The Coupling Coordination Degree and Spatial Correlation Analysis of the Digital Economy and Sports Industry in China

Abstract

Driven by a new round of scientific and technological revolution and industrial transformation, the interactive and integrated development of the digital economy and the sports industry has become the objective demand of current economic development. In order to explore the coupling coordination relationship between the digital economy and the sports industry, this paper uses the coupling coordination degree model and exploratory spatial data analysis method to analyze the coupling coordination degree and spatial correlation of the integrated development of China’s digital economy and its sports industry. It does this by taking the statistical data of 30 provinces, autonomous regions and municipalities in China from 2015 to 2019 as observation samples. The results show that the coupling coordination degree of China’s digital economy and sports industry is low, showing an unbalanced development trend of “low in the west, high in the east, low in the north and high in the south”. There is a significant positive clustering in spatial integration of the two, and the degree of clustering increases year by year. It is necessary to establish a regional coordinated development mechanism, define the stage of regional integration, optimize the spatial pattern of coupled development, and promote the balanced development of digital economy and sports industry.

1. Introduction

The digital economy is a new economic form with digital knowledge and information as the key production factors, digital technology as the core driving force, and modern information networks as important carriers [1]. It has become the leading driving force of global economic and social development and technological change. The 19th Party Congress report pointed out the need to “vigorously develop the digital economy, and promote the deep integration of artificial intelligence, the internet, big data and the real economy” [2]. “The Action to Foster the Development of New Economy Implementation Plan” proposed to “vigorously cultivate new digital economy and further accelerate the digital transformation of industries” [3] and to continuously promote the healthy development of the digital economy. “The Proposal of the Central Committee of the Communist Party of China on Formulating the 14th Five-Year Plan for National Economic and Social Development and the 2035 Visionary Goals” proposed to “give full play to the advantages of massive data and rich application scenarios, promote the deep integration of digital technology and the real economy, empower traditional industries to transform and upgrade, and give birth to new industries and new business models” [4], as the digital economy has become a new engine for China’s economic development. General Secretary Xi Jinping once again stressed the need to grasp the developmental trend and laws of the digital economy, promote the deep integration of digital technology and the real economy, empower the transformation and upgrading of traditional industries, and give rise to new industries and new business models [5], providing a direction for the acceleration of the integration of the digital economy and the real economy. The integration of the digital and real economies has become an important task for China in order to make the digital economy stronger, better, and bigger.

The integration of the digital and real economies is a major trend in the latest round of technological and industrial revolutions. The sports industry is an important part of the real economy, and the integration of the sports industry with the digital economy can help to continuously improve its digitalization and intelligence and provide an innovative driving force for high-quality development. “The Opinions on Promoting National Fitness and Sports Consumption to Promote the High-Quality Development of Sports Industry” states that “we should accelerate the deep integration of the Internet, big data, artificial intelligence and the real economy of sports” and vigorously develop “Internet + sports” [6] to further break down institutional barriers to the development of the sports industry and provide an institutional guarantee for its high-quality development. The digital transformation of the three industries has accelerated due to the impact of the COVID-19 pandemic, and the proportion of the digital economy in the tertiary industry has reached 40.7%. The digital economy has become a new driving force for the growth of the sports industry, and its integration has given rise to new industrial modes and product forms.

In recent years, scholars have shown great interest in the relationship between the digital economy and the sports industry. At the theoretical level, a large number of scholars have carried out relevant research, based on the concept of the digital economy and combined with the “push-pull theory”. Ye et al. studied and elaborated the driving mechanism behind the development of a sports industry driven by the digital economy [7]. Ren et al. believe that the sports industry gives play to economies of scale, economies of scope and the long tail effect through factor digitalization, process digitalization and product digitalization, forming the internal mechanism of the integration of the digital economy and the sports industry [8]. Dong et al. determined the mechanism by which the big data digital economy has driven the high-quality development of the sports industry from the micro, meso and macro levels. They also showed, through simulation experiments, that the digital economy can endow the sports industry with high-quality developmental power, improve industrial production efficiency, and contribute to reforms of the quality, efficiency, and power of the sports industry [9]. At the same time, the digital economy is gradually being applied and infiltrated into different aspects of the sports industry, Unmeng Chen, Li Haijie, Cai Jianhui, Luo Yuxin and Liu Chao respectively analyzed the dynamic mechanism, value dimension and strategic measures of the digital economy in the creative sports industry, in towns characterized as sports and leisure towns, the sporting goods manufacturing industry, the sports competition performance industry, the ice and snow sports industry and other fields [10,11,12,13,14]. Shen Keyin et al. further refined this program of research and its content when they took the 5G-enabled live ice hockey game of Beijing Sports Media, the block chain-enabled “Sports Time Bank” of Bluebird Sports, and the internet-enabled Zhejiang Huanglong Sports Center as examples for their analysis of the digitalization of the sports service industry and, from that, made a determination of the development trend [15]. Pan Wei, Wei Yuan and Chai Wangjun respectively analyzed the current development predicament and optimization path from the perspectives of a digital economy that enables a high-quality sports industry, drives the upgrading of the sports industry structure and boosts the digitalization of the sports industry [16,17,18].

Existing literature has shown that the digital economy can significantly improve the development of the sports industry; however, digital economies and sports industries are complex systems and, without accurate objects and appropriate indicators, it is difficult to quantitatively analyze the relationship between them. In order to solve this problem, a large number of scholars have carried out relevant research. Considering the potential relationship between a digital economy and energy transition, Muhammad Shahbaz et al. studied the impact of a digital economy on renewable energy consumption and the power generation structure, as well as the heterogeneity between the digital economy and energy transition [19]. Li Linhan et al. estimated the level of China’s digital economy from the perspectives of grey correlation, coupled coordination and a spatial correlation network [20]. Li Xiangyang and Han Zhaoan constructed an index system between digital economy and high-quality development from different dimensions [21,22]. Liu Jun et al. analyzed the driving factors of digital economy based on SAR model [23]. Yang Song et al. used the coupling coordination degree model to measure the coupling coordination degree of the Chinese sports and electronic information industries [24]. Wang Yuzhen et al. conducted an empirical analysis of the coupling coordination relationship between the development of China’s sports and tourism industries and sorted the explanatory power of different influencing factors on the coordinated development of these industries from the greatest to the smallest through an OLS regression model [25]. Yao Songbo et al. made an empirical analysis of the influence of sports industry agglomeration on regional economic growth by using static and dynamic panel models and measured the level of sports industry agglomeration in various provinces and cities with an industrial proportion index [26]. Xu Jinfu et al. studied the development of the sports industry and tourism industry from the perspective of industrial integration [27].

The above research results provide good experience for the further deepening of research on the coupling development of the digital economy and the sports industry, but there is still room for further expansion. First, the existing research results focus mainly on the power, process, path and effect of the coupling of “digital economy and sports industry” from a qualitative level. Most of the literature has analyzed specific cases, described the case’s phenomenon, analyzed the existing problems, and put forward countermeasures and suggestions that promote the coupling development of the two. Secondly, the research has focused on the one-way impact of the digital economy on the high-quality development of the sports industry; however, the two systems interact with each other, and so it is necessary to analyze the two-way relationship between them from the perspective of coupling and coordinated development. Third, the index system for the development of the digital economy and sports industry exists independently, and a coupling development system between the digital economy and sports industry has not been built, so that there are therefore few results for systematic quantitative research on the degree of coupling coordination between the two and the spatial distribution characteristics of this relationship.

Based on this, the main contribution of this study is reflected in the following two aspects: First, it constructs an evaluation index system of the coupling and integration developments of the digital economy and sports industry by using the coupling coordination theory. Additionally, it empirically tests the coupling development level and dynamic evolution of the Chinese digital economy and sports industry based on statistical data from 2015 to 2019, expanding quantitative research on the integration development of the two. Second, a spatial correlation analysis method is adopted to analyze the spatio-temporal evolution characteristics of the integrated development of China’s digital economy and sports industry, and the correlation and spatial agglomeration effect of the integrated development of the digital economy and sports industry in 30 provinces, autonomous regions and municipalities in China (except Xizang and Hong Kong, Macao and Taiwan, hereinafter referred to as the provinces) are analyzed from a spatial perspective. In order to provide theoretical and practical reference for other countries (regions) in the world to quantitatively evaluate the complex coupling relationship between a digital economy and sports industry. This study provides a quantitative decision-making reference for the relevant departments in China and its provinces to clearly position the integration stage, identify the two-way force between digital economy and sports industry, optimize the digital transformation strategy of a sports industry, and improve the driving force of a digital economy.

The rest of this article is organized as follows. The second part introduces the data source, index system structure and research methods. In Section 3, the dynamic coupling relationship with time is studied. The Section 4 further discusses the spatial correlation pattern of integrated development. Finally, Section 5 draws conclusions and recommendations.

2. Indicator Selection, Data Sources, and Research Methods

2.1. Selection of Indicators

Based on the integration mechanism and associated definitions of the digital economy and sports industry, and in accordance with the principles of scientific, systematic and available index selection, the index was screened and the index system was determined by using theoretical analysis and frequency statistics. First, the digital economy index system and sports industry index system were determined to be the main topics of literature retrieval on CNKI, and 29 and 62 studies related to Peking University core and CSSCI, respectively, were obtained. Through frequency statistics, the indexes with high frequency were selected to form the first round of indicators. Secondly, theoretical analysis was used to explain the connotation and elements of the digital economy and sports industry, and the primary indicators were checked and any gaps were filled, before the final determination and construction of a coupled coordination degree evaluation index system of the integration and development of the digital economy and sports industry, with 21 specific indicators in five dimensions (see

Table 1. Evaluation index system of the coupling and coordination degree of the integration development of the digital economy and sports industry.

Main Indicators	Tier 1 Indicators	Code Name	Secondary Indicators	Weights	Indicator Units	Direction
Digital economy indicators D	Informatization development indicators	D1	Fiber-optic cable density	0.0715	Million meters/square meter	+
		D2	Density of cellphone base stations	0.0715	Million/million square kilometers	+
		D3 1	Percentage of information technology practitioners	0.0713	%	+
		D4	Total telecommunications business	0.0715	Billion CNY	+
		D5	Software business revenue	0.0715	Billion CNY	+
	Internet development indicators	D6	Internet access port density	0.0714	pcs/10,000	+
		D7	Mobile phone penetration rate	0.0175	Department/100 People	+
		D8	Number of broadband Internet users as a percentage	0.0175	%	+
		D9	Percentage of mobile Internet users	0.0714	%	+
	Digital trading development indicators	D10	Percentage of corporate websites	0.0175	%	+
		D11	Number of computers used by enterprises	0.0712	%	+
		D12	E-commerce share	0.0713	%	+
		D13	E-commerce sales	0.0714	Billion CNY	+
		D14	Online retail sales	0.0715	Billion CNY	+
Sports industry development indicators standard S	Industrial economy indicators	S1 2	Culture, sports, and entertainment industry enterprise legal person unit business income	0.1666	Billion CNY	+
		S2 2	Main business income of culture, education, industrial arts, sports, and entertainment goods manufacturing industry	0.1665	Billion CNY	+
		S3	Sports lottery sales	0.1666	Million CNY	+
		S4	Number of marathon events	0.1662	Times	+
	Market size indicators	S5	Number of legal persons in culture, sports, and entertainment	0.1679	Individual	+
		S6	Number of employed persons in culture, sports, and entertainment urban units	0.1663	Million people	+

Note: ¹ Information technology employees refer to information transmission, computer services, and software industry personnel. ² Songbai Yao and Zhenghong Zhou point out that the sports industry is highly correlated and very highly integrated with the culture and entertainment industry, and thus, S1 and S2 were used instead of the sports industry income index.

). All of the aforementioned indicators are positive in nature. The index system of the digital economy mainly includes 14 specific indexes in three sub-systems of informatization development, internet development and digital transaction development, which are used to represent the comprehensive development level of the digital economy. The index system of the sports industry includes two dimensions of the industrial economy and market size and six specific indicators, which are used to represent the comprehensive development level of the sports industry. All of the aforementioned indicators are positive in nature.

2.2. Data Sources

“The Guidance of the State Council on Actively Promoting ‘Internet+’ Action” released in July 2015 is an important beginning for the development of China’s digital economy, while the number of broadband and mobile internet users and other specific indicators of the digital economy have been monitored since 2014. Due to the lagging nature of the statistics, the specific indicators of the sports industry were only updated in 2019. Additionally, the provincial data are not yet perfect and there are missing data for Tibet; therefore, they were excluded from the sample. As a result, this study selected the relevant data of 30 Chinese provinces from 2015–2019, which were obtained from the “China Statistical Yearbook”, “China Tertiary Industry Statistical Yearbook”, “China Sports Business Yearbook”, “China Industry Yearbook”, the official website of China Marathon, and relevant papers [28].

2.3. Research Methodology

2.3.1. Entropy Weight Method

This study adopted the entropy weight method to determine the weights of each index to ensure the scientificity, objectivity, and accuracy of the evaluation results. The entropy weight method assigns weights according to the amount of information conveyed by the indicators themselves and has the effect of weakening or strengthening the attribute parameters of different values. The processing steps are as follows.

Step 1: Standardization of indicator data. To eliminate the influence of the difference in magnitudes between the indicators, the indicator data are standardized to facilitate the weight assignment of the indicator data [29]. Since the entropy value method for indicator data processing involves the process of calculating natural logarithms, to avoid the meaninglessness of the assigned number, a positive number slightly higher than 0 is often added to the number after the standardization of the indicator data, which is generally chosen to be 0.01 [30].

Standardized treatment formula for positive indicators:

$${{\displaystyle \mathrm{u}}}_{ij}=\frac{{{\displaystyle x}}_{ij}-\min ({{\displaystyle x}}_{ij})}{\max ({{\displaystyle x}}_{ij})-\min ({{\displaystyle x}}_{ij})}+0.01$$

(1)

Standardized treatment formula for negative indicators:

$${{\displaystyle \mathrm{u}}}_{ij}=\frac{\max ({{\displaystyle x}}_{ij})-{{\displaystyle x}}_{ij}}{\max ({{\displaystyle x}}_{ij})-\min ({{\displaystyle x}}_{ij})}+0.01$$

(2)

In these formulae, ${{\displaystyle \mathrm{u}}}_{ij}$ is the value after normalization, ${{\displaystyle x}}_{ij}$ is the data of the jth indicator of the ith system, min(${{\displaystyle x}}_{ij}$) indicates the minimum value, and max(${{\displaystyle x}}_{ij}$) indicates the maximum value.

Step 2: The percentage of the jth data corresponding to the ith indicator is calculated. The formula is as follows:

$${{\displaystyle y}}_{ij}=\frac{{{\displaystyle x}}_{ij}}{{\displaystyle {\sum }_{i=1}^n{{\displaystyle x}}_{ij}}}$$

(3)

Step 3: The entropy value of the jth indicator is calculated, where j = 1, 2,…, m, and m is the number of indicators. The formula is as follows:

$${{\displaystyle e}}_j=\frac{1}{\ln m}{\displaystyle {\sum }_{i=1}^m{{\displaystyle y}}_{ij}{{\displaystyle \ln y}}_{ij}}$$

(4)

Step 4: The coefficient of variation is calculated. The formula is as follows:

$${{\displaystyle g}}_i=1-{{\displaystyle e}}_i$$

(5)

Step 5: The index weights are calculated. The formula is as follows:

$${{\displaystyle w}}_j=\frac{{{\displaystyle g}}_i}{{\displaystyle {\sum }_{j=1}^n{{\displaystyle g}}_i}}$$

(6)

2.3.2. Coupling Coordination Evaluation Method

The integration of the digital economy and sports industry does not belong to the category of industry integration in the traditional sense. It is a new digital business model of the sports industry spawned by the mode of penetration and integration. It is based on the premise that the digital economy has high growth, strong proliferation, and cost reduction, as well as the sports industry’s characteristics of high industrial relevance and high elasticity of demand and income. Coupling refers to the correlation and interaction between two or more objects in their processes of operation due to the correlation and interaction between elements, operating mechanisms, and other key factors, resulting in a joint and mutual influence between the objects, on that ultimately leads to the close cooperation and interdependence of the elements between the objects [31]. The integration and development of the digital economy and sports industry fit the coupling principle, where all elements in the two industries are closely connected; this is the prerequisite for the creation of a new business mode entailing the integration of the two industries. The new digital sports industry is generated under the influence of both internal and external factors. The external factors include policy documents, market demand, and enterprise competition, while the internal factors include the demands to reduce the operation costs, and deepen the division of labor, production synergy and network externality in order to aid the efficient development of the sports industry. Therefore, this study adopted the coupling principle to quantitatively analyze the coupling and coordination degree of the integration and development of the digital economy and sports industry. The basic analysis steps were as follows.

Step 1: Standardization of indicator data. Since the indicators for both the digital economy and the sports industry were positive, Equation (1) was used for the calculation.

Step 2: Measurement of the comprehensive development index. Utilizing the standardized data and the weights of each indicator, a linear weighting method was used to evaluate the comprehensive development level of the digital economy, where ${{\displaystyle u}}_i$ is the contribution value and ${{\displaystyle w}}_i$ is the weight of the indicators.

$$f\left(x\right)={\displaystyle \sum _{i=1}^n{w}_i{u}_i ,{\displaystyle \sum _{i=1}^n{{\displaystyle w}}_i}} =1$$

(7)

Similarly, we set up the horizontal analysis index of the sports industry as the variable ${{\displaystyle q}}_i$ (i = 1, 2,..., m), from which the comprehensive sports industry development index $g(x)$ was obtained.

Step 3: Construction of the coupling degree model of the digital economy and sports industry development level. The formula was as follows:

$$C=\frac{\sqrt{f(x)\times g(x)}}{f(x)+g(x)}$$

(8)

In Equation (8), $C$ is the coupling degree of the two industries (0 < $C$ < 1).

2.3.3. Spatiotemporal Coupling Coordination Model

The development of the digital economy and sports industry in 30 provinces is naturally unequal. To better reflect the coordination and cooperation, along with the virtuous cycle between the systems, a coupled coordination degree model was utilized to explore the spatial and temporal evolution of the coordination degree between the two industries by using the following equations [32]:

$$\{\begin{array}{l}D=\sqrt{C\times T}(0\le D\le 1)\\ T=af(X)+\beta g(x)\end{array}$$

(9)

In Equation (9), $D$ is the degree of coordinated development of the digital economy and sports industry, and $T$ is the comprehensive index of the coordinated development level. $\alpha$ and $\beta$ are coefficients to be determined, which are the contribution coefficients of the digital economy and sports industry, respectively. Since the digital economy and the sports industry are relatively independent, it is difficult to assess the strength of their mutual influence. Thus, based on relevant research results, $\alpha$ and $\beta$ were both taken to be 0.5. The coupling coordination level evaluation index classifications are given in

Table 2. Coupling coordination level evaluation index.

Level	Degree of Coupling Coordination	Coupling Coordination Degree Value
10	Quality coordination	[0.9, 1]
9	Good coordination	[0.8, 0.9)
8	Intermediate coordination	[0.7, 0.8)
7	Primary coordination	[0.6, 0.7)
6	Barely coordinated	[0.5, 0.6)
5	Nearly out of tune	[0.4, 0.5)
4	Mildly out of tune	[0.3, 0.4)
3	Moderate dissonance	[0.2, 0.3)
2	Severe dissonance	[0.1, 0.2)
1	Extremely dysfunctional	[0, 0.1)

[33].

2.3.4. Exploratory Spatial Data Analysis (ESDA)

This study used an exploratory spatial data analysis (ESDA) to explore the spatial correlation pattern evolution, e.g., spatial heterogeneity and spatial dependence, of the coupling coordination degree between the digital economy and the sports industry. The common spatial autocorrelation metrics used by ESDA are the global Moran index (global Moran’s I), which reflects the overall cluster level and distribution, and the local Moran index (local Moran’s I), which reflects the local spatial autocorrelation and clustering regions [34].

(1)

The global Moran index (GMI) is mainly used to reflect the average spatial differences and spatial correlations in the coupling coordination degree of the digital economy and sports industry in different provinces; it is calculated as follows:

$$I=\left[\left(\frac{N}{S_0}\right)\times \left\{{\displaystyle \sum _{i=1}^n{\displaystyle \sum _{j\ne 1}^n{W}_{ij}\left(X_i-\overline{X}\right)}}\right\}\left(X_j-\overline{X}\right)\right]/{\displaystyle \sum _{i=1}^n{(X_i-\overline{X})}^2}$$

(10)

In Equation (10), N is the number of provincial units; ${{\displaystyle x}}_i$ and ${{\displaystyle x}}_j$ are the observed values of provinces i and j, respectively; S₀ is the standardized element; X is the average value of the coupling coordination degree of the digital economy and sports industry; and ${{\displaystyle w}}_{ij}$ is a spatial weight matrix element based on distance criteria, where ${{\displaystyle w}}_{ij}$ = 1 indicates that the two provinces i and j are adjacent and ${{\displaystyle w}}_{ij}$ = 0 indicates that they are not adjacent. The global Moran’s I index falls in the range of [−1, 1] with the following characteristics: when the value is close to 1, the spatial agglomeration feature is obvious with less spatial variability; when the value is close to −1, the spatial agglomeration feature is less obvious with more spatial variability; and when the value is 0, it indicates a spatially random distribution [35].

(2)

The local Moran index (LMI) can make up for the shortcoming of the result of the global Moran’s I test being too general. The LMI was mainly used to evaluate the difference in the spatial association of the coupled development of the digital economy and sports industry in provinces and to characterize the local spatial aggregation of the digital economy and sports industry in different provinces. The equation is as follows:

$${{\displaystyle I}}_i={{\displaystyle Z}}_j{\displaystyle {\sum }_{j\ne 1}^n{{\displaystyle W}}_{ij}}{{\displaystyle Z}}_j$$

(11)

In Equation (11), ${{\displaystyle Z}}_i$ and ${{\displaystyle Z}}_j$ are the standardized observed values of provinces i and j, and ${{\displaystyle w}}_{ij}$ is the spatial weight relating i and j. The local spatial association types between the target province and its neighbors can be classified into four quadrants of spatial clustering forms based on the local Moran’s I index values and characterized by their significance levels using the LISA significance test: the first category has high levels for both the target province and its neighbors, which can be referred to as high–high (H–H); in the second category, both the target province and its neighbors are at the low level, which is referred to as low–low (L–L); the third category is when the target province is high and surrounded by low-level provinces (H–L); and the fourth category is when the province is low and surrounded by high-level provinces (L–H). Moreover, if the local Moran’s I index is <0, the spatial units with different attribute values of “L–H” and “H–L” are clustered together; if the local Moran’s I index is >0, the spatial units with similar attribute values of “H–H” and “L–L” are clustered together [36].

3. Spatial and Temporal Differences in the Coupling and Coordination of the Digital Economy and Sports Industry in China

3.1. Digital Economy and Comprehensive Development Level of the Sports Industry

3.1.1. Analysis of the Evolution of the Integrated Level of the Digital Economy

In terms of the temporal evolution, the digital economy of each province showed an overall upward trend from 2015 to 2019 after analyzing the overall digital economy score and ranking of China’s provinces (as shown in

Table 3. Overall scores and rankings of the digital economy.

	Year	2015		2016		2017		2018		2019		Average Score		Development Level
Province		Score	Rank	Score	Rank	Score	Rank	Score	Rank	Score	Rank	Score	Rank
Beijing		0.723	1	0.736	1	0.729	1	0.728	1	0.732	1	0.730	1	High development level
Shanghai		0.663	2	0.600	3	0.619	2	0.619	2	0.654	2	0.631	2
Guangdong		0.606	3	0.606	2	0.580	3	0.596	3	0.597	3	0.597	3
Zhejiang		0.541	4	0.557	4	0.524	4	0.518	4	0.520	4	0.532	4
Jiangsu		0.477	5	0.491	5	0.476	5	0.471	5	0.484	5	0.480	5
Fujian		0.316	6	0.321	6	0.297	7	0.301	7	0.295	7	0.306	6
Shandong		0.272	7	0.308	7	0.302	6	0.316	6	0.313	6	0.302	7	Medium development level
Hainan		0.268	8	0.296	8	0.269	8	0.260	10	0.259	10	0.270	8
Sichuan		0.247	10	0.278	9	0.264	9	0.271	8	0.291	8	0.270	9
Chongqing		0.219	13	0.251	12	0.246	10	0.262	9	0.269	9	0.249	10
Shaanxi		0.235	11	0.275	10	0.242	11	0.240	11	0.240	12	0.246	11
Liaoning		0.254	9	0.273	11	0.239	12	0.211	12	0.214	14	0.238	12
Tianjin		0.221	12	0.237	13	0.213	13	0.210	14	0.245	11	0.225	13
Hebei		0.184	16	0.223	14	0.209	14	0.201	15	0.214	15	0.206	14
Anhui		0.201	14	0.210	16	0.198	16	0.199	16	0.216	13	0.205	15
Hubei		0.200	15	0.222	15	0.200	15	0.193	17	0.206	16	0.204	16
Ningxia		0.167	17	0.193	17	0.193	17	0.210	13	0.199	17	0.192	17	Low development level
Henan		0.166	18	0.188	18	0.176	18	0.174	18	0.169	18	0.175	18
Qinghai		0.159	21	0.177	19	0.170	19	0.169	19	0.161	19	0.167	19
Inner Mongolia		0.148	23	0.171	22	0.166	20	0.157	20	0.147	22	0.158	20
Hunan		0.151	22	0.173	21	0.149	21	0.148	21	0.148	21	0.154	21
Shanxi		0.161	20	0.170	23	0.148	22	0.138	22	0.133	26	0.150	22
Jiangxi		0.161	19	0.148	26	0.139	25	0.136	24	0.146	23	0.146	23
Yunan		0.146	24	0.174	20	0.144	23	0.125	26	0.124	27	0.143	24
Guizhou		0.119	28	0.148	25	0.138	26	0.136	23	0.149	20	0.138	25
Gansu		0.126	27	0.146	27	0.133	27	0.130	25	0.144	24	0.136	26
Jilin		0.136	26	0.155	24	0.144	24	0.124	27	0.118	28	0.135	27
Xinjiang		0.138	25	0.144	29	0.114	29	0.110	30	0.116	29	0.124	28
Heilongjiang		0.115	29	0.145	28	0.126	28	0.112	29	0.107	30	0.121	29
Guangxi		0.103	30	0.124	30	0.107	30	0.117	28	0.135	25	0.117	30
Average score		0.254		0.271		0.255		0.253		0.258

). However, the trend was not obvious, with the highest average development index of 0.271 in 2016 and the lowest value of 0.254 in 2015. The overall level of the digital economy in the three major economic zones in China (east, central, and west) differed, with the overall level in the east being higher than that in the central region and the overall level in the west being the lowest. Within the three regions, the differences between provinces in the eastern region were the largest, followed by the central region, while the western region had the least noticeable differences. In general, there was a steady improvement in the overall development level of China’s digital economy, but the development is still immature, and more time and resources need to be invested to achieve further growth.

In terms of the spatial evolution, the average values of the comprehensive digital economy score from 2015 to 2019 were processed for natural break grading using ArcGIS 10.8 software, and were separated into three levels (see Figure 1). The first level represented a high development level, the second level represented a medium development level, and the third level represented a low development level. The results in Figure 1 indicate that the development status of China’s digital economy was clustered in different regions, and the high development level included six provinces, i.e., Beijing, Shanghai, Guangdong, Jiangsu, Zhejiang, and Fujian in the eastern region, among which Beijing stimulated the vitality of the digital economy with innovation resources with the benefits it offered from its policy advantages, technology advantages, location advantages, talent advantages, and so on. Beijing gathered digital economy innovation resources and became the national center of digital industrialization. It is a high-development province with “one core” leading (Beijing) and “one belt” supporting (the coastal economic belt). The medium development level included 10 provinces with a wide distribution, including Shandong in the eastern region; Hainan, Liaoning, Tianjin, Hebei, and Anhui in the central region; and Hubei and some other regions with advanced digital economy development, such as Sichuan, which became the backbone of the development of the western region by relying on the Western development strategy. The good economic foundation and the work of innovative scientists and engineers led to the rapid progress of the digital economy in these provinces. However, 14 provinces were still at a low development level, including Xinjiang, Qinghai, Ningxia, Gansu, and other provinces in the western region. The development of the digital economy in these regions was limited by the weak infrastructure construction, small digital industry, small talent pool, etc., which, together with the weak economic foundation, resulted in a relatively weak digital economic development. The development of the digital economy in Jilin and Heilongjiang has been stable for a long time, with no significant advantage in terms of comprehensive scores and growth rates.

3.1.2. Analysis of the Evolution of the Comprehensive Level of the Sports Industry

In terms of the temporal evolution, the comprehensive score and ranking of the sports industry in each province of China were analyzed (see

Table 4. Overall scores and rankings of the sports industry.

	Year	2016		2016		2017		2018		2019		Average Score		Development Level
Province		Score	Rank	Score	Rank	Score	Rank	Score	Rank	Score	Rank	Score	Rank
Jiangsu		0.685	1	0.755	1	0.749	1	0.656	2	0.639	2	0.697	1	High development level
Guangdong		0.632	3	0.733	2	0.575	4	0.758	1	0.764	1	0.692	2
Beijing		0.642	2	0.704	3	0.601	3	0.641	3	0.612	3	0.640	3
Zhejiang		0.507	5	0.517	5	0.607	2	0.583	4	0.604	4	0.564	4
Shandong		0.537	4	0.600	4	0.484	5	0.502	5	0.469	5	0.518	5
Henan		0.357	6	0.366	6	0.364	6	0.380	6	0.384	6	0.370	6	Medium development level
Fujian		0.294	7	0.333	7	0.353	7	0.330	7	0.343	7	0.331	7
Hubei		0.216	12	0.277	11	0.290	9	0.311	8	0.298	8	0.279	8
Hunan		0.250	10	0.289	9	0.303	8	0.273	10	0.261	11	0.275	9
Hebei		0.247	11	0.309	8	0.272	10	0.273	9	0.259	12	0.272	10
Shanghai		0.261	8	0.260	13	0.262	11	0.272	11	0.294	9	0.270	11
Sichuan		0.254	9	0.282	10	0.250	12	0.259	12	0.293	10	0.267	12
Anhui		0.200	14	0.272	12	0.245	13	0.228	13	0.234	13	0.236	13
Yunnan		0.176	16	0.187	15	0.205	14	0.195	14	0.201	14	0.193	14
Shaanxi		0.177	15	0.186	16	0.196	15	0.191	15	0.177	15	0.186	15	Low development level
Liaoning		0.209	13	0.214	14	0.175	16	0.151	18	0.157	18	0.181	16
Jiangxi		0.157	17	0.168	17	0.165	18	0.165	16	0.159	17	0.163	17
Chongqing		0.138	18	0.163	18	0.168	17	0.153	17	0.165	16	0.157	18
Shanxi		0.109	21	0.123	22	0.137	19	0.136	19	0.126	19	0.126	19
Heilongjiang		0.135	19	0.160	19	0.119	21	0.098	23	0.089	19	0.120	20
Tianjin		0.122	20	0.126	21	0.137	20	0.094	24	0.084	26	0.113	21
Guangxi		0.106	22	0.115	23	0.116	22	0.114	20	0.109	21	0.112	22
Inner Mongolia		0.094	26	0.130	20	0.111	23	0.104	22	0.107	22	0.109	23
Guizhou		0.095	25	0.106	25	0.109	24	0.109	21	0.116	20	0.107	24
Jilin		0.103	23	0.109	24	0.086	27	0.089	25	0.082	27	0.094	25
Gansu		0.096	24	0.094	26	0.099	25	0.081	27	0.087	25	0.091	26
Xinjiang		0.066	27	0.078	27	0.095	26	0.088	26	0.102	23	0.086	27
Hainan		0.048	28	0.060	28	0.034	28	0.033	28	0.028	28	0.041	28
Ningxia		0.017	29	0.018	29	0.017	29	0.019	29	0.019	29	0.018	29
Qinghai		0.011	30	0.012	30	0.012	30	0.010	30	0.014	30	0.012	30
Average score		0.231		0.258		0.244		0.243		0.243

) from 2015 to 2019. The development level of the sports industry in 30 provinces first rose and then leveled off, with a small rise seen in all provinces. The highest average development index was 0.258 in 2016 and the lowest was 0.231 in 2015 (a difference of 0.027), and there was a small decline in 2017. The data in the “2018–2023 China Culture, Education, Industry, Sports and Entertainment Goods Manufacturing Industry Development Prospects and Investment Strategic Planning Research Report” released by the China Industry Information Research Network shows that China’s culture, education, industry, beauty, sports, and entertainment goods manufacturing industry has a large overall market size, but the profit level is low. The main business income of China’s cultural, educational, industrial, aesthetic, sports, and entertainment goods manufacturing industries was 165.64 billion CNY in 2017, which was 0.76% less than in 2016, and the total profit of the industries was 93.6 billion CNY, which was 3.64% less than in 2016. The average composite score of the sports industry was 0.2–0.3, with the greatest fluctuation occurring between 2015 and 2016, and a tiny increase in the remaining years.

Regarding the spatial evolution (see Figure 2), the high development level of China’s sports industry covered five eastern provinces: Jiangsu, Guangdong, Beijing, Zhejiang, and Shandong. Jiangsu and Zhejiang are provinces with traditionally strong sports industries that take advantage of sports industry bases, large stadiums, and towns with characteristics that make them suitable for hosting sporting events. Guangdong has undergone rapid industrial structural adjustment and restructuring in recent years, and the sports industry will usher in a period of rapid growth in terms of marketization, industrialization, and scale, with the total scale and added value of the sports industry ranking among the top in the country. During the preparations for the 2022 Winter Olympics, the development of the sports industry entered a “fast track” mode due to the national fitness boom and the continuous influx of capital in Beijing. In recent years, Shandong, which is a typical province, has revitalized its cities through sporting events. With its vast territory and excellent natural conditions, Shandong has a rich resource base for the continuous development of the sports industry and has gradually become a region with a high development level in its sports industry. The medium development level included eight provinces: Henan, Hubei, Hunan, and Anhui in the central region; Fujian, Hebei, and Shanghai in the eastern region; and Sichuan in the west. The low development level included 17 provinces, which were mainly in the western region of China, where the development of the sports industry was slow.

3.1.3. Digital Economy and Sports Industry: Comprehensive Level Analysis

The synchronization of the comprehensive development level of the digital economy and the sports industry was analyzed. When $f(x)$ = $g(x)$, the two industries were in synchronous development; when $f(x)$ > $g(x)$, this indicated that the development of the digital economy was more advanced than that of the sports industry; when $f(x)$ < $g(x)$, this indicated that the development of the sports industry was more advanced than that of the digital economy. As can be seen in Figure 3, the comprehensive development levels of the sports industries in Guangdong, Zhejiang, Jiangsu, Fujian, Shandong, Anhui, Hubei, Hebei, Henan, Jiangxi, Hunan, and Yunnan were higher than the comprehensive development level of the digital economy, while the situation was the opposite in the rest of the provinces (see Figure 3). The overall comprehensive development level of China’s sports industry lagged the comprehensive level of the digital economy for the same period. The results of the analysis of the development speed of the industry show that the integrated level of the digital economy grew at a rate of 1.57% over the five years, while the sports industry increased at a rate of 5.20%. This means that China’s sports industry was developing rapidly, faster than the digital economy, and there was still huge room for improvement.

3.2. Spatial and Temporal Differentiation of the Integration and Coordination of the Digital Economy and Sports Industry

The coupling coordination degree is an index that accurately reflects the coordination between the digital economy and the sports industry; it was established based on the coupling degree [37]. The coupling degree and comprehensive evaluation index were brought into the coupling coordination degree equation to obtain the coupling coordination degree index for 30 Chinese provinces (see

Table 5. Table of coupling coordination scores by province.

	Year	2015	2016	2017	2018	2019	Average Value
Province
Eastern region	Beijing	0.584	0.600	0.575	0.585	0.578	0.584
	Tianjin	0.287	0.294	0.292	0.265	0.268	0.281
	Hebei	0.327	0.362	0.345	0.342	0.343	0.344
	Liaoning	0.339	0.348	0.320	0.299	0.303	0.322
	Shanghai	0.456	0.444	0.449	0.453	0.468	0.454
	Jiangsu	0.535	0.552	0.546	0.527	0.527	0.537
	Zhejiang	0.512	0.518	0.531	0.524	0.529	0.523
	Fujian	0.390	0.404	0.402	0.397	0.399	0.398
	Shandong	0.437	0.464	0.437	0.446	0.438	0.444
	Guangdong	0.556	0.577	0.537	0.580	0.581	0.566
	Hainan	0.238	0.258	0.218	0.215	0.207	0.227
Central region	Shanxi	0.257	0.269	0.267	0.262	0.254	0.262
	Jilin	0.243	0.255	0.236	0.229	0.222	0.237
	Heilongjiang	0.250	0.276	0.247	0.229	0.221	0.245
	Anhui	0.317	0.346	0.332	0.326	0.335	0.331
	Jiangxi	0.282	0.281	0.275	0.273	0.276	0.277
	Henan	0.349	0.362	0.356	0.358	0.357	0.357
	Hubei	0.323	0.352	0.347	0.350	0.352	0.345
	Hunan	0.312	0.334	0.326	0.317	0.313	0.320
Western region	Sichuan	0.354	0.374	0.358	0.364	0.382	0.366
	Chongqing	0.295	0.318	0.319	0.316	0.325	0.315
	Guizhou	0.230	0.250	0.247	0.247	0.256	0.246
	Yunnan	0.283	0.300	0.293	0.279	0.281	0.287
	Shaanxi	0.319	0.336	0.330	0.327	0.321	0.327
	Gansu	0.234	0.242	0.240	0.227	0.237	0.236
	Qinghai	0.146	0.150	0.152	0.145	0.155	0.150
	Ningxia	0.162	0.171	0.170	0.177	0.175	0.171
	Xinjiang	0.218	0.230	0.228	0.222	0.233	0.226
	Guangxi	0.229	0.244	0.236	0.240	0.247	0.239
	Inner Mongolia	0.243	0.273	0.260	0.253	0.251	0.256
Average score		0.324	0.340	0.329	0.326	0.328

).

3.2.1. Temporal Evolutionary Trend of the Coupling and Coordination Degrees of the Digital Economy and Sports Industry

As can be seen in Figure 4 in 2015 and 2019, the coupling and coordination degree of China’s digital economy and sports industry first increased, then decreased, and finally stabilized, indicating that the coupling and coordination degrees of the two industries were gradually improving. However, in terms of specific indices, the overall coupling coordination index of China’s digital economy and sports industry was still low, and thus, the level of coupling coordination still needs to be improved.

3.2.2. Spatially Divergent Characteristics of the Coupling Coordination Degree between the Digital Economy and Sports Industry

To further explore the coupled and coordinated development of the digital economy and sports industry in each province, the average value of the coupling coordination index of each province from 2015 to 2019 was selected to draw a coupling coordination degree interquartile graph (see Figure 5). As shown in Figure 5 and Table 5, there were large regional differences in the coupling and coordination degrees of the digital economy and sports industry in different provinces: the overall development and spatial differentiation were low in the west and high in the east, falling in the north and rising in the south, and there were a large number of regions with low coupling and coordination degrees. In the eastern region, Beijing, Jiangsu, Zhejiang, and Guangdong were at a barely coordinated level and ahead of other provinces due to their continuously growing digital economy scale and superior sports industry conditions; in the eastern region, Shanghai and Shandong were on the verge of disorder, with Shanghai’s sports development lagging the digital economy’s development, and Shandong’s development of the digital economy lagging its sports industry’s development. The comprehensive levels of the digital economy and sports industry in Shanghai and Shandong were in the middle-to-top level in the country. The mild dislocation areas consisted of 11 provinces: Henan, Hubei, Hunan, Anhui, and Henan in the central region; Hebei, Liaoning, and Fujian in the eastern region; and Shaanxi, Chongqing, and Sichuan in the western region. The moderate dislocation level includes 10 provinces, accounting for 33.33% of all provinces: Tianjin, Heilongjiang, Jilin, and Hainan in the east; Shanxi and Jiangxi in the central region; and Inner Mongolia, Guangxi, Gansu, and Xinjiang in the western region. The severe dissonance level was found in two provinces, namely, Qinghai and Ningxia in the western region, where the development level and integration and coordination of the digital economy and sports industry were relatively backward.

4. Spatial Correlation Patterns of the Integration and Development of the Digital Economy and Sports Industry in China

In terms of the spatial distribution, there was an obvious spatial correlation between the levels of coupling and coordination of the digital economy and sports industry integration and development in each region. To further analyze this spatial relationship, this study used the degree of coupling and coordination as an observational indicator to conduct a spatial correlation analysis on the degree of coupling and coordination between the digital economy and sports industry using exploratory spatial data analysis (EDSA) in ArcGIS 10.8 and GeoDa 1.20 software.

4.1. Global Spatial Analysis

From 2015 to 2019, the global Moran’s I index values of the coupling coordination degree of China’s digital economy and sports industry were all positive and increased year on year (see

Table 6. Global Moran index, 2015–2019.

Year	2015	2016	2017	2018	2019
Moran’s I	0.254	0.299	0.256	0.307	0.313
p-value	0.012	0.022	0.015	0.027	0.028

). All values passed the significance test at the 5% statistical level. The spatial pattern of the coupling coordination degree showed a significantly positive spatial correlation, and the spatial clustering characteristics between neighboring regions showed obvious “spatial spillover” and “Matthew” effects, with a high coupling coordination degree of regional space tending toward aggregated distribution, and low coupling coordination degree regions adjacent to each other.

4.2. Local Spatial Autocorrelation Analysis

The global Moran’s I index is a statistical measure of the overall spatial agglomeration effect of the coupled development of the digital economy and sports industry in China, which inevitably obscures certain key features of spatial agglomeration among regions. Therefore, it is necessary to apply the local Moran’s I index to evaluate the local spatial dependence characteristics and heterogeneous spatial pattern of each region and reflect its significance degree using the LISA significance test (see Figure 6).

The results in Figure 6a,b show that the 11 provinces of Xinjiang, Inner Mongolia, Gansu, Sichuan, Jiangsu, Anhui, Shanghai, Zhejiang, Jiangxi, Fujian, and Hainan passed the LISA significance test. The “H–H” agglomeration patterns of Jiangsu, Shanghai, Anhui, Zhejiang, and Fujian passed the significance test with a value of 0.05. These regions became important growth poles for the integration of the digital economy and sports industry in the southeastern coastal region. The “L–L” associations of Xinjiang, Gansu, and Inner Mongolia passed the significance test with a significance level of 0.05; although these regions were not the least developed regions in terms of integration, they were surrounded by lower regions, and thus, each one became the central pole of their “L–L” association and had difficulties in communication and cooperation with external regions. Sichuan was the only region that passed the significance test of the “H–L” association, with a significance level of 0.05. Sichuan was the first region in China to adopt the mixed-ownership model to set up a sports industry group and is also one of the places to conduct national experiments for the innovative development of the digital economy. Sichuan has strong development momentum and is leading in the western region, but there is still a gap compared with the eastern region. The “L–H” correlation regions were Jiangxi and Hainan, of which Hainan passed the significance test with a significance level of 0.001, while Jiangxi did not pass the LISA significance test because it was in the transition zone between regions with high and low degrees of coupling and coordination for integration development. There was no positive radiation effect and also no other regional driving effect. Jiangxi should take full advantage of its geographical proximity to the Zhejiang and Anhui provinces and strengthen regional cooperation and exchanges to form positive interactions.

The coupling coordination of the integration development in 2015, 2017, and 2019 was selected to make a LISA aggregation comparison chart to analyze the aggregation trend of the coupling coordination of the integration development of China’s digital economy and sports industry (see Figure 7). It was found that the coupling coordination degree of integration development improved over time, and the “H–H” agglomeration area gradually expanded from four provinces (Jiangsu, Zhejiang, Fujian, and Shanghai) in 2015 to five (Jiangsu, Shanghai, Anhui, Zhejiang, and Fujian) in 2019, indicating that the spillover effect in the southeastern coastal region was obvious, driving the integrated development of neighboring regions and forming a growth pole of benign interactions and strong associations. The “L–H” agglomeration area relocated from Anhui, Jiangxi, and Hainan in 2015 to Anhui in 2017, and then moved to Jiangxi and Hainan in 2019. These regions did not have a high degree of coupling and coordination between the digital economy and sports industry development. They were surrounded by “H–H” spatial agglomeration provinces. However, they were not affected by the “spatial spillover effect” from the regions with a high degree of coupling and coordination, but the “siphon effect” led to the loss of some capital, human resources, and other resources. The “L–L” clustering area was still in Xinjiang, Gansu, and Inner Mongolia, which are located in the western region of China. It is difficult to change the current situation in these regions in a short time due to the limitations of their geographical locations, climate conditions, and economic development. Therefore, to improve the overall level of integration between China’s digital economy and sports industry, we need to focus on the “L–L” agglomeration region. Sichuan, as the only region in the “H–L” agglomeration, remained unchanged for a long time, indicating that, although the coupling and coordination degree of this region was not low, it could not form an agglomeration effect with neighboring provinces due to the low level of development of the digital economy and sports industry.

5. Conclusions and Recommendations

5.1. Conclusions

It has become an academic consensus that the digital economy can drive the high-quality development of sports industry. Web of Science and CNKI were used to search the research results in this field. After reading a large amount of literature, we found that Bo Dong’s research results were similar to this paper. Both papers explored the relationship between the digital economy and sports industry by means of quantitative research. Firstly, an evaluation index system for the digital economy and the high-quality development of sports industry was constructed, and then the entropy method was used to determine the index weight. However, there are also differences. Bo Dong focuses on the dynamic relationship between the digital economy and the sports industry. In this paper, the data of the sports industry in a certain region is taken as the experimental sample to conduct a simulation test on the high-quality development evaluation system of the sports industry driven by the digital economy. We measured the spatial and temporal changes in the comprehensive development level of the digital economy and sports industry in 30 Chinese provinces from 2015 to 2019. The spatial and temporal divergences of the coupling coordination degree were analyzed, and their spatial correlation pattern was explored. The conclusions are as follows.

First, according to temporal changes in the comprehensive development level of China’s digital economy and sports industry, the development of the digital economy showed an overall upward trend during 2015–2019, though the trend was relatively flat, and the magnitude was small. The development level of the sports industry first increased and then leveled off, and the rise was not significant. There was a short decline in 2017 due to the negative growth of sports goods caused by the negative growth of the main business income of the manufacturing industry. One-third of the provinces in China had a higher comprehensive development level of the digital economy than the sports industry, while the development of the sports industry lagged the digital economy in the rest. In terms of the spatial pattern of the development level of the digital economy and sports industry, it indicated a gradual decrease from the southeast to the northwest. However, despite this, the gap between the two industries’ development levels in most provinces was still obvious.

Second, according to the temporal evolution of the coupling and coordination degree of the digital economy and sports industry in China, the coupling and coordination degree of the two industries showed a trend of first increasing, then decreasing, and finally stabilizing. The overall coupling and coordination index was still low, and ideal coordination was not achieved. The level of coupling and coordination needs to be further improved. The spatial variation of the coupling and coordination degree of the digital economy and sports industry indicated that there were large regional differences in the coupling and coordination degree of the digital economy and sports industry in different provinces.

Third, from the perspective of the spatial correlation pattern of the coupled development of China’s digital economy and sports industry, the two industries showed significant global and local positive spatial clustering patterns, and there were obvious “spatial spillover” and “Matthew” effects in the overall spatial correlations. The eastern and western regions belonged to the “H–H” and “L–L” agglomeration types, respectively, while the central region belonged to the “H–L” and “L–H” agglomeration types, where serious polarization existed. The eastern region was a growth pole with a high degree of integration, and both the digital economy and the sports industry were well developed. The western region had a low level of integration, and thus needs to be further improved.

5.2. Recommendations

According to the findings of the study, the following recommendations can be put forward.

First, establish a regional coordinated development mechanism to promote the balanced development of the digital economy and sports industry. The research results show that the comprehensive development level of the digital economy and sports industry is not balanced. The government should improve the top-level design, give targeted policy preference, financial support and technical guidance to different regions, and speed up the construction of digital economy and sports industry infrastructure according to local conditions. From the perspective of digital economy, the development priorities and division of labor and coordination mechanisms of high-level, medium and low-level regions are clarified. The experience gained from the successful models of digital economy innovation and development pilot zones will be promoted nationwide. One should continue to strengthen and give full play to the advantages of the development of the digital economy, make use of the achievements gained from the construction of the digital economy, and promote the digital transformation and in-depth development of the sports industry. At the sports industry level, we will implement the “Sports aid to Tibet” and other policies to promote the development of the regional sports industry in the western and central regions and seize on the “advantages of the northwest” to develop ice and snow sports, so that the regions that develop first will drive the regions that develop later, thereby forming a positive pattern of interactive development of the sports industries in the eastern, central and western regions.

Second, one should define the stage of regional integration and deepen the coupling and coordinated development. The eastern region should continue to maintain its development momentum, deepen the new business forms that integrate the digital economy with the sports industry, innovate the forms of services and products, and enhance the contribution of the sports industry to the development of the digital economy. At the same time, on the basis of ensuring its own development, the region should actively transfer talents, technology and experience to the outside, break the restrictions of administrative divisions, reduce industry barriers, and undertake the important task of driving the integrated development of China’s digital economy and sports industry. The western region should, based on its own resource endowment and industrial structure characteristics, highlight regional characteristics, and improve the application level of digital sports from the field of low difficulty and low cost. The central region should give full play to its geographical advantages in connecting the east with the west and the south with the north. Taking the “Opinions on Promoting High-quality Development of the Central Region in the New Era” as an opportunity, the central region should accelerate the application of digital, networked and intelligent technologies in the field of sports, promote inter-provincial cooperation and coordinated development of border areas, and create a carrier for the coupling development of the digital economy and sports industry. We will further expand the space for their coupled and coordinated development.

Third, one should attach importance to the positive spatial correlation function of coupling development and optimize its spatial pattern. Combined with the advantages of each province in economy, resources, location and industrial structure, the coupled and differentiated layout was carried out to enhance the coupled and coordinated high-quality development capability of the province itself. We will encourage neighboring provinces to develop comprehensive plans for coupled development and promote a new pattern of coordinated development among provinces. The “H–H” type in the eastern region should, on the premise of maintaining a good development momentum, attach importance to innovative development and explore new business forms, new models and new paths of coupled development. The “L–L” type in the western region can take Sichuan Province as the central city of the coupling and coordination of the two, and drive the improvement of the technical level, management mode, thinking concept and value concept of the surrounding areas with the growth pole effect [38]. The central region belongs to the type of “H–L” and “L–H”. It should focus on coupling development from the comparative advantages it derives from its huge market potential, profound cultural deposits and rich resource factors, and promote cooperation between the six inter-provincial border regions in the central region and the eastern border regions in terms of talents, technology, market and industrial chain, and enhance the internal force of coupling development in the process of cooperation.

5.3. Limitations and Future Research

This paper only selected indexes regarding the aspects of informatization development, internet development, digital transaction development, industrial economy and market size to construct the coupling evaluation index system of digital economy and sports industry. However, the coupling development of digital economy and sports industry is the result of multiple factors. The optimization of the evaluation index and consideration of other related factors are important issues that need to be solved in further research. At the same time, this paper mainly used the method of objective weighting (entropy value method) to determine the weight of indicators. In the future, the method of combining subjective weighting and objective weighting can be used to determine the weight of indicators more scientifically and reasonably. In addition, spatial Markov chain, Dagun Gini coefficient and other methods can be used to deeply analyze the spatial and regional differences of the integration development of the digital economy and sports industry.