UNWTO World Tourism Barometer and Statistical Annex, March 2021
Bibliographic record
Abstract
International tourism further weakens in January 2021 with a drop of 87% After the unprecedented 73% drop in international tourism recorded in 2020 under the impact of the COVID-19 pandemic, demand for international travel remained very weak at the beginning of 2021. International tourist arrivals (overnight visitors) plunged by 87% in January 2021, amid new outbreaks and tighter travel restrictions. This follows a decline of 85% in the last quarter of 2020. By regions, Asia and the Pacific (-96%), the region which continues to have the highest level of travel restrictions in place, recorded the largest decrease in international arrivals in January. Europe and Africa both saw a decline of 85% in arrivals, while the Middle East recorded a drop of 84%. International arrivals in the Americas decreased by 77% in January, following somewhat better results in the last quarter of the year. Due to the worsening of the pandemic with a surge of cases and the emergence of new variants, many countries reintroduced stricter travel restrictions, including mandatory testing, quarantines and in some cases a complete closure of borders, on top of local lockdowns, all weighing on the resumption of international travel. In addition, the speed and distribution of the vaccination roll-out have been slower than expected and quite uneven across countries and regions. With 32% of destinations worldwide showing complete border closures in early February and another 34% with partial closure, UNWTO expects international tourist arrivals to be down about 85% in the first quarter of 2021 over the same period of 2019. This would represent a loss of some 260 million international arrivals when compared to pre-pandemic levels. Looking ahead, UNWTO has outlined two scenarios for 2021.The first scenario points to a rebound in July, which would result in a 66% increase in international arrivals for the year 2021 compared to the historic lows of 2020. In this case, arrivals would still be 55% below the levels recorded in 2019. The second scenario considers a potential rebound in September, leading to a 22% increase in arrivals compared to last year. Still, this would be 67% below the levels of 2019. The scenarios consider a number of factors such as a gradual improvement of the epidemiological situation, a continued roll-out of the COVID-19 vaccine, a significant improvement in traveller confidence and a major lifting of travel restrictions, in particular in Europe and the Americas.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.103 | 0.104 |
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".