National tourism organizations and climate change
Bibliographic record
Abstract
There is a consensus that the global tourism system needs to undergo decarbonization and achieve net-zero emissions by mid-century. However, given the anticipated growth in the most energy-intensive subsector of tourism, air transport, achieving this goal seems unlikely. This paper focuses on the role of distance in the global geography of tourism, against evidence that National Marketing Organizations (NTOs) often seek to attract visitors from all over the world. The analysis of data for a sample of 12 NTOs in Europe, the USA and Canada reveals that the number of markets targeted varies between six and 33, with significant differences in the average distance to markets (<4,000 to 8,000 km), as well as emissions per arrival by market (0.2 t CO2 to 2.5 t CO2). For the countries studied, the 17% of the most distant arrivals cause 62% of the emissions. Results also show that more distant markets are more sensitive to disruptions such as COVID-19. These findings have relevance for destination marketing that point to new climate change related roles for NTOs such as rebranding, demarketing, market segmentation, and communication.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.001 |
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".