Science, Irving K. Barber Faculty of (Okanagan)
Earth, Environmental and Geographic Sciences, Department of (Okanagan)
DSpace
UBCO
Giesler, Wendelin
Master of Science - MSc
University of British Columbia
Ski resorts around the world are facing rapidly warming winter temperatures and shifts in precipitation patterns, likely impacting the demand for resort skiing and affecting regional businesses. Previous work investigates the future of alpine skiing in Europe, Eastern Canada, and in the United States (US), finding variable results depending on the analysed region and research methods. Within-seasonal impacts of changing climate conditions on customer demand are little researched and the ski region of British Columbia (BC) remains virtually unexplored by related studies. Via a multiple linear regression model, this research project investigates the reactivity of rental apartment bookings surrounding four selected ski resorts in BC to short-term changes in snowfall, rainfall, and temperatures. Expected future weather and snow conditions are then simulated using downscaled output from global climate models for each location under the emission scenarios RCP4.5 and RCP8.5, permitting insights into skiing conditions by the mid and late 21st century. Expected activity levels are finally attributed to future skiing conditions, providing an estimate of within-seasonal climate change impacts on the ski industry while holding supply and properties of rental apartments constant. The results differ among the sample of resorts with the colder resorts in the interior of BC displaying future increases in snowfall and moderate increases in rainfall levels. The resorts with warmer baseline temperatures are projected to experience significant increases in winter rainfall and steady or decreasing snowfall levels depending on the emission scenario. Demand levels surrounding all resorts are shown to increase with higher snowfall levels and decrease due to the impacts of winter rainfall. While expected climate change impacts differ insubstantially at the investigated resorts between the two emission scenarios by 2055, RCP4.5 leads to distinctly lower losses by 2085 than seen under RCP8.5. The study provides a first assessment of behavioural demand trends surrounding ski resorts in BC. Results indicate that while within-seasonal impacts of global warming might be modest at less than 10 % losses in total rental probabilities, the moderate warming scenario RCP4.5 would mitigate part of the projected losses, which can primarily be attributed to increasing rainfall during the ski season. The analysis provides resorts and stakeholders with tools to predict customer trends based on weather forecasts, first estimates of climate change impacts on demand levels based on local visitor data, as well as projections of long-term expected changes in climate conditions and demand levels.
SNOW CONDITIONS AND THE DEMAND FOR SKIER ACCOMMODATION IN BRITISH COLUMBIA: A COMPARISON OF CLIMATE CHANGE IMPACTS UNDER RCP4.5 AND RCP8.5 EMISSION SCENARIOS by Wendelin Giesler B.Sc. Environmental System Sciences and Economics, Karl-Franzens University Graz, 2020 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE COLLEGE OF GRADUATE STUDIES (Earth and Environmental Sciences) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) June 2023 © Wendelin Giesler, 2023ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis/dissertation entitled: SNOW CONDITIONS AND THE DEMAND FOR SKIER ACCOMMODATION IN BRITISH COLUMBIA: A COMPARISON OF CLIMATE CHANGE IMPACTS UNDER RCP4.5 AND RCP8.5 EMISSION SCENARIOS submitted by Wendelin Giesler in partial fulfillment of the requirements of the degree of Master of Science. Dr. Michael Pidwirny, Irving K. Barber Faculty of Science Supervisor Dr. John Janmaat, Irving K. Barber Faculty of Arts and Social Sciences Co-Supervisor Dr. John R. J. Thompson, Irving K. Barber Faculty of Science Supervisory Committee Member Dr. Julien Picault, Irving K. Barber Faculty of Arts and Social Sciences Supervisory Committee Member Dr. Patrick Baylis, University of British Columbia (Vancouver), School of Economics University Examiner iii Abstract Ski resorts around the world are facing rapidly warming winter temperatures and shifts in precipitation patterns, likely impacting the demand for resort skiing and affecting regional businesses. Previous work investigates the future of alpine skiing in Europe, Eastern Canada, and in the United States (US), finding variable results depending on the analyzed region and research methods. Within-seasonal impacts of changing climate conditions on customer demand are little researched and the ski region of British Columbia (BC) remains virtually unexplored by related studies. Via a multiple linear regression model, this research project investigates the reactivity of rental apartment bookings surrounding four selected ski resorts in BC to short-term changes in snowfall, rainfall, and temperatures. Expected future weather and snow conditions are then simulated using downscaled output from global climate models for each location under the emission scenarios RCP4.5 and RCP8.5, permitting insights into skiing conditions by the mid and late 21st century. Expected activity levels are finally attributed to future skiing conditions, providing an estimate of within-seasonal climate change impacts on the ski industry while holding supply and properties of rental apartments constant. The results differ among the sample of resorts with the colder resorts in the interior of BC displaying future increases in snowfall and moderate increases in rainfall levels. The resorts with warmer baseline temperatures are projected to experience significant increases in winter rainfall and steady or decreasing snowfall levels depending on the emission scenario. Demand levels surrounding all resorts are shown to increase with higher snowfall levels and decrease due to the impacts of winter rainfall. While expected climate change impacts differ insubstantially at the investigated resorts between the two emission scenarios by 2055, RCP4.5 leads to distinctly lower losses by 2085 than seen under RCP8.5. The study provides a first assessment of behavioural demand trends surrounding ski resorts in BC. Results indicate that while within-seasonal impacts of global warming might be modest at less than iv 10 % losses in total rental probabilities, the moderate warming scenario RCP4.5 would mitigate part of the projected losses, which can primarily be attributed to increasing rainfall during the ski season. The analysis provides resorts and stakeholders with tools to predict customer trends based on weather forecasts, first estimates of climate change impacts on demand levels based on local visitor data, as well as projections of long-term expected changes in climate conditions and demand levels. v Lay Summary Human caused climate change threatens skiing operations around the world due to general increases in temperatures and decreases in winter snowfall. This study investigates how customers at rental apartments surrounding ski resorts in British Columbia react to snowfall, rainfall, and temperatures and simulates changes in future skiing conditions. The results show that climate change will impact skiing operations in the province by generally lowering expected customer numbers and revenues. Ski resorts with warmer winter temperatures will see the majority of the impacts due to lower snowfall levels and increasing winter rainfall. Lowering greenhouse gas emissions could help avoid some of the predicted impacts. vi Preface The analysis in this thesis uses data collected or generated from the following resources: Snow-forecast.com, ClimateBC, Airdna, BC Assessment, and MarkSimGCM. The results and conclusions exclusively reflect my views on the topics covered without implications for the data providers. vii Table of Contents Abstract ................................................................................................................................... iii Lay Summary ........................................................................................................................... v Preface ...................................................................................................................................... vi Table of Figures........................................................................................................................ x List of Tables ......................................................................................................................... xiv Acknowledgements ............................................................................................................... xvi 1. Introduction ...................................................................................................................... 1 2. Literature Review: ............................................................................................................ 4 2.1 Climates of the Past, Present, and Future .................................................................... 4 2.1.1 Anthropogenic Enhancement of the Greenhouse Effect ...................................... 4 2.1.2 Future Climate Scenarios ..................................................................................... 5 2.1.3 Climate Models .................................................................................................... 8 2.1.4 Climate Drivers in British Columbia ................................................................. 12 2.2 Weather and Climate Impacts on the Ski Industry .................................................... 14 2.2.1 Weather and Tourism ......................................................................................... 14 2.2.2 Weather and Skiing Supply ............................................................................... 16 2.2.2.1 First Season Length Projections ..................................................................... 16 2.2.2.2 Skiing Supply with Artificial Snowmaking ................................................... 18 2.2.3 Weather and Skier Demand ............................................................................... 22 2.2.3.1 Snow Sensitivities .......................................................................................... 22 2.2.3.2 Reactivity of Overnight Stays to Snow Conditions ....................................... 26 2.2.3.3 Other Drivers of Demand ............................................................................... 28 2.3 Literature Gaps and Contributions ............................................................................ 29 3. Methods ........................................................................................................................... 32 3.1 Data Sources and Descriptions .................................................................................. 32 3.1.1 Snow and Weather Data..................................................................................... 32 3.1.2 Demand Data: Rental Apartment Approach ...................................................... 34 3.2 Introduction of Selected Ski Resorts ......................................................................... 36 3.2.1 Ski Resort Summaries ........................................................................................ 36 3.2.2 Winter Climate Conditions ................................................................................ 39 3.3 Logistic Regression Model........................................................................................ 47 3.3.1 Model Setup ....................................................................................................... 47 3.3.2 Expectations of Snow Conditions ...................................................................... 52 3.3.3 Data Summaries ................................................................................................. 56 viii 3.4 Future Climate Projections ........................................................................................ 61 3.4.1 Downscaling via MarkSimGCM ....................................................................... 61 3.4.2 Climate Model Selection.................................................................................... 63 3.5 Future Demand Projections ....................................................................................... 64 4 Results .............................................................................................................................. 66 4.1 Regression Model ...................................................................................................... 66 4.1.1 Marginal Effects................................................................................................. 66 4.1.2 Weather Reactivities .......................................................................................... 68 4.1.3 Calendar Effects and Property-Specific Impacts ............................................... 70 4.2 Climate Projections ................................................................................................... 72 4.2.1 Temperatures...................................................................................................... 72 4.2.2 Snowfall ............................................................................................................. 76 4.2.2.1 Mid Century – 2055 ....................................................................................... 76 4.2.2.2 Late Century – 2085 ....................................................................................... 78 4.2.2.3 General Trends ............................................................................................... 79 4.2.3 Rainfall ............................................................................................................... 84 4.2.3.1 Mid Century – 2055 ....................................................................................... 84 4.2.3.2 Late Century – 2085 ....................................................................................... 85 4.2.3.3 General Trends ............................................................................................... 87 4.3 Demand Projections .................................................................................................. 92 4.3.1 Mid Century – 2055 ........................................................................................... 92 4.3.2 Late Century – 2085........................................................................................... 97 4.3.3 General Trends ................................................................................................. 104 5 Discussion ...................................................................................................................... 106 5.1 Explaining Within-Seasonal Changes in Demand .................................................. 106 5.2 Future Winter Seasons ............................................................................................ 107 5.3 Climate Impacts on Rental Probabilities and Revenues.......................................... 109 5.4 Accounting for Endogeneity in the Demand Model ............................................... 111 5.5 Contributions and Future Research ......................................................................... 115 5.6 Limitations .............................................................................................................. 119 5.6.1 Short-term Rental Data .................................................................................... 119 5.6.2 Skier Demand................................................................................................... 120 5.6.3 Regression Model ............................................................................................ 123 5.6.4 Modelling Climate Scenarios ........................................................................... 125 6 Conclusion ..................................................................................................................... 127 ix Bibliography ......................................................................................................................... 129 Appendices ............................................................................................................................ 145 A. Geofenced ski resort apartments ................................................................................. 145 B. Regression Coefficients .............................................................................................. 148 C. Marginal Effects and Varying Snow Bins .................................................................. 151 D. Model Performance Comparison ................................................................................ 156 E. Rental Probabilities ..................................................................................................... 157 x Table of Figures Figure 1. Map of selected BC ski resorts, map data ©2023 Mapbox (Mapbox.com) ©OpenStreetMap (Openstreetmap.org) ................................................................................... 37 Figure 2: Average winter temperatures in °C at mid-elevation of Big White Ski Resort, 1951-2020 historical and model forecasts in 2025, 2055, and 2085. ...................................... 41 Figure 3: Average winter precipitation as snow and rain at mid-elevation of Big White Ski Resort, 1951-2020 historical and model forecasts in 2025, 2055, and 2085. .......................... 41 Figure 4: Average winter temperatures in °C at mid-elevation of Fernie Ski Resort, 1951-2020 historical and model forecasts in 2025, 2055, and 2085. ...................................... 42 Figure 5: Average winter precipitation as snow and rain at mid-elevation of Fernie Ski Resort, 1951-2020 historical and model forecasts in 2025, 2055, and 2085. .......................... 43 Figure 6: Average winter temperatures in °C at mid-elevation of Sun Peaks Mountain Resort, 1951-2020 historical and model forecasts in 2025, 2055, and 2085. ...................................... 44 Figure 7: Average winter precipitation as snow and rain at mid-elevation of Sun Peaks Mountain Resort, 1951-2020 historical and model forecasts in 2025, 2055, and 2085. ......... 44 Figure 8: Average winter temperatures in °C at mid-elevation Whistler Blackcomb, 1951-2020 historical and model forecasts in 2025, 2055, and 2085. ...................................... 45 Figure 9: Average winter precipitation as snow and rain at mid-elevation of Whistler Blackcomb, 1951-2020 historical and model forecasts in 2025, 2055, and 2085. .................. 46 Figure 10: Decision tree for combining and weighting expected snow conditions. ................ 55 Figure 11: w(x), weighting function for expected snowfall levels based on booking lead times. ........................................................................................................................................ 55 Figure 12: Apartment booking rates graphed by average daily temperatures, rain events, and expected snowfall at Big White. .............................................................................................. 59 Figure 13: Apartment booking rates graphed by average daily temperatures, rain events, and expected snowfall at Fernie. .................................................................................................... 59 Figure 14: Apartment booking rates graphed by average daily temperatures, rain events, and expected snowfall at Sun Peaks. .............................................................................................. 60 Figure 15: Apartment booking rates graphed by average daily temperatures, rain events, and expected snowfall at Whistler. ................................................................................................. 60 Figure 16: Marginal effects of expected 72h snowfall on rental probabilities under mean temperatures at Big White, Fernie, Sun Peaks, and Whistler. The impact at the reference scenario, [0-10 cm) is set at 0. Bars around estimates show 95% Confidence Intervals. ........ 68 xi Figure 17: Average daily winter temperatures at Big White under RCP4.5 2015, RCP4.5 2055, RCP8.5 2055, RCP4.5 2085, and RCP8.5 2085. ........................................................... 74 Figure 18: Average daily winter temperatures at Fernie under RCP4.5 2015, RCP4.5 2055, RCP8.5 2055, RCP4.5 2085, and RCP8.5 2085. ..................................................................... 75 Figure 19: Average daily winter temperatures at Sun Peaks under RCP4.5 2015, RCP4.5 2055, RCP8.5 2055, RCP4.5 2085, and RCP8.5 2085. ........................................................... 75 Figure 20: Average daily winter temperatures at Whistler Blackcomb under RCP4.5 2015, RCP4.5 2055, RCP8.5 2055, RCP4.5 2085, and RCP8.5 2085............................................... 76 Figure 21: Smoothed function of mean daily snowfall projections under RCP4.5 2015, RCP4.5 2055, RCP8.5 2055, RCP4.5 2085, and RCP8.5 2085 at Big White. ........................ 80 Figure 22: Average binned frequencies of expected 72h snowfall under RCP4.5 2015, RCP4.5 2055, RCP8.5 2055, RCP4.5 2085, and RCP8.5 2085 at Big White. ........................ 80 Figure 23: Smoothed function of mean daily snowfall projections under RCP4.5 2015, RCP4.5 2055, RCP8.5 2055, RCP4.5 2085, and RCP8.5 2085 at Fernie. .............................. 81 Figure 24: Average binned frequencies of expected 72h snowfall under RCP4.5 2015, RCP4.5 2055, RCP8.5 2055, RCP4.5 2085, and RCP8.5 2085 at Fernie. .............................. 81 Figure 25: Smoothed function of mean daily snowfall projections under RCP4.5 2015, RCP4.5 2055, RCP8.5 2055, RCP4.5 2085, and RCP8.5 2085 at Sun Peaks. ........................ 82 Figure 26: Average binned frequencies of expected 72h snowfall under RCP4.5 2015, RCP4.5 2055, RCP8.5 2055, RCP4.5 2085, and RCP8.5 2085 at Fernie. .............................. 82 Figure 27: Smoothed function of mean daily snowfall projections under RCP4.5 2015, RCP4.5 2055, RCP8.5 2055, RCP4.5 2085, and RCP8.5 2085 at Whistler Blackcomb. ........ 83 Figure 28: Average binned frequencies of expected 72h snowfall under RCP4.5 2015, RCP4.5 2055, RCP8.5 2055, RCP4.5 2085, and RCP8.5 2085 at Whistler Blackcomb. ........ 83 Figure 29: Smoothed function of mean daily rainfall projections under RCP4.5 2015, RCP4.5 2055, RCP8.5 2055, RCP4.5 2085, and RCP8.5 2085 at Big White. ...................................... 88 Figure 30: Average frequencies of expected 72h rain days under RCP4.5 2015, RCP4.5 2055, RCP8.5 2055, RCP4.5 2085, and RCP8.5 2085 at Big White. ................................................ 88 Figure 31: Smoothed function of mean daily rainfall projections under RCP4.5 2015, RCP4.5 2055, RCP8.5 2055, RCP4.5 2085, and RCP8.5 2085 at Fernie. .............................. 89 Figure 32: Average frequencies of expected 72h rain days under RCP4.5 2015, RCP4.5 2055, RCP8.5 2055, RCP4.5 2085, and RCP8.5 2085 at Fernie. ...................................................... 89 Figure 33: Smoothed function of mean daily rainfall projections under RCP4.5 2015, RCP4.5 2055, RCP8.5 2055, RCP4.5 2085, and RCP8.5 2085 at Sun Peaks. ...................................... 90 xii Figure 34: Average frequencies of expected 72h rain days under RCP4.5 2015, RCP4.5 2055, RCP8.5 2055, RCP4.5 2085, and RCP8.5 2085 at Sun Peaks. ................................................ 90 Figure 35: Smoothed function of mean daily rainfall projections under RCP4.5 2015, RCP4.5 2055, RCP8.5 2055, RCP4.5 2085, and RCP8.5 2085 at Whistler Blackcomb. ..................... 91 Figure 36: Average frequencies of expected 72h rain days under RCP4.5 2015, RCP4.5 2055, RCP8.5 2055, RCP4.5 2085, and RCP8.5 2085 at Whistler Blackcomb. ............................... 91 Figure 37: Projected daily rental probabilities 2055 (top bracket) and difference in total rental probabilities between RCP4.5 and RCP8.5 2055 (bottom bracket) at Big White. .................. 95 Figure 38: Projected daily rental probabilities 2055 (top bracket) and difference in total rental probabilities between RCP4.5 and RCP8.5 2055 (bottom bracket) at Fernie. ........................ 95 Figure 39: Projected daily rental probabilities 2055 (top bracket) and difference in total rental probabilities between RCP4. 5 and RCP8.5 2055 (bottom bracket) at Sun Peaks. ................. 96 Figure 40: Projected daily rental probabilities 2055 (top bracket) and difference in total rental probabilities between RCP4.5 and RCP8.5 2055 (bottom bracket) at Whistler. ..................... 96 Figure 41: Projected daily rental probabilities 2085 (top bracket) and difference in total rental probabilities between RCP4.5 and RCP8.5 2085 (bottom bracket) at Big White. ................ 100 Figure 42: Projected daily rental probabilities RCP8.5 2085 and RCP4.5 2015 (top bracket) and difference in total rental probabilities between the two scenarios (bottom bracket) at Big White. ..................................................................................................................................... 100 Figure 43: Projected daily rental probabilities 2085 (top bracket) and difference in total rental probabilities between RCP4.5 and RCP8.5 2085 (bottom bracket) at Fernie. ...................... 101 Figure 44: Projected daily rental probabilities RCP8.5 2085 and RCP4.5 2015 (top bracket) and difference in total rental probabilities between the two scenarios (bottom bracket) at Fernie. .................................................................................................................................... 101 Figure 45: Projected daily rental probabilities 2085 (top bracket) and difference in total rental probabilities between RCP4.5 and RCP8.52085 (bottom bracket) at Sun Peaks. ................. 102 Figure 46: Projected daily rental probabilities RCP8.5 2085 and RCP4.5 2015 (top bracket) and difference in total rental probabilities between the two scenarios (bottom bracket) at Sun Peaks. ..................................................................................................................................... 102 Figure 47: Projected daily rental probabilities 2085 (top bracket) and difference in total rental probabilities between RCP4.5 and RCP8.5 2085 (bottom bracket) at Whistler. ................... 103 Figure 48: Projected daily rental probabilities RCP8.5 2085 and RCP4.5 2015 (top bracket) and difference in total rental probabilities between the two scenarios (bottom bracket) at Whistler. ................................................................................................................................. 103 xiii Figure 49: Rental apartments surrounding Big White Ski Resort, BC. Retrieved from airdna.co, map data ©2023 Google. ...................................................................................... 145 Figure 50: Rental apartments in Fernie, BC. Retrieved from airdna.co, map data ©2023 Google. ................................................................................................................................... 146 Figure 51: Rental apartments surrounding Sun Peaks Mountain Resort, BC. Retrieved from airdna.co, map data ©2023 Google. ...................................................................................... 146 Figure 52: Rental apartments in Whistler, BC. Retrieved from airdna.co, map data ©2023 Google. ................................................................................................................................... 147 Figure 53: Marginal effects of snowfall under 5 cm, 10 cm, and 15 cm bins at Big White. . 154 Figure 54: Marginal effects of snowfall under 5 cm, 10 cm, and 15 cm bins at Fernie. ....... 154 Figure 55: Marginal effects of snowfall under 5 cm, 10 cm, and 15 cm bins at Sun Peaks. . 155 Figure 56: Marginal effects of snowfall under 5 cm, 10 cm, and 15 cm bins at Whistler. .... 155 xiv List of Tables Table 1: Summary of average historical (1950-2020, with standard deviations) and projected (2055, 2085) winter temperatures, snowfall at mid elevation of Big White, Fernie, Sun Peaks, and Whistler Blackcomb . ........................................................................................................ 47 Table 2: Summaries of categorical weather, snow, and booking data at Big White, Fernie, Sun Peaks, and Whistler. ......................................................................................................... 57 Table 3: Summaries of numerical weather, snow, and booking data at Big White, Fernie, Sun Peaks, and Whistler. ................................................................................................................. 58 Table 4: Summary of climate models included into the climate projection ensemble. ........... 64 Table 5: Marginal impacts of demand drivers on rental probability levels under average conditions. Impacts are divided into environmental, calendar, and property-specific effects. 67 Table 6: Share of rental apartment category per ski resort. Grouped into small (1-2 guests), medium (3-6 guests), and large (7+ guests) units. ................................................................... 72 Table 7: Comparison of rental activities under RCP4.5 2015, RCP4.5 2055, and RCP8.5 2055. Results show differences in rental probabilities as well as per-property differences in revenues (2016 CAD). ............................................................................................................. 94 Table 8: Comparison of rental activities under RCP4.5 2015, RCP4.5 2085, and RCP8.5 2085. Results show differences in rental probabilities as well as per-property differences in revenues (2016 CAD). ............................................................................................................. 99 Table 9: Marginal impacts of demand drivers on rental probability levels under average conditions in a two-stage estimation using instrumental variables. Impacts are divided into environmental, calendar, and property-specific effects. ........................................................ 115 Table 10: Logistic regression coefficients, standard errors, and significance levels for each ski resort. ..................................................................................................................................... 148 Table 11: Logistic regression coefficients, standard errors, and significance levels for each ski resort in a two-stage estimation using instrumental variables. .............................................. 149 Table 12: First-stage regression coefficients of the instrumental variable model. The regression approximates the per-person price at Big White, Fernie, Sun Peaks, and Whistler................................................................................................................................................ 150 Table 13: Marginal impacts of demand drivers on rental probability levels under average conditions. Impacts are divided into environmental, calendar, and property-specific effects with upper and lower 95% confidence intervals displayed. .................................................. 151 Table 14: Marginal impacts of rain events and varying snowfall levels under the standard model, a model excluding bookings with negative lead time (Alternative I), a model xv exclusively considering encountered snow conditions (Alternative II), a model with 15 cm snowfall bins, and a model with 5 cm snowfall bins. ............................................................ 152 Table 15: Model evaluation metrics are displayed for the standard model, a model with 15 cm snowfall bins, a model with testing a linear variable of snowfall, a model exclusively considering encountered snow conditions (Alternative I), and a model with a rigid cutoff time of expected snow conditions at a booking lead time of 14 days. ........................................... 156 Table 16: Mean rental probabilities and associated standard deviations per ski resort and per climate scenario. .................................................................................................................... 157 xvi Acknowledgements First of all, I would like to thank my co-supervisors Dr. Michael Pidwirny and Dr. John Janmaat for their guidance, support, and expertise throughout the last couple of years. This degree has been a valuable experience in an abundance of ways. I have learned many skills from you—academically and personally—and will take them with me to the next chapters of life. I would also like to thank my committee members Dr. John R. J. Thompson and Dr. Julien Picault for their support and help through the development of this research. Your advice has been incredibly helpful to me due to the interdisciplinary nature of this project, and I am grateful for the time and effort you committed to my education. Thank you to the team from the Thompson Okanagan Tourism Association which I was fortunate to join for 10 months through two Mitacs Accelerate Internships. During this time, I learned a great deal about stakeholder communication, working in the tourism industry, and public discussions of climate change. A special thanks goes to Ellen Walker-Matthews and Mike Overend who guided me through this experience. A final thank you goes to all the additional people, family, and friends, who have supported and endured me through this journey. I greatly appreciate the friendships, the advice, all the get-togethers, and the distractions after long days of schoolwork. 1 1. Introduction Due to anthropogenic enhancement of the greenhouse effect, the earth is experiencing unprecedented changes to the global climate system. Climate projections show that the recent warming of surface temperatures may continue to accelerate if greenhouse gas emissions are not drastically reduced within the next decades. Specifically, regions located in high latitudes and the coldest months out of the year are expected to see the most significant increases in surface temperatures throughout the 21st century (IPCC, 2021). The effects of changing climate conditions can be felt around the world already with ski resorts—being a good proxy of changing climate conditions due to a dependence on below-freezing temperatures and consistent snowfall—suffering from thinning snow packs, increased snowmelt, and overall shorter ski seasons (Gilaberte-Búrdalo et al., 2014). After recovering from closures due to the COVID-19 pandemic, skier visits in Western Canada accounted for 49% of total national visits in the winter of 2021/22 (CWSAA, 2022). Nonetheless, only sporadic research projects investigate the future of the ski industry in Western Canada and in Western North America in general. So, is there no need for concern in the Pacific Northwest due to sufficient winter snowfall and consistently cold temperatures? Projected future trends (Clark, 2018) and numbers from particularly warm seasons recently such as the winter of 2014/15 (Crocker, 2015; OWSC, 2015; Pique, 2015) challenge this statement. While researchers have begun analyzing how long future ski seasons may last depending on various warming scenarios, a fundamental factor has often been omitted, only recently receiving attention in academic studies; the demand for skiing. By approximating climate impacts exclusively with projections of future ski season lengths, it is frequently assumed that as long as skiing operations are possible, customer numbers will be stable, 2 leading to steady revenues for related businesses. But does this assumption necessarily reflect skier behavior and thus resort revenues? An analysis of previous studies suggests that the future of skiing operations in BC may depend on several factors: naturally provided conditions which are primarily observed as snowfall and sufficiently cold temperatures, artificial enhancement of skiing conditions such as snowmaking and snow capture techniques and, finally, customer behavior and trends in skier demand in response to changing environmental conditions. To provide further insights into the impacts of global warming on the local ski industry, we will take a closer look at demand trends and skiing conditions in the province of British Columbia. The project will attempt to answer the following three research questions: 1. Do short-term weather conditions (snowfall, rainfall, temperatures) affect demand levels around ski resorts? 2. How will global warming affect skiing conditions in BC? 3. Assuming constant supply and prices of rental apartments under future conditions, how will activity levels surrounding BC's ski resorts be affected by changing climate conditions? Demand trends surrounding four ski resorts in BC will be analyzed via proxy data from short-term rental apartments. Future skiing conditions will be investigated via projected changes in temperatures, snowfall, and rainfall, simulated under a moderate (RCP4.5) and a high emission scenario (RCP8.5). To demonstrate climate trends over time, projected skiing conditions during the middle of the century (2055) and the end of the century (2085) will be compared to current (2015) conditions. Expected demand levels under future climate conditions will finally be associated to each scenario, showing the within-seasonal impact of global warming and demand trends on activity levels surrounding BC’s ski resorts. 3 The results will indicate short-term reactivities of customers to marginal changes in snow conditions and future changes in skiing conditions surrounding BC’s ski resorts. Findings will help stakeholders predict customer streams depending on short-term weather forecasts, provide long-term estimates in climate trends at each resort location and provide policymakers with monetized lower-bound estimates of climate change impacts under the emission scenarios RCP4.5 and RCP8.5. 4 2. Literature Review: 2.1 Climates of the Past, Present, and Future 2.1.1 Anthropogenic Enhancement of the Greenhouse Effect Weather and climate differ by definition in the measurement of time and area. Weather describes the state of the atmosphere over a relatively short time period over a particular location or area. Climate states the general long-term atmospheric patterns in a particular region, providing statistical indices of weather conditions (NASA, 2005). First suggested in the early 19th century by Joseph Fourier, temperatures on earth support life as we know it due to specific gasses trapping solar radiation within the atmosphere—an idea that later led to the discovery of the greenhouse effect (Weart, 2022). Since the industrial revolution starting in the mid-18th century, increasing anthropogenic emissions of greenhouse gasses, specifically carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) have caused an enhancement of the greenhouse effect within the atmosphere and hence a warming of mean global temperatures. Atmospheric warming includes a collective of additional (feedback) effects, a small part of which will be discussed in this study. Identifying past climate change is the key to future projections, providing background and perspective to understand impacts of climate change. Atmospheric concentrations of CO2 have risen from around 280 ppm before the industrial revolution to current 412.5 ppm (+47%), CH4 rose from 700 to 1879.2 ppb (+168%), and N2O from 270 to 333 ppb (+23%) (Blunden & Boyer, 2021). Evidence of past climates established via proxy data from tree rings, lake sediments, bivalves, coral, and glacier ice cores enable detailed analyses over the last 2000 years (PAGES 2k Consortium, 2019) and general reconstructions of past climates over 10,000 years back in time (Smerdon & Pollack, 2016). All records indicate that the current warming in mean global temperatures is unprecedented in magnitude and speed. 5 Early studies investigating the greenhouse effect suggested significantly warming temperatures for the following decades and throughout the 21st century. Broecker (1975) predicted that the earth was coming out of a cooling trend—caused by natural factors and anthropogenic emissions of atmospheric dust—and was on the brink of pronounced global warming. Subsequent studies such as Hansen et al. (1981) predicted future global temperature increases which include today's warming and generally project similar temperature ranges as the current, highly sophisticated climate models (IPCC, 2021). Today’s global mean temperature levels are now found at 1.27°C above temperatures during the mid 19th century (globalwarmingindex.org, 2023), an evident warming trend which can distinctly be attributed to anthropogenic forcings (IPCC, 2021). Meanwhile, British Columbia has experienced warming of around 1.4°C per century between 1900 and 2013 with higher temperature increases during the winter months (British Columbia Ministry of Environment, 2015). 2.1.2 Future Climate Scenarios After rapid warming during the 1970s and 1980s, the Intergovernmental Panel on Climate Change (IPCC) was established in 1988 to deliver unbiased and scientific reports on the state of the Earth’s climate. Since then, regular assessment and special reports have been published in periodic intervals to summarize new and updated findings on the physical science, impacts, adaptation, vulnerability, and mitigation of climate change. The fifth IPCC Assessment Report (AR5) was published in 2013/2014, introducing several updated emission scenarios as representative concentration pathways (RCPs). Each number of the respective RCP represents the additional W/m² of radiative forcing on the earth's surface through anthropogenic greenhouse gas emission. These scenarios project likely effects of varying levels of greenhouse gas emissions onto the Earth’s climate until 2100. The most conservative and mitigation-heavy scenario is RCP2.6. It may only be 6 realized through immediate action and potentially negative emissions by the late 21st century since the remaining carbon budget will likely be depleted by the 2030s under constant emissions (IPCC, 2021). The two intermediate scenarios, RCP4.5 and RCP6.0 represent moderate warming pathways with varying levels of mitigation while RCP8.5 projects a high-emission scenario under business-as-usual developments. RCP2.6 is the only emission scenario to likely limit global warming to less than 2°C above pre-industrial temperatures, with RCP4.5 more likely than not exceeding the 2°C threshold (IPCC, 2014). RCP8.5 is frequently adopted as control scenario, assuming the state of the Earth’s climate if no action is taken. The realization of different pathways could prove crucial for future economic and social functioning as Lissner & Fischer (2016) show that the difference between 1.5°C and 2°C warming already might be able to push global climate to a new regime in terms of heat extremes. The most recent assessment report, AR6, introduces several new Shared Socio-Economic Pathways (SSPs) to complement the previously investigated emission scenarios. The SSPs project probable values for economic, population, and technological growth as well as education, urbanisation, and measures of inequality while assuming no global climate policies to be established. The scenarios can hence be used instead of the previous business-as-usual projection (RCP8.5) with the most conservative warming scenario by 2100 estimated at 3°C. The main pathways are summarized as follows: SSP1 projects a world empathizing on mitigation, equality, and sustainable growth. Global surface temperature warming by 2100 is estimated at 3-3.5°C relative to the pre-industrial level. SSP2 represents a middle-of-the-road scenario where future developments represent a continuation of historical patterns. Slow progress is made towards mitigation and sustainability while population growth peaks in the second half of the century (+3.8-4.2°C). SSP3 represents a world of rising nationalism, manifesting itself as a focus on regional policies, material-intense 7 consumption, and high challenges for global environmental issues such as climate change mitigation (+3.9-4.6°C). SSP4 projects fast economic growth under increasing global disparity. The gap between developed and developing countries widens, leading to high challenges for adaption to climate change impacts (+3.5-3.8°C). Finally, SSP5 represents fast, fossil-fuel powered economic growth. By focusing on rapid technological, economical, and social developments, this scenario results in the highest projected warming between 4.7°C and 5.1°C by 2100. The impact of different climate scenarios is frequently quantified by increases in average global temperatures. However, changing temperatures heavily impact precipitation patterns as well. Generally, warm air can hold more moisture, with saturated air containing 7% more water vapour with each additional °C (Coumou & Rahmstorf, 2012). Thus, globally intensified hydrological cycles via increases in evaporation and precipitation can be expected, causing increasing droughts in some regions and increases in precipitation elsewhere. The detection and attribution of changes in precipitation has been proven difficult due to high variability over time (Bindoff et al., 2013) and often non-normal distributions (Coumou & Rahmstorf, 2012). Models primarily predict changes depending on latitude over land as well as general increases in frequency and intensity of precipitation events. Fischer & Knutti (2015) calculate the probability of occurrence for the heaviest storms as 99th percentile events (based on preindustrial climate conditions) to increase by 30% under 2°C warming and the probability for events in the 99.99th percentile to increase by 100%. Local observations have matched some predictions by detecting likely more frequent droughts in the Mediterranean and West Africa and more frequent and intense precipitation events observed in North America (Hartmann et al., 2013). Increasing frequencies of tropical cyclones have been detected as well in the North Atlantic, closely matching warming sea surface temperatures. 8 Results in other ocean basins however are mixed and do not show similar distinct relationships (Hartmann et al., 2013). 2.1.3 Climate Models Since the global climate system is not suitable for studies via experimental methods, researchers use computer models to approximate and simulate essential mechanisms of the Earth’s climate. General Circulation Models (GCMs) have been developed since the mid-20th century, adopting 3-dimensional grid cells spanning the Earth to model horizontal and vertical energy exchange between cells and simulate weather and climate conditions. GCMs are based on differential equations representing physical processes of energy reflection, absorption, and transfer, thus simulating processes in the atmosphere such as solar radiation, winds, and heat and moisture exchange (Lloyd, 2009; Edwards, 2011). The size of the grid cells determines the resolution and detail of a simulation since energy exchange values are calculated between the individual cells. The models used for the fifth Assessment Report of the IPCC are based on 87.5 x 87.5km cells with 30x30km pixels in development (IPCC, 2014). Gradual step-ups in resolution have been defining of advances in meteorology and climatology over the last decades. However, with higher resolutions come increases in complexity, requiring up-to-date supercomputers as well as re-coding of elaborate climate models (Edwards, 2011). Climate models have become the primary tool to evaluate the impact of greenhouse gas emissions and simulate different climate change scenarios (Müller, 2010). While initially assessing climate change via the climate sensitivity—the equilibrium mean surface temperature under doubled CO2 concentrations in the atmosphere—models have been further developed to include several additional scenarios as well as other forcing agents (Edwards, 2011). Over time, the models have become increasingly complex thanks to the exponential rise in available computing power. Continuous improvements lead to inclusion of processes, 9 interactions and feedback effects in the hydrosphere, biosphere, and cryosphere. Current models now include parameters and equations to account for carbon cycles, aerosols, atmospheric chemistry, and ocean biogeochemistry (Flato et al., 2013). The most detailed models in terms of spatial and temporal resolution—GCMs—have been expanded into Earth System Models (ESMs), however, many model approaches exist for different purposes (Müller, 2010). Since climate models are based on differential equations to simulate physical, chemical, and biological processes, they are prone to specific errors and uncertainties. Notably, a model can only be as good as the model’s underlying assumptions and equations. The main sources of modelling errors are truncation errors stemming from discrete computerized equations, parameterization to capture within-cell effects, and susceptibility of model output to starting assumptions and model tuning (Müller, 2010). The impacts of clouds and related feedback effects specifically have been identified as unclear in current modelling approaches due to small-scaled processes and limited understanding of the involved physical mechanisms (Lloyd, 2009; Loehle, 2018). Despite described limitations, climate models are generally trusted for scientific and policy purposes due to several reasons. First, climate models serve as calculation tools which are based on the proven physical laws of nature by linking many simpler and established models and concepts together (Knutti et al., 2013). Second, they have shown to successfully reproduce temperatures during past and current climatic events (Loehle, 2018). For example, climate models are able to recreate temperatures during the Last Glacial Maximum and the Holocene by specifying forcing agents such as solar radiation, major volcanic eruptions, and greenhouse gas concentrations (Lloyd, 2009). Since the models are able to simulate a number of different forcings of past climates, it is generally assumed that they are suitable to predict future climate change as well. Third, recreating climate system patterns without explicit 10 coding such as monsoon systems, rain belts and storm tracks as well as forecasting of future weather and seasonal trends have been shown to be accurate (Lloyd, 2009). Successful results of this fashion have been achieved by several (partially independent) approaches of climate modelling across the world, yielding very similar behavior and results (Müller, 2010). Meanwhile, the Coupled Modelling Intercomparison Projects (CMIPs) have been created to compare the different models according to several criteria, control for quality and output, couple different models while also establishing a range of most likely scenarios based on the different approaches (Knutti et al., 2013). Instead of using a single model to predict future climate scenarios, it has become customary to use ensemble modelling. This approach involves using several different climate models in order to predict a range of likely outcomes. This can be performed in a variety of ways and is historically—while heavily discussed—mostly realized via a simple multi-model average (Tebaldi & Knutti, 2007; Knutti et al., 2013; Loehle, 2018). By using the output from many separate models, it is possible to account for uncertainty towards factors that have been coded and modelled differently as well as identify the natural variability in the global climate system. The inherit random variability of the climate system will yield different results for each model run and can hence be quantified via repeated slight adjustments in single models and ensemble modelling (Müller, 2010). This means that results from several model runs might differ due to varying starting conditions, hence representing multiple outcomes of a inherently random system. Here it is important to note that many climate models are not completely independent since climate modelling has evolved over time by developing previous models in a variety of ways as well as the widespread sharing of ideas and code. Interdependency complicates interpretation towards the reliability of climate and ensemble modelling due to the intertwined history of several modelling approaches (Müller, 2010; Knutti et al., 2013; Loehle, 2018). 11 Ensemble modelling has been improved throughout the time of the CMIP development stages thanks to increases in accuracy of the individual models and continued testing efforts. Widespread applications of ensemble modelling have shown that the approach of averaging available climate models best represents reality and reduces uncertainties (Knutti et al., 2013). Ensemble modelling is hence deemed more robust and expected to perform better for simulating future conditions than any individual model (Tebaldi & Knutti, 2007; Knutti et al., 2013; Loehle, 2018). While global circulation models are found to predict large-scale climate change well, the usefulness for local predictions and impact assessments is limited. Issues often arise when trying to simulate on smaller temporal or spatial scales, with traditional modeling approaches struggling to recreate climate and weather on regional and lower than decadal resolutions (Hargreaves & Annan, 2014). The models can only account for dynamics between the individual grid cells, while smaller events are accounted for by imperfect parameterization. As a result, it is necessary to downscale large circulation models to investigate local processes in greater detail. Downscaling techniques are used to derive regional or local output from GCMs at an initially coarse resolution. This is necessary as many processes take place at finer scale than can be produced by global circulation models, hence local events and impacts might be missed (Maraun et al., 2010). Broadly, two primary approaches can be identified: statistical and dynamic downscaling. Statistical downscaling methods make use of local records of weather data. Via a variety of statistical techniques, the large-scale output of circulation models is related to the recorded weather data, adjusting for inaccuracies in the GCM and for specific properties of the micro-climate to connect simulated and observed information. Future scenarios can then be simulated by the adjusted circulation models at greater detail (Mearns et al, 2018). Dynamic downscaling includes several techniques of applying 12 small-scaled models within GCMs to obtain a finer resolution in a specific area. The output can then be viewed in detail at the grid size of, for example, regional circulation models to account for smaller processes while limiting large time-consuming and potentially costly simulations (Maraun et al., 2010). 2.1.4 Climate Drivers in British Columbia Seasonal trends in winter temperatures and precipitation patterns in British Columbia are heavily influenced by several inter-connected oscillations in and above the Pacific Ocean. Periodically changing climate conditions in the region have been attributed, amongst others, to the phases of the El Niño Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO) (Lehr et al., 2012). The positive phase of ENSO, El Niño can be observed via abnormally warm sea surface temperatures in the central and eastern Pacific around the equator, at times featuring a reversal of wind direction into westerlies along the equator (Roberts et al., 2016). It generally manifests in the Pacific Northwest as warm and dry conditions due to a ridge of high pressure systems over the coastal region, particularly strong phases have been associated with marginal ski seasons as a consequence of reduced precipitation as well as early snowmelt (Newton et al., 2019; Pidwirny & Pedersen, 2020). El Niño has been observed as shorter-lived phenomenon at around a year and characterized by stronger anomalies than the negative counterpart of the ENSO (Timmermann et al., 2018). La Niña—the negative phase—is usually associated with wetter- and colder-than-usual winter conditions on the west coast of Canada (Brigode et al., 2013; Lindsey, 2017; Newton et al., 2019). This represents favorable conditions for skiers as more and longer lasting snow cover can be expected throughout the season. Similarly, the positive and negative phases of the PDO can be identified via sea surface temperature anomalies across the northern and tropical Pacific. While describing an ensemble of multiple individual processes, displaying oscillations on a longer timescale than 13 the ENSO and still being only partly understood, the effects on temperatures and precipitation patterns across western North America appear similar to the ENSO cycles (Mantua & Hare, 2001; Di Liberto, 2016). The negative phase of the PDO generally favors winter skiing activities by leading to wet and cold atmospheric conditions whereas the positive phases mostly align with positive temperature anomalies in the Pacific Northwest. Enhancements of the ENSO and PDO effects have been observed when both oscillations are found in the same phase (positive or negative) while the opposite phases of the oscillations tend to dampen the respective effects (Lehr et al., 2012; Islam & Gan, 2015). There are several other phenomena impacting general climate trends and thus winter temperatures and precipitation in British Columbia with teleconnections running between each of the oscillations. The additional drivers include the IPO, the Interdecadal Pacific Oscillation covering long-term trends in the ocean basins on both hemispheres, phases and paths of the polar jet stream, and the Pacific-North American Pattern (PNA), usually characterized as a atmospheric pressure pattern across the north-western hemisphere (Lehr et al., 2012; L’Heureux, 2014). Anthropogenic forcings have been found to impact atmospheric and oceanic circulations, thus possibly altering the long-term frequency and magnitude of mentioned oscillations (Timmermann et al., 2018). While many cause-effect relationships remain to be researched and fully understood in this subject, extreme ENSO events are expected to become more frequent and more predictable under warmer temperatures via enhanced sensitivity to ocean-atmospheric variability modes in the subtropical North Pacific (Jia et al., 2021). By analyzing daily winter geopotential height fields between 1949 and 2012, Newton et al. (2019) find significant increases in the frequency (linear increase) and persistence (step-change increase around 1977) of synoptic types—patterns of large scale atmospheric phenomena such as low/high pressure systems and weather fronts—responsible for warm and 14 dry conditions as well as decreases in the frequency (linear decrease) of synoptic states causing cold and wet conditions over western Canada. The increasingly common states of the atmosphere are associated with El Niño conditions and positive phases of the PDO while the coinciding conditions of La Niña and negative phases of the PDO appear to decrease in frequency (Newton et al., 2019). 2.2 Weather and Climate Impacts on the Ski Industry 2.2.1 Weather and Tourism Critiques of early tourism studies stress the issue that the connection between climate and tourism is often assumed, but rarely tested (De Freitas, 2003). Since then, tourism and recreational studies have increasingly focused on proving the relationship between weather, climate, and outdoor recreation with growing support throughout the last decade. A steady increase in publications since 2009 tackles questions concerning resource and site characteristics as well as weather perception and impacts (Verbos et al., 2018). Despite the growing body of literature, however, there is still a lack of consistent metrics and methodologies which could be used to compare and combine studies across different regions, themes, and activities. The impact of weather and climate on outdoor recreation can be investigated via demand-side, supply-side or joint approaches. Supply-side studies investigate changing availability of an outdoor activity due to weather and climate conditions, an approach that is usually favored by the accessibility of reliable data. Demand-oriented studies focus on behavioral responses to changing weather and climate conditions. The economic applications of such studies range from marketing to impact assessments and investment decision-making. For example, weather and climate research is receiving increased attention for weather insurance purposes (De Freitas, 2003; Tang & Jang, 2012; Olya & Altinay, 2016) as well as short and long-term risk assessments due to climate change impacts (WEDDA.at, n.d.). 15 For the demand-side branch of literature, researchers have repeatedly pointed out the importance of modelling the impact of on-site conditions as well as the expectations of conditions that are important for recreation. In other words, to successfully model the drivers of outdoor recreation, it is crucial to include the weather that is found at the time of use as well as finding a good approximation of the expected weather conditions (De Freitas, 2014; Verbos et al., 2018). Crucially depending on the investigated activities, previous studies have found a number of behavioral responses to deviations between expected and encountered weather conditions. The reactions range from no observed behavioral response to on-site adaptation, changes in activities and travel routes, and to shortening and cancellations of the trip (Becken & Wilson, 2013; De Freitas, 2014; McKercher et al., 2015). Expectations of weather conditions have been found to rely on short-term weather forecasts (Prettenthaler & Kortschak, 2015) as well as general information about a destination’s climate, typically relying on temperature indices (Denstadli et al., 2011). Here, it can be vital to take a closer look at heterogenous participant groups since the reactivity to forecasts may vary as shown by Landauer et al. (2009). The study investigates motives, perceptions, and preferences regarding cross country skiing in Finland, finding that the responses to changing climate conditions vary substantially between groups mainly motivated by social gathering, outdoor sceneries, or athletic ambitions. Furthermore, recent studies have coupled detailed short-term variation in climate variables with short-term demand responses in order to predict impacts under future climate conditions for outdoor activities such as recreational cycling and fishing (Chan & Wichman, 2020; Dundas & von Haefen, 2020). This methodology generally relies on high-resolution weather and demand data over a certain time period at several locations. After accounting for calendar effects (i.e., day-of-week, month-of-year and seasonal impacts), location effects, and long-term economic trends, the impact of short-term weather on demand can be 16 approximated. With a relationship between weather and demand identified, future impacts can be simulated under several climate scenarios. In the context of weather and outdoor recreation, skiing-related studies represent the most prominent group. Verbos et al. (2018) identify skiing as the single most investigated activity when reviewing 184 related articles over the last decade. This is attributed to the high vulnerability of mountain snowpack and hence skiing operations to warming temperatures and a changing climate. 2.2.2 Weather and Skiing Supply 2.2.2.1 First Season Length Projections The first studies investigating the impact of climate change on ski resorts were conducted in Eastern Canada in the late 1980s and, soon after, in the European Alps and Australia. The early literature focused on projecting changes in ski season length under several future warming scenarios. While becoming more sophisticated over time, this approach still represents the majority of studies connecting climate change to the ski industry today (Steiger et al., 2019). Pioneer studies by McBoyle et al. (1986) and Harrison et al. (1986) simulated future precipitation and temperature values to predict changes in season length for ski resorts in Ontario. While using a natural snow depth threshold of 5cm as minimum requirement for skiing, they found expected declines in season length of 30-100% (McBoyle et al., 1986) and 40-100% (Harrison et al., 1986) by the 2050s under business-as-usual scenarios with a complete termination of skiing operations hence in the range of predictions. The methodology of the initial research was soon applied and expanded to different regions with most studies painting a devastating image for the ski industry. McBoyle & Wall (1987) projected season losses of 40-89% in Québec for the 2050s, follow-up studies in the same province estimated losses of 42-87% (Lamothe & Periard Consultants, 1988). While 17 investigating climate projections in ski areas around the Great Lakes under continued greenhouse gas emissions, McBoyle & Wall (1992) found season shortenings of 28-90% in Québec, 35-100% in Ontario, and 39-100% in Michigan. Galloway (1988) used snow cover data on a monthly scale to predict the impact of a warming climate on ski resorts in Australia. Ski seasons in the investigated areas were simulated to decline by more than 50% under a warming of 2°C and decreases in precipitation by 20% during the winter season. The initial studies were followed by many application throughout the world’s ski regions with continuous improvements in methodologies. One key issue of the early literature was the lack of a appropriate statistic for a suitable snow base. The used indicators were based on minimal snow cover which, in reality, do not permit skiing operations. Witmer et al. (1986) first mentioned the metric of the 100-day rule which would become the most established criterion to evaluate the viability of ski resorts (Gilaberte-Búrdalo et al., 2014). The rule states that a resort is only deemed viable if it can provide a sufficient snow base (usually 30-50 cm) over 100 days for a minimum of 7 out of 10 seasons. Several other measures of viability have since been used to evaluate a ski resort’s viability with another frequently used indicator being the ability to guarantee skiing operations over the Christmas period (Gilaberte-Búrdalo et al., 2014). Further advances in skier supply studies include Abegg et al. (1994), who examined altitude changes of the equilibrium zone (where accumulation equals ablation) on glacier ski resorts in Switzerland. This approach was then applied to non-glacier resorts by calculating the altitudinal changes in snow thresholds under a warming climate and evaluating the snow reliability of Swiss ski resorts (Abegg, 1996; König & Abegg, 1997). The studies established the line of natural snow reliability (in agreement with the 100-day rule) at 1200m elevation with a projected rise of 300m under a warming scenario of 2°C. As a result, 85% of Swiss ski resorts were found as snow reliable due to a minimum altitude above the established snow 18 threshold. The share of snow reliable resorts would then drop to 63% under the simulated 2°C warming. A more extreme warming scenario sees the snow line pushed up to around 1800m by 2030-2050, which would leave the share of naturally viable resorts in Switzerland at 44% (Bürki, 2000, Elsasser & Bürki, 2002). In an extensive study, Abegg et al. (2007) later investigated 666 ski resorts across the Alps and found heterogeneous results between the different Alpine regions. Here, the threshold for natural snow reliability was adjusted to 1050m to account for the cold and dry continental climate in the eastern Alps and to 1500m in the southern and south-western Alps, factoring in the warmer Mediterranean climate. 91% of the alpine ski resorts were found to be currently snow reliable with the share dropping to 75% under 1°C warming, 61% under 2°C and 30% under 4°C warming. The ski resorts in the northern and north-eastern regions of the Alps (especially resorts in Germany, then followed by Austria) were identified as most threatened by the warming temperatures. 2.2.2.2 Skiing Supply with Artificial Snowmaking A series of snow-deficient winter during the 1980s and 1990s triggered large investments into artificial snowmaking facilities in the European Alps and in eastern North America (Steiger et al. ¸2019). Snowmaking has since been used extensively in the major ski hubs of the world, extending the ski season length and complementing natural snow conditions to increase snow reliability. There are several modes of artificial snowmaking. The process usually includes infusing water with pressurized air in the snow gun. The water is then sprayed out of a nozzle as tiny water droplets over a slope, forming snowflakes in the cold air before coating the ground with a layer of artificially created snow (Hanzer et al., 2014; El Achbar et al., 2019). Snowmaking can be used to provide a first base layer in the early season, thus insulating the natural snow on top of the artificial layer and guaranteeing scheduled season openings. Other 19 applications include general increases in snow reliability throughout the season, supplemental snowmaking on rocky and uncovered terrain and depot snowmaking in order to bridge potential warm periods later in the season (Hanzer et al., 2014; Steiger & Mayer, 2008). Short-term efficiencies of artificial snowmaking at a resort change substantially over time depending on the occurring weather conditions such as temperatures, wind speeds, relative humidity, and temperature lapse rates (López-Moreno et al., 2019). This natural variability can be addressed via automation of the snowmaking system to increase efficiency as shown by a case study in the Blue Mountain Resort, Pennsylvania (Blue Mountain Resort, 2017). The early 2000s saw first studies include the potential of artificial snowmaking into their models. The importance of snowmaking became apparent in the season length projections, clearly yielding less severe impacts of a warming climate onto the ski industry than previously estimated. Scott et al. (2003), for example, projected season shortenings at an Ontario ski resort with current and improved snowmaking capabilities. The improvements were defined as an increase in the snowmaking temperature threshold from -5°C to -2°C and increases in the daily production capacity by 50%. Several climate scenarios yielded season length reductions of 37-50% until the 2080s under current snowmaking, which could be mitigated to 26-39% with improvements in snowmaking capabilities. Multiple other studies followed, similarly integrating the impact of artificial snowmaking into the season length models and leading to the widely adopted SkiSim model (Scott et al., 2003; Steiger, 2009). With further developments of the model, SkiSim2.0 now includes measures of daily snow accumulation, snow melt, snowmaking potential and snowmaking requirements while accounting for different altitudes within the resort and management decisions throughout the season (Steiger, 2010). The case study and display of SkiSim2.0 methodology (Steiger, 2010) finds significant reductions in projected season lengths for 2 investigated low- (32%-90% reduction) and mid-altitude resorts (60%-99% 20 reduction) until the 2080s under low and moderate warming scenarios. This analysis would classify both resorts as not viable before the 2080s with 100% slopes covered by snowmaking, while an investigated high-altitude resort only remains viable with the help of artificial snowmaking under the moderate warming scenario. Supporting the natural snow base with artificial snow is found to extend the viability of resort for several decades with machine made snow contributing extensively to the ski season length especially in the most severe scenarios. Further research using SkiSim2.0 in the European Alps and eastern North America followed, displaying the mitigating effects of artificial snowmaking under different warming scenarios (Steiger, 2012; Scott & Steiger, 2013; Steiger & Abegg, 2013, 2015, 2017; Steiger & Stötter, 2013; Steiger, & Walser, 2015; Abegg & Steiger, 2016; Scott et al, 2017). Research across different parts of the world demonstrates that snowmaking can be used effectively to buffer natural climate variability and mitigate the impact of global warming. However, several key issues of artificial snowmaking have been identified, highlighting the limits and issues of the particular adaptation strategy. Steiger & Abegg (2017) demonstrate that the potential of snowmaking varies regionally in a large study in the European Alps. The northern and eastern extents of the Alps in Austria and Germany are projected to be most impacted by warming temperatures in the near future and might not be snow reliable by 2050s under a +2°C warming scenario despite artificial snowmaking. While artificial snowmaking can compensate for shorter natural ski seasons, the efficiency of snowmaking greatly decreases with warmer temperatures (El Achkar et al., 2019). Furthermore, snow production is not viable above -2°C with current technology, thus creating a natural limit for adaptation via artificial snowmaking (Steiger & Scott, 2020). Economic evaluations of artificial snowmaking have shown the additional strain on ski resorts due to rapidly increasing costs of snow production under warming temperatures. 21 An in-depth cost-revenue analysis of snowmaking in Austria predicts inevitable increases in operator costs under warmer conditions due to rapidly increasing costs of snowmaking (Damm et al., 2014). Results of the case study suggest a yearly increase in the nominal lift ticket prices by 3.3%-5.1% in order to compensate snowmaking costs and remain profitable. Simulations using stronger price elasticities than -0.1 yielded losses for the resort under all climate scenarios. Falk & Vanat (2016) investigate the profitability of artificial snowmaking based on investment, revenue, and visitation data of 109 French ski resorts. Positive returns to investment are found for high-elevation resorts, especially in large ski areas, however, the results indicate that artificial snow cannot fully compensate lacking natural conditions in marginal snow years. The high investment and electricity costs are accompanied by high amounts of water necessary to provide artificial snowmaking throughout the ski season. In the process of creating snow, large amounts of water are lost through evaporation and not re-issued into the basin by later snowmelt (Wemple et al.¸ 2007; De Jong, 2011). Water usage of ski hills has been reported to account for up to 36% of the drinking water consumption of the surrounding communities in Switzerland (Rixen et al., 2011). Because of the intensive use of resources, snowmaking has been widely criticized from an environmental perspective and discussions suggest it can be a maladaptive strategy to face climate change (Hopkins, 2013; Scott et al., 2021). In conclusion, season length projections provide a good measure of the impact of climate change on the supply for alpine skiing. A number of studies have been performed, mainly in the European Alps and in eastern North America (Ontario, Québec, Great Lakes area), progressively employing more sophisticated and more precise methods over time. This type of impact assessment is facilitated by the availability of the necessary data as past ski season lengths, climate records and climate models are generally accessible. When projecting 22 future ski season shortenings, it is crucial to include the potential of artificial snowmaking. Especially in the European Alps many resorts cover about a third (France and Switzerland) to more than half (Austria) of their slopes with artificial snowmaking (Falk & Vanat, 2016; Gonseth, 2013; Steiger & Abegg, 2018). Despite the costly and resource-intensive activities possibly yielding negative externalities, snowmaking has been shown to extend the viability of ski resorts for several decades under warming temperatures and changing precipitation patterns. 2.2.3 Weather and Skier Demand 2.2.3.1 Snow Sensitivities Recent studies in particular have noticed effects in skier behavior that might mitigate the impact of shorter ski seasons. Rutty et al. (2015) describe spatial and temporal substitution as the most common response to insufficient snow conditions at the local resort after evaluating skier surveys in Ontario. 48-61% of respondents were willing to travel to another resort if the local resort is closed due to a lack of snow while 31-36% would delay their skiing experience. Similarly, Scott et al. (2020) observe that reductions in season length cause a less than proportional reduction in skier visits due to temporal and spatial substitution. A study calculating the costs of climate change to the Swiss winter tourism finds that the national economy could benefit from intermediate-term climate change due to a decrease in competition (Gonseth & Vielle, 2019). Here, it is suggested that high altitude resorts with snowmaking will profit from additional visitors due to local snow conditions being less impacted than the European and global averages. All these findings indicate that supply-side studies on season length do not necessarily represent the changes in skier numbers that can be expect at a resort. During the last couple decades, the professional literature has frequently turned their focus on the impact of changing snow conditions on the demand for alpine skiing. The 23 research examines reactions of customers to different weather and snow conditions and projects future visitor numbers under a changing climate. Due to the sensitive nature of ski hill visitor data, this part of the literature is characterized by case studies and by using proxies for visitor numbers in the demand models. While suffering from imprecise data in terms of reliability as well as temporal and spatial resolution, the demand-side approaches permit projections of future impacts on ski resorts and on surrounding economies based on observed and simulated behavior. Although there are many factors influencing visitor numbers, snow conditions have been shown to have a direct impact on skier visits at the resorts. Through a survey of college students visiting 13 resorts in the Lake Tahoe area of Nevada and California, Englin & Moeltner (2004) establish an empiric demand model for skiers and snowboarders. The results show that the students react to changing amounts of snowfall with an elasticity of demand of 0.21. In other words, a 10% increase in weekly snowfall above mean snowfall levels results in an expected increase in visitor number of 2.1%. The study finds a negative price elasticity of demand at -0.8 and a negative marginal effect of higher temperatures on visitor numbers as well. Several studies have examined the reactivity of demand to seasonal snow and temperature trends, mainly due to availability of the data via reported seasonal lift ticket incomes. Falk (2015) analyses the impact of weather and economic factors on skier demand at 6 large resorts in the French Alps. Despite coarse 3-month averages of snow depth and temperature collected at only a single destination, a small snow elasticity of demand of 0.034 is found. The demand elasticity with respect to real GDP is found significantly stronger at 0.64. Inelastic values of income due to elasticities below 1.0 are commonly attributed to necessity goods as the consumption increases less than proportional with increases in income. 24 These results stand in contrast to later studies with similar approaches. Falk & Vieru (2017) investigate the reactivity to snow in Finland, determining snow elasticities of demand between 0.16 and 0.32 in the northern and southern part of the country respectively. Skiers in the southern part of Finland are hence found significantly more sensitive to changes in snow depth. The definition of elasticities slightly varies in this study as here, a 10% increase in average seasonal snow depth compared to the previous season results in a 3.2% change in lift ticket revenues in the south. Skiers in Sweden are found even more reactive to changing snow depths with a highly significant long-run elasticity of demand of 0.5 (Falk & Lin, 2018). The study finds declines in the snow elasticity over time which is attributed to recent increases in artificial snowmaking. Both studies in the Scandinavian countries find income elasticities above 1 (1.6 and 1.34 respectively), indicating that skiing can be interpreted as a luxury good in Finland and Sweden. In the discussed literature, the evidence for short-term impacts of snow and weather conditions is limited due to averaged seasonal values for snow depth and demand. This, however, allows for analyses of long-term trends, especially concerning particularly snow-rich or snow deficient winters. Another important conclusion is that the reactions to changing natural conditions generally appear significant while also yielding highly variable results across different ski markets. Data on shorter temporal resolutions can indicate the inner-seasonal variation explained by changing natural conditions. Regression models with monthly lift ticket revenues in Sweden find monthly inner-seasonal snow elasticities of demand of 0.14 in November and December, and 0.11 in January (Falk & Hagsten, 2016). Due to high variability of snowfall in the early season, one standard deviation in monthly snow depth therefore results in changes of lift ticket sales of 9.3 percentage points in the early season and 4.6 percentage points in January. The effect of one standard deviation changes in snow depth 25 on lift ticket sales is found more impactful than changes of economic factors such as real income and lift ticket prices. This can be attributed to the high variability of snow depth compared to relatively stable indices of income and prices. A few case studies have been performed with high-resolution lift ticket information provided form the resorts. While the analyses only cover individual resorts, the results can be assumed to be more indicative than previous studies due to the reliable data. With the help of daily visitor data at two New Hampshire resorts, Hamilton et al. (2007) simulate demand based on calendar effects (day of the week and seasonal indicators), temperature, snowfall, and snow depth. The model was able to explain between 55% and 67% of the daily variation in customer numbers. Measures of weather and snow variables were found significant in the mountains as well as in the nearby major city of Boston, providing support for the so-called backyard hypothesis. This hypothesis states that snowfall in urban areas significantly affects the demand for skiing in the nearby mountains. Similarly, Shih et al. (2009) were able to explain between 44% and 59% of variation in daily lift ticket sales at 2 resorts in Michigan. While several relationships were tested, the only significant variables were temperatures, snow depth, calendar effects and—although only via very light effects—wind chill and the consumer confidence index (a measure of perception of economic conditions). Snowfall was not found to significantly impact visitor numbers and the backyard effect could not be observed either. Holmgren & McCracken (2014) investigate the sensitivity of demand to economic factors as well as a number of weather-related variables at a ski resort around Salt Lake City based on daily visitor counts. Here, the study differentiates between different skier groups as well as lift ticket types with results displaying major heterogeneity across groups. The College group, for example, was found to be particularly reactive to changing prices with a price elasticity of demand of -6.95. Again, positive effects on demand are found for fresh snowfall with customers preferring lower temperatures at the same time. Additionally, the 26 study demonstrates that customers substitute between the different ski resorts in the region depending on the relative prices. This observed effect is not surprising in the case study as there are 7 major resorts within one hour form the Salt Lake City International Airport. 2.2.3.2 Reactivity of Overnight Stays to Snow Conditions Studies based on actual visitor numbers permit confident interpretations towards customer behavior of skiers. However, only individual case studies are available with reliable and detailed data due to the private data only being accessible to ski hill operators. To evaluate the reactivity to natural conditions in other regions, several studies have gone over to approximate the demand for skiing via overnight stays in the surrounding communities. Overnight stays can be expected to be less flexible than day-trips due to limited cancellation policies and more planning involved in long-distance trips, hence we expect lower reactivity to changes in snow and weather conditions than for simple lift ticket counts. Töglhofer et al. (2011) investigate the long-term elasticity of demand via seasonal overnight stays with at large sample of resorts in Austria. The model includes lagged effects of previous seasons’ bookings to account for persistence in tourist behavior and the primarily used snow index is measured via days with sufficient snow cover. The resulting snow elasticities are relatively small, ranging from 0.052 to 0.156, leading to changes in overnight stays between 0.6-1.9% due to a 1 standard deviation change in snow cover days. Interestingly, the authors find some of the higher altitude resorts to be negatively impacted by average Austrian snow conditions. This indicates that customers increasingly ski at the low-lying resorts under favorable snow conditions while the high altitude resorts profit from reduced competition during marginal snow winters. Other findings of the study include a strongly elastic demand to income; thus skiing (via overnight stays) is identified as a luxury good in the investigated locations. 27 Falk (2013) finds similar results with snow (depth) elasticities of 0.132 for domestic and 0.043 for foreign overnight stays in Austria as well. Here, the differentiation between domestic and foreign stays also shows that the demand is strongly income elastic for foreign visitors while being inelastic for domestic residents. Using coarse seasonal data, the study confirms lagged effects of snow depth onto the next season’s demand while not finding evidence that additional snow depth increases demand in the current season. On top of snow depth, Falk (2013) detects significant positive impacts of increasing cloudiness and increasing temperatures. This is somewhat surprising as previous studies report visitors generally preferring colder temperatures (Englin & Moeltner, 2004; Holmgren & McCracken, 2014). Further advancements of demand-side studies have included projections of the future impacts of warming temperatures based on the relationship between overnight stays and snow conditions. Prettenthaler & Dominik (2015) couple RCP warming scenarios with early SSP models to project the combined effect of changing temperatures, income, and population growth on overnight stays. Similarly, Damm et al. (2017) simulate future overnight stays using temperature and socio-economic models. The projections use historic snow sensitivities of demand in the European Alps based on a snow-water equivalent threshold (β = 0.13), indicating that the analysis mostly represents the reaction of visitors to changes in season length. This methodology permits to simulate climate induced changes in overnight stays as well as estimated socio-economic impacts. The applied +2°C warming scenario is projected to result in about 4 million nights per season at risk due to climate change, increasing to a total of 10 million nights after including socio-economic developments by the end of the century. Interestingly, previous studies have indicated that the timing of snowfall, especially in the early season can be critical for ski resort operators (Falk & Hagsten, 2016; Falk & Vieru, 28 2017; Steiger et al., 2019). Recent work by Parthum & Christensen (2019, 2022) expands this line of thought by showing that favorable timing of snowfall throughout a season can offset the effects of snow-deficient winters when periods of higher snow depth align with public holidays. The study correlates spatial and temporal variation in snow with rental apartment stays around ski resorts for the entire US ski market. Data on a daily resolution is used to determine state-specific rental revenue elasticities with respect to snow depth. This research is particularly relevant for our work as the climate and the skier demand in the western US can be expected to approximate conditions in BC better than previously discussed studies in Europe. Additionally, the high-resolution data on a daily level gives insights into short-term variation of demand due to snow conditions. The results show that the responsiveness to snow depth varies widely across the US. For example, large ski markets in Colorado are found much less responsive to changes in snow conditions than Vermont or California with revenue elasticities of 0.151, 0.443, and 0.895 respectively. This could be due to colder temperatures and a more secure snow base in the Rocky Mountains when compared to the warmer climates close to either coast. 2.2.3.3 Other Drivers of Demand An extensive study in the French Alps by François et al. (2014) investigates the relationships between weather, snow conditions and the socio-economic functioning of 142 resorts. The partial viability index of the ski resorts is created by slicing individual resorts into smaller parcels according to elevation, slope, and aspect and investigating impacts of changing precipitation and temperatures on each individual parcel. The seasonal viability indices are then compared to reported skier days and modeled skier day values, resulting in complex relationships between economic and natural variables. The impacts of snow-rich and snow-deficient winters can generally be seen in the modeled skier day values. While the smaller resorts appear to react strongly to changes in the viability index, resorts with the 29 largest ski-lift power seem rather insensitive to smaller changes in natural conditions. These findings are supported by research of Moreno-Gené et al. (2018), showing that larger resorts are better at absorbing shocks in natural conditions and suggesting that the skiing experience is often driven by other features at the big resorts. Increased resilience of large resorts to natural conditions is a reoccurring theme in the established literature (Falk & Vanat, 2016; Rutty et al., 2015). This appears to be attributed to a mix of higher investment in snowmaking, higher altitudes, more terrain to choose from and alternative activities in larger villages surrounding the resort. Additionally, Falk & Steiger (2020) find book profitability of resorts in Austria to depend most importantly on the length of ski slopes, followed by other factors such as maximum elevation, neighborhood ski resorts, and share of foreign overnight stays (all positive impacts). 2.3 Literature Gaps and Contributions The discussion of previous studies and findings reveals several literature gaps that will be addressed in this research. Despite the province’s resorts nationally displaying the highest visitor numbers and lift ticket revenues together with Québec (Canadian Ski Council, 2018), British Columbia has received little attention regarding climate change impacts on the ski industry. The few studies performed tend to focus on the supply of skiing via impact assessments and season length analysis (Clark, 2018, Pidwirny et al., 2017). To the best of my knowledge, no demand-side study linking customer numbers to snow and weather conditions has been conducted in the province to this date. This research will fill the gap with a first estimate of customer behavior surrounding BC's ski resorts. As discussed in the literature review, demand side studies suffer from data deficiency and imperfect proxy data. These shortcomings are further impaired by usage of coarse temporal and spatial weather and snow indices. Snowfall, snow depth, temperature, and other 30 weather variables are most accurately measured at each individual resort included in the study to obtain reliable information and reduce the probability of errors and spurious relationships. This study will add to the existing knowledge by including high resolution weather data on a daily scale at each resort’s location, permitting confident interpretations of on-site conditions and short-term impacts on customer numbers. For this purpose, the analysis closely follows the approach of Parthum & Christensen (2023)—analyzing short-term trends in skier demand via changes in surrounding rental apartment activity levels. Further, several reviewed demand-side studies measure snow conditions via snow-water equivalents or purely via snow depth. This analysis will include indices on snowfall in order to improve representation of observed and perceived snow and capture behavioral responses of customers to fresh precipitation. Furthermore, this research explores the impact of several additional drivers of demand such as apartment prices and simultaneously discusses potential endogeneity issues in this context. To understand changes in customer behavior and numbers on the ski hill, the drivers of demand must be analyzed. Therefore, it is important to investigate how far in advance skiers decide to book a skiing trip and what the expectations of snow conditions are based on at that time. This will indicate if expected weather and snow conditions can be attributed to long-term forecasts, recent weather, personal experience, or conditions in the previous season. The aspect of expected on-site conditions is previously mentioned in general tourism and climate research (De Freitas, 2014; Verbos et al., 2018), however, demand side studies on alpine skiing research struggle with adequate representation of expectations. It can be seen that the focus often lays on observed demand at the resort instead of on a concept to represent the drivers of demand. While difficult to address this issue, data on overnight stays, for example, can be examined for potential patterns in booking dates and previously observed skiing conditions to provide some additional information. This research will establish a first 31 measure of expected snow conditions which represents weather forecasts as well as previously experienced snow conditions depending on booking lead times before the date of stay. Finally, future climate scenario simulations are still primarily used for season length analyses and find limited attention in demand-side studies. Simulations of several climate scenarios typically yield averaged future conditions and are hence preferably used on a seasonal or larger temporal scale. This research will include projections of future climate scenarios via repeated random simulations to represent hypothetical seasons with daily temperature and precipitation values in order to facilitate predictions towards future conditions as well as impacts on the ski industry. This will be performed using downscaling of climate projections with the MarkSimGCM (Jones & Thorton, 2013) weather generator—introduced in the following methodology chapters. 32 3. Methods 3.1 Data Sources and Descriptions 3.1.1 Snow and Weather Data Most of the previously discussed demand-side studies focus on long-term trends in the demand for skiing by examining monthly and seasonal effects of snow depth or snow-water equivalent, thus omitting short-term reactivities to marginal changes in snow conditions. This approach can primarily be attributed to the difficulty of obtaining reliable information; first, demand data at daily resolution are necessary in order to identify short-term consumer choices. Second, weather and snow data of adequate quality require readings at a high temporal as well as spatial resolution due to significant variation over distance and elevation. To capture within-seasonal changes in demand due to changing snow conditions, daily measurements of snowfall, rainfall, temperatures, and additional weather-related drivers must be acquired from a single data source to permitting comparisons between the different locations. Local snow reports are usually provided by the respective ski resorts, occasionally coupled with local weather forecasts, thus providing a first indicator for expected snow conditions. However, this data provided by the resorts would not be suitable for the analysis due to concerns about the data quality and comparability. First, ski resorts have a incentive to report exaggerated snow levels to attract customers. This issue has been discussed and confirmed during personal communications with resort operators throughout the course of this study. Second, snow measurements are typically taken at a single location in the resort, thus providing a location-dependent estimate of snow conditions to represent the entire resort and potentially over- or underestimating average conditions due to snow drifts, elevation-dependent measurements, and other local effects on the mountain. For this research, only two out of the four investigated resorts provide information about the elevation of their snow and 33 temperature measurement stations (Big White, 2022c; Fernie Alpine Resort, 2022b; Sun Peaks Resort, 2022b; Whistler Blackcomb, 2022). As exact locations of snow measurements, potential local effects, and snow measurement techniques vary between different resorts and are not publicly available, the datasets would not be comparable using the self-reported snow reports, thus complicating and challenging interpretations. To address the discussed restrictions and guarantee comparability of snow and weather data, the research project draws information regarding natural snow conditions from one individual and independent source. Measures of snowfall, rainfall, wind, and additional weather information are taken from the online weather service Snow-forecast (https://www.snow-forecast.com). The global service reports and forecasts snow conditions at ski resorts by combining raw weather data of the Global Forecast System weather models from the National Oceanic and Atmospheric Administration (NOAA) with precipitation forecasts, thus benefitting from detailed and frequently updated data while focusing on modelling surface temperature changes at different elevations. Snow-forecast derives precipitation levels as either snow and rain via merging of weather information and digital elevation models at 1km resolution and by integrating temperature lapse rates, the rate of temperature and pressure changes over increasing altitude (Snow-forecast, 2022). The weather conditions are downscaled to mid-elevation of each ski resort, hence the temperature and precipitation values are not biased by heterogeneous on-mountain snow measurements as performed by the ski resorts themselves. Snow-forecast updates snow and weather forecasts in 6-hour intervals and the last—usually most accurate—forecast is archived for the respective time period and location. This results in “hindcasting” data records, logged weather and snow forecasts for 6-hour time periods, with historic records dating back to 2007 (Snow-forecast, 2022b). Therefore, instead of truly observed snow and weather information, the data used in this project are records of 34 short-term forecasts. Using data from Snow-forecast guarantees identical collection methods of information at all locations as well as snow and weather data downscaled precisely to mid-elevation of each resort. The information from Snow-forecast is preferred over comparable weather and snow reporting services for the purpose of this research due to historic data records, the high temporal and spatial precision of the data, and since the weather service does not rely on self-reported data from the ski resorts. This approach could be criticized for potentially introducing specific modelling biases into the data, leading to systematic over- or underestimation of snow conditions. However, any relative change in temperatures and snowfall levels at a location would still be captured in this case, permitting interpretations regarding demand reactivities to changing snow conditions. Further, the models from Snow-forecast have been continuously compared to new, local snow reports since the release of hindcasting models in 2007 in order to identify and adjust the models to systematic simulation errors caused by microclimatic features at each ski resort (Snow-forecast, 2022b). This inclusion of local snow reports addresses the issue of systematic biases and has improved the weather and snow models over time since the initial launch in 2007. Finally, since customers lack perfect information at the time of booking, Snow-forecast approximates expected weather and snow conditions of the skiers using this service. 3.1.2 Demand Data: Rental Apartment Approach Repeated and lengthy attempts to secure access to daily skier counts with several resorts in British Columbia—especially in the Thompson Okanagan Region—for this study were ultimately unsuccessful. Reasons for this include stakeholder worries about external access to important company data and seemingly a lack of interest in climate-related studies. Due to unavailability of skier numbers per day and resort, short-term rental data are used to approximate the demand for skiing. More specific, the relative change in demand for 35 skiing caused by varying snow conditions is investigated with booking information from short-term rental apartments surrounding each ski resort while assuming that the demand for short-term accommodation is highly correlated with the demand for skiing and similarly depends on local snow conditions. Rental data have been purchased from the online-service Airdna (https://www.airdna.co/), providing daily scraped data from several short-term rental services including Airbnb and Vrbo. The provided data are separated into daily records for each individual rental unit as well as monthly and all-time summary statistics for each unit dating back to 2015 or 2016 depending on the location until February 2023. This includes daily occupancy indicators (apartments can be reserved, blocked, unavailable, or available), booking prices (CAD and USD), booking dates, number of separate reservations, maximum guest occupancy, and an extensive list of property-specific information regarding available amenities and services. It is then possible to calculate several metrics describing activity levels surrounding each ski resort such as number of available apartments, overall occupancy rate, and total revenues generated by the apartments on any given day within the provided time period. Summaries of the respective rental markets surrounding each selected resort can be found in Table 2 and Table 3. Information for each resort is based on a geofenced area surrounding the respective ski hill. For Big White and Sun Peaks, the datasets cover the village surrounding the foot of the resort as well as additional on-mountain accommodation. Data from Fernie captures rental information from the village Fernie, located in the immediate proximity to the resort, however, excluding the on-mountain accommodation itself. The geofenced area around Whistler captures a larger and more populated zone than can be found at the other resorts by including the entire Whistler village, the two mountains with skiing operations (originally Whistler and Whistler Blackcomb), and additional apartments slightly north-east and 36 south-west of Whistler along Highway 99. Maps of the ski resorts, approximate apartment locations (graphs provided by Airdna, no information regarding the exact addresses) and the specific geofenced areas are included in Appendix A. Airdna and other customers of Airdna—most notably Sun Peaks Tourism—communicated several complications regarding the specific data collection methods and diverse changes throughout the data collection period. This includes additions of apartments and rental providers into the datasets over the years, specific apartments or dates being marked as “unavailable” without further specifications, as well as negative booking lead times for some bookings. Growth of the data sample is accounted for by choosing a logistic regression model, since the probability of each individual apartment being booked under given circumstances is investigated instead of changes in number of booked apartments. Negative booking lead times are recorded when the provided booking date is logged to occur after the date (or first day) of stay. Upon discussion with Airdna representatives, it was stated that data from some apartments with inconsistent or low activity levels are scraped irregularly (data scraping dates are not provided), therefore, in some occurrences, the date of stay precedes the date of booking. As any negative booking lead time is caused by delayed data scraping, the most likely true booking lead time for each case is zero days, followed by one day, two days and successively becoming less likely for higher true booking lead times. Information regarding apartment records not included by Airdna due to unavailability or data privacy regulations include cancellations of bookings, the number of guests for each stay, and the exact location of each apartment. 3.2 Introduction of Selected Ski Resorts 3.2.1 Ski Resort Summaries Weather and snow conditions, future climates, and skier demand are analyzed at four ski resorts in British Columbia: Big White Ski Resort, Fernie Alpine Resort, Sun Peaks Ski 37 Resort, and Whistler Blackcomb Ski Resort. The resorts were selected according to size, favoring larger ski resorts, location, guaranteeing a spread-out sample across BC as shown in Figure 1, and quantity of skier demand data, favoring resorts with higher number of adjacent rental unit observations. The map in Figure 1 was generated using the data graphing program Tableau 2020.3 (www.tableau.com). Figure 1. Map of selected BC ski resorts, map data ©2023 Mapbox (Mapbox.com) ©OpenStreetMap (Openstreetmap.org) Big White is located in the Southern Interior of BC. The most notable population center in proximity to Big White is Kelowna, located 56 km northwest of the ski resort with a population of 120,000 (all population stats from: Province of British Columbia (2021), unless otherwise stated; driving distances from Google, n.d.). The ski hill provides a vertical drop of 777 m from the peak at 2319 m elevation, total skiable terrain of 1147 ha, 16 lifts (Big White Ski Resort, 2022), and a cumulative 105 km of ski runs (Skiresort.info, 2022). The ski resort 38 relies on the reputation as “Canada’s Favourite Family Resort” (Big White, 2022b), providing a large share of beginner (18%) and intermediate (54%) terrain as well as alternative activities for families surrounding the resort (Big White, 2022). Fernie Alpine Resort is located in the East Kootenay region, in the southeastern corner of BC. The towns of Cranbrook (20,000) and Kimberley (7,000) are located within less than 100 km from Fernie, however, both towns have at least one ski resort in closer or similar proximity. Fernie itself holds a population of around 5,000 while the bigger agglomerations of Lethbridge (102,000) and Calgary (1,372,000) are over 200 km and 300 km removed from the resort respectively (Alberta.ca, 2021). Fernie Alpine Resort offers a vertical drop of 1082 m from the peak at 2134 m elevation, 1012 ha of terrain, 10 lifts (Fernie Alpine Resort, 2022), and 142 km of ski slopes (Skiresort.info, 2022). The resort has established a reputation for advanced all mountain terrain (OnTheSnow, 2022; Ski-Resort-Stats, 2022), while being located in the so-called Powder Highway, a chain of adventure-activity oriented towns with high ski resort concentration throughout south-eastern BC (Powderhighway, 2022). Sun Peaks is located in the Thompson Region, in the central Interior of BC and just north of Kamloops (population of 88,000). Additional influxes of skiers might come via Vernon (39,000 / 172 km), Kelowna (120,000 / 223 km) or the lower mainland which is connected to Kamloops via Highway 1. The Mountain Resort drops down 882 m from the summit at 2152 m elevation, provides transportation via 13 lifts over the skiable terrain of 1728 ha (Sun Peaks Resort, 2022) and 135 km of slope (Skiresort.info, 2022). Sun Peaks Resort stands out as Canada’s second-largest resort, providing a complete skiing experience due to varied terrain and a ski-through village (OnTheSnow, 2022b; Ski-Resort-Stats, 2022b). The evident outlier in the sample of ski resorts due to size and location is Whistler Blackcomb. After merging with Whistler Mountain in 2003, the resort is now the largest ski 39 resort in North America (Skimag, 2022) with 3307 ha of terrain (Whistler Blackcomb, 2022) and 200 km of slope (Skiresort.info, 2022)). The terrain drops 1609 m from the summit (2284 m) right into the town of Whistler and the two mountains offer a combined 32 lifts for uphill transportation (Tourism Whistler, 2022). Depending on the metric used, Whistler Blackcomb hence comes up to roughly double the size of each of the other 3 resorts in our sample. The location of Whistler Blackcomb differs significantly in the contexts of climate and demand as well, with the town of Whistler (population of 10,000) being situated near the coast and in proximity—at 122 km driving distance—to the largest agglomeration in BC, the Greater Vancouver region with a population of 2,375,000. Being dominated by a coastal climate regime, the resort benefits from higher snowfall levels than most ski resorts in BC while simultaneously suffering from frequent phases of warm temperatures and higher rainfall levels than the interior. 3.2.2 Winter Climate Conditions In order to set up the framework for the demand analysis at four selected ski resorts, past and future climate trends, as well as climatic differences between the resort locations must first be understood. For this purpose, general climatic trends are graphed in Figure 2 – Figure 9 using the IPCC’s Socio-economic Pathways SSP2-4.5 and SSP5-8.5 in the graphing program Datagraph v5.4.1 (2023). This approach provides a summary of general winter conditions (December, January, and February) at the selected four ski resorts before an in-depth analysis of expected future winter conditions is conducted under a moderate (SSP2-4.5) and a high (SSP5-8.5) emission scenario in the main analysis. The Socio-Economic Pathways (SSPs) follow the IPCC’s approach from the most recent Assessment Report AR6, thus including the impacts of social and economic drivers alongside the established climate projections and emission pathways from the previous Assessment Report AR5. With the first digit of the scenario (i.e., SSP5-8.5) noting the general pathway as state of the world, the last 40 two digits note the emission scenario with SSP5-8.5 equivalent to the emission scenario RCP8.5. Therefore, interpretations can be made depending on assumptions regarding emission levels as well as the socioeconomic development and the graphed pathways can be compared to the equivalent RCP scenarios which are analyzed later in the research project. The graphs (Figure 2 – Figure 9) display historic temperatures and precipitation as rain and snow—the primary factors influencing snow coverage and snow conditions—between 1951-2020 as well as projected conditions in 2025, 2055, and 2085 under 2 emission scenarios. Additionally, average historical and future temperature and precipitation levels are summarized in Table 1. The data for these projections are generated using the climate analysis program ClimateBC (Version 6.30, Wang et al., 2016), offering statistical downscaling of projected future weather data from several global circulation models with adjustments for latitude, longitude, elevation, and locally available data. The projections summarize expected changes under several warming scenarios downscaled to point location at mid-elevation of the resorts. Differences between climate impacts and scenarios provide the framework for the following demand analysis. At this point, it is important to note that the conversion from mm water equivalent to cm of snow on the ground will likely differ between the selected ski resorts. 1mm water equivalent of precipitation in the Rocky Mountains (i.e., at Fernie) can typically result in substantially higher amount of vertical snow depth of dry snow than the same amount of water would produce as wet, dense snow along the coast (i.e., at Whistler) (NOAA, n.d.). In this analysis 1mm water equivalent is assumed to be equivalent to 1cm of snowfall regardless of the resort location. 41 Figure 2: Average winter temperatures in °C at mid-elevation of Big White Ski Resort, 1951-2020 historical and model forecasts in 2025, 2055, and 2085. Figure 3: Average winter precipitation as snow and rain at mid-elevation of Big White Ski Resort, 1951-2020 historical and model forecasts in 2025, 2055, and 2085. Big White generally experiences winter seasons with average temperatures between -13°C and -4°C with recent years leaning towards the warmer side of the range. The smoothed trendline—fitted through seasonal mean temperature observations—appears to show a linear warming trend throughout the last 70 years, with mean temperatures rising by 42 just above 2°C. This trend is expected to continue throughout the 21st century with SSP2-4.5 projected to lead to winter temperatures between -5°C and -4°C by the late 21st century. The average winter temperatures would then correspond to the warmest winters experienced in the past 70 years. Corresponding snowfall levels are projected to mostly remain stable around 300mm water equivalent before dropping off in the late 21st century under the high emission scenario. Rainfall levels, however, already display a rising trend throughout the last decades and are projected to reach double of the mid-20th century levels by 2055. Figure 4: Average winter temperatures in °C at mid-elevation of Fernie Ski Resort, 1951-2020 historical and model forecasts in 2025, 2055, and 2085. 43 Figure 5: Average winter precipitation as snow and rain at mid-elevation of Fernie Ski Resort, 1951-2020 historical and model forecasts in 2025, 2055, and 2085. Due to a more continental location in the Rocky Mountains, Fernie displays similar temperature levels to Big White, despite the mid-mountain elevation at Fernie being located 340 m lower than found at Big White. The past warming trend has been slightly less pronounced at around 2°C over the last 70 years. Projections of future climate conditions show a similar trend, however, with the two scenarios displaying slightly greater divergence throughout the next decades. Snowfall levels in the Rocky Mountains are higher on average than in the Okanagan Region with historic data also showing significantly greater year-to-year fluctuation in snowfall levels as observations range from 150 mm – 700 mm water equivalent. Generally, a negative trend in snowfall levels can be observed in the data with some of the early recorded winter seasons clearly exceeding snowfall levels seen within the last 40 years. The historic data displays stable rainfall levels which are forecasted to rise rapidly under warming temperatures. The SSP5-8.5 scenario predicts expected rainfall levels above 200 mm by 2085. 44 Figure 6: Average winter temperatures in °C at mid-elevation of Sun Peaks Mountain Resort, 1951-2020 historical and model forecasts in 2025, 2055, and 2085. Figure 7: Average winter precipitation as snow and rain at mid-elevation of Sun Peaks Mountain Resort, 1951-2020 historical and model forecasts in 2025, 2055, and 2085. Sun Peaks Mountain Resort displays similar climatic characteristics as Big White, which can be attributed to the geographic proximity and, in particular, the location along a similar degree of longitude. Displayed warming trends over the last 70 years slightly exceed 45 2°C with a noticeable year-to-year variation between minimum (-14°C) and maximum (-3°C) mean winter temperatures. Snowfall levels are reported and expected to remain steady just below 300mm water equivalent until experiencing a significant decline in the late 21st century under the high emission scenario SSP5-8.5. Increases in future rainfall levels are similar to Big White, however, both emission scenarios would lead to slightly less projected winter rainfall than predicted for the nearby resort of Big White. Figure 8: Average winter temperatures in °C at mid-elevation Whistler Blackcomb, 1951-2020 historical and model forecasts in 2025, 2055, and 2085. 46 Figure 9: Average winter precipitation as snow and rain at mid-elevation of Whistler Blackcomb, 1951-2020 historical and model forecasts in 2025, 2055, and 2085. Of the resorts examined, Whistler displays the greatest altitudinal difference between top and bottom of the ski hill with ski slopes extending into the village at 675m elevation. Mid-mountain elevation is set at just 1480m, resulting in the lowest mid-elevation of the resort sample, roughly 100m below Fernie. Additionally, observed and projected trends in Whistler differ significantly from the other three ski resorts due to the dominant coastal climate regime. The baseline temperatures display a generally mild temperature level and a major change at almost 3°C of warming throughout the last 70 years. This trend is projected to continue with SSP5-8.5 leading to expected average winter temperatures of 0°C by 2085 – bound to force a termination of skiing operation in the lower parts of the mountain. While snowfall levels are consistently high along the coast, a negative trend of snowfall levels is visible at Whistler Blackcomb throughout past records and in future projections as well. Significant declines in snowfall levels are expected for both scenarios while rainfall levels are projected to continue rising, eventually surpassing snowfall levels in mm water equivalent at mid-elevation. It is noteworthy that the two warming scenarios 47 display great variation at Whistler Blackcomb and will most likely determine challenges and odds of future operations at the ski resort. Table 1: Summary of average historical (1950-2020, with standard deviations) and projected (2055, 2085) winter temperatures, snowfall at mid elevation of Big White, Fernie, Sun Peaks, and Whistler Blackcomb . 3.3 Logistic Regression Model 3.3.1 Model Setup In their large-scale study on skier demand, Parthum & Christensen (2022) investigate daily variation in rental apartment revenues surrounding ski resorts. Changes in revenues are explained by changes in snow depth (at selected and at nearby resorts), snowfall, temperatures, day of the week, holidays, and property-specific effects. The model aims to approximate the daily variation of the demand for skiing driven primarily by changes in snow depth. The model used for our study builds on Parthum & Christensen’s (2022) work, adopting the same approach and some of the independent variables in order to explain changes in demand for accommodation surrounding selected ski resorts and hence skiing in British Columbia. 48 Important changes to the reference study include the dependent variable, measures of snow conditions, the origin of environmental data, and the model type. Instead of explaining total revenues generated by rental apartments surrounding a ski resort on a given day, our regression model explains the probability of any apartment being booked—hence on average the share of booked apartments—in a logistic regression model. The approach via a logistic regression is less sensitive to changes in the rental market supply when additional apartments are introduced into the Airdna datasets, as is repeatedly observed throughout the period 2015-2020. Additionally, the binary outcome variable of an apartment being booked can be assumed less impacted by confounding effects such as changes in market prices or changes in quality of the experience than an analysis of generated rental revenues would be. Further, rather than basing the analysis on local snowpack values, measures of snowfall and rainfall are adopted as primary explanatory variables. With snow depth above a certain baseline threshold, it can be argued that marginal changes in snowpack might not be the factual driver of demand since skiing experiences depend on local weather phenomena such as the amount and physical state of precipitation rather than on the total depth of snow. Finally, Parthum & Christensen use self-reported snow data from the ski resorts. This may present an issue in the regression as several techniques can be used to observe and report high amounts of snow in order to attract additional customers, thus potentially resulting in heterogeneous snow reporting techniques between ski resorts. Following this rationale, the logistic regression model is used to analyze the probability of each short-term rental unit surrounding a specific ski resort being booked, based on snowfall and additional weather conditions while controlling for supplemental drivers of demand. The choice modelling package apollo (Hess et al., 2022) in the statistical analysis software R (2023) is used to solve the regression model and further predict apartment bookings under hypothetical future scenarios. Advantages of the used package 49 over standard approaches such as the glm() function include a more involved modelling approach – forcing the developer to write out the details of the equations – the opportunity of modelling complex nonlinear functional relationships and the automatic conversion from logit to probabilities in the prediction function. Using the apollo package, however, leads to the same results as when analyzing the dataset with glm() as was established after model completion. With predictors on a daily resolution, the demand analysis estimates the reactivity of skiers to marginal changes in weather and snow conditions, thus capturing short-term fluctuations in demand. The following equation describes the logistic regression model: 𝑃𝑟𝑜𝑏(𝐵𝑜𝑜𝑘𝑒𝑑𝑖𝑡) =𝑒𝑋𝑖𝑟𝑡∗𝑏1 + 𝑒𝑋𝑖𝑟𝑡∗𝑏+ 𝜀 Here, b contains parameters describing the relationships between each independent variable and the dependent variable. The error term 𝜀 denotes the residuals as difference between the binary outcome variable and the predicted probability of booking. X is a matrix of the following variables describing weather at resort r and characteristics of the rental unit i on date t: snowfall, rainfall, temperatures, indicators for regular winter holidays and events, apartment price, and indicators for apartment size. Snowfall measurements—taken at mid-elevation each respective resort—represent the cumulative snowfall over the last 72 hours, including the day of booking. To account for the low-frequency of snow days throughout the season—in particular at the resorts in the interior of BC—the cumulative snowfall values are then binned into no snowfall (0-10 cm), low snowfall (10-20 cm), medium snowfall (20-30 cm), strong snowfall (30-40 cm), heavy snowfall (40-50 cm), and extreme snowfall (50+ cm) categories. Approaches with alternative snow binning are displayed in Appendix C. The model further includes an indicator variable for rainfall at the resort, likewise recorded at mid-elevation, cumulatively lagged over 72h, and hence taking into account the lasting, deteriorating impact of rain on the snow base. Linear measures or binning of rainfall values 50 were not implemented due to the low number of rainy days during the ski season. Further, daily temperatures are derived as averages from three daily measurements (morning, noon, and evening) and included via a squared term to account for more sophisticated preferences. Calendar effects are included via indicators for weekends and holidays such as Christmas, Spring Break, Martin Luther King Day, and Family Day in BC and Alberta (coinciding with President’s Day in the US). Mirroring Parthum & Christensen’s (2022) approach, all holidays are linked to the nearest weekend via bridge days, which are included within the respective holiday indicator as well. For example, if Martin Luther King Day falls onto a Tuesday, then that Tuesday as well as the previous Monday, Sunday, and Saturday are marked with the Martin Luther King Day indicator. Only if falling onto a Wednesday, no bridge days are added around the holiday. Spring Break dates are recorded consistently with the local school district. Here, the local school districts either include the closest municipalities and therefore the primary assumed origin of customers (Kelowna for Big White, Kamloops for Sun Peaks, Fernie itself and nearby villages at Fernie) or—in the case of Whistler—the local Spring Break dates coincide with the dates at the larger nearby school districts (Vancouver for Whistler). Some variations between the dates were found for the four ski resorts, most notably at Sun Peaks where the school district switched between a 1 and 2-week spring break within time span of the data (Kamloops This Week, 2017). Further, by contacting the ski resorts, we were able to obtain event schedules at Big White and Sun Peaks, partially covering the period 2015-2020. The provided schedule at Big White was incomplete and subsequently complemented with dates found via event promotions on social media. Property-specific impacts are introduced into the model via the per-person per-night price, calculated via the asking price and the maximum guest number. As all other monetary values in this thesis, prices were adjusted to 2016 CAD via a consumer price index (Statistics 51 Canada, n.d.). Apartment size is tested as a driver of demand as categorical variable, dividing the rental properties into small (1-2 guests), medium (3-6 guests, reference scenario) and large apartments (7+ guests). Interaction effects were included into the model to account for the bilateral relationships between temperatures and snowfall, temperatures and rainfall, and their effect on customer demand. Precipitation generally increases with higher temperatures due to the air being able to hold more moisture, leading to higher levels of snowfall close to 0°C and increasing rainfall levels at warmer temperatures (NOAA, 2020). Simultaneously, precipitation and changes in the physical form of precipitation such as the melting of snowflakes influence local air temperatures (NSIDC, n.d.). When using temperatures and precipitation to represent the quality of skiing conditions, it is important to note that a cold, crisp day without fresh snowfall could be more attractive than a similarly cold day with high snowfall and associated limited visibility and additional discomfort. On the other side of the temperature range, fresh snowfall might also not be as important under particularly warm conditions as deep and consequentially wet snow can result in arduous skiing conditions. Further, an analysis of daily apartment rates showed, that Christmas, the family days, and the size of the apartment (categorized by number of guests) significantly impact per-person prices, whereas the impacts of weekends, spring break, Martin Luther King Day, and events on per-person prices remain minor and, at times, prove to be insignificant. Interaction effects are therefore introduced to investigate if the expected negative effect of higher prices on demand is less important on specific holidays. The logistic regression model assumes a binary dependent variable, independent observations, little-to-no multicollinearity, linearity between the independent variables and the log odds of the dependent variable, a sufficiently large sample size, the exclusion of influential outliers and independence of errors. Potential multicollinearity issues were tested 52 via a variance inflation factor (VIF) with only minor levels of multicollinearity (VIF < 2) being detected in a basic model without interaction effects or squared measures. Various interaction effects then introduce higher levels of multicollinearity, however, the coefficients of the variables of interest remain similar in the expanded model as previously found in the basic model. The assumption of independence is further examined in the discussion chapter of this research. Outliers were investigated via influence plots, displaying x-leverage, y-residuals, and Cook's distance of each observation, and therefore identifying potentially influential outliers. Following this procedure, the regression coefficients were compared to a model without the most influential outliers, resulting in only minimal changes. Consequently, the potential outliers were left in the dataset for the regression. Finally, the regression model itself was evaluated using McFadden's R² (see Appendix D) and the model accuracy of the regression (correct predictions divided by total predictions) was found significantly higher than the no-information rate for all four resort markets as assessed via confusion matrices. 3.3.2 Expectations of Snow Conditions Weather services generally forecast weather conditions up to 10-14 days in advance, with the 2-week barrier marking the current upper limit of reasonable predictability (Zhang et al., 2019). Additionally, weather forecasts decrease in predictive accuracy the further in advance they are taken and may, at some point, not represent expected snow conditions any more. Therefore, if skiers book accommodation at a resort with certain expected skiing conditions in mind, long-term expectations of weather and snow conditions must be formed using other available information. Due to lacking reliable weather forecasts above 14 days in advance, expectations may be based on knowledge on large-scale seasonal atmospheric trends such as the El-Nino Southern Oscillation, or snow conditions during past dates such as at the time of booking or during the same time period in the previous year. Given the date of booking for each 53 individual apartment reservation, it is possible to gain insights into the choice of booking and model expected snow conditions during the time of booking. The majority of rental units at each investigated resort (81%, 73%, 82%, and 83% respectively) are booked further in advance than 14 days before the date of stay, including dates outside of the current ski season. Parthum & Christensen (2022) choose to not account for long booking lead times in their primary model, ultimately explaining all revenues by short-term variations in the local snowpack at the time of stay, while mentioning the potential effect of weather on unobserved cancellations in the datasets as well. We hypothesize that the booking lead time may have an impact on expected snow conditions and therefore integrate expected snow conditions instead of encountered snow conditions into the demand model. A function is introduced, to appropriately weigh past and encountered snow conditions dependent on the booking lead time. The approach of integrating expected snow conditions follows suggestions from the professional literature such as in De Freitas (2014) and Verbos et al. (2018), where the impact of on-site weather on tourism demand as well as the importance of considering expected weather conditions are discussed. In a first step, it is determined whether the apartment was booked during a ski season (I) or during a time of no skiing operations (II), illustrated in Figure 10. In the former case (I), snow conditions at the date of booking are included in the index of expected snow conditions. This is based on the assumption that particularly good snow conditions during the time of booking might have motivated reservations for a future period. In some cases, however, reservations were made outside of the current or previous ski season (II), hence expected snow conditions cannot reasonably be explained by snow conditions during the time of booking. While analyzing related projects, Töglhofer et al. (2011) and Falk (2013) discuss seasonally lagged effects of snow on overnight stays in 54 Austria. The long-term relationships represent a measure of persistence and lagged expectations as "it is well known that winter tourists tend to adjust their travel behaviour based on past snow conditions" (Falk, 2013, p.5). In the related studies, overnight stays are found to be positively influenced by snow cover days (Töglhofer et al., 2011) and snow depth (Falk, 2013) in the previous season. Following a similar thought processes, our study includes snow conditions during the same time in the previous season into the index of expected snow conditions for the remaining long-term bookings (II). Since snowfall is cumulatively lagged over 72 hours and then binned, this method includes a slightly more general measure of the snow conditions during the previous year than a one-day capture of snow conditions would contain. Finally, the weighting function w(x) approximates expected snow conditions by summing encountered snow conditions (w(x), with 𝑥 being the lead time in days) and previous snow conditions (1-w(x)): 𝑤(𝑥): 1 − 1/(1 + 𝑒^((‑𝑥) + 7)) The weighting function w(x) as graphed in Figure 11, realizes a value of 0.5 for a booking lead time of 7 days and decreases steeply with increasing lead time. In other words, expected snow conditions for bookings taken further than a week in advance are modelled to be predominantly influenced by previous snow conditions. Expected snow conditions for short-term bookings (booked less than a week in advance) are modelled to be heavily influenced by weather forecasts of snow conditions to be encountered during the time of stay. 55 Figure 10: Decision tree for combining and weighting expected snow conditions. Figure 11: w(x), weighting function for expected snowfall levels based on booking lead times. In summary, snow records are first determined during the date of stay as well as at a previous date, either during the date of booking or during the same date in the previous year. Then, encountered and past snow conditions are assigned weights, combined into the expected snowfall variable, and binned into snowfall categories. All measures of snow conditions consist of cumulative lagged snowfall over 72h leading up to the date of interest. 56 3.3.3 Data Summaries Table 2 and Table 3 contain the summaries of rental market data and snow and weather records from Big White, Fernie, Sun Peaks, and Whistler. Fernie stands out as the smallest rental market with 60,500 observations across 333 units. Further, the rental apartments at Fernie display the largest share of small apartments (1-2 occupants) at 27% and the lowest average per-person price at around 50 CAD per night. Weather and snow trends show a large number of days with little-to-no expected snowfall (0-10 cm) at 72% and a relatively high number of days impacted by rainfall at 10%. Rental market properties in Big White and Sun Peaks seem comparable: both resorts display a similar number of rental units at 460 and 480 respectively, with the longer data coverage period at Big White leading to more observations. While Big White sees a higher share of large apartments, the Sun Peaks apartments are—on average—more expensive and booked further in advance with a booking lead time of 123 days as opposed to 100 days. The weather and snow trends are similar between the two resorts showing substantially fewer days impacted by rainfall at 4% and 6% respectively. The rental market at Whistler is noticeably bigger than at the other three resorts with around 928,000 observations across 3,940 rental units with more expensive prices as well at 86 CAD per person per night. Further, temperatures at Whistler are found distinctly higher compared to the other resorts at an average of -2.6°C. The warm winter temperatures lead to a smaller share of days with little-to-no snowfall at 41%, as well as a significantly larger share of days with extreme snowfall at 16% and days impacted by rainfall at 20% of observations. Figure 12 - Figure 15 show booking rates in each rental apartment market graphed by average daily temperatures (rounded to the next integer), rain events, and expected snowfall (by mean snowfall level of each snowfall bin respectively). The graphs serve as first glance into potentially causal relationships while any additional drivers are not controlled for. 57 Results for average daily temperatures show varying trends with the graphs at Sun Peaks and Whistler in particular suggesting that exceptionally high or cold tempartures might discurage customers. Further, the graphs generally show higher booking rates under higher snowfall levels and lower booking rates on days impacted by rainfall Table 2: Summaries of categorical weather, snow, and booking data at Big White, Fernie, Sun Peaks, and Whistler. 58 Table 3: Summaries of numerical weather, snow, and booking data at Big White, Fernie, Sun Peaks, and Whistler. 59 Figure 12: Apartment booking rates graphed by average daily temperatures, rain events, and expected snowfall at Big White. 95% confidence intervals are indicated in orange for the categorical variables. Figure 13: Apartment booking rates graphed by average daily temperatures, rain events, and expected snowfall at Fernie. 95% confidence intervals are indicated in orange for the categorical variables. 60 Figure 14: Apartment booking rates graphed by average daily temperatures, rain events, and expected snowfall at Sun Peaks. 95% confidence intervals are indicated in orange for the categorical variables. Figure 15: Apartment booking rates graphed by average daily temperatures, rain events, and expected snowfall at Whistler. 95% confidence intervals are indicated in orange for the categorical variables.61 3.4 Future Climate Projections 3.4.1 Downscaling via MarkSimGCM In order to analyze future rental demand levels—modelled under constant prices as well as unchanging market and apartment properties—snow and weather conditions must be projected for future time periods under selected climate scenarios. The climate models used in the IPCC’s Assessment Reports only provide estimates of future climate conditions at a low temporal and spatial resolution. ClimateBC—used in the previous section of this research project to provide insights into climate conditions at the selected ski resorts—increases the spatial resolution of climate model output to point estimates, precisely at mid elevation for our purpose (Wang et al., 2016). However, ClimateBC provides output at the monthly scale which is still too coarse to analyze day-to-day demand trends. MackSimGCM weather generator was created by climate scientists to increase the spatial and temporal resolution of IPCC climate model output (Jones & Thorton, 2013). MarkSimGCM uses statistical downscaling of the output from one or the average of several General Circulation Models (GCMs) of the IPCC’s fifth Assessment Report (AR5) to generate daily temperature and precipitation values for selected years and climate scenarios. First, output from large scale GCMs is statistically related to observations from local weather stations in order to downscale GCM projections to a finer spatial scale. Then, daily minimum and maximum temperature values are simulated by stochastically downscaling the climate projections. This means that daily temperature values are randomly generated, given Daily precipitation values are determined via a two-step procedure of a third order Markov chain: in the first step, historic weather records are used to establish monthly baseline probabilities of rainfall, determining the probability of rainfall on any given day by the occurrence of rainfall in the previous 3 days. Each day of the month is associated random rainfall probabilities via downscaling with a 12-point Fourier transform. In the second step, any day that was 62 determined as rainy day is attributed a certain amount of rainfall using the amount and variance of rainfall in historic records (Jones et al. 2002). In summary, the MarkSimGCM weather generator provides randomly simulated precipitation and temperature values on a daily resolution for a specific year under selected climate models, climate scenarios, and locations. To additionally account for the impact of climate change, MarkSimGCM also allows for climate typing, thus the climate system in a region transitioning to a different climate regime with varying temperature and precipitation patterns due to the impact of global warming (Jones & Thornton, 2013). While originally developed for agricultural applications in the tropics, the weather generator has since been utilized in variety of contexts and regions. For example, Robertson et al. (2015), Brammer et al. (2015) and Dickau et al. (2020) use the MarkSim Weather Generator to simulate the availability of outdoor ice skating in eastern Canada. The researchers investigate possible future conditions via repeated simulations with MarkSim: weather conditions are simulated 99-times for individual years over 10-year intervals, providing a large number of temperature scenarios with associated ranges and probabilities. This tested approach hence permits simulations of daily temperatures and precipitation while accounting for randomness in short-term weather conditions. For this reason, an adaptation of the approach is applied in our study. Here, thirty random simulations were performed to generate mean daily temperatures (average of maximum and minimum temperatures) and total daily precipitation values for a baseline scenario (RCP4.5 2015), and for a mid (2055) and late (2085) century period for the two climate scenarios RCP4.5 and RCP8.5 at each selected ski resort. RCP4.5 2015 is adopted as baseline scenario since it is the start of our data collection period and therefore represents average weather and snow conditions during the time period that the demand model is built on. RCP4.5 is chosen as moderate warming scenario and lower bound estimate of future warming whereas RCP8.5 63 represents the upper bound warming scenario with a buisiness-as-usual development of unmitiated greenhouse gas emissions. Each individual model run was initialized with a different, randomly generated model seed to project daily weather conditions under varying starting conditions. Each scenario is limited to thirty simulations due to the labor intensive nature of data collection in MarkSimGCM. The resulting climate projections provide insights into expected weather and snow conditions and will be discussed in the results chapters of this project via average projections of each scenario and location. 3.4.2 Climate Model Selection Climate modelling is subject to uncertainty from two general sources: uncertainty related to emission scenarios and uncertainty related to current understanding of the functioning of the climate system. Some uncertainty due to future levels of greenhouse gas emissions is addressed in this research by simulating future conditions under a moderate emission scenario—RCP4.5—and under a high emission scenario—RCP8.5. Uncertainty due to limited understanding and representation of climate system properties is addressed by the selection of climate models. Throughout the Coupled Model Intercomparison Projects (CMIPs), the quality and application of different climate models have been tested, leading to the conclusion that there is no single model best representing or predicting the functioning of the climate systems. Instead, it has been shown that the use of ensemble models improves predictions and reduces uncertainty related to individual model’s assumptions (Knutti et al., 2017, Tebaldi & Knutti, 2007). MarkSim provides climate projections using any individual or ensemble out of 17 climate models out of the IPCC’s CMIP5. Any combination of models can be chosen out of the 17 available climate models in a user-defined ensemble to provide the best possible selection for each application. For the purpose of projecting future winter climate conditions in British Columbia, 9 out of the 17 climate models were selected according to three criteria: 64 first, a maximum of one climate model per climate research center was picked. Second, exceedingly similar modelling approaches—potentially due to code sharing or models originating from a common previous model version—were excluded. Third, models producing output with low normalized standard distances from observed temperatures and precipitation values were favored. The exclusion of overly similar models and the ranking of models according to normalized standard distances was conducted following a in-depth analysis of CMIP5 models by Knutti et al. (2013). The selected climate models and respective developing climate centers are shown in Table 4. Table 4: Summary of climate models included into the climate projection ensemble. 3.5 Future Demand Projections First, results from the logistic regression model provided estimated rental probabilities for apartments on each day of the season depending on snow and weather conditions, the day of the year, and information on the induvial rental units. Then, climate projections via MarkSimGCM provided average daily temperatures and daily precipitation values for thirty randomly simulated years for each ski resort location and each of the following scenarios: RCP4.5 2015, RCP4.5 2055, RCP8.5 2055, RCP4.5 2085, and RCP8.5 2085. In the final step of the analysis, expected daily apartment rental probabilities are attributed to each climate 65 model run using the prediction function of the apollo_choice package. This results in thirty simulated years with daily rental probabilities for each scenario and ski resort location. Rental probabilities across the thirty model runs are then averaged and associated standard deviations are calculated, thus representing expected daily rental probabilities under future climate conditions and a measure of dispersion due to random weather variability. The expected daily rental probabilities exclusively represent the impact of changing climate and weather conditions as all other impacts related to demand and supply of rental apartments—such as apartment prices— are held constant. In addition to rental probabilities, associated changes in per-property rental revenues are calculated to establish a lower bound estimate of climate change impacts on individual apartment owners. Projected rental probabilities under future climate conditions—all else equal to the reference scenario—are multiplied with average daily revenues in the respective ski resort rental market. This approach hence estimates changes in expected daily revenues in a rental apartment market due to within-seasonal weather trends under future climate conditions while the supply in the rental market and apartment-specific properties are held constant. Finally, the discussion of results compares average rental probabilities and rental revenues between RCP4.5 and RCP8.5 for the mid and late century projections and analyzes differences between the status quo (RCP4.5 2015) and the worst-case scenario (RCP8.5 2085). 66 4. Results 4.1 Regression Model 4.1.1 Marginal Effects The skier demand model investigates the reactivity of demand to three environmental variables influencing the skiing experience—snowfall, rainfall, and temperatures—the impact of property specific identifiers—per person cost and apartment size—as well as several calendar effects—indicators for weekends, Christmas, Martin Luther King Day, Family Day in British Columbia and Alberta, spring break, and resort-specific events. Marginal effects from the logistic regression model explain changes in probability of apartments surrounding a specific resort being reserved due to a one-unit increase in the respective independent variable and all else equal. Table 5 shows that with all variables fixed to a reference scenario—set at mean temperatures, mean prices, and with holiday and weekend indicators at 0—a 1 unit increase in 𝑥𝑖 (predictor variable) leads to a 𝑦𝑖 (outcome variable) increase in rental probability. Further, the marginal effects of rainfall are computed with temperatures fixed at 0°C as the impact of rainfall at mean temperatures may result in unrealistic and extrapolated results. Marginal effects of snowfall are additionally graphed in Figure 16 to compare impacts between different snowfall levels and across the sample of resorts. Complete coefficient tables from the regression and marginal effects with the corresponding confidence intervals can be found in Appendix B and Appendix C respectively. All figures in this chapter were generated using the graphing program Datagraph v5.4.1 (2023). 67 Table 5: Marginal impacts of demand drivers on rental probability levels under average conditions. Impacts are divided into environmental, calendar, and property-specific effects. 68 Figure 16: Marginal effects of expected 72h snowfall on rental probabilities under mean temperatures at Big White, Fernie, Sun Peaks, and Whistler. The impact at the reference scenario, [0-10 cm) is set at 0. Bars around estimates show 95% Confidence Intervals. 4.1.2 Weather Reactivities The rental demand model (Table 5) shows varying effects for the three weather components that influence skiing conditions and contribute towards the quality of snow. Marginal rainfall impacts are analyzed and interpreted with temperatures set at 0°C in order to avoid extrapolation to unrealistic combinations of rainfall and temperatures. The impacts of rainfall on rental probabilities are consistent across all 4 investigated resorts, showing decreases in demand caused by any rainfall within the previous 72 hours. This effect is weakest in the Thompson-Okanagan Region with rainfall leading to decreases in rental probabilities of 4.23% and 4.91% at Big White and Sun Peaks respectively. Demand patterns display stronger adversities to rainfall at Fernie with 7.81% and obvious trends at Whistler with 14.5% reductions in rental probabilities caused by rainfall. 69 Snowfall generally appears to have a positive impact on demand with increasing strength for higher snowfall levels. However, the resorts display varying trajectories and magnitudes of impacts under higher expected 72h-snowfall levels, seen as differences between the estimates in Figure 16. The marginal effects at Big White and Fernie display similar tendencies of customer behavior: low expected snowfall levels (10-20 cm) show minor increases in rental levels, successively growing stronger under higher snowfall levels up to increasing average rental probabilities by around 50% under the highest expected snowfall levels. Marginal impacts at Sun Peaks display a positive, almost constant impact of expected snowfall levels between 10 – 50 cm, causing rental probabilities to increase by 10-23%. The highest snow-bin (50+ cm) shows comparable impacts on rental probabilities as the previously observed levels at Big White and Fernie with 42%. Heavy and extreme snowfall events therefore appear to be significant drivers of rental demand and substantially more impactful than low or intermediate snow events. The described relationships between snowfall and demand differ at Whistler. The impacts of expected snowfall remain at a basic levels for all snow bins below 50 cm with moderate increases of demand under extreme snowfall (50+ cm). Low expected snowfall levels (10-20 cm) appear to cause a minimal decrease in demand for rental apartments (-0.2%, confidence intervals include 0 as shown in Appendix C), while the higher snow-bins between 20 – 50 cm cause weak increases in demand just below 10% and, noticeably stronger, 21% for 50+ cm expected snowfall. The seemingly smaller impact of snowfall at Whistler, however, must be considered in the context of generally higher snowfall levels at the coastal resort, more frequent small, moderate, and big snowfall events, and the potentially different experience for customers skiing in heavy and wet snow at the coastal resort as opposed to lighter snow in the interior of BC. The discussed impacts of increasing snowfall levels on demand for rental apartments also hold under alternative snow-binning approaches as shown in Appendix C. 70 The regression model further shows minor influences of small temperature changes on rental demand. The squared temperature variable and interaction terms with snowfall and rainfall complicate interpretations, however, marginal effects of temperatures show that while assuming the baseline scenario, the preferred skiing temperatures at Big White and Fernie can be found at colder-than-average temperatures (average of observations at −6.64°C and −6.49°C respectively), whereas customers at Sun Peaks and Whistler appear to prefer warmer-than-average temperatures (average of -6.2°C and -2.6°C). 4.1.3 Calendar Effects and Property-Specific Impacts The results of the logistic regression show that any of the regular winter holidays increase the demand for rental apartments surrounding the BC ski resorts. First, it can be observed that weekends lead to regular increases in demand with the respective marginal effects on rental probabilities ranging from 8.9% (Sun Peaks) to 15.6% (Fernie). This effect is enhanced by Statutory Holidays and during school vacation dates in the ski season. Since holidays are included into the model via an indicator linking the holiday to the nearest weekend, holidays always include a weekend (unless falling on a Wednesday) and the weekend surges in rental demand are hence cumulative to the holiday effects. Christmas represents the most active period of the year for property managers and ski resorts with marginal impacts between 24% - 28.1% at all resorts. This period can be considered crucial to the ski industry since the Christmas period is modelled as 10-day period between the 23rd of December and January 1st and is hence responsible for an important portion of seasonal revenues. Family day weekends in February appear to be important drivers of demand as well at 15.8% - 27.4% increases in rental probabilities across the board, although the individual holiday effects are difficult to untangle as BC family day coincides with the Alberta and Ontario family day since 2019, which simultaneously coincide with Presidents’ Day in the US. Marginal impacts show a lower importance of spring break for 3 of the resorts (4.7% - 71 7% increases in demand) while remaining an important factor at Big White (20.4% increase). Apparent impacts of spring break on rental demand, however, might not capture the full effect of the seasonal holiday period, as they are based on local school district schedules and thus depend on the origin of the respective customers. The event indicator at Big White—derived from an incomplete schedule—results in a relatively weak marginal effect of 1.8%. The more extensive schedule at Sun Peaks hints that events might have a considerable impact on demand at the ski resort apartments with increases in rental probability of 16%. Further, decreases in demand can be observed for more expensive apartments at all 4 resorts. This effect ranges from 0.07% per CAD at Whistler to 0.32% per CAD at Fernie. It is noticeable that prices appear to play a lesser role in the bigger rental apartment markets as customers at Whistler are shown to be slightly less price sensitive than at Sun Peaks and Big White and significantly less sensitive than at Fernie. The size of the rental apartments appears to play a role as well, with different size categories (small, medium, or large) preferred, depending on the ski resort. This effect, however, might be influenced by the limited supply since small apartments, for example, represent the smallest category at Big White, Sun Peaks, and Whistler as seen in Table 6. Taking only significant coefficients into consideration, the regression model further suggests, that differences in impact between medium apartments and the other two size categories on demand matter slightly less if the per-person price of the apartment is increased. This can be seen due to the interaction effect coefficients (see Appendix B) between apartment size and per person price always displaying the opposing sign than the apartment size coefficients. Interpreting the per person price effect given a certain apartment size is inconclusive as the impacts vary depending on the resort. 72 Table 6: Share of rental apartment category per ski resort. Grouped into small (1-2 guests), medium (3-6 guests), and large (7+ guests) units. 4.2 Climate Projections 4.2.1 Temperatures Using the daily weather generator MarkSim GCM, future weather scenarios were simulated repeatedly, resulting in 30 hypothetical years with daily temperature and precipitation values per climate scenario and per resort. Each of the thirty model runs per scenario was initiated using a different model seed—thus guaranteeing varying starting conditions and including climate variability into the projections—under the previously specified selection of climate models. The resulting daily values were then graphed using cubed (global) regression lines in order to visualize general trends in Figure 17 - Figure 35. The regression lines enable comparisons of weather and snow conditions between emission scenarios in terms of total values and show within-seasonal trends otherwise hidden by the high variability between individual model runs and by the general scarcity of days with precipitation. Average daily temperatures for RCP4.5 2015, RCP4.5 2055, RCP8.5 2055, RCP4.5 2085, and RCP8.5 2085 are displayed in Figure 17 – Figure 20 to gain insights into within-seasonal temperature trends. Mean opening and closing dates at the respective ski resorts are derived from the last ski seasons (during the pre-COVID-19 period) and indicated in the graphs with vertical purple lines. Projections of daily temperatures display a clear warming trend for all scenarios compared to the baseline. Under RCP4.5 2055, temperatures are projected to uniformly 73 increase throughout the entire winter season by roughly 1.5°C at Big White, Sun Peaks, and Whistler. The projected warming at Fernie exceeds the effects at the other ski resorts with an expected shift of 2°C above baseline levels. Projected changes under RCP8.5 2055 differ slightly depending on the resort location: Big White is expected to see an additional degree of warming above RCP4.5 2055 during the first half of the season. Projected temperatures then converge towards the intermediate emission scenario in spring. Fernie does not display significant differences in temperatures between RCP4.5 and RCP8.5 for the 2055 projections as both scenarios show daily temperatures at around 2°C above the baseline scenario. Projections at Sun Peaks display RCP8.5 2055 slightly warmer than RCP4.5 2055 during the winter seasons with differences up to 1°C in the early and late ski season. Whistler is generally expected to see an additional degree of warming above RCP4.5 2055 under RCP8.5 2055. Similar to Sun Peaks, the temperature projections between the two emission scenarios diverge most noticeably during the shoulder seasons. The late century projections show additional warming with differences between RCP4.5 and RCP8.5 becoming obvious: winter temperature projections under RCP4.5 2085 match observations from RCP8.5 2055 closely at roughly 2°C – 2.5°C above baseline temperatures. Under RCP8.5 2085, the ski resorts are projected to experience warming temperatures of around 4°C above the baseline. The projections generally show the greatest warming to occur during the coldest period of the season – early to mid-January. In conclusion, the graphs display the expected additional warming impact of the high emission scenario RCP8.5 compared to RCP4.5 and the additional warming in the late-century projections as opposed to the mid-century projections. Major differences can be seen in baseline winter temperatures between the 4 ski resorts with the ski resorts in the Thompson Okanagan Region remaining the coldest at mid-elevation. RCP8.5 2085 would 74 push average temperatures above freezing about a month earlier than it is currently experienced at all locations, thus greatly impacting snow melt and hence spring skiing conditions and snow coverage. This can be seen in a particularly dramatic fashion in Whistler where the already mild temperatures would limit the period with below-freezing temperatures to less than 3 months under the most extreme warming scenario. Furthermore, the described scenarios will significantly impact precipitation levels and trends throughout the season. Atmospheric processes will change due to increasing humidity content in the atmosphere and changes in seasonal weather patterns, shifting the transition period between snow and rain earlier into the spring season. Figure 17: Average daily winter temperatures at Big White under RCP4.5 2015, RCP4.5 2055, RCP8.5 2055, RCP4.5 2085, and RCP8.5 2085. 75 Figure 18: Average daily winter temperatures at Fernie under RCP4.5 2015, RCP4.5 2055, RCP8.5 2055, RCP4.5 2085, and RCP8.5 2085. Figure 19: Average daily winter temperatures at Sun Peaks under RCP4.5 2015, RCP4.5 2055, RCP8.5 2055, RCP4.5 2085, and RCP8.5 2085. 76 Figure 20: Average daily winter temperatures at Whistler Blackcomb under RCP4.5 2015, RCP4.5 2055, RCP8.5 2055, RCP4.5 2085, and RCP8.5 2085. 4.2.2 Snowfall 4.2.2.1 Mid Century – 2055 Future snowfall trends are analyzed via average daily snowfall levels and expected seasonal frequencies of cumulative 72h snowfall levels – from here on simply referred to as snowfall events – using the 30 climate projections for each scenario. This approach permits insights into general climatic trends as well as into within-seasonal developments that might affect demand and skiing operations at the selected locations. Average daily snowfall and seasonal snowfall event frequencies for all resorts and scenarios are graphed in Figures 21-28. Big White displays significantly higher snowfall levels in the mid-century scenarios than in 2015. Under RCP4.5, snowfall levels are expected to increase throughout the entire ski season up to 0.35 mm WE/day above current snowfall during the peak snowfall period in early January. Under RCP8.5, increases in snowfall are projected to exceed 0.4 mm WE/day around the Christmas period. However, this peak snowfall under RCP8.5 is followed by a 77 drop-off below RCP4.5 levels in mid-January and eventually even below baseline snowfall levels at the tail end of the season. The described increases in snowfall primarily lead to higher frequencies of intermediate and higher snowfall events (20+ cm) as shown in Figure 22. 2055 Snowfall patterns at Fernie display similar patterns between both emission scenarios. Again, increases in snowfall can be expected during the peak snowfall season in early January with RCP8.5 showing the highest increases at 0.25 mm WE/day above the baseline snowfall levels. Both emission scenarios then display lower snowfall levels than the baseline scenario starting in mid-February with differences becoming more apparent in the late season. At Fernie, low (10-20 cm) and intermediate (20-30) snow events are expected to occur slightly more often by 2055, whereas heavier snow events (30-50 cm) might occur less frequently compared to current conditions. Sun Peaks displays minimal differences in snowfall between RCP4.5 and RCP8.5 by 2055 with both scenarios bringing increases in snowfall up to 0.25 mm WE/day above 2015 levels. Future snowfall levels are expected to exceed current conditions throughout the bulk of the ski season, but most significantly during the first half of the season. Snow event frequencies in Figure 26 hint that this will lead to increases in snow events at any level above 20 cm for RCP4.5, whereas RCP8.5 is mainly projected to experience a surge in intermediate snowfall events (20-30 cm). Whistler is the only resort out of the sample showing distinct trends of lower future snowfall levels compared to 2015. The emission scenarios lead to almost identical projected snowfall, both around 0.25 mm WE/day below the 2015 baseline in early January. The reduction in snowfall leads to rarer occurrences of any snowfall events except for extreme snowfall (50+ cm). Further, it is noticeable that 2055 patterns at Whistler do not show the 78 drop-off in spring snowfall compared to the 2015 baseline conditions which was observed at the other three resorts. 4.2.2.2 Late Century – 2085 By 2085, snowfall patterns at Big White are generally expected to approach current conditions again while differences between the two climate scenarios also become apparent. Under RCP4.5, late century snowfall is expected to exceed baseline snowfall consistently by 0.2mm WE/day. This can be observed throughout the entire ski season and will lead to higher frequencies of any snowfall event above 20 cm. The high emission scenario RCP8.5 displays similar snowfall patterns as the baseline scenario with slightly lower snowfall around the ski season margins in November and April. Snowfall projections at Fernie primarily display less future snowfall in the 2085 projections compared to current conditions. While RCP4.5 is expected to bring slightly higher snowfall levels in late December compared to 2015, the subsequent drop-off will result in 0.3 mm WE/day less snowfall during the last month of skiing operations. Low (10-20 cm) and intermediate (20-30) snowfall events are expected to occur more frequently, whereas the higher snowfall events are projected to become less common than seen under the baseline scenario. Projections under RCP8.5 2085 display consistently lower snowfall levels than the baseline scenario with differences exceeding 0.5 mm WE/day. Contrary to the RCP4.5 projections, RCP8.5 would lead to significantly fewer low (10-20 cm) snowfall events than seen under current climate conditions. Sun Peaks shows major differences between the moderate and high emission scenarios in the 2085 projections. Under RCP4.5, the resort is expected to see heavy increases in snowfall to exceed 2015 levels by up to 0.65 mm WE/day during the peak snowfall period – equivalent to an increase in daily snowfall of around 40%. This effect is not observable under RCP8.5 with projections showing snowfall patterns similar to 2015 79 throughout the season. Interestingly, snowfall frequencies in Figure 26 display how projected 2085 snow events differ depending on the emission scenario. RCP4.5 would lead to more frequent snow events above 20 cm, whereas RCP8.5 would result in more frequent low (10-20 cm) and intermediate (20-30 cm) snowfall events compared to the baseline scenario. Similar to the 2055 projections, Whistler shows little difference between the emission scenarios, but consistently lower snowfall levels compared to the baseline scenario by 2085. Both emission scenarios would lead to 0.5 mm WE/day less snowfall than currently experienced throughout the entire season. All snowfall events are expected to become rarer with the exception of extreme (50+ cm) snowfall. 4.2.2.3 General Trends Under warming climate conditions, future snowfall levels are expected to change significantly, however, the results differ between the four resorts. The colder resorts—Sun Peaks and Big White—can expect increasing snowfall levels due to low-enough average temperatures. The resorts with warmer baseline temperatures—Fernie and in particular Whistler—are projected to see smaller increases, steady, or steeply decreasing snowfall levels depending on the warming scenario. While mid-century projections of snowfall are generally similar between both climate scenarios and may even indicate improving snow conditions for some resorts, major differences between RCP4.5 and RCP8.5 become apparent towards the late century with RCP4.5 generally leading to higher snowfall. Common trends between the projections point towards early spring onsets with consistently lower snowfall levels towards the tail-end of the season. The Christmas period and middle of the ski season, however, might even benefit from increasing snowfall at Big White, Fernie, and Sun Peaks depending on the climate scenario. Further, the frequencies of future snowfall events generally differ depending on the resort and climate scenario, however, it can be observed that the resorts and scenarios with lower future snowfall 80 simultaneously display more frequent extreme (50+ cm) snowfall events, suggesting more intense snowfall patterns under warming temperatures. Figure 21: Smoothed function of mean daily snowfall projections under RCP4.5 2015, RCP4.5 2055, RCP8.5 2055, RCP4.5 2085, and RCP8.5 2085 at Big White. Figure 22: Average binned frequencies of expected 72h snowfall under RCP4.5 2015, RCP4.5 2055, RCP8.5 2055, RCP4.5 2085, and RCP8.5 2085 at Big White. 81 Figure 23: Smoothed function of mean daily snowfall projections under RCP4.5 2015, RCP4.5 2055, RCP8.5 2055, RCP4.5 2085, and RCP8.5 2085 at Fernie. Figure 24: Average binned frequencies of expected 72h snowfall under RCP4.5 2015, RCP4.5 2055, RCP8.5 2055, RCP4.5 2085, and RCP8.5 2085 at Fernie. 82 Figure 25: Smoothed function of mean daily snowfall projections under RCP4.5 2015, RCP4.5 2055, RCP8.5 2055, RCP4.5 2085, and RCP8.5 2085 at Sun Peaks. Figure 26: Average binned frequencies of expected 72h snowfall under RCP4.5 2015, RCP4.5 2055, RCP8.5 2055, RCP4.5 2085, and RCP8.5 2085 at Fernie. 83 Figure 27: Smoothed function of mean daily snowfall projections under RCP4.5 2015, RCP4.5 2055, RCP8.5 2055, RCP4.5 2085, and RCP8.5 2085 at Whistler Blackcomb. Figure 28: Average binned frequencies of expected 72h snowfall under RCP4.5 2015, RCP4.5 2055, RCP8.5 2055, RCP4.5 2085, and RCP8.5 2085 at Whistler Blackcomb. 84 4.2.3 Rainfall 4.2.3.1 Mid Century – 2055 Projections of future rainfall levels paint a more unanimous picture for the ski resorts than in the case of snowfall. This is shown by an evaluation of projected daily rainfall levels and expected seasonal frequencies of days with any rain within the previous 72 hours – from here on referred to as frequency of rain events. Daily rainfall projections and rain event frequencies are graphed in Figures 29 – 36. 2055 Projections at Big White display increases in rainfall throughout the entire ski season with notable differences between RCP4.5 and RCP8.5. The moderate emission scenario will primarily lead to higher rainfall levels around the shoulder seasons, especially in spring when RCP4.5 projections exceed the baseline scenario by 0.4 mm/day. The most striking changes under RCP8.5 consist in higher minimum rainfall levels throughout the ski season. Average daily rainfall levels are not expected to dip below 0.2 mm/day, thus eliminating the time period in the ski season showing little-to-no rainfall. Rainfall frequencies are consequently expected to rise from 10 days per season in the baseline scenario to above 17 days by 2055. General rainfall patterns at Fernie differ substantially from Big White as the Kootenay Resort shows rapid increases in rainfall during the spring season. By 2055, rainfall projections show little differences between RCP4.5 and RCP8.5, however, both scenarios would lead to substantial increases in rainfall. The most striking increases in rainfall would occur during the middle of the season as the projections show increases from below 0.1 mm/day under the baseline scenario to 0.3 mm/day by 2055 throughout most of January and February. This results in almost double the frequency of expected rain events from 15 under current conditions to 27 in 2055. 85 Sun Peaks generally displays similar trends as the nearby resort of Big White. Low seasonal rainfall levels under the baseline scenario are subject to minor increases under RCP4.5 2055 and more noticeable increases under RCP8.5 2055—especially during the spring season. Expected rain event frequencies display minor differences between the two climate scenarios as RCP4.5 would increase the amount of rain events from 12 (RCP4.5 2015) to 18, whereas RCP8.5 would lead to 21 expected rain events per season. Whistler faces the highest rainfall levels out of the ski resort sample and projections predict the coastal resort to experience the strongest increases as well. By 2055, projected rainfall levels would increase by a minimum of 0.25 mm/day under RCP4.5 and 0.35 mm/day under RCP8.5 during the main ski season. The smoothed functions of 2055 rainfall levels shown in Figure 35 generally follow the same trajectory as current conditions due to uniform increases throughout the season—meaning that the particular surge in spring rainfall as shown at the other resorts cannot be found in Whistler. Altogether, warm temperatures at Whistler—already leading to 39 rain events per season under the baseline scenario—are projected to cause more frequent rainfall by 2055 with 49 and 56 expected rain events under RCP4.5 and RCP8.5 respectively. 4.2.3.2 Late Century – 2085 The 2085 projections at Big White show consistent rainfall trends throughout the ski season with RCP4.5 leading to rainfall levels between 0.1 – 0.2 mm/day above the baseline scenario. RCP8.5 would further increase winter rainfall levels, projected to cause 0.5 mm/day above the baseline in the early season and 0.25 – 0.3 mm/day for the main season and throughout spring. The frequency of rain events reflects this pattern as RCP4.5 2085 remains similar to the 2055 projections at around 17 events per season, whereas RCP8.5 2085 would triple the baseline frequency of rain events from 10 to above 30. 86 Fernie displays clear increases in rainfall in the 2085 projections with noticeable differences between the two emission scenarios. RCP4.5 mirrors the mid-century projections at 0.3 mm/day above the baseline scenario in January and February while RCP8.5 would lead to an additional 0.2 – 0.3 mm/day throughout most of the season. This means that months with little expected rainfall would cease to exist at Fernie as expected daily rainfall levels would increase from below 0.1 mm/day under the baseline scenario to 0.5 – 0.7 mm/day under RCP8.5 2085 during the middle of the ski season. Consequently, rainfall frequencies see a substantial increase from 15 rain events under current conditions to 30 (RCP4.5) and 36 (RCP8.5) rain events per season in the late century projections. Compared to the projections at Fernie, Sun Peaks is expected to benefit from continuously low rainfall levels throughout most of the ski season even late in the 21st century. 2085 projections show rainfall during the early and main ski season at Sun Peaks at 0.1 mm/day (RCP4.5) and 0.2 – 0.3 mm/day (RCP8.5) above the baseline scenarios. Only in spring, the ski resort is expected to see major increases in future rainfall, especially under RCP8.5 which would increase daily rainfall levels by more than 0.5 mm in March and April. Overall, this leads to increases in the frequency of rain events from 12 in the baseline scenario to 23 (RCP4.5) and 33 (RCP8.5) per season. Late century projections at Whistler show heavy winter rainfall levels and dramatic increases above the baseline scenario. The already substantial rainfall levels in 2015 are expected to see the majority of additional rainfall between November and March, up to 0.7 mm/day under RCP4.5 and consistently above 1.0 mm/day additional rainfall under RCP8.5. The frequency of rain events is consequently expected to rise from 39 under the baseline scenario to 55 (RCP4.5) and 66 (RCP8.5) by the end of the century. 87 4.2.3.3 General Trends Contrary to the snowfall projections, future rainfall patterns generally paint a unanimous picture. Rainfall is expected to increase consistently with the more extreme emission scenario RCP8.5 and later in the century – with the exception of Fernie, where the trends are not contradictory, but remain less obvious. At all resorts, higher rainfall levels can already be detected in the 2055 projections compared to the baseline scenario of 2015. The expected changes range from minor differences in the Thompson Okanagan Region to severe rainfall increases in Whistler, possibly altering the skiing experience substantially. The general patterns predict the resorts with already high precipitation levels to see the heaviest future increases in rainfall as well. While the colder and drier resorts of Big White and Sun Peaks only show lesser increases in rainfall during the bulk of the season, Fernie might be subject to a noticeable shift in rainfall during the middle of the ski season and snow quality in Whistler might become dominated by rainfall for several months throughout the ski season. If we were to assume a constant ski season length at the historical average of 173 days at Whistler, roughly 1 out of 3 ski days would on average be impacted by rainfall during the previous 72h under RCP8.5 2055, RCP4.5 2085, and RCP8.5 2085. In conclusion, late-century rainfall levels during the middle of the ski season might be similar to the rain conditions currently experienced in November and April at all four investigated ski resorts, 88 Figure 29: Smoothed function of mean daily rainfall projections under RCP4.5 2015, RCP4.5 2055, RCP8.5 2055, RCP4.5 2085, and RCP8.5 2085 at Big White. Figure 30: Average frequencies of expected 72h rain days under RCP4.5 2015, RCP4.5 2055, RCP8.5 2055, RCP4.5 2085, and RCP8.5 2085 at Big White. 89 Figure 31: Smoothed function of mean daily rainfall projections under RCP4.5 2015, RCP4.5 2055, RCP8.5 2055, RCP4.5 2085, and RCP8.5 2085 at Fernie. Figure 32: Average frequencies of expected 72h rain days under RCP4.5 2015, RCP4.5 2055, RCP8.5 2055, RCP4.5 2085, and RCP8.5 2085 at Fernie. 90 Figure 33: Smoothed function of mean daily rainfall projections under RCP4.5 2015, RCP4.5 2055, RCP8.5 2055, RCP4.5 2085, and RCP8.5 2085 at Sun Peaks. Figure 34: Average frequencies of expected 72h rain days under RCP4.5 2015, RCP4.5 2055, RCP8.5 2055, RCP4.5 2085, and RCP8.5 2085 at Sun Peaks. 91 Figure 35: Smoothed function of mean daily rainfall projections under RCP4.5 2015, RCP4.5 2055, RCP8.5 2055, RCP4.5 2085, and RCP8.5 2085 at Whistler Blackcomb. Figure 36: Average frequencies of expected 72h rain days under RCP4.5 2015, RCP4.5 2055, RCP8.5 2055, RCP4.5 2085, and RCP8.5 2085 at Whistler Blackcomb. 92 4.3 Demand Projections 4.3.1 Mid Century – 2055 Using repeated simulations of future weather and snow conditions as well as the previously established demand model, expected rental probabilities and associated rental revenues were predicted. The mid-century analysis focuses on differences between the moderate and high emission scenario by 2055, showing the impacts depending on the respective emission scenario. Differences in rental activity levels between RCP4.5 2015, RCP4.5 2055, and RCP8.5 2055 are displayed in Table 7; average rental probabilities for each scenario and associated standard deviations between the 30 model runs of each scenario can be found in Appendix E. Differences in rental probabilities between emission scenarios are graphed in Figure 37 - Figure 48. The general trends are highlighted by a locally weighted scatter-plot smoother (LOESS)—displaying within-seasonal trends using simple regression methods on local subsets of the data. The LOESS function (fitted and included via the graphing program Datagraph) helps determine general tendencies within the ski season by smoothing the otherwise dominant impact of climate variability on day-to-day rental probabilities. The analysis of projected rental impacts at Big White in 2055 displays consistently higher rental probabilities under emission scenario RCP4.5 than under RCP8.5 as shown in Figure 37. This trend is observable throughout the entire season with only few dates where RCP8.5 projections of rental probabilities are higher than under RCP4.5, with the exceptions generally located in the early or late season. This leads to an average loss of 1.71% of total daily rental probability, equivalent to 1,120 CAD per property per season under RCP8.5 2055 compared to RCP4.5 2055. 2055 projected rental probabilities in Fernie suggest no major differences between the two climate scenarios as shown in Figure 38. RCP8.5 leads to 0.06% higher rental 93 probabilities than RCP4.5, therefore matching the projected weather conditions with similar rainfall patterns between the scenarios and slightly higher snowfall levels under RCP8.5. At Sun Peaks, differences in rental probabilities between the two climate scenarios are virtually non-existent by 2055 as shown in Figure 39. RCP8.5 results in marginally lower rental probabilities by 0.04%. The day-to-day differences between the two scenarios mostly remain under 5%, with RCP8.5 slightly outperforming RCP4.5 through the bulk of the season and RCP4.5 leading to higher rental levels around the beginning and end of the ski season. Whistler shows distinct differences between RCP4.5 and RCP8.5 in the mid-century projections with RCP4.5 leading to 1.4% higher rental probabilities than RCP8.5 – graphed in Figure 40. The differences in rental activity levels can be observed throughout the entire season with larger variations in projected rental probabilities during the second half of the season. The loss in rental probabilities under RCP8.5 2055 appears to be caused by increasing rainfall levels as snowfall and temperatures only differ marginally between the two scenarios. Losses in rental probabilities are equivalent to 1,180 CAD per property seasonally as a consequence of differences between the climate scenarios and high rental prices in Whistler. 94 Table 7: Comparison of rental activities under RCP4.5 2015, RCP4.5 2055, and RCP8.5 2055. Results show differences in rental probabilities as well as per-property differences in revenues (2016 CAD). 95 Figure 37: Projected daily rental probabilities 2055 (top bracket) and difference in total rental probabilities between RCP4.5 and RCP8.5 2055 (bottom bracket) at Big White. Figure 38: Projected daily rental probabilities 2055 (top bracket) and difference in total rental probabilities between RCP4.5 and RCP8.5 2055 (bottom bracket) at Fernie. 96 Figure 39: Projected daily rental probabilities 2055 (top bracket) and difference in total rental probabilities between RCP4. 5 and RCP8.5 2055 (bottom bracket) at Sun Peaks. Figure 40: Projected daily rental probabilities 2055 (top bracket) and difference in total rental probabilities between RCP4.5 and RCP8.5 2055 (bottom bracket) at Whistler. 97 4.3.2 Late Century – 2085 The late century analysis focuses on rental activity level differences between the two climate scenarios RCP4.5 and RCP8.5, showing the impacts under the respective climate scenario, as well as the differences between the worst case scenario—RCP8.5 2085—and the baseline scenario—RCP4.5 2015—to show the maximum climate-induced damages compared to current conditions. Differences in rental activity levels between RCP4.5 2015, RCP4.5 2085, and RCP8.5 2085 are displayed in Table 8. At Big White, the trend favoring rental activities under the intermediate emission scenario is further strengthened by 2085 as shown in Figure 41 with RCP4.5 leading to 2.28% higher rental probabilities than RCP8.5 and day-to-day differences in rental probabilities occasionally exceeding 10%. Individual days favoring RCP8.5 over RCP4.5 are slightly less common by 2085 compared to 2055, thus further demonstrating the divergence of weather trends between the two scenarios by the late century. Comparisons of the most extreme warming scenario, RCP8.5 2085 to current conditions—graphed in Figure 42—show clearly lower rental probabilities for the future scenario. Losses are most pronounced in early December and in March, with differences repeatedly exceeding 10% rental probability. The gap between rental probabilities slightly decreases in the mid-season (mid-December throughout late February) and fluctuates between favoring either scenario in mid-April, around the end of the ski season. On average, current conditions lead to 3.94% higher total rental probabilities than predicted under RCP8.5 2085, equivalent to 2580 CAD in seasonal rental revenues per property. Unlike in the 2055 projections, Fernie displays a trend of generally higher rental levels under RCP4.5 by 2085 as shown in Figure 43. Differences, however, remain relatively small at an average of 1.33% throughout the season and without systematic trends depending on the period within the ski season. Total seasonal losses of the high emission scenario are 98 estimated at around 430 CAD per property. Rental losses and inner-seasonal trends become more apparent when comparing RCP8.5 2085 to current conditions – graphed in Figure 44. Around early January, RCP8.5 2085 results in equal and, at times, higher rental probabilities than under current conditions. This is contrasted by strong differences in the second half of the season, where projected rental levels in 2015 exceed the future projections consistently and by up to 15%. Altogether, this results in rental probability differences of 1.96% between RCP8.5 2085 and RCP4.5 2015, equivalent to 630 CAD per property per season, with the majority of future losses expected to accrue in the late season. 2085 Projections at Sun Peaks – graphed in Figure 45 – show RCP4.5 yielding higher rental probabilities than RCP8.5, even though the margin remains relatively small at 0.7% on average, equivalent to 500 CAD per property seasonally. The most noticeable differences in rental probabilities occur in the early season, when RCP4.5 outperforms RCP8.5 more distinctly than later in the ski season. Comparing RCP8.5 2085 to current conditions shows similar results with generally higher rental probabilities in the 2015 simulations as seen in Figure 46. Several dates display higher probabilities for the future emission scenario, however, the 2015 probabilities dominate at an average difference of 0.66%, without any clear within-seasonal trends detectable. By 2085, the previously observed differences at Whistler become heavily pronounced as rental probabilities average 3.43% higher under RCP4.5 than under RCP8.5 – graphed in Figure 47. Probabilities remain close only during a brief period around the beginning of January before consistently favoring RCP4.5 throughout the rest of the season up to differences in rental probabilities of around 10%. The comparison between RCP8.5 2085 and 2015 yields a striking average difference of 7.49% in rental probability, equivalent to 6310 CAD per property per season – as shown in Figure 48. Again, the only period with similar probabilities between the two scenarios remains in early January. 99 Table 8: Comparison of rental activities under RCP4.5 2015, RCP4.5 2085, and RCP8.5 2085. Results show differences in rental probabilities as well as per-property differences in revenues (2016 CAD). 100 Figure 41: Projected daily rental probabilities 2085 (top bracket) and difference in total rental probabilities between RCP4.5 and RCP8.5 2085 (bottom bracket) at Big White. Figure 42: Projected daily rental probabilities RCP8.5 2085 and RCP4.5 2015 (top bracket) and difference in total rental probabilities between the two scenarios (bottom bracket) at Big White. 101 Figure 43: Projected daily rental probabilities 2085 (top bracket) and difference in total rental probabilities between RCP4.5 and RCP8.5 2085 (bottom bracket) at Fernie. Figure 44: Projected daily rental probabilities RCP8.5 2085 and RCP4.5 2015 (top bracket) and difference in total rental probabilities between the two scenarios (bottom bracket) at Fernie. 102 Figure 45: Projected daily rental probabilities 2085 (top bracket) and difference in total rental probabilities between RCP4.5 and RCP8.52085 (bottom bracket) at Sun Peaks. Figure 46: Projected daily rental probabilities RCP8.5 2085 and RCP4.5 2015 (top bracket) and difference in total rental probabilities between the two scenarios (bottom bracket) at Sun Peaks. 103 Figure 47: Projected daily rental probabilities 2085 (top bracket) and difference in total rental probabilities between RCP4.5 and RCP8.5 2085 (bottom bracket) at Whistler. Figure 48: Projected daily rental probabilities RCP8.5 2085 and RCP4.5 2015 (top bracket) and difference in total rental probabilities between the two scenarios (bottom bracket) at Whistler. 104 4.3.3 General Trends Projections of rental probabilities at Big White generally match the average weather projections as RCP4.5 consistently outperforms RCP8.5 in terms of expected rental probabilities. The occurrences of higher daily rental probabilities under RCP8.5 than under RCP4.5 become less frequent and less pronounced by 2085 and are most likely caused by weather variability and the closely matched rainfall patterns under both scenarios during the spring season. At Fernie, differences between RCP4.5 and RCP8.5 are generally less obvious at than they can be observed at Big White. This is caused by a closer match between projected climate conditions, especially in the case of snowfall. Further, it appears that Fernie is projected to experience a higher degree of weather variability than Big White, seen as projected future rental probabilities oscillate between favoring RCP4.5 and RCP8.5 with a total span of roughly 20 % total rental probability between the scenarios. Differences between projected rental impacts at Sun Peaks remain minor at below 1% for all projections and comparisons. Additionally, it is noteworthy that Sun Peaks displays higher future rental probabilities for all climate projections except RCP8.5 2085 compared to the 2015 baseline scenario. The location is therefore the only ski resort out of the sample with potentially higher future rental revenues due to changing climate conditions according to the demand model. Following major changes in climate conditions, Whistler displays the greatest differences in rental probabilities between the selected scenarios. Due to the high sensitivity of demand to rain events, projected losses at Whistler appear to be predominantly caused by heavy increases in future rainfall. In conclusion, all 4 resorts display similar general developments, however, inner-seasonal trends and the magnitude of change remain resort-specific. Differences 105 between RCP4.5 and RCP8.5 remain rather small by 2055 with Fernie and Sun Peaks displaying neglectable differences in projected rental revenues. By 2085, RCP4.5 leads to clearly higher probabilities at each resort with heaviest impacts shown at Whistler. With a generally low expected level of change, Sun Peaks remains the only resort showing consistently higher expected revenues under several future scenarios compared to the baseline scenario, RCP4.5 2015. 106 5. Discussion 5.1 Explaining Within-Seasonal Changes in Demand The regression results show that within-seasonal weather patterns have a significant impact on short-term demand of rental apartments surrounding the selected ski resorts. Since customers react to short-term changes in snowfall, rainfall, and temperatures, climate impacts studies approximating damages purely via changes in season might omit part of the impacts—positive and negative—of changing climate conditions on demand surrounding the ski resort. The results also suggest that warming temperatures, generally associated with lower snowfall and increased snow melt in the late season cannot entirely be compensated by artificial snowmaking as customers have been shown to react to changing snow conditions even when a sufficient snow base for skiing is provided. Further, it can be seen that customer behavior varies between the four analyzed ski resorts. Marginal effects of the environmental drivers of demand display significant heterogeneity among the resorts, thus complicating general interpretations for BC. Given discussed differences between customer behavior, winter conditions, and changes in future climate conditions, impacts of climate change on the ski industry in BC should be analyzed at each ski resort individually or the province must be divided into smaller regions with similar tendencies. A possible remedy for this issue would, for example, divide the resorts by dominant climate regime into coastal and continental resorts as seen in Clark (2018). In the sample of selected ski resorts, the snow coefficients between Fernie and Big White display similar patterns, hinting substantial impacts of high snowfall events on demand. Whistler—analyzed with distinctly more rental observations than the other three resorts together—displays weaker impacts of snowfall under all snowfall levels. Demand patterns with respect to snowfall are less obvious at Sun Peaks due to a lower impact of strong snow events (30-40 cm) compared to moderate (20-30 cm) snowfall events. Marginal 107 impacts of snowfall at Sun Peaks hint towards a steady positive impact of snowfall on rental levels with demand patterns—apart from rare extreme snowfall events—potentially less dependent on the amount of snow than the occurrence of any snow event. The adverse impacts of rainfall on rental levels can be observed across the four resorts with the marginal impacts of rainfall showing up significantly stronger at Whistler than at the other locations. This result suggests a potential challenge for future skiing operations at Whistler due to the expected surges in rainfall levels on top of already high amounts of rainfall. Some of the additional tested variables have been shown to significantly impact the demand for accommodation surrounding ski resorts as well. As expected, holidays—most notably the Christmas period and the family days—have been shown to attract customers to the ski resorts. Additionally, the impact of events at Sun Peaks suggests that adverse snow conditions could potentially be compensated by organized activities at the ski resort. Further, it can be seen that increases in short-term rental prices have the expected negative impact on demand with varying magnitude between the ski resorts. The strongest price effect is observed at Fernie, the resort with the fewest apartments available for rent. Interaction effects between prices and holidays as well as prices and apartment size are observed as shown in Appendix B, however, no general consensus can be established between the variables across all 4 ski resort markets. 5.2 Future Winter Seasons The climate projections show clear warming trends during the entire winter season with differences between RCP4.5 and RCP8.5 becoming most pronounced by the late century. Both analyzed climate scenarios will lead to significant warming at all 4 ski resorts, which—amongst others—sensitively affects precipitation patterns throughout the ski season. While general precipitation trends differ considerably due to the location of the four BC resorts in the Coastal Range, the Thompson Okanagan, and the Kootenays, projected rainfall 108 levels and the number of rainy days can be seen to increase substantially across the board. There is no consensus in within-seasonal timing of the major rainfall increases between the resorts, however, it can be concluded that winter seasons will experience more regular rainfall—most notably under RCP8.5 and later in the 21st century. Further, steady or increasing snowfall levels are predicted between mid-December and early February for Big White, Fernie, and Sun Peaks. The shoulder seasons are expected to see less favorable changes with early season snowfall generally projected similar to current conditions, whereas the spring ski season will be subject to lower snowfall levels. Precipitation patterns generally differ depending on the emission levels under RCP4.5 and RCP8.5, with rather subtle deviations by 2055 turning into ample divergences by 2085. Differences in snowfall levels between the two emission scenarios are only detectable at minor levels around 2055. Snowfall trends then become clear by 2085 with RCP4.5 leading to slightly (Whistler) and distinctly (Big White, Sun Peaks, and Fernie) higher snowfall levels. Rainfall trends differ by the mid-century projections already—with the exception of Fernie—with more frequent rain events under RCP8.5. Late century projections then continue to show major differences in rainfall between the two emission scenarios. More regular rainfall is generally predicted throughout the entire ski season at all locations and rainfall frequencies are shown to increase by 20-70% between the two scenarios in 2085. It is noticeable that the resorts and scenarios showing losses in snowfall relative to 2015 primarily display fewer low (10-20 cm) and intermediate (20-30 cm) snowfall events. Heavy and extreme snowfall events are projected to remain stable or increase in frequency under most climate projections. This suggests that while decreasing snowfall levels are generally expected under the most extreme case—RCP8.5 2085—weather conditions might simultaneously become more variable or intense with the relative frequency of extreme snow 109 events increasing. A higher number of climate model runs would be necessary to investigate possible changes in climate variability under future emission scenarios at greater detail. By analyzing within-seasonal trends of rainfall and snowfall, conclusions can further be drawn towards skiing conditions during the main and shoulder ski seasons. The discussed projections at the 4 resorts in BC display negative changes to skiing conditions particularly occurring during the late season. This implies that spring skiing might be subject to marginal and unfavorable snow conditions on a more frequent basis. Simultaneously, the analysis suggests that snow conditions in British Columbia during the economically vital Christmas period can generally be expected to remain favorable for skiing operations if not superior to current conditions. 5.3 Climate Impacts on Rental Probabilities and Revenues Comparison of rental revenues between each climate scenario and the baseline scenario approximate total costs of changing climate conditions on rental activities, whereas comparisons between RCP4.5 and RCP8.5 in 2055 and in 2085 can be seen as avoidable costs as both climate scenarios remain in the realm of possibilities. Projected rental levels and associated revenues lead to the conclusion that RCP4.5 is generally preferred over RCP8.5 for skiers, landlords, ski resorts, and other stakeholders—although this result only truly becomes apparent with the late century projections. By 2055, Big White and Whistler display lower rental revenues under RCP8.5, whereas Fernie and Sun Peaks show virtually no difference in rental revenues between the scenarios. By 2085, differences become unambiguous at all resorts with Whistler leading the chart (Table 8) at 3.43% lower total activity levels under RCP8.5 than under RCP4.5 and Fernie showing rapidly increasing losses compared to 2055. Notably, most future climate scenarios are projected to lead to lower rental levels and revenues compared to RCP4.5 2015 with the only exceptions seen at Sun Peaks. 110 At seasonal losses in total rental probability of 4% by 2055 and 7.49% by 2085 under RCP8.5 – equivalent to 3,371 CAD and 6,307 CAD per property– it must be asked whether Whistler can absorb the climate-induced shocks to the demand under the high emission scenario. Climate projections show decreasing snowfall levels on top of increasing rainfall levels under any future climate scenario. While decreases in snowfall might not impact the local economy dramatically due to generally high snowfall levels and a low reactivity of demand to snowfall, rainfall affecting more than 1 out of 3 ski days and the strong reactivity of demand to rain events could substantially alter rental and skiing operations in Whistler (22% of days with rain events under the baseline scenario to 38% under the RCP8.5 2085). One mitigating factor for the coastal resort could be that the mid-mountain elevation used for climate projections in this analysis lays below the majority of ski lifts and runs. Therefore, a part of the projected increase in rainfall might in fact be received as snowfall on most of the ski slopes and compensate some of the projected losses in snowfall. The lower parts of the mountain, however, will become less available for skiing operation than currently seen and any ski-out to the Whistler village will likely need substantial support via artificial snowmaking or transport of snow from higher altitudes. Sun Peaks marks the least impacted resort out of the sample with significantly smaller differences in activity levels compared to the 2015 projections, potential increases in revenues, and generally low changes in rental levels and revenues between RCP4.5 and RCP8.5. So how is it possible that the ski resort with the lowest seasonal snowfall will fare the best under changing climate conditions? As temperatures warm, Sun Peaks will see substantial increases in winter precipitation, part of which—thanks to the generally low temperatures—will come as snow. The demand model shows a moderate impact of snow events on demand as well as a comparably low impact of rain events on rental demand at Sun 111 Peaks. This, on one side dampens the positive effects of increasing projected snowfall, however, on the other side, mitigates the effects of distinctly increasing rainfall levels. The within-seasonal analysis revealed changes in winter precipitation trends, distinguishing impacts of globally warming temperatures on the early, main, and late ski season. Consistent results on such a short temporal resolution, however, cannot be observed in the projected rental impacts. Individual resorts show alleged within-seasonal trends under specific climate scenarios without providing enough information or sufficiently distinct trends for a general conclusion on a provincial scale. This heterogeneity of results could be caused by weather conditions generally being too variable, hence requiring additional repeated simulations to observe any distinct within-seasonal trends in rental levels. Another reason could be changes in climate variability under warmer temperatures, thus masking any trends that may otherwise be identified. Finally, it is possible that the entire ski season will be impacted at a relatively constant rate, without specific time sections within the season being affected systematically by the generally warming climate system. 5.4 Accounting for Endogeneity in the Demand Model The logistic rental demand model explained the probability of a unit being rented given a certain per-person price for the apartment. However, it could be argued that the relationship between prices and rental probabilities are more complicated, potentially introducing endogeneity into the regression model. This would be possible if the probability of an apartment being rented influences the owner of the apartment to adjust the price, leading to a case of reverse causality in the model. Strategies using price adjustments over time to maximize profits—often referred to as dynamical pricing strategies—are commonly found in the hotel business (Gibbs et al., 2018, Leoni & Nilsson, 2021). If similar strategies were to be present in the Airbnb market for short-term rental apartments, then endogeneity might indeed pose an issue in the demand 112 model of this research project. A scan of the professional literature reveals that the consideration of endogeneity often depends on the topic and purpose of research: skier demand studies have not mentioned the issue of endogeneity despite the frequent use of visitor expenses in regression models. Most commonly, lift ticket prices are included to explain changes in demand, however, accommodation prices have been used and discussed as independent variables as well (Töglhofer et al., 2011; Falk, 2013). On the other hand, a increasing number of recent studies have investigated the use and potential of dynamical pricing strategies in the context of short-term rental markets. While the extent of price adjustments has generally been found at a lesser degree compared to the hotel business and primarily dependent on experience and professionalism of the host—observed via the number of managed rental apartments—some level of dynamical pricing is identified (Gibbs et al., 2018; Kwok & Xie, 2019; Abrate et al., 2023). This leads to the conclusion that the presence of endogeneity and how to account for it should be discussed. Endogeneity issues are commonly addressed via instrumental variables (IVs) in a two-stage regression model, with IVs explaining changes in the outcome variable exclusively through the endogenous variable (Koladjo et al., 2022; Becker, 2016; Foster, 1997). In other words, the problematic endogenous variable is estimated via a separate set of explanatory variables before being used in the regression to estimate the outcome variable. While typically employed in linear regression models, the r package rvtools (Sjolander et al., 2020) allows for IVs in a non-linear two-stage model as well (Sjolander & Martinussen, 2018). To address potential endogeneity in the demand model, appropriate IVs explaining the final asking price of a rental apartment must be found. The assumptions for the regression model are summarized as follows: the instrumental variables must be associated with the endogenous variable, the instrumental variables must affect the outcome variable only 113 through the endogenous variable, and the relationship between the instrumental variables and the outcome variable must be unconfounded (Sjolander & Martinussen, 2018). Prices are generally chosen by the owner of the rental apartment in order to maximize profits and may therefore depend on factors from the supply side (owners) such as expected rental levels and the value of the rental apartment as well as factors from the demand side (visitors) such as income levels of potential visitors. In this research, three instrumental variables were chosen to approximate the apartment price: the local annual GDP of nearby population centers by census metropolitan area (Statistics Canada, n.d. (c)), monthly occupancy levels of the apartment during the same period in the previous year, and the approximated apartment value depending on number of bedrooms (BC Assessment, 2020). The instrumental variables are used in a first-stage linear model to approximate the apartment price. The resulting estimate is then used in the logistic second stage of the model to approximate the probability of each apartment being booked. The results from the first stage model generally show the expected signs: apartments with previously high occupancy levels tend to be priced lower than average, whereas increases in local GDP and apartment value tend to increase the asking price. The final results of the model show minor differences to the previously discussed standard model: increasing per-person apartment prices are still shown to decrease rental probabilities at a relatively low level, even though the impact of prices has strengthened at all four ski resort locations. The other regression coefficients also display small differences compared to the standard model with the most distinct changes being slightly higher positive coefficients for most variables, including slightly stronger effects of snowfall. The results of the two-stage regression with IVs are displayed as marginal impacts in Table 9—results might also differ from the standard model due to different ways of calculating marginal impacts—full coefficient tables are included in Appendix B. 114 As an alternative to the instrumental variable approach, the standard model was also tested after excluding the per-person price from the regression. The results showed only minimal differences in the variables of primary interest—snowfall, rainfall, and temperatures—thus further suggesting that potential endogeneity of the price variable does not affect the results of the regression in a significant manner. Furthermore, it would be possible to explain the general price level of each apartment with a measure less dependent on the unobserved daily drivers of rental supply. This could be performed—for example—by including average prices over a longer period of time such as prices during the week of booking, or by using the price levels of each specific rental apartment during the off-season. In summary, the inclusion of instrumental variables adds context to the previous regression results and specifically for the impact of the apartment price on rental probabilities by showing only minor total changes. For the sake of projecting future impacts of climate change on rental probabilities while holding prices and the supply of rental apartments constant, the previous standard model is assumed to be appropriate. 115 Table 9: Marginal impacts of demand drivers on rental probability levels under average conditions in a two-stage estimation using instrumental variables. Impacts are divided into environmental, calendar, and property-specific effects. 5.5 Contributions and Future Research This research has contributed to previous studies and findings in three primary aspects: first, the demand for skiing was estimated in the previously unexplored region of British Columbia. First estimates in local reactivities to snowfall, rainfall, and temperatures 116 were established. This complements the existing literature with a new region while simultaneously providing further evidence of the importance of demand-side studies. Second, future snow conditions in BC were simulated using downscaled RCP4.5 and RCP8.5 projections at mid-elevation and on daily resolution at four ski resorts. This provides future studies with an approach to similarly investigate day-to-day climate change impacts using the MarkSimGCM weather generator. Additionally, it displays another application of the weather generator in the mid-latitudes. Third, minimum impacts of climate change on the ski industry and the surrounding rental markets were estimated. The findings primarily indicate that the consequences of warming temperatures are highly variable, even at a within-provincial level. Comparisons between findings from this study and results in the established literature are complicated by the large pool of indicators and metrics used in skier demand research. Skier numbers, resort revenues, rental probabilities, rental revenues, and occupied beds have all been used as independent variables to approximate skier demand. Additionally, the chosen explanatory variables include many indicators for changing climate conditions such as variations of snow-water equivalent, snow depth, snow cover days, snowfall, rainfall, temperatures, wind, and weather conditions, thus leading to a great number of possible combinations. In a case study investigating college students visiting ski resorts in Nevada and California, Englin & Moeltner (2004) find an elasticity of demand with respect to weekly snowfall of 0.21—meaning that a 10% increase in weekly snowfall results in a 2.1% increase in demand. Using average historic snowfall records and computed marginal impacts of snowfall, we can establish elasticities of rental probabilities—and hence shares of booked apartments—with respect to (expected) 72h snowfall. In our sample of resorts, this results in comparably weak snowfall elasticities with 10% (1 standard deviation) increases in expected 72h snowfall above mean snowfall resulting in changes in rental probabilities of 0.31% 117 (9.8%) at Big White, -0.19% (+12.2%) at Fernie, 2.12% (13.73%) at Sun Peaks, and 0.61% (20.47%) above mean rental probabilities at Whistler. Elasticity estimates were calculated using the fitted functions of marginal snowfall impacts graphed Appendix C (standard model with 10 cm snowfall bins). The negative elasticity value at Fernie is a result of the nature of the fitted cubic function and turns into a positive value if different polynomial functions or different elasticity versions (20% or 30% increase) are used. Although the studies are not directly comparable due to different indicators of demand and cumulative snowfall measures, it can be seen that Englin & Moeltner found a snowfall elasticity of demand which is 195% higher than the average elasticity found in our study. While not surprising, that visitors react stronger to changing snow conditions than rental apartment bookings due to higher flexibility in planning and visiting a ski resort—apartment renters generally commit more time and expenses to a skiing trip on top of potentially non-refundable payments or deposits—further conclusion cannot be drawn in this case due to limited similarities in study approach and used metrics. It can be said, however, that the general study results from this analysis are conform with previous ideas and findings in the sense that within-seasonal snowfall increases the demand, whereas higher costs deter customers from the ski resort. The analysis further complements studies in the related literature by developing previous ideas and applying them in the little-researched province of British Columbia, granting first insights into customer behavior surrounding ski resorts in western Canada. The discussed results add to the ever growing evidence that within-seasonal impacts should be considered in related climate change impact studies and the employed methodology advances previous demand studies by using high-precision daily measurements of snowfall, rainfall, and temperatures at a resort level. Finally, the effects of rainfall in particular have seen little attention in previous studies, likely due to constraints to weather data accuracy and reliability. This study hints that rainfall 118 might have a significant impact on customer numbers surrounding ski resorts and should therefore be considered. For future reference, there are several ways in which studies could improve and complement this analysis. A first step would be obtaining more representative skier data, such as daily skier visits. Additional information on personal identifiers of the customers would further be useful in improving modelling decision-making, expected snow conditions and other influential factors with, for example, agent based modelling approaches. Agent based modelling can be used to improve simulations of skier behavior given information on different agent groups (e.g., skier origin, age, income, etc.), for instance by accounting for varying climate change impacts due to spatial substitution effects as discussed by Pons et al. (2014). Further, season length analyses could be combined with the established demand behavior in relation to snow conditions, thus including within-seasonal impacts and external season length impacts to approximate full damages under future climate conditions. For this demand analysis, including shorter future ski seasons would result in larger losses around the season margins as the first and particularly last days of the season might not support skiing operations on a profitable level anymore. Further, coupling demand and supply side impacts would improve any previous season length analysis by accounting for within-seasonal impacts of climate change and, for most resort locations and climate scenarios, increase the projected losses. Finally, to account for possible substitution effects, rental levels at the season margins could be investigated as other spring activities such as hiking and mountain biking might buffer some climate induced losses due to shorter ski seasons by attracting a share of customers. 119 5.6 Limitations 5.6.1 Short-term Rental Data The short-term rental information from Airdna indicates the reactivity of renters surrounding ski resorts to weather and snow conditions and is used to approximate skier behavior. The data, however, comes with a list of limitations and the results of the analysis must therefore be put into perspective regarding missing information or further data issues. First, the data does not provide any information on the people purchasing ski tickets. Generally, it can be assumed that the primary purpose of booking rental apartments surrounding ski resorts during the winter season is for skiing activities, however, villages such as Whistler provide alternative activities and attractions for potential customers, some of which could then again be snow dependent. The analyzed data is hence only based on a fraction of the skiers at the respective resort, some of which might not have booked accommodation with skiing as their primary purpose. Second, additional apartments are continuously introduced into the datasets between 2015-2020. This does not necessarily correspond to a growing market of rental units but might simply be due to Airdna expanding their data scraping horizon or more rental apartments being listed on Airbnb, Vrbo, and additional rental services. The new apartments could differ from the initially listed rental units and hence affect the regression. Additionally, demand trends could have changed over time due to a growth in demand of rental apartments and hence lead to corresponding change in consumer profiles. Third, the data does not include information which could be used to characterize the customers and hence improve the demand model. Information such as customer origin – and hence distance from the ski resort – income, age, profession, and whether a customer is a season pass holder could be used to develop a more directed regression model and adjust observed market transactions for personal characteristics. Fourth, there have been some inconsistencies within the data which were addressed during the data 120 cleaning process such as negative booking lead times (assigned a booking lead time of 0), rental units being marked as “unavailable” without further explanation, and impossible price and booking values (excluded from the regression). Finally, the data spans a relatively short time period and therefore limits the strength of the demand model. The earliest scraped bookings only date back to 2015 at Big White and any data past February 2020 is excluded due to the potentially confounding impacts of the COVID-19 pandemic. Finally, the booking data does not provide any information on cancellations. The potentially relevant impact of adverse weather and snow conditions on cancellations can therefore not be approximated. It would be reasonable to assume that extremely warm winter conditions with frequent rainfall might lead to cancelled bookings, further lowering the probability of the respective rental unit being re-booked as it is available on the market for a shorter period of time. 5.6.2 Skier Demand This study yields changes in average rental probabilities as well as associated rental revenues for several climate scenarios, however, one might ask how representative are the results for the entire skier population or how do the results affect the ski resorts themselves? Due to limited information from the available data, it can be assumed that general reactivities to short-term weather conditions as well as total climate impacts on local revenues are substantially underestimated. Most importantly, only overnight stays are observed in this analysis, thus capturing a part of the skier population which might also not adequately represent the average skier. Apartment renters can be expected to react to short-term weather and snow conditions at a lesser degree than single day-trippers due to constraining cancellation policies, potentially longer travel approaches, bigger monetary commitments, and—associated with a longer approach—less information about local snow conditions. Further, only rental probabilities and revenues have been analyzed and approximated with this analysis. To approximate total impacts on the local economy, the 121 analysis would have to be complemented with lift ticket sales, rental equipment sales, expenses on food and beverages, and other local expenditures taken by the apartment renters and by day-trippers. The calculated changes in rental revenues therefore present a low-bound estimate of climate change impacts, likely underestimating total monetary impacts on local ski town economies manifold. The projections of future demand levels are based on the assumption of unchanging rental markets in terms of rental prices, number of apartments, associated amenities, and potential customers (ergo skier populations). These assumptions facilitate first estimates of local climate change impacts driven exclusively by changing snow conditions—however—they oversimplify future developments in the short-term rental markets. Over the data collection period (2015-2020), the number of available apartments saw a significant increase across the four ski resort locations. Over the same period, real per-person prices of rental apartments have remained steady or seen slight increases. While it is unclear which share of apartments were previously available for rent and later introduced into the datasets due to expanding of data scraping operations by Airdna, an increase in the supply of short-term housing appears evident, thus suggesting possible further increases in rental apartment supply. Generally, increasing supply levels would be expected to cause lower price levels, however, this could be offset by simultaneous increases in demand via higher income levels and additional customers or changes in product quality such as the type, size, or amenities of newer apartments. Any projections of future changes in rental markets are additionally complicated by the novelty and recent surges in popularity of the sharing economy—including Airbnb (Gibbs et al., 2023)—resulting in a relatively small pool of background data available to related studies. Finally, modelling of future rental demand is challenged by uncertainty regarding future skier populations. Projections of future demand levels will be impacted by assumptions regarding changes in skier demographics as well as potential 122 decreases in competition of skiing operations internationally under a warming climate (Gonseth & Vielle, 2019). For example, a transition of demographics away from local skiers towards international customers could mean a reduction of future climate change impacts as the customer base might become less sensitive to changes in weather and snow conditions. Alternatively, an increase in general demand levels matching the increase in rental supply without changes in skier demographics could lead to the same impacts as previously estimated (in terms of relative impacts on demand levels), however, at a higher total level (in terms of real CAD). In conclusion, the discussion shows that—while not considered in this research—future changes in supply and prices of rental apartments as well as changes in skier populations have potential to affect the demand for rental apartments and therefore the impact of changing climate conditions on the ski and short-term housing industry. With the adopted rental demand model, it is assumed that a sufficient snow base is available throughout the entire ski season thanks to artificial snowmaking and that skiers react equally to changing environmental conditions throughout the entire ski season. This might not be adequate as marginal snow conditions in the early and late season force ski resort operators to limit access to only part of the mountain. Including appropriate indicators for snow bases below a certain threshold and available skier terrain into the analysis may be problematic, however, since they would depend on currently unavailable information of total snow depth as well as on resort operator decision-making which might also be influenced by land leases, access, and additional schedules during the spring season. Finally, a bigger sample of resorts within BC would provide a better picture on regional trends. Simultaneously, this would require the inclusion of spatial substitution between ski resorts via a measure of snow conditions at nearby resorts in order to explain local customer streams, similar as shown in Parthum & Christensen (2022). 123 5.6.3 Regression Model The analysis approximates the probability of each rental apartment being booked on a specific day, given information on weather, snow, and additional drivers of demand. The results of the regression model are based on the assumption, that the observations are independent, hence a specific apartment being booked is independent of the same apartment being booked on any other day. It could be argued that the individual observations are not independent in the case of bookings covering multiple consecutive days. In this case, the probability of individual bookings would have to be approximated given average weather, snow, and additional information over several days. Such an analysis might be especially complicated, given that there is no information on whether the initially planned stay was extended or perhaps cut short due to changing conditions or plans. Additionally, criteria influencing decision-making towards length of stay would have to be included. Further, the regression model does not include several drivers of demand—some of which have been used and tested in similar studies such as customer origin, income, stated preferences, and group size—due to missing client information or other data limitations. Based on availability of information and under a longer data range, factors such as business cycles, access to the ski resort, advertising, and gas prices could be tested against changes in demand for skiing. Gas prices, for example, experienced significant changes during the winter seasons between 2015-2020 as prices fluctuated between 105.9 CAD in February 2016 and 157.95 CAD in April 2019 (Vancouver monthly gasoline prices adjusted to 2016 CAD, Statistics Canada, n.d.(b)). This information, however, could only reasonably be included into the regression model with information regarding the approach (vehicle and distance) of customers to the ski resort. Several other drivers of demand and model versions were tested during the model building process and not included as they impacted the model performance negatively or 124 complicated analysis and interpretations without providing substantial improvements to model performance. The additionally tested variables include, for example, a indicator for apartments with a hot tub and several interaction effects between apartment prices and snowfall. The hot tub indicator was to be used as a distinguishing factor for apartment quality and tested with price and temperature interaction effects. Results did not show significant model improvements (in terms of adjusted R², AIC, and BIC statistics) or consistent results for the hot tub factor as a driver of demand. The interaction effects between snowfall and per person costs were introduced to investigate whether the reaction to high snowfall events differed between bookings at low and high priced apartments. This, however, lead to complicated inferences depending on the initial percentage of booked apartments and their respective per-person prices without definitive model improvements in terms of adjusted R², AIC, and BIC statistics. Different iterations of the adopted model have been tested and compared to the selected model version as well. This includes different bins for expected snowfall values and different snow weighting functions. The respective benchmarks for model performance for the selected and several alternative model iterations can be found in Appendix D. Finally, the model approximates the decision-making process of potential skiers due to limited information and constraints related to overly complicated modelling approaches. In reality, rainfall and unusually warm conditions might not deter customers who are looking for a day of skiing in soft and wet snow-slush during the spring ski season. Similarly, heavy snowfall events coinciding with or followed by strong winds would turn skiing conditions highly unpleasant and potentially dangerous. In order to include such detailed effects, interactions would first have to be investigated with the help of insightful data such as daily skier counts. 125 5.6.4 Modelling Climate Scenarios Analyses based on climate modelling underlay several sources of uncertainties, classified in previous discussions as inexactness, unreliability, and ignorance (Funtowicz & Ravetz, 1991; Müller, 2010). This ranges from the data used, model developments, predictability of climate in general, future climate scenarios, to finally human error in modelling climates and evaluating results. While some sources of uncertainty can be mitigated, Knutti & Sedlácek (2013) show that modern and more complex modelling approaches only provide limited reductions of uncertainty when compared to their predecessor versions. This means that the climate models used in this analysis are limited by several sources of uncertainty in their capacity to represent natural processes. Modelled future climate conditions consequently represent – in absence of a most likely future state of the climate – a best guess of future climate conditions surrounding the selected ski resorts. Further, the research approach via MarkSimGCM is subject to several limitations: first, the developers of the Weather Generator state that MarkSimGCM tends to overestimate the variance of rainfall in the Köppen climate group D (Jones & Thornton, 2013) which includes most of British Columbia (Plantmaps, 2022). This issue of rainfall variances is addressed by using repeated climate simulations with the average model representing an approximation of the true expected precipitation levels. Second, the impact of the ENSO cannot be included in the climate projections as the natural oscillation in the Pacific Oceans can lead to extreme deviations from the average rainfall amounts and patterns (Jones & Thornton, 2013). Third, the MarkSim Weather Generator used to project future conditions produces values which naturally deviate from the data used from Snow-forecast to establish the demand model. This is inherently caused by MarkSim randomly calculating daily temperature and precipitation levels under average expected conditions, whereas the recorded data from Snow-forecast – in the best case – represents just one random model run of the 126 2015 baseline climate projections which is equally likely as any other model run. The precipitation data provided from Snow-forecast and MarkSim can hence be expected to differ significantly. This could ultimately be due to differences in included variables, downscaling methods, used circulation models between the Weather Generator and Snow-forecast, and due to inherently random properties of the weather system. Since there is no weather and climate service available for mountain regions which could simulate past, current, and future conditions with reliable output on a daily resolution, it was necessary to combine different models and methods for the historic analysis and future simulations. To compensate for potential differences between MarkSim and Snow-forecast data, MarkSim projections of current and future conditions are used to simulate demand levels. While the demand model is based on rental data from Airdna and weather information from Snow-Forecast, future demand levels are compared to the simulated current demand levels under average 2015 winter conditions using MarkSim GCM. Finally, there is general uncertainty and no obvious consensus when modelling the physical form of precipitation as snow or as rain, specifically between 0°C and 2°C (Newton et al., 2019). Assumptions are made to define the physical form of precipitation in this grey zone with all precipitation occurring at above 0°C modelled as rainfall. 127 6. Conclusion This study has investigated the impacts of short-term weather and snow conditions on short-term rental apartments surrounding four resorts in BC, projected future skiing conditions under the climate scenarios RCP4.5 and RCP8.5, and estimated climate change impacts on future rental activity levels and revenues via changing demand levels. A snow expectation model was built, approximating customer expectations of to-be-encountered snow conditions using forecasted, present, and past snowfall levels at the time of booking. Rental probabilities surrounding all four resorts have subsequently been shown to be significantly influenced by expected snowfall, rainfall, and temperatures. Snowfall tends to increase the demand for rental apartments at an increasing magnitude for higher snow events with up to 50% increases in total rental probability observed for extreme snow events. Rainfall has been shown to decrease the demand with the strongest effect observed at Whistler at -14.5% total rental probability for days impacted by rainfall within the previous 72h. Projections of future snow and weather conditions in British Columbia show minor to substantial changes in future weather patterns depending on the climate scenario and ski resort location. Since 2015, daily winter temperatures could warm between a minimum of 1°C (under RCP4.5 2055) up to 4.5°C (under RCP8.5 2085). Snowfall levels are expected to generally increase at the colder resorts in the Thompson Okanagan Region (Big White and Sun Peaks), while remaining similar (Fernie) or decreasing (Whistler) at the warmer resorts in the Kootenays and Coastal Mountains. Mid-century projections and snowfall levels under RCP4.5 tend to be higher than projections for the late century and RCP8.5 respectively. All ski resorts show substantial increases in the amount and frequency of winter rainfall under any climate projection with rain events possibly tripling in frequency under RCP8.5 2085 compared to 2015. Variations between the intermediate (RCP4.5) and high-emission 128 (RCP8.5) scenario remain slight by 2055, however, could lead to vastly different precipitation levels by 2085. Projected rental probabilities show lower activity levels under future climate conditions for Big White, Fernie, and Whistler, while the benefits of higher snowfall could outweigh the losses under increasing rainfall levels at Sun Peaks by the mid-century. RCP4.5 generally results in smaller climate-induced impacts than RCP8.5, which is particularly apparent in the late century projections. With small impacts at Fernie and Sun Peaks, intermediate losses at Big White up to 3.94% total rental probabilities and considerable losses in activity levels at Whistler up to 7.49%, climate impacts vary across the province and show that the future of the individual ski resorts might be determined by their location. By analyzing changes in rental apartment probabilities, a lower bound approximation of climate-induced damages is provided for the ski resorts at Big White, Fernie, Sun Peaks, and Whistler. This study delivered a first assessment of short-term demand reactivities to snow conditions in BC, an estimate of within-seasonal climate change impacts, and a tool for future impact assessments for the local ski industry. The provided information suggests that limiting greenhouse gas emissions may sustain the ski industry in BC and limit the economic impacts of global warming. 129 Bibliography Abegg, B. (1996). Klimaänderung und Tourismus. 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What Is the Predictability Limit of Midlatitude Weather? Journal of the Atmospheric Sciences, 76(4), 1077–1091. https://doi.org/10.1175/JAS-D-18-0269.1 145 Appendices A. Geofenced ski resort apartments Figure 49: Rental apartments surrounding Big White Ski Resort, BC. Retrieved from airdna.co, map data ©2023 Google. 146 Figure 50: Rental apartments in Fernie, BC. Retrieved from airdna.co, map data ©2023 Google. Figure 51: Rental apartments surrounding Sun Peaks Mountain Resort, BC. Retrieved from airdna.co, map data ©2023 Google. 147 Figure 52: Rental apartments in Whistler, BC. Retrieved from airdna.co, map data ©2023 Google. Figure 49 - Figure 52 show the geofenced area surrounding the selected ski resorts and approximate location of individual rental apartments included in the data sets – indicated by purple squares. Rental units at Big White and Sun Peaks are found in immediate proximity to the sky hill and mostly clustered in villages among the bottom of the ski lifts. The rental units at Fernie are spread out over the village of Fernie, therefore removed from the ski resort itself and not including on-mountain accommodation. The geofenced zone at Whistler includes a large area around the two ski hills and around the Sea-to-Ski Highway. The majority of apartments, however, is still concentrated around the bottom of the ski lifts. 148 B. Regression Coefficients Table 10: Logistic regression coefficients, standard errors, and significance levels for each ski resort. Big White Fernie Sun Peaks Whistlerx β s.e. p.value sig β s.e. p.value sig β s.e. p.value sig β s.e. p.value sig p>0.05intercept -0.125 0.034 <0.001 *** 0.194 0.037 <0.001 *** 0.384 0.030 <0.001 *** 0.094 0.006 <0.001 *** p<0.05 +rain -0.208 0.052 <0.001 *** -0.369 0.037 <0.001 *** -0.227 0.045 <0.001 *** -0.622 0.007 <0.001 *** p<0.01 *rain x temp 0.144 0.015 <0.001 *** 0.072 0.017 <0.001 *** -0.055 0.019 0.002 * -0.056 0.003 <0.001 *** p<0.005 **rain x temp² 0.014 0.006 0.010 ** 0.017 0.002 <0.001 *** -0.005 0.008 0.258 0.015 0.000 <0.001 *** p<0.001 ***temp -0.105 0.005 <0.001 *** -0.074 0.005 <0.001 *** 0.046 0.005 <0.001 *** -0.103 0.001 <0.001 ***temp² -0.004 0.000 <0.001 *** -0.003 0.000 <0.001 *** 0.005 0.000 <0.001 *** -0.005 0.000 <0.001 ***snow: 10-20 0.479 0.038 <0.001 *** 0.401 0.044 <0.001 *** 0.762 0.041 <0.001 *** 0.028 0.008 <0.001 ***snow: 10-20 x temp 0.104 0.009 <0.001 *** 0.081 0.011 <0.001 *** 0.004 0.009 0.327 0.122 0.002 <0.001 ***snow: 10-20 x temp² 0.004 0.000 <0.001 *** 0.004 0.001 <0.001 *** -0.004 0.000 <0.001 *** 0.007 0.000 <0.001 ***snow: 20-30 1.727 0.084 <0.001 *** 1.976 0.138 <0.001 *** 1.145 0.069 <0.001 *** 0.214 0.010 <0.001 ***snow: 20-30 x temp 0.467 0.022 <0.001 *** 0.205 0.028 <0.001 *** -0.067 0.015 <0.001 *** 0.139 0.003 <0.001 ***snow: 20-30 x temp² 0.026 0.001 <0.001 *** 0.004 0.001 <0.001 *** -0.008 0.001 <0.001 *** 0.013 0.000 <0.001 ***snow: 30-40 3.327 0.166 <0.001 *** 3.935 0.477 <0.001 *** -0.172 0.096 0.0368 + 0.392 0.012 <0.001 ***snow: 30-40 x temp 0.769 0.043 <0.001 *** 1.019 0.131 <0.001 *** -0.245 0.024 <0.001 *** 0.162 0.003 <0.001 ***snow: 30-40 x temp² 0.040 0.003 <0.001 *** 0.049 0.008 <0.001 *** -0.013 0.001 <0.001 *** 0.008 0.000 <0.001 ***snow: 40-50 5.296 0.472 <0.001 *** 23.025 3.313 <0.001 *** 3.221 0.409 <0.001 *** 0.435 0.014 <0.001 ***snow: 40-50 x temp 0.913 0.088 <0.001 *** 3.860 0.574 <0.001 *** 1.041 0.132 <0.001 *** 0.211 0.004 <0.001 ***snow: 40-50 x temp² 0.038 0.004 <0.001 *** 0.132 0.022 <0.001 *** 0.066 0.010 <0.001 *** 0.012 0.000 <0.001 ***snow: 50+ 26.391 2.149 <0.001 *** 5.913 1.093 <0.001 *** 36.504 8.517 <0.001 *** 0.399 0.010 <0.001 ***snow: 50+ x temp 7.069 0.575 <0.001 *** 0.780 0.222 <0.001 *** 10.652 2.534 <0.001 *** 0.286 0.004 <0.001 ***snow: 50+ x temp² 0.433 0.037 <0.001 *** 0.022 0.010 0.0144 + 0.758 0.186 <0.001 *** 0.031 0.000 <0.001 ***small app 1.500 0.169 <0.001 *** -0.597 0.042 <0.001 *** -0.143 0.128 0.132 0.106 0.014 <0.001 ***small app x pp_price -0.014 0.003 <0.001 *** 0.019 0.001 <0.001 *** -0.008 0.001 <0.001 *** 0.000 0.000 0.492large app -0.783 0.033 <0.001 *** -0.491 0.050 <0.001 *** 0.036 0.033 0.141 -0.262 0.009 <0.001 ***large app x pp_price 0.010 0.001 <0.001 *** 0.006 0.001 <0.001 *** -0.002 0.000 <0.001 *** 0.000 0.000 0.288pp_price -0.013 0.000 <0.001 *** -0.021 0.001 <0.001 *** -0.003 0.000 <0.001 *** -0.003 0.000 <0.001 ***weekend 0.597 0.015 <0.001 *** 0.683 0.019 <0.001 *** 0.411 0.018 <0.001 *** 0.601 0.005 <0.001 ***christmas 1.030 0.059 <0.001 *** 0.959 0.073 <0.001 *** 2.171 0.089 <0.001 *** 1.037 0.017 <0.001 ***christmas x pp_price 0.003 0.001 <0.001 *** 0.006 0.001 <0.001 *** -0.012 0.001 <0.001 *** 0.001 0.000 <0.001 ***spring break 0.896 0.025 <0.001 *** 0.208 0.032 <0.001 *** 0.278 0.030 <0.001 *** 0.316 0.008 <0.001 ***family day BC 0.860 0.138 <0.001 *** 0.959 0.132 <0.001 *** 1.184 0.186 <0.001 *** 0.819 0.039 <0.001 ***family day BC x pp_price -0.001 0.002 0.266 -0.004 0.002 0.0414 + -0.001 0.002 0.254 -0.001 0.000 <0.001 ***family day AB 0.914 0.157 <0.001 *** 1.280 0.150 <0.001 *** 1.144 0.210 <0.001 *** 0.914 0.043 <0.001 ***family day AB x pp_price 0.000 0.002 0.441 -0.002 0.002 0.222 -0.004 0.002 0.022 + 0.000 0.000 0.333m.l.king day US 0.587 0.045 <0.001 *** 0.050 0.056 0.186 0.948 0.058 <0.001 *** 1.016 0.018 <0.001 ***event 0.077 0.021 <0.001 *** 0.741 0.047 <0.001 ***149 Table 11: Logistic regression coefficients, standard errors, and significance levels for each ski resort in a two-stage estimation using instrumental variables. 150 Table 12: First-stage regression coefficients of the instrumental variable model. The regression approximates the per-person price at Big White, Fernie, Sun Peaks, and Whistler Table 10 shows the individual regression coefficients (β), standard errors, p-values, and significance levels for each of the four ski resorts. Interaction effects are indicated via 𝑟𝑎𝑖𝑛 𝑥 𝑡𝑒𝑚𝑝 for the interaction effects between the rainfall dummy and the temperature variable. Table 11 shows the individual regression coefficients (β), standard errors, p-values, and significance levels for each of the four ski resorts using from the two-stage regression analysis using instrumental variables. R marks the control function to the regressors in the second stage of the model (Sjolander & Martinussen, 2018). Table 12 shows the instrumental variable coefficients from the first-stage regression. 151 C. Marginal Effects and Varying Snow Bins Table 13: Marginal impacts of demand drivers on rental probability levels under average conditions. Impacts are divided into environmental, calendar, and property-specific effects with upper and lower 95% confidence intervals displayed. Reference Scenario Estimate Lower Upper sig Estimate Lower Upper sig Estimate Lower Upper sig Estimate Lower Upper sigReference Values Rental Probability 0.4 0.36 0.54 0.47Expected Snow (cm) [0-10) [0-10) [0-10) [0-10)Temp_ave (°C) -6.64 -6.49 -6.2 -2.6Temp_sq (°C) 65.08 72.38 66.44 42.02ppprice 56.59 48.56 74.64 86.07Apartment size Medium Medium Medium MediumMarginal EffectsEnvironmental Rain -0.0592 -0.218 0.100 0.0933 0.045 0.142 *** -0.0510 -0.303 0.201 0.0356 0.027 0.045 ***Effects Rain at 0°C* -0.0423 -0.063 -0.022 *** -0.0781 -0.093 -0.063 *** -0.0491 -0.068 -0.030 *** -0.1450 -0.148 -0.142 ***Expected Snow [10-20) 0.0172 0.010 0.024 *** 0.0358 0.026 0.046 *** 0.1087 0.100 0.117 *** -0.0019 -0.005 0.001Expected Snow [20-30) 0.0670 0.055 0.079 *** 0.2170 0.194 0.240 *** 0.2046 0.192 0.217 *** 0.0956 0.092 0.100 ***Expected Snow [30-40) 0.1947 0.173 0.216 *** 0.2049 0.130 0.280 *** 0.1121 0.094 0.130 *** 0.0722 0.068 0.077 ***Expected Snow [40-50) 0.3462 0.303 0.390 *** 0.5634 0.556 0.571 *** 0.2279 0.163 0.293 *** 0.0934 0.088 0.099 ***Expected Snow 50+ 0.5319 0.524 0.540 *** 0.4505 0.376 0.525 *** 0.4159 0.399 0.433 *** 0.2083 0.204 0.213 ***Temp_ave -0.0015 -0.003 0.000 -0.0072 -0.009 -0.005 *** 0.0077 0.006 0.010 *** -0.0030 -0.003 -0.003 ***Temp_sq 0.0003 0.000 0.001 *** 0.0001 0.000 0.000 0.0006 0.000 0.001 *** 0.0013 0.001 0.001 ***Calendar Effects Weekend 0.1359 0.129 0.143 *** 0.1555 0.147 0.164 *** 0.0887 0.081 0.096 *** 0.1347 0.133 0.137 ***Christmas 0.2730 0.260 0.286 *** 0.2811 0.265 0.297 *** 0.2685 0.250 0.288 *** 0.2436 0.239 0.249 ***Spring_break 0.2038 0.193 0.215 *** 0.0474 0.033 0.062 *** 0.0601 0.047 0.073 *** 0.0708 0.067 0.075 ***Family_Day_BC 0.1795 0.154 0.205 *** 0.1806 0.148 0.213 *** 0.2350 0.201 0.269 *** 0.1610 0.152 0.171 ***Family_Day_AB 0.2035 0.174 0.233 *** 0.2737 0.237 0.311 *** 0.1769 0.138 0.216 *** 0.2019 0.191 0.213 ***King_Day 0.1336 0.114 0.154 *** 0.0114 -0.014 0.036 0.2047 0.180 0.229 *** 0.2278 0.220 0.236 ***Event 0.0176 0.008 0.027 *** 0.1600 0.140 0.180 ***Property-Specific ppprice (CAD/night) -0.0015 -0.002 -0.001 *** -0.0032 -0.003 -0.003 *** -0.0011 -0.001 -0.001 *** -0.0007 -0.001 -0.001 ***Effects Small Apartment 0.1497 0.131 0.168 *** 0.0699 0.060 0.080 *** -0.1615 -0.187 -0.136 *** 0.0234 0.020 0.027 ***Large Apartment -0.0542 -0.061 -0.048 *** -0.0507 -0.060 -0.042 *** -0.0195 -0.026 -0.013 *** -0.0588 -0.061 -0.057 ***Fernie Sun Peaks Whistler95 % CI 95 % CI 95 % CI 95 % CIBig White152 Table 14: Marginal impacts of rain events and varying snowfall levels under the standard model, a model excluding bookings with negative lead time (Alternative I), a model exclusively considering encountered snow conditions (Alternative II), a model with 15 cm snowfall bins, and a model with 5 cm snowfall bins. Removing observation with negative booking lead times from the data frame (Alternative I) results in almost identical marginal snow effects compared to the standard sig. Alternative I' sig. Alternative II'' sig. sig. sig.Rainfall (0°C) -0.042 *** -0.042 *** -0.052 *** Rainfall (0°C) -0.039 *** Rainfall (0°C) -0.035 **10-20 0.017 *** 0.027 *** -0.004 15-30 0.037 *** 5-10 0.021 ***20-30 0.067 *** 0.081 *** 0.002 30-45 0.194 *** 10-15 0.026 ***30-40 0.195 *** 0.229 *** 0.022 45-60 0.529 *** 15-20 0.029 ***40-50 0.346 *** 0.377 *** -0.184 ** 60+ 20-25 0.083 ***50+ 0.532 *** 0.563 *** 0.496 *** 25-30 0.082 ***30-35 0.409 ***' Without negative booking lead time 35-40 0.034 +'' Only 72h snowfall 40-45 0.365 ***45+ 0.539 ***sig. Alternative I' sig. Alternative II'' sig. sig. sig.Rainfall (0°C) -0.078 *** -0.081 *** -0.09 *** Rainfall (0°C) -0.072 *** Rainfall (0°C) -0.069 ***10-20 0.036 *** 0.035 *** 0.041 *** 15-30 0.155 *** 5-10 -0.015 **20-30 0.217 *** 0.221 *** 0.084 *** 30-45 0.186 *** 10-15 -0.00130-40 0.2049 *** 0.209 *** 0.083 45-60 0.517 *** 15-20 0.120 ***40-50 0.5634 *** 0.572 *** 0.337 60+ 0.396 *** 20-25 0.192 ***50+ 0.4505 *** 0.454 *** 0.423 *** 25-30 0.236 ***30-35 0.197 ***' Without negative booking lead time 35-40''''' Only 72h snowfall 40-45 0.556 ***''' insufficient observations 45+ 0.411 ***sig. Alternative I' sig. Alternative II'' sig. sig. sig.Rainfall (0°C) -0.049 *** -0.070 *** -0.052 *** Rainfall (0°C) -0.051 *** Rainfall (0°C) -0.049 ***10-20 0.109 *** 0.119 *** 0.083 *** 15-30 0.173 *** 5-10 0.124 ***20-30 0.205 *** 0.240 *** 0.024 ** 30-45 0.102 *** 10-15 0.113 ***30-40 0.112 *** 0.139 *** 0.025 + 45-60 0.408 *** 15-20 0.222 ***40-50 0.228 *** 0.262 *** 0.221 *** 60+ 20-25 0.129 ***50+ 0.416 *** 0.460 *** -0.595 *** 25-30 0.344 ***30-35 0.178 ***' Without negative booking lead time 35-40 0.139 ***'' Only 72h snowfall 40-45 0.208 ***45+ 0.471 ***sig. Alternative I' sig. Alternative II'' sig. sig. sig.Rainfall (0°C) -0.145 *** -0.143 *** -0.1117 *** Rainfall (0°C) -0.137 *** Rainfall (0°C) -0.152 ***10-20 -0.0019 -0.003 + -0.037 *** 15-30 0.077 *** 5-10 -0.008 ***20-30 0.0956 *** 0.101 *** -0.039 *** 30-45 0.094 *** 10-15 -0.049 ***30-40 0.0722 *** 0.077 *** 0.037 *** 45-60 0.248 *** 15-20 0.024 ***40-50 0.0934 *** 0.092 *** 0.074 *** 60+ 0.257 *** 20-25 0.107 ***50+ 0.2083 *** 0.216 *** 0.058 *** 25-30 0.080 ***30-35 0.092 ***' Without negative booking lead time 35-40 0.048 ***'' Only 72h snowfall 40-45 0.078 ***45+ 0.203 ***p>0.05p<0.1 +p<0.05 *p<0.01 **p<0.001 ***Standard Model 15cm Snowfall Bins 5cm Snowfall BinsBig WhiteStandard Model 15cm Snowfall Bins 5cm Snowfall BinsStandard Model 15cm Snowfall Bins 5cm Snowfall BinsFernieSun PeaksStandard Model 15cm Snowfall Bins 5cm Snowfall BinsWhistler153 model as shown in Table 13. Slight differences to the standard model are observed as Alternative I results in marginally stronger positive effects for snowfall and stronger negative effects for rainfall. Additionally, our confidence in a couple of variables increased slightly due to a smaller p-value of the estimate. Alternative II omits the effect of expected snow conditions in favor of encountered snow conditions or, alternatively, assumes perfect information of expected snow conditions for customers. Variations in rental probability are explained exclusively by encountered snow conditions without accounting for past snow conditions via weighting past and present snow conditions as in the standard model. Increasing positive effects of higher snowfall levels on demand can conditionally be observed at Fernie (partially insignificant results), Sun Peaks (excluding extreme snowfall events which suggest the opposite effect), and Whistler. Results at Big White are mostly inconclusive and only partially significant. Impacts of rainfall on rental demand, however, are found to be significant and at similar levels to the standard model. Finally, alternative snowfall binning in 5 cm and 15 cm bins respectively shows that the results of the standard model are consistent and not dependent on arbitrarily chosen snowfall bins as seen in Figure 53 – Figure 56. The marginal snowfall effects and associated cubic regression lines of best fit barely differ between snowfall bin size at Big White, revealing a seemingly exponential increase of impact under higher snowfall levels. At Fernie, the marginal impacts of snowfall appear to level off under the highest snowfall levels. Sun Peaks shows more disparity between smoothed regression lines than the other resorts, hinting at a stepwise increase of marginal snow effect from no snowfall to some snowfall to heavy snowfall. Marginal effects of snowfall at Whistler are similar between the different snow bins, showing a seemingly linear increase in impact under higher snowfall levels. 154 Figure 53: Marginal effects of snowfall under 5 cm, 10 cm, and 15 cm bins at Big White. Figure 54: Marginal effects of snowfall under 5 cm, 10 cm, and 15 cm bins at Fernie. 155 Figure 55: Marginal effects of snowfall under 5 cm, 10 cm, and 15 cm bins at Sun Peaks. Figure 56: Marginal effects of snowfall under 5 cm, 10 cm, and 15 cm bins at Whistler. 156 D. Model Performance Comparison Table 15: Model evaluation metrics are displayed for the standard model, a model with 15 cm snowfall bins, a model with testing a linear variable of snowfall, a model exclusively considering encountered snow conditions (Alternative I), and a model with a rigid cutoff time of expected snow conditions at a booking lead time of 14 days. Comparisons of different model versions in Table 15 show that the standard model generally performs best, however, alternative approaches of binning or modelling expected snowfall are not substantially worse at explaining variation in apartment bookings. Standard Model 15cm Binned Snowfall Linear Snowfall Alternative I'AIC 130547.1 130401.5 132562.3 132445BIC 130890 130687.3 132771.9 132787.9Log Likelihood -65237.54 -65170.76 -66259.14 -66186.47R² 0.0697 0.0707 0.0552 0.0562R² adj 0.0692 0.0702 0.0548 0.0557Standard Model 15cm Binned Snowfall Linear Snowfall Alternative I'AIC 77214.37 77341.54 78339.08 78252.59BIC 77529.73 77602.84 78528.3 78567.95Log Likelihood -38572.18 -38641.77 -39148.54 -39091.29R² 0.0788 0.0771 0.065 0.0664R² adj 0.078 0.0765 0.0645 0.0656Standard Model 15cm Binned Snowfall Linear Snowfall Alternative I'AIC 97565.35 98025.15 98917.31 99274.71BIC 97899.32 98303.46 99121.41 99608.68Log Likelihood -48746.67 -48982.58 -49436.66 -49601.36R² 0.0687 0.0642 0.0556 0.0524R² adj 0.0681 0.0637 0.0551 0.0517Standard Model 15cm Binned Snowfall Linear Snowfall Alternative I'AIC 1168858 1173550 1194458 1193314BIC 1169269 1173890 1194705 1193725Log Likelihood -584394.1 -586745.8 -597208.1 -596622R² 0.0907 0.087 0.0707 0.0716R² adj 0.0906 0.087 0.0707 0.0716' Only 72h snowfallBig WhiteFernieSun PeaksWhistler157 E. Rental Probabilities Table 16: Mean rental probabilities and associated standard deviations per ski resort and per climate scenario. Table 16 shows that despite generally lower rental probabilities under scenarios with higher emission levels and later in the century, the spread of probabilities between the 30 respective simulations tends to increase as indicated by the standard deviation values. Resort Scenario Mean Rental Probability Standard DeviationBig White RCP4.5 2015 0.4943 0.0816RCP4.5 2055 0.4877 0.0974RCP8.5 2055 0.4707 0.0989RCP4.5 2085 0.4777 0.0993RCP8.5 2085 0.4549 0.1044Fernie RCP4.5 2015 0.4518 0.1062RCP4.5 2055 0.4495 0.1166RCP8.5 2055 0.4500 0.1184RCP4.5 2085 0.4454 0.1171RCP8.5 2085 0.4321 0.1267Sun Peaks RCP4.5 2015 0.6177 0.0615RCP4.5 2055 0.6192 0.0702RCP8.5 2055 0.6188 0.0709RCP4.5 2085 0.6180 0.0743RCP8.5 2085 0.6110 0.0699Whistler RCP4.5 2015 0.5170 0.1155RCP4.5 2055 0.4909 0.1204RCP8.5 2055 0.4770 0.1228RCP4.5 2085 0.4764 0.1257RCP8.5 2085 0.4421 0.1264
Thesis/Dissertation
eng
Earth and Environmental Sciences
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Pidwirny, Michael J., 1958-
Janmaat, John
Snow conditions and the demand for skier accommodation in British Columbia : a comparison of climate change impacts under RCP4.5 and RCP8.5 emission scenarios
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