Climate change and the distribution of climatic resources for tourism in North America
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Tourism is a major sector of the global economy, and it is strongly influenced by climate. At some travel destinations, climate represents the natural resource on which the tourism industry is predicated. Global climate change has the potential to alter the distribution of climate assets among tourism destinations, with implications for tourism seasonality, demand and travel patterns. Changes in the length and quality of the tourism season have considerable implications for the long-term profitability of tourism enterprises and competitive relationships between destinations. This analysis utilizes a 'tourism climate index' (TCI) that incorporates 7 climate variables relevant to general tourism activities (i.e. sightseeing) to assess the spatial and temporal distribution of climate resources for tourism in North America under baseline conditions and 2 climate change scenarios (CGCM2-B2 and HadCM3-A1F1) for the 2050s and 2080s. The analysis found that a substantive redistribution of climate resources for tourism will be possible in the later decades of the 21st century, particularly in the warmer and wetter HadCM3-A1F1 scenario. The number of cities in the USA with 'excellent' or 'ideal' TCI ratings (TCI > 80) in the winter months is likely to increase, so that southern Florida and Arizona could face increasing competition for winter sun holiday travelers and the seasonal 'snowbird' market (retirees from Canada and the northern states of the USA, who spend 2 to 6 mo in winter peak and optimal climate destinations). In contrast, lower winter TCI ratings in Mexico suggest it could become less competitive as a winter sun holiday destination. In Canada, a longer and improved warm-weather tourism season may enhance its competitiveness in the international tourism marketplace, with potentially positive implications for its current international tourism account deficit.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it