Exploring the Coupling Coordination of Green Transformation of Industry and Novel Infrastructure in the Context of Low-Carbon Economy

Abstract

In response to the huge economic impact of the new pneumonia epidemic, the “new infrastructure” has become an important hedge against the downward pressure of the economy. We believe that we should take this opportunity to ensure that the “new infrastructure” projects can strongly support the green and low-carbon transformation of the economy, so whether the new infrastructure can promote the green transformation of the industry has become the focus of academic circles, whereas the existing literature has ignored the coupling and coordination between the green transformation of the sports industry (GTSI) and the novel infrastructure in the context of a low-carbon economy. This study uses data of 31 provinces and cities in China from 2013 to 2020, and a linked coordination degree model is selected to assess the relationship between novel infrastructure and GTSI. The conclusions are as follows. (1) China’s comprehensive index of “novel infrastructure” was 0.228 from 2013 to 2020, comprised of 0.705 convergence infrastructure, 0.227 information infrastructure, and 0.200 innovation infrastructure. (2) The sports industry’s average green total factor productivity is 1.223, with an annual growth rate of 11.2%. The yearly growth rates for green technology efficiency, green pure technology efficiency, and green scale efficiency are correspondingly 10.5%, 6.8%, and 4.5%. 83.6% of provinces and cities are in the growing return to size phase. (3) The mean coupling coordination value between novel infrastructure and GTSI is 0.449. Except for Beijing, Shanghai, and Guangzhou, the majority of provinces and cities led in the development of novel infrastructure but lagged in GTSI. From 2013 to 2020, the coupling coordination degree of novel infrastructure and its three subsystems in specific provinces and cities, such as Beijing and Shanghai, and GTSI show an upward trend, while the overall trend displays a downward trend. (4) Novel infrastructure and GTSI have mutual promoting effect; Government intervention negatively affects the coupling and coordination level; Consumption structure, industrial structure and foreign investment also have a certain positive impact on the two.

1. Introduction

The sports industry, on the whole, is a low-carbon industry, a resource-saving and environment-friendly industry. The development of a sports industry, especially a green sports industry, is undoubtedly a strategic choice to bring into play the advantages of domestic industry. Green sports industry refers to the innovative industry that adopts low energy consumption and no pollution in the process of producing sports material and spiritual products in the above-related industries, inhibits damage to the environment and ecology, and achieves coordinated development of industry and environment. Its development needs to be based on sustainable development theory, ecological economics theory, ecological ethics theory, benefit maximization theory, consumption theory, and so on. It is to improve the whole industrial chain from the perspective of the environment. The specific measures are to improve the energy consumption of sports product manufacturers, to improve the ecological damage brought by the construction of sports stadiums, to improve the ecological pressure brought by sports activities, and to improve the concept of mass sports consumption, and its significance is to form a green industry with sustainable symbiosis and harmony with the environment. China emphasized the need to “accelerate the green transformation of development mode “and emphasized that “promoting green and low-carbon economic and social development is crucial to achieving development of high-quality”. He advocated for “the acceleration of adjustments and optimizations in industrial, energy, and transportation structures, as well as the promotion of low-carbon production techniques and lifestyles” [1], as “The stronger sports are, the stronger China will be, and when a country is wealthy, sports will be prosperous” [2]. it is believed that a robust sports sector reflects a robust nation and vice versa. In the following decade or even longer, the percentage of the Chinese sports industry in the national economy will continue to rise, and it will become a pillar industry [3]. Therefore, for the country and the people, the green development of the sports industry is very important. In actuality, ecologically responsible growth has become a new trend in the global athletics sector. For instance, numerous athletics organizations have pledged to adhere to the UN Framework Convention on Climate Change [4]. Guided by the concept of green development, the 2022 Winter Olympics held in China indirectly guides the sports industry to transform to a green, low-carbon, and circular economy mainly through the cultural and ideological values contained in the Olympic Movement and reconstructs the unsustainable development concept of high ecological input in the sports industry [5]. However, at present, extensive development with high input and low output is still common in Chinese sports manufacturing, stadiums, and competitive and mass sports. For example, in 2018, China produced 1.09 billion pairs of sports shoes with a carbon emission of 14 million tons, which is about CNY400 million (IMedia Research data) [6]. The Action Plan for Carbon Peak by 2030 suggests improvements are to be made to the industrial structure, as well as the green and low-carbon in conventional sectors in light of the considerable carbon emissions connected with particular businesses. Transportation, venue services, and infrastructure are the primary contributors to the tertiary athletics industry’s carbon footprint. According to the data of the Olympic Organizing Committee, it is estimated that carbon emissions from transportation and venue facilities during the Beijing Winter Olympic Games in 2018 were 1.637 million tons (actual emissions were 1.02 million tons), accounting for 87.2% of the total emissions [5]. For large carbon emitters, the Ministry of Ecology and Environment of China released the Pilot Plan for Carbon Neutrality in Large-scale Activities in 2019 [7], which incorporated the carbon emissions of large-scale athletic events into the macro-management system, and the green transformation was imperative. It is a necessary condition to promote the sports industry to transform into a pillar in the general economy and to fulfill the Chinese “3060” dual-carbon target and the “14th Five-Year Plan” and the Vision 2035 goals of “Green transformation of production and life style”.

The 2020 meeting of the PBSC of the CPC emphasized “the importance of investment in novel infrastructure”. During the 14th Five-Year Plan era, Chinese central and local governments published plans for “novel infrastructure building” in 2021. The integration of novel infrastructure and the green transformation of the sports industry are seen as significant steps toward achieving a greater degree of dynamic equilibrium in the sports industry. The decentralized, immutable, and anonymous qualities of blockchain in novel infrastructure, for instance, give new growth potential for the expansion of the sports industry [8]. The release of “sports productivity” using artificial intelligence is an efficient way to promote sports consumption [9], etc. However, the use of novel infrastructure in the sports business is still in its infancy, and the effect of novel infrastructure on the growth of the sports sector is not yet known [10]. Without adequate consideration of industrial factor endowments and economic development levels, blind promotion may result in misaligned resources and inadequate productivity. Consequently, it is essential to comprehend the present condition of novel infrastructure and the environmental improvement of the sports sector, as well as to empirically assess whether novel infrastructure provides new prospects for the green transformation of the sports industry.

The main contributions of this study are as follows: first, based on the summary of existing literature and practical experience, this study theoretically deduces the interaction mechanism between the new infrastructure and the green transformation of the sports industry, which is a further supplement to the new infrastructure theory and the sports discipline theory and has certain theoretical innovations. Second, empirically testing the influence mechanism of coupling coordination between the new infrastructure and green transformation of the sports industry and exploring the new driving force of the green transformation of the sports industry can provide decision-making consultation and reference solutions for relevant government departments. Third, the sports industry is not only a new growth point of the national economy, but also a pillar industry of the national economy in the future. It also belongs to the happiness industry, the sunrise industry, and the green industry in people’s traditional cognition. Promoting the “coupling and coordinated development between the new infrastructure and the green transformation of the sports industry” plays an important leading role in the “extensive formation of green production and life style” proposed by the Chinese government. As a study path, this issue has both theoretical merit and practical need.

In the next section, we will sort out the relevant literature. Section 3 describes the research method. Section 4 describes the temporal and spatial characteristics of green transformation of new infrastructure and sports industry, gives the main measurement results, and makes a thematic analysis of regional differences, and the fifth part is the Conclusions and Recommendations.

2. Literature Review

2.1. Novel Infrastructure

The notion of “novel infrastructure construction” (also known as “novel infrastructure”) was initially proposed under the context of economic reform and development [11]. In the years that followed, several academics have characterized “novel infrastructure” differently. For instance, Sheng Lei et al. (2020) defined novel infrastructure as infrastructure geared toward meeting the requirements of the brand new scientific and technological revolution, on the basis of connection and centered on calculation. It supports several data-related and other operational aspects, offering the new generation of digital infrastructure systems, which is exhibited by its fast technological updates, hardware and software, and collaborative integration, among other characteristics [12]. Huang Qunhui (2020) defined novel infrastructure as the infrastructure of new industrialization, which encompasses all types of infrastructure related to greening. It is not confined to the “seven domains”; the infrastructure also enables the ongoing technological and industrial improvement [13].

In April 2020, an official definition of the idea and meaning of “novel infrastructure” was released by the official agency. The term “novel infrastructure” is used to describe a system of infrastructure that provides digital enhancement, intelligent updating, integrative innovation, and other solutions with a view toward meeting the demands of high-quality development in accordance with new development ideas and technology. Primarily, it comprises infrastructure types of information, convergence, and innovation. The first type of infrastructure includes network infrastructure, which is a type of new technology infrastructure characterized by A.I., blockchain, etc., and computing infrastructure includes data- and computing-related infrastructure; convergence infrastructure refers to an upgrade upon a conventional type of infrastructure utilizing new technologies to deliver integration and concomitant development of the old and the new; innovation infrastructural covers the infrastructure used to support scientific research, education, science, and technology [14]. The fundamental traits and standards of infrastructure still conform to Frischmann’s proposed parameters for various types of public and capital goods, and inputs of commodities as well as services [15]. The “newness” of the novel infrastructure is relative to the traditional infrastructure, and the advanced nature of technology is the biggest difference between the novel infrastructure and the traditional infrastructure [16]. “Novel infrastructure” is considered a potent drive for unleashing economic activities, a realistic road to accomplishing innovation-driven growth, and a crucial aid for promoting high-quality development [12]. Short term, it may stimulate and drive investment and consumption improvement and upgrading; mid to long term, production factors as well as business output can be optimized and enhanced, therefore driving the high-quality growth of the country [17]. Of course, the significance and breadth of novel infrastructure are not predetermined. The academic community and government will continue to increase their knowledge of “novel infrastructure” as technology and industry advance.

2.2. Green Transformation of Sports Industry

The root of green transition research is green growth [18]. Green growth was originally publicly advocated as a new social improvement approach by British environmental economists Pearce et al. in their 1989 paper, “Blueprint for Green Economy” [19]. Green growth refers to a social improvement approach and acts as the transformation to a low-carbon and resource-saving world, fostering economic development, lowering environmental strain, and enhancing well-being and equality in society [20]. As a future pillar industry of the national economy, the proportion of the sports industry increases year by year, but the challenges of widespread expansion, limited size, and poor structural advantages remain constant [21]. For instance, “business forms without systems”, “chains without being smooth”, “uncoordinated parts”, and “high input and poor efficiency” are significant paradoxes [22]. However, existing research covering sports industry advancement concentrates primarily on the sports industry’s linear expansion [23,24], without tying the growth of the sports sector to carbon emissions. As the percentage of the service sector within the athletics business rises, some academics believe it to be a green industry nonobjectively. Considering the declining marginal benefit of emission reduction in the primary and secondary sectors, a significant opportunity exists in fully utilizing the emission reduction in the tertiary industry [25]. Current research on the green transformation of the sports industry (hereinafter referred to as GTSI) is in its infancy and consists mostly of qualitative descriptions devoid of rigorous economic theory and empirical study [26]. To encourage GTSI, it is vital to learn from the strategies utilized in other industries. Research on green transition focuses mostly on three areas. First, in examining the quantification of ecological transition, parametric and non-parametric methodologies are generally used. Examples include the use of data envelopment analysis (DEA), as well as stochastic frontier analysis (SFA). The latter approach mostly emphasizes a unique production function paradigm to evaluate the allocative efficiency of components [27], and it has been extensively used in several research studies to analyze ecological transition [28,29]. In contrast to SFA, the DEA approach operates without the assumption of a production function, hence eliminating estimate bias caused by an inappropriate distribution of hypothesis error terms. Tone (2001) [30] created the non-radial SBM-DSE model, which is frequently used in the assessment of green transition [31,32,33]. It is the most-used DEA approach. Second, we investigate the attributes of green transformation. Green transformation is a dynamic process whose properties should be represented in time and place [34]. Some researchers have examined the geographical aspects of green transformation and discovered substantial variation, including regional and industry variances [35,36]. Some researchers have researched the time variation trend of green transformation in an area and discovered a certain convergence or divergence [37]. Third, we investigate the elements that affect green transformation. Previous research has concentrated on the effects of green transformation in view of environmental regulation [38], technical advancement [39], and international commerce [40].

2.3. Study of the Novel Infrastructure Impact on the Green Transformation of Sports Industry

The novel infrastructure offers systematic and technical endorsement for the Chinese athletics sector, which is advantageous to the industry’s long-term growth. The theoretical foundation for its impact dates back to the 1950s. Solow and Swan’s neoclassical economic growth theory investigated the multiplier effect on economic production in relation to the investment in infrastructure. The 1980s saw a surge of interest from economists studying the correlation among improved infrastructure, rising levels of human capital, and new technical developments. Krugman and other new economic geographers complemented the economic consequences of infrastructure with a geographical distribution viewpoint in the 1990s. A multitude of theoretical investigations into infrastructure and industrial growth have arisen since then. Previous research has examined the effects of environmental legislation [41], technical advancement [42], and industrial concentration [43] on industrial green transformation. Regarding the influence of novel infrastructure on industrial green transformation, the majority of experts feel that infrastructure building encourages energy-efficiency improvement [43] and has a beneficial impact on green transformation [44,45]. Nonetheless, a number of studies indicate that its future function is unknown and may be altered by government size [46], income level [47], and other variables. In addition, a number of researchers have shown that a rise in infrastructure investment would have an impact on energy consumption and consequently increase pollution emissions [48].

In terms of the sports industry, existing research mainly focuses on the application of new infrastructure in the sports industry. For example, big data, 5G, blockchain, and other technologies in the new infrastructure can constantly reshape the organizational structure, management mode, and consumer value of the sports industry when they become key production factors [10]. With the help of new infrastructure, the operation process of traditional sports venues, live events, mass consumption, and other fields can be replaced and transformed to provide a more convenient, comfortable, environmentally friendly, and participatory venue service experience [49]. With the help of internet of things technology, users, coaches, and fitness equipment can be connected, which helps to improve the coaching efficiency and fitness effect of users. It meets users’ personalized fitness needs [50], does not discuss its impact on the GTSI, and does not carry out economic theoretical and empirical analysis on its impact mechanism and path. However, the application of novel infrastructure in the development of the sports industry still has some problems, such as insufficient effective supply, unreasonable resource allocation, and a low degree of industrial integration [50]. Whether novel infrastructure will enable the GTSI and to what extent, the key lies in the degree of coupling and coordination between it and the green development of the sports industry [51]. Coupling coordination degree can reflect the overall balanced development degree of coordination and development level among different systems in a region [52]. The coupling coordination degree of novel infrastructure and GTSI can not only directly reflect the fit degree and trend of the “novel infrastructure” embedded in the GTSI, but also further reflect the direction and path of its empowerment of green development of the sports industry. Therefore, it is necessary to study the current situation and characteristics of coupling coordination and coupling mechanism and the influence mechanism between the two. Based on the above analysis, this paper intends to empirically test and analyze the coupling and coordinated development of novel infrastructure and the sports industry before green transformation.

3. Methods

3.1. Theoretical Mechanisms

3.1.1. Novel Infrastructure Is the Underlying Support for the Green Transformation of the Sports Industry

In addition to compensating for “weaknesses” in the creation of new business forms and business models, the novel infrastructure may provide “forward-looking” technical direction and market demand. It may effectively support the GTSI by encouraging technological innovation, reforming the sports industry structure, and maximizing sports resources allocation.

First of all, the novel infrastructure can effectually encourage the GTSI by encouraging technological innovation. As the development level of novel infrastructure improves, the overall systematic and technological innovation environment of the athletics industry is constantly improved, which can stimulate the innovative thinking of sports enterprises, speed up enterprises’ capital investment in green production technologies, and change the production mode of enterprises. For example, Anta, a leading enterprise in the Chinese athletic goods manufacturing industry, has actively explored green and low-carbon improvement in recent years. By integrating environmental protection into the whole process of product design, production, R & D, and manufacturing, carbon intensity has been reduced from 5.53 tons in 2015 to 2.63 tons of CO₂ equivalent in 2020 [53]. It also cuts transaction and production costs and eliminates information asymmetry by offering digital platforms and technological assistance for the iterative innovation and growth of the athletics sector. For example, the outdoor intelligent gym of the Lvjing Home in Dezhou, Shandong Province, can provide citizens with physical tests, health analyses, and personal exercise plans. Second, the novel infrastructure can effectively encourage the GTSI by reshaping the industrial structure. The novel infrastructure can continuously improve the digital, intensive, networked, and intelligent level of the sports industry through digital industrialization, industrial digitalization, and other modes, reduce energy consumption, and encourage the digital advancement of the industry. Industrial digital improvement can subvert the traditional business model and resource utilization mode, reduce pollution, and improve environmental quality, thus promoting industrial green transformation. In addition, the novel infrastructure can promote profound changes in the traditional sports consumption field by encouraging online trade, eco-friendly travel, and sustainable consumption by releasing the promise of multi-level and multi-channel athletic consumption, reforming sports business models, developing new consumption formats, and providing consumers with more individualized and varied product and service options. For example, the 2022 Beijing Winter Olympics actively fulfilled the commitment of carbon neutrality and realized low-carbon improvement through the whole process of product design, product purchase, production and packaging, product logistics, product sales, and product recycling in the supply chain. Third, the novel infrastructure can efficiently encourage the GTSI by optimizing the allocation of resources. The effective flow of resources is the key to improving the distribution of assets. “Novel infrastructure” investment can not only improve the agglomeration of resources in geographical space but also generate scale effect and specialization to improve the allocation of resources. At the same time, the information sharing brought by “novel infrastructure” can also greatly improve the effectiveness of asset management and application. For example, the Likoo movement takes the venue SaaS as the entry point and integrates online booking, training guidance, e-commerce ticketing, and other functions to provide residents with intelligent, convenient, efficient, and diversified services such as online booking, ball booking, social networking, training, competition, e-commerce, and health.

3.1.2. Sports Industry Is One of the Important Practical Carriers for Sustainable Development of “Novel Infrastructure”

As a catalyst for the revitalization of the national economy and a pillar industry of the national economy in the future, the sports industry also has a certain contribution to the diversification of market demand, the diversification of investment subjects, and the rich input of production factors for novel infrastructure.

First, the development of the sports industry can foster multiple market demands for “novel infrastructure”. For example, in the facilities and equipment market, the National Development and Reform Commission and the General Administration of Sport of the State issued the Implementation Plan of the National Fitness Facilities Strengthening Project in the Period of the 14th Five-Year Plan in 2021, proposing to build or expand 1000 sports parks in the country by 2025 and requiring the promotion of “Internet + fitness” to improve the intelligence, information, and digitalization of national fitness public services. This provides huge potential market demand for novel infrastructure; in terms of service demand market, according to the statistics of the 2019 China Sports Industry Market Status and Development Trend Analysis, there were more than 120 million monthly active users of digital sports in China in 2019, among which more than 30 million monthly active users watched basketball games through digital media, more than 20 million were football users and 20 million digital fitness users. This provides a huge consumer base for novel infrastructure. Second, the development of the sports industry can cultivate various types of investors for “novel infrastructure”. With the volume growth and quality improvement of the sports industry, relevant enterprises or governments will spontaneously invest in various fields of “novel infrastructure”, gradually becoming the key component of investment in the “novel infrastructure” market. For example, in 2014, Alibaba injected RMB 1.2 billion to acquire 50% of Evergrande Football Club, which ushered in the “Internet era” for Chinese football. In 2015, Alibaba established Ali Sports with the aim of innovating the sports industry supply chain using digital economy principles. For instance, in the national “14th Five-Year Plan for Athletic Development”, the government clearly proposed to use central funds to support the construction of key sports parks in local areas, national fitness centers, and other public service facilities and required the promotion of “Internet + fitness”. Under the guidance of this policy, the government and enterprises will increase capital and resource investment in this “just need” market. Third, the development of the sports industry can further enrich the input of various new elements of “novel infrastructure”. New-generation information technologies such as 5G, big data centers, artificial intelligence, and industrial internet are the core components of “Novel infrastructure”. With the long-term cooperation and practice of novel infrastructure and sports industry, a large number of innovative sports science and technology products of higher level will be derived, including cutting-edge technology, high-quality data, knowledge network, innovation ecology, etc. These sports science and technology innovation products can become the factor inputs of the continuous upgrading and transition of “novel infrastructure” and become among the inexhaustible power sources for the sustainable development of “novel infrastructure”.

In short, “Novel infrastructure” constantly promotes the GTSI by constantly promoting technological innovation of the sports industry, reshaping the sports industry structure along with maximizing the use of sporting assets, while the sports industry further cultivates market demand and investment subjects, further enriching its input factors for “novel infrastructure”. The two are coupled and coordinated, jointly promoting the further integration of novel infrastructure and sports industry resources, optimizing the management system and mechanism, and even signing strategic agreements to achieve strategic coordination. In this process, the higher the development level of “novel infrastructure”, the more helpful it is to encourage resource sharing, to realize the improvement of the sports industry from supply to demand, and to improve the green transformation development level and governance ability of the athletics industry. Conversely, the higher the environmental improvement development level of the sports industry, the more powerful external impetus for sustainable development of “novel infrastructure” can be provided (Figure 1).

3.2. Research Design

3.2.1. Comprehensive Evaluation Method of Novel Infrastructure

In accordance with the National Development and Reform Commission’s 2020 characterization and by incorporating relevant research, this study has developed a novel infrastructure index measurement system (

Table 1. Novel infrastructure measurement index system.

First-Level Indicators	Second-Level Indicators	Unit	The Weight	First-Level Indicators	Second-Level Indicators	Unit	The Weight
A. Information Infrastructure	A1 Local switch capacity	Thousands of the door	0.033	A. Information Infrastructure	A13 Number of websites per 100 businesses	Unit	0.033
	A2 Mobile phone switch capacity	Thousands of families	0.032		A14 E-commerce sales	CNY 100 million	0.056
	A3 Mobile phone base station	10,000	0.035	B. Convergence infrastructure	Coupling coefficient of traditional infrastructure and enterprise informatization	-	0.029
	A4 Length of cable line	m	0.024	C. Innovative infrastructure	C1 R & D investment intensity	%	0.055
	A5 Number of domain names	10,000	0.045		C2 Number of mechanisms	Unit	0.033
	A6 Number of websites	10,000	0.055		C3 Total R & D personnel	People	0.050
	A7 Number of IPv4 addresses	10,000	0.050		C4 R & D personnel are equivalent to full hours	Person/year	0.050
	A8 Internet broadband access port	10,000	0.037		C5 Internal R & D expenditure	CNY 10,000	0.055
	A9 Software business revenue	million	0.057		C6 Government funds	CNY 10,000	0.052
	A10 The number of computers used at the end of the term	tai	0.055		C7 Investment in R & D projects	CNY 10,000	0.040
	A11 Every 100 people use computers	tai	0.036		C8 Number of patent applications	Unit	0.041
	A12 Number of websites owned by enterprises	unit	0.045

) that comprises three dimensions: information infrastructure, convergence infrastructure, and innovative infrastructure. Six metrics are used to measure the degree of enterprise informatization: the percentage of companies engaging in e-commerce transactions (%), e-commerce revenues (RMB 100 million), the web sites of the top 100 companies (number), the web sites of the businesses (number), and the quantity of computers in use at the term end (units). The level of traditional infrastructure is expressed by railway operating mileage (km) and area of operating road per person (square meters), rail transport mileage (km), and expressway mileage (km).

For the research, the entropy weight approach is employed to measure novel infrastructure, information infrastructure, convergence infrastructure, and innovative infrastructure. That is, on the basis of data standardization, the ratio, information entropy, and weight of each index under each scheme are calculated successively, and finally, the comprehensive score of each scheme is calculated. This method can carry out objective weight evaluation of multiple indicators and avoid subjective deviation as far as possible. This paper realizes the calculation of related data with the help of SPSS16 software.

3.2.2. Evaluation Approach of the Green Transformation of the Sports Industry

Referring to the existing research [47,48], this paper uses the “Green Total Factor Productivity (hereinafter referred to as GTFP)” index to evaluate the GTSI. There are mainly two methods for measuring TFP: parametric and non-parametric. The traditional DEA model, no matter the BBC or CCR model, cannot fully consider the non-zero slack of input or output, and the efficiency value equal to 1 is not subdivided. In addition, the classic data envelopment analysis (DEA) paradigm does not take into account undesirable outputs such as pollution. The research integrates the idea of unexpected output into the super-efficiency stochastic frontier model (SBM) and uses it to construct the green productivity index (GML) for the athletics sector so that it more closely matches real production. The green productivity index is then applied to measure the GTFP of the sports industry. GTFP can be decomposed into green technology progress change (GTC) and green technology efficiency change (GEC), and green technology efficiency change (GEC) can be further decomposed into green pure technology efficiency change (GPEC) and green scale efficiency change (GSEC).

The construction of the GTFP of the sports industry is mainly according to the ideologies of scientific, validity, and data availability and combined with existing research (

Table 2. Evaluation index system of GTFP in China’s sports industry.

Type of Elements	First-Level Indicators	Second-Level Indicators	Third-Level Indicators	Unit
Factor of input	Consumption of resources	Consumption of capital	Total sports manufacturing assets	CNY 100 million
			Total sports services assets	CNY 100 million
		Consumption of manpower	Average number of workers employed in sports manufacturing	Ten thousand people
			Average number of workers employed in sports services	Ten thousand people
Factor of output	Undesired output	Exhaust gas emission	Sulfur dioxide emissions from sports manufacturing	ton
			Sulfur dioxide emissions from sports services	ton
		Wastewater discharge	Chemical oxygen demand emissions from sports manufacturing industry	ton
			Emissions of chemical oxygen demand in sports services	ton
	Expected output	Development of industry	Sports manufacturing revenue	CNY 100 million
			Sports service industry revenue	CNY 100 million

). Relevant athletic industry data relate to previous research concepts on data stripping [54] and apply the percentage technique of indicators to remove pertinent index information. The sports manufacturing and sports service sectors were separated from their respective and correlated industries, and the original information of the input–output indicators necessary for the assessment of the sports industry were subsequently gathered. In this work, the required data are calculated using MaxDEA8 Ultra.

3.2.3. Evaluation Method of Coupling Coordination Degree

(1) Calculation of coupling coordination degree

In accordance with the present literature, the steps of coupling coordination degree measurements are as follows: first, the coupling degree of the two is calculated; second, the coordination index of the two is calculated; finally, the coupling coordination degree is calculated.

The calculation of coupling degree refers to the concept of capacitive coupling in physics and the model of the capacitive coupling coefficient and refers to the practice of Cong [55]. Its function is as follows:

$$C_n=2\times {\lfloor \frac{u_1\times u_2}{{(u_1+u_2)}^2}\rfloor }^{\frac{1}{2}}$$

(1)

where C_n represents coupling degree, and $u_1$ and $u_2$ represent the comprehensive scores of novel infrastructure and the sports industry, respectively.

The T value of coordination index is:

$$T=\alpha f\left(x\right)+\beta g\left(y\right)$$

(2)

Among them, α and β are the weights to be determined.

The calculation formula of the D value of the coupling coordination degree is:

$$D=\sqrt{C·T}$$

(3)

(2) Types, stages, and relationship characteristics of coupling and coordination degrees

According to the coupling coordination development status of new urban infrastructure and urban sports in different cities and referring to the existing research’s “ten points method” [56,57] and the coupling stage division method [58,59], this paper divides the coupling coordination degree of the two systems into 4 stages and 10 types (

Table 3. Types and stages of the coupling and coordinated development of novel infrastructure and GTSI.

Coupling Coordination Degree (Coupling Degree)	Coupling Coordination Type	Coupling Coordination Phase
0.9–1	Quality coordination	High level coupling
0.8–0.89	Good coordination
0.7–0.79	Intermediate level coordination	Running-in stage
0.6–0.69	Primary coordination
0.5–0.59	Barely in tune
0.4–0.49	On the verge of disorder	Period of antagonism
0.3–0.39	Mild disorder
0.2–0.29	Moderate dissonance	Low level coupling
0.1–0.19	Serious dissonance
0–0.09	Extreme dissonance

).

This study also classifies the characteristics of the relationship between novel infrastructure and GTSI into three categories based on the comprehensive evaluation function of each system (see

Table 4. Characteristics of coupling and coordination relationship between novel infrastructure and GTSI.

Comprehensive Evaluation Function	Characteristics of Coupling and Coordination Relationship between Novel Infrastructure and GTSI
f(x) > g(x)	The GTSI leads, while the development of novel infrastructure lags behind
f(x) = g(x)	The two synchronize the coordination type
f(x) < g(x)	The development of novel infrastructure is leading, while the green transformation of the sports industry lags behind

).

3.2.4. Data Source and Processing

In this paper, 31 provinces (municipalities) of China were taken as observation samples. Considering the availability and timeliness of data, 2014–2021 was selected as the period span. All data were derived from the China Statistical Yearbook, China Industrial Statistical Yearbook, China Tertiary Industry Statistical Yearbook, China Environmental Statistical Yearbook, China Science and Technology Statistical Yearbook, China Urban Construction Statistical Yearbook, etc. Individual missing data were supplemented by interpolation method and average growth rate method. Considering that some statistical data intersected with culture, education, industrial, art, and entertainment industries, to accurately reflect the real situation of the sports industry, relevant data-stripping processing was conducted by referring to the data-stripping ideas of Han Yuanjun, Wu Pu, and Lin Tan et al. (2015) [60].

Among them, the divestiture coefficient of total assets of the sports manufacturing industry, the divestiture coefficient of average employment number, and the divestiture coefficient of main business income are, respectively, the proportion of the total assets of the sports goods manufacturing industry in the total assets of cultural, industrial, sports, and entertainment goods manufacturing industry in the sub-industry data of the China Industrial Statistics Yearbook. The proportion of the average employment number of the sports goods manufacturing industry in the manufacturing of cultural, industrial, sports, and entertainment goods, the proportion of the average number of employees, and the proportion of the main business income of the sports goods manufacturing industry in the main business income of the cultural, educational, industrial, sports, and entertainment goods manufacturing industry are among the proportion of total assets of the sporting goods manufacturing industry in total assets of cultural, educational, industrial, sports and entertainment goods manufacturing industry. The divestiture coefficients of total assets, average employment and main business income of sports service industry are respectively determined by the proportion of total assets of sports enterprises in total assets of cultural, sports, and entertainment enterprises, the proportion of average employment of sports enterprises in average workers of cultural, sports and entertainment enterprises, and the proportion of main business income of sports enterprises in cultural, sports, and entertainment enterprises. The relevant data of cultural, sports, and entertainment enterprises as legal entities are obtained by combining the relevant data of the press and publication industry, radio, television, film and audio industry, culture and art industry, sports and entertainment industry, etc. See

Table 5. Stripping coefficient.

Year Index	2013	2014	2015	2016	2017	2018	2019	2020
Average employment in sports manufacturing industry	0.121	0.120	0.115	0.116	0.107	0.099	0.130	0.139
Average employment in sports service industry	0.093	0.094	0.091	0.092	0.096	0.103	0.103	0.105
Total assets of sports manufacturing industry	0.105	0.098	0.096	0.097	0.100	0.103	0.111	0.117
Total assets of sports service industry	0.091	0.090	0.170	0.135	0.076	0.093	0.095	0.098
Sports manufacturing business income	0.089	0.087	0.088	0.087	0.088	0.090	0.095	0.108
Revenue of sports service industry	0.058	0.059	0.059	0.065	0.076	0.073	0.075	0.072
Total output value of sports industry	0.049	0.058	0.072	0.077	0.080	0.088	0.095	0.087

for the specific stripping coefficient. All variables related to price change factors are deflated. Among them, government funds, internal R & D expenditure, R & D project investment expenditure, per capita GDP, government financial expenditure, and other data are all deflated using the GDP deflator with 2013 as the base period. The total assets of the sports manufacturing industry, the operating revenue of the sports manufacturing industry, the revenue of software business, and the sales of e-commerce are adjusted by the industrial production price index with 2013 as the base period. The total assets of the sports service industry, the operating revenue of the sports service industry, and other data are adjusted by the consumer price index of 2013.

4. Results

4.1. Appraisal of Comprehensive Chinese Novel Infrastructure Development

The entropy weight approach measured the development level of Chinese novel infrastructure and its three subsystems during 2013–2020. The results showed (

Table 6. Comprehensive development level of China’s novel infrastructure and its three subsystems from 2013 to 2020.

	The National	The Eastern Region	The Central Region	The Western Region	Northeast China
Novel infrastructure w	0.228 (0.830)	0.407 (0.568)	0.201 (0.299)	0.112 (0.641)	0.144 (0.422)
Information infrastructure w1	0.227 (0.796)	0.396 (0.565)	0.193 (0.268)	0.119 (0.585)	0.162 (0.379)
Convergence infrastructure w2	0.705 (0.141)	0.757 (0.163)	0.724 (0.104)	0.672 (0.100)	0.628 (0.065)
Innovative infrastructure w3	0.200 (1.042)	0.396 (0.656)	0.172 (0.398)	0.073 (0.955)	0.107 (0.549)

Note: Coefficient of variation is in parentheses.

, Figure 2) that first, from the general level of novel infrastructure development, the average comprehensive development index of Chinese novel infrastructure was 0.228, indicating a low overall development level. By region, the highest development level was attained in the eastern region (0.407 on average), succeeded by the central (0.201 on average) and the northeast region (0.144 on average), and the western region has the lowest level (0.112 on average). The eastern region is 2.09 times more than the central region, 2.82 times more than the northeast region, and 3.626 times more than the western region, showing a large regional gap. Second, in view of the three subsystems, the development level of national information infrastructure (average 0.227) is equal to that of novel infrastructure, and innovation infrastructure (average 0.200) is slightly lower than that of novel infrastructure, while the convergence infrastructure development level is the highest (average 0.705), which is 3.106 and 3.525 times of information infrastructure and innovation infrastructure, respectively. This phenomenon may be linked to the relatively advanced degree of development of conventional infrastructure in convergence infrastructure. When evaluating the three subsystems of novel infrastructure in the four areas, it is found that the information infrastructure and innovative infrastructure display a cascading decrease pattern throughout the eastern, central, northeastern, and western regions as a whole. In contrast, the convergence infrastructure has a somewhat different trajectory, with a pattern of cascading decrease evident in the eastern, central, western, and northeastern sectors. Eastern Chinese information infrastructure is 2.052 times that of central China, 2.444 times that of northeast China, and 3.356 times that of western China. Eastern Chinese convergence infrastructure is 1.046 times more than that of central China, 1.126 times greater than that of western China, and 1.205 times greater than that of northeastern China. Eastern China has 2.302 times the innovative infrastructure of central China, 3.701 times that of northeast China, and 5.425 times that of western China. These numbers indicate that regional disparities in information infrastructure and innovative infrastructure are significant, whereas regional disparities in convergence infrastructure are relatively insignificant. This disparity can be attributed to the relatively insignificant differences in the levels of traditional infrastructure development across China. Fourth, in terms of differences between regions, among the novel infrastructure and its three subsystems at the national level, innovation infrastructure has the largest regional difference (coefficient of variation is 1.042), which is a high difference, followed by novel infrastructure (coefficient of variation is 0.827) and information infrastructure (coefficient of variation is 0.796), and convergence infrastructure has the least regional difference (0.141). By region, the differences of novel, information, and innovation infrastructure are in the western, eastern, northeast, and central regions from large to small, whereas the convergence infrastructure shows a decreasing rule in the eastern, central, western, and northeast regions. Fifth, from the development trend (Figure 2), from 2013 to 2020, the novel infrastructure and its three subsystems generally presented a wavy and spiraling development trend, but the overall change range was not large, and the high level was high, while the low level was low. According to the coefficient of variation, the coefficient of variation of novel infrastructure and its three subsystems presents a wavy advance and a downward spiral trend, indicating that the overall difference of novel infrastructure and its three subsystems is gradually decreasing.

4.2. Green Transformation Evaluation of China’s Sports Industry

The Super-SBM model in MaxDEA8 Ultra software was used to evaluate the green total factor productivity of the Chinese sports industry during 2013–2020. The results showed (

Table 7. Average GTFP and its decomposition of sports industry in various regions of China from 2013 to 2020.

Area	GTFC	GMI	GTC	GEC	GPEC	GSEC	Return to Scale
							Increasing	Constant	Decreasing	Bar Mini Chart
The national	1.223	1.112	1.105	1.073	1.068	1.047	206	3	39
In the east	1.252	1.062	1.252	0.988	1.032	0.988	55	0	25
In the middle	1.269	1.106	1.269	1.008	1.017	1.007	36	0	12
In the west	1.349	1.207	1.349	1.220	1.167	1.131	91	3	2
The northeast	0.525	0.910	0.525	0.884	0.893	0.986	24	0	0

Note: GTFC stands for green total factor productivity, GMI stands for green productivity index, GTC stands for change in green technical progress, GPEC stands for change in green pure technical efficiency, and GSEC stands for change in green scale efficiency.

, Figure 3) that the mean yearly rate of growth for the Chinese sports industry’s GTFP is 11.2%, which is much greater than the mean yearly rate of growth for the Chinese economy (6.18%) in the same period, indicating that the overall ecological efficiency level of the Chinese sports industry is good, and it has become a new growth pole of the country. Second, technical advancement is the primary cause for the rise of green total factor productivity (GTFC) in the sports industry, as shown by the average yearly growth rates of green technology progress change (GTC) (10.5%), green pure technology efficiency (GPEC) (6.8%), and scale efficiency (GSEC) (4.7%). Pure technical efficiency, especially scale efficiency, contributes relatively little. Further analysis shows that among 248 spatio-temporal coordinates of 31 provinces from 2013 to 2020, provinces in the stages of increasing, constant, and decreasing returns to scale account for 83.06%, 1.21%, and 15.73% of the total, respectively. This suggests that the Chinese sports industry is relatively modest and remains in the stage of developing returns on scale throughout the research period. There is ample potential to improve scale efficiency. Third, by region, the green total factor productivity (GTFC) of the athletic industry is highest (mean 1348), with a mean yearly rate of growth of 20.7%, followed by the central region (mean 1269) and the eastern region (mean 1252), with average annual growth rates of 10.6% and 6.0%, respectively. The northeast Chinese athletics sector has the lowest green total factor productivity (GTFC) (mean: 0.525) and the lowest growth (average annual decline: −9%). Further analysis of the growth drivers reveals that technological progress efficiency is the primary driver for the growth of green total factor productivity (GTFC) in the eastern, central, and western regions, as well as the primary cause for the decline of green total factor productivity (GTFC) in the athletics industry in northeast China. Fourth, from the perspective of the development trend (Figure 2), green total factor productivity (GTFC) of the athletics industry generally presents an inverted U-shaped growth trend of initially increasing and then declining. Green technology progress change (GTC) of the athletics industry is mostly identical to the general trend of green total factor productivity (GTFC), which decreased around 2019. However, the green pure technology efficiency change (GPEC) in the green technology efficiency change (GEC) of the athletics industry began to grow in 2019; especially, the green scale efficiency change (GSEC) rapidly increased at a speed of 27.4% in 2020. It shows that the potential advantage of green technology efficiency change (GEC) in the sports industry, especially the green scale efficiency, starts to emerge.

4.3. Evaluation of Coupling Coordination Degree of China’s Novel Infrastructure and Green Transformation of Sports Industry

Based on the coupling coordination degree calculation formula, the coupling coordination degree between the novel infrastructure and the GTSI from 2013 to 2020 is estimated. The results show (

Table 8. Mean values of coupling coordination degrees of novel infrastructure and the GTSI from 2013 to 2020.

Area	Coupling Coordination Degree between W and S	Coupling Coordination Degree between W1 and S	Coupling Coordination Degree between W2 and S	Coupling Coordination Degree between W3 and S
The national	0.449	0.446	0.577	0.450
In the east	0.592	0.590	0.638	0.587
In the middle	0.495	0.488	0.629	0.497
In the west	0.354	0.347	0.558	0.358
The northeast	0.267	0.276	0.343	0.270

Note: W, W1, W2, W3, and S represent novel infrastructure, information infrastructure, convergence infrastructure, innovative infrastructure, and sports industry, respectively (the same below).

, Figure 4) that first, from the national level, the average coupling coordination degree between novel infrastructure and the GTSI is 0.449, which belongs to the category on the verge of imbalance in the antagonistic period, indicating that the overall coupling coordination degree between novel infrastructure and the GTSI is low, and the interaction between the two has not formed resonance and synergy and remains evasive of the high-level coupling coordination phase. This may be because novel infrastructure is just emerging in our country, and the sports industry is also a sunrise industry. Second, from the perspective of the national system, the coupling coordination degree of information infrastructure and the GTSI (mean 0.446) is slightly lower than that of innovation infrastructure and the GTSI (mean 0.450), but the two types of coupling coordination degree are the same as the coupling coordination degree of novel infrastructure and the GTSI, both in the antagonistic period. However, the coupling coordination degree of convergence infrastructure and the GTSI (mean 0.577) is greater than the other three coupling coordination degrees and has been upgraded to the type of reluctant coordination in the run-in stage, which can be attributed to the relatively good development of traditional infrastructure in convergence infrastructure. Third, by comparing the mean values of the four types of coupling coordination degrees in the four regions, the mean values of the four types of coupling coordination degrees in the eastern region are all greater than 0.5, which has crossed the antagonistic period and entered the run-in stage. The coupling coordination degrees of novel, information, and innovation infrastructure, as well as the environmental improvement of the athletics industry, are all reluctant coordination types. The coupling coordination of convergence infrastructure and the GTSI is the primary coordination type. In general, the four types of coupling coordination degrees in the eastern region are observed to be higher than the other three regions, indicating that the coupling coordination development level for novel infrastructure and the GTSI is relatively highest in the eastern region. In the center and western areas, all four kinds of coupling coordination degrees had mean values larger than 0.3 and were in the antagonistic phase. The corresponding mean values in the central region (0.495, 0.488, 0.629, and 0.497, respectively) were all greater than those in the western region (0.354, 0.347, 0.558, and 0.358, respectively). The mean values of the four types of coupling coordination degrees in northeast China were the lowest (0.267, 0.276, 0.343, and 0.270, respectively). Except for the high coupling coordination degrees of integrated infrastructure and the GTSI during the hostile era, the other three categories of coupling coordination degrees were less than 0.3, indicating that they were in the low-level coupling stage. Fourth, the degree of linkage coordination between the three subsystems of novel infrastructure in the central and western regions and the GTSI is comparable to that of the whole nation, according to a comparison of the three subsystems in the four areas. The mean value of coupling coordination degree is the coupling coordination degree of convergence infrastructure and the GTSI, the coupling coordination degree of innovation infrastructure and the GTSI, and the coupling coordination degree of information infrastructure and the GTSI. The east and northeast differ slightly from the rest of the country, in that the coupling coordination degree of convergence infrastructure and the GTSI still ranks first, and the coupling coordination degree of information infrastructure and the GTSI ranks second, not innovation infrastructure. Overall, the degree of linkage coordination between convergence infrastructure and the GTSI is the greatest among the three subsystems in the four areas. Fifth, from a development trend standpoint, the overall trend of coupling coordination degree between China’s novel infrastructure construction (including its three subsystems) and the GTSI during 2014–2020 is similar, both of which show a wave-like downward trend. Although some provinces (cities) such as Beijing and Shanghai show a wave-like upward trend, they cannot change the reality of the overall downward trend. It indicates that the coupling and coordination between the novel infrastructure (including its three subsystems) and the GTSI need to be improved.

Seeing further by provinces (

Table 9. Mean value types of coupling coordination between novel infrastructure and its three subsystems and GTSI in 31 provinces from 2014 to 2020.

Type of Coupling Coordination	Coupling Coordination Degree between W and S	Coupling Coordination Degree between W1 and S	Coupling Coordination Degree between W2 and S	Coupling Coordination Degree between W3 and S
Quality coordination
Good coordination	Beijing ★,	Beijing ★
Intermediate level coordination	Guangdong ★, Jiangsu ★	Guangdong ★	Beijin ★, Guangdong ★, Jiangsu ★, Zhejiang ★, Anhui ★, Shanxi, Sichuan ★	Beijin ★, Jiangsu ★, Guangdon ★
Primary coordination	Zhejiang ★, Shanghai ★	Jiangsu ★, Zhejiang ★, Shanghai ★	Hunan ★, Henan ★, Hubei ★Guizhou ★, Fujian ★, Yunnan ★, Chongqing ★, Gansu, Hebei ★, Tianjin	Zhejiang ★, Shanghai ★
Barely in tune	Henan, Sichuan, Fujian, Shanxi, Anhui, Hunan, Hubei, Shandong ★, Tianjin	Henan, Sichuan, Fujian, Shaanxi, Hunan, Anhui, Hubei, Shandong ★, Hebei	Shandong ★, Hainan ★, Tibet ★ Jiangxi, Shanxi, Inner Mongolia ★, Ningxia ★	Shanxi, Henan, Sichuan, Tianjin, Anhui, Fujian, Hunan, Shandong ★, Hubei
On the verge of disorder	Hebei, Chongqing, Yunnan, Guizhou	Tianjin, Chongqing, Yunnan, Guizhou, Shanxi, Jiangxi	Qinghai ★, Shanghai, Jilin	Chongqing, Hebei, Gansu
Mild disorder	Shanxi, Jiangxi, Gansu, JilinInner Mongolia	Gansu, Xinjiang, Jilin, Hainan, Inner Mongolia	Xinjiang, Liaoning ★	Shanxi, Yunnan, Guizhou, Jiangxi, Jilin, Xinjiang, Inner Mongolia, Ningxia
Moderate dissonance	Xinjiang, Hainan, Guangxi and Liaoning ★, Ningxia, Heilongjiang ★	Guangxi, Liaoning ★, Heilongjiang ★	Heilongjiang ★, Guangxi ★	Guangxi, Liaoning ★, Hainan, Qinghai
Serious dissonance	Inner Mongolia, Qinghai	Inner Mongolia, Qinghai,		Heilongjiang ★
Extreme dissonance		Ningxia		Inner Mongolia

Note: With leading ★ for sports development, the corresponding novel infrastructure development lags behind.

): first, from the number of provinces, the proportion of provinces at various phases of coupling development, from low to high, is shaped like a spindle; that is, the number of provinces at two ends is less, and the number in the middle is more. However, the spindle weight centers were different. The coupling coordination centers of novel infrastructure, information infrastructure, innovation infrastructure, and GTSI were all located within the barely coordination type (all nine, accounting for 29.3%), while the coupling coordination centers of convergence infrastructure and the GTSI were located in the primary coordination type (10, 32.26%). Second, from the perspective of provinces (cities) with a high coupling level, the mean values of the four types of coupling coordination degrees of Beijing, Guangdong, and Jiangsu are always in the top three of various coupling coordination development levels. In addition to the good coordination of novel infrastructure, information infrastructure and the GTSI, the other two types belong to the intermediate coordination, and Guangdong has always been in the intermediate coordination. The coupling coordination mean value of Jiangsu’s information infrastructure and the GTSI is even in the primary coordination stage, indicating that the coupling coordination development level of provinces (cities) in a relatively high coupling type is not high on the whole. Third, from the characteristics of coupling relationship, most provinces (cities) in China from 2014 to 2020 belong to the leading type of novel infrastructure development and the lagging type of the GTSI, indicating that the GTSI is relatively lagging behind the development of novel infrastructure, which may be the main reason for the low degree of overall coordinated development of coupling. Beijing, Shanghai, Guangdong, Jiangsu, Zhejiang, Shandong, Liaoning, and Heilongjiang provinces and cities take the lead in the GTSI and lag behind in the development of novel infrastructure. Among them, the political and economic centers of China, respectively, are Beijing and Shanghai. In addition, Beijing, Shanghai, Guangdong, Jiangsu, Zhejiang, and Shandong are all developed regions in eastern China. The rapid development of the economy may be the main reason for the leading GTSI in these areas. In addition, Liaoning and Heilongjiang belong to northeast China, which may be related to the rich resources and high development level of sports tourism in these regions.

4.4. Empirical Test

4.4.1. Empirical Model Construction

To assess the interaction between “novel infrastructure” and the GTSI, the following simultaneous equation model is produced:

$$\{\begin{array}{c}spor{t}_i=C_1+{\alpha }_{11}infr{a}_i+\beta ·Contro{l}_{1,i}+{\delta }_{1,t}+{\gamma }_{1,i}+{\mu }_{1,i}\\ infr{a}_i=C_2+{\alpha }_{21}spor{t}_i+\beta ·Contro{l}_{2,i}+{\delta }_{2,t}+{\gamma }_{2,i}+{\mu }_{2,i}\end{array}$$

(4)

where sport is the development level of “ the GTSI “; infra denotes “novel infrastructure”; δ represents the time fixed effect; γ symbolizes the individual fixed effect; μ is the disturbance term that follows independent and identically distributed; α and β are parameters that need to be estimated; t is time; i is province (city); C is a constant term; $Contro{l}_{1,i}$ refers to the factors that affects the GTSI, including industrial structure (ind), measured by the ratio of the secondary and tertiary industries’ added value. Government intervention (gov) is calculated by the proportion of local general budget expenditure in GDP; openness to the outside world (fdi) is measured by the proportion of foreign direct investment in GDP; consumption structure (con) is represented by the ratio of residents’ education, culture, and entertainment consumption to residents’ consumption expenditure. $Contro{l}_{2,i}$ refers to the factors impacting the development of “novel infrastructure” including the economic development level (pgdp), in addition to the variables mentioned above, which is measured by per capita regional product.

Theoretically, the coupling coordination degree of “novel infrastructure” and the GTSI is the result of their coupling interaction. To investigate the influence mechanism of “novel infrastructure” and the GTSI on the coupling and coordinated development of them, this study constructs the following econometric empirical model further:

$$y_i=C+{\alpha }_{1}infr{a}_i+{\alpha }_2·spor{t}_i+{\alpha }_{1}infr{a}_i·spor{t}_i+\beta ·X_i+{\delta }_t+{\gamma }_i+{\mu }_i$$

(5)

where y is the explained variable, representing the coupling and coordination degrees of w and s, w1 and s, w2 and s, and w3 and s respectively in different models; X refers to other influencing factors of coupling and coordination degree.

It should be noted here that to more accurately identify the interaction between novel infrastructure, GTSI, and their impact on their coupling and coordinated development, time fixed effect δ and individual fixed effect γ are introduced into Equations (4) and (5) to eliminate the endogeneity problem caused by time and individual change to a certain extent.

4.4.2. Analysis of Empirical Results

According to Equations (4) and (5), regression analysis was conducted on the data of 31 provinces (cities) from 2014 to 2020 (see

Table 10. The coupling interaction between “novel infrastructure” and GTSI and the test of the influence mechanism of coupling coordination degree from 2014 to 2020.

Independent Variables	Model 1 Dependent Variable w	Model 2 Dependent Variable s	Model 3 W and s Degree of Coupling Coordination	Model 4 W1 and s Degree of Coupling Coordination	Model 5 W2 and s Degree of Coupling Coordination	Model 6 W3 and s Degree of Coupling Coordination
s	0.006 * (0.011)		0.066 *** (0.010)	0.071 *** (0.011)	0.229 *** (0.070)	0.077 *** (0.009)
$w_x$		1.063 *** (2.704)	0.319 *** (0.067)	0.342 *** (0.078)	0.522 *** (0.143)	0.262 *** (0.062)
$w_x\times s$			0.184 ** (0.043)	0.158 ** (0.046)	0.496 *** (0.102)	0.183 * (0.044)
lngov	−0.214 ** (0.035)	−0.226 * (0.231)	−0.159 (0.023)	−0.172 (0.027)	−0.031 (0.034)	−0.148 * (0.023)
lnfdi	0.034 (0.014)	0.175 * (0.090)	0.009 (0.009)	0.013 (0.010)	0.008 (0.012)	0.006 (0.009)
lncon	0.198 (0.049)	0.169 ** (0.322)	0.075 ** (0.032)	0.082 (0.036)	0.086 ** (0.046)	0.002 ** (0.031)
lnind	−0.089 * (0.029)	−0.476 * (0.174)	−0.027 ** (0.019)	−0.013 * (0.022)	−0.078 (0.026)	−0.044 *** (0.019)
lnpgdp	0.067 * (0.032)		0.063 (0.020)	0.074 (0.023)	0.030 (0.027)	0.035 (0.021)
Constant term	−1.206 (0.321)	−0.747 * (1.021)	−0.574 (0.211)	−0.656 (0.239)	−0.143 ** (0.309)	−0.453 (0.216)
Value of observation	217	217	217	217	217	217
R2	0.800	0.851	0.945	0.916	0.932	0.956

Note: ***, **, and * represent the significance level of 1%, 5%, and 10%, respectively; standard errors are in parentheses.

). Models 1 and 2 correspond to Equation (4), and Models 3–6 correspond to Equation (5). The findings indicate that there is a reciprocal relationship between the implementation of novel infrastructure and the GTSI, as demonstrated by the data presented in Table 8 Models 1 and 2. Specifically, a 1% increase in the GTSI corresponds to a 0.006% increase in the advancement of novel infrastructure, while a 1% increase in the advancement of novel infrastructure corresponds to a 1.063% increase in the GTSI. Additionally, the results in Table 9 Models 3–6 reveal an effect of both novel infrastructure and the GTSI on the coordination and integration of the two sectors that is positively significant at the 1% level. Furthermore, it was found that both novel infrastructure and its three subsystems have a greater promoting effect on the four types of coupling coordination degree than the GTSI, with convergence infrastructure having the largest promoting effect, preceded by information and innovation infrastructure. This examination also highlights that government intervention, while it can significantly boost the environmental improvement capability of the sports industry at a 10% level, has a significant negative impact on the advancement of novel infrastructure development level at a 5% level and also has a detrimental effect on the improvement of the coupled coordination level of novel infrastructure and its three subsystems. However, it is important to note that in the current scenario, government intervention only has a significant negative impact on the coupled coordinated development level of innovation infrastructure and the GTSI and does not have a significant negative impact on the other three types of coupled coordination. Additionally, other factors such as foreign investment, consumption structure optimization, industrial structure optimization, and an increase in per capita GDP were found to have a positive impact on the GTSI and the coordination of the two sectors, with foreign investment and consumption structure optimization significantly improving the GTSI and promoting the coupling and coordinated development of novel infrastructure and the GTSI, particularly convergence infrastructure and innovative infrastructure. Furthermore, industrial structure optimization was found to significantly promote the GTSI, the coupling and coordinated development of novel infrastructure, and the GTSI, specifically information infrastructure and innovation infrastructure. While an increase in per capita GDP significantly promoted the improvement of novel infrastructure development level, it had a positive but nonsignificant impact on the improvement of the four types of coupling and coordinated development levels.

Considering differences in the development of novel infrastructure and the GTSI in different regions, this will also affect the coupling coordination level of the two. To further examine the influence of regional differences, this paper further conducts an empirical study on the regional differences in the impact of the GTSI and novel infrastructure (including three subsystems) on the coupled and coordinated development level of the sports industry. The results show (

Table 11. Impacts of green transformation and novel infrastructure of sports industry and their three subsystems on their coupling and coordination degrees in the four regions from 2014 to 2020.

Area	Independent Variables	Model 3 W and s Degree of Coupling Coordination	Model 4 W1 and s Degree of Coupling Coordination	Model 5 W2 and s Degree of Coupling Coordination	Model 6 W3 and s Degree of Coupling Coordination
In the east In the middle	s	0.154 *** (0.020)	0.153 *** (0.019)	0.186 *** (0.028)	0.152 *** (0.022)
	$w_x$	0.396 *** (0.065)	0.392 *** (0.058)	0.735 *** (0.117)	0.373 *** (0.063)
In the west	s	0.104 *** (0.008)	0.105 *** (0.007)	0.103 *** (0.014)	0.098 *** (0.008)
	$w_x$	0.437 (0.159)	0.039 (0.109)	0.742 (0.128)	0.658 *** (0.113)
In the east In the middle	s	0.069 *** (0.008)	0.071 *** (0.011)	0.087 *** (0.013)	0.067 *** (0.007)
	$w_x$	1.298 *** (0.189)	1.392 *** (0.275)	1.530 *** (0.201)	1.314 *** (0.177)
In the west	s	0.062 * (0.041)	0.066 ** (0.046)	0.031 * (0.052)	0.061 *** (0.038)
	$w_x$	1.328 (0.895)	0.631 (0.779)	0.228 (0.722)	1.710 * (0.933)

Note: ***, **, and * represent the significance level of 1%, 5%, and 10%, respectively; standard errors are in parentheses.

) that first, in view of GTSI, the GTSI in four regions features a major positive impact on the improvement of the four types of coupling coordination degree. From the perspective of impact range of the GTSI, the GTSI in the eastern region has the largest bearing on the four types of coupling coordination degree, followed by regions in central and western China and regions in the northeastern encountering the least impact range. Second, in terms of the novel infrastructure, the regions in eastern and western China exhibit a significant positive impact on the improvement of the coupling coordination degree of the two, but the influence array of the western region is 3.278 times more than the eastern region, with a considerable difference. This may be due to the western region possessing a plethora of athletics resources, yet the novel infrastructure and sports industry volume is relatively small. Most provinces (cities) are in the stage of increasing returns to scale. If novel infrastructure is taken as a new factor to invest in the GTSI, its marginal output level may exhibit explosive growth. The novel infrastructure in central and northeast China has no significant positive effect on the coupling coordination degree. Third, when considering the three subsystems of the novel infrastructure, the three subsystems of the novel infrastructure in the eastern and western regions have significant impacts on the coupling coordination degree, and the impact range of the convergence infrastructure is the largest, succeeded by the information and innovative infrastructure. Among the three subsystems of novel infrastructure in the center and northeast, only innovative infrastructure has the largest and significant effect, which indicates that among the novel infrastructure in these two regions, innovative infrastructure makes the largest contribution to the improvement of their coupled and coordinated development level. Generally speaking, only innovative infrastructure substantially affects the three subsystems across four regions.

5. Conclusions and Discussion

Based on the data of 31 provinces (municipalities) in China from 2013 to 2020, the coupling coordination degree of “novel infrastructure” and the GTSI is calculated and analyzed, and the coupling coordination development mechanism of the two is theoretically deduced and empirically tested. The following conclusions are drawn:

(1) From the empirical test results, in addition to the green transformation factors of novel infrastructure and the sports industry, the local consumption structure, and industrial structure, foreign investment and government intervention will to some extent positively or negatively affect the coupling, coordination, and interaction between the two. Therefore, provinces (municipalities) should combine their actual economic and social development and existing resource advantages and make full use of provincial (municipal) industry, capital, consumption, market opening, and other advantages to cultivate the green ecological consumption of the sports industry, promote the green transformation of the sports industry, and promote the sports industry from the new growth pole of the national economy to further develop into a pillar industry of the national economy; for new infrastructure construction in provinces (cities), in addition to the construction according to the regional economic development level, industrial structure, consumption level, and other differences, the significant negative impact of government intervention should also be considered. Especially for the urgent development of innovative infrastructure and information infrastructure, government intervention should be reduced. State-owned enterprises, private enterprises, and foreign capital can be appropriately guided to participate in the new infrastructure in accordance with the Regulations on the Administration of Enterprise Investment Project Approval and Filing, Measures for the Supervision and Administration of Central Enterprises Investment, Special Management Measures for Foreign Investment Access (2020 Version), and other management regulations and measures, so as to fully activate the efficiency of market resource allocation in the new infrastructure and finally promote the coupling and coordination of the two.

(2) From the characteristics of the coupling relationship, most of the Chinese provinces (municipalities) were leading in novel infrastructure development and lagging in GTSI during 2014–2020. Therefore, in terms of improving the coupling of the two, the ecological efficiency of the sports industry should be enhanced. Based on the current status of the green pure technical efficiency change (GPEC) of the sports industry, especially the relatively low contribution of green scale efficiency change (GSEC), on the one hand, for some provinces (cities) that are already in the stage of unchanged or declining returns to scale, investment should not be increased, but they should focus on improving their green pure technical efficiency; that is, under the current technical level, with the help of novel infrastructure, the coordination among various resource elements in the sports industry can be increased, so as to release the potential of the current technical level to a greater extent, improve the resource allocation efficiency of various elements of the sports industry, and realize the GTSI. On the other hand, for most provinces (cities) still in the rising stage of return to scale, in addition to considering increasing the green pure technical efficiency, the potential scale advantage of the sports industry can be given full play by increasing the human, financial, material and other factors input related to the new infrastructure, so as to improve the green scale efficiency of the sports industry and thus enhance the green total factor productivity of the sports industry. Finally, we realize the GTSI.

(3) From the level of coupling coordination: on the whole, the coupling coordination degree between novel infrastructure construction and GTSI is low, which belongs to the verge of imbalance in the antagonistic period and has not entered the stage of high-level coupling coordination. From the comparison of the mean values of the four types of coupling coordination degrees in the four regions, the eastern region has the highest coupling coordination development level, followed by the central and western regions, and the northeast region has the lowest coupling coordination degrees, with significant regional differences. Therefore, it is not recommended to adopt a “one-size-fits-all” large-scale rollout of “novel infrastructure” nationwide. It can be conducted in the eastern regions such as Beijing and Shanghai, which lead the GTSI and lag in the development of novel infrastructure, and the provinces (cities) in northeast China such as Liaoning and Heilongjiang, which have a rigid demand for novel infrastructure development, and we can summarize their successful experience. For example, Beijing, Shanghai, Guangdong, Jiangsu, Zhejiang, and other “sports industry green transformation leading” provinces (cities) moderately increase their “novel infrastructure” investment to improve the carrying capacity of “novel infrastructure” sports industry green transformation development, so as to promote the benign coupling and coordinated development of the two when the time is ripe to promote to other provinces (city). For most of the provinces (cities) in central and western China, which are in the low coupling coordination type, the so-called “new infrastructure development takes the lead and sports industry green transformation lags behind”. While appropriately developing novel infrastructure, the development focus should be placed on improving the quality and efficiency of the integration of the two systems, so as to promote the GTSI.

(4) From the novel infrastructure development level, the overall level of integrated infrastructure in the four regions is high and the interval gap is not big, but the development level of “information infrastructure”, especially “innovation infrastructure”, is relatively low in most provinces (municipalities), which leads to a relatively low coupling coordination degree between the two and the ecological efficiency of the sports industry, but the development potential of “information infrastructure”, especially “innovation infrastructure” is relatively large. At present, technological progress efficiency is the main motivation for the growth of green total factor productivity (GTFC) in the sports industry in the eastern, central, and western regions of China, and also the main reason for the decline of green total factor productivity (GTFC) in the sports industry in northeast China. Sports enterprises can further integrate into information infrastructure and innovate infrastructure technology to break through the bottleneck of existing technology development and promote the GTSI.

There are also a few limitations in this study. The first is that although this paper has included per capita GDP as a control variable when analyzing the impact of the sports industry on new infrastructure, it still fails to prove whether this correlation is caused by China’s relatively high GDP growth rate. Second, it is difficult to investigate whether the correlation is direct or indirect because of the availability of data. Third, there are many sources of endogeneity problems and many solutions. In this paper, there are some limitations to solving endogeneity problems only by using dual fixed effects. In the future, we can further test and revise the endogeneity of the system by means of the instrumental variable method and system GMM. Fourth, the analysis in this paper is limited to the macroscopic level. To know the contents of the interaction, we need to consider the decomposed model and check the main driver. Otherwise, concluding the coupling only from this superficial consideration is difficult. Future research is expected to provide a more accurate analysis of “the coupling and coordinated development of new infrastructure and sports industry”.