Promoting or inhibiting? The impact of sports industry investment on regional health equity

Promoting or inhibiting? The impact of sports industry investment on regional health equity | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Promoting or inhibiting? The impact of sports industry investment on regional health equity Zhibin Zou, Weihui Hu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7835154/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Addressing regional health equity ( RHE ) is a pivotal challenge for sustainable development. The innovative and rapidly expanding sports industry investment ( SII ) presents a potential avenue to mitigate these disparities. Based on a panel dataset of 287 Chinese prefecture-level cities from 2003 to 2023, we systematically investigate this relationship using fixed effects, mediation models, and a spatial Durbin model. Our findings show that SII significantly enhances RHE, with the effects being more pronounced in economically advanced regions, cities with net capital inflows, and sports-derivative sectors. We identify three key mechanisms driving this effect: boosts in resident income, industrial structure upgrades, and advancements in health-related technology. Notably, we uncover substantial positive spatial spillovers, demonstrating that investments in one locality generate health equity benefits in neighboring regions. These indirect spillovers constitute the primary component of the total effect. To our knowledge, this is the first study to empirically map the comprehensive pathway - including both direct mechanisms and spatial spillovers - through which SII influences RHE . Our findings offer crucial policy implications for the synergistic formulation of industrial and public health strategies. Sports Industry Investment Regional Health Equity Spatial Spillover Effect Chinese Cities Figures Figure 1 Figure 2 Introduction Health is fundamental to human well-being and a cornerstone of societal progress. Consequently, ensuring its equitable access has become a paramount objective on the global public health agenda. WHO has called for a “Health in All Policies” approach to advance universal health coverage and foster RHE (WHO, 2024). Although China has achieved remarkable progress in public health with 2024 statistics indicating a life expectancy of 79 years, an infant mortality rate of 4.0‰, and a maternal mortality rate of 14.3 per 100,000, surpassing the average of upper-middle-income nations - significant challenges persist. The country’s progress is impeded by population aging, a rising prevalence of chronic diseases, substantial healthcare costs, and pronounced imbalances in the allocation of health resources across urban-rural and regional divides. As a result, disparities in health outcomes and healthcare accessibility remain stark (Han et al., 2023 ; Liu et al., 2024 ). This inequality not only compromises social justice but also erodes the quality and sustainability of human capital - the primary engine of economic growth - thereby threatening long-term macroeconomic stability. It is therefore imperative to bridge the regional “health gap” and promote balanced health development across the nation (Yin et al., 2018 ). Sports represent a proactive, universal, and cost-effective mode of health promotion, offering a new paradigm for resolving regional health inequities. Many developed nations have long integrated their sports economies into national strategies to enhance population health and address the challenge of chronic diseases, exemplified by Japan’s “Health Japan 21” initiative and the “Healthy People” plans in the United States. Meanwhile, China is vigorously fostering new economic engines to drive high-quality growth, and the sports industry, with its powerful linkage effects and immense market potential, is attracting significant private investment. The value-added of China’s sports industry has recently grown at an average annual rate of 15.4%, substantially outpacing GDP growth. Projections from the 2025 policy document, Opinions on Unleashing Sports Consumption Potential and Further Promoting the High-Quality Development of the Sports Industry, forecast that the industry will maintain this trajectory, reaching a total scale of 7 trillion RMB by 2030. This raises critical questions within the “Healthy China” strategy, which champions universal health for all: Can SII foster RHE ? If so, through what mechanisms does it operate? This paper addresses these questions by investigating the impact of SII on RHE , examining its heterogeneous effects and underlying mechanisms, and analyzing its spatial dimensions. Literature Review The concept of health equity, as defined by Braveman & Gruskin (2003), posits that all individuals realize their full health potential regardless of socioeconomic position, race, or geography. Its central tenet is the elimination of health disparities that are avoidable and unjust. A vast body of research has sought to identify the determinants of health inequity. Recent studies reaffirm that socioeconomic status remains one of the most potent predictors of health outcomes; for example, Wan & Jiang (2021) demonstrated a durable link between socioeconomic inequality and disparities in well-being life expectancy among older adults. Furthermore, structural factors such as the lack of health insurance and geographical barriers to healthcare access are known to significantly compound health inequalities (Woolf & Schoomaker, 2019). Environmental justice research has also shown how consumption patterns create disparate exposure to air pollution across racial and ethnic groups, exacerbating health risks (Tessum et al., 2019). Indeed, systemic discrimination is increasingly understood as a root cause of health disadvantages (Williams et al., 2019), while higher educational attainment is robustly associated with improved health behaviors and lower mortality (Fu et al., 2024). In parallel, the literature on SII has evolved from a narrow focus on economic impacts to a more holistic consideration of its value. This investment spans core activities, such as professional sports and recreational fitness, as well as derivative sectors like sporting goods and media (Downward et al., 2017). While much research has quantified its economic contributions - such as the effects of mega-events on tourism (Fourie & Santana-Gallego, 2022) or the impact of professional teams on local employment and revenue (Coates & Humphreys, 2020) - a new research frontier is emerging. Moving beyond purely economic metrics, scholars like Gosselin et al. (2020) have developed frameworks to capture the broader societal benefits of sports. This work has begun to highlight the increasingly significant role of sports investment in generating positive social outcomes, most notably in the enhancement of public health. The net effect of SII on RHE remains a subject of considerable debate. Proponents argue that it is an effective tool for advancing health equity. Investments can directly expand the provision of public sports infrastructure, such as urban green-ways and community recreation centers, thus lowering barriers to physical activity, particularly for low- and middle-income populations (Han & Zhou, 2024). By promoting widespread participation in sports, these investments can help curtail the incidence of chronic diseases and enhance overall public health (Bull et al., 2020). Moreover, the sports industry can indirectly bolster the socioeconomic determinants of health by fostering job creation and local economic growth, leading to improved household incomes and living standards. Conversely, critics question the inclusive nature of such investments, contending that they risk reinforcing or even worsening regional health disparities. Given the profit-driven motives of capital, sports investments tend to gravitate toward affluent regions with high consumption capacity. This concentration can lead to a “Matthew Effect” in resource allocation, where high-end sports facilities cluster in wealthy areas while underserved regions face acute shortages (Lou & Chen, 2024). Furthermore, investment models predicated on mega-events and landmark stadiums can divert public funds away from essential public health services and grassroots community sports. The purported “trickle-down” benefits of these high-profile projects often do not materialize as promised (Zhang, 2025). Consequently, if not guided by an equity-focused framework, sports investment may fail to bridge the “health gap” and instead act as a further catalyst for social stratification. In summary, the existing literature provides a valuable foundation for understanding the relationship between SII and health equity. However, several gaps remain. (1) Current research has predominantly focused on the micro-level of individual behavior or the meso-level of community projects, lacking a macro-level perspective from regional economics that systematically examines the net effect of industry-level capital flows on the overall landscape of RHE. (2) Existing studies have not sufficiently explored the internal transmission pathways through which sports investment affects health equity, and the complex mechanisms behind this relationship require deeper investigation. (3) The literature has generally overlooked the potential spatial dependence and spillover effects that may exist in the impact of sports investment on RHE . The marginal contributions of this paper are as follows: (1) We construct and test a macro-analytical framework for SII and RHE . (2) We identify the multiple transmission mechanisms of SII through three key dimensions: enhancing resident income, optimizing industrial structure, and boosting health technology innovation. (3) We reveal the spatial spillover effects of SII on RHE , providing an empirical basis for formulating more holistic and coordinated regional sports and health development strategies. Theoretical background and hypothesis development The Direct Effect of SII on RHE We posit that SII directly and positively influences RHE by enhancing the provision of health-related resources and intervening in key health determinants. This argument is grounded in Grossman's (1972) seminal theory of health demand, which conceptualizes health as a capital good whose stock can be augmented through individual investments like exercise. Investments in the sports industry, especially those targeting public facilities and services, substantially reduce the costs and barriers associated with these personal “health investments”. For example, the expansion of urban green-ways, community fitness centers, and subsidized coaching services directly improves the accessibility of physical activity, thereby dismantling “health barriers” linked to socioeconomic status (Kumar et al., 2018). Moreover, investments in sports cultivate social and physical environments that encourage healthy lifestyles, generating positive externalities for population health (Zhang et al., 2024). As the distribution of sports resources within a region becomes more equitable, residents across diverse social and geographical backgrounds receive fairer opportunities for health enhancement. This process mitigates avoidable health disparities and ultimately elevates the overall level of RHE . Therefore, we hypothesize: H1: SII positively affects RHE . Indirect Impact of SII on RHE SII may also indirectly affect RHE through a series of socioeconomic mediating variables. This process can be realized through the following three transmission mechanisms. The first is the resident income enhancement mechanism. As a combination of a labor-intensive and high value-added industry, investment in the sports sector can effectively create jobs across multiple fields, including event organization, venue operation, and the manufacturing and sales of sporting goods, thereby raising the overall regional employment level and resident income (Jiang, 2024). An increase in income is one of the most fundamental socioeconomic determinants for improving health outcomes, as it can directly enhance residents’ ability to pay for medical care, improve their nutritional status, and reduce life stress, thus narrowing health inequalities caused by poverty (Jiang & Feng, 2021). The second is the industrial structure optimization mechanism. The sports industry facilitates the transition from traditional manufacturing to a knowledge-based, service-oriented economy. This optimization of the industrial structure not only brings higher-quality employment opportunities but also promotes the improvement of urban functions and environmental quality. These factors collectively constitute a favorable macroeconomic environment for promoting health equity (Li et al., 2025). The final mechanism is health technology innovation. Investment in competitive sports and mass fitness is often accompanied by R&D investment in related fields such as sports science, rehabilitation medicine, and wearable devices. The innovations resulting from this health technology do not solely serve professional athletes; their technology spillover effects can also benefit the general public, elevating the overall regional levels of disease prevention, health management, and rehabilitation therapy, thereby providing technical support for the promotion of health equity (He, 2019). Based on this, the present study proposes the following hypothesis: H2: SII can indirectly promote RHE through the three dimensions of increasing resident income, optimizing industrial structure, and promoting health technology innovation. Spatial Spillover Effects of SII on RHE Regional economic activities are not isolated from one another but are spatially correlated through channels such as factor mobility, technology diffusion, and policy interactions. Similarly, the impact of SII on RHE may transcend administrative boundaries and generate spatial spillover effects. According to “the first law of geography”, interactions are more intense between nearby regions (Tobler, 1970). The prosperity of the sports industry in one region may produce a demonstration effect and competitive pressure on neighboring regions, prompting them to increase their own public investments in sports and health, thereby forming a pattern of benign regional competition and coordinated development. Furthermore, the construction of large-scale sports venues can have a “growth pole” effect, with impacts radiating to surrounding areas. For instance, residents of adjacent regions might cross administrative borders to enjoy high-quality sports facilities and services, or benefit from collaborative divisions of labor within inter-regional sports industry chains. Conversely, if sports investment is overly concentrated in a few central cities, it could create a “siphoning effect” on peripheral areas, further exacerbating the imbalance of health resources between regions (Hyun, 2022). To accurately identify the true effect of SII , it is essential to examine it within a broader geospatial framework. Based on this, the present study proposes the following hypothesis: H3: The impact of SII on RHE has significant spatial spillover effects. Empirical Analysis Model Specification Baseline Regression Model To estimate the impact of SII on RHE , we specify the following baseline model: $$RH{E_{it}}={\alpha _0}+{\alpha _1}SI{I_{it}}+{\alpha _2}contro{l_{it}}+{\mu _i}+{\nu _t}+{\varepsilon _{it}}$$ 1 In this context, i denotes the city, and t represents the time. RHE represents regional health equity. The term α 0 is the constant term. The core independent variable is SII , sports industry investment. CV encompasses all the control variables. µ i represents the individual fixed effect, v t represents the time fixed effect, and ε it is the random error term. Mediation Analysis To test whether SII can indirectly promote RHE by increasing resident income, optimizing industrial structure, and promoting health technology innovation, this paper constructs the following mediation effect model: $${M_{it}}={\alpha _0}+{\alpha _1}SI{I_{it}}+{\alpha _2}contro{l_{it}}+{\mu _i}+{\nu _t}+{\varepsilon _{it}}$$ 2 $$RH{E_{it}}={\beta _0}+{\beta _1}{M_{it}}+{\beta _2}SI{I_{it}}+{\beta _3}contro{l_{it}}+{\mu _i}+{\nu _t}+{\varepsilon _{it}}$$ 3 Here, M is the mediating variable, representing the level of resident income, the level of industrial structure optimization, and the level of health technology innovation, respectively. The other variables are the same as in Eq. ( 1 ). Spatial Durbin Model To test whether the impact of SII on RHE has spatial spillover effects, this paper constructs the following dynamic Spatial Durbin Model: $$RH{E_{it}}={\alpha _0}++\rho (WRH{E_{it}})+{\alpha _1}SI{I_{it}}+\theta (WSI{I_{it}})+{\alpha _2}contro{l_{it}}+\delta (Wcontro{l_{it}})+{\mu _i}+{\nu _t}+{\varepsilon _{it}}$$ 4 Here, W is the spatial weight matrix. ρ is the spatial autoregressive coefficient, θ represents the spatial spillover coefficients of the independent variables. The other variables are defined as in the baseline model. Variable Selection Dependent Variable: Regional Health Equity ( RHE ) Following the research of Wei et al., ( 2024 ) and Murray et al., ( 2022 ), this paper constructs an evaluation index for RHE from two dimensions: economic development and health security. This index is decomposed into 5 secondary indicators and 14 tertiary indicators. Using the CRITIC (Criteria Importance Through Intercriteria Correlation) method, we measure the level of RHE for 287 prefecture-level cities in China from 2003 to 2023. The indicator system and corresponding weights are shown in Table 1 . Table 1 Indicator system and weights for the RHE . Primary indicator Secondary indicator Tertiary indicator Weight Economic Development Economic Aggregate Gross Domestic Product (10,000 RMB) 0.046404 Local Government General Budget Revenue (10,000 RMB) 0.039511 Proportion of Tertiary Industry Value-added in GDP (%) 0.128105 Economic Efficiency Per Capita Gross Domestic Product (RMB) 0.098936 Per Capita Total Retail Sales of Consumer Goods (RMB) 0.056287 Urbanization Rate (%) 0.100727 Health Security Government Expenditure Per Capita Expenditure on Science 0.030730 Per Capita Expenditure on Education 0.046726 Social Security Number of Participants in Basic Medical Insurance for Urban Employees 0.045503 Number of Participants in Basic Old-age Insurance for Urban Employees 0.057609 Number of Participants in Unemployment Insurance 0.050557 Healthcare Security Number of Hospital Beds per 10,000 People 0.108848 Number of Licensed Physicians per 10,000 People 0.090969 Number of Health Institutions per 10,000 People 0.099091 Explanatory Variable: Sports Industry Investment ( SII ) Using Python web scraping techniques, we obtained 57,596,248 records of basic corporate registration information. Each record was assigned a unique identifier, and 24 indicator items were processed, with irrelevant information being removed. The operational duration of each firm was calculated based on its business status (e.g., canceled, revoked, or active), and all registered capital figures were standardized to units of 10 million RMB. After cleaning, the equity penetration method was used to establish investment links between firms. First, we performed branch penetration, removing extraneous information, de-duplicating records, and precisely matching unique identifiers. Second, shareholder-branch penetration was conducted, removing irrelevant data and retaining only corporate shareholders, which were matched by “Shareholder Name + Year of Subscribed Capital”. The data from both types of penetration were then merged (assuming 100% holding of branches by headquarters and calculating shareholder investment based on shareholding ratios). This process ultimately yielded a total of 8,961,483 equity penetration records, comprising 5,072,508 from branch penetration and 3,888,975 from corporate shareholder penetration. These data were then aggregated to the 287 prefecture-level cities in China. After obtaining the equity penetration data, we used latitude and longitude coordinates to determine the province, city, and district/county for each firm. As industry classifications were often missing from the basic registration data, we extracted keywords for 71 sub-class industries based on the Statistical Classification of the Sports Industry (2019) released by the National Bureau of Statistics of China. We further categorized these into a sports industry classification system (Table 2 ) consisting of 2 major categories (Core Sports Industry and Related Sports Industry) and 11 one-digit industry codes, with each two-digit code corresponding to specific keywords. By matching these keywords with the equity penetration data, we identified a total of 745,110 records of enterprise investment data in China’s sports sector. Table 2 Classification system for the sports industry. Code Industry name Major category 1 Sports Management Activities Related Sports Industry 11 Management of Social Affairs in Sports 12 Management of Social Organizations in Sports 13 Management of Support Organizations in Sports 2 Sports Competition and Performance Activities Core Sports Industry 21 Professional Sports Competition and Performance 22 Non-Professional Sports Competition and Performance 3 Sports Fitness and Leisure Activities Core Sports Industry 31 Sports and Leisure Activities 32 Mass Sports Activities 33 Other Sports and Leisure Activities 4 Management of Sports Venues and Facilities Core Sports Industry 41 Sports Venue Management 42 Sports Service Complex Management 43 Management of Sports Parks and Other Facilities 5 Sports Brokerage, Advertising, Exhibition, and Design Related Sports Industry 51 Sports Brokerage and Agency Services 52 Sports Advertising and Exhibition Services 53 Sports Performance and Design Services 6 Sports Education and Training Core Sports Industry 61 School Sports Education Activities 62 Sports Training 7 Sports Media and Information Services Related Sports Industry 71 Sports Publication Services 72 Sports Film, Television, and Other Media Services 73 Internet Sports Services 74 Sports Consulting 75 Sports Museum Services 76 Other Sports Information Services 8 Other Sports Services Related Sports Industry 81 Sports Tourism Services 82 Sports Health and Athletic Rehabilitation Services 83 Sports Lottery Services 84 Sports Finance and Asset Management Services 85 Sports Science, Technology, and IP Services 86 Other Unspecified Sports Services 9 Manufacturing of Sports Goods and Related Products Core Sports Industry 91 Manufacturing of Sporting Goods and Equipment 92 Manufacturing of Sports Vehicles, Boats, and Aviation Equipment 93 Manufacturing of Sports-related Materials 94 Manufacturing of Other Sports-related Products and Equipment 10 Sales, Rental, and Trade of Sports Goods Related Sports Industry 101 Sales of Sports and Related Products 102 Rental of Sporting Goods and Equipment 103 Trade Agency for Sports and Related Products 11 Construction of Sports Venues and Facilities Core Sports Industry 111 Construction and Decoration of Sports Venues 112 Engineering and Installation of Sports Facilities Mediating Variables We select per capita disposable income, the value-added of the tourism industry, and the number of patents in strategic emerging industries as the mediating variables. Per capita disposable income (income) is used to represent the level of resident income. Value-added of the tourism industry (tourism) is used to represent the level of industrial structure optimization. A more developed tourism industry indicates a more pronounced trend toward an advanced industrial structure. Number of patents in strategic emerging industries (patent) is used to represent the level of health technology innovation, as such innovations are primarily concentrated in cutting-edge fields like biotechnology, high-end medical devices, and AI-powered health management. Control Variables Given what might be regarded as potentially playing the role of errors due to omitted variables and/or to gain much more substantially in terms of the estimation of the effect of SII on RHE , based on what might be regarded as pertinent prior work by Hao et al. ( 2021 ) and Xu & Lin ( 2022 ), seems to tend to control by the following pertinent variables: Population density ( density ). In this general framework, this variable seems to be measured as the number of ten thousand persons per square kilometer. Financial development level ( finance ). From these considerations, it seems that this factor is measured as the ratio of the loan balance of financial institutions to GDP. Level of foreign openness ( fdi ). The evidence seems to reveal that this is measured by the actual utilized foreign investment and it is measured in units of 10,000 RMB. Human capital ( hum ). This is measured as the number of students enrolled in regular institutions of higher education and it is measured in units of 10,000 persons. R&D investment ( rd ). This seems to indicate that this is measured by R&D expenditure and it is measured in units of 10,000 RMB. Investment level ( asset ). Given the rather complex nature of these findings, this is mainly measured by the amount of fixed asset investment and it is measured in units of 10,000 RMB. Fiscal support ( gov ). This variable seems to be measured as the ratio of local government general budget expenditure to the gross regional product. Data Sources and Descriptive Statistics This paper uses a panel dataset of 287 prefecture-level cities in China from 2003 to 2023 as the study sample. The sample of Tibet Autonomous Region was removed because of the serious lack of data. The SII data were scraped and aggregated from the Qichacha data platform ( https://www.qcc.com ) by using web scraping tools. Other necessary data were collected from China Statistical Yearbook, National Intellectual Property Administration and National Bureau of Statistics. The descriptive statistics of all variables are shown in Table 3 . Table 3 Descriptive statistics. Variable count mean sd min max RHE 6027 0.1931 0.0795 0.0683442 0.7288619 SSI 6027 0.0316 0.1468 0 4.5 density 6027 4.2774 3.3531 0.05 30.05 finance 6027 0.9777 0.6359 0.0753187 12.81711 fdi 6027 0.0760 0.1939 0 3.1 hum 6027 0.8772 1.6260 0 15 rd 6027 0.0580 0.1666 0 3.8 asset 6027 0.2402 1.4690 0 74 gov 6027 0.1823 0.1449 0.0312843 3.667161 Empirical Results Spatial Pattern Evolution Spatial Pattern of the SII Level We visualized China's SII to map its geographical distribution (Fig. 1 ). The map clearly shows that total investment value first grew rapidly and then slowed down. At the same time, the number of smaller investments increased significantly. This trend was especially apparent in 2023, when more than 250 cities were classified in tier Ⅰ for investment. Spatial Pattern of the RHE Index Figure 2 maps the geographical distribution of RHE. Overall, the level of RHE showed a clear upward trend from 2003 to 2023. Its presence, initially scattered in a few locations, gradually spread to surrounding cities. The RHE classification for cities also improved markedly over time: in 2003, Tier I cities were the most common; by 2013, Tier II had become more numerous; and by 2023, Tier III cities constituted the majority. Baseline Regression Analysis Table 4 reports the baseline regression results examining the direct impact of SII on RHE . Under the specification that simultaneously controls for both city and year two-way fixed effects and includes a series of region-level control variables (column 1), the regression coefficient of the core explanatory variable, SII is 0.0205 and is significantly positive at the 1% level. This result indicates that SII has a significant promotional effect on RHE , thus verifying Hypothesis H1 . SII not only directly improves the accessibility and quality of public sports facilities, reducing the cost for residents to participate in physical activities, but also fosters a health-promoting social atmosphere by hosting events and advocating for healthy lifestyles, thereby enhancing the overall health level of the region (Zhai et al., 2024 ). We also conducted a sensitivity analysis of the model specification. In models that only control for two-way fixed effects (column 2) or only include control variables (column 3), the coefficient for SII remains significant at the 1% level and consistent in its direction. This fully demonstrates that the baseline regression result is not driven by a specific model choice.。 Table 4 Regression results of the benchmark model. Variable (1) (2) (3) SII 0.0205*** 0.0364*** 0.0312*** (0.003) (0.003) (0.005) density 0.0121*** 0.0005** (0.001) (0.000) finance 0.0052*** 0.0519*** (0.001) (0.001) fdi 0.0341*** 0.0726*** (0.003) (0.005) hum 0.0103*** 0.0049*** (0.001) (0.001) rd 0.0488*** 0.1166*** (0.003) (0.005) asset 0.0019*** 0.0076*** (0.000) (0.000) gov -0.0151*** -0.0212*** (0.004) (0.005) Constant 0.1233*** 0.1919*** 0.1246*** (0.003) (0.000) (0.002) Observations 6,027 6,027 6,027 R-squared 0.940 0.917 0.624 yearfix YES YES NO idfix YES YES NO Notes: 1. Standard errors in parentheses. 2. *p < 0.1, **p < 0.05, ***p < 0.01. Robustness Tests This paper conducts robustness tests to verify the baseline results (Table 5 ). Replacing the core dependent variable. We employ the entropy weight method to re-measure the regional health equity ( RHE ) index and substitute it into the model. As shown in column (1), the coefficient of SII remains significantly positive. Adding Control Variables We further introduce a dummy variable indicating whether a city is a provincial capital. The results in column (2) of Table 5 show that after enhancing the model's control capacity, the promotional effect of SII remains unchanged. Excluding Special Samples and Years To account for potential biases, we perform two exclusion tests. First, considering the COVID-19 pandemic as a major external shock, we exclude the sample for the 2020–2021 period, with results presented in column (3) of Table 5 . Second, to eliminate potential estimation bias arising from the unique economic scale and policy environments of first-tier cities (Beijing, Shanghai, Guangzhou, and Shenzhen), we re-run the regression after removing them from the sample (column 4) of Table 5 . In both scenarios, the coefficient for SII maintains a high degree of positive significance. Addressing Extreme Values To mitigate potential interference from outliers, we apply 1% bilateral winsorization to both the core explanatory and dependent variables. As reported in column (5) of Table 5 , the core conclusion remains valid after controlling for the influence of extreme values. Table 5 Robustness tests. Variable Alternative dependent variable Add control variables Exclude special years Exclude special sample Winsorize at 1% (1) (2) (3) (4) (5) SII 0.0313*** 0.0205*** 0.0218*** 0.0214*** 0.0366*** (0.002) (0.003) (0.003) (0.005) (0.006) CV YES YES YES YES YES Constant -0.0423*** 0.1233*** 0.1163*** 0.1571*** 0.1366*** (0.002) (0.003) (0.003) (0.004) (0.004) N 6,026 6,027 5,453 5,901 6,027 R 2 0.944 0.940 0.934 0.926 0.943 yearfix YES YES YES YES YES idfix YES YES YES YES YES Notes: 1. Standard errors in parentheses. 2. *p < 0.1, **p < 0.05, ***p < 0.01. 3. CV represents the control variables. Endogeneity Test Given the potential for reverse causality between SII and RHE , regions with higher health levels may attract more SII , this paper addresses endogeneity. We select the one-period lag of SII as an instrumental variable and employ the Two-Stage Least Squares method to conduct the endogeneity test (Table 6 ). After applying the 2SLS method, the impact of SII on RHE remains significantly positive, with a coefficient of 0.0425, which is significant at the 1% level. This indicates that after controlling for endogeneity, SII still promotes RHE . We then use the Difference GMM method to control for endogeneity, and the results remain consistent with the 2SLS estimation. Table 6 Endogeneity test results. Variable 2SLS Difference GMM First-stage regression Second-stage regression SII 0.4923*** 0.0425*** 0.0126*** (0.012) (0.006) (0.001) CV YES YES YES Kleibergen-Paap rk LM statistic P-value 1432.19 (0.0000) Cragg-Donald Wald F statistic 1803.95 N 5,740 5,740 5,453 yearfix YES YES YES idfix YES YES YES Notes: 1. Standard errors in parentheses. 2. *p < 0.1, **p < 0.05, ***p < 0.01. 3. CV represents the control variables. Heterogeneity Analysis Analysis of Regional Heterogeneity China’s vast territory has led to significant regional imbalances in socioeconomic development (Deng et al., 2022 ). Consequently, the impact of SII on RHE may exhibit regional heterogeneity. We divide the national sample into four major regions, Eastern, Central, Western, and Northeastern - and conduct sub-sample regressions (Table 7 ). The results show that the positive effect of SII is primarily concentrated in the Eastern and Central regions, where the regression coefficients are significantly positive at the 1% level. In contrast, the impact in the Northeastern region, while positive, is not statistically significant, and the effect in the Western region is also insignificant. The Eastern and Central regions typically possess stronger fiscal capacity, more mature market mechanisms, and higher levels of resident consumption, which provide a robust foundation for translating SII into inclusive health resources. In these areas, sports investment can not only fund high-quality public sports facilities but also effectively stimulate the growth of related service industries, thereby promoting health equity from both ends. In comparison, the Western region has a relatively weaker economic base, which may limit the scale and effectiveness of SII , making it difficult to achieve scale effects. The Northeastern region, meanwhile, may be facing challenges related to industrial structure transformation and population outflow, which could prevent the health-promoting effects of sports investment from being fully realized (Chen, 1997 ). Table 7 Results of regional heterogeneity analysis. Variable Eastern regions Central regions Western regions Northeast regions (1) (2) (3) (4) SII 0.0204*** 0.0197*** -0.0268 0.0067* (0.003) (0.007) (0.021) (0.004) CV YES YES YES YES Constant 0.1597*** 0.1655*** 0.1764*** 0.0633*** (0.005) (0.005) (0.014) (0.020) N 1,806 1,680 1,827 714 R 2 0.974 0.973 0.887 0.953 yearfix YES YES YES YES idfix YES YES YES YES Notes: 1. Standard errors in parentheses. 2. *p < 0.1, **p < 0.05, ***p < 0.01. 3. CV represents the control variables. Other Heterogeneity Analyses To further deepen the understanding of the health effects of SII , we conduct heterogeneity tests from three dimensions: investment flow, industry attributes, and the policy environment (Table 8 ). Heterogeneity of investment flows. There are significant differences in the economic logic between investment inflows and outflows. This paper disaggregates SII into inflows and outflows for separate examination. As shown in columns (1) and (2), both investment inflows (coefficient 0.0920) and outflows (coefficient 0.0193) significantly promote RHE , but the marginal effect of inflows is much larger than that of outflows. This may be because investment inflows, as external capital, often directly impact local sports facility construction and health service provision, allowing them to more rapidly fill the local capital gap in the health sector. Consequently, their promotional effect is more direct and efficient. In contrast, investment outflows more closely reflect the external strategic positioning of local capital, and their feedback effect on local health equity is relatively indirect, requiring longer chains such as capital returns and knowledge spillovers to be realized (Özmen & Taşdemir, 2021 ; Davis & Van Wincoop, 2021). Heterogeneity of industry attributes. The sports industry can be divided into core industries (e.g., fitness and leisure, competitive performances) and derivative industries (e.g., sporting goods manufacturing, sports media). As shown in columns (3) and (4), investment in sports-derivative industries (coefficient 0.0204) has a significant promotional effect on RHE , whereas investment in core industries is not significant. A possible reason is that derivative industries, particularly sporting goods manufacturing, have stronger industrial linkage effects and a greater capacity for employment absorption, which can effectively increase resident income and regional economic vitality, making them more inclusive. In contrast, if investment in core sports industries is concentrated in a few high-end events or venues, it may not benefit the general public in the short term, thus limiting its direct contribution to health equity (Kenyon & Postlethwaite, 2026 ). Heterogeneity of the policy environment. The “Healthy Cities” initiative, as an important national strategy, provides a policy framework and resource support for local governments to advance public health. This paper divides the sample into pilot and non-pilot cities based on their participation in the “Healthy Cities” program. The regression results in columns (5) and (6) show that SII significantly promotes health equity in both types of cities, but the marginal effect is slightly stronger in non-pilot cities (coefficient 0.0253) than in pilot cities (coefficient 0.0180). This suggests that in areas lacking specific preferential policies, market-driven SII may play a more critical “gap-filling” role, becoming an important marginal force for promoting health equity, while the national strategy provides a policy framework and resource support for local health development (Bai et al., 2022 ; Yan & Yan, 2025 ). Table 8 Results of other heterogeneity analyses. Variable Outflow Inflow Core sports industry Related sports industry Pilot cities Non-pilot cities (1) (2) (3) (4) (5) (6) SII 0.0193*** 0.0920*** 0.0366 0.0204*** 0.0180*** 0.0253*** (0.003) (0.013) (0.033) (0.003) (0.004) (0.004) CV YES YES YES YES YES YES Constant 0.1232*** 0.1225*** 0.1224*** 0.1231*** 0.2206*** 0.1211*** (0.003) (0.003) (0.003) (0.003) (0.013) (0.003) N 6,027 6,027 6,027 6,027 735 5,292 R 2 0.939 0.939 0.939 0.940 0.964 0.932 yearfix YES YES YES YES YES YES idfix YES YES YES YES YES YES Notes: 1. Standard errors in parentheses. 2. *p < 0.1, **p < 0.05, ***p < 0.01. 3. CV represents the control variables. Mediation Effect Analysis This paper conducts a mediation effect test (Table 9 ). As shown in columns (1), (3), and (5) of Table 9 , SII has a significant positive impact on resident income ( income ), industrial structure optimization ( tourism ), and health technology innovation ( patent ). When these three mediating variables are introduced into the baseline model separately, the results in columns (2), (4), and (6) show that the coefficients of all mediators are significantly positive. Furthermore, while the coefficient of the core explanatory variable ( SII ) decreases, it remains significant. This indicates that SII can indirectly influence RHE through the three channels of increasing resident income, optimizing industrial structure, and promoting health technology innovation, thus verifying Hypothesis H2 . The promotional effect of SII on resident income not only increases household wealth but also comprehensively enhances “health stock” by improving healthcare affordability and alleviating psychological burdens caused by economic stress. Its role in optimizing the industrial structure fosters the development of sports and related service industries, which in turn improves the quality of urban public spaces and creates a healthier living environment. Finally, sports investment stimulates demand for sports science and wearable devices; through technology spillovers and market-based applications, these innovations are ultimately transformed into accessible health management tools for the general public, narrowing the regional “health gap” from a technological standpoint (Li et al., 2025 ). Table 9 The mediation effects test. Variable income RHE tourism RHE patent RHE (1) (2) (3) (4) (5) (6) SII 0.2300*** 0.0133*** 0.2536*** 0.0191*** 0.5347*** 0.0087*** (0.038) (0.002) (0.020) (0.002) (0.041) (0.003) income 0.0299*** (0.001) tourism 0.0134*** (0.002) patent 0.0221*** (0.001) CV YES YES YES YES YES YES Constant 2.4071*** 0.0535*** 0.2367*** 0.1148*** -1.5718*** 0.1580*** (0.040) (0.003) (0.022) (0.002) (0.039) (0.003) N 5,937 5,937 5,227 5,227 6,027 6,027 R 2 0.963 0.951 0.850 0.966 0.863 0.946 yearfix YES YES YES YES YES YES idfix YES YES YES YES YES YES Notes: 1. Standard errors in parentheses. 2. *p < 0.1, **p < 0.05, ***p < 0.01. 3. CV represents the control variables. Spatial Spillover Effect Analysis This paper calculates the Global Moran’s I for the RHE of each prefecture-level city from 2003 to 2023 (Table 10 ). It is evident that the Global Moran’s I is significantly positive for all years, which indicates a significant positive spatial autocorrelation among the prefecture-level cities. Therefore, it is necessary to employ a spatial econometric model. Table 10 Spatial Moran’s I Test. Year Global Moran Index 2003 0.3019*** 2004 0.3283*** 2005 0.3376*** 2006 0.3586*** 2007 0.3834*** 2008 0.3909*** 2009 0.4018*** 2010 0.3981*** 2011 0.4120*** 2012 0.4301*** 2013 0.4402*** 2014 0.4368*** 2015 0.4403*** 2016 0.4409*** 2017 0.4430*** 2018 0.4306*** 2019 0.4166*** 2020 0.4213*** 2021 0.4286*** 2022 0.4262*** 2023 0.3724*** Notes: *p < 0.1, **p < 0.05, ***p < 0.01. Following the research of Guo et al. ( 2020 ), the spatial correlation of RHE is influenced not only by geographical distance but is also closely related to the economic development levels between regions. This paper selects an economic-geographical weight matrix as the baseline spatial weight matrix, supplemented by a nested economic-geographical matrix and an inverse distance matrix for robustness checks. The results are presented in columns (1)–(3) of Table 11 . The regression results in column (1) of Table 11 show that the spatial autoregressive coefficient, rho, is significantly positive at the 1% level. This indicates the existence of significant positive spatial dependence in RHE , meaning the health equity status of one region is positively affected by that of its neighbors. The coefficient of the core explanatory variable, SII , is significantly positive, indicating that local SII effectively promotes local health equity. Simultaneously, the coefficient of the spatially lagged term, W*SII , is also significantly positive at the 1% level, indicating a significant spatial spillover effect from SII . The successful implementation of sports investment in one region, such as the construction of iconic sports facilities or the hosting of major events, can create a “demonstration effect”, inducing neighboring governments to adopt similar public investment strategies to enhance their own competitiveness. Furthermore, the sports industry has a cross-regional industrial chain integration effect. Core projects in the industry (such as event operations or equipment manufacturing) can integrate upstream and downstream industries like tourism, logistics, and support services in surrounding areas through forward and backward linkages. This promotes the optimal inter-regional allocation of production factors, thereby spilling over the benefits of economic growth to neighboring regions (Campayo-Sanchez et al., 2026 ). This paper further employs a nested economic-geographical matrix and a spatial distance matrix for robustness tests, as shown in columns (2) and (3) of Table 11 , respectively. After changing the spatial weight matrix, the coefficients of the core explanatory variable ( SII ) and its spatially lagged term ( W*SII ) remain significantly positive. This indicates that the paper’s conclusion, SII has both a positive local effect and a positive spatial spillover effect. Therefore, Hypothesis H3 is validated. Drawing on the research of Lesage & Pace ( 2009 ), this paper uses partial differential decomposition on the Spatial Durbin Model to break down the total effect into direct and indirect effects, with the results presented in columns (4)–(6) of Table 11 . The results show that the coefficient for the direct effect of SII is 0.0136 and is significant at the 1% level, which indicates that local SII has a robust direct promotional effect on local health equity. The indirect effect, which represents the spatial spillover, has a coefficient of 0.0581 and is also highly significant at the 1% level. Notably, the coefficient of the indirect effect is larger than that of the direct effect. This suggests that the promotional impact of SII on health equity is more prominently reflected in its radiating influence on neighboring regions. A possible reason for this is that the sports industry is characterized by strong “network externalities” and “mobility”. On the one hand, core sports products like high-level sporting events and sports tourism destinations have a powerful cross-regional appeal, with a service radius extending far beyond the local area, which can effectively attract external consumption and promote inter-regional exchange. On the other hand, the healthy lifestyles, scientific fitness knowledge, and spirit of sports culture engendered by SII are easily transmissible. They can be disseminated to neighboring regions at a low cost through channels such as media and interpersonal exchanges, thereby promoting the realization of health equity on a broader scale (Aral & Nicolaides, 2020 ). Table 11 Results of Spatial Econometric Models. Variable Spatial Durbin Model Regression Decomposition of Spatial Effects Economic-geographical weight matrix Nested economic-geographical matrix Spatial distance matrix Direct effect Indirect effect Total effect (1) (2) (3) (4) (5) (6) SII 0.0116*** 0.0140*** 0.0164*** 0.0136*** 0.0581*** 0.0717*** (0.002) (0.003) (0.002) (0.003) (0.012) (0.012) CV YES YES YES YES YES YES W*SII 0.0372*** 0.0158** 0.0985*** (0.009) (0.007) (0.036) W*CV YES YES YES rho 0.3051*** 0.1268*** 2.6390*** (0.023) (0.025) (0.030) sigma2_e 0.0003*** 0.0003*** 0.0003*** (0.000) (0.000) (0.000) N 5,964 5,964 5,964 R 2 0.381 0.402 0.064 yearfix YES YES YES idfix YES YES YES Notes: 1. Standard errors in parentheses. 2. *p < 0.1, **p < 0.05, ***p < 0.01. 3. CV represents the control variables. Conclusions and Implications Based on a panel dataset of 287 prefecture-level cities in China from 2003 to 2023, this paper systematically investigates the impact of SII on RHE , including its heterogeneous effects, mechanisms of action, and spatial effects. The main research conclusions are as follows: SII has a significant direct promotional effect on RHE , but its impact exhibits significant heterogeneity. Specifically, this promotional effect is more pronounced in the Eastern and Central regions, which have stronger economic foundations, in cases of capital inflow, and within sports-derivative industries. SII can significantly improve regional health levels through three mediating pathways: enhancing resident income, optimizing the regional industrial structure, and promoting health technology innovation. The health-promoting effect of SII is characterized by significant spatial spillovers. Sports investment in a local region not only improves its own level of health equity but also has a positive radiating effect on neighboring regions. Furthermore, the effect decomposition results show that this indirect spillover effect is the main component of the overall impact. The implications of these findings are discussed below: Optimize investment layout and implement differentiated, targeted promotion strategies. The investment structure should be optimized to guide more capital toward highly inclusive sports-derivative industries and public community sports services, while also encouraging the inflow of high-quality external capital. A regionally differentiated strategy is necessary, in the Eastern and Central regions, the focus should be on promoting the deep integration of the sports industry with related sectors. In the Western and Northeastern regions, investment should be prioritized to address fundamental shortcomings and solidify the foundation for health equity. Strengthen mechanistic synergy and unblock the value conversion pathways of sports investment. Policy design should connect the three transmission channels of “sports-economy-health”. This includes using fiscal and tax incentives to support the creation of more high-quality jobs in the sports industry; integrating sports industry development into the overall planning for urban renewal and industrial upgrading; and establishing specialized funds to encourage R&D and the application of innovations in fields such as smart fitness and athletic rehabilitation. Leverage spatial spillovers to foster a pattern of coordinated regional health development. Cross-regional coordination mechanisms for sports development should be established. Central cities should be encouraged to leverage their radiating and leading roles to disseminate capital, talent, and health concepts to peripheral areas through methods like jointly building sports industrial parks and sharing event resources. Sports facilities and health services should be integrated into regional planning to maximize the spatial spillover dividends of sports investment, thereby promoting the overall enhancement of RHE . Declarations Author Contribution Zhibin Zou: Conceptualization, Methodology, Software, Data curation, Writing – original draft, Writing- Reviewing and Editing, Weihui Hu: Conceptualization, Methodology, Software, Data curation, Writing – original draft, Writing- Reviewing and Editing, Visualization, Investigation, Validation. Data Availability Data is provided within the supplementary information files.https://github.com/SmartPatrick1/Supporting-Information References Aral, S., Nicolaides, C. (2020). Exercise contagion in a global social network. 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The geographical agglomeration effect of the sports industry cluster: an economic analysis. GeoJournal, 90(173). https://doi.org/10.1007/s10708-025-11414-5 Zhang, Z., Wang, M., Xu, Z., et al. (2021). The influence of Community Sports Parks on residents’ subjective well-being: A case study of Zhuhai City, China. Habitat International, 117, 102439. https://doi.org/10.1016/j.habitatint.2021.102439 Additional Declarations No competing interests reported. Supplementary Files SupportingInformation.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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1","display":"","copyAsset":false,"role":"figure","size":1951518,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial pattern of the SII.\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7835154/v1/ca7f801e378b6155ec08dc2f.jpg"},{"id":93477598,"identity":"5fc6b576-f040-4a81-8d32-2d726e3bad57","added_by":"auto","created_at":"2025-10-14 09:28:25","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1967755,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial pattern of the RHE.\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7835154/v1/6c048b8158b10a0918383dda.jpg"},{"id":94988554,"identity":"aabd25fd-de05-4cd1-a108-f6cfce10142f","added_by":"auto","created_at":"2025-11-03 07:09:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5793277,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7835154/v1/f95450d0-aefc-4d19-8a71-4a452265f47e.pdf"},{"id":93477917,"identity":"cef69855-e704-4614-80e9-0bb1e78a31de","added_by":"auto","created_at":"2025-10-14 09:36:25","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1348860,"visible":true,"origin":"","legend":"","description":"","filename":"SupportingInformation.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7835154/v1/c1ed580db55d3ff30d2ff4d0.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Promoting or inhibiting? The impact of sports industry investment on regional health equity","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHealth is fundamental to human well-being and a cornerstone of societal progress. Consequently, ensuring its equitable access has become a paramount objective on the global public health agenda. WHO has called for a \u0026ldquo;Health in All Policies\u0026rdquo; approach to advance universal health coverage and foster RHE (WHO, 2024). Although China has achieved remarkable progress in public health with 2024 statistics indicating a life expectancy of 79 years, an infant mortality rate of 4.0\u0026permil;, and a maternal mortality rate of 14.3 per 100,000, surpassing the average of upper-middle-income nations - significant challenges persist. The country\u0026rsquo;s progress is impeded by population aging, a rising prevalence of chronic diseases, substantial healthcare costs, and pronounced imbalances in the allocation of health resources across urban-rural and regional divides. As a result, disparities in health outcomes and healthcare accessibility remain stark (Han et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This inequality not only compromises social justice but also erodes the quality and sustainability of human capital - the primary engine of economic growth - thereby threatening long-term macroeconomic stability. It is therefore imperative to bridge the regional \u0026ldquo;health gap\u0026rdquo; and promote balanced health development across the nation (Yin et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSports represent a proactive, universal, and cost-effective mode of health promotion, offering a new paradigm for resolving regional health inequities. Many developed nations have long integrated their sports economies into national strategies to enhance population health and address the challenge of chronic diseases, exemplified by Japan\u0026rsquo;s \u0026ldquo;Health Japan 21\u0026rdquo; initiative and the \u0026ldquo;Healthy People\u0026rdquo; plans in the United States. Meanwhile, China is vigorously fostering new economic engines to drive high-quality growth, and the sports industry, with its powerful linkage effects and immense market potential, is attracting significant private investment. The value-added of China\u0026rsquo;s sports industry has recently grown at an average annual rate of 15.4%, substantially outpacing GDP growth. Projections from the 2025 policy document, Opinions on Unleashing Sports Consumption Potential and Further Promoting the High-Quality Development of the Sports Industry, forecast that the industry will maintain this trajectory, reaching a total scale of 7 trillion RMB by 2030. This raises critical questions within the \u0026ldquo;Healthy China\u0026rdquo; strategy, which champions universal health for all: Can \u003cem\u003eSII\u003c/em\u003e foster \u003cem\u003eRHE\u003c/em\u003e? If so, through what mechanisms does it operate? This paper addresses these questions by investigating the impact of \u003cem\u003eSII\u003c/em\u003e on \u003cem\u003eRHE\u003c/em\u003e, examining its heterogeneous effects and underlying mechanisms, and analyzing its spatial dimensions.\u003c/p\u003e"},{"header":"Literature Review","content":"\u003cp\u003eThe concept of health equity, as defined by\u0026nbsp;Braveman \u0026amp; Gruskin (2003), posits that all individuals realize their full health potential regardless of socioeconomic position, race, or geography. Its central tenet is the elimination of health disparities that are avoidable and unjust. A vast body of research has sought to identify the determinants of health inequity. Recent studies reaffirm that socioeconomic status remains one of the most potent predictors of health outcomes; for example,\u0026nbsp;Wan \u0026amp; Jiang (2021)\u0026nbsp;demonstrated a durable link between socioeconomic inequality and disparities in well-being life expectancy among older adults. Furthermore, structural factors such as the lack of health insurance and geographical barriers to healthcare access are known to significantly compound health inequalities (Woolf \u0026amp; Schoomaker, 2019). Environmental justice research has also shown how consumption patterns create disparate exposure to air pollution across racial and ethnic groups, exacerbating health risks (Tessum et al., 2019). Indeed, systemic discrimination is increasingly understood as a root cause of health disadvantages (Williams et al., 2019), while higher educational attainment is robustly associated with improved health behaviors and lower mortality (Fu et al., 2024).\u003c/p\u003e\n\u003cp\u003eIn parallel, the literature on \u003cem\u003eSII\u003c/em\u003e has evolved from a narrow focus on economic impacts to a more holistic consideration of its value. This investment spans core activities, such as professional sports and recreational fitness, as well as derivative sectors like sporting goods and media (Downward et al., 2017). While much research has quantified its economic contributions - such as the effects of mega-events on tourism (Fourie \u0026amp; Santana-Gallego, 2022) or the impact of professional teams on local employment and revenue (Coates \u0026amp; Humphreys, 2020) - a new research frontier is emerging. Moving beyond purely economic metrics, scholars like\u0026nbsp;Gosselin et al. (2020)\u0026nbsp;have developed frameworks to capture the broader societal benefits of sports. This work has begun to highlight the increasingly significant role of sports investment in generating positive social outcomes, most notably in the enhancement of public health.\u003c/p\u003e\n\u003cp\u003eThe net effect of \u003cem\u003eSII\u003c/em\u003e on \u003cem\u003eRHE\u003c/em\u003e remains a subject of considerable debate. Proponents argue that it is an effective tool for advancing health equity. Investments can directly expand the provision of public sports infrastructure, such as urban green-ways and community recreation centers, thus lowering barriers to physical activity, particularly for low- and middle-income populations (Han \u0026amp; Zhou, 2024). By promoting widespread participation in sports, these investments can help curtail the incidence of chronic diseases and enhance overall public health (Bull et al., 2020). Moreover, the sports industry can indirectly bolster the socioeconomic determinants of health by fostering job creation and local economic growth, leading to improved household incomes and living standards.\u003c/p\u003e\n\u003cp\u003eConversely, critics question the inclusive nature of such investments, contending that they risk reinforcing or even worsening regional health disparities. Given the profit-driven motives of capital, sports investments tend to gravitate toward affluent regions with high consumption capacity. This concentration can lead to a \u0026ldquo;Matthew Effect\u0026rdquo; in resource allocation, where high-end sports facilities cluster in wealthy areas while underserved regions face acute shortages (Lou \u0026amp; Chen, 2024). Furthermore, investment models predicated on mega-events and landmark stadiums can divert public funds away from essential public health services and grassroots community sports. The purported \u0026ldquo;trickle-down\u0026rdquo; benefits of these high-profile projects often do not materialize as promised (Zhang, 2025). Consequently, if not guided by an equity-focused framework, sports investment may fail to bridge the \u0026ldquo;health gap\u0026rdquo; and instead act as a further catalyst for social stratification.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn summary, the existing literature provides a valuable foundation for understanding the relationship between \u003cem\u003eSII\u003c/em\u003e and health equity. However, several gaps remain. (1) Current research has predominantly focused on the micro-level of individual behavior or the meso-level of community projects, lacking a macro-level perspective from regional economics that systematically examines the net effect of industry-level capital flows on the overall landscape of RHE. (2) Existing studies have not sufficiently explored the internal transmission pathways through which sports investment affects health equity, and the complex mechanisms behind this relationship require deeper investigation. (3) The literature has generally overlooked the potential spatial dependence and spillover effects that may exist in the impact of sports investment on \u003cem\u003eRHE\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eThe marginal contributions of this paper are as follows: (1) We construct and test a macro-analytical framework for \u003cem\u003eSII\u003c/em\u003e and \u003cem\u003eRHE\u003c/em\u003e. (2) We identify the multiple transmission mechanisms of \u003cem\u003eSII\u003c/em\u003e through three key dimensions: enhancing resident income, optimizing industrial structure, and boosting health technology innovation. (3) We reveal the spatial spillover effects of \u003cem\u003eSII\u003c/em\u003e on \u003cem\u003eRHE\u003c/em\u003e, providing an empirical basis for formulating more holistic and coordinated regional sports and health development strategies.\u003c/p\u003e"},{"header":"Theoretical background and hypothesis development","content":"\u003cp\u003e\u003cstrong\u003eThe Direct Effect of \u003cem\u003eSII\u003c/em\u003e on \u003cem\u003eRHE\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe posit that \u003cem\u003eSII\u003c/em\u003e directly and positively influences \u003cem\u003eRHE\u003c/em\u003e by enhancing the provision of health-related resources and intervening in key health determinants. This argument is grounded in\u0026nbsp;Grossman\u0026apos;s (1972)\u0026nbsp;seminal theory of health demand, which conceptualizes health as a capital good whose stock can be augmented through individual investments like exercise. Investments in the sports industry, especially those targeting public facilities and services, substantially reduce the costs and barriers associated with these personal \u0026ldquo;health investments\u0026rdquo;. For example, the expansion of urban green-ways, community fitness centers, and subsidized coaching services directly improves the accessibility of physical activity, thereby dismantling \u0026ldquo;health barriers\u0026rdquo; linked to socioeconomic status (Kumar et al., 2018). Moreover, investments in sports cultivate social and physical environments that encourage healthy lifestyles, generating positive externalities for population health (Zhang et al., 2024). As the distribution of sports resources within a region becomes more equitable, residents across diverse social and geographical backgrounds receive fairer opportunities for health enhancement. This process mitigates avoidable health disparities and ultimately elevates the overall level of \u003cem\u003eRHE\u003c/em\u003e. Therefore, we hypothesize:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH1: \u003cem\u003eSII\u003c/em\u003e positively affects \u003cem\u003eRHE\u003c/em\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIndirect Impact of \u003cem\u003eSII\u003c/em\u003e on \u003cem\u003eRHE\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSII\u003c/em\u003e may also indirectly affect \u003cem\u003eRHE\u003c/em\u003e through a series of socioeconomic mediating variables. This process can be realized through the following three transmission mechanisms.\u003c/p\u003e\n\u003cp\u003eThe first is the resident income enhancement mechanism. As a combination of a labor-intensive and high value-added industry, investment in the sports sector can effectively create jobs across multiple fields, including event organization, venue operation, and the manufacturing and sales of sporting goods, thereby raising the overall regional employment level and resident income (Jiang, 2024). An increase in income is one of the most fundamental socioeconomic determinants for improving health outcomes, as it can directly enhance residents\u0026rsquo; ability to pay for medical care, improve their nutritional status, and reduce life stress, thus narrowing health inequalities caused by poverty (Jiang \u0026amp; Feng, 2021).\u003c/p\u003e\n\u003cp\u003eThe second is the industrial structure optimization mechanism. The sports industry facilitates the transition from traditional manufacturing to a knowledge-based, service-oriented economy. This optimization of the industrial structure not only brings higher-quality employment opportunities but also promotes the improvement of urban functions and environmental quality. These factors collectively constitute a favorable macroeconomic environment for promoting health equity (Li et al., 2025).\u003c/p\u003e\n\u003cp\u003eThe final mechanism is health technology innovation. Investment in competitive sports and mass fitness is often accompanied by R\u0026amp;D investment in related fields such as sports science, rehabilitation medicine, and wearable devices. The innovations resulting from this health technology do not solely serve professional athletes; their technology spillover effects can also benefit the general public, elevating the overall regional levels of disease prevention, health management, and rehabilitation therapy, thereby providing technical support for the promotion of health equity (He, 2019). Based on this, the present study proposes the following hypothesis:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH2: \u003cem\u003eSII\u003c/em\u003e can indirectly promote \u003cem\u003eRHE\u003c/em\u003e through the three dimensions of increasing resident income, optimizing industrial structure, and promoting health technology innovation.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpatial Spillover Effects of \u003cem\u003eSII\u003c/em\u003e on \u003cem\u003eRHE\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRegional economic activities are not isolated from one another but are spatially correlated through channels such as factor mobility, technology diffusion, and policy interactions. Similarly, the impact of \u003cem\u003eSII\u003c/em\u003e on \u003cem\u003eRHE\u003c/em\u003e may transcend administrative boundaries and generate spatial spillover effects. According to \u0026ldquo;the first law of geography\u0026rdquo;, interactions are more intense between nearby regions (Tobler, 1970). The prosperity of the sports industry in one region may produce a demonstration effect and competitive pressure on neighboring regions, prompting them to increase their own public investments in sports and health, thereby forming a pattern of benign regional competition and coordinated development. Furthermore, the construction of large-scale sports venues can have a \u0026ldquo;growth pole\u0026rdquo; effect, with impacts radiating to surrounding areas. For instance, residents of adjacent regions might cross administrative borders to enjoy high-quality sports facilities and services, or benefit from collaborative divisions of labor within inter-regional sports industry chains. Conversely, if sports investment is overly concentrated in a few central cities, it could create a \u0026ldquo;siphoning effect\u0026rdquo; on peripheral areas, further exacerbating the imbalance of health resources between regions (Hyun, 2022). To accurately identify the true effect of \u003cem\u003eSII\u003c/em\u003e, it is essential to examine it within a broader geospatial framework. Based on this, the present study proposes the following hypothesis:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH3: The impact of \u003cem\u003eSII\u003c/em\u003e on \u003cem\u003eRHE\u0026nbsp;\u003c/em\u003ehas significant spatial spillover effects.\u003c/strong\u003e\u003c/p\u003e"},{"header":"Empirical Analysis","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eModel Specification\u003c/h2\u003e\n \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\n \u003ch2\u003eBaseline Regression Model\u003c/h2\u003e\n \u003cp\u003eTo estimate the impact of \u003cem\u003eSII\u003c/em\u003e on \u003cem\u003eRHE\u003c/em\u003e, we specify the following baseline model:\u003c/p\u003e\n \u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e$$RH{E_{it}}={\\alpha _0}+{\\alpha _1}SI{I_{it}}+{\\alpha _2}contro{l_{it}}+{\\mu _i}+{\\nu _t}+{\\varepsilon _{it}}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eIn this context, \u003cem\u003ei\u003c/em\u003e denotes the city, and \u003cem\u003et\u003c/em\u003e represents the time. \u003cem\u003eRHE\u003c/em\u003e represents regional health equity. The term \u003cem\u003e\u0026alpha;\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e is the constant term. The core independent variable is \u003cem\u003eSII\u003c/em\u003e, sports industry investment. \u003cem\u003eCV\u003c/em\u003e encompasses all the control variables. \u003cem\u003e\u0026micro;\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e represents the individual fixed effect, \u003cem\u003ev\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e represents the time fixed effect, and \u003cem\u003e\u0026epsilon;\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e is the random error term.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003eMediation Analysis\u003c/h3\u003e\n\u003cp\u003eTo test whether \u003cem\u003eSII\u003c/em\u003e can indirectly promote \u003cem\u003eRHE\u003c/em\u003e by increasing resident income, optimizing industrial structure, and promoting health technology innovation, this paper constructs the following mediation effect model:\u003c/p\u003e\n\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e$${M_{it}}={\\alpha _0}+{\\alpha _1}SI{I_{it}}+{\\alpha _2}contro{l_{it}}+{\\mu _i}+{\\nu _t}+{\\varepsilon _{it}}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e$$RH{E_{it}}={\\beta _0}+{\\beta _1}{M_{it}}+{\\beta _2}SI{I_{it}}+{\\beta _3}contro{l_{it}}+{\\mu _i}+{\\nu _t}+{\\varepsilon _{it}}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eHere, \u003cem\u003eM\u003c/em\u003e is the mediating variable, representing the level of resident income, the level of industrial structure optimization, and the level of health technology innovation, respectively. The other variables are the same as in Eq. (\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eSpatial Durbin Model\u003c/h2\u003e\n \u003cp\u003eTo test whether the impact of \u003cem\u003eSII\u003c/em\u003e on \u003cem\u003eRHE\u003c/em\u003e has spatial spillover effects, this paper constructs the following dynamic Spatial Durbin Model:\u003c/p\u003e\n \u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e$$RH{E_{it}}={\\alpha _0}++\\rho (WRH{E_{it}})+{\\alpha _1}SI{I_{it}}+\\theta (WSI{I_{it}})+{\\alpha _2}contro{l_{it}}+\\delta (Wcontro{l_{it}})+{\\mu _i}+{\\nu _t}+{\\varepsilon _{it}}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eHere, \u003cem\u003eW\u003c/em\u003e is the spatial weight matrix. \u003cem\u003e\u0026rho;\u003c/em\u003e is the spatial autoregressive coefficient, \u003cem\u003e\u0026theta;\u003c/em\u003e represents the spatial spillover coefficients of the independent variables. The other variables are defined as in the baseline model.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eVariable Selection\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eDependent Variable: Regional Health Equity (\u003c/strong\u003e\u003cstrong\u003eRHE\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFollowing the research of Wei et al., (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) and Murray et al., (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e), this paper constructs an evaluation index for \u003cem\u003eRHE\u003c/em\u003e from two dimensions: economic development and health security. This index is decomposed into 5 secondary indicators and 14 tertiary indicators. Using the CRITIC (Criteria Importance Through Intercriteria Correlation) method, we measure the level of \u003cem\u003eRHE\u003c/em\u003e for 287 prefecture-level cities in China from 2003 to 2023. The indicator system and corresponding weights are shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eIndicator system and weights for the \u003cem\u003eRHE\u003c/em\u003e.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePrimary indicator\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSecondary indicator\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTertiary indicator\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWeight\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eEconomic Development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eEconomic Aggregate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGross Domestic Product (10,000 RMB)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.046404\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLocal Government General Budget Revenue (10,000 RMB)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.039511\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProportion of Tertiary Industry Value-added in GDP (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.128105\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eEconomic Efficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePer Capita Gross Domestic Product (RMB)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.098936\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePer Capita Total Retail Sales of Consumer Goods (RMB)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.056287\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrbanization Rate (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.100727\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"8\"\u003e\n \u003cp\u003eHealth Security\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eGovernment Expenditure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePer Capita Expenditure on Science\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.030730\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePer Capita Expenditure on Education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.046726\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eSocial Security\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of Participants in Basic Medical Insurance for Urban Employees\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.045503\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of Participants in Basic Old-age Insurance for Urban Employees\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.057609\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of Participants in Unemployment Insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.050557\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eHealthcare Security\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of Hospital Beds per 10,000 People\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.108848\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of Licensed Physicians per 10,000 People\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.090969\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of Health Institutions per 10,000 People\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.099091\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eExplanatory Variable: Sports Industry Investment (\u003c/strong\u003e\u003cstrong\u003eSII\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing Python web scraping techniques, we obtained 57,596,248 records of basic corporate registration information. Each record was assigned a unique identifier, and 24 indicator items were processed, with irrelevant information being removed. The operational duration of each firm was calculated based on its business status (e.g., canceled, revoked, or active), and all registered capital figures were standardized to units of 10\u0026nbsp;million RMB. After cleaning, the equity penetration method was used to establish investment links between firms. First, we performed branch penetration, removing extraneous information, de-duplicating records, and precisely matching unique identifiers. Second, shareholder-branch penetration was conducted, removing irrelevant data and retaining only corporate shareholders, which were matched by \u0026ldquo;Shareholder Name\u0026thinsp;+\u0026thinsp;Year of Subscribed Capital\u0026rdquo;. The data from both types of penetration were then merged (assuming 100% holding of branches by headquarters and calculating shareholder investment based on shareholding ratios). This process ultimately yielded a total of 8,961,483 equity penetration records, comprising 5,072,508 from branch penetration and 3,888,975 from corporate shareholder penetration. These data were then aggregated to the 287 prefecture-level cities in China.\u003c/p\u003e\n\u003cp\u003eAfter obtaining the equity penetration data, we used latitude and longitude coordinates to determine the province, city, and district/county for each firm. As industry classifications were often missing from the basic registration data, we extracted keywords for 71 sub-class industries based on the Statistical Classification of the Sports Industry (2019) released by the National Bureau of Statistics of China. We further categorized these into a sports industry classification system (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) consisting of 2 major categories (Core Sports Industry and Related Sports Industry) and 11 one-digit industry codes, with each two-digit code corresponding to specific keywords. By matching these keywords with the equity penetration data, we identified a total of 745,110 records of enterprise investment data in China\u0026rsquo;s sports sector.\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eClassification system for the sports industry.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCode\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIndustry name\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMajor category\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSports Management Activities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eRelated Sports Industry\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eManagement of Social Affairs in Sports\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eManagement of Social Organizations in Sports\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eManagement of Support Organizations in Sports\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSports Competition and Performance Activities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eCore Sports Industry\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProfessional Sports Competition and Performance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Professional Sports Competition and Performance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSports Fitness and Leisure Activities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eCore Sports Industry\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSports and Leisure Activities\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMass Sports Activities\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther Sports and Leisure Activities\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eManagement of Sports Venues and Facilities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eCore Sports Industry\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSports Venue Management\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSports Service Complex Management\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eManagement of Sports Parks and Other Facilities\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSports Brokerage, Advertising, Exhibition, and Design\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eRelated Sports Industry\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSports Brokerage and Agency Services\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSports Advertising and Exhibition Services\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSports Performance and Design Services\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSports Education and Training\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eCore Sports Industry\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSchool Sports Education Activities\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSports Training\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSports Media and Information Services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"7\"\u003e\n \u003cp\u003eRelated Sports Industry\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSports Publication Services\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSports Film, Television, and Other Media Services\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInternet Sports Services\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSports Consulting\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSports Museum Services\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther Sports Information Services\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther Sports Services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"7\"\u003e\n \u003cp\u003eRelated Sports Industry\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSports Tourism Services\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSports Health and Athletic Rehabilitation Services\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSports Lottery Services\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSports Finance and Asset Management Services\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSports Science, Technology, and IP Services\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther Unspecified Sports Services\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eManufacturing of Sports Goods and Related Products\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eCore Sports Industry\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eManufacturing of Sporting Goods and Equipment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eManufacturing of Sports Vehicles, Boats, and Aviation Equipment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eManufacturing of Sports-related Materials\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eManufacturing of Other Sports-related Products and Equipment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSales, Rental, and Trade of Sports Goods\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eRelated Sports Industry\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSales of Sports and Related Products\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRental of Sporting Goods and Equipment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrade Agency for Sports and Related Products\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstruction of Sports Venues and Facilities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eCore Sports Industry\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstruction and Decoration of Sports Venues\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEngineering and Installation of Sports Facilities\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eMediating Variables\u003c/h3\u003e\n\u003cp\u003eWe select per capita disposable income, the value-added of the tourism industry, and the number of patents in strategic emerging industries as the mediating variables. Per capita disposable income (income) is used to represent the level of resident income. Value-added of the tourism industry (tourism) is used to represent the level of industrial structure optimization. A more developed tourism industry indicates a more pronounced trend toward an advanced industrial structure. Number of patents in strategic emerging industries (patent) is used to represent the level of health technology innovation, as such innovations are primarily concentrated in cutting-edge fields like biotechnology, high-end medical devices, and AI-powered health management.\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eControl Variables\u003c/h2\u003e\n \u003cp\u003eGiven what might be regarded as potentially playing the role of errors due to omitted variables and/or to gain much more substantially in terms of the estimation of the effect of \u003cem\u003eSII\u003c/em\u003e on \u003cem\u003eRHE\u003c/em\u003e, based on what might be regarded as pertinent prior work by Hao et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Xu \u0026amp; Lin (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e), seems to tend to control by the following pertinent variables: Population density (\u003cem\u003edensity\u003c/em\u003e). In this general framework, this variable seems to be measured as the number of ten thousand persons per square kilometer. Financial development level (\u003cem\u003efinance\u003c/em\u003e). From these considerations, it seems that this factor is measured as the ratio of the loan balance of financial institutions to GDP. Level of foreign openness (\u003cem\u003efdi\u003c/em\u003e). The evidence seems to reveal that this is measured by the actual utilized foreign investment and it is measured in units of 10,000 RMB. Human capital (\u003cem\u003ehum\u003c/em\u003e). This is measured as the number of students enrolled in regular institutions of higher education and it is measured in units of 10,000 persons. R\u0026amp;D investment (\u003cem\u003erd\u003c/em\u003e). This seems to indicate that this is measured by R\u0026amp;D expenditure and it is measured in units of 10,000 RMB. Investment level (\u003cem\u003easset\u003c/em\u003e). Given the rather complex nature of these findings, this is mainly measured by the amount of fixed asset investment and it is measured in units of 10,000 RMB. Fiscal support (\u003cem\u003egov\u003c/em\u003e). This variable seems to be measured as the ratio of local government general budget expenditure to the gross regional product.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eData Sources and Descriptive Statistics\u003c/h2\u003e\n \u003cp\u003eThis paper uses a panel dataset of 287 prefecture-level cities in China from 2003 to 2023 as the study sample. The sample of Tibet Autonomous Region was removed because of the serious lack of data. The \u003cem\u003eSII\u003c/em\u003e data were scraped and aggregated from the Qichacha data platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.qcc.com\u003c/span\u003e\u003c/span\u003e) by using web scraping tools. Other necessary data were collected from China Statistical Yearbook, National Intellectual Property Administration and National Bureau of Statistics. The descriptive statistics of all variables are shown in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive statistics.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ecount\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003emean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003esd\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003emin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003emax\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eRHE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1931\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0683442\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7288619\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSSI\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003edensity\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.2774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.3531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003efinance\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.9777\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0753187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.81711\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003efdi\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1939\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ehum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.6260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003erd\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1666\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003easset\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.4690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003egov\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1823\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0312843\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.667161\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Empirical Results","content":"\u003ch2\u003eSpatial Pattern Evolution\u003c/h2\u003e\u003cp\u003e\u003cb\u003eSpatial Pattern of the\u003c/b\u003e \u003cb\u003eSII\u003c/b\u003e \u003cb\u003eLevel\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe visualized China's SII to map its geographical distribution (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The map clearly shows that total investment value first grew rapidly and then slowed down. At the same time, the number of smaller investments increased significantly. This trend was especially apparent in 2023, when more than 250 cities were classified in tier Ⅰ for investment.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eSpatial Pattern of the\u003c/b\u003e \u003cb\u003eRHE\u003c/b\u003e \u003cb\u003eIndex\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e maps the geographical distribution of RHE. Overall, the level of RHE showed a clear upward trend from 2003 to 2023. Its presence, initially scattered in a few locations, gradually spread to surrounding cities. The RHE classification for cities also improved markedly over time: in 2003, Tier I cities were the most common; by 2013, Tier II had become more numerous; and by 2023, Tier III cities constituted the majority.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003eBaseline Regression Analysis\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e reports the baseline regression results examining the direct impact of \u003cem\u003eSII\u003c/em\u003e on \u003cem\u003eRHE\u003c/em\u003e. Under the specification that simultaneously controls for both city and year two-way fixed effects and includes a series of region-level control variables (column 1), the regression coefficient of the core explanatory variable, \u003cem\u003eSII\u003c/em\u003e is 0.0205 and is significantly positive at the 1% level. This result indicates that \u003cem\u003eSII\u003c/em\u003e has a significant promotional effect on \u003cem\u003eRHE\u003c/em\u003e, thus verifying Hypothesis \u003cb\u003eH1\u003c/b\u003e. \u003cem\u003eSII\u003c/em\u003e not only directly improves the accessibility and quality of public sports facilities, reducing the cost for residents to participate in physical activities, but also fosters a health-promoting social atmosphere by hosting events and advocating for healthy lifestyles, thereby enhancing the overall health level of the region (Zhai et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). We also conducted a sensitivity analysis of the model specification. In models that only control for two-way fixed effects (column 2) or only include control variables (column 3), the coefficient for \u003cem\u003eSII\u003c/em\u003e remains significant at the 1% level and consistent in its direction. This fully demonstrates that the baseline regression result is not driven by a specific model choice.。\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRegression results of the benchmark model.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSII\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0205***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0364***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0312***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.005)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003edensity\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0121***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0005**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.000)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003efinance\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0052***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0519***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.001)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003efdi\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0341***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0726***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.005)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ehum\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0103***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0049***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.001)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003erd\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0488***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1166***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.005)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003easset\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0019***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0076***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.000)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003egov\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0151***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0212***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.005)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1233***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1919***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1246***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.002)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6,027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6,027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6,027\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR-squared\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.940\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.917\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.624\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eyearfix\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eidfix\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eNotes: 1. Standard errors in parentheses. 2. *p \u0026lt; 0.1, **p \u0026lt; 0.05, ***p \u0026lt; 0.01.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003eRobustness Tests\u003c/h2\u003e\u003cp\u003eThis paper conducts robustness tests to verify the baseline results (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Replacing the core dependent variable. We employ the entropy weight method to re-measure the regional health equity (\u003cem\u003eRHE\u003c/em\u003e) index and substitute it into the model. As shown in column (1), the coefficient of \u003cem\u003eSII\u003c/em\u003e remains significantly positive.\u003c/p\u003e\u003ch2\u003eAdding Control Variables\u003c/h2\u003e\u003cp\u003eWe further introduce a dummy variable indicating whether a city is a provincial capital. The results in column (2) of Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e show that after enhancing the model's control capacity, the promotional effect of \u003cem\u003eSII\u003c/em\u003e remains unchanged.\u003c/p\u003e\u003ch2\u003eExcluding Special Samples and Years\u003c/h2\u003e\u003cp\u003eTo account for potential biases, we perform two exclusion tests. First, considering the COVID-19 pandemic as a major external shock, we exclude the sample for the 2020–2021 period, with results presented in column (3) of Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Second, to eliminate potential estimation bias arising from the unique economic scale and policy environments of first-tier cities (Beijing, Shanghai, Guangzhou, and Shenzhen), we re-run the regression after removing them from the sample (column 4) of Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. In both scenarios, the coefficient for \u003cem\u003eSII\u003c/em\u003e maintains a high degree of positive significance.\u003c/p\u003e\u003ch2\u003eAddressing Extreme Values\u003c/h2\u003e\u003cp\u003eTo mitigate potential interference from outliers, we apply 1% bilateral winsorization to both the core explanatory and dependent variables. As reported in column (5) of Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the core conclusion remains valid after controlling for the influence of extreme values.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRobustness tests.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAlternative dependent variable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAdd control variables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eExclude special years\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eExclude special sample\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eWinsorize at 1%\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(5)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSII\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0313***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0205***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0218***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0214***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0366***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.005)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.006)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCV\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eConstant\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0423***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1233***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1163***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.1571***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.1366***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.004)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6,026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6,027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5,453\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5,901\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6,027\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.944\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.940\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.934\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.926\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.943\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eyearfix\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eidfix\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNotes: 1. Standard errors in parentheses. 2. *p \u0026lt; 0.1, **p \u0026lt; 0.05, ***p \u0026lt; 0.01. 3. CV represents the control variables.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003eEndogeneity Test\u003c/h2\u003e\u003cp\u003eGiven the potential for reverse causality between \u003cem\u003eSII\u003c/em\u003e and \u003cem\u003eRHE\u003c/em\u003e, regions with higher health levels may attract more \u003cem\u003eSII\u003c/em\u003e, this paper addresses endogeneity. We select the one-period lag of \u003cem\u003eSII\u003c/em\u003e as an instrumental variable and employ the Two-Stage Least Squares method to conduct the endogeneity test (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). After applying the 2SLS method, the impact of \u003cem\u003eSII\u003c/em\u003e on \u003cem\u003eRHE\u003c/em\u003e remains significantly positive, with a coefficient of 0.0425, which is significant at the 1% level. This indicates that after controlling for endogeneity, \u003cem\u003eSII\u003c/em\u003e still promotes \u003cem\u003eRHE\u003c/em\u003e. We then use the Difference GMM method to control for endogeneity, and the results remain consistent with the 2SLS estimation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEndogeneity test results.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e2SLS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDifference GMM\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFirst-stage regression\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSecond-stage regression\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSII\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.4923***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0425***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0126***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.012)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.006)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.001)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCV\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eKleibergen-Paap rk LM statistic P-value\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1432.19\u003c/p\u003e\u003cp\u003e(0.0000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCragg-Donald Wald F statistic\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1803.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5,740\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5,740\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5,453\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eyearfix\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eidfix\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eNotes: 1. Standard errors in parentheses. 2. *p \u0026lt; 0.1, **p \u0026lt; 0.05, ***p \u0026lt; 0.01. 3. CV represents the control variables.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003eHeterogeneity Analysis\u003c/h2\u003e\u003ch2\u003eAnalysis of Regional Heterogeneity\u003c/h2\u003e\u003cp\u003eChina’s vast territory has led to significant regional imbalances in socioeconomic development (Deng et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Consequently, the impact of \u003cem\u003eSII\u003c/em\u003e on \u003cem\u003eRHE\u003c/em\u003e may exhibit regional heterogeneity. We divide the national sample into four major regions, Eastern, Central, Western, and Northeastern - and conduct sub-sample regressions (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe results show that the positive effect of \u003cem\u003eSII\u003c/em\u003e is primarily concentrated in the Eastern and Central regions, where the regression coefficients are significantly positive at the 1% level. In contrast, the impact in the Northeastern region, while positive, is not statistically significant, and the effect in the Western region is also insignificant. The Eastern and Central regions typically possess stronger fiscal capacity, more mature market mechanisms, and higher levels of resident consumption, which provide a robust foundation for translating \u003cem\u003eSII\u003c/em\u003e into inclusive health resources. In these areas, sports investment can not only fund high-quality public sports facilities but also effectively stimulate the growth of related service industries, thereby promoting health equity from both ends. In comparison, the Western region has a relatively weaker economic base, which may limit the scale and effectiveness of \u003cem\u003eSII\u003c/em\u003e, making it difficult to achieve scale effects. The Northeastern region, meanwhile, may be facing challenges related to industrial structure transformation and population outflow, which could prevent the health-promoting effects of sports investment from being fully realized (Chen, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1997\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of regional heterogeneity analysis.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEastern regions\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCentral regions\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWestern regions\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNortheast regions\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSII\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0204***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0197***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.0268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0067*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.007)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.021)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.004)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCV\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eConstant\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1597***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1655***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1764***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0633***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.005)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.005)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.014)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.020)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,806\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,680\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,827\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e714\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.974\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.973\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.887\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.953\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eyearfix\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eidfix\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNotes: 1. Standard errors in parentheses. 2. *p \u0026lt; 0.1, **p \u0026lt; 0.05, ***p \u0026lt; 0.01. 3. CV represents the control variables.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003eOther Heterogeneity Analyses\u003c/h2\u003e\u003cp\u003eTo further deepen the understanding of the health effects of \u003cem\u003eSII\u003c/em\u003e, we conduct heterogeneity tests from three dimensions: investment flow, industry attributes, and the policy environment (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eHeterogeneity of investment flows.\u003c/b\u003e There are significant differences in the economic logic between investment inflows and outflows. This paper disaggregates \u003cem\u003eSII\u003c/em\u003e into inflows and outflows for separate examination. As shown in columns (1) and (2), both investment inflows (coefficient 0.0920) and outflows (coefficient 0.0193) significantly promote \u003cem\u003eRHE\u003c/em\u003e, but the marginal effect of inflows is much larger than that of outflows. This may be because investment inflows, as external capital, often directly impact local sports facility construction and health service provision, allowing them to more rapidly fill the local capital gap in the health sector. Consequently, their promotional effect is more direct and efficient. In contrast, investment outflows more closely reflect the external strategic positioning of local capital, and their feedback effect on local health equity is relatively indirect, requiring longer chains such as capital returns and knowledge spillovers to be realized (Özmen \u0026amp; Taşdemir, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Davis \u0026amp; Van Wincoop, 2021).\u003c/p\u003e\u003cp\u003e\u003cb\u003eHeterogeneity of industry attributes.\u003c/b\u003e The sports industry can be divided into core industries (e.g., fitness and leisure, competitive performances) and derivative industries (e.g., sporting goods manufacturing, sports media). As shown in columns (3) and (4), investment in sports-derivative industries (coefficient 0.0204) has a significant promotional effect on \u003cem\u003eRHE\u003c/em\u003e, whereas investment in core industries is not significant. A possible reason is that derivative industries, particularly sporting goods manufacturing, have stronger industrial linkage effects and a greater capacity for employment absorption, which can effectively increase resident income and regional economic vitality, making them more inclusive. In contrast, if investment in core sports industries is concentrated in a few high-end events or venues, it may not benefit the general public in the short term, thus limiting its direct contribution to health equity (Kenyon \u0026amp; Postlethwaite, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2026\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eHeterogeneity of the policy environment.\u003c/b\u003e The “Healthy Cities” initiative, as an important national strategy, provides a policy framework and resource support for local governments to advance public health. This paper divides the sample into pilot and non-pilot cities based on their participation in the “Healthy Cities” program. The regression results in columns (5) and (6) show that \u003cem\u003eSII\u003c/em\u003e significantly promotes health equity in both types of cities, but the marginal effect is slightly stronger in non-pilot cities (coefficient 0.0253) than in pilot cities (coefficient 0.0180). This suggests that in areas lacking specific preferential policies, market-driven \u003cem\u003eSII\u003c/em\u003e may play a more critical “gap-filling” role, becoming an important marginal force for promoting health equity, while the national strategy provides a policy framework and resource support for local health development (Bai et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Yan \u0026amp; Yan, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of other heterogeneity analyses.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOutflow\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInflow\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCore sports industry\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRelated sports industry\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePilot cities\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNon-pilot cities\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(5)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(6)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSII\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0193***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0920***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0366\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0204***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0180***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0253***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.013)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.033)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.004)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCV\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eConstant\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1232***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1225***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1224***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.1231***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.2206***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.1211***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.013)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6,027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6,027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6,027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6,027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e735\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5,292\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.939\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.939\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.939\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.940\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.964\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.932\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eyearfix\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eidfix\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNotes: 1. Standard errors in parentheses. 2. *p \u0026lt; 0.1, **p \u0026lt; 0.05, ***p \u0026lt; 0.01. 3. CV represents the control variables.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003eMediation Effect Analysis\u003c/h2\u003e\u003cp\u003eThis paper conducts a mediation effect test (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). As shown in columns (1), (3), and (5) of Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, \u003cem\u003eSII\u003c/em\u003e has a significant positive impact on resident income (\u003cem\u003eincome\u003c/em\u003e), industrial structure optimization (\u003cem\u003etourism\u003c/em\u003e), and health technology innovation (\u003cem\u003epatent\u003c/em\u003e). When these three mediating variables are introduced into the baseline model separately, the results in columns (2), (4), and (6) show that the coefficients of all mediators are significantly positive. Furthermore, while the coefficient of the core explanatory variable (\u003cem\u003eSII\u003c/em\u003e) decreases, it remains significant. This indicates that \u003cem\u003eSII\u003c/em\u003e can indirectly influence \u003cem\u003eRHE\u003c/em\u003e through the three channels of increasing resident income, optimizing industrial structure, and promoting health technology innovation, thus verifying Hypothesis \u003cb\u003eH2\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eThe promotional effect of \u003cem\u003eSII\u003c/em\u003e on resident income not only increases household wealth but also comprehensively enhances “health stock” by improving healthcare affordability and alleviating psychological burdens caused by economic stress. Its role in optimizing the industrial structure fosters the development of sports and related service industries, which in turn improves the quality of urban public spaces and creates a healthier living environment. Finally, sports investment stimulates demand for sports science and wearable devices; through technology spillovers and market-based applications, these innovations are ultimately transformed into accessible health management tools for the general public, narrowing the regional “health gap” from a technological standpoint (Li et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe mediation effects test.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eincome\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eRHE\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003etourism\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eRHE\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003epatent\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eRHE\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSII\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.2300***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0133***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2536***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0191***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.5347***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0087***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.038)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.020)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.041)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eincome\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0299***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003etourism\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0134***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003epatent\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0221***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.001)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCV\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eConstant\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.4071***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0535***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2367***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.1148***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.5718***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.1580***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.040)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.022)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.039)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5,937\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5,937\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5,227\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5,227\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6,027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6,027\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.963\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.951\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.850\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.966\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.863\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.946\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eyearfix\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eidfix\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNotes: 1. Standard errors in parentheses. 2. *p \u0026lt; 0.1, **p \u0026lt; 0.05, ***p \u0026lt; 0.01. 3. CV represents the control variables.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003eSpatial Spillover Effect Analysis\u003c/h2\u003e\u003cp\u003eThis paper calculates the Global \u003cem\u003eMoran’s\u003c/em\u003e I for the RHE of each prefecture-level city from 2003 to 2023 (Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). It is evident that the Global \u003cem\u003eMoran’s\u003c/em\u003e I is significantly positive for all years, which indicates a significant positive spatial autocorrelation among the prefecture-level cities. Therefore, it is necessary to employ a spatial econometric model.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSpatial \u003cem\u003eMoran’s\u003c/em\u003e I Test.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGlobal \u003cem\u003eMoran\u003c/em\u003e Index\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.3019***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.3283***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.3376***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.3586***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.3834***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.3909***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.4018***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.3981***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.4120***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.4301***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.4402***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.4368***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.4403***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.4409***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.4430***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.4306***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.4166***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.4213***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.4286***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.4262***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.3724***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"2\"\u003eNotes: *p \u0026lt; 0.1, **p \u0026lt; 0.05, ***p \u0026lt; 0.01.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFollowing the research of Guo et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), the spatial correlation of \u003cem\u003eRHE\u003c/em\u003e is influenced not only by geographical distance but is also closely related to the economic development levels between regions. This paper selects an economic-geographical weight matrix as the baseline spatial weight matrix, supplemented by a nested economic-geographical matrix and an inverse distance matrix for robustness checks. The results are presented in columns (1)–(3) of Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e. The regression results in column (1) of Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e show that the spatial autoregressive coefficient, rho, is significantly positive at the 1% level. This indicates the existence of significant positive spatial dependence in \u003cem\u003eRHE\u003c/em\u003e, meaning the health equity status of one region is positively affected by that of its neighbors. The coefficient of the core explanatory variable, \u003cem\u003eSII\u003c/em\u003e, is significantly positive, indicating that local \u003cem\u003eSII\u003c/em\u003e effectively promotes local health equity. Simultaneously, the coefficient of the spatially lagged term, \u003cem\u003eW*SII\u003c/em\u003e, is also significantly positive at the 1% level, indicating a significant spatial spillover effect from \u003cem\u003eSII\u003c/em\u003e. The successful implementation of sports investment in one region, such as the construction of iconic sports facilities or the hosting of major events, can create a “demonstration effect”, inducing neighboring governments to adopt similar public investment strategies to enhance their own competitiveness. Furthermore, the sports industry has a cross-regional industrial chain integration effect. Core projects in the industry (such as event operations or equipment manufacturing) can integrate upstream and downstream industries like tourism, logistics, and support services in surrounding areas through forward and backward linkages. This promotes the optimal inter-regional allocation of production factors, thereby spilling over the benefits of economic growth to neighboring regions (Campayo-Sanchez et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2026\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis paper further employs a nested economic-geographical matrix and a spatial distance matrix for robustness tests, as shown in columns (2) and (3) of Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e, respectively. After changing the spatial weight matrix, the coefficients of the core explanatory variable (\u003cem\u003eSII\u003c/em\u003e) and its spatially lagged term (\u003cem\u003eW*SII\u003c/em\u003e) remain significantly positive. This indicates that the paper’s conclusion, \u003cem\u003eSII\u003c/em\u003e has both a positive local effect and a positive spatial spillover effect. Therefore, Hypothesis \u003cb\u003eH3\u003c/b\u003e is validated.\u003c/p\u003e\u003cp\u003eDrawing on the research of Lesage \u0026amp; Pace (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), this paper uses partial differential decomposition on the Spatial Durbin Model to break down the total effect into direct and indirect effects, with the results presented in columns (4)–(6) of Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e. The results show that the coefficient for the direct effect of \u003cem\u003eSII\u003c/em\u003e is 0.0136 and is significant at the 1% level, which indicates that local \u003cem\u003eSII\u003c/em\u003e has a robust direct promotional effect on local health equity. The indirect effect, which represents the spatial spillover, has a coefficient of 0.0581 and is also highly significant at the 1% level.\u003c/p\u003e\u003cp\u003eNotably, the coefficient of the indirect effect is larger than that of the direct effect. This suggests that the promotional impact of \u003cem\u003eSII\u003c/em\u003e on health equity is more prominently reflected in its radiating influence on neighboring regions. A possible reason for this is that the sports industry is characterized by strong “network externalities” and “mobility”. On the one hand, core sports products like high-level sporting events and sports tourism destinations have a powerful cross-regional appeal, with a service radius extending far beyond the local area, which can effectively attract external consumption and promote inter-regional exchange. On the other hand, the healthy lifestyles, scientific fitness knowledge, and spirit of sports culture engendered by \u003cem\u003eSII\u003c/em\u003e are easily transmissible. They can be disseminated to neighboring regions at a low cost through channels such as media and interpersonal exchanges, thereby promoting the realization of health equity on a broader scale (Aral \u0026amp; Nicolaides, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of Spatial Econometric Models.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eSpatial Durbin Model Regression\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eDecomposition of Spatial Effects\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEconomic-geographical weight matrix\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNested economic-geographical matrix\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSpatial distance matrix\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDirect effect\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eIndirect effect\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTotal effect\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(5)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(6)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSII\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0116***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0140***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0164***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0136***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0581***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0717***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.012)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.012)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCV\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eW*SII\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0372***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0158**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0985***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.009)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.007)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.036)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eW*CV\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003erho\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.3051***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1268***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.6390***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.023)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.025)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.030)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003esigma2_e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0003***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0003***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0003***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5,964\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5,964\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5,964\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.381\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.402\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.064\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eyearfix\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eidfix\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNotes: 1. Standard errors in parentheses. 2. *p \u0026lt; 0.1, **p \u0026lt; 0.05, ***p \u0026lt; 0.01. 3. CV represents the control variables.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e"},{"header":"Conclusions and Implications","content":"\u003cp\u003eBased on a panel dataset of 287 prefecture-level cities in China from 2003 to 2023, this paper systematically investigates the impact of \u003cem\u003eSII\u003c/em\u003e on \u003cem\u003eRHE\u003c/em\u003e, including its heterogeneous effects, mechanisms of action, and spatial effects. The main research conclusions are as follows: \u003cem\u003eSII\u003c/em\u003e has a significant direct promotional effect on \u003cem\u003eRHE\u003c/em\u003e, but its impact exhibits significant heterogeneity. Specifically, this promotional effect is more pronounced in the Eastern and Central regions, which have stronger economic foundations, in cases of capital inflow, and within sports-derivative industries. \u003cem\u003eSII\u003c/em\u003e can significantly improve regional health levels through three mediating pathways: enhancing resident income, optimizing the regional industrial structure, and promoting health technology innovation. The health-promoting effect of \u003cem\u003eSII\u003c/em\u003e is characterized by significant spatial spillovers. Sports investment in a local region not only improves its own level of health equity but also has a positive radiating effect on neighboring regions. Furthermore, the effect decomposition results show that this indirect spillover effect is the main component of the overall impact. The implications of these findings are discussed below:\u003c/p\u003e\u003cp\u003eOptimize investment layout and implement differentiated, targeted promotion strategies. The investment structure should be optimized to guide more capital toward highly inclusive sports-derivative industries and public community sports services, while also encouraging the inflow of high-quality external capital. A regionally differentiated strategy is necessary, in the Eastern and Central regions, the focus should be on promoting the deep integration of the sports industry with related sectors. In the Western and Northeastern regions, investment should be prioritized to address fundamental shortcomings and solidify the foundation for health equity.\u003c/p\u003e\u003cp\u003eStrengthen mechanistic synergy and unblock the value conversion pathways of sports investment. Policy design should connect the three transmission channels of “sports-economy-health”. This includes using fiscal and tax incentives to support the creation of more high-quality jobs in the sports industry; integrating sports industry development into the overall planning for urban renewal and industrial upgrading; and establishing specialized funds to encourage R\u0026amp;D and the application of innovations in fields such as smart fitness and athletic rehabilitation.\u003c/p\u003e\u003cp\u003eLeverage spatial spillovers to foster a pattern of coordinated regional health development. Cross-regional coordination mechanisms for sports development should be established. Central cities should be encouraged to leverage their radiating and leading roles to disseminate capital, talent, and health concepts to peripheral areas through methods like jointly building sports industrial parks and sharing event resources. Sports facilities and health services should be integrated into regional planning to maximize the spatial spillover dividends of sports investment, thereby promoting the overall enhancement of \u003cem\u003eRHE\u003c/em\u003e.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eZhibin Zou: Conceptualization, Methodology, Software, Data curation, Writing \u0026ndash; original draft, Writing- Reviewing and Editing, Weihui Hu: Conceptualization, Methodology, Software, Data curation, Writing \u0026ndash; original draft, Writing- Reviewing and Editing, Visualization, Investigation, Validation.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData is provided within the supplementary information files.https://github.com/SmartPatrick1/Supporting-Information\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAral, S., Nicolaides, C. (2020). 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The influence of Community Sports Parks on residents\u0026rsquo; subjective well-being: A case study of Zhuhai City, China. Habitat International, 117, 102439. https://doi.org/10.1016/j.habitatint.2021.102439\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Sports Industry Investment, Regional Health Equity, Spatial Spillover Effect, Chinese Cities","lastPublishedDoi":"10.21203/rs.3.rs-7835154/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7835154/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAddressing regional health equity (\u003cem\u003eRHE\u003c/em\u003e) is a pivotal challenge for sustainable development. The innovative and rapidly expanding sports industry investment (\u003cem\u003eSII\u003c/em\u003e) presents a potential avenue to mitigate these disparities. Based on a panel dataset of 287 Chinese prefecture-level cities from 2003 to 2023, we systematically investigate this relationship using fixed effects, mediation models, and a spatial Durbin model. Our findings show that \u003cem\u003eSII\u003c/em\u003e significantly enhances RHE, with the effects being more pronounced in economically advanced regions, cities with net capital inflows, and sports-derivative sectors. We identify three key mechanisms driving this effect: boosts in resident income, industrial structure upgrades, and advancements in health-related technology. Notably, we uncover substantial positive spatial spillovers, demonstrating that investments in one locality generate health equity benefits in neighboring regions. These indirect spillovers constitute the primary component of the total effect. To our knowledge, this is the first study to empirically map the comprehensive pathway - including both direct mechanisms and spatial spillovers - through which \u003cem\u003eSII\u003c/em\u003e influences \u003cem\u003eRHE\u003c/em\u003e. Our findings offer crucial policy implications for the synergistic formulation of industrial and public health strategies.\u003c/p\u003e","manuscriptTitle":"Promoting or inhibiting? The impact of sports industry investment on regional health equity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-14 09:28:20","doi":"10.21203/rs.3.rs-7835154/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0a739a63-d2a9-45c4-848d-0b41766cffb5","owner":[],"postedDate":"October 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-28T05:53:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-14 09:28:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7835154","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7835154","identity":"rs-7835154","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}