Climate suitability for tourism in China in an era of climate change: a multiscale analysis using holiday climate index
Bibliographic record
Abstract
Climate change is increasingly influencing tourism policy and practice and there is a growing need to assess climate risk for destinations and the potential implications for global tourism demand patterns. Climate-dependent tourism markets, such as beach tourism, are particularly sensitive to changes in climate, and understanding the future redistribution of tourism climate resources remains a gap in many world leading tourism regions. This paper presents the first climate change assessment of tourism climate resources in China. The Holiday Climate Index:beach (HCI:beach) and Holiday Climate Index:urban (HCI:urban) are calculated for 775 climate stations across China for the 1981–2010 baseline and mid and late-twenty-first century using projections from six CMIP5 Global Climate Models under low and high emission futures. The projected geographic and seasonal redistribution of tourism climate resources are advantageous for many climate-limited destinations but pose high heat risks for some major city destinations. The differential results for the HCI:beach and HCI:urban reinforce the importance of utilising market-specific indices to assess future climate risk. The results provide new decision-relevant climate information for tourism managers and destination planners throughout China.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".