Tourist demand and destination development under climate change: complexities and perspectives
Bibliographic record
Abstract
Climate change is becoming increasingly impactful on global tourism, including climate patterns and weather extremes, degraded and lost assets (snow, water, biodiversity, beach), business disruption and damage, rises in travel and hospitality cost, as well as deteriorating socio-economic stability. Based on a narrative review of the literature, the paper develops a series of conceptualizations that represent the complexities and implications for tourist demand. Findings confirm that while tourists have diverse options to adapt to climate change - including spatial substitution, temporal shifts, and activity modification - the rising cost of tourism due to climate change is likely to be the primary driver of tourist demand responses in the immediate future. There is a considerable risk that severe disruptions in the tourism system will occur within the next two decades unless mitigation efforts are significantly scaled up, with salient impacts on destination competitiveness and tourism demand. Important research gaps persist, specifically in relation to the implications of cost changes and compounding impacts, which inhibit robust projections of future tourism losses and gains at the destination scale.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".