal regions have significant tourism economies, and rely on the tourism sector for socio-economic stability, we seek to understand how assessments of vulnerability have been applied in coastal communities by systemically assessing literature that describes the concepts of adaptation, resilience, and vulnerability. We conducted a systematic search across three peer-reviewed journal databases (Web of Science, Scopus, and PubMed) to compare the types of methods and models used to determine coastal adaptation and vulnerability in various geographical and demographic settings. The systematic process found 205 unique articles; 143 were deemed relevant as they focused on adaptation, resilience, or vulnerability assessment. A qualitative analysis of the key themes of relevant studies, identified using thematic coding, found that vulnerability and adaptation research in tourism-based coastal communities is predominantly done through qualitative methods, researched more frequently in higher-populated areas of Europe and North America, have a focus on water as a driver of vulnerability, focus on socio-economic impacts of vulnerability, and emphasize locally-based knowledge in understanding adaptive capacity. Bibliometric analysis of citations and mentions in social-media reveal a larger number of papers with a higher citation rate focused on coastal-based systems (salt-water) with an emphasis on regional studies rather than those with a specific urban or rural focus. We found few studies aimed at coastal communities using tourism operators as informants, but rural coastal communities had higher social-media metrics indicating their comparative public importance. Thus, we highlight the need for locally-informed case studies in vulnerability and adaptation assessment for small 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 | 0.010 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.019 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".