This study used panel data from 31 provinces and cities in China between 2011 and 2019 for empirical testing, aiming to understand the contribution of new infrastructure investment in China to the international tourism industry. The research findings indicate that infrastructure investment in China had a positive impact on the development of international tourism. With the increase in infrastructure investment, the number of inbound overnight tourists and the international tourism revenue also increased. Mechanism analysis shows that transportation accessibility and information technology levels influenced international tourism; that is, new infrastructure investments improved the regional transportation environment and enhanced the level of information technology, which was beneficial for international tourism. In addition, there was apparent regional heterogeneity in the impact of new infrastructure investments in China on the international tourism industry. Overall, China's investment in new infrastructure has led to enormous opportunities for the development of the international tourism industry.
Research Article
The impact of new infrastructure investment on the international tourism industry: new evidence from China
https://doi.org/10.21203/rs.3.rs-5258323/v1
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infrastructure investment in China
international tourism industry
transportation accessibility
information technology level
regional heterogeneity
As an ancient civilization with a long history, China has a profound cultural heritage and abundant tourism resources that attract countless foreign tourists for exploration and sightseeing. According to the China Statistical Yearbook, there were 49.1136 million inbound foreign tourists and the international tourism revenue reached USD 131.554 billion in 2019, with average annual growth rates of 7.26% and 12.41% from 2010 to 2019, respectively. Although the growth in the number of inbound foreign tourists was lower, international tourism revenue was higher than that recorded from 2001 to 2010, when the average annual growth rates of inbound foreign tourists and international tourism revenue were 9.84% and 11.08%, respectively. These data demonstrate that the expenditure of foreign tourists per capita is constantly increasing, which has driven the development of China's tourism economy. In March 2010, the Chinese government highlighted "Internet of Things technology" as one of the five emerging strategic industries in the country, marking a new era in China's new infrastructure development. From then on, the rapid expansion of China's international tourism coincided with a significant increase in new infrastructure investment, which sparked our thinking on how new infrastructure investment may help stimulate China's international tourism expansion.
Investments in new infrastructure include 5G technology, artificial intelligence, big data centers, industrial Internet, ultra-high-voltage transmission, intercity high-speed rail and rail transit, and new-energy vehicle charging stations. To further illustrate the rapid development of infrastructure investment in China, we use high-speed rail and communication technology as examples and describe the achievements made in these two fields in detail. Since 2016, the Chinese government has planned to construct the main channels of the "Eight Verticals and Eight Horizontals" high-speed railway, which will include eight vertical and eight horizontal high-speed railways across provinces and regions, running to the east, west, north, and south, to improve the transportation environment. According to data from the National Railway Administration of China, as of the end of November 2023, 36100 kilometers of the main channels of China's "Eight Verticals and Eight Horizontals" high-speed rail network have been completed, accounting for about 80% of the total mileage. The accelerated construction of China's high-speed rail provides strong support for regional coordinated development, which is conducive to visitors carrying out tourism activities. This study offers statistics on the operating mileage and passenger volume of high-speed rail in China from 2008 to 2022 (See Appendix A and B).
In recent years, the Chinese government has actively promoted the construction of communication infrastructure in various regions and increased the coverage of essential communication networks nationwide, with the target of becoming a "network power" and a "digital China". According to statistics from the Chinese Ministry of Industry and Information Technology, as of June 2023, China has built a total of 2.844 million 5G base stations, with over 2.05 billion cellular IoT terminal users, and the total amounts of IPv6 address resources and computing infrastructure are the highest in the world. In addition, according to the China Statistical Yearbook, as of the end of 2022, the capacities of mobile phone switches, mobile phone base stations, and fiber-optic cable lines in China were 2.752 billion households, 10.834 million, and 595.8 billion kilometers, respectively. Their average annual growth rates from 2010 to 2022 were 5.17%, 18.61%, and 16.07%, respectively. China's informatization construction will promote the operation of related industries, strengthen regional connections, and drive the development of the tourism economy. This study provides statistics on the construction of communication infrastructure in China from 2010 to 2022 (See Appendix C).
Theoretically, new infrastructure investment can increase the stock of regional infrastructure, which stimulates private investment and consumption, thereby driving regional economic development (Shioji 2001; McQuinn et al. 2007). New infrastructure investment constantly generates market demands, promoting high-quality development of the tourism industry. A case in point is that the opening of high-speed rail has reshaped the economic activities of surrounding areas, promoting the circulation of goods and the transfer of the labor force (Qin 2017; Behrens et al. 2018), which creates favorable conditions for the development of the regional tourism industry. In addition, new infrastructure empowers industrial development through technological means such as networking, digitization, and intelligence, continuously optimizing the regional industrial structure. Relying on the latest technological advancements, tourism enterprises can constantly improve business models and enhance operational efficiency and service quality (Tang et al. 2021; Pan et al. 2022; Wang et al. 2022). In Travel & Tourism Development Index 2024, According to the evaluation of the tourism industry in various countries around the world based on indicators such as passenger experience, infrastructure, and natural resources, the top seven are all developed countries (See Appendix D).
In practice, countries with world-renowned tourism industries, such as the United States, France, and Germany, generally have complete and advanced infrastructure systems. Regarding transportation infrastructure, the highway system in the United States covers the whole country, the railway network operates over 250000 kilometers, and there are 15000 airports, ranking first in the world. The total length of France's road network exceeds 1.105 million kilometers, and the total length of its inland waterways is 14900 kilometers. Its subway has a long history, with over 500 platforms in the capital city of Paris alone. The total length of German highways is about 830000 kilometers, the total length of the core railway network is 38600 kilometers, and more than 100 modern public seaports and river ports connect 56 cities across the country via waterways. From the perspective of communication capabilities, these countries have developed network facilities and high levels of information technology. Specifically, the United States is one of the world's largest Internet technology innovation centers and has the world's most powerful network technology and information resources (DeNardis 2020). The penetration rate of e-commerce in France is relatively high, and its digital content industry is very competitive (Guthrie et al. 2021). Germany attaches importance to the development of small- and medium-sized enterprises, and many new enterprises have made breakthroughs in the Internet field (Heider et al. 2021).
Using panel data from 31 provinces in China between 2011 and 2019, this study contributed to the literature by investigating the relationship between infrastructure investment in China and the international tourism industry. From 2020 to 2022, the global economy fluctuated greatly due to COVID-19, and China's epidemic prevention and control policies delayed the development of the tourism industry. Thus, this range was not included in this study. The specific empirical method involved constructing a panel regression model and controlling for time and individual bidirectional fixed effects (Fernandez et al. 2016; Kutlu et al. 2019). Multiple variables were considered to control for supply-side factors related to accessibility and tourism in designated areas to study the impact of infrastructure investment in China on international tourism. Moreover, robustness and endogeneity tests were conducted to avoid missing variables, bidirectional causality, and sample bias and to enhance the reliability of the estimation results. Furthermore, the transmission path between new infrastructure investment and international tourism development was examined through mechanism analysis. The mechanism analysis model referred to Bolin’s (2014) and Hayes’s (2017) criticisms of complete mediation and partial mediation and selected direct and obvious mediating variables in the channel through which the core explanatory variable affected the dependent variable. Last but not least, we divided China into four regions based on their geographical locations (northeast, east, center, and west) to test the heterogeneous impact of new infrastructure investment in China on the international tourism industry (See Appendix E).
The purpose of these works is to explore to what extent the new infrastructure investment in China has affected the international tourism industry, how new infrastructure investment in China has affected the international tourism industry, and how the impact of new infrastructure investment in China on the international tourism industry has changed in different regions of China. We can draw many novel conclusions through empirical methods, and their practical value lies in providing reliable empirical evidence for policymakers to formulate better investment policies to promote the sustainability of the international tourism industry.
The rest of this study is organized as follows: The second section provides a literature review. The third section introduces the research field and empirical models, defines the main variables, and discusses the data sources. The fourth section summarizes the regression results, robustness checks, mechanism analysis results, and heterogeneity analysis results of the empirical model. Finally, the conclusion and discussion are presented in the fifth section. A technical roadmap is shown in Fig. 1.
2.1 Tourism resource endowment
Tourism resources include natural landscapes, cultural landscapes, historical and cultural heritage, and tourism service facilities. Yang et al. (2010) analyzed the determinants of international tourists visiting China, particularly the World Heritage Sites and various tourist attractions that greatly appeal to foreign tourists. Strengthening cultural heritage protection and improving the quality of cultural heritage sites are crucial for developing the international tourism industry (Carbon et al. 2020). Terkenli et al. (2021) argued that the connection between tourism and landscapes is evident and inseparable. For instance, China is renowned for its long history and cultural assets, and foreign tourists are drawn to its cultural and natural tourist attractions (Bi et al. 2020; Lin et al. 2020; Wang et al. 2020). Richards (2020) believed that the relationship between culture and tourism has become increasingly close over time and that culture has gradually become one of the main contents of tourist experiences. For example, religious activities and attractions in India have brought opportunities for the development of religious tourism (Shinde 2022).
In addition to natural landscapes, cultural landscapes, and historical and cultural heritage, the quality of tourism facilities also affects tourists' satisfaction. When narrowly defined, tourism facilities include hardware facilities for catering, accommodation, transportation, and cultural entertainment. When broadly defined, they also include tourism environments, tourism policies, tourism management, and tourism service personnel (Chao et al. 2021). In general, regions with high levels of economic development have more advanced and complete tourism service facilities, which explains the prosperous tourism markets in developed countries, even though they are not as rich in history and culture as countries such as India and China (Salehi et al. 2021). Khairi et al. (2021) found that Malang is a tourist city in Indonesia that already has a strong tourism service foundation and strong tourism characteristics. Improving its tourism service quality has provided it with more significant opportunities for development than other cities.
2.2 Transportation accessibility
Transportation accessibility is the foundation for tourists to achieve long-distance tourism and an important determining factor for developing the regional tourism industry. Improving transportation can revive declining tourist destinations (Le Klhn et al. 2015; Shafiullah et al. 2019). Scholars have explored the definition, connotation, and constituent elements of transportation accessibility at tourist attractions. Adeola et al. (2020), Calero et al. (2020), and Park et al. (2019) pointed out that transportation accessibility can be defined as the degree of convenience for tourists to reach tourist attractions through various modes of transportation, including factors such as transportation travel time, transportation costs, and transfer efficiency. Khadaroo et al. (2007) analyzed the impact of transportation facilities on tourism demand. They found that the capital stock of air, land, and sea transportation represents the level of development of regional transportation infrastructure, which affects the number of tourists in Mauritius. According to a study by Massidda et al. (2012), the total length of highways plays a vital role in the development of Italy's tourism industry [34]. Another study confirmed that airport infrastructure is an essential factor in attracting more international tourists to the United States (Khoshnevis et al. 2017).
As for the development of high-speed rail, Román et al. (2014) claimed that compared to aviation, high-speed rail is punctual, safe, and saves time. Therefore, they argued that high-speed rail greatly shortens the distances between starting and ending points. In Europe, there is a market complementarity effect between high-speed rail and aviation (Dobruszkes et al. 2014). In Spain, high-speed rail development has positively impacted the international tourism industry, particularly increasing international tourism revenue (Guirao et al. 2016). In Japan, the number of tourists has increased dramatically in cities that are connected to the expanded high-speed rail network (Kurihara et al. 2016). Accordingly, Pagliara et al. (2017) reviewed the impact of high-speed rail on Italy's tourism industry, and substantial evidence suggests that high-speed rail has a positive effect on the number of domestic tourists and overnight arrivals in Italy. However, as argued by Albalate et al. (2017), the opening of high-speed rail has shortened the stays of destination tourists, which may lead to decreases in per capita tourism expenses and decreases in the total tourism revenues of tourist destinations.
China has the world's longest high-speed rail network, and in research on its impact on the tourism industry, Gao et al. (2019) proposed that, as an important way for passengers to travel, it is a modern transportation facility that improves the accessibility of tourist destinations in remote areas. Through an analysis of China's tourism industry at the urban level, Li et al. (2019) concluded that high-speed rail connections increase tourism flow and have a more significant impact on international tourists than domestic tourists. Moreover, the research results of Yao et al. (2022) indicate that both domestic tourism revenue and tourist numbers are positively affected by high-speed rail, and this impact is greater in underdeveloped areas and areas that are less critical geopolitically such as inland or prefecture-level cities. Furthermore, Li et al. (2023) evaluated the impact of high-speed rail on China's economic development. They found significant evidence to support the contribution of high-speed rail connectivity to China's tourism industry.
2.3 Information technology level
Information technology mainly promotes the development of the tourism industry by reducing tourist travel costs and improving the quality of tourism services. With the development of digital technology, tourists increasingly rely on digital technology and information platforms to meet their personalized travel needs (Kumar et al. 2020). The research results of Almeida et al. (2020) demonstrate that communication technology and infrastructure have a positive impact on the development of tourism. With increases in information and communication technology and infrastructure, the number of tourists is also increasing in Africa. Advanced information technology can help tourists search for helpful information, reducing their time, information, and communication costs when interacting with tourism suppliers. Accordingly, tourists obtain information promptly through digital information platforms, minimize the risk of tourism purchases, and have better travel experiences (Seetanah 2019; Pencarelli 2020).
Undeniably, the digital economy has provided new opportunities for the development of the tourism industry. For one, the deep integration of digital technology and the tourism industry promotes a refined division of labor and improves the production efficiency of enterprises (Pan 2022). Enterprises are constantly changing traditional production methods and business models, giving rise to new forms of tourism and providing diverse combinations of elements for product innovation (Gössling 2020), which has led to the tourism market expanding in scope and size (Kayumovich 2020; Perelygina et al. 2022). For instance, Blockchain online travel agencies have changed the market structure of the tourism industry by shifting power from suppliers to consumers (Rashideh 2020), and the exploration and application of artificial intelligence in the tourism industry have enriched diversified forms of tourism and enhanced new experiences for tourists (Van et al. 2021). In addition, Carlisle et al. (2023) claim that digital technology has significantly impacted tourism companies' organizational structures and management capabilities. Further, enterprises may improve their staff's management efficiency and skill level using advanced digital technology and intelligent devices, ultimately achieving massive consumer and tourism revenue.
Based on the above analysis, we proposed the following research hypotheses:
H1
under a certain level of tourism resource endowment, new infrastructure investment significantly impacts international tourism.
H2
transportation accessibility and the information technology level are the mechanisms through which new infrastructure investment affects international tourism.
3.1 Econometric models
In the literature, Gao et al. (2019) and Yao et al. (2023) used a double-difference model to study the impact of opening high-speed rail on China's tourism economy, while Adeola et al. (2020) used a dynamic panel gravity model to examine the relationship between communication technology, infrastructure, and tourism development in Africa. This study referred to the existing literature to establish a benchmark regression model to test the relationship between new infrastructure investment in China and international tourism. Based on the benchmark regression model, we developed a mechanism analysis regression model to examine the impact mechanism of new infrastructure investment in China on international tourism development.
Tourit = α + β1Ninfit + β2Xit + ui + ρt + εit
Medit = α + β1Ninfit + β2Xit + ui + ρt + εit
where Tourit represents international tourism, Medit represents mechanism variables, Ninfit represents new infrastructure investment, β1 is the investment coefficient for new infrastructure, Xit represents a set of control variables, β2 is the coefficient of the control variable, ui is an individual fixed effect, ρt is a fixed time effect, and εit is a random perturbation term.
3.2 Explanatory variables
This study's dependent variable was international tourism. We adopted two indicators to measure it: one was the number of international tourists (Tourist), which was expressed as the logarithm of the number of inbound overnight tourists (Li et al. 2019), and the other was international tourism revenue (Revenue), expressed as the logarithm of the international tourism revenue.
The core explanatory variable used in this study was new infrastructure investment (Ninf), which was expressed as the logarithm of the stock of new infrastructure investment in each region.[1] We first used the perpetual inventory method to calculate the stock of fixed-asset investments based on information infrastructure and innovative infrastructure. Information infrastructure was measured using fixed-asset investments in the information transmission, software, and information technology service industries. Innovation infrastructure was measured using the sum of the fixed assets invested in scientific research, the technical service industry, and health and social work. The perpetual inventory method is the most commonly used method for measuring the stock of material capital; for example, it was used by Cubas (2020) to measure public capital and Zolfaghari (2020) et al. to measure social capital. In October 2003, Qinhuangdao Shenyang Railway, the first passenger-dedicated railway in China, was officially opened, marking the beginning of new infrastructure investments in China. Therefore, we calculated the stock of fixed-asset investment in new infrastructure and the infrastructure stock index with 2003 as the base period. The following calculation formula was used:
Ninfit = Ninfit-1(1-δ) + Inveit
where Ninfit represents province I's total new infrastructure capital stock in year t, Inveit represents the province's new infrastructure investment in that year, and δ is the capital depreciation rate (9.2%).
Next, the entropy method was used to calculate the sum of traditional infrastructure investment (the electricity, heat, gas, water production and supply, water conservancy, environmental and public facility management, transportation, warehousing, and postal industries), information infrastructure investment, and innovation infrastructure investment. Then, the coupling degree of the two, the fusion coefficient, was calculated. Finally, the product of the traditional infrastructure investment and the integration coefficient was adopted to represent infrastructure investment integration. The fusion coefficient was calculated as follows:
Finally, we combined the information infrastructure investment, innovation infrastructure investment, and integration infrastructure investment to obtain the specific infrastructure investment values.
The control variables of this study, including tourism resources (Resource), were measured using the total number of scenic spots (A-level and above). The fixed capital stock of accommodation and catering companies was used to measure tourism capital (Capital). The tourism labor force (Labor) was expressed as the number of employees at each region's star-rated hotels, travel agencies, and tourist attractions. The average investment amounts of foreign-invested enterprises in the various areas measured openness to the outside world (Open). These variables were used in studies by Gao et al. (2019), Li et al. (2019), and Yao et al. (2022).
According to the literature analysis, tourism resource endowment, transportation accessibility, and information technology levels are essential channels that affect international tourism. Therefore, in this study, we selected two mechanism variables: one was transportation accessibility (Turn), which was expressed as the logarithm of passenger turnover (Khadaroo et al. 2007), and the other was the level of information technology (Inter), which was described by the logarithm of the number of Internet broadband access ports (Gössling et al. 2020).
3.3 Data
This study employed data from the 2011 and 2019 provincial panels in China for empirical research. The data covered 31 provincial-level local administrative units in China, while Hong Kong, Macau, and Taiwan were excluded. These data were obtained from the official websites of the National Bureau of Statistics, the Ministry of Industry and Information Technology of China, and the National Railway Administration of China, as well as the China Statistical Yearbook, China Tourism Statistical Yearbook, China Fixed Assets Investment Yearbook, and China Communications Yearbook. Summary statistical data of the relevant variables are presented in Table 1.
|
Variables |
Samples |
Mean |
Std. Dev. |
Min. |
Max. |
|---|---|---|---|---|---|
|
Tourist |
279 |
13.813 |
1.399 |
9.525 |
16.023 |
|
Revenue |
279 |
6.856 |
1.606 |
1.696 |
9.929 |
|
Ninf |
279 |
8.276 |
0.869 |
5.481 |
10.019 |
|
Resource |
279 |
5.413 |
0.778 |
3.367 |
7.324 |
|
Capital |
279 |
6.199 |
1.103 |
2.664 |
7.953 |
|
Labor |
279 |
11.044 |
0.836 |
8.261 |
12.538 |
|
Open |
279 |
0.108 |
0.056 |
0.023 |
0.412 |
|
Turn |
279 |
5.571 |
1.037 |
2.332 |
7.002 |
|
Inter |
279 |
7.105 |
1.044 |
3.285 |
9.052 |
4.1 Benchmark regression
As shown in Table 2, new infrastructure investment had significant positive impacts on inbound overnight tourists and international tourism revenue, and the significance did not change when the control variable was added. Based on the analysis of the results, new infrastructure investment in China was conducive to increasing the number of inbound overnight tourists and international tourism revenue, promoting the development of the international tourism industry. Nevertheless, only openness to the outside world significantly impacted the control variables of the number of inbound overnight tourists and international tourism revenue. In contrast, the other control variables were insignificant, indicating that China's opening-up policy is more important in promoting international tourism development than endowment conditions such as tourism resources, capital, and labor. This result verified hypothesis H1.
| Variables | Tourist | Revenue | ||
|---|---|---|---|---|
| (1) | (2) | (3) | (4) | |
| Ninf | 0.313** (0.122) | 0.373** (0.154) | 0.693*** (0.147) | 0.570*** (0.179) |
| Resource | 0.031 (0.061) | -0.020 (0.071) | ||
| Capital | -0.039 (0.101) | 0.150 (0.118) | ||
| Labor | -0.116 (0.081) | -0.111 (0.094) | ||
| Open | 1.024* (0.523) | 2.800*** (0.611) | ||
| Cons | 11.458*** (0.920) | 12.238 (1.093) | 1.372 (1.108) | 2.687** (1.276) |
| Fixed effects | Yes | Yes | Yes | Yes |
| R² | 0.252 | 0.274 | 0.328 | 0.387 |
| Samples | 279 | 279 | 279 | 279 |
| Note: * p < 0.10, ** p < 0.50, *** p < 0.01. The robust standard error is indicated in parentheses. | ||||
4.2 Robustness testing
The robustness test results are reported in Table 3. Firstly, considering the particularities of Chinese municipalities, although they have the same administrative level, their scale is smaller than that of ordinary provinces. To this end, we changed the sample interval and removed the data from the four municipalities directly under the central government (Beijing, Tianjin, Shanghai, and Chongqing). We recalculated with new samples, and the calculation results remained unchanged (see columns 1 and 5).
Secondly, considering that outliers or extreme values may have affected the accuracy of the estimation results, we applied a 1% tail reduction to the estimated core explanatory variable (Ninf) and then recalculated. The regression results showed that the significance of the core explanatory variable and the control variable was consistent with the baseline regression results (see columns 2 and 6).
Thirdly, to overcome a possible reverse causal relationship between the core explanatory variable and the dependent variable, we referred to the general practice in the existing literature and adopted the second-order least squares method. Due to an inability to find suitable instrumental variables, we selected the lagged period of new infrastructure investment as the instrumental variable for calculation. The estimated results using instrumental variables were consistent with the benchmark regression results. The F-value of the weak instrumental variable test and the P-value of the over-identification test met the requirements, indicating that the estimation results were accurate (see columns 3 and 7).
Fourthly, to reduce the bias of the estimation results, this study used the system GMM estimation method proposed by Blundell et al. (1998) to establish a dynamic regression model and introduced the one-period-lagged dependent variables L.Tourist and L.Revenue. There was no substantial change in the significance of the core explanatory variables in the estimation results of the system GMM. At the same time, the P-values of the AR (2) test and Sargan test of the system GMM were more significant than 0.1, which met the test requirements. This indicated that the selected instrumental variables did not have over-identification problems and were influential. The test results were reasonable and reliable (see columns 4 and 8).
| Variables | Tourist | Revenue | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | ||||
| L.Tourist | 0.765*** (0.025) | ||||||||||
| L.Revenue | 0.924*** (0.009) | ||||||||||
| Ninf | 0.389** (0.175) | 0.399** (0.154) | 0.236*** (0.079) | 0.641*** (0.196) | 0.575*** (0.180) | 0.262*** (0.044) | |||||
| L.Ninf(IV) | 0.418** (0.177) | 0.589*** (0.210) | |||||||||
| Resource | 0.002 (0.096) | 0.029 (0.061) | 0.026 (0.055) | -0.053 (0.038) | -0.020 (0.107) | -0.021 (0.071) | -0.035 (0.065) | 0.007 (0.029) | |||
| Capital | -0.072 (0.119) | -0.049 (0.101) | -0.090 (0.111) | -0.237*** (0.071) | 0.101 (0.133) | 0.147 (0.118) | 0.143 (0.132) | -0.111** (0.048) | |||
| Labor | -0.121 (0.087) | -0.120 (0.081) | -0.149** (0.074) | 0.220*** (0.046) | -0.097 (0.097) | -0.114 (0.094) | -0.110 (0.087) | -0.080* (0.046) | |||
| Open | 1.165** (0.554) | 1.005* (0.522) | 0.974** (0.473) | 1.495*** (0.185) | 3.009*** (0.621) | 2.791*** (0.611) | 2.746*** (0.562) | -0.843*** (0.284) | |||
| Cons | 12.363*** (1.324) | 12.144*** (1.088) | 13.550*** (1.281) | 0.475 (0.418) | 2.034 (1.482) | 2.693** (1.272) | 3.754** (1.522) | 0.053 (0.359) | |||
| Fixed effects | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | |||
| AR(1) | 0.000 | 0.003 | |||||||||
| AR(2) | 0.720 | 0.307 | |||||||||
| Sargan | 0.127 | 0.300 | |||||||||
| R² | 0.291 | 0.277 | 0.978 | 0.425 | 0.328 | 0.976 | |||||
| Samples | 243 | 279 | 248 | 248 | 243 | 279 | 248 | 248 | |||
| F-value | 534.964 | 534.964 | |||||||||
| P-value | 0.000 | 0.000 | |||||||||
| Note: * p < 0.10, ** p < 0.50, *** p < 0.01. The robust standard error is indicated in parentheses, and the output results of the AR (2) and Sargan tests are P-values. The F-value represents the weak instrumental variable test result, and the P-value represents the over identification test result. | |||||||||||
4.3 Mechanism analysis
As seen in Table 4, new infrastructure investment had a significant positive impact on transportation accessibility and the information technology level, and the significance did not change when the control variables were added. Based on the analysis of the results, transportation accessibility and the information technology level were the channels through which new infrastructure investment in China affected international tourism. The mechanism analysis result was consistent with hypothesis H2. This indicates that with the increase in new regional infrastructure investment, the stock of regional transportation infrastructure and the information technology level will continue to improve, which will help increase the number of inbound overnight tourists and international tourism revenue. This research illustrates that transportation accessibility is a mechanistic pathway that affects international tourism, which is consistent with the research findings of Kurihara et al. (2016), Li et al. (2019), and Yao et al. (2022). The level of information technology is a mechanism that affects international tourism, which is similar to the research conclusion of Kayumovich et al. (2020), who found that digital technology helps tourism companies achieve higher profits.
| Variables | Turn | Inter | ||
|---|---|---|---|---|
| (1) | (2) | (3) | (4) | |
| Ninf | 0.143*** (0.053) | 0.271*** (0.067) | 0.471*** (0.059) | 0.327*** (0.073) |
| Resource | -0.000 (0.027) | 0.005 (0.029) | ||
| Capital | -0.101** (0.044) | 0.122** (0.048) | ||
| Labor | -0.064* (0.035) | 0.043 (0.038) | ||
| Open | -0.309 (0.228) | 0.626** (0.249) | ||
| Cons | 4.267*** (0.404) | 4.516*** (0.477) | 2.718*** (0.444) | 2.667*** (0.521) |
| Fixed effects | Yes | Yes | Yes | Yes |
| R² | 0.713 | 0.701 | 0.953 | 0.955 |
| Samples | 279 | 279 | 279 | 279 |
| Note: * p < 0.10, ** p < 0.50, *** p < 0.01. The robust standard error is indicated in parentheses. | ||||
4.4 Heterogeneity analysis
As seen in Table 5, new infrastructure investment, the tourism labor force, and capital significantly impacted inbound overnight tourists and international tourism revenue in the northeast region. However, the level of openness to the outside world had no effect. It is noteworthy that tourism resources did not affect inbound overnight tourists and had a significant positive impact on the international tourism revenue coefficient. In the eastern region, only the level of openness had a significant positive impact on inbound overnight tourists and international tourism revenue, whereas the other variables had no effect. In the central region, new infrastructure investment and tourism capital significantly positively impacted inbound overnight tourists and international tourism revenue, while the other variables had no effect. In the western region, tourism capital significantly impacted inbound overnight tourists but did not affect international tourism revenue. On the contrary, the level of openness to the outside world did not affect inbound overnight tourists but had a significant positive impact on international tourism revenue. The other variables did not affect inbound overnight tourists or international tourism revenue.
Based on the analysis of the results, the impact of new infrastructure investment on international tourism did not pass the significance test in the eastern and western regions, indicating that the driving effect of new infrastructure investment on international tourism was relatively poor in these areas. This was mainly because most provinces in the eastern region of China are coastal areas with advantageous geographical locations, relatively developed economies, and relatively complete infrastructure construction that still lack the driving force for international tourism. However, most provinces in the western region need to be developed, and the infrastructure construction in these areas needs to be revised. Even worse is the harsh geographical environment in western China. Its mountainous plateaus are not attractive to foreign tourists, especially in the presence of transportation accessibility issues. On the other hand, the significance in the northeastern and central regions was relatively good, indicating that new infrastructure investment significantly improved the tourism facilities and environments in these regions of China, thereby promoting the development of the international tourism industry. In conclusion, although new infrastructure investment in China has a positive impact on international tourism, regional differences are apparent.
| Variables | Tourist | Revenue | ||||||
|---|---|---|---|---|---|---|---|---|
| Northeast | East | Midst | West | Northeast | East | Midst | West | |
| Ninf | 1.656** (0.701) | -0.188 (0.248) | 1.862*** (0.617) | -0.006 (0.356) | 2.070** (0.693) | -0.060 (0.314) | 2.535*** (0.633) | -0.098 (0.338) |
| Resource | 0.053 (0.219) | 0.122 (0.076) | 0.026 (0.055) | -0.219 (0.145) | 0.423* (0.240) | -0.062 (0.096) | 0.026 (0.055) | 0.164 (0.138) |
| Capital | -0.867* (0.418) | 0.035 (0.280) | 0.026* (0.055) | -0.339** (0.179) | -0.839* (0.413) | -0.385 (0.356) | 0.026** (0.055) | -0.225 (0.171) |
| Labor | -0.491** (0.243) | -0.080 (0.223) | 0.165 (0.127) | -0.126 (0.134) | -0.599** (0.240) | 0.111 (0.283) | 0.201 (0.130) | -0.180 (0.128) |
| Open | 1.280 (4.782) | 1.571* (0.841) | 1.051 (1.449) | 1.156 (0.788) | -3.849 (4.724) | 3.119*** (1.066) | 1.545 (1.487) | 2.691*** (0.749) |
| Cons | 10.819 (6.548) | 16.192*** (2.461) | 1.454 (3.009) | 16.668*** (2.238) | -0.535 (6.469) | 9.369*** (3.121) | -6.120* (3.009) | 8.015*** (2.129) |
| Fixed effects | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| R² | 0.729 | 0.364 | 0.586 | 0.439 | 0.763 | 0.254 | 0.684 | 0.737 |
| Samples | 27 | 90 | 54 | 108 | 27 | 90 | 54 | 108 |
| Note: * p < 0.10, ** p < 0.50, *** p < 0.01. The robust standard error is indicated in parentheses. | ||||||||
5.1 Conclusions
This study sheds light on the relationship between new infrastructure investments in China and the development of the international tourism industry concerning overnight inbound tourists and international tourism revenue. During the research process, we evaluated new infrastructure investments in various regions of China and conducted empirical tests using panel data from 31 provinces and cities between 2011 and 2019. Draw the following conclusions:
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The empirical results show that the coefficients of new infrastructure investment's effects on inbound overnight tourists (5% level) and international tourism revenue (1% level) and the coefficients of the effects of external openness on overnight inbound tourists (10% level) and international tourism revenue (1% level) were significantly positive. Accordingly, new infrastructure investment in China and the level of openness had positive effects on the development of international tourism.
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Mechanism analysis showed that the coefficients of new infrastructure investment's effects on transportation accessibility (1% level) and the information technology level (1% level) were significantly positive. Hence, it was confirmed that transportation accessibility and the information technology level are mechanism variables that affect international tourism. With the increase in investments in new infrastructure, the regional transportation environment has improved. The level of regional information technology continues to improve and has become a mechanism for promoting the development of international tourism in China.
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The heterogeneity analysis results suggest significant regional differences in the impact of new infrastructure investment in China on international tourism development. Specifically, the coefficients of new infrastructure investment's effects on inbound overnight tourists and international tourism revenue were insignificant in the eastern and western regions. In the northeastern region, the coefficients of new infrastructure investment's effects on inbound overnight tourists (5% level) and international tourism revenue (5% level) were significantly positive. In the central region, the coefficients of new infrastructure investment's effects on inbound overnight tourists (1% level) and international tourism revenue (1% level) were significantly positive.
5.2 Inspiration
Chinese policymakers should pay more attention to these research conclusions, introduce targeted and differentiated policies, and promote balanced regional economic development. For example, it would be beneficial to improve the tourism industry model in the developed eastern region, leverage the driving effect of new infrastructure investment on the tourism industry, further tap into the potential of the tourism industry, increase financial investment in the underdeveloped western regions, and develop and improve new infrastructure construction projects. Furthermore, the investment strategy can not only increase fiscal budget funds, but also through state-owned capital investment, real estate investment trust fund, and guiding private capital into the new infrastructure sector, etc.
5.3 Contribution
Most existing literature has analyzed the impact of high-speed rail, information technology, and infrastructure on the tourism industry. Compared with many existing studies, the novelty of this study lies in exploring the impact of new infrastructure investment in China on international tourism, which is a new topic in the development of international tourism. Correspondingly, we evaluated the stock of new infrastructure investment in China, which can reflect the accessibility of transportation and the degree of information construction. In addition, a clear and important contribution of this study is that the impact of infrastructure investments in China on international tourism development is constrained by factors such as the regional economic level, geographical location, and tourism resources, with significant heterogeneity. This means that the strategy for making new infrastructure investments should be friendly and sustainable.
5.4 Limitations
This study had certain limitations. Firstly, we evaluated new infrastructure investments in China based on the existing literature, which reflect the accessibility of transportation and the level of information technology in this region. Unfortunately, there were no relevant data on the specific values of new infrastructure investment. Thus, we applied the internationally recognized perpetual inventory method to calculate investment stock based on the infrastructure classification data provided in the China Statistical Yearbook. This method is only for evaluating data, and the results may differ from the actual data. Thus, efforts should be made in subsequent research to find better methods to evaluate this indicator. Secondly, to measure international tourism development, we chose two indicators: the number of overnight tourists and international tourism revenue. However, they only represent one aspect of the international tourism industry, and factors such as international tourist preferences and consumption habits are also important factors affecting the development of international tourism. Obtaining these data to enrich this research is another goal we need to work towards. Thirdly, exploring how macro policies (financial policies, foreign trade policies) affect the relationship between new infrastructure investment and international tourism is of greater reference value for improving investment policies. Future research requires additional experiments, such as selecting macroeconomic policies as moderating variables and attempting to establish a regression model with moderating variables as testing tools.
Author Contribution
This paper is a joint work by the two authors. S.S. is responsible for the overall framework and content of the article, P.L. completed the main content of the article, including data collection, model inference, result analysis, and conclusion.
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- Note: New infrastructure investment included 5G technology, artificial intelligence, big data centers, industrial Internet, ultra-high-voltage transmission, intercity high-speed rail and rail transit, and new-energy vehicle charging stations. These investments eventually form society's fixed-asset investment stock, so we calculated the new infrastructure investment stock according to the sub-item evaluation of fixed-asset investments in the China Statistical Yearbook.
No competing interests reported.