The Spatial Synergy of the Ice–Snow Tourism Industry and Its Related Industries in Jilin Province

Abstract

The rapid development of the ice–snow tourism industry in recent years has promoted the sustainable development of the regional economy, and it has become one of the critical supporting industries affecting local economic development. However, there is an obvious imbalance in the ice–snow tourism industry in the region, resulting in significant differences in the synergy between the ice–snow tourism industry and its related industries in the country. This study used grey relation analysis, Copula joint function, and structural equation methods to analyze the correlation and interaction between the ice–snow tourism industry and other regional industries. It analyzed the synergistic relationship between the ice–snow tourism industry and its related industries in Jilin Province. The results showed that the ice–snow tourism industry highly correlates with the financial, real estate, and other service industries. The contribution rate of ice–snow tourism to the three closely related service industries has reached 25.1%. This study provides decision-making support for the synergistic development of regional and ice–snow industries.

1. Introduction

Under the background of sustainable development and carbon neutrality/peaking, the ice–snow industry has become an important field with the government’s primary support [1,2,3]. Ice–snow tourism and sports have gradually entered the scope of public consumption. After the 2022 Beijing Winter Olympic Games, China’s ice–snow industry ushered in a new development opportunity and received unprecedented attention [4]. The ice–snow industry is a resource-based industry formed around the development of ice–snow resources and is a new industry in the country [5]. The ice–snow industry mainly includes four core areas: ice–snow tourism, ice–snow sports, ice–snow culture, and ice–snow equipment. The rapid development of the ice–snow industry is limited by ice–snow resources and is also closely related to the integration of related industries. In Northeast China, climate conditions are suitable for ice and snow resources. However, as a new industry, the synergetic development between the ice–snow industry and related industries has become a key factor restricting its competitiveness.

Industrial synergy is an important measure to promote regional economic development, which has a profound impact on the quality and effectiveness of regional development. Regional industries cooperate in various forms. To achieve industrial synergy, each industry needs to utilize internal resources, brands, funds, information, and other factors within the group to accelerate its development speed and realize the maximization of group interests [6]. At present, industrial synergy has become a hotspot [7,8]. Some studies have analyzed the efficiency of the ice–snow tourism industry using the DEA (Data Envelopment Analysis) model [9]. Researchers have studied the collaborative development relationship between industries in the interval from the perspective of industrial resource coordination [10], network coordination [11], and sustainable development [12]. Some researchers have also put forward new industrial development models from the perspective of industrial symbiosis and put forward some new methods to analyze the synergy in industrial parks [13]. With numerous studies on maximizing the implementation of industrial symbiosis, researchers have also provided methods to evaluate and compare the quality of industrial synergy in terms of three aspects: environmental quality, economic quality, and geographical proximity [14,15].

The high-quality development of the ice–snow industry is closely related to its related industrial structure [16]. The synergetic development of the ice–snow industry is crucial to the improvement of the quality of regional economic growth. The ice–snow industry has strong compatibility and is a good driving industry. From the perspective of related industries, the ice–snow industry has promoted the industrial upgrade of the consumption structure, production structure, and infrastructure, on the one hand, while promoting innovation in and integration of related industries on the other hand. The ice–snow industry chain includes three links: upstream design, equipment, midstream operation and service, and downstream consumption. Its related industries include sports facility manufacturing, culture, entertainment, catering, retail, accommodation, transportation, medical, and other industries related to the ice and snow industry. The research on ice and snow industry integration and industry synergy has become a key issue in research related to the ice–snow industry and regional economies.

Since 2011, the growth of mature markets in the global ice–snow industry has slowed, emerging markets have gradually developed, and the overall number of participants in the industry has remained relatively stable. However, due to the impact of the COVID-19 lockdowns, the size of skiing participation groups decreased from 2020 to 2022, and the global market penetration rate of the skiing population also decreased, with a reduction in the snow season year by approximately 2.58% from 2020 to 2021 [17]. After 2022, the global market penetration rate of skiing participants gradually returned to the levels of the pre-COVID-19 period. Since the Beijing Winter Olympics, winter sports have grown tremendously in China, along with skiing and learning facilities. The number of skiers grew from about 3 million in 2012 to 13 million in the 2022 issue of the report [17]. Jilin Province in China has a superior geographical location and is one of China’s best ice–snow tourism destinations. In recent years, Jilin Province has proposed building an entire ice–snow industry chain centered on ice–snow tourism, sports, and culture. The ice–snow tourism industry has shown strong competitiveness. Ice–snow tourism in Jilin Province has become one of the leading industries in China, becoming an international ice–snow tourism industry.

To reveal the synergistic relationship between the ice–snow tourism industry and its related industries, this study takes Jilin Province as the study area and measures the driving effect of the ice–snow tourism industry on related industries in Jilin Province, as well as the impact of related industries on the ice–snow tourism industry. In this study, first, the spatial pattern of the distribution of the ice–snow tourism industry in Jilin Province was analyzed. Secondly, the grey correlation model and Copula method were used to analyze the correlation and interdependence between the ice–snow tourism industry and related industries. This study reveals the mutual influence mechanism between the ice–snow tourism industry and regional industries, providing decision-making support for the spatial layout and industrial structure adjustment of the ice–snow tourism industry.

2. Study Area and Data

2.1. Study Area

The study area is located in the middle of Northeast China (40°52′–46°18′ N, 121°38′–131°19′ E) and belongs to the temperate monsoon climate zone. The central and eastern regions of the study area are hilly and mountainous, and the central and western regions are plain. The snow period is 180 days, and the length of snow cover is 80–120 days. This area is abundant in ice–snow tourism resources. The eastern part of the study area has heavy snowfall and deep snow cover, while the western region has less snowfall, but more rivers, lakes, and reservoirs, forming a spatial pattern of “West Ice–East Snow”. The combination of advantages in terrain, climate, hydrology, and other aspects makes the region have great potential for winter ice–snow tourism (Figure 1).

In terms of infrastructure, railway transportation is convenient, and the highway is centered in Changchun, Jilin, Yanji, Tonghua, and other cities, forming a highway network of cities, prefectures, and counties. Changchun, Jilin, Yanji, and other airports have opened to over 20 cities in China. Hospitality facilities such as hotels, restaurants, shopping, catering, and entertainment services have been continuously improved. The post and telecommunications industry has developed rapidly, and 26 independent tourism websites have been built. The study area has been a ski training base for athletes since the 1950s and 1960s. There are over 10 international ice and snow tourist attractions, including Jingyue Ski Resort, Beidahu Ski Resort, Songhua Lake Ski Resort, Beishan Ice–Snow World, and Changbai Mountain Plateau Ice–Snow Training Base. Compared with other regions in Northeast China, the suitable climate conditions make the high snow cover and good snow quality, and less cold wind, which is conducive to skiing and outdoor activities. Therefore, the development of ice–snow tourism in the study area has low cost, distinctive cultural characteristics, and resource advantages, which makes it the main area of winter ice–snow tourism.

The ice–snow tourism industry has gradually become a new growth pole driving the economic development of the study area. According to the snow–ice tourism survey report of Jilin Province in the snow season of 2018–2019, the province received 84.3184 million tourists and the tourism revenue was CNY 169.808 billion. In 2022, this area built 54 ski resorts of different scales for 279 ski trails, with a total area of 1139 hectares and a total length of more than 330 km. By 2035, the number of people participating in ice–snow sports in this area will reach 15 million, and the number of various ski resorts will reach 120.

2.2. Data Sources

The data in this study mainly include data related to the ice–snow tourism industry and economic data of various industries in the study area, including the hotel and catering industry, transportation industry, wholesale and retail industry, etc. These data are from the statistical yearbook of Jilin Province and the statistical yearbooks of various cities from 2006 to 2021. Meanwhile, this study also obtained road traffic network data and Point of Interest (POI) data of Jilin Province in 2022 (Figure 2), which include 564,605 pieces of data on ice–snow scenic spots, hotels, catering, food, shopping malls, supermarkets, etc., in Jilin Province. These data are used to analyze the spatial dependence of the development of the ice–snow tourism industry on related industries.

3. Methods

In this study, industrial synergy refers to the joint development of regional industries. Firstly, we need to confirm the correlation between industries and analyze whether there is joint development among related industries in the region. Three methods are utilized in this research. This study first uses grey correlation to determine which industries are related to the ice–snow tourism industry. Secondly, the Copula function is used to analyze the spatial synergism distribution of its primary related industries. Finally, the structural equation model is used to analyze the contribution rate of the ice–snow tourism industry to its related industries.

3.1. Structural Equation Model (SEM)

SEM [18] is a statistical method for analyzing the relationship between variables based on the covariance matrix of variables. It is a multivariate statistical technique that combines factor analysis and path analysis [19]. Its advantage lies in the quantitative study of the interaction between multiple variables. This method is suitable for analyzing the internal relationship between the ice–snow tourism industry and its related industries. It is assumed that the tourism industry is affected by GDP output value. The ice–snow tourism industry also affects tourism-related industries (wholesale and retail industry, hotel and catering industry, transportation industry), and tourism-related industries are also affected by the GDP output value. Then, the structural equation of tourism industry synergy can be expressed as the following formula:

The measurement model is defined as:

$$x={\phi }_x\xi +\epsilon$$

(1)

$$y={\phi }_y\eta +ϵ$$

(2)

The structure model is defined as:

$$\eta =\lambda \eta +\mu \xi +\zeta$$

(3)

where 𝑥 is the vector of exogenous observation variables. We selected nine exogenous observational variables: employment population, fixed investment, per capita GDP, hotel and catering industry income, wholesale and retail industry income, transportation industry income, the number of domestic tourists, the number of international tourists, and the per capita consumption of tourism. 𝜉 is the vector of exogenous potential variables, ${\phi }_x$ is the factor load matrix of external observation variables on external potential variables, $\epsilon$ is the residual term vector of external observation variables, and 𝑦 is the endogenous observation variable vector. We set three endogenous observation variables: socio-economic development, development of ice–snow tourism industry, and development of ice–snow tourism-related industries. 𝜂 is the endogenous potential variable vector, ${\phi }_y$ is the factor load matrix of the endogenous observation vector on the endogenous potential variable, $ϵ$ is the residual term vector of the endogenous observation vector, λ and $\mu$ denote the path coefficient matrix in the structural model, where λ indicates the relationship between endogenous potential variables and $\mu$ indicates the influence of exogenous potential variables on endogenous potential variables, and 𝜁 is the error vector of the structural equation.

3.2. Grey Relation Analysis (GRA)

Grey Relation Analysis (GRA) is a multi-factor statistical analysis method [20]. It was prosed on the basis of grey systems theory [21], and the basic idea is to judge whether the relationship between different series is close according to the similarity of the geometric shape of the data time series curve, so the difference between the curves is the measure of the degree of correlation. The discrete behavior observation values of system factors are converted into piecewise continuous polylines by linear interpolation, and then a model to measure the degree of correlation is constructed according to the geometric characteristics of the polylines. The closer the geometry of the polyline is, the greater the correlation between the corresponding sequences, and vice versa. This method is suitable for analyzing the correlation between various industries and the ice–snow tourism industry in the region. It is used to filter industries with strong correlation.

It is assumed that the time series of ice–snow tourism income in the study area is $X_0=(x_0\left(1\right), x_0\left(2\right), x_0\left(3\right),\cdots ,x_0\left(n\right))$, and the time series of ice–snow tourism-related industries is $X_i=\left(x_i\left(1\right), x_i\left(2\right), x_i\left(3\right),\cdots ,x_i\left(n\right)\right), i=1, 2, 2, 3,\cdots ,m$.

$$X_i^{\prime }\left(k\right)=(X_i\left(k\right)-\overline{X_i\left(k\right)})/{\delta }_i$$

(4)

where k is the year, $X_i^{\prime }\left(k\right)$ is the data standardization for the same industry, ${\delta }_i$ is the standard deviation of the same industry, and the absolute value is calculated by taking the number of ice–snow tourism income as the reference number sequence:

$${∆}_i\left(k\right)=\left|X_0\left(k\right)-X_i\left(k\right)\right|$$

(5)

Then, the grey correlation coefficient between the ice–snow tourism industry and its related industries is expressed as:

$${\phi }_i\left(k\right)=(a+\rho \times b)/({∆}_i\left(k\right)+\rho \times b)$$

(6)

where $a=\underset{i}{\min }\underset{k}{\min }\left|X_0\left(k\right)-X_i\left(k\right)\right|$, $b=\underset{i}{\max }\underset{k}{\max }\left|X_0\left(k\right)-X_i\left(k\right)\right|$, $\rho$ is the resolution coefficient, $\rho =0.5$. Then, the grey correlation between the ice–snow tourism industry and its related industries is expressed as:

$$r_{oi}\left(k\right)=\frac{1}{n}\sum _{k=1}^n{\phi }_i\left(k\right), i = 1, 2, 3,\cdots , m$$

(7)

3.3. Copula Joint Distribution

This study uses the Copula function to analyze the joint probability of two variables of the ice–snow tourism industry and its related industries to characterize the close relationship between them. The Copula is a kind of function that couples a multivariable distribution function with its marginal distribution function [22], which can effectively describe the dependence of nonlinear tails between variables. The Copula function can be used to analyze the dependence structure of the ice–snow tourism industry and its related industries for scientific characterization. The Copula method can be described as follows:

It is supposed that F(x, y) is the bivariate joint function of F₁(x) and F₂(y). Then, there is a unique Copula function C:

$$F\left(x,y\right)=C(F_1\left(x\right),F_2\left(y\right))$$

(8)

where F₁(x) is the time series of the ice–snow tourism industry, and F₂(y) is the time series of related industries and has continuous edge distribution. Let $x=F_1^{-1}\left(u\right)$, and $y=F_2^{-1}\left(v\right)$. Then, C of the Copula can be determined as follows:

$$F\left(x,y\right)=F\left(F_1^{-1}\left(u\right),F_2^{-1}\left(v\right)\right)=C(u,v)$$

(9)

The single-parameter ($\theta$) Archimedean Copula function is defined as follows:

$$C\left(u,v;\theta \right)={\phi }^{-1}[\phi \left(u,\theta \right)+\phi \left(v,\theta \right);\theta ]$$

(10)

The kinds of Archimedes Copula mainly include Clayton, Frank, and Gumbel, where parameter θ can be calculated by using the Kendall-τ method:

$$\tau ={\left(\begin{array}{c}N\\ 2\end{array}\right)}^{-1}\sum _{j=1}^N\sum _{i=1}^{j}sign\left[(x_i-x_j){(y}_i-y_j)\right]$$

(11)

where x and y are the output values of the ice–snow tourism industry and its related industries, respectively. In this study, a pairwise Copula analysis of the ice–snow tourism industry and its related industries is conducted.

4. Results

4.1. Spatial Distribution of Synergetic Industrial Groups of Ice–Snow Tourism

According to the two indicators of richness in ice–snow resource types and abundance of ice–snow resource reserves, the study area has a total of 89,531 ice–snow resources, with a unit density of 0.48/km², of which the number of high-quality ice–snow resources is 6312. Therefore, the study area is rich in ice–snow tourism resources, with rapid industrial synergetic development, and has formed four major industrial spatial clusters (Figure 3). (1) The Chang-Jilin Urban Ice–Snow and Leisure Resort Cluster (Zone Ⅰ). This zone includes the three administrative regions Changchun, Jilin and Siping. In this region, the deep integration of ice–snow tourism with automobile, film, optics, science and technology, commerce, rural and other industries is primarily performed. Major developments include ice–snow exhibitions, urban ice–snow industry, high-level ski resorts, training venues, ice–snow talent training bases, industrial parks, ice–snow entertainment projects, etc. In this area, the Northeast Asia International Ice–Snow Industry Exchange Center will be built, including an ice–snow talent training center, an ice–snow equipment R&D and manufacturing center, and a light ice–snow equipment industrial park, integrating research and development, production, and sales. (2) The Changbai Mountain Ice–Snow Ecological Resort Cluster (Zone Ⅱ). This zone includes the two administrative regions Yanji and Baishan. By integrating the ice–snow ecological holiday resources in the Changbai Mountain area, an ice–snow ecological holiday and hot spring leisure gathering area will be developed in this area, with alpine ice and snow sports, ski vacations, hot spring healthcare, and forest vacations as its main purpose. Major developments include skiing training and competitive competitions, and famous international and domestic competitions in ice–snow sports are held. An ice–snow ecotourism and folk culture gathering area, an ice–snow depth experience area, an ice–snow tourism resort area, a hot spring health experience area, and an ice–snow rural tourism area will be built in the area. (3) The Tong-Mei ice–snow integration development cluster (Zone Ⅲ). This zone includes the three administrative regions Tonghua, Meihekou, and Liaoyuan. This is the third pole of the ice–snow industry in Jilin Province. The region is focused on the construction of major ice–snow projects and a deep integration is promoted with agricultural culture, anti-war culture, wine culture, healthcare using traditional Chinese medicine, and urban culture, as well as the integration of ice–snow resorts, cultural experience, urban leisure, and other industry clusters. (4) The Song-Bai Ice Industry Cluster (Zone Ⅳ). This zone includes the two administrative regions Songyuan and Baicheng. With Chagan Lake–Nen River Bay as the leading line and the Lake–Grassland–Wetland Tourism Grand Loop as the main line, in this area, ice lake fishing and hunting culture, winter fishing and leisure tourism, ice sports, hot spring vacations and industries characteristic of cold areas have been the primary developments. All four zones are connected by highways.

4.2. Grey Correlation Analysis of Ice–Snow Tourism and Its Related Industries

To analyze the industrial synergy of the four ice–snow tourism industry zones, in this study, the grey correlation between regional industries and ice–snow tourism was analyzed using the number of ice–snow tourists in the study area and the regional output value of various industries. As can be seen from Figure 4, the growth rate of ice–snow tourism (23.02%) has been significantly faster than that of the three largest industries in the region (Figure 4a). Before 2017, the grey correlation between ice–snow tourism and the secondary industry was higher than that with the first and third industries. After 2017, the grey correlation between ice–snow tourism and the third industry was higher than that with the first and second industries. This is mainly because, before 2017, ice–snow tourism was in its infancy, and the development of ice–snow tourism was constrained by economic development. Infrastructure construction urgently needed to be improved, while the growth of industry promoted infrastructure upgrade. After 2017, the construction of infrastructure for the ice–snow tourism industry gradually started to be completed, and there was an increase in the correlation between the service industry and the ice–snow tourism industry that was higher than the correlation between the first and second industries during this period (Figure 4b). By comparing Figure 4a,b, it can be found that the three major industries in the study area were affected by COVID-19 (2019–2021), with the second industry being the most seriously affected, followed by the first industry, resulting in a similar gray correlation between the two industries and ice–snow tourism.

Table 1. Grey correlation degree of ice–snow tourism-related industries in four zones (Zone I, II, III, and IV). TR is the overall grey correlation degree in Jilin province.

Related Industries	TR	I	II	III	IV
Farming, forestry, animal husbandry, and fishery	0.672	0.941	0.578	0.727	0.579
Second industry	0.743	0.962	0.605	0.628	0.616
Construction	0.773	0.708	0.643	0.782	0.776
Transportation, storage, post, and telecommunication	0.672	0.946	0.647	0.646	0.675
Wholesale, retail trade	0.697	0.970	0.622	0.672	0.682
Hotel and catering service	0.720	0.956	0.734	0.616	0.664
Finance	0.838	0.947	0.620	0.789	0.640
Real estate	0.812	0.913	0.642	0.747	0.518
Other social services	0.802	0.973	0.626	0.795	0.621

shows the grey correlation degree of ice–snow tourism-related industries in the four zones. From the perspective of the entire region, the ice–snow tourism industry has a high correlation with the financial industry, real estate industry, and other service industries, followed by the construction industry, industry, accommodation, and catering industry, and finally the agriculture, forestry, animal husbandry, and sideline fishing industries, as well as the transportation, warehousing, and postal industries. However, in four different zones, there are significant differences in their related industries. The ice–snow tourism industry in Zone I mainly affects the wholesale and retail industry, and has a strong driving effect on other industries. The ice–snow industry in Zone II mainly affects the hotel and catering industry, while in Zone III it mainly affects the service industry, and in Zone IV the construction industry. The driving force of ice–snow tourism industry is relatively low in Zones II, III, and IV. The strong grey correlation between the industries in Zone I indicates good industrial synergy, while in other Zones, the grey correlation between industries is weak. For all zones, there is weak industrial synergy between ice–snow tourism and the first industry—transportation, storage, post, and telecommunication.

4.3. Copula Joint Distribution of Ice–Snow Tourism-Related Industries

To explore the correlation between ice–snow tourism and related industries, in this study, spatial POI data of the ice–snow tourism industry were used to measure and analyze the correlation between the number of ice–snow tourists and the following three service industries: wholesale and retail, hotel and catering, and transportation. Based on an overall analysis of the study area, the dependency between the tourism population and the wholesale and retail industry was R = 0.7674 (u = 5.0862), while the dependency between the tourism population and the hotel and catering industry was R = 0.7674 (u = 13.3988). The dependence of the tourism population on the hotel and catering industries was relatively low (<0.4) (Figure 5 and Figure 6).

On the basis of an analysis of industrial synergy in various counties, it was found that the number of ice–snow tourists in Changchun, Liaoyuan, and Siping counties demonstrated a high degree of dependency on the wholesale and retail industries, as well as on the hotel and catering industries (>0.85), while the dependency between the Baishan and Songyuan counties was relatively low. The dependence of the number of ice–snow tourists on the transportation industry was very low, with Liaoyuan and Jilin City having a relatively high dependency (

Table 2. The number of tourists (NT) and the wholesale and retail industry (WR), the hotel and catering industry (HC), and the transportation industry (TS).

	NT & WR		NT & HC		NT & TS
	R	u	R	u	R	u
Changchun	0.871	4.779	0.900	4.016	0.538	1.661
Jilin	0.752	7.700	0.799	7.349	0.560	15.769
Siping	0.864	2.020	0.888	1.796	0.425	2.682
Liaoyuan	0.870	3.4 × 107	0.865	3.4 × 107	0.639	281.389
Tonghua	0.753	4.930	0.765	5.535	0.412	3.551
Baishan	0.591	8.360	0.713	9.006	0.439	5.566
Songyuan	0.618	2.188	0.719	4.839	0.364	3.456
Baicheng	0.720	2.776	0.730	4.405	0.475	5.057
Yanji	0.780	3.239	0.822	6.138	0.507	5.279

).

Taking the gross domestic product of the regional third industry as the control variable, the partial correlation between the number of tourists and the wholesale and retail trade, hotel and catering, and transportation industries in Jilin Province were analyzed to obtain the correlation coefficients between the ice–snow tourism industry and related industries. It was found that the partial correlation coefficients between the ice–snow tourism industry and its related industries were 0.1887 (p = 0.3885), 0.7133 (p = 0.00013288), and −0.4231 (p = 0.0443), respectively. It was also found that there was no significant correlation between wholesale and retail trade and ice–snow tourism in Jilin Province, while there was a significant correlation between the hotel and catering industry, the transportation industry, and ice–snow tourism. The contribution of the hotel and catering industry to tourist arrivals was positively correlated, while the transportation industry was negatively correlated with ice–snow tourism. The main reason for this is that the majority of tourists are primarily self-driving, while the number of tourists using public transportation has significantly decreased.

4.4. Analysis of the Contribution of the Ice–Snow Tourism Industry to Related Industries Based on SEM

In this study, three industrial populations were evaluated in terms of investment and per capita GDP to characterize socio-economic development. The tourism industry was characterized using the number of domestic tourists (TOUR2), the number of international tourists (TOUR1), and the per capita consumption of tourism (PCONSUM). The tourism-related industries characterized were the hotel and catering industry (HOTEL), the wholesale and retail industry (SALE), and the transportation industry (TRANS). On this basis, a structural state equation between the social economy, ice–snow tourism, and related industries was constructed (Figure 7). We selected the employed population (EMPOP), social investment (INVEST), and per capita GDP (PGDP) to describe social and economic development.

As can be seen from Figure 7, social and economic development has a promoting effect on both ice–snow tourism and its related industries. On the one hand, social and economic development has promoted the construction of ice–snow tourism infrastructure in society, while the consumption level among the public will also increase, thereby increasing the number of potential tourists and potential consumers. Social and economic development had a promoting effect on ice–snow tourism-related industries. Social and economic development increased the construction of infrastructure for ice–snow tourism-related industries (R = 0.753), which had a far greater impact than that exerted by ice–snow tourism on its related industries (R = 0.251). These results are consistent with those reported in relevant studies [23].

To verify the results of this study, the industry influence and industry sensitivity of tourism were calculated using the input–output table of regional industry. Industry influence and industry sensitivity refer to the economic connections and spillover effects between industries that must influence and be influenced by the economic activities of other industries, including the production, supply, and sales activities of any industry. The ability and intensity with which an industry affects other industries is called industrial influence, and the greater the coefficient of influence, the greater the boosting effect of the industry on other industries. The ability and intensity with which an industry can be influenced by other industries is called industry sensitivity. The complete-consumption coefficient refers to the number of products and services that need to be fully consumed by each department to increase the total output of a unit in a certain department. This study analyzed and utilized the input–output table of various industries in Jilin Province (2017) to calculate the industrial influence, industry sensitivity (Figure 8), and complete-consumption coefficient (Figure 9) of each industry in Jilin Province.

Figure 8 shows that the influence coefficient of the industry in which ice–snow tourism belongs (I41) is 1.011, and the sensitivity coefficient is 0.433. The industrial influence is greater than 1, indicating that the ice and snow tourism industry has a strong influence and plays a significant role in promoting the development of other industries. The industrial sensitivity coefficient reflects the degree of forward linkage of an industry and refers to a coefficient whereby changes in production in other industries cause corresponding changes in production in another given industry. If the sensitivity coefficient of a given industry is lower than 1, this indicates that the industry has low sensitivity and is not easily affected by other industrial sectors. Under conditions of rapid economic growth, industries with low sensitivity coefficients generally have slow rates of development. Figure 9 shows the demand for other related industries due to the increase in the output value of the ice–snow tourism industry. This indicates the degree of dependence of the ice–snow tourism industry on other industries. It was found that the three industries most driven by the ice–snow tourism industry include culture–education–sports (I10), electricity and heat production (I25), and the metal processing industry (I14). From the above analysis, it can be seen that ice–snow tourism in Jilin Province has a strong promoting effect on other industries in the region [24], but the promoting effect of related industries on ice–snow tourism is relatively limited.

5. Discussion

Industrial synergism is a complex and open system composed of multiple industries, where each industry is coordinated in time, space, and function. It is an effective way of promoting the development of industries, regional economies, and macroeconomies. It is a new form of industrial organization that promotes industrial agglomeration, complementary advantages, resource sharing, and win–win cooperation through government guidance, enterprise entities, and market operation, achieving sustainable economic and social development. In recent years, researchers have focused on scientific issues in the ice–snow tourism industry, including regional synergism [25], driving force factors [26], high-quality development [27], and the optimal development path of ice–snow tourism destinations [28]. However, there is a lack of research on the industrial synergism of ice–snow tourism and related industries from a micro perspective.

On the basis of a grey correlation analysis of the ice–snow tourism industry and other social industries, it was found that finance and real estate correlate significantly with the ice–snow tourism industry, indicating that the ice–snow tourism industry plays a vital role in promoting social development. This effect varies significantly in different zones, and for zones with low correlation, managers should enhance the driving role of the ice–snow tourism industry.

Traditionally, tourism-related industries are believed to include the hotel and catering industry, wholesale and retail, transportation, etc. However, statistical analysis shows that the correlation between transportation and ice–snow tourism is relatively low, and a partial correlation analysis showed a negative correlation between ice–snow tourism and transportation. This indicates that the impact of transportation on ice–snow tourism is not explicit. The development of the transportation industry has a potential promoting effect on the ice–snow tourism industry. With the significant changes in the mode of travel by which tourists reach ice–snow tourism activities, self-driving has become one of the main ways for tourists to travel.

On the basis of gray analysis and Copula analysis, it was found that, except for Zone I, the synergy between the ice–snow tourism industry and other social industries in Jilin Province is relatively low. The increased output value of the ice–snow tourism industry in the other three zones is mainly dependent on tickets, and there is a lack of integration between industries.

6. Conclusions

As a newly emerging industry, ice–snow tourism has developed rapidly in Jilin Province in recent years, and its industrial level has been greatly improving, forming four major ice–snow tourism industry belts in Jilin Province. The development of ice–snow tourism has driven that of closely related industries such as hotel and catering, wholesale and retail, and transportation, and has had a strong impact on other industries. However, the driving effect of other industries on ice–snow tourism is limited. Therefore, it is necessary to explore other industries with elements of the ice–snow industry to improve inter-industry correlation in Jilin Province.

The correlation between tertiary industries and the ice–snow tourism industry has gradually increased, surpassing the first and second industries after 2017. At the regional level, there is a strong correlation between the industries in Zone I and the ice–snow industry, followed by Zone III. Therefore, it is necessary to strengthen research into the the collaborative development of the ice–snow tourism industry in Zone II and Zone IV.

We proposed three methods for establishing the correlation between the ice–snow tourism industry and the other industries in the region, measuring the level of spatially synergetic development between the ice–snow tourism industry and related industries, and analyzing the contribution of the ice–snow tourism industry to closely related industries. These methods have important guiding significance for quantifying the integrated development between the regional ice–snow tourism industry and other regional industries, improving and optimizing industrial structure. By analyzing the three major service industries that are closely related to ice–snow tourism—finance, wholesale and retail, and hotel and catering—it was found that the correlations of the ice–snow tourism industry with wholesale and retail, as well as with hotel and catering, were strong, reaching 0.7674, while the correlation with transportation was weak. From a regional perspective, the correlation between Zone I and Zone III was strong, while the correlation between Zone II and Zone IV was weak. Overall, the level of contribution of ice–snow tourism to the three closely related service industries reached 25.1%.

Grey correlation analysis and Copula analysis indicated that there was insufficient synergy between the development of the ice–snow tourism industry and the related social industries. The ice–snow tourism industry has a weak driving force for the first- and second-largest industries in the economy, and each zone should develop ice–snow tourism-related industries in accordance with its own characteristics. The elements of the first- and second-largest industries in the economy in the zones were integrated into the ice–snow tourism industry to enhance the regional characteristics of the ice–snow tourism industry. As a service industry itself, the synergy between the ice–snow tourism industry and other service industries also needs to be strengthened, such as by enhancing the quality of hotel and catering services and improving the service provided by the transportation industry.

While we obtained the correlation between the ice–snow tourism industry and other social industries in the region, as well as the contribution rate of the ice–snow tourism industry to its related industries and the synergy between the ice–snow tourism industry and its related industries, these relationships were all nevertheless determined using statistical methods, and there is a lack of analysis of the mechanism by which the ice–snow tourism industry drives other social industries, which is also a key issue for our future research. Our next step will be to study the driving mechanism and implementation path of synergy between the ice–snow tourism industry and regional industries.