Sustainability of snowmaking as climate change (mal)adaptation: an assessment of water, energy, and emissions in Canada’s ski industry
Bibliographic record
Abstract
As climate change continues to impact the snowpack in ski areas globally, operators rely increasingly on snowmaking to maintain ski seasons and visitor experience. Increased reliance on machine-made snow has implications for the sustainability of ski tourism. This study provides the first national estimate of water, energy, and CO2 emissions and projected changes under low (RCP2.6), mid (RCP4.5), and high emission (RCP8.5) climate futures by the 2050s. A central estimates of snowmaking efficiency found Canada currently uses 478,000 megawatts (MWh) of electricity (with 130,095 tonnes of associated CO2 emission) and 43.4 million m3 of water to produce over 42 million m3 of technical snow. With snowmaking production requirements projected to increase between 55% and 97% by 2050 across low to high-emission climate futures, energy, and water use will increase proportionally. In contrast, future emissions associated with increased snowmaking would nonetheless decline substantially as provincial electricity grids are decarbonized under current policy targets. Regional differences in snowmaking requirements and emissions caused by provincial electricity-grid emission intensity and their important implications for ski tourism sustainability and snowmaking as (mal)adaptation are discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".