The Dynamic Nexus Between International Tourism and Environmental Degradation in Top Twenty Tourist Destinations: New Insights From Quantile-on-Quantile Approach
Bibliographic record
Abstract
Tourism is one of the important factors that can affect the environmental and economic situation of any economy. This study investigates the relationship between tourist arrivals and CO2 emission in the top 20 tourist destinations using data from quarterly observations from 1995 to 2018. A unique technique via quantile-on-quantile regression and Granger causality in quantiles was used. In particular, how the quantiles of tourist arrivals impact quantiles of CO2 emission was analyzed. The empirical results suggest a combination of both positive and negative effects of tourist arrivals and CO2 emission in most tourist destinations. Predominantly, at both high and low tails, in the USA, Spain, Hong Kong, and Austria, tourist arrival has a positive effect on CO2 emission, whereas in the case of Canada, France, Germany, Mexico, and Malaysia, the association was negative. On the other hand, China, Greece, Russia, Japan, Italy, South Korea, Thailand, and Turkey have both positive and negative effects of tourism on CO2 emissions at low and high tails. Tourism can be an important factor while formulating policy for environmental and climate aspects.
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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 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".