The Demand of Car Rentals: a Microeconometric Approach with Count Models and Survey Data
Bibliographic record
Abstract
This study analyzes the demand side of the tourism market in the Autonomous Region of the Azores, Portugal, ranked by National Geographic as the second island destination for sustainable tourism among 111 islands in the world. Due to the high frequency of car rentals, this region is a “fly-and-drive” destination, experienced rapid growth in the tourism sector in recent years. It is well known that the excessive use of cars leads to negative externalities such as pollution and the degradation of roads. Considering ecological fragility, typical for small islands, it is crucial to investigate the extent of negative externalities for internalizing the congestion costs. This topic is very important in terms of policy-making for developing sustainable tourism destination as well as in a global environmental context, from the perspectives of eco-taxes used as instruments for enhancing environmental protection. A distinctive contribution of this study is the attention paid to the diversity of tourists used car rental services in the Azores. The demand function of car rentals is analyzed based on highly disaggregated, individual data containing a large number of tourists visited the Azores and the family of count models. Then, based on the price elasticity of demand for car rentals, the desired tax rates are suggested for internalizing the congestion costs.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".