Artificial Snowmaking: Winter Sports Between State-Owned Company Policy and Tourist Demand

Abstract

Due to changing climatic conditions, artificial snowmaking has become a major method of ski resort adaptation globally. It is a financially intensive operation requiring high start-up investment and involving operating costs that are dependent on weather conditions. Operational costs and the expansion of artificial snowmaking systems increase the price of ski passes. In our work, we analyzed the operations of a public company that operates the largest ski resorts in Serbia and directs the flow of winter sports tourism. We tried to determine, by means of correlation, the extent to which ski pass prices in the biggest winter resort in Serbia are influenced by factors such as natural snow cover, number of tourist overnights, ski run length, and local wages. The results of the survey indicate an increasing transformation of the ski resort into a thematic attraction independent of climatic factors in terms of determining ski pass prices.

Keywords

artificial snowmaking correlation ski pass price public policy Kopaonik

Introduction

The impact of climate change on natural snow conditions, resulting in the reduction of the length of the winter season, has a direct economic impact on resorts relying on winter tourism activities. Artificial snowmaking is seen by many authors (Falk & Vanat, 2016; Rixen et al., 2011; Rutty et al., 2017) as the key adaptation strategy in response to climate change. Research has followed various directions since ski resorts have had to begin dealing with the effects of climate change. The connection between ski tourism and climate change was first recognized in the mid-1980s (Steiger et al., 2017). Since Witmar (1986) proposed and defined the “100-day rule” (a minimum 30-cm depth of snow cover with a duration of 100 days) as a climatic threshold that could help the financial viability of a ski resort, research has focused on assessment studies of future natural snow reliability in winter destinations (Elsasser & Bürki, 2002; Koenig & Abegg, 1997; Walters & Ruhanen, 2015). At the beginning of the 21st century, natural snow has ceased to be an indicator of the impact of climate change on the ski industry and winter sports tourism, as many authors have incorporated artificial snowmaking as an important adaptive strategy (Bark et al., 2010; Chin et al., 2018; Demiroglu et al., 2016; Gonseth & Vielle, 2018; Hennessy et al., 2008; Pickering & Buckley, 2010; Scott et al., 2006; Steiger & Mayer, 2008). Campos Rodrigues et al. (2018) have characterized various adaptation measures in response to climate change apart from the incorporation of artificial snow: technological innovation in snow production, implementation of nocturnal skiing, protection and conservation of the snowpack, diversification of snow-based activities, expansion of skiable area, public and private economic assistance and management solutions, transformation of ski resorts into multirecreational mountain resorts, redefinition of the local economic model, and marketing strategies. Although employing different methodologies, all the studies have shown that the skiing industry will face negative consequences that are reflected in shallower snow cover, a shorter ski season, and increased snowmaking costs.

Artificial snowmaking is used to insulate the ski industry against the effects of variations in natural snow in terms of providing snow cover at the start of the winter season, maintaining cover on high use areas at lower altitudes, and extending the season at its close (Pickering & Buckley, 2010; Steiger & Mayer, 2008). Since the implementation of artificial snowmaking technology (in 1952 in the United States), many countries known for their winter sports tourism have incorporated it into their great efforts to respond to climate change (Scott et al., 2007). In locations such as the Alps, for instance, recent years have seen a substantial increase in artificial snowmaking facilities (Rixen et al., 2011). According to Hanzer et al. (2014), almost half of the total skiable terrain in the Alpine countries is equipped with snowmaking systems. Snowmaking coverage on ski slopes has reached 48%, 65%, and almost 100% in Switzerland, Austria, and Italy, respectively (Demiroglu et al., 2016). Corresponding numbers for other countries range from 12% in Australia to more than 50% in Canada and 66% in the United States (Hanzer et al., 2014). By the end of this century, the projected demand for artificial snow is expected to increase by 330% (Damm et al., 2014). This adaptation strategy has substantially decreased the climatic vulnerability of the ski industry, helping to secure the economic viability of ski resorts (Rutty et al., 2017).

Although snowmaking is recognized as an effective climate adaptation strategy, there are numerous requirements: appropriate equipment (snow cannons, snow towers, snow fans); large amounts of water, requiring the construction of new power lines, water pipelines, and reservoirs (spring, river, or lake); high electricity demand; and recommended temperature. Although snow can be made at temperatures of approximately −1 °C (with low humidity levels and special snowmaking additives), Scott et al. (2006) have pointed out that ski area operators and snowmaking equipment manufacturers generally identify −5 °C as a threshold for efficient snowmaking.

Artificial snowmaking is obviously a financially intensive operation, involving high investment and operational costs. The former range from €25,500 to €150,000 per hectare, and the latter are estimated as between €10,000 and €30,000 per hectare annually (Damm et al., 2014). During the 2001-2002 ski seasons in Eastern North America, snowmaking represented 5.6% (northeast ski region), 6.7% (southeast ski region), and 4.5% (Midwest ski region) of the total operating costs (Scott et al., 2006). It is projected that snowmaking costs in Québec City will increase from 13% to 22% by the 2020s, and 31% to 108% by the 2050s (Scott et al., 2007). When considering the cost of snowmaking, many variables come in to play according to the efficiency of the snowmaking system, power and labor costs, and climatic conditions (Scott et al., 2006). It is expected that increased snowmaking requirements will cause expenditure to rise in skiing areas (Dawson et al., 2009).

The null hypothesis would be that the price of ski passes increases with less natural snow cover, that is, the use of artificial snow systems (ASS). Our initial hypothesis in this article is that the price of ski passes in the largest ski center in Serbia is not in line with the depth of natural snow cover, the increase in the number of ski runs, or the increase in artificial snowmaking infrastructure. Based on previous experience, the main aim of this article was to determine the correlation between the ski pass prices in the largest ski resort in Serbia and several variables: average snow cover during the season, number of overnight stays, average local wages, and average allocation of funds by families for sports tourism, recreation, and hospitality purposes. A secondary aim was to determine the ratio of the ski pass price and the total ski run length, allowing Serbia’s ski resorts to be compared with the competition.

Literature Review

The main restriction on the use of artificial snowmaking is related to the profitability of the ski tourism providers (Dawson et al., 2009). To remain profitable, the ski industry compensates for its losses through the capital investment in and operational costs of artificial snowmaking by increasing the price of ski passes (Malasevska & Haugom, 2018). Many authors have noted this correlation (Gonseth & Vielle, 2018; Scott et al., 2007), but there has been no research on the degree to which it affects the final price. It is therefore necessary to investigate the extent to which the costs of snowmaking investments have been directly passed on to consumers (Töglhofer et al., 2011).

According to some authors (Bark et al., 2010; Campos Rodrigues et al., 2018; Gonseth, 2013; Pickering & Buckley, 2010) low-lying ski areas are and will be more affected by the predicted warmer temperatures than high-elevation ski areas; consequently, increases in capital and operating costs will be greater for the former than for those located at cooler, higher altitudes (Rixen et al., 2011). As a result of the additional costs of artificial snow, Bark et al. (2010) have noted that lower elevation resorts will need to raise ski pass prices. The results of Tang and Jang’s (2012) research show that the insurance against the risk of insufficient snowfall has the greatest effect on ski resorts generating the highest profit through activities directly related to whether or not there is sufficient snow, namely, selling ski passes to ski schools.

Using a data set of 84 Austrian ski resorts for the 2005-2006 seasons, Falk (2008) investigated the relationship between ski pass prices and ski resort characteristics. His research found the following: Increase in ski runs length by 10% has led to 6-day ski pass price rising of 0.36%; availability of high-speed lifts and cable cars increase by 10% leads to a 1.4% increase in the price of ski pass; a 10% increase of ski runs covered by ASS leads to a 0.6% increase in the price of 6-day ski pass. He was one of the first, if not the first, to research the effect of the use of artificial snowmaking on the increase in ski pass prices. Malasevska et al. (2017) examined price discounts for various difficult weather conditions and tourist demand for skiing on those days at three ski resorts in Norway, while Malasevska and Haugom (2018) investigated the relationship between ski pass price, tourist demand, and ski resort revenues, attempting to find the optimal prices for alpine ski passes for the ski resorts mentioned. Both studies have shown that ski resorts can substantially increase their revenue and skiing demand by offering lower prices when the quality of the skiing experience is reduced by difficult weather (Malasevska et al., 2017), that is, lower prices, especially midweek (optimal midweek prices are 43% to 52%, and optimal weekend prices are 11% to 17% lower than the current prices; Malasevska & Haugom, 2018). Falk and Vanat (2016) examined the impact investment in snowmaking has on the output (measured as the number of ski visits) of French ski lift companies, finding that higher investment in snowmaking systems has a significant and positive impact on the number of skier visits. On average, increasing capital investment in snowmaking infrastructure by 10% leads to an 8% increase in the number of skier visits. The basic limitation of their work lies in the fact that they failed to take into account that greater investment in artificial snowmaking will contribute to increases in ski pass prices, which may have consequences for the tourist’s willingness to pay, that is, the profitability of the ski industry. In a study conducted by Filo et al. (2013), the authors concluded that the primary motivation for sports tourists is participation in some sport or watching competitions. They will return to the same destination if there is a primary motivation for their stay; in this case, the motivations are ski runs and suitable infrastructure. Furthermore, ski resorts have to find ways to keep the motivation of regular tourists at a high level in order to make their visits as frequent as possible and to sustain the level of consumption (Vassiliadis et al., 2018). Shih et al. (2009) conducted a study, that is, a regression analysis, and the results suggest that the depth of snow cover has a statistically significant impact on the sale of ski passes in the U.S. state of Michigan: a 2.5-cm increase in snow cover depth increases ski pass sales by between 7% and 9% on a daily basis.

Damm et al. (2014) have investigated a ski resort operator’s cost–revenue forecasts for snowmaking under the climatic conditions for Styria (Austria) up to 2050, taking into account projected daily snowmaking hours, projected daily skiers, changing energy prices, ski pass prices, and discount rate scenarios. The volume of artificial snow produced and also the electricity costs are calculated on the basis of the modeled daily water consumption, given that 0.4 m³ of water is required to produce 1 m³ of snow and that electricity costs of around €0.38 currently accrue per cubic meter of snow. Total snowmaking costs amount to approximately €3.6 million per season, with depreciation accounting for about half of the total, and electricity costs around one third. They predict that the energy costs will increase on average by over 61% in the period between 2021 and 2050 in comparison with the snowmaking energy costs of the period between 1971 and 2000. Positive annuities are achieved at a discount rate of 7% with ski pass prices increasing on average by at least 4% per year. Their research suggest that ski industry will remain profitable in the future if the annual increase in ski pass prices is slightly higher than in previous years.

Regional Settings

With a history of more than 90 years, winter sports tourism in mountain resorts has been recognized as an important component of Serbia’s tourist portfolio (Ministry of Trade, Tourism, and Telecommunications, Serbia, 2016). While ski resorts are not numerous in Serbia, due to the country’s geographical setting, climate, and terrain characteristics, they are heterogeneous in a number of features, such as the length, number, capacity, and average altitudes of the ski runs and the distance from domestic and foreign markets.

Many of the articles about the development of sports tourism in Serbian mountain resorts have dealt with the environmental problems that accompany artificial snowmaking: erosion processes, flood and flood risks, deforestation, and aesthetic degradation (Potić et al., 2015; Ristić et al., 2009; Ristić & Radić, 2008a, 2008b). In some articles, the influence of selected climate parameters on tourist traffic is analyzed (Stojsavljević et al., 2016). There are total of 80 ski runs in Serbia with accompanying equipment, of which 46% are equipped with artificial snowmaking systems. Such systems have been built in six mountain ski resorts: Kopaonik, Stara Planina, Zlatibor, Tara, Brezovica, and Divčibare (Joksimović et al., 2019). The largest ski resorts in Serbia (Kopaonik, Stara Planina, Brezovica, and Zlatibor) are managed by a state-owned company, Skijališta Srbije, which is financed from several sources: the budget of the Republic of Serbia, sale of ski passes, advertising space, rental of business premises, selling schools the right to instruct skiers, provision of services in the ski resorts (organization of races, engagement of special machinery), subsidies, capital projects, and financial loans.

Kopaonik, the highest mountain in Central Serbia at 2,017 m, covers an area in the south of Central Serbia and the north of Kosovo and Metohija. It stretches approximately 80 km in NNW-SSE direction between the two rivers, the Janošica to the north, and the Lab to the south. Kopaonik has a complex geological formation due to its location on the border of two geotectonic units. Magmatic rocks are found on extensive surfaces, while sedimentary ones are found on small surfaces. This is why the mountain itself has a branching waterway network. There are several spas on Kopaonik’s tectonic borders. The tourist resorts and the spas are located in Central Serbia, while the Kosovo and Metohija sector of Kopaonik is well-known for animal husbandry, mining, and forestry. The Kopaonik National Park (Supplemental Figure 1 available online) is an area of 11,809 ha, ranging between 800 and 2,017 m above sea level. Even though the name of the mountain is semantically related to mining, Kopaonik today is synonymous with sport, more precisely, Serbian ski tourism. Ski runs and a cable car system have been built at altitudes between 1,100 and 2,000 m, along with an artificial snowmaking system. The territory of National Park and the ski resort overlap, leading to numerous conflicts. The proximity of the administrative border between Central Serbia and Kosovo and Metohija, which splits Kopaonik into two parts, is a political problem for building tourist infrastructure since Kosovo has been declared to be an independent state.

Kopaonik has history of organized winter sports and sports tourism, and significant place in the market among mountains in neighboring countries. With a distribution of 66% of overnight stays in the winter season and 34% during the rest of the year, Kopaonik has the highest winter season overnight rate when compared with other ski centers in Serbia. In the period between 1991 and 2016, there was a significant correlation between the average number of days with snow cover by month and the average number of tourist overnights in Kopaonik (Joksimović et al., 2019). In the period between 2010 and 2018, the number of overnights doubled. Among Serbian ski resorts, Kopaonik has the best ratio of tourists who buy ski pass to other tourists—average ski pass duration was 2.4 days (Ski Resorts of Serbia, 2019). Kopaonik is consequently the most sensitive of the resorts to climate change and thus falls within the interest sphere of companies engaged in artificial snowmaking. The resort has 62 km of Alpine ski runs and 12 km of Nordic ski runs, 25 lifts, and two mobile lanes. The first artificial snowmaking system was built in 1992. The finishing touches were added to the artificial snowmaking systems on Kopaonik during several stages in the period between 2008 and 2012. The construction of the system was accompanied by the construction of new ski runs and the extension of the ski lift. With 300 fixed and 15 mobile devices for artificial snow, Kopaonik is the best equipped ski resort in the country. Ten new cable cars were built between 2004 and 2010, as well as 12 new ski runs, and two further artificial lakes for snowmaking were added to the existing one. Ski resorts began working longer hours during the high season, and a 10% discount for groups was introduced, alongside 15% discounts during promotional weeks (Infokop, 2014).

The artificial snowing system is software controlled. Fresh water without chemical additives is collected in built accumulations. The process of snowmaking can in this way be controlled to optimize snow properties, relative to temperature, humidity, and wind direction. During the last 2,085 days of lift operation, among 25 lifts, efficiency was 8.1% to 96.6% per lift, and the average efficiency was 59%. The most inefficient lifts in fact worked only every 10th day, and there were only five lifts with an operational average above 80% (Infokop, 2014). Although conditions were suitable for artificial snowmaking and ski passes were valid for all runs, some runs were unavailable to tourists during the observed period with no explanation offered by the ski resort management.

The ski pass price list is approved by the Government of the Republic of Serbia and published by the Official Gazette of the Republic of Serbia (2018), thereby somewhat politicizing the pricing and management of ski resorts. In the business program of Serbian ski resorts, ski pass prices are affected by fuel prices, employment of labor, and electricity consumption (Ski Resorts of Serbia, 2019). The quality of natural snow does not therefore affect the price of a ski pass. The price list is adjusted so that the prices are highest during national holidays and school breaks when the domestic population travels the most. Exceptionally, the director can offer discounts in cases where, due to the lack of snow or of the climatic conditions for producing artificial snow, only the part of the installation in the ski resort is operating.

Methodology

Before approaching methodology and appropriate databases, we will mention the criteria on the basis of which the case study was selected. In the context of the topic, the concept of the “winter sports tourist” resort was considered. Under this term, we considered resorts with constructed ski runs, cable cars, lifts, provision of services to professional and recreational skiers, and visits from tourists during the 4 months of the main ski season (from mid-December to the end of March). Winter sports infrastructure has been built in 19 ski resorts in Serbia, 17 of which are in the mountainous areas. Among them, Kopaonik, Stara Planina, Zlatibor, Brezovica, and Divčibare represent complex resorts with several managed ski systems. With the exception of Divčibare, some parts of these mountains are protected natural areas (national parks, nature parks). The specificity of the goal of our work has imposed criteria for the selection of the case study. Kopaonik was selected because of the availability of three groups of data, namely, climatological, economic, and tourist. Time series of climate and tourist data do exist for other tourist resorts, but no data are available for their business activity.

Data Collection

The methods used called for data for different space levels and are based on different time series. The databases used are those of the Statistical Office of the Republic of Serbia (2019), hydro-meteorological service and unpublished data of the Ski Resorts of Serbia (Infokop, 2014), and field research findings. The winter tourist seasons between 2006 and 2018 form the period considered in the research.

Procedure

We have proposed a new methodology consisting of two segments. In the first segment, the correlation between the time series of different variables is determined, and in the second, each of the correlation results is quantified by scoring. By adding the scores, we obtained an index determining the ratio of ski pass prices to the factors that affect them. Correlation method was used to determine the connection of the average price of 1-day (S1) and 7-day (S7) ski passes in Kopaonik, the average depth of natural snow cover there (D), the number of overnights during winter season (December 1 to March 31) in the resort (O), the average wage of workers employed at registered companies in Serbia (W), and recreation and hospitality expense as a percentage of total consumption in Serbia (E).

To determine the correlation (Correl) of the time series of mentioned indicators, the following equation was used:

Correl (X, Y) = \frac{\sum (x - \bar{x}) (y - \bar{y})}{\sqrt{\sum {(x - \bar{x})}^{2} {(y - \bar{y})}^{2}}}

where x and y are the average values of given variables of time series X and time series Y. The more the value is closer to 1, the correlation is more significant.

Once the correlations were obtained, the results were classified into the hierarchical matrix (Table 1). Scores (positive absolute numbers) were assigned to correlation values, according to the importance of the connection with ski pass prices. The first four components (C1-C4), from Table 1, Correl S1–D, Correl S7–D, Correl S1–O, and Correl S7–O have a maximum score of 2; the remaining two components (C5 and C6) can have a maximum of 1. The first four components (C1-C4) contribute to the final assessment as they are connected with the Kopaonik study, and the last two components (C5 and C6) are indicators of general developments in the state as a whole. Of course, the choice and scoring of variables is largely subjective and characteristic of the chosen case study.

Table 1

Correlation Score Matrix

Correlation Score	C1: Correl S1–D	C2: Correl S7–D	C3: Correl S1–O	C4: Correl S7–O	C5: Correl S1–W	C6: Correl S1–E
Negative to 0	0	0	0	0	0	0
.1-.25	0.5	0.5	0.5	0.5	0.25	0.25
.26-.50	1	1	1	1	0.5	0.5
.51-.75	1.5	1.5	1.5	1.5	0.75	0.75
.76-1.00	2	2	2	2	1	1

Note: S1 = 1-day ski pass average price in Kopaonik; S7 = 7-day ski pass average price in Kopaonik; D = average depth of natural snow cover in Kopaonik; O = number of overnights during winter season (December 1 to March 31) in Kopaonik; W = average wage of workers employed at registered companies in Serbia; E = recreation and hospitality expense of total consumption in Serbia (%).

The ski pass index (SP index) is determined by adding all six components:

SP index = SUM (C 1, C 2, C 3, C 4, C 5, C 6)

The ski pass index illustrates that ski pass prices, related to variables that were previously obtained using the correlation method, are justified. The minimum value of the SP index can be 0 and the maximum 10. The minimum value represents a total mismatch of the time series of selected variables with which the ski pass price is related, while ski resorts where ski pass price is in full compliance with the selected variables score the maximum value. This means that a ski resort with ski pass index of 10 would have the best ski pass price in relation to the chosen indicators and criteria. The problem of this method is the degree of subjectivity involved in determining the importance of the variables related to ski pass prices. For example, in some resorts it will be snow cover, while in others the consumer habits of foreign tourists will be the most important. Despite the above imperfection, we consider that this method can determine the connection between ski pass prices and certain factors, depending on the availability and quality of the data used.

In addition to the above methods, the length/price index (LP index) for competitor ski resorts in Europe is calculated. This is based on the length of the ski runs and the price of 6-day ski passes. The LP index highlights ski pass price in relation with available quantity of ski runs, giving the following quantitative context:

LP index = \frac{L (km)}{P (euros)}

where L is the cumulative length of the ski runs and P the price of the ski pass. The higher the value of the index, the more favorable the resort for skiers. The limitation of this index is that it cannot point to the ratio of price and qualitative features of the ski runs, ski lifts, and other skiing services.

Results

In the period from 2008 to 2016, Kopaonik had five ski seasons with an average of 43 days of snow cover (minimum snow depth of 30 cm), justifying the building of an ASS. In the worst season for sports tourism, 2013-2014, natural snow cover with a depth of 30 cm last only for a few days (Figure 1). With ASS, the quantity of snow was maintained at a relatively sufficient level (15-30 cm), which is made possible by low temperatures. Amount of 1 m³ of artificial snow in Kopaonik costs 0.15 euro cents, which is significantly less than that in Alpine resorts (€2-€5 per 1 m³ of snow; Badre et al., 2009; Hinnerth, 2012). Among others, this is due to lower electricity prices and the use of water from natural sources (such as precipitation), which does not have to be paid for. For example, 1 kWh of electricity for commercial purposes costs 15.1 euro cents in Germany, 14.5 in Italy, 10.5 in Austria, 9.2 in France, 7.8 in Slovenia, and 7.5 in Serbia (Eurostat, 2018). One-day ski pass in Alpine resorts in mentioned countries costs from €30 to €40, while in Kopaonik, in the 2016-2017 season, it cost €26.80. The price of a 1-day ski pass during the 2010-2011 season, when there were only 18 days with snow cover of 30 cm, was €19.60. During the worst skiing season (2013-2014), the 1-day ski pass reached €28.

Figure 1

Snow Cover and Temperature in Kopaonik in Season 2013-2014

It is clear that the depth and duration of snow cover were not the main factors in determining ski pass prices and therefore the null hypothesis is rejected. Through the correlation method, we have established and hierarchically distinguished the connection of ski pass prices with several factors. In the observed period, annual averages of depth of natural snow cover (D) ranged between 13.4 and 73.4 cm, depending on the influence of climatic factors. The price of a 1-day ski pass (S1) ranged between €19.60 and €28.10. There is a correlation of minor significance (.155) between D and S1, suggesting that the price of the 1-day ski pass was not determined by the depth and duration of natural snow cover—the ski resort was independent of natural factors and well adapted to snow cover variations. The price of a 7-day ski pass (S7) varied between €99.30 and €133.40. A negative correlation was found between D and S7 (−.061), illustrating that the price of a 7-day ski pass was not influenced by the condition of natural snow. The number of overnights (O) was from 160,725 to 349,696, and the determined correlation between S1 and O is significant (.534) and between S7 and O is very significant (.807). Average salaries of employees in registered companies in Serbia (W) amounted to between €501.70 and €589.10. The determined correlation between S1 and W was relatively significant (.500). The expense for recreation and hospitality in total consumption was from 5.2% to 8.1%, and the correlation between S1 and E was insignificant (.07). On the basis of the calculated components, the SP index for Kopaonik for the period between 2006 and 2018 was 4.5/10.

Based on the length of the runs in 19 ski resorts in nine countries, the LP index (Figure 2) was calculated. Kopaonik is among the ski resorts in the middle with an index of 0.41. A few resorts with short ski runs have a low LP index: in Serbia (Tornik, Stara Planina), Slovakia (Jasna), Slovenia (Krvavec), and Bulgaria (Borovets, Bansko). The major competitors are large integrated ski resorts in Austria, France, and Italy with over 100 km of ski runs. The LP index indicates that long-distance ski resorts, in terms of ski pass price, are more favorable for skiers who stay for 6 days: they have a greater selection of ski runs at a lower cost. However, skiing is not the only cost for skiers during the season, there are costs for many other services as well. The LP index only partially determines the relative economies of ski resorts.

Figure 2

Length/Price Index for Competitive Ski Resorts in Europe

Discussion

Based on the goals set and the research methodology, the results illustrate that there are certain correlations between ski pass prices and the chosen indicators, which we consider to be the scientific contribution of this article. First, we should consider the time series of the natural depth of snow cover in Kopaonik. In the period studied, there have been many oscillations, and in three of 12 ski seasons, the average snow cover depth was not more than 30 cm. However, this does not mean that there was not enough snow for sports activities during these seasons. The depth of snow cover on the ski runs was maintained by artificial snowmaking (Figure 1). The results show an insignificant correlation between natural snow cover depth and 1-day (.155) and weekly (−.0061) ski pass prices. Fluctuations in the depth and the quality of snow cover did not affect ski pass prices, they did not go up. Second, the number of overnight stays in Kopaonik did not fall or rise in a similar way to ski pass prices. A significant correlation (.534) has been found between the number of overnight stays and 1-day ski pass price, as well as a highly significant correlation between the number of overnight stays and weekly ski pass prices (.807). This leads us to the conclusion that the number of overnight stays has grown since 2012, regardless of the ski pass price rise. Falk and Vanat (2016) concluded that investment in artificial snowmaking infrastructure increases the number of tourist overnights, and the Kopaonik case study has confirmed this correlation. Third, a significant correlation between ski pass prices and average wages in Serbia (.500) was noted, which implies the higher financial solvency of the domestic market. This leads us to conclude that Skijalište Srbije, which suggests ski pass prices, starts adjusting price lists before the season begins in accordance with the population’s financial solvency. Furthermore, the share of household budget that people spend on recreation and holidays is not in accordance with ski pass price rises because the correlation between these two parameters is relatively insignificant (.007).

In the period between 2004 and 2012, when the last investment in 10 new cable cars and 12 new ski runs was made, ski pass prices did not fall until the 2011-2012 season. Since 2012, ski pass prices have been growing progressively, regardless of the fact that no new investments in ski infrastructure have been made (Table 2). We compared our results with those obtained by the methodologies used by other authors. Falk’s (2008) conclusion that the length of ski runs affects ski pass price rises, as in Kopaonik, has been proved to a minor extent: Ski pass prices have been growing progressively in the years subsequent to the investments in ski infrastructure (2012-2018). If Falk’s theory were applied to Kopaonik, the 34% increase in the price of a 7-day ski pass over the 2010-2018 period should follow on from a 944% extension of the ski runs, while in fact the length of ski runs in Kopaonik has been extended by about 20%. While it lies within the discretion of the resort director to offer a discounted ski pass price in the eventuality of unfavorable weather and closed ski runs, this has so far not happened in Kopaonik. Based on the calculated components, the ski pass index for Kopaonik was 4.5/10. This means that ski pass prices are only partially correlated with natural snow depth, tourist demand, average income, and the amount of money people can spend on recreation in Serbia. The remaining 5.5 index points must therefore be determined by other variables.

Table 2

Basic Indicators Used for Correlation for the Period Between 2006 and 2018

Season	Average Natural Snow Cover Depth in cm (D)	1-Day Ski Pass Average Price in Euros (S1)	7-Day Ski Pass Average Price in Euros (S7)	Number of Overnights (Winter Season Only; O)	Average Wage of Workers Employed at Registered Companies in Euros (W)	Recreation and Hospitality Expense of Total Consumption in % (E)
2006-2007	43.3	22.9	115.7	289,111	n/a	n/a
2007-2008	47	21.2	107.1	296,905	n/a	n/a
2008-2009	54.7	21.2	108.4	255,240	n/a	6.7
2009-2010	30.2	20	107.3	160,725	509.6	6.9
2010-2011	13.4	19.6	99.3	186,218	501.7	6.6
2011-2012	65.8	23.1	106.8	211,472	576.9	6.2
2012-2013	62.3	27.1	105.5	240,156	511.3	5.8
2013-2014	15.8	28.1	110	234,497	559.8	6.7
2014-2015	73.4	26.8	110.5	285,686	547.6	6.9
2015-2016	20.9	26.6	129.8	304,293	532.6	7.9
2016-2017	31.3	26	129.2	341,293	551.7	5.2
2017-2018	57.1	26.8	133.4	349,696	589.1	8.1

Note: n/a = data not available. D = average natural snow cover depth in cm in Kopaonik; S1 and S7 = based on prices in Kopaonik; O = Number of overnights in Kopaonik; W = based on whole country data as the closest market, E = based on whole country data as a closest market.

Source: Hydro-Meteorological Service of Serbia (2018); Statistical Office of Republic of Serbia (2019); Infokop (2014).

Based on the results presented, the basic hypothesis of the work—that the price of ski passes at the Kopaonik resort is not in accordance with natural snow cover, the increase in the number of ski runs and the infrastructure for artificial snow—has been confirmed. The price of a ski pass both determines and is based by winter sports demand. The price in Kopaonik is roughly double that of prices in other Serbian resorts. In the case of Zlatibor, the altitude of 1,110 to 1,490 m should create higher demands for artificial snow, which should in turn lead to higher ski pass prices. So far, this has not been the case. Ski pass prices in Kopaonik not only are conditioned by the quantity and quality of the runs but are also artificially controlled according to the number of tourists, average income in the country and the profile of consumers. We believe that the main tools for manipulating the number and profile of tourists during the winter season will be artificial snow making systems, due to cheap electricity and water prices in Serbia. Since most of the electricity in Serbia is generated from lignite, it could be said that in order to gain profits from skiing in protected natural assets (itself a subject of criticism) on the one hand, polluted areas are created near thermal power plants on the other.

A policy that manipulates prices so that the ski pass is the most expensive during public holidays and school holidays is not in line with natural factors affecting skiing, among other things, the depth and quality of snow cover. Skiers are further discouraged by the price increase at the beginning of the season, that is, the reduction of the discount from 70% to 30%.

Conclusion

While high ski pass prices, especially during school and state holidays, may discourage skiers, especially if the ski services offered are not of a high standard, the extensive construction of luxury accommodation in the immediate vicinity of the ski runs can attract tourists who do not ski. As an alternative to this type of accommodation, there is a trend to build small apartments and residential hotels outside ski resorts and national parks, without planning and building permission.

Thanks to numerous articles focused on artificial snow strategy and quality–demand ratio in the ski industry, we have identified the fixed and variable costs that affect ski pass prices. Among the main limitations of our research are those related to methodology. It is based in part on subjectively selected indicators, such as the salaries of employees in and the amount allocated by families for recreational purposes in Serbia. Future research should be based on data relating to the actual users of ski resort services who are actively involved in skiing or purchasing a ski pass. The research should further include qualitative details of the profile of tourists in the sample, based on their characteristics: gender, age, education, salary, and so on. Future research could cover all national or regional ski resorts to obtain comparative data. If other developed ski resorts have more detailed databases on investments in the infrastructure of artificial snowmaking and other factors that influence ski pass prices, the methodology can be modified. Accordingly, while methodological flexibility on the one hand represents limitations, on the other it provides certain kinds of tools for improvement.

The results of our research may be of use to users of ski services, skiers, tour operators, travel agencies, and ski resort management. Providing ski pass index information contributes to the formation of offers by agencies and tour operators, which can also make it easier for skiers to make arrangements. The results of our research, such as the significant correlation between the increase in the number of tourists and the increase in the price of ski passes, can assist investors in making decisions about investing in ski resorts in Serbia. The results can also be used for promotional and marketing purposes for each ski resort.

The relationship between state-owned companies that manage ski resorts and protected natural assets as well as qualitative research into tourist demand in relation to ski pass prices in privately owned companies are potential open fields for further research. The question remains as to whether tourists will be willing to participate in winter sports activities if the current trend of increasing ski pass prices continues.

Supplemental Material

Supplement_Figure_1 – Supplemental material for Artificial Snowmaking: Winter Sports Between State-Owned Company Policy and Tourist Demand

Supplemental material, Supplement_Figure_1 for Artificial Snowmaking: Winter Sports Between State-Owned Company Policy and Tourist Demand by Marko Joksimović, Mirjana Gajić, Snežana Vujadinović, Jelena Milenković and Vladimir Malinić in Journal of Hospitality & Tourism Research

Supplemental Material

Supplement_Figure_2 – Supplemental material for Artificial Snowmaking: Winter Sports Between State-Owned Company Policy and Tourist Demand

Supplemental material, Supplement_Figure_2 for Artificial Snowmaking: Winter Sports Between State-Owned Company Policy and Tourist Demand by Marko Joksimović, Mirjana Gajić, Snežana Vujadinović, Jelena Milenković and Vladimir Malinić in Journal of Hospitality & Tourism Research

Supplemental Material

Supplement_Figure_3 – Supplemental material for Artificial Snowmaking: Winter Sports Between State-Owned Company Policy and Tourist Demand

Supplemental material, Supplement_Figure_3 for Artificial Snowmaking: Winter Sports Between State-Owned Company Policy and Tourist Demand by Marko Joksimović, Mirjana Gajić, Snežana Vujadinović, Jelena Milenković and Vladimir Malinić in Journal of Hospitality & Tourism Research

Footnotes

Authors’ Note:

The article is part of a research project (No. 176008) funded by the Ministry of Education, Science, and Technological Development of the Republic of Serbia.

ORCID iD

Marko Joksimović

Supplemental Material

Supplemental material for this article is available online.

Marko Joksimović (corresponding author; email: dellmare10@gmail.com) is an assistant professor at the Faculty of Geography, University of Belgrade in Serbia.

Mirjana Gajić (email: mir.gajic@gmail.com) is an associate professor at the Faculty of Geography, University of Belgrade in Serbia.

Snežana Vujadinović (email: vujadinovicsnezana@gmail.com) is a full professor at the Faculty of Geography, University of Belgrade in Serbia.

Jelena Milenković (email: jelenamilenkovic311@gmail.com) is a PhD candidate in Geosciences at the Faculty of Geography, University of Belgrade.

Vladimir Malinić (email: vmalinic92@gmail.com) is a PhD Candidate in Geosciences at the Faculty of Geography, University of Belgrade.

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