The impact of the COVID-19 pandemic on revenues of visitor attractions: An exploratory and preliminary study in China
Bibliographic record
Abstract
The COVID-19 pandemic has made a detrimental impact on various tourism subsectors. The financial consequences of this impact should be carefully evaluated to set benchmarks for industry recovery. This study assessed the financial impact of the pandemic on the tourism subsector of visitor attractions in China; 4222 A-grade visitor attractions accounting for over one-third of the national market were surveyed. Data triangulation was subsequently applied to undertake a comprehensive assessment of potential revenue loss. Triangulation was based upon the (1) lost revenue estimates made by tourist attractions’ administrations, (2) reverse estimation of past macroeconomic data, and (3) expert opinion estimates. The assessment results demonstrated that A-grade visitor attractions in China may have lost up to 140 billion RMB (circa US$21 billion) due to COVID-19, with up to 65% of all losses incurred in the first quarter of 2020. The scale of revenue loss varied significantly depending on visitor attraction’s grade, type, and location. Potential strategies for industry recovery are discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".