2.1 Policy background

Digital tourism, which has emerged at the intersection of digital technologies, the digital economy, and traditional tourism, has become a strategic priority in China’s policy agenda. The Ministry of Culture and Tourism’s Opinions on Deepening “Internet + Tourism” and Promoting High-Quality Tourism Development (2020) explicitly links digital infrastructure advancement to industrial upgrading, advocating enhanced policy-platform-technology synergies to strengthen the digital cultural sector. The 14th Five-Year Plan for Cultural and Tourism Technological Innovation (2021) further codified this course of action by placing an emphasis on smart tourism development via innovations in digital transformation, the integration of the “Internet + Tourism” concept, and new models of cultural-tourism consumption. As part of its ongoing endeavor to adapt policy frameworks to the changing digital landscape, the ministry has been selecting top digital innovation demonstration cases in culture and tourism every year since 2022. These cases exhibit innovative solutions to pressing problems in the field. While these initiatives do shed light on China’s approach to digital tourism development, there are still many unanswered questions, such as how to optimize digital infrastructure, how to tailor tactics to local resources, and how to foster the systemic expansion of digital tourism.

China’s digital infrastructure advancement has been significantly propelled by landmark policies, such as the State Council’s Broadband China Strategy and Implementation Plan (2013), a cornerstone initiative for enhancing global competitiveness in the digital economy. The initiative established a framework for regional specialization in digital infrastructure development through three annual selections of pilot cities (2014–2016) that were accompanied by substantial policy and financial support. Key implementation measures included coordinated regional broadband development with differentiated strategies; infrastructure optimization through innovative public-private partnerships; enterprise digital transformation promotion; industry chain enhancement via collaborative innovation; and cybersecurity capacity building.

Notably, the development of innovative digital tourism forms serves as a critical metric for evaluating the strategy’s effectiveness in fostering digital entrepreneurship and industry transformation. Pilot cities’ experiences provide helpful information about leveraging localized digital advantages while addressing persistent challenges in cross-sectoral resource integration and path-dependent development models.

2.2 Core concept definition

The reference to new industries has been around for a long time, but the systematic conceptualization and definition of the scope of new industries in digital tourism still needs to be supplemented. Some studies have pointed out that the core of the new digital tourism industry is the innovative integration and application of digital technology with traditional tourism, resulting in a series of new tourism products, services, experiences, and scenarios [15]. Through the publication of the “Guidance Catalogue for Industrial Structure Adjustment (2024)” in December 2023, the National Development and Reform Commission made it clear that there are a total of 23 distinct types of new tourism industries. Among these, the ones that are directly associated with digital technology are “tourism information service, smart tourism, technology tourism, and other new tourism industries.”

When it comes to the tourist business, some of the new digitally associated topics include “tourism information service,” “intelligent tourism,” “technology tourism,” and “other emerging tourism mode service systems.” This paper makes reference to this directory, which is the first official document that specifies the scope of the new tourism business. The purpose of this article is to establish the new digital tourist sector. This paper describes “digital tourism as a new industry” based on current research and documents, specifically detailing how the tourism industry management body utilizes digital infrastructure, big data, the Internet of Things, artificial intelligence, blockchain, and other digital technologies to update and transform its business model, development strategy, product and service types, product forms, and integrate with the traditional tourism industry. To meet the increasing demand for digital tourism products and services, a new industry model has emerged that integrates the most effective aspects of both traditional and modern business practices in the tourism sector. Two key features of the emerging digital tourism industry stand out: first, the widespread adoption of digital technology has made it an integral part of the tourism industry’s growth, operations, and management; and second, the industry’s expansive nature allows for the constant creation of innovative digital tourism products and services.

2.3 Theoretical analysis and research hypotheses

Digital infrastructure development policies have a significant impact on the emergence of emerging urban tourism forms. In the urban economic system, the institutional environment is like the soil, which profoundly affects the germination and growth of the “seed” of emerging forms. According to Tan and Sheng [16], the impact of policy and market environments on urban entrepreneurial activities is particularly essential. The development, cultivation, and eventual entrepreneurial practice of new industry sectors are all dependent to a large extent on the institutional context in which new industry sectors are located. It is important to note that in China, the direction and support of policy on the promotion of urban industry is highly prominent. Industrial new-format cultivation activities are not isolated, but rather the entrepreneurial topic and the interaction of various resources and conditions in the region are [17]. Changes have been brought about in the traditional urban tourism business as a result of the policies for the development of digital infrastructure. This industry has been afflicted by major homogenization, single experiences, high risk, and other chronic difficulties. As a result of these changes, the profound incorporation of innovation into tourism products, market services, and industry demand began to increase. To start, business owners can have better insight into developing markets thanks to the digital infrastructure strategy, which encourages the use of digital technologies in the tourist industry. Thanks to advancements like 5G networks and lightning-fast broadband, entrepreneurs can now monitor the dynamics of the tourist sector in real time. Big data and AI allow them to precisely predict future demand, which in turn inspires the creation of novel goods and services like virtual reality tourism experiences and tailored smart tour guides. Secondly, digital infrastructure promotes the digital transformation of the tourism industry and improves synergy and operational efficiency. According to Xiong et al. [18], the implementation of the Internet of Things in scenic areas creates the possibility of equipment interconnection, and intelligent management systems optimize resource deployment. This results in the industry chain becoming more tightly connected, and it also reduces the operational expenses of startups. Entrepreneurs will find it easier to integrate resources and develop their entrepreneurial space as a result of the fact that each link links to the online tourist platform in real time. In addition, the strategy on digital infrastructure offers financial investments and incentives to promote new businesses that are involved in urban tourism. Government investments are the driving force behind connected businesses, the provision of technology and supporting services for startups, such as cloud computing to give arithmetic support, tax incentives and subsidies to reduce the threshold and risk of entrepreneurship, and the attraction of talent and cash. In conclusion, the implementation of digital infrastructure has the potential to enhance the whole visitor experience, boost the allure of urban tourism, and generate a higher level of market demand [19]. The development of intelligent tourism scenarios significantly enhances and simplifies the travel experience of tourists, which in turn attracts a large number of tourists, which in turn promotes the ongoing increase of the scale of the tourism market. The persistent increase in market demand creates development space for entrepreneurial firms, encourages the excitement and creativity of entrepreneurs, and motivates them to explore emerging forms, models, and service types. Moreover, the market demand continues to climb.

In summary, this paper proposes research hypothesis H1.

H1: The implementation of the Digital Infrastructure Development Policy has helped to generate emerging forms of urban tourism.

The digital infrastructure development policy has multiple mechanisms for urban tourism entrepreneurship, mainly through three core paths. First, the implementation of the policy can promote the entrepreneurial process of the new urban tourism industry by encouraging the convergence of talents. The urban tourism industry requires a comprehensive digital infrastructure to establish a high-quality development platform. This includes a fast and reliable network, sophisticated digital office equipment, and other tools that enable talent to establish a comfortable and productive work environment [20]. With the help of the digital infrastructure, experts in the tourist industry are able to obtain a more precise understanding of the demand in the market and develop tourism products and services that are more innovative and competitive. This is especially true for the big data analysis tools and virtual reality creation platforms currently available. At the same time, digital infrastructure eliminates geographical limitations, making it possible for talented individuals to participate in project collaboration and communication from a remote location, as well as expanding the opportunities for professional advancement [11]. In order to create a talent agglomeration effect, diverse talents communicate and collaborate with one another. This effect brings the collision of knowledge, experience, and innovative thinking to the entrepreneurial projects of the new tourism industry, which in turn further promotes the entrepreneurial activities of the new tourism industry in the city’s tourism sector.

Second, the implementation of digital infrastructure development policies can promote emerging forms of urban digital tourism by enhancing the level of digital technology application. The development of digital infrastructure provides a solid foundation for the wide application of digital technologies in the field of urban tourism, making the application of cloud computing, the Internet of Things, artificial intelligence, and other technologies in tourism scenarios smoother. With the help of these digital technologies, the urban tourism industry is able to realize the digital integration and management of tourism resources and the construction of an intelligent tourism service system. For example, the use of Internet of Things (IoT) technology to achieve intelligent monitoring and management of scenic area equipment, provide personalized tourism recommendations through artificial intelligence, and rely on digital technology to give rise to virtual tourism and immersive light and shadow tourism experiences and other modes. Additionally, the innovative application of digital technology provides the groundwork for the supply of new tourism products and services, which in turn dynamically meets the ever-changing needs of tourists, thereby distinguishing itself from the competition in the market and promoting the prosperity of new urban tourism entrepreneurship [21].

Last but not least, the implementation of laws for the development of digital infrastructure might help new urban tourist entrepreneurs by boosting the support they receive from the government. accompanying funding, subsidies, and incentives brought about by the digital infrastructure policy and its accompanying policies have the potential to significantly contribute to the rapid development of scenarios connected to emerging forms. In terms of policy, the government has implemented a number of preferential policies, such as tax breaks, venue subsidies, and entrepreneurial incentives, with the goal of lowering the threshold and cost of entrepreneurship and encouraging a greater number of individuals to devote themselves to urban tourism and new-format entrepreneurship [18]. In terms of funding, the government has increased its investment in digital infrastructure while setting up special support funds and guiding financial institutions to provide loan support for entrepreneurial projects to alleviate the financial pressure on startups. In addition, the government also provides technical guidance and resource docking for entrepreneurs by organizing training and building communication platforms so as to support the entrepreneurial activities of emerging urban forms in all aspects.

In summary, this paper proposes research hypothesis H2.

H2: Digital infrastructure development policy implementation influences the generation and breeding of emerging urban forms through three paths: promoting digital talent pooling, innovating digital technologies and applications, and improving government support. Spillover effects of digital infrastructure policy implementation on entrepreneurial activities in new urban tourism sectors. The resources and technologies involved in digital infrastructure, such as network communication and data processing, have the characteristic of transcending geographical boundaries and can be rapidly disseminated and shared in a wide range. However, when it comes to the actual impact on new tourism entrepreneurial activities in different regions, it will have different effects due to the differences in regional spatial relations, which are manifested in the promotion of neighboring regions and the siphoning effect on non-neighboring regions.

For neighboring regions, the implementation of digital infrastructure policies will have a significant catalytic effect. First, geographic proximity makes information dissemination more convenient and rapid. The information bridge built by digital infrastructure allows information such as tourism market dynamics, changes in consumer preferences, and the application of emerging tourism technologies to circulate quickly among neighboring regions [22]. By combining the qualities of local tourism resources, entrepreneurs in neighboring regions can seize market opportunities quickly, gain knowledge from advanced experiences, and create innovative products and services. This, in turn, spurs more entrepreneurial projects in new tourism industries. Second, the development of digital infrastructure has greatly improved the communication, logistics, and transportation systems in the region [23]. This improvement helps to actualize the sharing of resources and complementary advantages, encourages synergistic development, strengthens economic ties between regions, and sets the groundwork for the establishment of the cross-regional tourism industry chain. As an example, adjacent areas can collaborate to develop cross-regional tourism routes and advertise each other’s brands to increase visitor numbers. This kind of collaboration promotes entrepreneurial activity by making the market more favourable to new types of entrepreneurship. But digital infrastructure plans might have a draining effect on areas that aren’t next to one another. The development of digital infrastructure will lead to a rapid increase in the competitiveness of the tourism industry in core cities or regions, attracting a large concentration of high-quality resources such as capital, talent, and technology. Non-contiguous regions are at a disadvantage in the competition for resources due to their distance. On the one hand, funds are more inclined to be invested in areas with perfect digital infrastructure and strong development prospects, leading to the plight of new tourism industry entrepreneurship in non-contiguous areas facing a shortage of funds [24]. On the other hand, high-quality tourism professionals and innovative talents will also be attracted by the numerous development opportunities and favorable conditions in the core regions, resulting in a serious brain drain and weakened entrepreneurial and innovative capabilities in non-adjacent regions [4]. In addition, from the perspective of market competition, regions with advanced digital infrastructure are able to create more high-quality and diversified tourism products and service systems by relying on cutting-edge technologies and efficient operation modes. This makes these regions much more attractive to tourists, thus further capturing the tourism market share of non-neighboring regions. These circumstances have significantly suppressed the entrepreneurial activities of new settlers in non-neighboring regions.

In summary, this paper proposes research hypothesis H3.

H3: While the implementation of digital infrastructure development policies generates spillover effects to neighboring regions, it also creates siphon effects in non-neighboring regions.

3.1 Selection and description of variables

3.2 Modeling

3.2.1 Baseline model.

To estimate the impact of the pilot policy of Broadband China on the entrepreneurial activities of the new tourism industry in the region, this paper adopts a multi-period DID model for regression analysis, and the specific measurement model is as follows:

(1)

In Eq.(1), i(i = 1,2,...,287) is the city, t(t = 2012,2013,...,2024) is the year, and the explanatory variable NTE_it denotes the level of development of new tourism entrepreneurial activities in city i in year t. is the DID estimator, if city i is the pilot area of Broadband China policy, is 1, otherwise it is 0. indicates the pilot time of city i, if city i is set up as the pilot area of Broadband China in year t, is 1, and 0 otherwise. is other influential factors that may affect the entrepreneurial activities. denotes the time fixed effect. is the individual fixed effect. denotes the randomized perturbation term.

3.2.2 Mechanism testing model.

After verifying the impact of the pilot policy of Broadband China, this paper builds upon the ideas of Jiang Ting [27] and establishes the following model based on the benchmark regression model to verify the mechanism of digital infrastructure influencing the emerging forms of urban digital tourism entrepreneurship:

(2)

in Eq.(2) denotes the three mechanism variables proposed by the above theoretical analysis: talent agglomeration level, the level of technological innovation application, and government support intensity. The rest of the symbols are consistent with Eq.(1). Given the significant effect of the core explanatory variables in the benchmark regression, we analyze the coefficient of the pilot policy on the mechanism variable . If is significant, it indicates the existence of the mechanism path.

3.2.3 Spatial effects test model.

To further test the spatial spillover effects, the following model is set up:

(3)

Building on the benchmark regression, Eq.(3) excludes samples from the original pilot cities to examine the net effect in neighboring cities. Following the approach of Cao [28], introduces the explanatory variable , the parameter s represents the geographical distance between cities (in kilometers, with s ≥ 50), calculated using the spherical distance based on latitude and longitude coordinates. Specifically, if in year t, there exists a Broadband China pilot city within the spatial range of (s – 50, s] kilometers from city i, then takes the value of 1; otherwise is 0. For example, is used to determine whether a Broadband China pilot city was present within 50 kilometers of city i in year t. The regression coefficient of measures the impact of the Broadband China pilot policy on emerging tourism forms in nearby cities. Furthermore, this paper reports regression results at 50-kilometer intervals (i.e., s = 50, 100,..., 350, 400) to examine the spatial spillover effects of the policy. By comparing the statistical significance of regression coefficients across different distance thresholds, we assess how the policy’s influence varies with spatial proximity.

3.3 Data sources and descriptive statistics

This paper explores the impact of digital infrastructure development policies on entrepreneurial activities in new tourism sectors in urban areas using city-level panel data from 2012 to 2024. In view of the availability of data and the changes to administrative divisions in the time dimension, some cities with serious missing data are excluded from this paper. We eventually generated a balanced panel data set that includes 297 Chinese cities at the prefecture level after screening, and you can see the descriptive statistics for it in Table 1. The following sources provide the empirical data: As a first step, we use a Python crawler software to collect data about entrepreneurial activities in prefecture-level cities from the websites of Tianyancha and Qichacha. Two of China’s most prominent business data platforms are Tianyancha and Qichacha. Their information comes from government databases like China Judgments Online and the National Enterprise Credit Information Publicity System. Both systems have seen heavy application in empirical studies conducted in the fields of economics and management, and the results are known to be thorough and dependable. In addition, the MIIT-published pilot city documents are the source for the list of cities that have adopted the Broadband China pilot program. Third, the control variables (economic, financial, education, consumption, industrial, postal infrastructure, and communication levels) are derived from various sources (e.g., the EPS data platform, the China Urban Construction Statistical Yearbook, and the China Urban Statistical Yearbook 2013–2022). When values are missing, they are reasonably filled in using linear interpolation.

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Table 1. Definition of variables and descriptive statistics.

https://doi.org/10.1371/journal.pone.0343903.t001

4.1 Baseline regression results

This paper adopts a progressive regression strategy to conduct the study. In Table 2, columns (1) and (2) present the regression results without the inclusion of control variables; column (3) shows the regression without the addition of time and individual fixed effects; and column (4) shows the regression results with the addition of control variables, time, and individual variables. According to these findings, the impact of the Broadband China pilot policy on emerging form cultivation coefficients are consistently positive, even when controlling for time fixed effects and other variables. This suggests that building digital infrastructure plays a significant role in encouraging the growth of new tourist entrepreneurial activities. The results show that the installation of digital infrastructure clearly promotes the growth of new entrepreneurial activities in urban tourism, proving hypothesis H1.

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Table 2. Benchmark regression results.

https://doi.org/10.1371/journal.pone.0343903.t002

4.2 Robustness tests

4.2.1 Parallel trend test and placebo test.

When utilizing the DID model for policy assessment, the treatment and control groups need to satisfy a key precondition, i.e., a parallel trend before the policy implementation. To effectively avoid the interference of the multicollinearity problem, the previous period before the implementation of the policy is excluded from the sample in this paper, which is used as the base period. The estimation results of the parallel trend test are presented in Fig 1, from which it can be seen that the estimated coefficients of the core explanatory variables do not show significance (with confidence intervals covering 0) in the periods before the official implementation of the Broadband China pilot strategy. This result is a strong indication that before the implementation of the policy, there was no significant difference between pilot and non-pilot cities in terms of entrepreneurial activities in the new tourism sector. The implementation of the policy gradually highlighted its promotion effect on the cultivation of emerging forms in cities.

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Fig 1. Parallel trend test.

Note: d_i denotes year i before policy implementation, and d_i denotes year i after policy implementation.

https://doi.org/10.1371/journal.pone.0343903.g001

To guarantee the reliability of the results of the regression analysis and exclude the interference of random factors, this paper adopts the strategy of randomly designating the control group and the experimental group and implements 1,000 repetitions of sampling as a means of constructing a placebo test. Specifically, within the full sample, randomly select cities with the same number of pilot cities, set them as the “pseudo-experimental group,” and identify the remaining cities as the “pseudo-control group.” We used a multi-period DID model to perform regression analysis after randomly assigning the year of policy implementation to each of these “pseudo-experimental group” locales. Fig 2 shows the distribution of p-values and regression coefficients for 1000 random samples. The dots representing p-values and kernel density estimation curves indicate that the regression coefficients follow a nearly normal distribution. The regression coefficients of the Broadband China pilot policy in the randomized experiments cluster around 0, which differs significantly from the true estimates. As a result, the estimation results are robust and unaffected by the omitted variables, supporting hypothesis H1. Additionally, the P-values are mostly greater than 0.1, further confirming this.

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Fig 2. Placebo test.

https://doi.org/10.1371/journal.pone.0343903.g002

4.2.2 Considering the impact of other policies.

In addition to the Broadband China pilot policy, the implementation of other policies may also have an impact on the cultivation of new tourism entrepreneurial activities. The pilot policy of the national-level big data comprehensive experimental zone will promote the construction of urban information infrastructure, thereby stimulating regional innovation capacity and fostering new tourism entrepreneurial activities. This paper sets up the pilot policy variable of the big data comprehensive pilot zone; if the city belongs to the pilot policy list of the big data comprehensive pilot zone in that year and later, the variable is assigned to 1, and the rest is assigned to 0. The pilot list originates from the official websites of the government and various functional departments. Second, the opening of high-speed rail signifies the facilitation of urban transportation conditions, which contributes to the development of the local tourism industry and empowers the entrepreneurial activities of the new tourism industry. This paper establishes a dummy variable to represent the opening of high-speed rail, using the year when local high-speed rail lines open as the starting point. The variable is assigned a value of 1 for the year of opening and subsequent years, while it is assigned a value of 0 for all other years. Lists of high-speed rail opening times are compiled from announcements made by the railroad department. Third, the telecom universal service pilot policy for the purpose of broadening network coverage also contributes to the construction of information infrastructure in prefectural-level cities to stimulate regional innovation capacity, which may have an impact on the conclusions of this paper, and therefore, a dummy variable for telecom universal service is set, which is assigned a value of 1 if the city belongs to the pilot list in the current year and thereafter, or 0 otherwise, and the list of the pilot cities is obtained from the website of the Ministry of Industry and Information Technology. According to the fourth point, the national tourism standardization demonstration units that were announced by the Ministry of Culture and Tourism contribute to the leadership of the industry’s development, optimize the tourism environment, enhance the level of urban tourism management and service quality, and have a positive impact on the activities of entrepreneurs. The purpose of this article is to establish a dummy variable for the national tourist standardized demonstration units. If the city is included on the list both in the current year and in subsequent years, then the variable is assigned a value of 1, and if it is not included, then it is assigned a value of 0. The General Office of the Ministry of Culture and Tourism is the source of the list of communities. The fifth point is that the development of innovative cities has a positive impact on the innovative behavior of regional small and medium-sized businesses. These businesses work toward the establishment of a technological innovation system that will facilitate the deep integration of industry, academia, and research. Additionally, these businesses work to accelerate the transfer and transformation of major scientific and technological achievements, which in turn helps to cultivate the entrepreneurial activities of tourism in new industries. Both the Ministry of Science and Technology and the National Development and Reform Commission are responsible for compiling the list of cities that are currently undergoing construction.

This paper incorporates five variables into the regression model, to exclude the impact of other policies on the entrepreneurial activities of emerging forms. These variables are the pilot policy of a national-level big data pilot zone, the opening of high-speed rail, the pilot project of universal telecommunication service, the national tourism standardization demonstration policy, and the policy of innovative city construction. The specific results are shown in Table 3, and the paper is based on the policies that have been discussed above. The findings demonstrate a thorough consideration of the effects of various policies. The results show that, after considering the effects of other policies, the regression coefficient of the Broadband China pilot policy is still significantly positive, indicating that it has a significant role in promoting the cultivation of new tourism entrepreneurial activities in the city, which further confirms the validity of hypothesis H1.

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Table 3. Robustness tests excluding other policy effects.

https://doi.org/10.1371/journal.pone.0343903.t003

4.2.3 Substitution of explanatory variables.

The benchmark regression uses PCA to measure the explanatory variables; however, this method may not be able to visualize the changes in the number of enterprises in the new tourism entrepreneurial activities in the city, and it does not take into account the differences in the registered capital of the enterprises and other circumstances. Therefore, this paper employs two methods to re-evaluate the new tourism entrepreneurial activities and perform regression analysis.

Based on the above seven keywords, this paper simply aggregates the number of newly registered enterprises in the same city each year as an explanatory variable so as to intuitively reflect the development of the entrepreneurial activities of the new tourism industry in each city.

The specific operation process is as follows: firstly, calculate the average registered capital for each city and each enterprise corresponding to each keyword; then, calculate the ratio of the average registered capital of each enterprise belonging to each keyword in each city to the sum of the average registered capital of all keywords; lastly, use the ratio as the weight to summarize the number of newly registered tourism enterprises under each keyword in that year, and use the summarized result as an explanatory variable. The summarized results are used as the explanatory variables. The relevant results are presented in columns (1) and (2) of Table 4. The table shows that the Broadband China pilot policy’s regression coefficient remains significantly positive even after replacing the explanatory variables.

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Table 4. Other robustness tests.

https://doi.org/10.1371/journal.pone.0343903.t004

4.2.6 Tests based on the PSM-DID.

In the selection of pilot cities for Broadband China, there is a self-selection bias that tends to select cities with relatively complete digital infrastructure and a high proportion of digitized production technology applications. And such cities usually have conditions conducive to the development of new tourism entrepreneurial activities, which makes the treatment and control groups in the original model systematically different in nature. Given this information, the purpose of this research is to reevaluate the impact that the Broadband China policy has had on the implementation of the policy by applying the means of Propensity Score Matching-Difference in Differences (PSM-DID). To be more specific, this paper adheres to the concept of cross-section matching, treats all control variables as covariates, matches the samples using 1:1 nearest neighbor matching, caliper matching, and radius matching, and then then performs regression analysis on the samples that have been matched once more. The specific results are presented in Table 5. According to the findings presented in Table 5, the regression coefficients are consistent with the results of the baseline regression under a variety of matching approaches.

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Table 5. PSM-DID matching results.

https://doi.org/10.1371/journal.pone.0343903.t005

4.2.7 Exclusion of endogeneity.

A preliminary comparative analysis of digital infrastructure conditions in pilot and non-pilot cities reveals that pilot cities are generally better than non-pilot cities in terms of digital infrastructure, with obvious natural differences, which suggests that the selection of pilot cities may not be completely random, and that there is a self-selection bias that causes endogenous problems in the model, leading to a biased assessment of policy effects. This suggests that the selection of pilot cities may not be completely random, and there is self-selection bias, which causes endogenous problems in the model, leading to bias in the evaluation of policy effects. Therefore, this paper adopts the Heckman two-step method to deal with the above endogeneity problem. In the first stage, this paper constructs the interaction term between terrain relief and regional GDP per capita as well as the control variables as covariates, and uses the Probit model to calculate the Inverse Mills Ratio (IMR). In the second stage, in order to solve the endogeneity dilemma that may be caused by policy choices, this paper incorporates the IMR into the benchmark regression model for regression analysis. The results show that the regression coefficients corresponding to the Broadband China pilot policy are significantly positive (see Table 6). This finding provides strong support for hypothesis H1 and fully confirms the rationality of the hypothesis.

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Table 6. Heckman two-stage regression results.

https://doi.org/10.1371/journal.pone.0343903.t006

4.3 Mechanism analysis

Table 7 shows the test results of the mechanism of the digital infrastructure policy to promote the development of emerging forms of urban digital tourism, according to which it can be seen that hypothesis 2 is valid. Specifically analyzed as follows:First, the talent agglomeration level. In the development of emerging forms of urban digital tourism, talent is a key element. The test results of the talent pooling mechanism are displayed in column (2). Due to the fact that the regression coefficients of the core explanatory variables are significantly positive, it is evident that the digital infrastructure policy has the potential to effectively drive the convergence of computer talents and other information talents in order to better serve the emerging forms of urban digital tourism. The second factor is the degree of technological innovation and technology use. The advancement of technology is a significant factor that plays a driving role in the promotion of the development of new types of urban digital tourism. Column three illustrates the influence that the policy on digital infrastructure has on technical innovation. The coefficient of this policy is significantly positive at the 1% level, which indicates that it is able to successfully motivate the city to plan innovation and new applications. This brings us to the third effect: the strength of government backing. According to the findings presented in Column 4, the coefficient of digital infrastructure policy on government support is both positive and significant. This suggests that digital infrastructure policy has the potential to encourage the local government to increase the amount of money it spends on science and technology. Furthermore, this financial support effect can assist the city in developing its tourism industry sector.

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Table 7. Mechanism analysis.

https://doi.org/10.1371/journal.pone.0343903.t007

4.4 Heterogeneity analysis

4.4.1 Tourism endowment conditions.

The development and improvement of digital infrastructure can significantly improve the dissemination speed of regional tourism information and the ability of tourists to access information [18]. However, digital infrastructure is a “lone tree,” and the natural differences in the endowment of regional tourism resources may affect the implementation of digital infrastructure policies. The richness and level of regional tourism resources will directly affect the regional layout of the new tourism industry. Specifically, cities with higher endowments of tourism resources are more likely to form industrial cluster effects and promote the cultivation and development of new tourism industries and new modes. In view of this, this paper counts the number of 4A and 5A scenic spots in each prefecture-level city from 2012 to 2024 in accordance with the current guidelines for the classification of tourist attraction quality levels. And take the median as the boundary, divide the cities into two groups of non-tourism endowment and tourism endowment, and then carry out regression analysis, and the results are presented in Table 8. As can be seen from Table 8, the regression coefficient of the tourism-endowed cities is 0.2783, which is valid at a significant level of 5% and is higher than that of the non-tourism-endowed cities. This implies that the promotion effect of digital infrastructure represented by the Broadband China pilot policy on the cultivation of emerging forms is more significant in cities with high tourism resource endowment.

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Table 8. Heterogeneity analysis.

https://doi.org/10.1371/journal.pone.0343903.t008

4.5 Analysis of spatial effects

In order to investigate whether there is a spillover effect of the Broadband China policy pilot at the geospatial level, based on the theoretical analysis in the previous section, the constructed spatial spillover model is used to investigate the spatial effect of the development of digital infrastructure on the cultivation of new tourism industries in neighboring cities. Fig 3 demonstrates the trend of explanatory variables with spatial distance (confidence interval is 95%). Specifically, the driving effect of the Broadband China pilot on the development of emerging forms in neighboring cities shows an inverted S-shaped change with increasing spatial distance, which first decreases, then increases, and then decreases. In the range of 50–100 km around the pilot city, digital infrastructure has a significant pulling effect on the development of tourism, and the positive effect of the pilot on the cultivation of the new tourism industry in the neighboring cities is weakening after exceeding 100 km; while in the range of 150–200 km, the siphoning effect is obvious, which is not conducive to the cultivation of tourism in the cities within the range. This suggests that the digital infrastructure promoted by the Broadband China policy pilot is closely related to spatial distance and that there are obvious heterogeneous spatial effects of digital infrastructure on the cultivation of emerging forms in neighboring cities.

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Fig 3. Spatial effects.

https://doi.org/10.1371/journal.pone.0343903.g003

The reason for this is that the catalytic effect of the Broadband China policy pilots on emerging forms is concentrated in the 50–100 km range. When the spherical distance between neighboring cities and the pilot city is too close, the good level of digital infrastructure in the pilot city forms a clustering shadow area. As a result, tourism resources of neighboring cities gradually converge to the pilot city due to the scale effect of the agglomeration area. The Broadband China policy pilot will only benefit the local digital tourism industry when the spherical distance is 50–100 kilometers, free from the agglomeration shadow area. If the pilot city is too far away, its positive impacts diminish and become a siphon effect. This may be because spatial remoteness raises information dissemination and factor flow costs. Information dissemination and production factor allocation are crucial to new tourist industry development [31]. If neighboring cities are too far from pilot cities, the information dissemination advantage of digital infrastructure is gradually weakened in long-distance transmission, and it is difficult for neighboring cities to quickly obtain the advanced experience and innovative mode of digital tourism development in pilot cities, so they cannot quickly integrate into the trend. On the other hand, the pilot cities occupy a more favorable competitive position in the tourism market by virtue of their digital infrastructure advantages, and their perfect digital infrastructure platforms attract more tourist flows and form a strong market agglomeration. The gap between the pilot cities and the remote neighboring cities in terms of digital infrastructure capacity leads to an imbalance in digital tourism resources. At the same time, the convenient payment and intelligent marketing services brought by digital infrastructure give the pilot cities a greater advantage in tourism consumption experience, further attracting tourists and related tourism enterprises to gather, resulting in the continuous flow of production factors such as tourism resources, sources of tourists, and industrial investment from neighboring cities to the pilot cities, which is not conducive to the cultivation and development of new tourism industries in remote neighboring cities.

5.1 Conclusions

The Chinese government’s sustained support for digitalization policies has created transformative opportunities for urban tourism development. This study employs the Broadband China pilot policy as a quasi-natural experiment, utilizing a multi-period DID approach to investigate how digital infrastructure policies drive entrepreneurship in urban digital tourism sectors. Four important findings emerge from the investigation. First, there is strong evidence from studies of parallel trends, placebo effects, variable substitutions, and endogeneity controls that shows how developing digital infrastructure greatly encourages new types of urban digital tourism. Secondly, the strategy is able to accomplish its goals by means of three complementary processes: increasing the concentration of digital talent, encouraging technical innovation, and strengthening government support structures. Third, cities with unique cultural capital, efficient deployment of administrative resources, and an abundance of tourist resources see a multiplicative effect of policy efficacy. As a fourth point, the results of the spatial analysis show a “proximity paradox”: nearby cities, like the Rhine-Ruhr regional cluster in Germany, reap the benefits of industrial synergies and information sharing, while faraway cities, like the Amazon in Brazil, risk marginalization as a result of resource drainage effects.

5.2 Talking about it

The study’s findings shed light on the interdependence of digital infrastructure and new types of urban digital tourism in China, and they also serve as a resource for the international advancement of digitalization in the tourism industry, thanks to their fundamental reasoning and real-world consequences. Digital infrastructure serves as the technical backbone for new business models in tourism, with its core essence lying in lowering the technological barriers for market players and optimizing resource allocation efficiency to foster innovative tourism scenarios. This underlying logic holds true across diverse national contexts. Generally, against the backdrop of steadily rising economic development levels and household consumption capabilities, government-led tourism infrastructure policies often catalyze robust growth in regional tourism industries—a pattern validated globally. For instance: In 2019, Germany launched the Innovative Capability Enhancement in Tourism initiative, supporting Lübeck Bay’s development of an “intelligent crowd control” traffic management system. Under this policy framework, novel formats like virtual tours and online performances flourished across the country’s urban attractions. In 2023, Singapore’s Tourism Board and Infocomm Media Development Authority jointly introduced the Industry Digital Plan (IDP) for tourism attractions. With state backing, the IDP effectively integrates emerging technologies to advance the digital transformation of physical sites and empowers operators to adopt digital solutions. The EU’s European Capital of Smart Tourism 2022 award, meanwhile, spurred the adoption of smart tourism practices by promoting accessibility, sustainability, and digitalization, accelerating sectoral growth across Europe. Despite variations in technological starting points and policy instruments, these cases collectively underscore the research insight that synergies between digital infrastructure and policy frameworks drive the emergence of new tourism models. Such experiences offer valuable pathways for other nations seeking to harness digital infrastructure for tourism industry advancement.

Based on the aforementioned findings and discussions, this paper derives the following policy implications:

First, it is essential to expand the coverage of digital infrastructure policies and deepen their impact on the upgrading of the digital tourism industry in urban areas. From the perspective of urban regions themselves, proactive efforts should be made to enhance the construction, supporting facilities, and utilization of digital infrastructure, thereby seizing the developmental opportunities it presents. This includes accelerating the adoption of emerging digital tourism forms and services, such as intelligent tourism navigation, virtual reality tourism, and immersive experiences, to facilitate the transformation of urban tourism toward greater intelligence and convenience.

Second, considering the heterogeneous endowments of different cities, policies should be implemented in a categorized and phased manner to achieve “localized adaptation.” Specifically, cities abundant in tourism resources should tailor policy implementation plans based on their comparative advantages, fostering a stronger awareness of integrating digital technologies with the tourism industry during policy execution. Priority should be given to promoting the digital transformation of the tourism sector while strengthening urban organizational capacity to accelerate tourism-oriented development through administrative measures. For cities with rich historical and cultural heritage, efforts should focus on deepening the integration of digital technologies with high-quality cultural resources, expanding the presentation and experiential dimensions of historical and cultural assets, and enabling immersive tourism experiences to revitalize and effectively preserve these resources. For instance, augmented reality (AR) projects could be developed in historical cities to restore ancient sites, allowing tourists to experience time travel and immersive participation. Cities reliant on natural resources could establish digital twin scenic areas, integrating meteorological and ecological monitoring and tourist behavior data to shift tourism from “sightseeing-based” to “deeply participatory.” Metropolitan leisure cities could explore “metaverse + consumption” models to avoid homogeneous competition.

Finally, priority should be given to encouraging and nurturing tourism cities with exemplary policy implementation outcomes, leveraging their demonstration and spillover effects to promote inter-city collaborative tourism development. Examples include establishing cross-city tourism alliances and encouraging core cities to export smart scenic area management systems to less-developed cities, with the latter reciprocating through ecological resource sharing, thereby forming a “technology-for-resources” complementary model.

The positive effects of China’s digital infrastructure policies clearly demonstrate that accelerating digital infrastructure development is an effective pathway for driving the digital transformation of the tourism industry and fostering new tourism business models. This provides valuable insights for the development of urban tourism and its digitalization on a global scale. As substantiated by the case studies examined in this paper—Germany’s Innovation Demonstration Projects for Enhancing Tourism Competitiveness, Singapore’s Tourism (Attractions) Industry Digital Plan (IDP), and the EU’s European Capital of Smart Tourism 2022—accumulated empirical evidence conclusively demonstrates that digitalization has become an indispensable component of future urban development, and “digital solutions” are equally essential for the advancement of urban tourism industries.