Bibliographic record
Abstract
The impact of the climate change response on the labour market is an important question for policymakers, while the net positive effect of green policies on the labour market is seen as one of the arguments in favour of a green transition. This is particularly important for the tourism labour market, which was severely hit by the COVID-19 pandemic. This study examined the effect of carbon taxes on tourism employment for European countries that have levied a carbon tax over the past thirty years. A macroeconomic panel data regression model ex-post study was applied by contrasting the obtained results via a robustness check. The estimation results indicate a slightly positive and significant association between the carbon tax and tourism employment, which was additionally tested by considering revenue recycling, early adopters of the carbon tax, and a higher carbon tax compared to countries with a lower carbon tax. We cannot conclude that these factors matter for tourism employment, proving the robustness of the results. Revenue-neutral carbon taxation, policies to address the skills gap, push and pull incentives, and active labour market policies to facilitate the quick re-integration of jobseekers into employment are viewed as pivotal to ensure a smoother transition toward a sustainable tourism labour market.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".