Does Tourism Opportunity or Threat to Green Economıc Growth Evidence From The Top 10 Countries
Bibliographic record
Abstract
The aim of the study is to examine the impact of tourism on green economic growth in the top 10 countries in international tourism (USA, Austria, Canada, France, Germany, Spain, UK, Italy, Greece, Mexico) using panel data analysis method for the period 2010-2022. In the study, control variables (energy and financial development variables) were used in addition to the tourism variable. This context, four models have been created. According to the findings, an increase in international tourism numbers reduces green growth. The variables of renewable energy and financial institutions are statistically insignificant; but the variables of fossil energy and financial markets have significant effects on green economic growth, with fossil energy having a negative impact and financial markets having a positive impact. It is expected that this study will contribute to the literature by being one of the first studies to examine the impact of work tourism on green economic growth.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".