Disclosure of environmental sustainability activities by large ski lift firms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 it