Global trends in length of stay: implications for destination management and climate change
Bibliographic record
Abstract
Length-of-stay (LOS) is a key parameter in destination management that determines the number of guest nights relative to arrival numbers, with concomitant repercussions for revenue generation and other performance indicators. This article investigates the development of LOS for 32 destinations in developed and emerging economies as well as Small Islands and Developing States (SIDS). The analysis is based on UNWTO data for 478.5 million international tourist arrivals, or about 40% of the global total in 2015, for the years 1995–2015. Results show considerable differences in LOS between destinations, with a global trend of falling LOS, by 14.8% over the study period. However, in individual destination countries, LOS was found to be increasing. Analyses of LOS trends reveal that these can neither be explained by distance–decay relationships nor business to leisure arrival ratios. Results are discussed with regard for destination management and revenue optimisation, transport infrastructure needs, as well as sector greenhouse gas emissions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".