Regional ski tourism risk to climate change: An inter-comparison of Eastern Canada and US Northeast markets
Bibliographic record
Abstract
Climate change has become a business planning reality in the ski industry, with differential impacts and adaptive capacity important for intra- and inter-regional market competitiveness. Potential climate change impacts are examined at 171 ski areas in Ontario, Québec and the US Northeast using the SkiSim2 model with regional parameterizations of snowmaking capacity. With advanced snowmaking, mid-century season length losses are limited to 12–13% under a low emission pathway (RCP 4.5), increasing to 15–22% under high emissions (RCP 8.5). By late-century, low and high emission pathways diverge creating very different futures for the ski industry. Season length and skiable terrain losses increase only marginally in the low emission pathway, while transformational impacts occur under a high emission pathway, with only 29 ski areas in Québec and high-elevation areas of the US Northeast able to maintain a 100-day season and open regularly for the economically important Christmas-New Year holiday. A low emission future, where current national pledges to Paris Climate Agreement are achieved, is crucial to preserve the Eastern North America ski tourism marketplace. The results are compared with previous studies that have neglected the adaptive capacity of snowmaking and substantially overestimated the impact of mid-century and lower emission climate change scenarios.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".