The Great Canadian (Electric) Road Trip: Evaluating EV Use in National Park Tourism
Bibliographic record
Abstract
As part of its 2050 net zero emission commitment, Canada has a mandate to transition to electric vehicles (EVs) to decarbonize transportation. While the EV charging infrastructure is expanding in urban areas, the infrastructure for inter-city and nature-based tourism has not been assessed. This study combined four metrics of feasibility and convenience into an EV readiness index to evaluate 94 road trip itineraries associated with Canada’s iconic national parks. Index scores ranged from not possible (0) to equal to the travel experience of internal combustion vehicle (20). With the summer range, the average one-day route score was 19.2, as 82% of one-day itineraries could be completed without requiring additional charging. Multi-day routes scored lower (14.0), with 24% being highly inconvenient or impossible due to gaps between charging stations. With a reduced winter EV range, average index scores for one- and multi-day trips declined as charging needs increased (18.4 and 13.2, respectively). Across the 94 travel routes, EVs were estimated to produce only 17% of the carbon dioxide emissions of internal combustion vehicles (this was much lower in some provinces). The findings also highlight key regional differences in tourism EV readiness and where infrastructure is needed to provide equitable access to Canada’s national parks for tourists seeking to decarbonize their holiday travel.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".