Snow reliability and climate elasticity: case of a Slovak ski resort

Abstract

Purpose – The aim of this paper is to present the relationship between climate and tourism development data as an example of an emerging winter and ski tourism destination in Slovakia. Design/methodology/approach – The method aims to discover the relationship through snow-reliability and regression analyses and to further implicate the consequences of such established relationship under a changing (warming) climate. Findings – As a result of the research, the authors can predict that a 1 per cent fall in snow depth and visibility would erode the ski demand by 1.2 and 0.12 per cent, respectively, a 1°C rise of the mean temperature, on the other hand, would indicate a 6 per cent loss of skipass sales. The latter finding translates into a further 6.6 to 19.2 per cent loss of sales on account of the anticipated temperature increases for the twenty-first century. The capacity of the resort for the utmost adaptation strategy, snowmaking, is also to deteriorate with the daytime/fulltime annual good quality production range to reduce from 33/45 days to 10-26/14-34 days, according to the emissions-related warming scenarios and in terms of the commonly available current technology. Practical implications – The results of the study can help the management of ski resorts to adopt strategies for the future development by taking into account the predicted climatic changes. Originality/value – This study is the first type of study performed in Slovakia and can contribute to the better understanding of the relationship between climate change and the performance of the ski tourism resorts. It also delivers innovation by considering wet-bulb temperature in snow-reliability analyses and also by coining the “climate elasticity” concept.

Microclimatic Volatility and Elasticity of Glacier Skiing Demand

“…In Central Europe, Damm et al [26] found no significant relationship between the snow index and overnight stays in Czechia but a positive relationship with a magnitude of 3-6% in Slovakia, whereas Demiroglu et al [32] also revealed significant relationships between daily snow depth, visibility, and temperature and skier visits to a particular Slovak ski area, with the snow depth elasticity as high as 1.2, implying that the demand is positively and highly sensitive to changes in snow cover. Temperature, on the other hand, was a negative regressor, as lower temperatures implied a higher snow depth.…”

“…The relationship of weather and climate with ski area/resort/destination performance had been reported in a total of 22 papers at the time of the preparation of this manuscript. These studies provide us with statistical and econometric models for France [19], Switzerland [20], Austria [21][22][23][24], Italy [25], Germany, and Slovenia in the Alps [26], Sweden [27,28], Norway [29], and Finland [30,31] in Nordic Europe [26], Czechia, Slovakia [32], and Romania [33,34] in Eastern Europe [26], Spain in Western Europe [26], the USA [13,[35][36][37], Australia [6], and Japan [38].…”

“…As evident in the state of the art research on climate and skiing relationship, most models, so far [6,13,[19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38], have utilized indicators based on weather and climate variables such as snow depth, snow water equivalent, snowfall, snow density, avalanche index, temperature, wind chill, visibility, and cloudiness as the main regressors, whereas other variables such as precipitation (rain, freezing rain, etc.) and wind speed have also been mentioned as important in consumer surveys with winter [49] and summer [50] skiers.…”

“…Additionally, many studies, especially those based on long-term aggregate data [13,19,21,[24][25][26][27][28]31,37], have attempted to control for changes in economic variables such as income, consumer prices, gas and airfare prices, exchange rates, unemployment rates, and changes in ski tourism supply in question. Regarding the calendar effects, especially in models with daily data [13,26,27,31,32,36], dummy variables have been inserted to identify peak times from weekends, holidays such as Christmas, New Year, and Easter, and school breaks at the market origins. As for the dependent variables that represent the ski area/resort/destination performance, lift transports [24], skier visits [6,19,20,32,[36][37][38], lift ticket revenues [27,28,31], closures [23], and foreign and domestic accommodation arrivals, overnight stays, and occupancy rates [21,22,25,26,31,33,34] have been the main indicators.…”

“…Regarding the calendar effects, especially in models with daily data [13,26,27,31,32,36], dummy variables have been inserted to identify peak times from weekends, holidays such as Christmas, New Year, and Easter, and school breaks at the market origins. As for the dependent variables that represent the ski area/resort/destination performance, lift transports [24], skier visits [6,19,20,32,[36][37][38], lift ticket revenues [27,28,31], closures [23], and foreign and domestic accommodation arrivals, overnight stays, and occupancy rates [21,22,25,26,31,33,34] have been the main indicators. Most studies have utilized regression models in estimating the effects based on these variables, and more recently autoregressive models have been in use [19,[24][25][26]28,31,36] to analyze the possible impacts of past dynamics of observations on current values of the data.…”