Will the Mediterranean Become “Too Hot” for Tourism? A Reassessment
Bibliographic record
Abstract
Climate, particularly temperature, is one of the most important resources of a tourist destination. With projected climate change in the twenty-first century, this attribute of tourism destinations is anticipated to change, leading some to conclude that the Mediterranean region will become “too hot” for tourist comfort in the peak summer season by as early as the 2020s or 2030s. This study sought to reassess these claims in the literature and media. Perceptions of “too hot” for comfortable tourism activities at beach and urban destinations was quantified for the young adult travel segment by means of a survey of 850 university students in five countries that represent source markets for the Mediterranean (Austria, Germany, the Netherlands, Sweden and Switzerland). The threshold that defines “unacceptably hot” for the majority of respondents was then compared against thermal conditions (temperature and humidity) in a baseline climate (1961–1990), and an early (2011–2035), mid (2046–2065) and late century (2080–2099) climate change scenario (A1B) for 10 Mediterranean destinations. By early century under the warmest available climate change scenario, no additional beach or urban destination became unacceptably hot. By mid century, thermal conditions for two additional beach and one additional urban destination became “too hot” during the peak summer months. In the late century scenario, several, but not all, of the destinations (four beach and five urban destinations) were found to exceed the stated “unacceptably hot” thresholds in the summer months. However, given this length of time and the potential for northern European travellers to acclimatize to warmer average temperatures at home, it remains uncertain whether the thermal comfort threshold identified by this sample will persist. An important contrasting point is that at the same time there is a larger decrease in the number of months that are considered “unacceptably cool” for both a beach and urban holiday and an increase in months that become “ideal”. The findings hold important implications for critically assessing the potential impact of climate change in the study area and other destinations more broadly, and can be used to refine models intended to predict the influence of climatic change on the geographic and temporal patterns of international tourism.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".