Introduction

With the acceleration of urbanization, smart cities have increasingly become a vital approach to enhancing urban governance and improving residents’ quality of life. A smart city utilizes advanced technologies, such as information and communication technologies (ICT) and the Internet of Things (IoT), to achieve intelligence and efficiency in urban infrastructure, public services, and management. This, in turn, enhances the city’s sustainable development capabilities and the living standards of its residents1,2. The core objective is to optimize resource allocation and improve public service efficiency through data-driven decision-making and management, ultimately creating an intelligent, green, and human-centered urban environment3. Smart city policies consist of strategic plans, policy measures, and guiding documents formulated by governments at various levels to promote smart city development. In China, the initiative for smart city construction began in 2012 when the National Development and Reform Commission and the Ministry of Housing and Urban-Rural Development jointly launched the first batch of smart city pilot projects. Specific measures include advancing infrastructure development, establishing data and information platforms, implementing intelligent city management, enhancing public services, promoting industrial upgrades, and ensuring information security4. To date, over 500 cities across the country have engaged in smart city construction, encompassing diverse regions such as eastern, central, and western China. The eastern and central regions have made more rapid progress in smart city development due to their stronger economic foundations, resulting in initial application demonstrations in various fields, including smart transportation, smart healthcare, and smart education5. Conversely, the western region has been gradually advancing smart city construction with the support of national policies and funding; however, there remain significant gaps in infrastructure and technology applications.

With the continuous expansion of artificial intelligence application technology, smart cities have become a strategic choice for promoting new urbanization and enhancing the quality of public services6,7,8. The impact of smart city development on residents’ participation in physical exercise has also garnered increasing attention, with numerous practical cases emerging. For instance, Shenzhen City has significantly boosted residents’ motivation for physical activity by constructing smart fitness trails and smart gyms, which provide real-time exercise data monitoring and personalized fitness guidance. Evidence suggests that the development of smart cities contributes to increased participation in physical exercise among residents9. However, existing studies primarily focus on the effects of smart city development on urban governance and economic growth, leaving a gap in the systematic exploration of how it specifically influences residents’ engagement in physical activity. Based on this, this paper empirically analyzes the impact of smart city development on residents’ participation in physical activity using data from the China Family Panel Studies (CFPS) conducted between 2010 and 2020. The analysis employs a Difference-in-Differences (DID) model, concentrating on specific measures such as the spatial optimization of smart city infrastructure, the construction of public facilities, information technology platforms, and smart fitness amenities. This study explores the effects of built environments, information channels, and the role of enjoyment in consumption, revealing the varying responses of different resident groups to smart city policies. Ultimately, it provides a scientific basis for integrating smart city development with national fitness policies to promote the establishment of a healthy China and a robust sports nation.

Literature review and theoretical analysis

The direct impact of smart city construction on residents’ physical activity participation

Classical theories of the urban built environment offer a vital theoretical framework for understanding how smart city development influences residents’ participation in physical activity. These theories primarily address key aspects such as land use, transportation and accessibility, the built environment, diversity, public space, and infrastructure10. First, land use highlights the impact of a well-planned layout of different land uses on residents’ lives and activities. Effective land use planning can enhance the number and distribution of public facilities, including urban green spaces, parks, and walking paths, thereby promoting outdoor activities and physical exercise among residents. Kang, Y et al. demonstrated that an increase in the area of urban green space is significantly and positively correlated with the frequency of residents’ physical exercise11. Secondly, transportation and accessibility are critical factors influencing residents’ engagement in physical activity. A convenient transportation system and good accessibility can facilitate residents’ access to sports facilities and venues, thereby increasing their participation in physical activities. The implementation of intelligent transportation systems optimizes the transportation network, enhances the efficiency of public transit, and simplifies access to sports venues for residents. Moreover, the built environment, which encompasses characteristics such as building density, height, and mixed-use developments, affects residents’ activity patterns12. High-quality built environments can create inviting spaces for exercise and encourage residents to partake in outdoor activities. The design of smart buildings and green spaces enhances residents’ movement experiences. Additionally, diversity underscores the importance of a variety of urban functions and spaces to meet the diverse needs of residents13. Benis et al. argue that providing different types of public spaces and facilities can cater to the physical activity needs of residents across various ages, occupations, and interests, thereby enhancing overall quality of life. Finally, the quality and quantity of public space and infrastructure are essential for facilitating residents’ activities. High-quality public spaces, comprehensive sports facilities, and robust infrastructure create the necessary conditions for residents to engage in physical activity. Therefore, the construction of smart cities may have a penetrative impact on various aspects of residents’ participation in physical exercise (Fig. 1).

Based on the above analysis, the research hypothesis 1 is proposed: Smart city construction can significantly promote residents’ participation in physical exercise.

Indirect effects of smart city construction on residents’ physical activity participation

Built environment effect

The concept of the built environment effect refers to the social, economic, environmental, and health impacts of physical infrastructure and spatial layout within a city. Research has demonstrated that the built environment significantly influences residents’ participation in physical activity14. In terms of land use, smart cities leverage big data and artificial intelligence to enhance the precision and effectiveness of urban planning, optimize land use, and increase the availability of public green spaces and sports facilities. Regarding transportation and accessibility, Hemwood et al. found that smart cities can effectively improve residents’ access to sports venues and public spaces by optimizing transportation networks through intelligent transportation systems15, providing real-time traffic information, and enhancing the efficiency and convenience of public transportation16. Concerning the built environment, smart cities promote the construction of smart and green buildings, utilizing IoT technology to enable intelligent building management, improve indoor and outdoor environmental quality, and enhance residents’ sports experiences17,18. In terms of diversity, smart cities address the varied needs of residents through the development of smart communities, the provision of shared spaces, and the organization of cultural and sports activities, thereby fostering social pluralism and inclusivity. With respect to public space and infrastructure, smart cities increase investment in public areas and adopt technologies such as smart security, smart lighting, and environmental monitoring to enhance the safety and comfort of these spaces19,20, ultimately providing residents with better conditions for physical exercise. Therefore, smart city construction indirectly promotes residents’ physical activity participation by improving the built environment (Fig. 1).

Based on the above analysis, Research Hypothesis 2 is proposed: Smart city construction promotes residents’ participation in physical exercise by enhancing the built environment.

Information channel effect

The impact of information technology refers to the utilization of information technology and communication networks within smart cities to establish an efficient and convenient system for information dissemination and communication21. This influence significantly affects residents’ participation in physical exercise. This effect is particularly notable in the following aspects: First, the convenience of information access. With the widespread adoption of smartphones and the Internet, there has been a marked increase in residents’ access to health and exercise-related information22,23. Li et al. argue that smart cities leverage information technology to disseminate health knowledge and exercise guidance through various platforms and applications, thereby raising residents’ awareness of the importance of physical activity24. Second, online interaction and community building. Social platforms in the development of smart cities have become vital channels for residents to share their exercise experiences and results. These platforms foster interaction and communication among residents, enhancing their motivation to participate and their sense of belonging. Third, online exercise courses and personalized guidance. Zhou et al. contend that smart cities offer a wealth of exercise courses and professional guidance through online platforms, thereby lowering the barriers for residents to learn about and engage in physical exercise1. The variety and richness of online fitness courses, coupled with guidance from professional coaches, enable residents to select appropriate programs based on their individual needs. Additionally, smart fitness devices and applications can record and provide real-time feedback on exercise data, offering personalized exercise suggestions and adjustment plans based on data analysis25,26. This enhances the scientific basis and effectiveness of exercise.

Based on the above analysis, research hypothesis 3 is proposed: The construction of smart cities changes residents’ participation in physical exercise by expanding the channels of information access, and then changes the residents’ participation in physical exercise.

Enjoyment consumption effect

The construction of smart cities not only enhances residents’ living environments and access to information but also significantly optimizes and upgrades their consumption patterns, which indirectly influences their participation in physical exercise. First, there is an improvement in income and consumption. The economic development of smart cities has led to an increase in residents’ disposable income, prompting a shift in consumption from basic survival needs to development and enjoyment27. As a result, residents are more inclined to invest in fitness and health. For instance, an increasing number of residents are choosing to purchase gym memberships, high-quality sports equipment, and gear. These investments not only enrich their sports experiences but also enhance their motivation and frequency of participation in physical exercise28. Second, the consumption experience has improved. Intelligent fitness equipment, virtual reality technology, and big data analysis in smart cities have elevated the experience and technology associated with sports consumption, attracting more residents to participate. Suhua et al. found that the application of intelligent fitness equipment and new technologies enables residents to engage in personalized fitness training anytime and anywhere, providing an immersive sports experience that increases the enjoyment and interactivity of participation29. Third, the sports industry is developing. In the smart city environment, the sports industry is bolstered by supportive policies and market promotion, offering a diverse range of high-quality sports products and services30. This expansion provides residents with richer choices for engaging in physical exercise. For example, emerging products and services such as smart fitness equipment, online exercise courses31, and personalized training guidancenot only meet residents’ diverse exercise needs but also stimulate their interest in physical activity, further promoting the normalization and diversification of their participation in exercise.

Based on the above analysis, research hypothesis 4 is proposed: Smart city construction influences residents’ physical activity participation by enhancing enjoyable consumption, which in turn affects their physical activity levels.

Fig. 1
figure 1

Theoretical analysis framework.

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Methods

Data sources

The data utilized in this paper were primarily obtained from 2010 to 2020 databases of the China Family Panel Studies (CFPS). The CFPS is a comprehensive, household-based national social survey project organized and conducted by the Institute of Social Science Surveys (ISSS) at Peking University32. All experimental protocols were reviewed and approved by the Biomedical Ethics Committee of Peking University, and all methods were carried out in accordance with relevant guidelines and regulations. The ethical review lot numbers for the various investigation rounds remained consistent and were unified under IRB00001052-14010. Before the investigations commenced, the researchers obtained informed consent from the participants in writing. When minors were involved, information was provided by their parents or guardians, who had also given consent33. The CFPS data encompassed 25 provinces, autonomous regions, and municipalities in mainland China, making it broadly representative32. The survey respondents provided extensive information regarding family members’ basic details, health status, educational background, employment status, and income levels34. To enhance the scientific validity of the data, this study also collated relevant data from the China Urban Statistical Yearbook and supplemented information on city-level control variables such as city GDP per capita, population size, and the number of people working in the sports system to ensure the comprehensiveness of the study data9. The sample data were processed as follows: (1) eliminating samples with missing values; (2) pruning the continuous variables.

Model setting

Based on the existing data and literature methods, this paper constructs a Difference-in-Differences (DID) model to analyze the impact of smart city construction on residents’ physical activity participation. The model setting is shown below:

$$\:{activity}_{it}={\beta\:}_{1}{post*treat}_{it}+{\beta\:}_{i}{control}_{it}+{\delta\:}_{i}+{\gamma\:}_{t}+{\beta\:}_{0}+{\epsilon\:}_{it}$$
(1)

In order to explore the mechanism effects of the built environment (envir), information channels (internet), and enjoyment-based consumption (cons), the following mediation effect model was constructed with the model setup shown below:

$$\:{envir}_{it}={\beta\:}_{1}{post*treat}_{it}+{\beta\:}_{i}{control}_{it}+{\delta\:}_{i}+{\gamma\:}_{t}+{\beta\:}_{0}+{\epsilon\:}_{it}$$
(2)
$$\:{activity}_{it}={\beta\:}_{1}{post*treat}_{it}+{\beta\:}_{2}{envir}_{it}+{\beta\:}_{i}{control}_{it}+{\delta\:}_{i}+{\gamma\:}_{t}+{\beta\:}_{0}+{\epsilon\:}_{it}$$
(3)
$$\:{internet}_{it}={\beta\:}_{1}{post*treat}_{it}+{\beta\:}_{i}{control}_{it}+{\delta\:}_{i}+{\gamma\:}_{t}+{\beta\:}_{0}+{\epsilon\:}_{it}$$
(4)
$$\:{activity}_{it}={\beta\:}_{1}{post*treat}_{it}+{\beta\:}_{2}{internet}_{it}+{\beta\:}_{i}{control}_{it}+{\delta\:}_{i}+{\gamma\:}_{t}+{\beta\:}_{0}+{\epsilon\:}_{it}$$
(5)
$$\:{cons}_{it}={\beta\:}_{1}{post*treat}_{it}+{\beta\:}_{i}{control}_{it}+{\delta\:}_{i}+{\gamma\:}_{t}+{\beta\:}_{0}+{\epsilon\:}_{it}$$
(6)
$$\:{activity}_{it}={\beta\:}_{1}{post*treat}_{it}+{\beta\:}_{2}{cons}_{it}+{\beta\:}_{i}{control}_{it}+{\delta\:}_{i}+{\gamma\:}_{t}+{\beta\:}_{0}+{\epsilon\:}_{it}$$
(7)

In the above expression i denotes the individual, t denotes the year, \(\:{post*treat}_{it}\) denotes the smart city policy effect term, \(\:{control}_{it}\) denotes the ensemble of control variables, \(\:{\beta\:}_{0}\)is the constant term, \(\:{\beta\:}_{i}\) denotes the parameter to be estimated, \(\:{\epsilon\:}_{it}\) denotes the random error term, \(\:{\delta\:}_{i}\) denotes the individual fixed effect, and \(\:{\gamma\:}_{t}\)denotes the time fixed effect.

Variable selection

This paper designates the experimental group (treatment) as the pilot city, assigning a value of 1 to the experimental group and 0 to the control group. The policy time (post) denotes the year of policy implementation; if the year is greater than or equal to the year of policy implementation, it is assigned a value of 1, otherwise, it is assigned a value of 0. The focus is on the coefficient of \(\:{\beta\:}_{1}\). When \(\:\:{\beta\:}_{1}\) > 0, it indicates a positive policy effect; when \(\:{\beta\:}_{1}\) < 0, it indicates a negative policy effect. The selection of variables is presented in Table 1.

Table 1 Selection of variables.
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Descriptive statistics results

Table 2 presents the full-sample descriptive statistics of the impact of smart city construction on residents’ physical activity, including the mean, standard deviation, minimum value, maximum value, and sample size of each variable. From the table, it can be found that the total number of samples is 24,739.

Table 2 Descriptive statistics for the full sample.
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Results

Benchmark regression analysis

The Difference-in-Differences (DID) is used to conduct an empirical study of the benchmark model. Table 3displays the regression results of the benchmark model. In the table, N represents the number of samples, R2 indicates the goodness of fit, standard deviation is shown in parentheses, Year represents the year fixed effect, Individual indicates the individual fixed effect, and Columns (1) to (4) present the regression results of gradually adding individual-level, household-level, and city-level control variables. From the regression results in column (4), it is evident that the coefficient of the smart city policy (post_treat) is 0.310, and this coefficient is statistically significant at the 1% level. This suggests a significant positive impact of the smart city policy on residents’ physical activity participation.

Table 3 Results of the baseline regression analysis.
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Heterogeneity analysis

In order to explore the differences in smart city policy effects in urban and rural areas, east-central and western areas, low and high education, and low and high age, this paper conducts a heterogeneity analysis. First, according to the attributes of the individual’s residence, the sample is divided into urban and rural area samples. Model regression is performed separately, and the regression results are shown in columns (1) and (2) in Table 4. Second, according to the geographic region where the individual is located, the sample is divided into the east-central region and the western region. Model regression is performed separately, and the regression results are shown in columns (3) and (4) in Table 4. Third, according to the individual’s education level, the sample is divided into low education (less than junior high school education) and high education (junior high school education and above). Model regression is performed separately, and the regression results are shown in column (5) and column (6) in Table 4. Fourth, according to the age of the individuals, the sample is divided into low age (less than 60 years old) and high age (60 years old and above). Model regression is performed separately, and the regression results are shown in columns (7) and columns (8) in Table 4.

Table 4 Eterogeneity regression results.
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Robustness tests

Propensity score matching

The propensity score matching method is used to address the sample selection bias issue, particularly by employing the nearest neighbor matching method for the experimental and control groups. Table 5 illustrates the results of the covariate balance test before and after matching. Following matching, the absolute values of T-statistics for most covariates are notably decreased, suggesting a significant reduction in differences between the experimental and control groups. Additionally, the decrease in the deviation rate further signifies that the disparities between the groups are minimized after matching.

Table 5 Balance test.
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Table 6 presents the regression results of the model after propensity score matching. The results in column (4) reveal that the coefficient of the smart city policy (post_treat) is significantly positive, suggesting that the smart city policy has a notable positive impact on residents’ physical activity participation. This finding aligns with the earlier benchmark regression results, affirming the model’s robustness and validity with a high level of confidence.

Table 6 Regression results after propensity score matching.
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Replacement of explanatory variables

In order to further verify the impact of smart city policies on residents’ physical activity participation, this paper considers residents’ physical activity hours (exercise) as an explanatory variable for regression analysis. The regression results in Table 7 show that the coefficient of smart city policy (post_treat) in Column (4) is significantly positive, suggesting that the smart city policy effectively promotes residents’ physical activity participation. This result aligns with the previous benchmark regression findings, indicating the model’s robustness and high credibility.

Table 7 Regression results with replacement of explanatory variables.
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Changing the sample

In order to be closer to the most recent point in time and to reflect the most recent impact of smart city policies on residents’ physical activity participation, this paper selects sample data from the three periods of 2016, 2018, and 2020 for analysis. The regression results in Table 8 show that the coefficient of the smart city policy variable (post_treat) in model column (4) is significantly positive, indicating that the smart city policy significantly promotes residents’ physical activity participation. This finding is consistent with the previous benchmark regression results, further demonstrating the robustness and credibility of the model.

Table 8 Changing sample regression results.
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Placebo test

In order to verify the robustness of the results, this paper adopts the Bootstrap technique to randomly assign the samples into experimental and control groups and perform model regression, repeating the experiment a total of 500 times. Figure 2 shows that the impact coefficients of smart city policies are approximately normally distributed, mainly concentrated around 0 and very rarely around ± 3. This suggests that the proportion of constructed smart city policies with significantly positive or significantly negative regression coefficients in 500 randomized experiments is small, making it a rare event. This rules out the possibility of a spurious treatment effect of the policy.

Fig. 2
figure 2

Placebo test results.

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Parallel trend test

Figure 3 below illustrates the results of the parallel trend test. In the graph, before1 and before2 represent the dummy variables for the experimental group one to two years prior to the policy implementation, while after1 and after2 denote the dummy variables for the experimental group one to two years following the policy. The term the dummy variables for the experimental group during the current period of the policy. current denotes the dummy variables for the experimental group in the current policy period. The results depicted in the graph indicate that the confidence intervals for the coefficients of before1 and before2 intersect with the value of 0. This suggests that neither coefficient is statistically significant, implying that there is no meaningful difference in residents’ participation in physical activity between the experimental group and the control group prior to the policy implementation. Consequently, this finding supports the assumption of parallel trends.

Fig. 3
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Parallel trend.

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Analysis of mediating effects

Table 9 presents the regression results of the built environment (envir), information channel (internet), and enjoyment-based consumption (cons) as mediating variables. Columns (1), (3) and (5) display the regression results of the explanatory variables on the mediator variables, respectively, while columns (2), (4), and (6) show the regression results of the explanatory variables and the mediator variables on the explained variables.

From the regression results of column (1), it can be seen that the smart city policy significantly promotes environmental accessibility. The results of column (2) show that both the smart city policy and environmental accessibility have a significant positive impact on residents’ participation in physical activity. Combined with the results of Sobel’s test, it can be found that the built environment plays a significant mediating role in the relationship between the smart city policy and residents’ physical activity.

The regression results from column (3) indicate that smart city policies significantly contribute to the importance of information channels. Additionally, the results of column (4) demonstrate that smart city policies and the importance of information channels have a significant positive effect on residents’ physical activity participation. When considering the results of Sobel’s test, it becomes evident that information channels also play a significant mediating role between smart city policies and residents’ physical activity.

The regression results in column (5) show that smart city policies significantly contribute to the development of enjoyment-based consumption. The results in column (6) indicate that smart city policies and the development of enjoyable consumption have a significant positive effect on residents’ physical activity participation. Combining these findings with Sobel’s test results reveals that enjoyable consumption also plays a significant mediating role between smart city policies and residents’ physical activity.

Table 9 Intermediation effect regression results.
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Discussion

The direct impact of smart city construction on residents’ physical activity behavior

Using data from the China Family Panel Studies (CFPS) spanning from 2010 to 2020, this study empirically analyzes the impact of smart city construction on residents’ participation in physical activity through a Difference-in-Differences (DID) model. The results indicate that the development of smart cities significantly enhances residents’ engagement in physical activities. A variety of robustness tests are employed in this paper, including propensity score matching, substituting explanatory variables, and altering samples, to ensure the reliability and scientific rigor of the research findings. This approach addresses the limitations of previous studies that relied on a single method and lacked comprehensive testing. The regression results consistently demonstrate that smart city construction positively influences residents’ participation in physical activities. The potential reasons for this effect include: (1) Smart city construction leverages advanced information and communication technology to expand the opportunities for residents’ physical exercise. Through online fitness platforms and intelligent fitness equipment, residents can overcome physical space limitations35, receive personalized exercise guidance, and achieve a seamless integration of online and offline activities, thereby enriching the forms and content of physical exercise. (2) Smart city initiatives have enhanced the planning and development of urban public sports facilities, introducing amenities such as smart gyms, smart sports parks, and smart trails36, which improve the accessibility and convenience of physical exercise for residents. (3) The data collection and sharing mechanisms inherent in smart city construction facilitate the optimal allocation of sports resources. By utilizing intelligent sensors and Internet of Things technology, data on residents’ physical activity is collected and analyzed37,38, allowing for a more accurate response to residents’ needs and alleviating the imbalance between the supply and demand for physical activity resources.

Indirect influence of smart city construction on residents’ physical exercise behavior

This study further investigates the indirect influence mechanisms of smart city construction on residents’ participation in physical activity. It constructs a mediating effect model and analyzes the roles of the built environment, information channels, and enjoyment-based consumption. This multi-dimensional analysis enriches the theoretical framework regarding the relationship between smart city construction and residents’ physical activity behaviors. The results of the mediating effect analysis indicate that: (1) smart city construction indirectly promotes residents’ participation in physical activity by optimizing the built environment. It enhances urban planning, improves the quality of public spaces, strengthens intelligent management, and comfort in engaging in physical activities39. (2) Smart city construction indirectly encourages residents’ participation in physical exercise by expanding information channels. The widespread application of information technology has made it easier for residents to access health knowledge and information about physical activities40. Smart city initiatives utilize smartphone applications, social media, online fitness courses, and other platforms to enhance residents’ awareness and willingness to engage in physical exercise. (3) Smart city construction indirectly drives residents’ participation in physical exercise by fostering enjoyment-based consumption. The development of smart cities enhances residents’ income levels and consumption capabilities, leading them to prioritize quality of life and increase spending on enjoyable activities such as fitness and leisure, thereby promoting participation in physical exercise.

Heterogeneity analysis of smart city construction on residents’ physical exercise behavior

This study conducted a heterogeneity analysis to examine the differences in physical activity participation among various groups of residents. The analysis reveals the differentiated impact of smart city development on these groups, providing innovative insights and empirical evidence for precise policymaking. The results of the heterogeneity analysis indicate the following: (1)Urban versus rural residents.Smart city construction has a more significant impact on the physical activity participation of urban residents, while rural areas benefit relatively less. This disparity may be attributed to the more comprehensive smart city infrastructure in urban areas, the wider application of smart technologies, and easier access to smart sports services for urban residents. (2) East-Central versus western regions.The positive effect of smart city construction on residents’ participation in physical activity is significantly stronger in the east-central region compared to the western region. The east-central region enjoys a higher level of economic development and more rapid progress in smart city initiatives. In contrast, the western region experiences lagging economic development, slower advancement in smart city construction, and a relative lack of infrastructure, resulting in a diminished impact on residents’ physical activity participation.(3) Low versus high education. Residents with higher education levels respond more positively to smart city construction and demonstrate higher rates of physical activity participation. This may be due to their greater knowledge of health and physical activity, making them more likely to accept and utilize the smart fitness facilities and services offered by the smart city. (4) Younger versus older residents.Younger residents exhibit a more favorable response to smart city construction. This may be because younger individuals tend to have a higher level of acceptance of new technologies and innovations, making them more inclined to utilize the smart fitness facilities and services provided by the smart city. Overall, this analysis highlights the varying impacts of smart city construction on different demographic groups, underscoring the need for tailored policies to enhance physical activity participation across diverse populations.

Conclusions

(1) Smart city construction significantly promotes residents’ physical activity behavior. Smart city policies have a significant positive effect on residents’ participation in physical exercise. This conclusion is further verified in robustness tests such as replacing explanatory variables and changing samples, indicating that smart city construction has a significant effect on enhancing the frequency of residents’ physical activity. (2) Smart city construction indirectly affects residents’ physical activity behavior through the built environment, information channels, and enjoyable consumption. The mediation effect analysis shows that the built environment, information channels, and enjoyable consumption play a significant mediating role between smart city policies and residents’ physical activity behavior. (3) There is heterogeneity in the impact of smart city construction on the physical activity behavior of residents with different attributes. The results of the study show that smart city construction has a more significant effect on the promotion of physical activity behavior of urban residents, the highly educated group, and the younger age group.

Based on the above conclusions, the following policy insights are drawn:

  1. (1)

    The investment in smart city infrastructure construction should be increased, especially in the planning and construction of public sports facilities to ensure that residents in both urban and rural areas can enjoy convenient sports venues and facilities. (2) Information technology should be utilized to broaden health information dissemination channels and promote healthy lifestyles and scientific exercise methods. (3) Focus on the balanced development of smart city policies among different populations. In the process of promoting the construction of smart cities, consider the differences between regions and groups, and formulate differentiated policy measures to enhance residents’ participation and enthusiasm in physical exercise.

Limitations

Although this study systematically explored the impact of smart city construction on residents’ physical activity behaviors through a Difference-in-Differences (DID) model, and mediated effects analysis, several limitations remain. (1) The limited time span of the CFPS data fails to cover the long-term impact of smart city construction. Future studies may consider data with a longer time span to verify the robustness of the results. (2) Despite the introduction of multilevel control variables in this paper, there may still be unobserved latent variables, such as individual psychological factors and socio-cultural background, which may impact the results. (3) The specific measures and implementation effects of smart city construction vary among different cities. This paper fails to analyze the specific effects of various smart city construction measures in detail. Future research can further refine the specific content and implementation effects of smart city construction. (4) The research object of this paper mainly focuses on China. The universality of the research conclusions needs to be verified in other countries and regions to enhance the external validity of the research further.