International tourism and climate change
Bibliographic record
Abstract
Abstract Tourism is a major global economic sector that is undergoing tremendous growth in emerging economies and is often touted as salient for development and poverty alleviation in developing countries. Tourism is recognized as a highly climate‐sensitive sector, one that is also strongly influenced by environmental and socioeconomic change influenced by climate change, and is also a growing contributor to anthropogenic climate change. This article outlines the complex interrelationships between climate change and the multiple components of the international tourism system. Five focal themes that have developed within the literature on the consequences of climate change for tourism are then critically reviewed: climatic change and temporal and geographic shifts in tourism demand, climate‐induced environmental change and destination competitiveness within three major market segments (winter sports tourism, coastal tourism, and nature‐based tourism), and mitigation policy developments and future tourist mobility. The review highlights the differential vulnerability of tourism destinations and that the resultant changes in competitiveness and sustainability will transform some international tourism markets. Feedbacks throughout the tourism system mean that all destinations will need to adapt to the risks and opportunities posed by climate change and climate policy. While notable progress has been made in the last decade, a number of important knowledge gaps in each of the major impact areas, key regional knowledge gaps, and both tourist and tourism operator perceptions of climate change risks and adaptive capacity indicate that the tourism sector is not currently well prepared for the challenges of climate change. WIREs Clim Change 2012. doi: 10.1002/wcc.165 This article is categorized under: Climate and Development > Decoupling Emissions from Development
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.002 |
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".