Potential consequences of a CO2 aviation tax in Mexico on the demand for tourism
Bibliographic record
Abstract
There is limited evidence on the potential consequences of the implementation of a CO2 aviation tax in developing countries. In this paper we analyze the potential impact of a CO2 aviation tax on the inbound tourism demand from the United States, Canada and Europe to Mexico. The methodology consists of a panel cointegration estimation of the demand for international tourism to Mexico. Unlike previous studies we analyze the potential effect of the tax on both tourism expenditure and the number of airplane arrivals. The results indicate an income elasticity of 1.9 for tourism expenditure and 2.9 for the number of airplane tourist. The price elasticities of airplane tourism expenditure and the number of airplane tourists are -0.94 and -0.39, respectively. The difference in price elasticity between tourism expenditure and number of tourists suggest that a CO2 aviation tax in Mexico would lead to a larger adjustment in total expenditure rather than in trip decisions. The implementation of such tax is therefore consistent with a continuous growth of the demand for tourism. Furthermore, the tax has the potential to generate additional fiscal revenue for 163 - 480 million dollars. The price elasticity of the competitive destination highlights the importance of considering a regional agreement for the implementation of an international CO2 aviation tax.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".