Does the Digital Economy Improve Urban Tourism Development? An Examination of the Chinese Case

Abstract

The digital economy, a new economic form based on Information and Communications Technology (ICT), has profoundly changed the tourism industry. Based on a theoretical analysis framework, this paper measured the digital economy index and urban tourism development index. It empirically tested the impact of the digital economy on urban tourism development through the benchmark regression model, panel threshold model (PTM), and spatial Durbin model (SDM) according to panel data of 284 prefecture-level and above cities in China from 2011 to 2019. The results show that the digital economy can directly drive urban tourism development. The positive impact in mid-western, non-tourist, key urban agglomerations, and low-level cities is more fully realised. Moreover, the digital economy has positive, nonlinear effects on urban tourism development, and the marginal effects are increasing. Additionally, the impact of the digital economy on the tourism development of neighbouring cities can be realised through spatial spillover effects, which are more dependent on inclusive digital finance; this impact has a boundary effect, reaching a maximum at 300 km. Furthermore, the conclusions are still valid after a robustness test and quasi-natural implementation based on smart cities. Finally, specific recommendations are proposed for the digital economy to improve urban tourism development according to the above findings.

1. Introduction

The digital economy is a new economic form that integrates fast-growing information technology and the traditional economy with the characteristics of high permeability and speedy dissemination [1]. For example, a Non-Fungible Token (NFT) is evidence of the penetration and dissemination of the digital economy into the cultural industry, which has the advantages of convenient collection and low price, unlike physical cultural and creative products [2]. It mainly includes digital souvenirs, digital music, digital pictures, and electronic tickets. In addition, the digital economy is gradually becoming the main driving force to help the world economy get out of its predicament in the post–COVID-19 era, playing a pivotal role in stimulating consumption, boosting investments, and promoting industrial upgrades. The “White Paper on Global Digital Economy (2022)” showed that the scale of the digital economy in 47 countries reached $38.1 trillion in 2021, accounting for 45% of the GDP. As the world’s second-largest economy, the scale of China’s digital economy is already second only to that of the United States, reaching 45.5 trillion yuan in 2021 and accounting for 39.8% of GDP [3,4]. In recent years, with China’s economic development transitioning from high growth to high-quality development, the digital economy supported by digital technology has received special attention from the Chinese government. The latest “14th Five-Year Plan for the Development of the Digital Economy” promulgated by the State Council of China proposed that the digital economy has become the main economic form after the agricultural and industrial economies. Improving the level of the digital economy is a strategic choice to seize new opportunities from the technological revolution and industrial change. Moreover, the digital economy is rapidly penetrating various fields, such as agriculture, industry, and service. It can not only improve the efficiency of industrial development by reducing transaction costs and resource mismatch but also help optimize and upgrade the industrial structure [5,6].

As a comprehensive industry, tourism provides a wide range of application scenarios for the digital economy. Moreover, the digital economy is rapidly promoting the reform of the tourism industry. China’s tourism has rapidly grown since the country’s reform and opening, contributing 11.05% of the GDP and 10.31% of employment in 2019, according to “China Tourism Statistics Yearbook” [7,8]. However, China is currently facing predicaments due to a development model that ignores quality and the negative impact of COVID-19. In this context, the digital economy could become a vital antidote to drive China’s tourism industry out of the woods. In 2020, China’s Ministry of Culture and Tourism issued the “Opinions on Deepening ‘Internet + Tourism’ to Promote High-Quality Development of Tourism,” which proposed accelerating the application of digital technology in the tourism industry to achieve a higher quality and efficiency of tourism. In 2021, “The 14th Five-Year Plan for Tourism Development” proposed fully utilizing digital technologies to promote a shift in the tourism development model from resource-driven to innovation-driven, highlighting the importance of the digital economy in tourism development [9]. Therefore, the digital economy offers a significant opportunity to achieve accelerated recovery from COVID-19 and the high-quality development of the tourism industry, which has received strong support from the Chinese government. Studying whether the digital economy can drive tourism development is essential.

Therefore, based on constructing a theoretical analysis framework of the digital economy driving urban tourism development, this paper was devoted to hypothesis formulation and empirical research from different perspectives. First, is there a positive effect of the digital economy on urban tourism development in a linear perspective? Second, from a nonlinear perspective, is the positive marginal effect of the digital economy on urban tourism development increasing? Finally, from a spatial perspective, does the digital economy have positive spatial spillover effects on tourism in neighbouring cities? The answers to the above questions help respond to scholars’ concerns and enrich tourism research. In summary, based on measuring the digital economy index and urban tourism development index in 284 prefecture-level and above cities in China from 2011 to 2019, this paper used the benchmark regression model, panel threshold model (PTM), and spatial Durbin model (SDM) to explore the impact of the digital economy on urban tourism development. In addition, implementing a series of robustness tests guaranteed the findings’ robustness. There were three marginal contributions: First, a theoretical framework of the digital economy on urban tourism development was constructed. Second, the impact of the digital economy on urban tourism development was empirically tested using China as an example. Finally, in China’s development reality, targeted suggestions were made for the digital economy to promote urban tourism development.

The remainder of this paper is organized as follows. Section 2 presents the literature review. Section 3 includes the theoretical framework and research hypothesis. In Section 4, the variables are explained, and econometric models and data sources are stated. In Section 5, the empirical results are illustrated. Finally, the research findings are provided, and targeted recommendations are made in Section 6.

2. Literature Review

2.1. The Effect of the Digital Economy

With the development of ICT, the digital economy as a new economic form has gradually become an important booster of global economic development and industrial transformation. In 1995, Tapscott et al. [10] first explained the connotation of the digital economy: The digital economy is an emerging economic form in the era of intelligence, eliminating the role of the “middleman” in the traditional economy and creating new consumption channels that rely on the internet platform. After this, countries worldwide, led by the United States, successfully launched the definition of the digital economy. In 1999, the “Emerging digital economy” report issued by the US Department of Commerce indicated that e-commerce and the IT industry are two aspects of the digital economy, of which e-commerce is a means of conducting transactions, and the IT industry is the basis of e-commerce. In China, the definition of the digital economy is mainly based on the “G20 Digital Economy Development and Cooperation Initiative”. This initiative refers to a broad range of economic activities that includes using digitized information and knowledge as the key factor of production, modern information networks as the important activity space, and the effective use of ICT as an important driver for efficiency-enhancing and economic structural optimization [11]. In addition, the UK, France, and Japan define the digital economy in their context, but the connotation cannot be separated from the internet, e-commerce, and data elements.

The impact of the digital economy has become a hot topic of current academic research, which primarily revolves around the three aspects of economic, environmental, and social effects. First, in terms of the economic effects of the digital economy, some scholars have confirmed that the digital economy has a significant positive impact on economic growth. Using China as a case, Jiao et al. [12] confirmed that the digital economy boosts economic growth by increasing the city dwellers’ employment rate and has a spatial spillover effect on economic growth. Zhang et al. [13] pointed out that the digital economy can drive economic growth in countries along the Belt and Road by promoting industrial structure upgrades, boosting total employment, and adjusting the employment structure in the context of COVID-19. Moreover, the digital economy is a booster for Africa’s economic growth, enhancing the continent’s ability to absorb international trade’s positive economic growth effects [14].

Furthermore, some scholars have paid attention to the role of the digital economy in promoting the quality of economic development [15], industrial structure upgrades [6], and green total factor productivity [16]. The environmental effects of the digital economy are also a key topic of interest for scholars, who have confirmed that the digital economy has a positive impact on improving the ecological environment from different perspectives. Using China as an example, Li et al. [17] confirmed that by relying on the digital economy’s scale effect, technology effect, and structural effect, pollutant emissions could be reduced. Che et al. [18] argued that the development of the digital economy is the key to solving the haze problem in China. Li et al. [19] found an inverted U-shaped relationship between the digital economy and carbon emissions. In addition, the digital economy has a positive impact on high-quality green urban development [20], industrial wastewater discharge [11], and renewable energy utilization [21]. The social effects of the digital economy have also received widespread attention. Niu [22] pointed out that the digital economy plays a crucial role in establishing solid social governance mechanisms. Senyo et al. [23] provided a research framework for transforming the public sector, driven by the digital economy, and offered propositions using Ghana’s paperless port as a case study. In addition, Lyu et al. [24] demonstrated that the digital economy could improve government performance and regulatory quality, thus improving public health services’ efficiency and ability to respond to public health crises during COVID-19 in China. Some scholars have also focused on the role of the digital economy in solving employment problems. For example, Shen et al. [25] confirmed that the digital economy could effectively reduce the degree of labour mismatch, in which intelligent manufacturing plays a mediating role.

2.2. The Impact of the Digital Economy on Tourism Development

The relationship between the digital economy and tourism development is a frontline academic topic, and there is little research specifically focusing on the impact of the digital economy on tourism development. In representative studies, Tang [26] confirmed that the digital economy had a driving effect on the UK’s tourism with an increasing marginal trend. Wang et al. [27] pointed out that the digital economy could significantly improve the efficiency of tourism resource allocation in China. In the context of COVID-19, some scholars have focused on the impact of the digital economy on the cultural tourism industry, demonstrating the accelerated integration of the cultural and tourism industries due to the digital economy [28].

Currently, more scholars have focused on the impact of the application of ICT on tourism development. Buhalis [29] paid attention to the critical role of ICT in the transformation of tourism, providing general ideas for studying the impact of ICT on tourism. From the perspective of tourism enterprises, the creation of computer reservations and global distribution systems has fundamentally changed the business model of tourism. From the perspective of tourists, IT helps save them time choosing, booking, and buying and stimulates tourism spending potential. Scholars have performed rich academic research combined with local realities. Karanasios et al. [30] focused on the impact and potential problems of ICT application on tourism development in small and medium-sized countries. Using Malaysia and Ecuador as examples, they point out that local governments have been paying attention to the benefits of adopting the internet. However, it is difficult to achieve the popularization of ICT because of the poor pioneering spirit and closed business environment. Adeola et al. [31] found that the initial increase in smartphone and internet penetration could not promote tourism development in 40 countries in Africa; however, as the number of users increased rapidly, its positive effects gradually emerged. Yang [32] demonstrated that the quality and efficiency of China’s tourism development have improved with internet usage. Some scholars have also focused on the enormous changes generated by ICT in local tourism, such as in Dubai and Turkey [33,34]. In addition, ICT helps achieve a dynamic balance of tourism distribution channels, and product innovation has been confirmed [35,36]. With the rapid development of artificial intelligence, 5G, and big data technologies, the scope of academic research has broadened. For example, Nuenen et al. [37] found that adopting immersive technologies (e.g., VR headsets) can enrich the tourist experience and that algorithms and AI embedded in the tourism industry can help improve tourism efficiency. Kwok et al. [38] focused on the application of blockchain technology in the tourism industry, arguing that it can help enhance the tourism experience, improve the security of tourism payments, and reduce the operating costs of tourism businesses. However, the digital economy is a double-edged sword that has imposed severe challenges to tourism development, such as the digital gap and the monopolization of tourism platforms [39,40].

In summary, existing studies have focused more on the impact of ICT on tourism development. There is a lack of research on how the digital economy affects tourism development based on the measurement of the digital economy index, especially when using developing countries, such as China, as research cases. Moreover, the theoretical framework of the impact of the digital economy on urban tourism development has not been fully developed. Therefore, from the perspectives of supply and demand and stakeholders, this paper constructed a theoretical analysis framework for the impact of the digital economy on urban tourism development according to China’s reality and designed a testing procedure based on theoretical analysis and research assumptions. Finally, most scholars have only focused on the linear relationship between the digital economy and tourism development; whether the digital economy has a nonlinear and spatial spillover effect on tourism development is also worth studying. In this study, using 284 prefecture-level and above cities in China as an example, we measured the digital economy index based on entropy-weighted TOPSIS and then explored the direct, nonlinear, and spatial spillover effects of tourism development on urban liveability using the benchmark model, the SDM, and the PTM, respectively. Finally, recommendations are put forward according to the conclusions reached.

3. Mechanism Analysis and Theoretical Hypothesis

3.1. Direct Effects of the Digital Economy on Urban Tourism Development

The direct effect of the digital economy on tourism can be analysed in two dimensions: product supply and tourist demand. From the supply side, emerging technologies such as cloud computing and big data can analyse and provide travellers’ spatial and temporal footprints and consumption behaviours to tourism enterprises, significantly reducing the high costs of obtaining consumer information and improving tourism productivity [37,41]. In addition, due to the small scale of Chinese tourism enterprises and their lack of fixed collateral, it is difficult for them to obtain bank loans, but inclusive digital finance helps solve this problem. The lending platform provides credit loans to tourism enterprises after assessing their operational status and risk level through big data and financial modelling. The convenience of loans alleviates the problem of insufficient entrepreneurial funds and facilitates their scale expansion and continuous innovation [27]. Furthermore, digital technologies, such as the internet and blockchain, could stimulate cross-border integration with tourism, cultural, entertainment, and leisure industries and contribute to breeding new tourism formats (e.g., cloud tourism, digital playfulness) to expand the scale of the tourism market [36,38].

From the demand side, internet travel platforms, such as Ctrip and Mafengwo, integrate the links among information search, purchase, and evaluation, which is conducive to tourists grasping travel information, formulating strategies, and booking products promptly, which contributes to stimulating tourists’ desire to travel [5]. Moreover, big data can analyse internet users’ historical browsing behaviour to shape user portraits, helping to push the precise tourism information that interests visitors, which stimulates tourists’ consumption enthusiasm [42]. Furthermore, personal travel loan products launched by digital financial platforms, such as Ctrip’s Naquhua and Touniu’s Shoufuchufa, meet tourist demands to consume first and pay later. WeChat payments and Alipay products supported by digital technology have eliminated the trouble of cash payments during travel and have further stimulated travel consumption in China [27]. Finally, the digital economy contributes to building a model of tourism product cocreation dominated by tourists’ needs. In this model, the changes in tourists’ identity from passive recipients to cocreators and, finally, to leaders of tourism product innovation help stimulate tourists’ participation in tourism and unleash tourism potentiality.

Hypothesis 1.

The digital economy directly drives urban tourism development.

3.2. Nonlinear Spillover Effects of the Digital Economy on Urban Tourism Development

Economies of scale, economies of scope, and long-tail effects constitute the fundamental economic environment of the digital economy and stimulate the expansion of the scale of urban tourism [17]. First, as digital technologies continue to penetrate the tourism industry, the cost structure of tourism enterprises has formed two new characteristics: high fixed cost and low marginal cost. Concretely, the composition of the former is the sunk cost and the cost of subsidy activities to attract users, and the latter is determined by the network externality of the digital economy [43]. “Metcalfe’s Law” can satisfactorily explain why the driving force of the digital economy on urban tourism development will become stronger [44]. Furthermore, tourism companies can obtain ancillary sources of profit in addition to the sales profits of core products in the era of the digital economy. The reason for realising this profit model is that the operators have understood and applied the scope economy effect of the digital economy to the extreme [41]. In the digital economy, tourism operators can rely on customers accumulated in their primary businesses, selling them diversified but less relevant products at low marketing costs, which expands the company’s revenue sources and scale of operations [43]. For example, Ctrip, China’s largest travel OTA, was founded with its main business in ticket booking, hotel booking, and tailor-made travel. However, as platform users increased significantly, the managers expanded the business to car rentals and financial loans, diversifying the company’s revenue sources. Finally, in the era of the digital economy, the diversity of travel product categories and low search costs meet the individual needs of consumers in niche markets and promote the full release of the long-tail effect of the tourism consumption market and demand-side economies of scale.

Hypothesis 2.

The digital economy has nonlinear spillover effects on urban tourism development, and the marginal effects may be increasing.

3.3. Spatial Spillover Effects of the Digital Economy on Urban Tourism Development

As an essential development factor, data profoundly affect the tourism industry, promoting the deep integration of the digital economy and tourism. The digital economy has the characteristics of network externalities, which may have significant spatial spillover effects on urban tourism development [5]. From the supply side, tourism as a comprehensive industry needs the support of upstream and downstream industries and neighbouring regions. However, the digital economy can break industrial boundaries and geographical barriers to achieve “virtual agglomeration.” Doing so helps mobilize tourism resources rapidly, reduces communication costs, and promotes the evolution of the tourism industry chain from a single linear model to a network model, ultimately realizing the development of regional tourism [5,27]. The internet makes the long-distance dissemination of codable forms of knowledge a reality, breaking the spatial limitations under traditional knowledge dissemination channels and allowing tourism enterprises, as well as city managers, to promptly exchange information and experiences with the help of voice, text, and video [40]. Ultimately, tourism enterprises and management departments of different cities learn from each other’s experiences, contributing to realizing tourism’s joint development. Since the birth of the digital tourism platform, tourism suppliers in different regions have been able to combine tourism products from different cities through multiple channels and expand the geographical scope of the business due to the limitations of geographical distance and the regional monopoly of the tourism market has been broken by the digital economy [26].

From the demand side, in an imperfect information environment, it is difficult for tourists to purchase tourism products from different cities and design travel itineraries across multiple cities, inhibiting tourists’ large-scale spatial displacement. However, tourists always prefer multi-destination travel to maximize the tourism experience [16]. By relying on digital technology, the convenience of collecting tourism information makes the circuit tour a popular choice for tourists, benefiting the cities along the route from the economic growth brought by tourism consumption [44]. In addition, tourists often overlook remote areas with rich tourism resource endowments because they are far from the main tourist markets. However, with the advent of the digital economy era, social media, represented by Xiaohongshu and Weibo, has broken down information barriers and has provided a platform for travel bloggers to share travel tips, stimulating travellers’ desire to travel to remote destinations in China. The positive spatial spillover effect of the digital economy on tourism development in remote cities has emerged.

Hypothesis 3.

The digital economy can drive urban tourism development in neighbouring areas through spatial spillover effects.

4. Variables, Econometric Models, and Data

4.1. Variables

4.1.1. Explained Variables

Urban tourism development (Tour). At present, scholars mainly use two types of indicators to measure tourism development: tourism specialization (TS) and tourist population proportion (TP). TS is characterized by the ratio of total tourism revenue to the city’s GDP [45], and TP is characterized by the ratio of the total number of tourists to the total year-end population of the city [46]. In this paper, tourism specialization was used as an explanatory variable to characterize the urban tourism development index. In addition, in the robustness test, the tourist population proportion was used as a proxy variable for the explanatory variable.

4.1.2. Explanatory and Threshold Variables

Digital economy (Dige). Based on the current research results, this paper constructed a digital economy evaluation system from three dimensions: digital infrastructure, digital industry development, and inclusive digital finance [20]. Among them, digital infrastructure is the foundation of the digital economy, measured by two variables: internet users and mobile phone users. Digital industry development is the core of the digital economy, which was evaluated by three variables, such as the proportion of employees in computer services and the software industry. Inclusive digital finance is a necessary component of the digital economy, measured in three parts: coverage of digital finance, depth of digital finance, and digitalization degree of digital finance (

Table 1. Evaluation index system of digital economy.

Target Level	Standard Level	Index Level	Indicator Attribute
Digital economy	Digital infrastructure	Number of Internet users per 100 people	positive
		Number of mobile phone users per 100 people	positive
	Digital industry development	Proportion of employees in computer services and software industry (%)	positive
		Telecommunications business per capita (yuan)	positive
		Proportion of software business income to GDP (%)	positive
	Inclusive digital Finance	Coverage of digital Finance	positive
		Depth of digital Finance	positive
		Digitalization degree of digital Finance	positive

).

After constructing the index system of the digital economy, this paper adopted the entropy-weighted TOPSIS method, which is widely used in the evaluation of development levels, to calculate the digital economy index.

The first step was to normalize the data. In order to eliminate the influence of different dimensions of each indicator on the evaluation results, we used the range method to standardize the original data. Among them, formulas (1) correspond to the calculation process of the positive indices.

$$X_{ij}=\frac{x_{ij}-\min x_{ij}}{\max x_{ij}-\min x_{ij}}$$

(1)

where X_ij denotes the normalized value of indicator j of city i, and x_ij denotes the original value of indicator j of city i.

The second step was to normalize the data.

$$P_{ij}=Y_{ij}/{\displaystyle \sum _{i=1}^m{Y}_{ij}}$$

(2)

The third step was to calculate the information entropy E_j of the indicator j in year t.

$$E_j=-\frac{1}{\ln m}{\displaystyle \sum _{t=1}^m{P}_{ij}}\ln P_{ij}$$

(3)

The fourth step was to calculate the weight W_j of each indicator.

$$W_j=\frac{1-E_j}{{\displaystyle \sum _{j=1}^n(1-E_j)}}$$

(4)

The fifth step was to calculate the weighting matrix.

$$\begin{array}{l}R={(r_{ij})}_{mn},r_{ij}=w_j{x}_{ij}\\ (i=1,2,\cdots ,m;j=1,2,\cdots ,n)\end{array}$$

(5)

The sixth step was to determine the optimal solution and the worst solution.

$$S_j^{+}=\max (r_{1j},r_{2j},\cdots ,r_{nj}),S_j^{-}=\min (r_{1j},r_{2j},\cdots ,r_{nj})$$

(6)

The seventh step was to calculate the Euclidean distance between various schemes and the optimal solution and the worst solution.

$$se{p}_i^{+}\sqrt{{\displaystyle \sum _{j=1}^n{(s_j^{+}-r_{ij})}^2}},se{p}_i^{-}\sqrt{{\displaystyle \sum _{j=1}^n{(s_j^{-}-r_{ij})}^2}}$$

(7)

Finally, this paper calculated the evaluation index of digital economy.

$$C_i=\frac{se{p}_i^{-}}{se{p}_i^{+}+se{p}_i^{-}}$$

(8)

4.1.3. Control Variables

To increase the results’ accuracy, the following six control variables were selected regarding the current research results [47]: density of starred hotels (Sh), tourism resources (Tr), density of road network (Rn), industrial structure (Is), economic development level (Eco), and openness (Open). Specifically, the density of starred hotels is expressed as the ratio of the number of starred hotels to urban land areas. Tourism resources are expressed as the number of class 4A and above tourist attractions; the density of the road network is characterized by the ratio of highway mileage to the urban land area; and the industrial structure is characterized by the proportion of output value of the tertiary industry in GDP. The economic development level is expressed as per capita GDP, and openness is characterized by the total import and export proportion to GDP (

Table 2. Variable selection and descriptive statistics.

Variables	Variable Name	Symbol	Interpretation	Obs	Mean	Std. Dev.	Min	Max
Explained variable	Tourism development	lnTour	Proportion of tourism revenue in GDP	2556	−2.0161	0.7576	−6.5528	0.8487
Explanatory variable	Digital economy	lnDige	Dige calculated by entropy weight TOPSIS method	2556	−2.4798	0.4842	−4.5818	−0.1985
Threshold variable	Digital economy	Dige	Ditto	2556	0.0591	0.0591	0.0020	0.8440
Control variables	Density of starred hotels	lnSh	Ratio of number of starred hotels to urban land area	2556	−7.1434	1.1862	−10.7709	−3.1221
	Tourism resources	lnTr	The number of class 4A and above tourist attractions	2556	2.1276	0.7737	0	4.6821
	Density of road network	lnRn	Ratio of highway mileage to urban land area	2556	−0.0704	0.5988	−2.6899	1.4270
	Industrial structure	lnIs	Proportion of output value of tertiary industry in GDP	2556	3.6846	0.2453	2.3223	4.4248
	Economic development level	lnEco	Per Capita GDP	2556	10.6934	0.5765	8.7729	13.0556
	Openness	lnOpen	Proportion of total import and export to GDP	2556	−3.1344	1.5645	−8.8048	0.2703

).

4.2. Econometric Models

4.2.1. Benchmark Model

To verify if the digital economy has a positive impact on urban tourism development, based on hypothesis one, the benchmark Model (9) was constructed.

$$\ln Tou{r}_{it}=a_0+{\beta }_1\ln Dig{e}_{it}+{\beta }_2\ln Contro{l}_{it}+{\mu }_i+{\delta }_t+{\epsilon }_{it}$$

(9)

where lnTour_it stands for urban tourism development, that is, the logarithm of TOUR in city i at time t; lnDige_it is represented by the urban digital economy; lnControl_it indicates control variables μ_i and δ_t are the city and time fixed effects, respectively, and ε_it denotes the error term.

4.2.2. Threshold Model

To test hypothesis two, whether the digital economy has a nonlinear effect on urban tourism development, the panel threshold model proposed by Hansen [48] was constructed for estimations. The panel threshold model was used to examine whether the correlation between the explanatory variables and the explained variables changed when the threshold was crossed. This paper chose the digital economy (Dige) as the threshold variable, and the model is expressed in Equation (10).

$$\begin{array}{l}\ln Tou{r}_{it}=a_0+a_{1}Dig{e}_{it}·I\left(Dig{e}_{it}\le {\gamma }_1\right)+a_{2}Dig{e}_{it}·I\left({\gamma }_1<Dig{e}_{it}<{\gamma }_2\right)+\\ a_{3}Dig{e}_{it}·I\left(Dig{e}_{it}\ge {\gamma }_2\right)+\lambda Contro{l}_{it}+{\mu }_i+{\delta }_t+{\epsilon }_{it}\end{array}$$

(10)

where lnTour_it denotes the explanatory variable, and Dige_it is represented by the urban digital economy, which is both a threshold variable and a core explanatory variable. i represents the region, t represents the year, and γ represents the threshold. I(·) represents an indicator function taking the value 1 or 0, with the numerical value set as 1 if the condition in parentheses is met and 0 otherwise. Control_it denotes the exogenous control variables μ_i and δ_t are the city and time fixed effects, respectively, and ε_it denotes the random perturbation term.

4.2.3. Spatial Econometric Model

Based on hypothesis three, a spatial econometric model was selected for empirical analysis to examine the spatial spillover effect of the digital economy on urban tourism development. However, before constructing the spatial econometric model, Moran’s I and Getis-Ord $G_i^{*}$ indices were used to explore the spatial characteristics of the digital economy and urban tourism development. The expressions are in Equations (11) and (12).

$$I=\frac{n{\displaystyle \sum _{i=1}^n{\displaystyle \sum _{j=1}^n{w}_{ij}(Y_i-\overline{Y})(Y_j-\overline{Y})}}}{S^2{\displaystyle \sum _{i=1}^n{\displaystyle \sum _{j=1}^n{w}_{ij}}}}(i\ne j)$$

(11)

where i, j denotes different cities, $S^2=\frac{1}{n}{\displaystyle \sum _{i=1}^n(Y_i}-\overline{Y}{)}^2$ and $\overline{Y}=\frac{1}{n}{\displaystyle \sum _{n=1}^n{Y}_i}$, Y_i, Y_j, represent the observations on the spatial cell, w_ij is the spatial weight matrix, and I takes values between −1 and 1. The spatial correlation is negative when the value of I is less than −1, spatially uncorrelated when it is equal to 0, and spatially positive when it is greater than 0. The larger the absolute value of I is, the stronger is the spatial correlation.

$$G_i^{*}(d)={\displaystyle \sum _{j=1}^n{w}_{ij}(d)}{P}_j/{\displaystyle \sum _{j=1}^n{P}_j}$$

(12)

where n denotes the number of cities, P_j represents the observed values of digital economy and urban tourism development on spatial units, w_ij is the spatial weight matrix, and $G_i^{*}$ is close to 0, which means that the observed values are randomly distributed in the region, and the larger the absolute value of $G_i^{*}$ is, the more likely it is to form a hot spot area or a cold spot area.

After measuring the spatial agglomeration index, the spatial econometric models were subsequently constructed. Spatial econometric models mainly included the SRM, SEM, and SDM. The spatial Durbin model, as a comprehensive form of the SEM and SRM and without endogeneity problems [49], fits well with the study of the spatial spillover effect of the digital economy on urban tourism development, and the model expression is in Equation (13).

$$\begin{array}{l}\ln Tou{r}_{it}=a_0+\rho W\ln Tou{r}_{it}+{\beta }_{1}W\ln Dig{e}_{it}+{\theta }_1\ln Dig{e}_{it}+\\ {\beta }_{2}W\ln Contro{l}_{it}+{\theta }_{2}W\ln Contro{l}_{it}+{\mu }_i+{\delta }_t+{\epsilon }_{it}\end{array}$$

(13)

where W is the spatial weight matrix, and the geographical weight matrix (W1) and the economic and geographical nested matrix (W2) are selected in this paper. Among them, the geographical distance between cities was calculated based on the geographical coordinates between cities, and the economic data were selected as the difference between the average value of GDP per capita of each city from 2011 to 2019. ρ is the spatial regression coefficient of the explanatory variable, and β₁ and β₂ are the spatial regression estimation coefficients of the explanatory variable and the control variables, respectively. θ₁ and θ₂ are the spatial regression estimated coefficients of the explanatory and control variables, respectively. μ_i and δ_t are the city and time fixed effect, ε_it is the random error term, and X denotes the control variable.

4.2.4. Difference-in-Differences (DID) Model

Since 2012, the Chinese government has released three batches of smart city pilot lists, and the adoption of smart city pilot policies as policy variables has been widely used in studies on urban development quality, carbon emissions, and other areas. To guarantee the reliability of the research findings, we adopted the difference-in-differences (DID) model to examine the effects of China’s smart city pilot policy on urban tourism development. Ma’s [15] research design methodology indicated that if a city is a smart city listed in 2012 and later, the value is 1; otherwise, it is 0. The propensity score matching difference-in-differences (PSM-DID) method was used as a comparative study to verify the reliability of the DID model results. The model expression is in Equation (14):

$$\ln Tou{r}_{it}=a_0+{\beta }_1\ln Polic{y}_{it}+{\beta }_2\ln Contro{l}_{it}+{\mu }_i+{\delta }_t+{\epsilon }_{it}$$

(14)

where Policy_it is the variable of the smart city pilot policy, and Control_it denotes the exogenous control variable. If β₁ is significantly greater than 0, then the smart city pilot policy significantly promotes the development of urban tourism.

4.3. Data

Due to the lack of data on China’s digital economy before 2011 and some of the data not being updated after 2020, this paper collected statistics for 284 prefecture-level and above cities in China from 2011–2019. The data on the digital economy were obtained from the China Statistical Yearbook (2012–2020), China Urban Statistical Yearbook (2012–2020), China Information Industry Yearbook (2012–2020), China Internet Development Statistics Bulletin (2011–2019), and China urban Statistics Bulletin (2011–2019). The Digital Financial Inclusion Index was measured by Peking University Digital Finance Research Center in cooperation with ANT GROUP (Website: https://tech.antfin.com/research/data).(Accessed on 2 October 2022) Tourism data were obtained from China Tourism Statistical Yearbook (2012–2020). The data of control variables were obtained from China Urban Statistical Yearbook (2012–2020) and China Tourism Statistical Yearbook (2012–2020).

5. Empirical Results and Analysis

5.1. Direct Effects

According to

Table 3. Direct effects.

Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)
lnDige	0.8071 ***	0.6245 ***	0.5705 ***	0.5285 ***	0.3044 ***	0.3085 ***	0.3047 ***
	(57.38)	(41.51)	(30.61)	(27.63)	(14.66)	(11.52)	(11.36)
lnSh		0.8582 ***	0.8713 ***	0.8491 ***	0.5922 ***	0.5881 ***	0.5877 ***
		(22.67)	(23.07)	(22.73)	(16.17)	(14.60)	(14.59)
lnTr			0.0955 ***	0.1066 ***	0.1042 ***	0.1059 ***	0.1081 ***
			(4.86)	(5.48)	(5.83)	(5.49)	(5.60)
lnRn				0.3075 ***	0.1755 ***	0.1756 ***	0.1716 ***
				(7.98)	(4.87)	(4.87)	(4.75)
lnIs					0.9705 ***	0.9704 ***	0.9634 ***
					(20.32)	(20.31)	(20.12)
lnEco						0.0092	0.0198
						(0.24)	(0.51)
lnOpen							0.0222 *
							(1.92)
constant	−0.0145	0.9980 ***	1.2318 ***	1.1078 ***	−3.4616 ***	−3.353 ***	−3.2862 ***
	(−0.41)	(18.19)	(16.93)	(15.08)	(−14.74)	(−6.60)	(−6.46)
City FE	YES	YES	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES	YES	YES
Observations	2556	2556	2556	2556	2556	2556	2556
R−squared	0.1121	0.2349	0.2458	0.2149	0.2635	0.2640	0.2707
F−statistic	60.45	63.63	63.84	65.73	75.09	74.84	74.46

Note: The clustered standard error values are in parentheses. *, and ***, respectively, represent significance at the 10% and 1% significance levels.

, the regression coefficients of the digital economy are positive at the 1% significance level regardless of whether control variables were considered, indicating that the digital economy has a significant direct effect on urban tourism development. Hypothesis 1 is supported. Among the control variables, the density of starred hotels (lnSh), tourism resources (lnTr), the density of road networks (lnRn), and industrial structure (lnIs) contribute significantly to urban tourism development at the 1% significance level. The findings indicate that good tourism infrastructure facilities and resources are necessary to promote urban tourism development, and the rapid development of the tertiary industry can provide good business support for tourism development, consistent with Hu et al.’s [47] research. Moreover, economic development (lnEco) has a nonsignificant positive effect on urban tourism development, indicating that tourism development is not significantly dependent on the level of the urban economy. Openness (lnOpen) has a driving effect on urban tourism at the 10% significance level, which may be because as openness increases, so does urban visibility.

The paper further investigated the direct impact of the digital economy on urban tourism development in different types of cities to guarantee the robustness of the findings. The selected samples were considered from the characteristics of urban location, tourist cities, key urban agglomerations, and city level. According to the Chinese regional division method promulgated by the NBSPRC, all cities were divided into eastern and mid-western cities. Based on the 14th Five-Year Plan for Tourism Development, all cities were divided into tourist and non-tourist cities. According to the list of national urban agglomerations approved by the State Council of the PRC, cities in 11 national urban agglomerations were urban agglomeration cities, while others were not. Moreover, based on the list of China’s city grades published by the First Finance and New Tier Cities Institute, first-tier, new first-tier, second-tier, and third-tier cities were classified as high-level cities, while others were classified as low-level cities.

The results of the heterogeneity test are shown in

Table 4. Heterogeneous analysis.

Variables	Eastern Cities	Mid-Western Cities	Tourist Cities	Non-Tourist Cities	Urban Agglomeration Cities	Non-Urban Agglomeration Cities	High-Level Cities	Low-Level Cities
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
lnDige	0.1426 ***	0.4243 ***	0.1764 **	0.3406 ***	0.3307 ***	0.2558 ***	0.2392 ***	0.3812 ***
	(3.40)	(12.09)	(2.75)	(11.43)	(9.38)	(6.24)	(7.23)	(10.25)
Constant	−5.9693 ***	−1.1907 *	−5.7135 ***	−2.9299 ***	−1.6009 **	6.8570 ***	4.3219 ***	2.8976 ***
	(−6.67)	(−1.85)	(−4.43)	(−5.25)	(−2.57)	(7.88)	(6.53)	(4.13)
Control variables	YES	YES	YES	YES	YES	YES	YES	YES
City FE	YES	YES	YES	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES	YES	YES	YES
Observations	900	1656	441	2115	1449	1107	1233	1476
R−squared	0.3981	0.2945	0.0199	0.2951	0.2102	0.3081	0.2667	0.3076
F−statistic	51.29	74.88	41.79	71.03	82.14	65.78	61.05	76.85

Note: The clustered standard error values are in parentheses. *, **, and ***, respectively, represent significance at the 10%, 5%, and 1% significance levels.

, indicating that the direct impact of the digital economy on urban tourism development is significantly positive in all types of cities at the 1% significance level. However, the degree of influence is different in various types of cities. First, the promotion of the digital economy to the tourism development of mid-western cities is greater than that of eastern cities. This may be because eastern cities have a well-developed industrial system, and urban development is mainly supported by traditional industries such as industry and commerce rather than tourism. In contrast, many mid-western cities have rich tourism resources, and the application of the digital economy to urban tourism development has also received special attention. Second, the promotion of the digital economy to the tourism development of non-tourism cities is greater than that of tourist cities. This may be because traditional tourist cities have high visibility through good marketing and word-of-mouth. However, internet platforms can quickly increase the visibility of non-tourist cities, narrowing their development gap with tourist cities. Third, the digital economy significantly affects the tourism development of cities in urban agglomerations. The reason may be that urban agglomeration cities have developed economies, good tourism infrastructure, and tourism talent reserves. With the development of the digital economy, they are more likely to grasp the significant opportunities brought by the digital economy for urban tourism development compared to non-urban agglomerations. Finally, the tourism development of lower-level cities is more significantly positively affected by the digital economy. The reason may be that high-level cities have a complete industrial system, so tourism does not play a decisive role in urban development. In contrast, most lower-level cities, especially those in central and western China, have a single industrial structure but rich tourism resources, so the positive impact of the digital economy on tourism development is more obvious.

5.2. Nonlinear Spillover Effects

According to the theoretical analysis, the digital economy may have a nonlinear impact on urban tourism development with increasing marginal effects. Therefore, the panel threshold regression model proposed by Hansen [48] was used to analyse the nonlinear relationship between the digital economy and urban tourism development. The samples were repeatedly drawn 1000 times by the bootstrap method to test whether there was a threshold effect on the digital economy.

Table 5. Threshold test.

Regions	Model	Threshold	F-Statistic	Prob	10%	5%	1%
All cities	single	0.0825 ***	86.51	0.0000	28.4505	33.2175	47.7482
	double	0.1396	29.35	0.1200	43.9673	87.2816	110.4774
	triple	0.2258	16.16	0.3800	42.9359	59.7197	92.4269
Eastern cities	single	0.0730 ***	46.87	0.0000	21.2263	25.6161	30.2195
	double	0.1553 ***	37.42	0.0000	17.5118	19.3588	27.9366
	triple	0.3045	17.75	0.7000	47.8011	55.8973	65.1162
Mid-western cities	single	0.0745	22.65	0.1700	26.5994	28.8156	34.2094
	double	0.1211 ***	32.02	0.0000	24.0732	25.6139	26.4553
	triple	0.1239	6.74	0.8400	36.9712	53.6869	62.0245

Note: *** represents significance at the 1% significance levels.

shows the threshold test results, indicating that the digital economy passes the single-threshold test in all cities and the dual threshold test in eastern and mid-western cities. Specifically, the threshold of the full sample is 0.0825, the thresholds of eastern cities are 0.0730 and 0.1553, and the thresholds of mid-western cities are 0.0745 and 0.1211.

The threshold regression results are shown in

Table 6. Threshold regression results.

Variables	All Cities		Eastern Cities			Mid-Western Cities
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
Threshold	Dige ≤ 0.0825	Digel > 0.0825	Dige ≤ 0.0730	0.0730 < Dige ≤ 0.1553	Dige > 0.1553	Dige ≤ 0.0745	0.0745 < Dige ≤ 0.1211	Dige > 0.1211
Dige	0.7640 ***	1.0088 ***	0.6360 **	0.9182 ***	1.2299 ***	0.0880	0.1703 **	0.4788 ***
	(5.57)	(9.30)	(2.44)	(7.15)	(7.00)	(1.15)	(2.80)	(6.25)
Control variables	YES	YES	YES	YES	YES	YES	YES	YES
City FE	YES	YES	YES	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES	YES	YES	YES
R-squared	0.1195		0.9171			0.9488
F-statistic	36.86		10.98			9.64

Note: ** and ***, respectively, represent significance at the 5%, and 1% significance levels.

. When the digital economy index exceeds 0.0825, the regression coefficient of the digital economy increases from 0.7640 to 1.0088 at the 1% significance level, indicating that the digital economy has a significant increasing marginal effect on urban tourism development. Hypothesis 2 is confirmed. Moreover, we take the eastern cities as the sample. When Dige ≤ 0.0730, the regression coefficient of the digital economy is 0.6360 at the 5% significance level, and when the digital economy index crosses the second and third thresholds, the regression coefficient gradually increases to 0.9182 and 1.2299 at the 1% significance level, respectively. Then, in mid-western cities, when Digel ≤ 0.0745, the positive effect of the digital economy on urban tourism development is not significant, and when the digital economy index crosses the thresholds of 0.0745 and 0.1211, the regression coefficient gradually increases to 0.1703 and 0.4788, respectively, at the 5% significance level. The above results further verify the validity of Hypothesis 2 and are consistent with the findings of Cheng et al. [41].

5.3. Spatial Spillover Effects

5.3.1. Spatial Autocorrelation Test

Before modelling a spatial econometric model, it was necessary to detect the spatial correlation of the variables. First, Moran’s I was used to detect the global spatial autocorrelation of the digital economy (lnDige) and urban tourism development (lnTour) in China from 2011 to 2019. The results in

Table 7. The spatial correlation tests.

Year	W1		W2
	lnDige	lnTour	lnDige	lnTour
2011	0.110 ***	0.054 ***	0.396 ***	0.068 **
2012	0.082 ***	0.057 ***	0.374 ***	0.077 **
2013	0.118 ***	0.056 ***	0.444 ***	0.085 **
2014	0.084 ***	0.054 ***	0.424 ***	0.086 **
2015	0.075 ***	0.060 ***	0.421 ***	0.106 ***
2016	0.078 ***	0.057 ***	0.457 ***	0.100 **
2017	0.087 ***	0.060 ***	0.422 ***	0.100 **
2018	0.063 ***	0.063 ***	0.361 ***	0.100 **
2019	0.054 ***	0.063 ***	0.370 ***	0.113 ***

Note: **, and ***, respectively, represent significance at the 5%, and 1% significance levels.

show that Moran’s I was positive during 2011–2019 at the 5% significance level under the geographical weight matrix (W1) and the economic and geographical nested matrix (W2).

Getis-Ord $G_i^{*}$ was used to examine the characteristics of the local spatial autocorrelation of lnDige and lnTour. Figure 1 and Figure 2 show that the major “hot-spot” agglomerations of lnDige were located in the southeast coastal cities, and the “cold spot” agglomerations were located in the mid-west. Moreover, there are two major “hot-spot” agglomerations of lnTour, which were located in the Yangtze River Delta urban agglomerations and the Yunnan–Guizhou Plateau, and there are two “cold spot” agglomerations, which were located in Shandong Peninsula urban agglomerations and the Hetao Plain.

5.3.2. Statistical Testing of Model Selection

Spatial econometric models were constructed based on W1 and W2. The LM, LR, Wald, and Hausman tests were performed to discern which type of spatial econometric model was best to use. As shown in

Table 8. Statistical testing of model selection.

Inspection Method	W1		W2
	Characteristic Value	p-Value	Characteristic Value	p-Value
LM-Lag test	334.713	0.000	102.964	0.000
Robust LM-Lag test	36.782	0.000	5.155	0.023
LM-Error test	881.176	0.000	110.536	0.000
Robust LM-Error test	583.244	0.000	12.727	0.000
LR-Lag test	32.3	0.000	32.19	0.000
LR-Error test	43.23	0.000	44.15	0.000
Wald-Lag test	31.97	0.000	32.34	0.000
Wald-Error test	44.14	0.000	44.51	0.000
Hausman test	22.7	0.002	12.86	0.075

, based on the results of the LM test, the SDM, which was combined with the SEM and SAR model, was appropriate. Then, the LR and Wald tests showed that the SDM model is optimal compared to the SEM and SAR models, so the SDM model should not be changed. Finally, according to the results of the Hausman test, the fixed-effects model was optimal. Therefore, the SDM based on fixed effects was used to construct the spatial econometric model in the following.

5.3.3. Spatial Effect Estimation Results

As shown in

Table 9. Spatial effect estimation results.

Variables	SDM		SAR
	W1	W2	W1	W2
	(1)	(2)	(3)	(4)
lnDige	0.0494 **	0.2681 ***	0.0546 **	0.3222 ***
	(1.88)	(7.03)	(2.04)	(8.56)
W × lnDige	1.0094 **	0.1643 *
	(2.75)	(1.93)
Direct effect	0.0336	0.2762 ***	0.0574 **	0.3239 ***
	(1.15)	(7.57)	(2.02)	(8.83)
Indirect effect	0.5595 **	0.3124 **	0.0921 **	0.1065 ***
	(2.51)	(2.99)	(2.03)	(5.32)
Total effect	0.5932 **	0.5886 ***	0.1495 **	0.4305 ***
	(0.01)	(5.45)	(2.02)	(8.53)
Control variables	YES	YES	YES	YES
City FE	YES	YES	YES	YES
Year FE	YES	YES	YES	YES
Observations	2556	2556	2556	2556
R-squared	0.0093	0.1751	0.0064	0.1480
Log-likelihood	976.0490	799.1717	928.4773	736.1689

Note: The clustered standard error values are in parentheses. *, **, and ***, respectively, represent significance at the 10%, 5%, and 1% significance levels.

, the regression coefficients of W × lnDige are significantly positive at the 10% significance level, whether under the W1 or W2 weight matrix. However, LeSage and Pace [50] argued that testing the spatial spillover effect using the point estimation method results in significant errors, so we further analysed it based on the results of the partial differential estimation. Under the W1 weight matrix, every 1% increase in the digital economy could result in a corresponding increase of 0.5595% in the urban tourism development of adjacent cities at the 5% significance level. Under the W2 weight matrix, every 1% increase in the digital economy could result in a corresponding increase of 0.3124% in the urban tourism development of adjacent cities at the 5% significance level. The results show that the digital economy has a significant positive spatial spillover effect on urban tourism development, so Hypothesis 3 is confirmed. The conclusion is also verified in the SAR model.

5.3.4. Further Tests

We further explored the specific path of the spatial spillover effect, examining the spatial spillover effects of digital infrastructure, digital industry development, and inclusive digital finance, which constitute the digital economy index on urban tourism development. As shown in

Table 10. The specific path of the spatial spillover effect estimation results.

Variables	Digital Infrastructure		Digital Industry Development		Inclusive Digital Finance
	W1	W2	W1	W2	W1	W2
	(1)	(2)	(3)	(4)	(5)	(6)
Direct effect	0.0544 **	0.0935 ***	0.0052 **	0.0054 **	0.0302 **	0.0401 **
	(2.42)	(7.51)	(2.12)	(2.21)	(2.47)	(3.45)
Indirect effect	1.0996	0.0585	0.0106	0.0011	1.0976 *	0.1800 ***
	(0.40)	(1.57)	(0.33)	(0.18)	(1.67)	(5.18)
Total effect	1.1541	0.1521 ***	0.0159	0.0065	1.1277 *	0.2202 ***
	(0.42)	(3.89)	(0.49)	(1.05)	(1.71)	(6.04)
Control variables	YES	YES	YES	YES	YES	YES
City FE	YES	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES	YES
Observations	2556	2556	2556	2556	2556	2556
R-squared	0.1112	0.2109	0.9765	0.9654	0.4786	0.6671
Log-likelihood	3629.3836	827.8738	4598.5286	4594.3076	1740.259	1665.1372

Note: The clustered standard error values are in parentheses. *, **, and ***, respectively, represent significance at the 10%, 5%, and 1% significance levels.

, they all have positive spatial spillover effects on urban tourism development. Specifically, the positive spatial spillover effect of inclusive digital finance is the most significant. Every 1% increase in inclusive digital finance can increase the urban tourism development index by 1.1277% and 0.2202% at the 10% significance level under the W1 and W2 weight matrices, respectively. In contrast, digital infrastructure and digital industry development have a nonsignificant positive spatial spillover effect on urban tourism development. This may be because inclusive digital finance can provide development funds for tourism enterprises and more tourism consumption opportunities to disadvantaged groups and residents in remote areas, promoting the rapid development of tourism from the supply and demand sides.

However, digital infrastructure and industries are still in the early development stages, so their positive spatial spillover effects on urban tourism have not yet been fully realised. Therefore, we conducted a regression analysis of the spatial spillover effect every 100 km within the range of 100–1500 km to explore the changes in the direct effects and spatial spillover effects of the digital economy on urban tourism development under different spatial thresholds. As shown in Figure 3, the direct effect of the digital economy on urban tourism development remains largely stable at different spatial thresholds. The level of spatial spillover effects shows an inverted U-shaped trend, reaching a peak (1.0383) at 300 km and then gradually attenuating, indicating that the spatial spillover effect of the digital economy on urban tourism development has the characteristics of distance attenuation. This is because the synchronicity of the production and consumption of tourism products has not changed, and geographical distance is still an essential barrier to the cross-regional flow of tourists, although the digital economy has broken down the barrier of geographical distance and realised the cross-regional flow of data.

5.4. Robustness Check

5.4.1. Replace Explanatory Variable

To improve the reliability of the findings, we conducted a series of robustness tests. First, referring to Zhao et al. [51], we used the tourist population proportion, the surrogate variable of tourism specialization, to characterize urban tourism development. The regression results were significantly positive for the FE model, RE model, and OLS models, which is consistent with the previous findings and further supports the hypothesis (see Columns (1)–(3) in

Table 11. Endogenous test.

Variables	Replace Explanatory Variable			Control the Fixed Effect		Instrumental Variable
	(1) FE	(2) RE	(3) OLS	(4)	(5)	(6)	(7)
lnDigital	0.3634 ***	0.3625 ***	0.2819 ***	0.0364 ***	0.0917 ***	0.2276 ***	0.0279 ***
	(15.96)	(16.07)	(5.63)	(1.43)	(3.99)	(16.74)	(17.84)
Control variables	YES	YES	YES	YES	YES	NO	YES
Province×Year FE	NO	NO	NO	NO	YES	NO	NO
Province FE	NO	NO	NO	YES	YES	NO	NO
City FE	YES	YES	YES	YES	YES	YES	YES
Year FE	YES	YES	YES	YES	YES	YES	YES
Kleibergen-Paap rk LM statistic						280.171	13.5819
						[0.0000]	[0.0000]
Kleibergen-Paap rk Wald F statistic						343.29	14.0829
						{16.38}	{16.38}
Observations	2556	2556	2556	2556	2556	2556	2556
R-squared	0.8499	0.8497	0.4616	0.8812	0.9315	0.1185	0.7328

Note: The clustered standard error values are in parentheses. *** represents significance at the 1% significance levels.

).

5.4.2. Control the Fixed Effect

Cities with a higher level of tourism development may have complete infrastructures and good economic conditions, which give them some advantages in developing a digital economy; so, potential endogeneity problems may be generated. Therefore, the province fixed effect and the province and year interaction effect were set to alleviate the macro environment changes that the digital economy’s development may bring about. As shown in Columns (4) and (5) in Table 11, all of the regression results are significantly positive, which is consistent with the previous tests.

5.4.3. Instrumental Variable Approach

In addition, using appropriate instrumental variables is a critical way to address endogeneity issues. According to Ma’ s research [20], historical data of post and telecommunications data for each city in 1984 were used as an instrumental variable for the digital economy. This is because the internet is a continuation of the development of traditional communication technologies, and the local historical telecommunications infrastructure affects the subsequent application of internet technologies. Moreover, the impact of traditional telecommunications tools on economic development decreases as the use frequency decreases, so the choice of the tool variable satisfies exclusivity. However, the original data are cross-sectional, which cannot be directly applied to panel data analysis. Therefore, we refered to Nunn et al.’s [52] method and constructed the interaction term using the number of telephones per 10,000 persons in 1984 and China’s internet users in the previous year as another instrumental variable that could be applied to panel data. As shown in Columns (6) and (7) in Table 11, the Kleibergen-Paap rk LM and Wald F statistics significantly reject the null hypothesis, indicating that there are no problems of insufficient and weak identification of instrumental variables. On this basis, the regression results are significantly positive, further confirming the significant promoting effect of the digital economy on urban tourism development.

5.4.4. Exogenous Policy Shocks

China’s digital economy cannot be developed without government policy support, and policies related to the digital economy may profoundly affect urban tourism development. Therefore, by referring to Ma’s research [20], we examined the smart city pilot policy to evaluate whether the digital economy drives urban tourism development. This is because smart city pilot policies provide good policy support for digital economy development and an appropriate strategy for quasi-natural experiments.

In 2012, the Chinese government announced a list of smart city pilots covering 90 cities, counties, and districts. To accurately assess the net effect of smart city pilot policies on urban tourism development, the counties and districts in the experimental group were excluded, and 33 cities were retained. On this basis, the DID model was used for empirical testing. First, if a city is listed as a smart city, the value is 1; otherwise, it is 0. Second, the PSM-DID model was further established for the robustness test. As shown in Columns (1) and (2) in

Table 12. The effects of smart city pilot policy on urban tourism development.

Variables	DID		PSM-DID
	(1)		(2)
Policy	0.5242 ***	0.4995 ***	0.0631 **	0.5192 ***
	(5.19)	(9.12)	(2.81)	(7.64)
Constant	6.3969 ***	2.0626 ***	5.8489 ***	2.2907 ***
	(52.64)	(219.65)	(10.45)	(72.94)
Control variables	YES	NO	YES	NO
City FE	YES	YES	YES	YES
Year FE	YES	YES	YES	YES
R-squared	0.9560	0.0017	0.9215	0.0112
F-statistic	8943.43	83.09	357.51	58.3

Note: The clustered standard error values are in parentheses. ** and *** respectively, represent significance at the 5% and 1% significance levels.

, the regression coefficients are positive at the 1% significance level regardless of whether the control variable was considered, indicating that smart city pilot policies have a significant positive impact on urban tourism development.

Moreover, if unobservable missing variables enter the disturbance term, it may affect the estimated results [20]. An indirect placebo test was employed to solve the problem. Figure 4 shows the kernel density distribution of the estimated coefficients for 1000 randomly generated experimental groups. The distribution of coefficients is close to a normal distribution. Moreover, the mean is close to 0, which is in line with the expectations of the placebo test. Therefore, the estimates are not subject to error due to omitted variables.

6. Conclusions and Discussion

6.1. Conclusions

The digital economy has brought new opportunities for tourism development, but there is a lack of scientific research on whether the digital economy can drive urban tourism development. Therefore, first, we proposed research hypotheses based on the theoretical framework for the impact of the digital economy on urban tourism development. Then, we took 284 cities at the prefecture level and above in China as case study sites and constructed the benchmark regression model, PTM, and SDM to explore the impact of the digital economy on urban tourism development. The findings are as follows.

First, the digital economy can significantly contribute to urban tourism development. When the control variables were not considered, for every 1% increase in the digital economy index, the urban tourism development index can increase by 0.8071%. Moreover, when a series of control variables were added, for every 1% increase in the digital economy index, the urban tourism development index can increase by 0.3047%. However, the extent to which the digital economy promotes tourism development varies significantly in different cities. Specifically, the digital economy in mid-western, non-tourist, urban agglomeration, and low-level cities is more significant in regard to promoting urban tourism development.

Second, the digital economy has nonlinear spillover effects on urban tourism development, and the marginal effects gradually increase. In the full sample, when the digital economy index crossed the threshold (0.0825), the driving effect of the digital economy on urban tourism development was significantly increased. Moreover, when taking China’s eastern and mid-western cities as research samples, the degree of the promotion of the digital economy in urban tourism development increased twice after crossing the two thresholds.

Third, the impact of the digital economy on urban tourism development also has a significant, positive, spatial spillover effect. Every 1% increase in the digital economy index generates positive spatial spillover effects of 0.5595% and 0.3124% on the tourism development of neighbouring cities under the geographic weight matrix and the economic and geographic nesting matrix, respectively. Moreover, in the three dimensions of the digital economy, digital financial inclusion plays a more significant role in promoting urban tourism development than digital infrastructure and digital industry development. Furthermore, the spatial spillover effect of the digital economy on urban tourism development shows the characteristics of distance attenuation after reaching a maximum of 300 km.

6.2. Discussion

This paper confirms that the digital economy is an important driving force for urban tourism development. The conclusion can be verified not only in the related research but also in China’s cities. Hangzhou is an important tourist destination in China, but its attractiveness to tourists has declined due to the inefficient tourism development model. In order to promote tourism transformation, Hangzhou Tourism Bureau, together with well-known technology enterprises, has launched ten digital economy tourism scenes, including Hikvision, Yunqi Town, Information Harbor Town, Turing Town, Dachuang Town, Dahua Zhilian, Robot Town, Wasu Group, Alibaba, and Alibaba Cloud supET Innovation Center. They bring new opportunities for Hangzhou’s tourism development and enhance the city’s image [53]. Specifically, (1) Hikvision is a well-known Chinese company focusing on digital technology innovation. It cooperated with the government to turn the company lobby into an immersive scenic spot to attract many tourists with the help of artificial intelligence and holographic projection technology. (2) Yunqi Town is an intelligent industrial park jointly built by the government, enterprises, and research institutions. Unlike traditional industrial zones or tourist attractions, it has beautiful scenery and a complete industrial layout based on digital economy industries. (3) Information Port Town is a cluster of intelligent industries, including artificial intelligence, intelligent transportation, and smart homes. It is known as the “Silicon Valley” of Hangzhou’s digital economy. (4) Turing Town, as a digital economy application demonstration zone in Hangzhou, is a complex containing business, tourism, and digital industry, allowing visitors to experience the convenience of digital technology in a beautiful natural environment. (5) Dachuang Town is an innovation base for the digital industry, which includes well-known scientific research institutes, maker spaces, and incubators. Visitors can get an unforgettable tourist experience in the town with the help of 4K projectors, multilingual translators, and flexible material wearable devices. (6) Dahua Zhilian is an intelligent industrial park that profoundly integrates the digital economy and traditional manufacturing industry. It is also a demonstration point to realize the transformation and upgrading of the manufacturing industry. (7) Robot Town is an innovative application park where the digital economy empowers intelligent manufacturing industries’ development. Tourists can interact with intelligent robots, such as dancing robots, chess-playing robots, and vending robots, in scenic spots. (8) Wasu Group is a well-known digital media company in China. In the exhibition hall of the group headquarters, visitors can experience 5G + 8K ultra-high-definition video images based on 5G networks, as well as immersive tourism experience projects such as 3D cloud games. (9) Alibaba is a renowned e-commerce company and a pioneer in digital economy innovation. It opened several digital economic attractions, including “Qinchengli,” Alibaba’s first intelligent business space, and “FlyZoo Hotel,” the world’s first full-scene face recognition hotel. (10) Alibaba Cloud supET Innovation Center includes industrial Internet of Things (IoT) platform, digital factory, and industrial intelligence platform, which aims to make the super easy transformation of industrial digitalization. Overall, the ten digital economy scenic spots are the integration of digital economy and tourism, which inject vitality into the tourism development in Hangzhou. In addition, in some cities with developed digital economies, digital tourism has become a new engine of tourism economic growth during COVID-19.

6.2.1. Theoretical Implications

This paper provides some theoretical innovations based on the shortcomings of existing studies. First, this study constructed a theoretical framework of the digital economy driving urban tourism development from the supply and demand perspectives and a multi-subject perspective. On this basis, we used the econometric models to conduct empirical tests and analyse the differences in the results of different types of cities, providing a complete paradigm for the further study of this issue in other case sites. Second, this study confirmed the effectiveness of Metcalfe’s law in the digital economy driving urban tourism development by using a panel threshold model. With the improvement of the digital economy, its positive effect on urban tourism development presents the characteristics of increasing marginal effects. This theoretical evidence provides a scientific reference for regional policy formulation. Finally, this paper also confirmed that the digital economy has a positive spatial spillover effect on tourism development in neighbouring cities from a spatial perspective by using the spatial Durbin model. Compared with previous studies [26,27], we incorporated the spatial ideas of geography into tourism economics research, which not only improved the objectivity of the conclusions but also promoted interdisciplinary communication.

6.2.2. Practical Implications

Based on the above conclusions, recommendations for the two dimensions of government and enterprises are advanced.

From the government side, tourism departments should promote the deep integration of digital technologies, such as the internet, 5G, cloud technology, AI technology, and tourism, as an essential task in current tourism development to expand the application scenarios in tourism in the digital economy. Moreover, it is essential to establish a better policy system and legal environment that matches the digitalization of the tourism industry to ensure the effectiveness of the digital economy in promoting tourism and avoiding transaction risks and product infringements caused by the disorderly application of digital technology.

From the enterprise side, tourism enterprises should fully seize the development opportunities brought by the digital economy and transform traditional business strategies and models. Moreover, it is crucial to use digital production factors and build digital platforms to allow consumers to participate in the tourism product production processes to realise the value of the cocreation of tourism products. Specifically, travel agencies and starred hotels should fully utilize the digital platform to broaden their sales channels, accurately grasping the changes in consumer demand and industry operation to realize the transformation of the business model from “supply-oriented” to “demand-oriented.” Tourist attractions should also actively promote the digital transformation to allow for digital reservations, paperless tickets, and intelligent travel experiences. Moreover, building a big data tourism platform is essential to realizing real-time traffic monitoring and resource deployment and warnings related to tourism.

In addition, tourists and media are essential stakeholders who can also contribute to tourism digitalization. The former can participate in creating digital tourism products through the consumer co-creation platform built by enterprises. The latter is a vital disseminator of digital economy tourism information and a bridge for communication between various subjects.

6.2.3. Limitations and Future Research

Although this study confirms that the digital economy has a positive effect on urban tourism development in China and provides theoretical contributions and practical guidance, it still has some limitations, which also provide direction for future research. First, the empirical study did not consider the mediating effect, which may lead to the neglect of multiple paths of the digital economy driving urban tourism development. Therefore, the question of how the digital economy improves urban tourism development through multiple paths requires further research. Second, due to the limitation of the data, we only used a single variable to measure urban tourism development. Although this approach conforms to the paradigm of economic research, it is difficult to comprehensively evaluate the level of urban tourism development from multiple dimensions. Therefore, compound evaluation indicators can be used in future studies to measure the urban tourism development index. Finally, this study only conducted empirical research on prefecture-level cities in China and did not expand the study object to the district, county, or provincial level. In the future, scholars can use Chinese provinces, counties, districts, or other countries as research subjects to obtain more convincing empirical results.