A Behavioral Assessment of Tourism Transportation Options fo r Reducing Energy Consumption and Greenhouse Gases
Bibliographic record
Abstract
energy use and GHG emissions in tourism destinations. The second phase conducts a discrete-choice experiment (DCE) to estimate tourist travel-mode choices under different trans-portation scenarios. In the final phase, the findings from the choice experiment are linked with the bottom-up modeling procedure to derive more behaviorally realistic estimates of energy consumption and GHG emissions associated with each of the scenarios. To illustrate the utility of the preceding approach, the methods are applied to a case study in Whistler, British Columbia. Whistler is a four-season destination located about 120 kilometers from Vancouver. It attracts an estimated 2 million visitors annually to its mountains for various leisure pursuits. Because of the strategic importance of the area’s high-quality natural resources to the destination’s competitive-ness, Whistler stakeholders have committed to implementing a range of transportation strategies designed to make it more sustainable (RMOW 2004). This study examines summer-visitor responses to various transportation options associated with travel to Whistler and models the direct energy consump-tion and associated GHG emissions resulting from these travel choices. The study focuses on one specific component of the travel to Whistler—trips between Vancouver and Whistler, which currently are dominated by private and rental cars. Day users from as close as Vancouver as well as overnight visitors from far and wide all must travel this corridor. In combination, the article’s theoretical and methodolog-ical frames as well as applied case study illustrate a behav-Joe Kelly (PhD, resource and environmental management) is director of strategic services at InterVISTAS Consulting Inc. in
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".