Systems Analysis of Climate Change Vulnerability for the US Northeast Ski Sector
Bibliographic record
Abstract
One of the greatest challenges to the sustainability of the winter tourism sector is climate change. Studies examining the implications of climate change for the ski tourism industry have mainly focused on vulnerability of the supply side (i.e. ski area infrastructure and operators) with limited attention given to the demand side (i.e. how tourists will respond to changing climate and ski conditions). A more holistic understanding of how the winter tourism marketplace may evolve under a changed climate is required for managers and communities to develop and plan specific adaptation strategies. Using a systems approach this study examines climate change vulnerability of both the supply and demand sides of the US Northeast ski tourism sector (i.e. a marketplace of some 103 ski areas across the states of New York, Vermont, New Hampshire, Maine, Massachusetts, Rhode Island and Connecticut). Multiple methods were employed including a climate change analogue (demand and supply side), future climate change and operations modeling (supply side), and a skier survey (demand side). Findings reveal a complexity of interacting and opposing impacts including the projected contraction northward of viable ski areas. In response to projected ski area closures in the region, demand for skiing opportunities is not likely to decrease proportionally. Ski areas that are able to remain operational under changed conditions should plan for a possible market-shift (i.e. spatial substitution) and may expect crowding issues and residual development pressure in association with the concentration of ski areas in fewer climate-advantaged regions.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".