Disclosure of environmental sustainability activities by large ski lift firms
Bibliographic record
Abstract
This study investigates how environmental sustainability practices and reporting are disclosed by a group of six large ski lift operators across the world (Compagnie des Alpes, CDA (France), Silvrettaseilbahn AG (Ischgl) (Austria), Skistar (Sweden/Norway), Vail resorts (United States), Whistler Blackcomb (Canada) and Zermatt (Switzerland). Different types of practices are assessed. Results show that ski lift operators are highly active even if the extent of disclosure varies across resorts. Publicly listed ski lift operators in France and Sweden provide a detailed sustainability report and have also implemented environmental management programmes. Other firms develop their own sustainability strategies (Whistler Blackcomb, Vail resorts and Zermatt Bergbahnen AG). The practices range from monitoring of greenhouse gas emissions, 100 per cent green electricity, zero emission goals, energy reduction, fuel switching, water consumption, waste management and adaptation measures to climate change. Two ski lift operators show a decreasing trend in Co2 emissions per skier day or energy costs. Some operators report water usage in snowmaking per visitor which ranges between 250 to 1400 litres per skier day. Carbon offsetting and environmentally friendly diesel are also common tools. No ski lift operator actively participates in the UN global compact programme while three provide a sustainability report following the Global Environmental Reporting Initiative. There is an overemphasis on the use of easily available renewable energy sources, while other more complicated environmental concerns such as climate change risk are de-emphasised. Information on the main source of locally generated emissions, the fuel consumption of “piste” vehicles and snowmobiles is scarce.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".