A Global Meta-Analysis of Climate Change Impacts on National-Level Tourism Demand
Bibliographic record
Abstract
The purpose of this study is to examine the global impact of climate change on national-level tourism demand. To this end, a meta-analysis was conducted to synthesize quantitative findings from previous research to estimate the average effect of temperature on national tourism demand. Following the PRISMA guidelines, relevant studies were identified, collected, and reviewed, and their coefficients were extracted and converted into standardized elasticity measures. These temperature–tourism elasticities were used as the dependent variable, while temperature and national-level conditions served as independent variables. The model was applied to countries worldwide and to the 16 regional groupings suggested by the FUND model to evaluate the impact of temperature rise on tourism demand. The results show that temperature rise increases tourism demand in high- and some mid-latitude regions, such as China, Russia, North America, Lower South America, Canada, and Australia–New Zealand, but decreases demand in low-latitude regions, including Southeast Asia, South Asia, Africa, and the Middle East. These findings demonstrate that climate change impacts on tourism demand are uneven, depending on the structural conditions of each country and region. The key contribution of this study is the integration of fragmented empirical findings into a global, national-level model that identifies the macro-patterns of climate change impacts on tourism demand.
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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 | 0.025 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.034 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".