Modelling Tourism Destination Energy Consumption and Greenhouse Gas Emissions: Whistler, British Columbia, Canada
Bibliographic record
Abstract
As awareness of tourism’s energy impacts on global environments increases, and as knowledge of energy consumption’s effects on tourism destination sustainability grows, so does the need for planners to develop proactive energy management strategies. However, the unique characteristics of energy consumption behaviour in resort destinations make it difficult to assess the relative merits of various energy management options. This research identifies a unique ‘bottom-up’ modelling procedure for assessing the relative effects of various destination planning strategies on energy use and GHG emissions. It then applies the model to energy management strategies being considered for implementation in Whistler, British Columbia – one of North America’s leading mountain resort destinations. The research suggests that the model’s dynamic character makes it a potentially valuable tool for quantitatively assessing what dimensions of various destination transportation, building design and community infrastructure development strategies have the greatest influence on energy use and greenhouse gas emissions. The research contributes to existing destination planning and sustainable tourism development theory and practice by developing a first generation forecasting model for identifying and assessing energy use and GHG emissions, and then illustrating its practical application in the context of emerging sustainable destination planning practices.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".