The Future of Canada's Ski and Mountain Destinations in an Era of Climate Change
Bibliographic record
Abstract
Climate change represents a grand challenge for society and the far-reaching risks for the global tourism sector is no exception. As one of the largest sectors globally, tourism is not only highly impacted by the biophysical impacts of climate change but is also a major source of greenhouse gas emissions contributing to anthropogenic climate change. Tourism’s entrenchment in global socio-ecological systems mean that how tourism identifies and responds to climate change risks will have extensive implications for sport, recreation, livelihoods, culture, real estate, infrastructure, and community resilience in tourism destinations worldwide. While the international tourism sector has highlighted climate change as the primary threat to tourism sustainability, lack of viable climate change adaptation and mitigation strategies raise fundamental questions about the place of tourism in a warmer and decarbonized future. Considering the urgency and salience of these questions for winter tourism specifically, research on highly climate sensitive ski tourism provides important learnings and potential leadership for other tourism sectors that will inevitably face transformative risks as climate change accelerates. \nThis dissertation therefore investigates the complex physical climate and carbon risk within the Canadian ski and mountain tourism system to explore potential pathways towards sustainability and climate resiliency. Canada's diverse ski tourism industry provides an exemplary case study to identify the range of climate and carbon risks, investigate climate adaptations, and understand other socio-ecological factors contributing to or hindering climate impacts, responsiveness, and resilience. Through three interrelated studies, this dissertation combines qualitative and quantitative methodologies using a tourism geography lens to; (1) apply industry-specific climate risk modeling, (2) conduct empirical analysis on the sustainability of snowmaking as a climate adaptation, and (3) understand diverse and inter-connected stakeholder climate risk and response perspectives. Through this process, the research aims to understand the "wicked" challenge of climate change in complex tourism systems, provide information needed for relevant and dynamic climate response planning, decision-making and action, and enable discussions on sustainability transformations and climate resilient futures for diverse mountain tourism destinations. \nFindings suggest climate risk and resilience is relative across temporal and spatial scales, with potential cross-regional implications for competitiveness and demand patterns. Empirical assessments of snowmaking as a climate adaptation further demonstrate that the national scale is too coarse to evaluate (mal)adaptation or sustainability, instead showing how regional and destination-scale differences in climate impacts, tourism markets, ecosystems (e.g., water availability), energy sources result in differing assessments of adaptation sustainability. Multi-stakeholder narratives situate modelled and observed climate and carbon risks within complex socioeconomic systems and identify diverse actors, structures, and perspectives influencing destination-scale climate (in)action and potential levers to affect more transformative change towards climate resilient futures. \n\tMore broadly, this dissertation broaches important sustainable tourism and climate change theories, concepts and paradoxes including: temporal and spatial scale, relative climate risk (impacts and adaptive capacity); private-public sector relations and responsibility; (mal)adaptation; scope 3 emissions and current-future tourism mobility; tourism growth and decarbonization; pluralistic value(s) of tourism and sustainability; top-down vs bottom up decision-making; and climate justice, with the aim of extending the important conversation on [sustainable] tourism’s place in a decarbonized economy and warmer world. In investigating the intersection of these research questions, this dissertation presents novel conceptual frameworks, empirical analysis and participatory methods which could be replicated in other tourism contexts and applied to support ski tourism operators and local mountain communities responding to climate change.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.006 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".