high speed rail tourism

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here .

Loading metrics

Open Access

Peer-reviewed

Research Article

Impact of high-speed rail on tourism in China

Contributed equally to this work with: Kehan Shi, Jinfang Wang

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

Affiliation Institute of Industrial Economics of CASS, Beijing, China

Roles Formal analysis, Methodology, Resources, Software, Validation, Visualization, Writing – review & editing

Affiliation School of Economics and Management, Beijing Forestry University, Beijing, China

Roles Conceptualization, Data curation, Investigation, Project administration, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation School of Economics and Management, Jiangxi Agricultural University, Nanchang, Jiangxi, China

Roles Data curation, Investigation, Writing – original draft

Affiliation College of Agriculture, Guangxi University, Nanning, China

• Kehan Shi, 
• Jinfang Wang, 
• Xiaojin Liu, 
• Xiaoying Zhao

• Published: December 8, 2022
• https://doi.org/10.1371/journal.pone.0276403
• Peer Review
• Reader Comments

The “time-space compression” effect of high-speed rail (HSR) has effectively improved the accessibility of the cities and has had a profound impact on tourism. This study explores the impact of HSR on tourism development in cities along HSR lines from the perspective of transfer of transport advantages, then evaluates the impact of HSR on tourism development using panel data of 286 cities in China from 2005 to 2013 by the difference-in-differences (DID) method. The empirical results show that the opening of HSR has significantly increased the tourism revenue and tourist arrivals. These results are still holds after considering endogenous HSR lines placement, and by various robustness checks. Further analysis of nodal effect shows that node cities experienced greater growth in tourism revenue than non-node cities. The analysis of mechanism found that tourism development in node cities relied on hotel industry, while tourism development in non-node cities relied on scenic spots industry. The findings of this study validate the role of HSR as a catalyst for urban tourism development, and reveal the comparative advantages of tourism in different cities under the influence of HSR. This study has important reference value for the development of tourism industry policies in cities along and around HSR lines.

Citation: Shi K, Wang J, Liu X, Zhao X (2022) Impact of high-speed rail on tourism in China. PLoS ONE 17(12): e0276403. https://doi.org/10.1371/journal.pone.0276403

Editor: Jun Yang, Northeastern University (Shenyang China), CHINA

Received: June 30, 2022; Accepted: October 5, 2022; Published: December 8, 2022

Copyright: © 2022 Shi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data underlying the results presented in the study are available from National Bureau of Statistics of the People's Republic of China ( http://www.stats.gov.cn/ ) and CRAD database of Chinese research data services platform ( https://www.cnrds.com/ ).

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Transportation infrastructure is an important connection between tourist sources and tourist destinations. As a representative of modern transportation infrastructure, the "time-space compression" effect of high-speed rail (HSR) weakens the impediment of spatial and temporal distance to the diffusion and concentration of tourism flows [ 1 ]. It provides a new impetus for tourism development. Since the first HSR in China (Beijing-Tianjin Intercity Railway) opened in 2008, China’s HSR has developed rapidly. By the end of 2021, China had nearly 41,000 km of HSR in operation. Approach 93% of cities that population over 500,000 connected by HSR. It has become the main means of transportation for travelers [ 2 ]. In this context, Chinese local governments have attempted to use HSR to drive local tourism development. For example, regions along HSR, such as Sichuan, Jiangxi, and Yunnan, have successively established tourism alliances to jointly build tourism brands based on HSR connections to enhance regional tourism attraction.

At present, scholars have discussed a lot about the relationship between HSR and the development of tourism. Yang and Li [ 3 ] found that HSR significantly promoted the growth of the inbound tourism market. Zhang et al. [ 4 ] found that HSR promoted the growth of the domestic tourism market. Xin and Li [ 5 ] subdivided HSR into intercity HSR and non-intercity HSR, and found that intercity HSR could not promote the development of urban tourism, but could enhance the contribution of non-intercity HSR to urban tourism development. Wei et al. [ 6 ] verified the positive impact of HSR on urban tourism from the industrial efficiency. In addition, scholars have also found time lags [ 7 ] and persistence [ 8 , 9 ] in the impact of HSR on urban tourism development. While most of the literature supports the view that HSR can promote the development of tourism, some scholars argue that this growth is only a level effect, not a rate effect, and suggest that only a small proportion of cities’ tourism development can benefit from HSR construction [ 10 ].

The impact of HSR on urban tourism stems from the improvement of urban accessibility [ 11 ], which may be positive or negative. On the one hand, improved urban accessibility reduces travel resistance for tourists and releases significant tourism demand, but it also widens the travel time gap between tourists and destinations [ 12 ], so HSR will only have a positive impact on tourism if urban tourism appeal increases as a result of improved accessibility [ 13 ]. On the other hand, improved urban accessibility helps to weaken the geographical constraints on factor mobility, accelerating the concentration of tourism factors [ 14 ] and promoting the formation of tourism centres [ 15 ]. However, it has also been suggested that the relationship between tourism factor concentration and tourism development is not simply a positive one, and that excessive concentration of tourism enterprises and tourism labour can reduce the efficiency of the tourism industry [ 16 ]. As for the heterogeneity of the influence of HSR on the development of urban tourism, most of the existing literature attributed the reasons to the differences in urban socio-economic characteristics such as the geographical distribution [ 5 ], economic scale [ 17 ], resource endowment [ 18 ], city size [ 9 ], and level of administrative division [ 10 ], emphasizing the influence of Matthew effect, filtering effect, diffusion effect and superposition effect [ 19 ]. In fact, the impact of a city’s tourism development from the HSR is closely related to the period of its construction. Existing literature shows that HSR mainly shows siphon effect in the formation period of main trunk line, and diffusion effect in the completion period of branch lines [ 20 ]. Kong and Li [ 21 ] also found that the strength of the promotion effect of HSR on urban tourism was related to whether the city was an original station or not.

The impact of HSR on tourism development is essentially the result of the adjustment of tourism resources configuration due to the change of transportation advantages [ 1 , 15 ]. Chen et al. [ 22 ] elaborated this logic more clearly. They pointed out that the transportation network provides a supporting connection for urban connectivity and promotes cities to participate more in a wider range of factor flows relying on the "flow space". However, the "flow space" has nodes and centers, with cities having a hierarchy of influence [ 23 ]. The node level of cities in the HSR network determines its transportation advantage, and further determines its influence in the "flow space". Yin et al. [ 24 ] analyzed the influence of the improvement of HSR network on the tourism attraction of cities, and found that the greater the network density, the higher the tourism attraction of regions. Li et al. [ 25 ] confirmed the existence of node effect, but did not further discuss the mechanism of node effect.

To sum up, the existing research has conducted various discussions on the influence of HSR opening on tourism development. However, it has shown the following limitations: First, more literature analyzed the level effect of HSR on the economic growth of tourism, and rarely discussed the rate effect, so the contribution of HSR to tourism may be overestimated. Second, few literature discuss the reasons for heterogeneity of the impact of HSR on tourism development based on the differences in HSR factors, so it is difficult to explain the differences in the role of HSR at different stages of construction. Third, the existing literature is mostly localized case studies on individual HSR lines, with the findings implying obvious regional characteristics, while possibly being influenced by other cross lines and poor generalization representation. In order to make up for the lack of available literature, this paper attempts to extend the existing research from the following aspects: Firstly, we use incremental values to measure changes in the tourism economy to examine the rate effect of HSR on tourism economic growth. Secondly, based on the point-axis theory, we constructed the mechanism of node effect, and divided cities into node and non-node cities based on the number of HSR lines passing through the city to test the node effect. Thirdly, we collated data on all HSR line in China since 2008 and conducted empirical tests on a sample of 286 cities in China to avoid possible regional limitations of the study findings and estimation bias caused by the omission of cross lines. The results show that HSR has a rate effect on tourism economic growth, but the effect strength is lower than the level effect estimated by the existing literature. Furthermore, the existence of the node effect makes different cities have different comparative advantages in the divided tourism industry. Our findings have good policy reference value for the local governments of HSR cities in promoting tourism development in the context of HSR network operations.

Framework and hypothesis

Time-space compression effect.

Distance decay rate and destination accessibility are important factors affecting the implementation of tourism activities [ 26 ]. For tourists, the spatial distribution probability of tourism demand is related to the distance between the tourist destination and source. When the travel time becomes the main influencing factor of tourists’ destination choice, the distribution probability gradually decreases with the distance [ 1 ]. For tourist destinations, the accessibility of the city is a prerequisite for ensuring that tourism activities can be carried out successfully. Fig 1 illustrates the impact of HSR on "time-space distances" between cities. When the spatial distance between cities is fixed, the opening of HSR shortens the commuting time between cities on the basis of improving the accessibility of cities ( Fig 1A ). When the commuting time to the destination city is fixed, the opening of HSR makes the spatial structure between cities more compact ( Fig 1B ). The emergence of HSR, on the one hand, weakened the negative impact of distance decay rate on tourism demand [ 27 , 28 ], providing the possibility of transforming potential tourism demand into real tourism demand. On the other hand, it has enhanced the accessibility of cities along the route [ 29 ], which is conducive to expanding tourist source market [ 30 ] and increasing tourist attractiveness [ 31 ].

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

(a) Time compression and (b) Space compression.

https://doi.org/10.1371/journal.pone.0276403.g001

In addition, the HSR has accelerated the process of inter-regional tourism cooperation. The integration of tourism resources through regional cooperation is conducive to the creation of high-quality tourism products and itineraries and the formation of tourism economic corridors [ 32 ]. Tourists will also give priority to cities along HSR lines when choosing destinations to reduce the time and cost spent on transportation and maximize tourism utility [ 33 ]. Therefore, the following hypotheses are proposed:

• H1: Given other conditions being equal, the opening of HSR is positively correlated with tourism revenue in cities along the route.
• H2: Given other conditions being equal, the opening of HSR is positively correlated with tourist arrivals in cities along the route.

Nodal effect

Based on the pole-axis theory, economic centres usually initially formed in a few well-located areas, and form a point-axis spatial structure by connecting with the surrounding areas through transportation routes [ 34 ]. Then industry sectors are clustered and growing in economic centres. After that, the product flow, capital flow, labor flow, technology flow, information flow, policy flow, etc. spreads to the surrounding areas through transportation routes, and regroups in the areas with sub-optimal locational conditions [ 15 ]. For tourism, the spatial distribution of tourism centres is also inextricably linked to transport factors [ 35 ]. Fig 2 draws the logical framework for the impact of HSR on urban tourism development based on the pole-axis theory.

https://doi.org/10.1371/journal.pone.0276403.g002

The nodal effect of HSR reinforces the agglomeration effect of old tourism centres [ 36 ] and gives some cities that were less accessible the opportunity to develop into new tourism centres [ 37 ]. The nodal effect may cause heterogeneous development of urban tourism because of the different roles and benefits played by tourism centres and non-tourism centres in tourism activities [ 38 , 39 ]. Node cities become distributing centres on travel routes, taking on a transit role, promoting tourism development by gathering more tourist flow and tourist consumption activities. Non-node cities become the radiating areas of the tourism centres. Their tourism development benefits from the increased local accessibility on the one hand [ 40 ], and the diffusion effect from the tourism centres on the other [ 41 ]. Furthermore, the differentiation between the tourism centre and the non-tourism centre is closely related to the development of the tourism subdivided industry [ 42 ]. The impact of HSR has led to differences in the positioning of cities’ tourism functions. The subdivided industries matching the cities’ tourism function positioning forms a comparative advantage while influencing the tourism consumption structure, ultimately leading to heterogeneous tourism development in cities along the route. This gives rise to the following testable hypotheses:

• H3: Given other conditions being equal, HSR has a greater positive impact on tourism revenue in node cities than in non-node cities.
• H4: Given other conditions being equal, HSR has a greater positive impact on tourist arrivals in node cities than in non-node cities.

Methods and data

Econometric model.

Where subscripts i and t denote city and year, respectively. Tour it represents tourism development. HSR it is a dummy variable indicating whether the city has an HSR in operation. Node it denotes whether the city is a node city. CONTROL it represents a vector of control variables. μ i and λ t indicate city fixed effects and time fixed effects. ε it is random disturbances. Firstly, we use Eq ( 1 ) to test the average effect of the impact of HSR on urban tourism development. Secondly, we use Eq ( 2 ) to capture the nodal effect of HSR impact on tourism. To avoid possible cross-sectional correlation, time-series correlation, and heteroscedasticity between cities, we use robust standard errors clustered at the city level. in all regressions.

Variable selection

Dependent variables..

In this study, we use two variables to measure tourism development ( Tour ), tourism revenue ( TR ) and tourist arrivals ( TA ). In the robustness testing section, dependent variables will be replaced by domestic tourism revenue ( DTR ) and domestic tourist arrivals ( DTA ). To test the rate effect of HSR on tourism development, all dependent variables use incremental data. For example, TR is calculated by subtracting the total tourism revenue in period t-1 from the total tourism revenue in period t .

Key independent variable.

The key independent variable we are interested in here, HSR , is a dummy variable that if the city opened HSR in period t , it takes the value of 1 in t and subsequent periods and 0 otherwise. Considering that there is a time lag in the impact of HSR on tourism development, we treat HSR that open in the first half of the year as opening in the current year and those that open in the second half as opening in the following year.

Moderator variables.

To test the heterogeneity influence of HSR on tourism development caused by nodal effect, we construct a dummy variable ( Node ), which takes the value of 1 if the city with 2 or more HSR lines and 0 otherwise, as a moderating variable.

Mechanism variables.

Differences in the positioning of tourism functions mean that the supply structure of tourism products differs from city to city. We focus on the two main categories of tourism product supply, including food and accommodation, and sightseeing.

• Food and accommodation supply capacity ( FASC ). The number of hotels can reflect a city’s capacity to meet the food and accommodation needs of tourists. Therefore, we select the number of star-rated hotels to measure FASC .
• Sightseeing supply capacity (SSC). The ability of a city to meet the sightseeing needs of tourists is linked to the amount of high-quality tourism resources it possesses. Therefore, we select the number of China’s 5A scenic spots to measure SSC .

Control variables.

Drawing on existing research, we control for a range of city-level factors that may affect tourism development.

• Economic scale ( ES ). The larger the city’s economy, the better the foundation for tourism development. TS is measured by the natural logarithm of gross regional product.
• Fiscal expenditure ( FE ). Higher fiscal expenditure means better urban infrastructure development, which helps to improve the tourism reception capacity of the city [ 8 ]. FE is measured by the natural logarithm of local government expenditure.
• Transportation convenience degree ( TCD ). The cities with higher accessibility are more attractive to tourists, but it may have a negative impact on tourism revenue due to increased passenger mobility [ 5 ]. TCD is measured by the natural logarithm of highways passenger traffic.
• Industrial structure ( IS ). Industrial structure change means the redistribution of production factors among different sectors, which affects the scale and the mode of economic growth, and in turn changes the disposable income and consumer awareness of potential source markets for tourism, ultimately affecting tourism development in terms of both the scale and structure of demand. We follow Wei et al. [ 6 ] use the industrial structure index to measure IS .
• Service industry support ( SIS ). As one of the most interrelated service industries, the development of tourism depends on the support of other related industries. The scale of tertiary industry employees can reflect the overall supply capacity of the urban service industry. SIS is measured by the natural logarithm of the number of employed persons in the tertiary industry.
• Population size ( PS ). Population size changes can influence tourism demand from local sources. Usually, the larger the population size, the higher demand for tourism from the local area [ 44 ]. PS is measured by the natural logarithm of the city’s resident population.
• Income level ( IL ). Income is the basis and prerequisite for consumption, and usually the higher the income, the higher the consumption. This means that cities with high income levels usually receive more tourism revenue [ 45 ]. IL is measured by the natural logarithm of the average wage of employed persons.
• Opening degree ( OD ). The degree of opening facilitates the tourism industry to actively participate and share the information, technology, resources, and other valuable factors brought by internationalization. Usually, the higher degree of external openness, the higher the inbound tourism revenue of the region. OD is measured by the proportion of FDI in the GDP.

Data description

China’s first HSR construction started in 2005 and operated in 2008. To 2013, China’s HSR operating mileage reached the total mileage of other countries combined. The main framework of China’s HSR network has basically taken shape. After 2013, China’s HSR networked layout has been further improved and cities have introduced industrial policies regarding “all-for-one tourism” strategies. To avoid the interference of these industrial policies in testing the impact of HSR on tourism development, the time window was set at a golden period of China’s HSR development from 2005 to 2013. Data on tourism revenue and tourist arrivals are from China Statistics Yearbook for Regional Economy. Data on HSR opening times and HSR line information are from Chinese High-speed Rail and Airline Database (CRAD) in Chinese Research Data Services Platform (CNRDS). The data of HSR opening times and HSR lines in CRAD database are mainly from the construction and opening of HSR lines published by China State Railway Group Co., Ltd. since 2003. The rest of the city-level macroeconomic data are mainly sourced from the China City Statistical Yearbook, with some missing data supplemented from each city’s Statistical Communique on National Economic and Social Development and each city’s Statistical Yearbook. After removing the samples with serious data missing, we finally used the panel data of 286 cities, including four municipalities of Beijing, Tianjin, Shanghai, and Chongqing. The number of sample observations is 2574. The descriptive statistics of the above variables are shown in Table 1 .

https://doi.org/10.1371/journal.pone.0276403.t001

Benchmark regression results

Table 2 presents the estimation results of the benchmark regression based on Eq ( 1 ). After controlling for both city fixed effects and time fixed effects, the coefficients on HSR were all significantly positive at the 1% statistical level (columns (1) and (3)). The direction and significance of the coefficients on HSR did not change after further inclusion of control variables (columns (2) and (4)). These results indicate that the opening of HSR has the rate effect on tourism revenue and tourist arrivals. In terms of economic significance, the opening of HSR increases tourism revenue increment and tourist arrivals increment in cities along HSR lines by 1.18% (0.313/26.420) and 9.03% (0.221/2.448) on average. H1 and H2 are proven.

https://doi.org/10.1371/journal.pone.0276403.t002

The estimation results of the control variables are generally consistent with the established research. The economic scale of cities is positively correlated with tourist arrivals increment. The service industry support and population size of cities are positively correlated with tourism revenue increment. The degree of transportation convenience is negatively correlated with tourism revenue increment. Fiscal expenditure is negatively correlated with tourism revenue increment and tourist arrivals increment. Fiscal expenditure represents the degree of government intervention in the economy [ 5 ]. Although the original intention of government intervention is to play the macroeconomic control role of the government, it often produces excessive administrative intervention in the market and enterprises of tourism, which retards the basic role of the market in resource allocation and leads to the hindrance of the tourism economy. HSR, on the other hand, may further strengthen the administrative intervention of local governments in tourism.

Parallel trend and dynamic effect

The average treatment effect of HSR affecting tourism development was analyzed in the previous section, but the year-on-year impact of HSR opening could not be observed from it. Moreover, the DID implies an important premise that tourism development in the sample cities should have parallel temporal trends if the HSR had not been in operation. Therefore, it is necessary to analyze the changes each year before and after the opening of HSR in the sample cities, which can observe the annual treatment effect of HSR opening on urban tourism development on the one hand, and the rationality of the identification strategy can be verified on the other hand.

Where θ − τ denotes the treatment effect in τ periods before the opening of HSR; θ τ denotes the treatment effect in τ periods after the opening of HSR; θ 0 denotes the treatment effect in the period of HSR opening. The rest of the variables have the same meaning as in Eq ( 1 ).

Fig 3A and 3B plots the average treatment effect of HSR on tourism revenue increment ( TR ) and tourist arrivals increment ( TA ) in periods before and after HSR opening and the 95% confidence interval of θ -τ , θ + τ , and θ 0 , respectively. As shown in Fig 2A and 2B , we can accept the null hypothesis of the consistent time-varying trends of TR and TA in both the HSR and non-HSR cities before the initiation of operations.

https://doi.org/10.1371/journal.pone.0276403.g003

We can also find that the enhancement of TR in cities along HSR lines by opening of HSR is not only significant in the period of opening but also significant at a 5% level for 8 years after the opening ( Fig 3A ). The intensity of the impact will increase over time and eventually remain stable. This means that there is a temporal continuity in the impact of HSR on tourism revenue growth. The annual treatment effect of HSR opening on TA ( Fig 3B ) is different from that on TR . The opening of HSR significantly increases tourist arrivals in cities along HSR lines only in the short term (T≤t+5). This may be attributable to the fact that at the beginning of the opening of HSR, the cities along HSR lines had the transportation location advantage and the tourism attractiveness of the cities was greatly enhanced, and the number of tourist arrivals increased significantly. However, as the density of the HSR network increases, the highway transportation support between cities along HSR lines and non-HSR cities gradually improved. The co-opetition effect of HSR and other transportation begins to manifest. The number of tourists to non-HSR cities increases, and the transportation location advantage of cities along HSR lines gradually disappears, which eventually leads to the promoting effect of the opening of HSR on the tourist arrivals becoming less significant in the long run.

Results from the IV method

In addition to satisfying the assumption of parallel trends, the use of the DID to estimate the impact of HSR opening on tourism also requires the satisfaction that the opening of the HSR is exogenous, i.e., whether cities have HSR should be random and should not be disturbed by other measurable or unmeasurable factors that can affect tourism development. But this may not be the case. The main purpose of HSR construction is to reduce travel times between central or large cities, so economically and politically important cities are more likely to have HSR. In addition, omitted variables may affect both the opening of HSR and the development of tourism in the city, which would have led to biased estimates of the parameter estimates. For example, an official with a strong desire to develop local tourism may lobby hard for his city to become a HSR city; cities with advantageous tourism resource endowment are also more likely to be prioritized for HSR cities, etc. These uncontrollable and unobservable variables subject the HSR opening to endogeneity, which results in biased and inconsistent coefficient estimates of the variables and affects the accuracy of the estimation results in this study. We attempt to select appropriate instrumental variables to address the endogeneity of HSR opening in multi-period DID.

Where IVHSR it is an instrumental variable of HSR it constructed with the straight-line strategy, and it takes the value of 1 when the city is located on straight lines between end cities in year t and 0 otherwise. Other variables and symbols have the same meaning as in Eq ( 1 )

Table 3 reports the results of 2SLS estimations based on Eq ( 4 ). The results of the first stage show that the instrumental variable ( IVHSR ) is significantly and positively correlated with the endogenous variable ( HSR ) at a 1% level (column (1)). The F-statistic of 481.12 is much larger than the critical value of 10. These results indicate that the instrumental variable has a strong explanatory power for the endogenous variable. The results of the weak identification test show that the Cragg-Donald Wald statistic is much larger than the critical value corresponding to the tolerance of 10% distortion provided by Stock and Yogo [ 47 ], which indicates that IVHSR is not a weakly instrumental variable. The results of the underidentifiability test show that the Anderson LM statistic rejects the null hypothesis of "underidentifiable of instrumental variables" at a 1% level. Columns (2) and (3) report the results of the second stage estimation with TR and TA as dependent variables, respectively. It can be found that the coefficients of HSR on TR and TA are still significantly positive at a 1% level, and the coefficient values are larger compared to the estimated results in Table 2 . It shows that after relieving the endogeneity of HSR lines placement, the opening of HSR still significantly boosts tourism development. The conclusions of this study remain unchanged.

https://doi.org/10.1371/journal.pone.0276403.t003

Robustness test

Replacement of dependent variables..

The dependent variables TR and TA used in benchmark regression both include the domestic tourism market and the inbound tourism market. In fact, the impact of HSR on tourism development is mainly focused on the domestic tourism market, while the inbound tourism market is more vulnerable to the airline system. Therefore, there may be some deviation in identifying the impact of HSR on tourism development using aggregate indicators. Accordingly, we re-estimated Eq ( 1 ) using domestic tourism revenue increment ( DTR ) and domestic tourist arrivals increment ( DTA ) as the dependent variables. The results show that the direction of coefficient effects and statistical significance of the main test variables remain consistent with the main regression results in Table 2 after replacing the dependent variables (columns (1) and (2) of Table 4 ).

https://doi.org/10.1371/journal.pone.0276403.t004

Eliminate the impact of national central cities.

As mentioned earlier, HSR aims to connect central cities with important economic and political functions. Central cities have an advantage over neighboring cities in terms of access to resources, which makes them more conducive to tourism development [ 18 ]. The sample used in benchmark regression includes national central cities, which may affect the identification of the impact of HSR on tourism development. For this purpose, we re-estimated Eq ( 1 ) using the sample that excludes national central cities. The results show that the main empirical findings of this study have not changed (columns (3) and (4) of Table 4 ).

Changing the time window of data.

One may argue that estimation using data with shorter pre-processing periods gives more accurate results. The reason is that the longer the pre-processing period, the more likely the data will contain other noise shocks. During the period 2005–2013, China’s tourism industry was affected by two shocks. The first was the formal proposal by the State Council in 2006 to make tourism a strategic pillar industry and the impact of tourism expanded across the board. The second was the gradual reform of the tourism market with the implementation of the Tourism Law of the People’s Republic of China in 2013. To reduce these interferences, we re-estimated Eq ( 1 ) narrowing the time window to 2006–2012. The regression results show that the findings are still robust after shortening the pre-processing period of the data (columns (5) and (6) of Table 4 ).

Heterogeneity analysis and mechanism identification

Heterogeneity analysis.

We estimated Eq ( 2 ) to analyse the heterogeneity impact of HSR on tourism caused by the nodal effect. The estimation results are presented in Table 5 .

https://doi.org/10.1371/journal.pone.0276403.t005

In Table 5 , we find that the coefficient of HSR*Node is 0.588 and significant at a 1% level (column (1)). This indicates that the impact of HSR on tourism revenue is significantly different between node cities and non-node cities. It is easier for node cities to transform the location advantages brought by HSR into tourism industry development advantages. The coefficient of HSR*Node on tourist arrivals increment ( TA ) is not significant (column (2)). From the previous theoretical analysis, the opening of HSR increases the accessibility of non-node cities and strengthens the function of nodal cities as tourism distributing centres. Tourists visiting node cities may also visit the surrounding cities along HSR lines. Due to the superimposed effect, the growth of tourist arrivals in the cities along HSR lines may not be lower than that in node cities. Therefore, the nodal effect does not cause the heterogeneity impact of HSR on tourist arrivals. Comparing the results in columns (1) and (2) of Table 5 with columns (2) and (4) of Table 2 , respectively. It can be observed that the coefficients of HSR are significantly smaller in all regressions after considering the nodal effect. The coefficient of HSR*Node is significantly positive at a 1% level in the regression of TR (column (1) of Table 5 ). This result shows that the nodal effect of the opening of HSR is mainly reflected in the rate effect on tourism revenue. In terms of economic significance, when cities along HSR lines become node cities, the HSR will increase the increment of tourism revenue by 0.588. This is equivalent to 187.86% of the average marginal effect of the opening of the HSR on tourism revenue growth (0.588/0.313). The above results support H3.

Mechanism identification

The previous theoretical analysis shows that the opening of HSR forms new transportation network nodes, which causes the transfer of transportation location advantages, and eventually the transportation location advantages are transformed into tourism development advantages. Cities along HSR lines evolve into tourism centres and non-tourism centres, respectively. In this case, the positioning of tourism functions in each city will be different, and the tourism subdivided industry which matches the functional positioning will gain the comparative advantage. If HSR is used blindly for tourism development, it may lead to an irrational allocation of tourism production factors, which in turn may hinder tourism growth. According to the theoretical framework presented in the previous section, node cities are more likely to develop into tourism centres, while non-node cities and other cities without HSR will become non-tourism centres. From the tourism demand perspective, node cities with convenient transportation and good facilities are more appropriate as distributing centres and transit areas for tourists, while non-tourism centres with abundant tourism attractions are better able to meet tourists’ needs for excursions and sightseeing. Therefore, tourism consumption in node cities may be dominated by accommodation, dining, shopping, and entertainment, while non-node cities will be dominated by sightseeing. To verify the above conjecture, we divide the samples into node and non-node cities. Then we perform group regression using the number of star-rated hotels measuring the capacity of the food and accommodation supply, and the number of China’s 5A scenic spots measuring the capacity of the sightseeing supply. This section uses samples from 2007 onwards for the regressions because China’s 5A scenic spots assessment was first carried out in 2007. The results are presented in Table 6 .

https://doi.org/10.1371/journal.pone.0276403.t006

The results in columns (1) and (3) of Table 6 show that the coefficient of SSC on TR is not significant in node cities, but significantly positive at a 1% level in non-node cities. The results in columns (2) and (4) of Table 6 show that the coefficient of FASC on TA is significantly positive at a 1% level in node cities, but not has a significant effect in non-node cities. The results show that the construction of hotel industry produces a significant boost to tourism development in node cities, while the construction of scenic spot industry produces a significant boost to tourism development in non-node cities and confirm the validity of the previous theoretical derivation. This also provides a theoretical basis and empirical evidence for how cities along the route can rationalize the layout of the tourism industry, as well as having important policy implications.

Conclusions and discussion

Based on the panel data of 286 cities in China from 2005 to 2013, this study empirically analyzes the impact of HSR on tourism development and heterogeneity caused by the nodal effect by using the DID method. It is revealed that: (1) The impact of HSR opening on tourism revenue and tourist arrivals growth are significant positive. (2) There has a significant nodal effect of HSR on tourism development, and the positive effect of HSR on tourism revenue growth is significantly higher in node cities than in non-node cities. (3) The results of the mechanism analysis show that the tourism development in node cities depends on hotel construction, while on scenic spot industry in non-node cities. These findings validate the theoretical hypotheses of this study. The opening of HSR not only improves the accessibility of non-node cities, but also strengthens the function of tourism distributing centres in node cities. Tourism revenue and tourist arrivals in both nodal and non-node cities are enhanced. The growth effect of node cities is higher than that of non-node cities in terms of tourism revenue, as tourism distributing centres may attract more tourism consumption. The results of the mechanism analysis provide evidence that the nodal effect leads to differences in the functional positioning of tourism in cities along HSR lines. As the influence of spatial and temporal distance on tourism activities gradually weakens, most tourists choose tourism centres (node cities) with convenient transportation and well-developed infrastructure as the base for accommodation and transit points throughout the journey. The hotel industry, which caters to the food and accommodation needs of tourists, has become an important driving force in supporting tourism development in such cities. However, non-tourism centres (non-node cities) tend to have high-quality tourism attractions that can better satisfy tourists’ needs for sightseeing, so the scenic spot industry becomes an important driver to support tourism development in these cities.

As an academic research, this research has some innovative contributions. First, our study complements the findings reported by Zeng and Chen [ 8 ], Xin and Li [ 5 ], Feng and Cui [ 10 ]. Zeng and Chen [ 8 ] and Xin and Li [ 5 ] used the absolute value of tourism economic indicators to analyze the impact of HSR on tourism development only reflects the level effect rather than rate effect. Feng and Cui [ 10 ] performed logarithmic treatment on the tourism economic indicators, and the results reflected the percentage change of the urban tourism economic indicators by HSR. Our research uses the incremental value of tourism economic indicators in logarithmic form, which can better reflect the rate effect of HSR on tourism development and avoid the overestimation of the contribution of HSR to tourism development. The results obtained in this paper for the impact of HSR on tourism revenue tourist arrivals are indeed lower than those obtained by Zeng and Chen [ 8 ]. Second, this study focuses on the differences in HSR factors, which breaks the limitation of analyzing the heterogeneous impact of HSR on tourism from the perspective of urban socio-economic characteristics [ 9 , 17 , 18 , 24 , 36 ] verifies the existence of node effect in HSR network. Third, this study reveals the comparative advantages of tourism development between nodal cities and non-nodal cities under the influence of HSR, which provides new insight to explain the failure of HSR to improve tourism industry efficiency [ 6 ].

Based on the above research findings, this study makes the following policy recommendations from both enterprise and government dimensions, with a view to better exploiting the key role of HSR in driving the transformation of China’s tourism industry towards high-quality growth. (1). When investing in cities along HSR lines, enterprises should consider the subdivided industry of tourism to which the investment project belongs and the node level of the city. According to the different needs of the cities for subdivided industry of tourism reasonable investment. It will not only enhance the rate effect of HSR on urban tourism with greater effectiveness, but also help to improve the productivity of enterprises and avoid the negative effects due to misallocation of resources. (2). The government should combine resource endowment and node level in a comprehensive judgment when laying out the tourism industry in the region. Node cities should first focus on the development of accommodation, catering, tourist transportation and travel agency industries to play the function of tourist distributing centres and strengthen the construction of tourism infrastructure services. Secondly, they also need to pay attention to the development of tourism shopping and cultural entertainment. Non-node cities should make tourism resource development and scenic spots construction the key elements of tourism development. They also need to improve the quality of tourism products, enrich tourism products, and promote tourism consumption upgrading. In this way, not only can haphazard investment be avoided, but it is also conducive to the formation of comparative advantages of each city, which in turn will enhance the attractiveness of city’s tourism.

This study innovatively discusses the nodal effect in the impact of HSR on tourism development from the perspective of transportation advantage transfer and analyzes the differentiated tourism function positioning of cities along HSR lines caused by the nodal effect. Even though this study has contributed to an improved understanding of the relationship between HSR and tourism development, it still has some limitations. The degree of impact of HSR on tourism flows is correlated with the degree of HSR network improvement. The impact of HSR on tourism development may have changed as the density of China’s HSR network increased after 2013. However, the time window of this study is set to 2005–2013, which does not capture the changes in the impact of HSR on tourism development in the context of further improvement of HSR network after 2013, although it avoids mixing the effect of HSR with the effect of the post-2013 successive introduction of regional tourism industrial policies. This limitation is compensated to some extent by the robustness test. In 2013, China opened 12 new HSR lines, accounting for 44% of the total number of HSR lines from 2005 to 2012. It can be assumed that the difference between 2012 and 2013 in the degree of HSR network improvement is significant. If the effect of the degree of HSR network improvement on the HSR tourism effect is non-negligible, then the regression results using 2006–2012 in the robustness test should be significantly different from the benchmark regression results. However, the results are not. This suggests that the findings of this study are still informative.

Supporting information

S1 table. abbreviation comparison table..

https://doi.org/10.1371/journal.pone.0276403.s001

• View Article
• Google Scholar
• 23. Castells M. The rise of the network society. Blackwell Publishers.

Travel on the road: does China’s high-speed rail promote local tourism?

• Research Article
• Published: 28 July 2022
• Volume 30 , pages 501–514, ( 2023 )

Cite this article

• Xiaoxiao Zhou 1 ,
• Siyu Chen 2 &
• Hua Zhang   ORCID: orcid.org/0000-0002-6665-8265 3  

2398 Accesses

6 Citations

Explore all metrics

Following a Chinese saying: To be rich, roads first, high-speed rail (HSR) opening and station construction are indispensable for economic developing. Probing the nexus between HSR, as a vital part of modern transportation system, and local tourism development provides a scan for reviving tourism and gaining low-carbon transition after COVID-19 pandemic. Drawing on prefecture-level panel data, this study takes difference-in-difference and instrument variable methods to detect the overall and heterogeneous effects of HSR connection on cities’ tourism development. The results showed that HSR connection had an overall positive effect on cities’ domestic tourist arrivals. The heterogeneity of the effect from HSR to tourism development appears to be that central and western cities, non-resource-based cities, and small cities benefited more from the opening of HSR. From a dynamic perspective, HSR connection promoted local tourism development in the 0 and 1 year of HSR opening but failed to show a positive effect in the long term. Hence, the study proposed some adjustments for evaluating the efficiency of HSR with consideration for the tourism effect, redesigning the system of HSR with consideration for local heterogeneity, and optimizing the HSR environment. These measures can optimize China’s HSR management and the design of HSR systems.

Similar content being viewed by others

Spatial Impact of High-speed Railway on the Urban Scale:An Empirical Analysis from Northeast China

Hsr mechanisms and effects on the spatial structure of regional tourism in china.

How Does High-Speed Rail Impact the Industry Structure? Evidence from China

Explore related subjects.

• Environmental Chemistry

Avoid common mistakes on your manuscript.

Introduction

There was an old Chinese saying: To be rich, roads first. As a crucial component of modern transportation systems (Castillo-Manzano et al. 2018 ; Zhang et al. 2020 ), high-speed rail (HSR) has developed rapidly and shows a gradually increasing influence on economic developing, especially in China. With almost 20 years of endeavors including preliminary planning, technology improvement, large-scale construction, stagnation, and going global (Li et al. 2021 ), the largest HSR network has been a buildup in China (China Railway Maps 2021 ). Compared with traditional modes of travel, HSR has the advantages of saving time by providing comfortable, convenient, and highly efficient transport (Li et al. 2021 ). HSR development has profoundly changed the mode of transport and led to a suite of impacts on economic, social, and environmental aspects (Castillo-Manzano et al. 2018 ; Zhang et al. 2020 ; Li et al. 2021 ). The tourism industry, particularly, depends heavily on transport (Wang et al. 2018 ; Chen and Haynes 2015 ). The mode, scale, structure, and distribution of tourism have been strongly affected by HSR development (Pagliara et al. 2017 ; Yin et al. 2019 ; Zhang et al. 2020 ).

Research on both HSR and tourism has attracted the attention of academics and policymakers (Chen and Haynes 2015 ; Pagliara et al. 2017 ; Li et al. 2021 ). Indeed, from different perspectives, a variety of studies have drawn on diverse data and applied multitudinous methods to detect the nexus between HSR and tourism development. Some economists used case studies to reveal the changes in the arrivals of tourists and peaks of tourism activities (Delaplace et al. 2014 ; Guirao and Campa 2016 ). Some others considered the tourism corridor effect and proved that both aggregation and diffusion effects appear between HSR and tourism development (Dai et al. 2018 ; Jin et al. 2020 ). Numerous works have embraced this issue by connecting HSR with tourism development using an econometric method based on panel data, yielding commixed results covering positive effects (Pagliara et al. 2017 ; Castillo-Manzano et al. 2018 ; Pagliara and Mauriello 2020 ), negative effects (Albalate and Fageda 2016 ; Gao et al. 2019 ), and non-significant effects (Pagliara et al. 2015 ; Campa et al. 2016 ). These studies shape a complicated understanding of the connection between HSR and tourism. The links from HSR to tourism remains controversial and shows heterogeneity (Pagliara and Mauriello 2020 ). Thus, we focused on the mechanism behind the relationship between HSR connection and tourism development and aimed at a credible and robust empirical test based on China’s city data using difference-in-differences (DID) and instrument variable (IV) methods.

Essentially, few studies have addressed the causality between HSR construction and tourism development in China (Chen and Haynes 2015 ; Xu et al. 2018 ). As of 2020, China’s HSR network has a length of over 37,900 km (23,550 mi) and serves 94.7% of the population of more than one million cities. China’s HSR construction supports the new concept of “Green, Intelligent, Safety and Humanity” and endeavors to contribute to sustainable development. Green transformation is trendy in the context of economic recession and sustainability. Coincidentally, tourism is a representative sector of the green industry. In recent years, the role of tourism in the economy has increased. From Fig.  1 , both tourism expenditure and its ratio to gross domestic product (GDP) have an upward tendency. In 2019, the added value of tourism and its related industry reached RMB 4.4989 trillion, accounting for 4.56% of GDP ( NBSC ). HSR shows a direct and strong correlation with regional tourism industry. However, in China, whether the nexus of HSR and tourism development is significant and positive or negative; how HSR connections influence tourism development; and whether the connection between HSR and tourism is heterogeneous among different regions, to address these issues, we focused on the mechanism, level, direction, and heterogeneity of the relation between HSR and tourism in China. Our findings could inform HSR system optimization and tourism policy formulation.

The tourism and GDP in China (2000–2019)

The present study may make some contributions as follows. First, form both macro and microscopic perspectives, we provide a comprehensive mechanism analysis based on a comprehensive literature review. Given that previous studies have mainly attempted to explore the nexus of HSR and tourism from a specific view (Guirao and Campa 2016 ; Dai et al. 2018 ), our analysis would identify the pathway from HSR to tourism development by accounting for macro and micro subjects and expecting a comprehensive and deep understanding of them. Second, the prefecture-level city data covering 283 samples used in our study provide detailed and referential cases in this area. Existing studies, especially on China, mainly conduct empirical tests using province-level data or small samples, such as a tourism corridor or a special region or city (Yin et al. 2019 ; Jin et al. 2020 ). Third, our methods included DID, IV, and propensity score matching combined with DID (PSM-DID) to treat endogeneity and econometric selection bias, as well as determine robust and believable conclusions.

Literature review and hypothesis

Literature review.

From a positive viewpoint, by an extended gravitational model, Wang et al. ( 2012 ) found that China’s HSR development can redistribute and transform the tourist market, extend market competition, and reallocate tourism centers. Through a questionnaire analysis of the main Italian cities, Cartenì et al. ( 2017 ) found that not only the “Faster services” of HSR, but also “hedonic services” attract more tourists. Pagliara et al. ( 2017 ) analyzed 2006–2013 data from 77 Italian municipalities and reported that HSR service has a positive effect on Italian visitors’ arrivals and the number of nights spent. In 28 EU countries for the period 1996–2014, Castillo-Manzano et al. ( 2018 ) found that HSR is more conducive to domestic tourism, whereas air travel is more conducive to foreign tourism. Based on 99 Italian provinces, Pagliara and Mauriello ( 2020 ) proved that HSR has a satisfying effect on both Italian and foreign tourists and the effect shows spatial heterogeneity.

To the negative results, based on the DID method using Spain’s panel data covering 50 provinces for 1998–2013, Albalate and Fageda ( 2016 ) indicated that HSR may have a positive (weak) direct effect on tourism, but it may also lead to a negative indirect effect by impacting air traffic. Drawing on China’s city-level data, Gao et al. ( 2019 ) used DID and IV methods and reported that connecting to HSR boosts tourist arrivals but fails on stimulating tourism revenue, resulting in a negative nexus between HSR and tourism revenue per arrival. In addition, the effect was heterogeneous.

Regarding the non-significant effect, Pagliara et al. ( 2015 ) analyzed data gathered using a revealed preference survey and found that owing to the high percentage of foreign tourists, HSR has no significant impact on the choice to travel to Madrid. Campa et al. ( 2016 ), drawing on 47 provincial panel data in 1999–2015, revealed a positive but lower effect of Spanish HSR construction on foreign arrivals and revenues but not on domestic tourism. Guirao and Campa ( 2016 ) examined the cross effect of tourism and HSR with Spain as a case study and showed that the former has positive effects on the latter, but that the HSR cannot bring about additional tourism demand.

Others have also pointed out that the impact from HSR to tourism is the result of multiple interacting forces, which may lead to nonlinearity or conditional results. Albalate et al. ( 2017 ) pointed out that, in large cities in Spain, HSR has positive influence on the tourism aspects, but the effects are minimal or even negative to most cities. Xu et al. ( 2018 ) constructed an index of the connectivity-accessibility of cities and found that cities in the Yangtze River Delta would suffer the most, whereas central and western cities would gain the most from the HSR network. Gao et al. ( 2019 ) acknowledged that the accessibility of HSR can facilitate tourism and stimulate tourism promotion; however, the redistribution from asymmetric accessibility may lead to the agglomeration of tourism resources and hamper the tourism development of surrounding cities.

To explore the link from HSR connection to tourism in China, we reviewed the related literature in Table 1 and handled them as comparable to our research. In almost all contexts, HSR shows a positive effect on tourism-related factors in China (Wang et al. 2018 ; Jin et al. 2020 ; Zhang et al. 2020 ). Regarding tourist arrivals and tourism competition, connecting to the HSR system always has a positive influence (Gao et al. 2019 ; Jin et al. 2020 ). As for tourism-related revenue and travel times, the impact of HSR appears to be weak and even disappointing (Liu and Zhang 2018 ; Gao et al. 2019 ). The divergence and incoherence of the conclusions provide reference to our research but rather prove the necessity and urgency of our work.

Framework and hypothesis

To elucidate the connection between HSR and tourism development, we investigated their mechanism from both macro and micro perspectives covering economic development, industrial structure, firm performance, and consumer preference as the intermediary determinants. Figure  2 outlines the framework of our theoretical analysis.

Links between HSR and tourism development

Essentially, for HSR, its promotion of accessibility and mobility is the main reason that it can affect cities’ tourism. In other words, HSR connection brings about acceleration in factor-flow and space–time compression. Connecting to HSR makes factors including capital, labor, information, and other resources flow faster, ultimately reducing the cost of regional resource mobility, expediting resources reallocation and promoting factor productivity. From a macroscopic perspective, these impacts eventually boost the economy and optimize the industrial structure (Cartenì et al. 2017 ; Li et al. 2020 ). Economic growth also reversely drives tourism development by providing adequate capital and employment supports and stimulating local consumption, especially tourism consumption (Gao et al. 2019 ). Meanwhile, the coordinated development of industries simultaneously drives tourism and related industry development. Resource agglomeration brought by HSR connection may benefit key cities but limit the surrounding cities’ tourism, the so-called core-periphery polarization. Gao et al. ( 2019 ) noted that redistribution caused by asymmetric accessibility would not be conducive to the tourism development of surrounding cities.

Additionally, the mobility increase and cost reduction not only result in direct firm performance promotion by reducing costs and removing face-to-face meeting obstacles (Lin 2017 ) but also strengthen the competition of local firms by expanding the market scope and forcing firms to be more creative and effective, especially tourism and related firms (Matas et al. 2020 ). Zhang et al. ( 2020 ) confirmed that HSR fundamentally influences the business environment by substantially lessening actual travel time and boosting the mobility of production factors. Ma et al. ( 2021 ) showed that the opening of HSR can enhance the market potential of a region by improving information flow and face-to-face communication, ultimately manifested in the promotion of regional innovation and entrepreneurship. This effect is more prominent in the large cities of China.

HSR is a viable way to mitigate travel time immediately compared with traditional transport modes (e.g., cars, trains, boats). The space–time compression provides another choice for consumers and further changes the tourists’ preferences. With the availability of “faster train” services, unplanned trips increases, resulting higher tourist arrivals. Jin et al. ( 2020 ) proved that HSR connection promoted 1-day and weekend trips and tourism development in nearby cities. Also, HSR can provide “hedonic services” by its high frequency, reliability, and easy access, thus attracting more people to travel with HSR instead of other vehicles. Cartenì et al. ( 2017 ) indicated that HSR influences tourists’ choice of travel mode through “faster train” services and “hedonic services.” The travel time reduction by HSR makes long-distance travel reliable, and its high-quality services make long-distance travel acceptable. However, considering transfer and accessibility, tourists prefer airplanes in international travel. Castillo-Manzano et al. ( 2018 ) found that, in EU countries, HSR is beneficial to domestic tourism, whereas airplanes benefit international travel.

In general, considering economic development, industrial structure, and firm performance as intermediary factors, HSR connection ameliorates factor free-flow; leads to mobility cost reduction, resource reallocation, and agglomeration; and finally formats the so-called aggregate channels, via the two competing effects of siphonage and positive effect. Given consumer preferences, HSR provides both “faster train” and “hedonic services” and attracts more tourists and more HSR travels, ultimately enabling the so-called substitution effect, which is more apparent in domestic rather than international tourism. Thus, we proposed the following hypothesis:

Hypothesis 1: HSR has positive but varying effect on cities’ domestic tourism.

The regional disparity of the HSR effects on tourism (Pagliara and Mauriello 2020 ) has been attributed to two aspects: the asymmetric allocation of the HSR system and the heterogeneity of regional characteristics (Albalate et al. 2017 ; Xu et al. 2018 ; Gao et al. 2019 ). In essence, the asymmetrical distribution of HSRs is produced internally from regional heterogeneity, to some extent. Thus, HSR services may have different effects on different cities with heterogeneous endowments and conditions. A typical classification is the geographic location; in this study, we also paid attention to classifications based on city size and resource dependency.

In China, eastern, central, and western cities have apparent differences in economic, social, environmental, transport, and resource endowments. HSR services also have vital differences in these cities. The divergence of HSR and other endowments explains the variation in the territorial impact of HSR on tourism. In eastern regions, which originally have adequate and advanced transport systems, HSR may have little impact on tourism promotion. To central and western cities with scant traffic channels, HSR construction may lead to a wide promotion of tourism development, which is similar to the so-called catch-up effect (Liu and Zhang 2018 ; Xu et al. 2018 ; Gao et al. 2019 ). Generally, non-resource-based cities always pose abundant tourism resources and have a higher reliance on tourism, but resource-based cities tend to be tourism-lagged. Similarly, owing to the disparity in tourism endowment, HSR’s effects on tourism in resource-based and non-resource-based cities would be heterogeneous; the former benefits little, whereas the latter gains more (Gao et al. 2019 ). Compared with large cities with more HSR stations, small cities should benefit more from connecting to the HSR system, which satisfies the diminishing marginal utility of HSR construction. Accordingly, on the divergence of the territorial impacts of HSR, we proposed the following hypotheses:

Hypothesis 2a: HSR has more positive effects on tourism development in central and western cities than in eastern cities in China.

Hypothesis 2b: HSR has more positive effects on tourism development in non-resource-based cities than in resource-based cities.

Hypothesis 2c: HSR has more positive effects on tourism development in small cities than in large cities.

Empirical strategy

Empirical framework.

Referring to Gao et al. ( 2019 ), Zhang et al. ( 2020 ), and Deng et al. ( 2020 ), we treated HSR connection as a quasi-natural experiment and adopted a DID method. We constructed a benchmark model as follows:

The dependent variable \(tourism development\) can be measured by tourist arrivals or tourism revenue. \({HSR}_{it}\) is the city-level HSR connection: \({HSR}_{it}=1\) if a city has opened HSR service in year t ; otherwise, \({HSR}_{it}=0\) . \(Z\) covers all the control variables. \({\mu }_{t}\) , \({\lambda }_{i}\) , and \({\varepsilon }_{it}\) size the time-fixed effect, individual-fixed effect, and the error term, respectively. To avoid potential heteroscedasticity and serial correlation among others, we clustered standard errors at the city level. We defined the “connected” city as a city that has at least one station on the HSR line.

Data and variables

City-level tourism development.

As a tertiary industry, tourism is closely related to and difficult to distinguish from related industries, such as the transport and catering industry (Campa et al. 2016 ). Based on the discussion in the “ Literature review and hypothesis ” section, tourism development can be measured from the perspectives of tourist arrivals, distance, revenue, and scenic spots (Campa et al. 2016 ; Gao et al. 2019 ; Zhang et al. 2020 ). Moreover, it is reasonable to separately discuss domestic and foreign tourism development. Similar to Chen and Haynes ( 2015 ), we mainly considered domestic tourist arrivals, domestic tourism revenue, international tourist arrivals, and international tourism revenue as the measurement of city-level tourism development. Given that international tourists cannot substitute flying with HSR travel, the influence of the opening of HSR on international tourism is small (Pagliara et al. 2015 ; Campa et al. 2016 ). Therefore, we considered the relevant data of domestic tourism as the main research object.

City-level HSR connections

Many indices have been used to proxy HSR (Albalate and Fageda 2016 ; Gao et al. 2019 ; Zhang et al. 2020 ). Given the data limitations and potential endogeneity of indexes, we followed Gao et al. ( 2019 ) and set a dummy variable to proxy HSR. Specifically, HSR = 1, if a city is connected to the HSR network; otherwise, HSR = 0.

City-level control variables

Based on previous studies (Campa et al. 2016 ; Gao et al. 2019 ; Yin et al. 2019 ; Deng et al. 2020 ), we introduced per capita income, industrial structure, population density, FDI, education level, financial expenditure, wage level, and the number of 5A scenic spots into the models as control variables. The detailed definitions of the control variables are presented in Table 2 . All of the variables were derived from the China Statistical Yearbook, China City Statistical Yearbook, The Yearbook of China Tourism Statistics, and Statistical Communique on National Economic and Social Development of Cities (2004–2017). Variables involving prices were deflated into the year 2000.

Empirical findings

Benchmark results.

Table 3 gives the benchmark regression results of the effect of HSR connections on tourism development. Models (1) and (2) are the basic results for all cities; models (3) and (4) provide the results for the samples excluding municipalities; and models (5) and (6) show the results for the samples excluding municipalities, provincial capitals, and specific plan-oriented cities.

The positive and significant coefficients of HSR in Table 3 implied that HSR had a dramatically positive impact on cities’ tourism development. That is, connecting to the HSR and HSR station construction enhanced connectivity and accessibility among cities in China, bringing in more tourism travelers and a boom in local tourism development. Our results affirmed the positive effect of HSR in Hypothesis 1 and agreed with the conclusions in Gao et al. ( 2019 ) and Jin et al. ( 2020 ). The value of \({\beta }_{1}\) in model (2) indicated that, with ceteris paribus, the domestic tourist arrivals in the cities with HSR increased by 4.25% on average compared with cities without HSR. Consequently, after the 2008 HSR opening, connecting to HSR averagely increased domestic tourist arrivals by 0.47% (4.25%/9) annually.

As for the control variables, according to model (2), GDP per capita had a significant and positive coefficient. Therefore, economic development had a positive effect on tourism development, also confirming the results of Gao et al. ( 2019 ). The positive coefficients of the ratio of FDI and ratio of financial expenditure indicated that financial support can improve regional tourism facilities and the environment and could drive local tourism development. The ratio of college students showing a negative effect on tourist arrivals may be attributed to the fact that younger people may prefer to travel to other cities. The unexpected coefficient of the number of 5A scenic spots indicated that cities have not made good use of local 5A scenic spots to attract tourists (Gao et al. 2019 ). The coefficients of the other variables were insignificant, indicating an absence of influence on tourism development.

To provide insight into the main influence of HSR on tourism, we introduced other indexes of tourism development into the baseline model as shown in Table 4 . The HSR had a positive influence on local tourism development, which can be attributed to the main effect of HSR on domestic tourist arrivals. In the regression with other tourism indicators, the HSR shows no significant impact on cities’ tourism revenue and international tourist arrivals. In other words, the effect of HSR connection was mainly observed for domestic tourist arrivals rather than tourist revenue and foreign arrivals, partly confirming the conclusions of Gao et al. ( 2019 ). The reason may be similar to the claim of Guirao and Campa ( 2016 ): a constrained indicator of tourists may be the main reason for the lack of significant effects of HSR on tourism in Spain.

• Heterogeneity

The heterogeneity of HSR’s effect is a critical issue (Pagliara and Mauriello 2020 ). According to the analysis in the “ Framework and hypothesis ” section, we identified the heterogeneity of the HSR connection effect from the perspectives of geographical location, city size, and resource endowment (shown in Table 5 ).

Table 5 shows that the coefficients of HSR are strikingly positive in models (2) and (3), but not in model (1), implying that connecting to HSR has a positive effect for the central and western cities but not for the eastern cities in China. As less developed regions, central and western China gain more profits by connecting to HSR. Liu and Zhang ( 2018 ) pointed out that the opening of HSR can reduce travel time, and given that the western region itself has a large travel time base, it could benefit greatly from HSR. Although the opening of HSR could reduce accessibility gaps between regions, the differences persist. Meanwhile, the eastern regions already have mature transportation systems, high connectivity and accessibility of cities, and flourishing tourism, thereby gaining less from HSR connection. Hypothesis 2a was thus confirmed.

The results of models (5) and (6) showed a significant positive coefficient in the resource-based city regression and a non-significant coefficient in the non-resource-based city regression. Therefore, non-resource-based cities can enjoy the benefits of HSR, whereas resource-based cities do not. This regional disparity may be because non-resource-based cities have abundant tourism resources and a higher reliance on tourism, whereas resource-based cities tend to be tourism-lagged. Owning to the difference in tourism endowments, HSR’s effects on tourism in resource- and non-resource-based cities were heterogeneous: the former benefited little, whereas the latter gained more. The results were similar to those reported by Gao et al. ( 2019 ). Hypothesis 2b was therefore supported.

In models (6), (7), and (8), the HSR had a significant coefficient in the regression with small cities but a non-significant coefficient with large and medium-sized cities. That is, in China, only small cities could benefit from the connectivity and accessibility brought by connecting to HSR. These results were contrary to the findings in Spain by Albalate et al. ( 2017 ). The potential explanation behind these phenomena is that for large and medium cities, it may be difficult to promote tourism by connecting to HSR as a convenient transportation network. Thus, we observed a marginal positive utility of HSR in small cities featuring primary transport networks and lower connectivity. Hypothesis 2c was proved to some degree. In general, not all cities had their tourism boosted by HSR. In the process of HSR development, attention should be paid to avoiding the mismatch of demand and deliberate choices of overinvestment, overdesign, and over-quality (Beria et al. 2016 ). Ongoing endeavor is needed to optimize the composition of different sized cities and towns in the HSR plan of China (Liu and Zhang 2018 ).

Robustness checks

To prove the validity of our results, we performed robustness checks according to Gao et al. ( 2019 ) and Zhang et al. ( 2020 ). The corresponding results are listed in Table 6 .

In Table 6 , model (1) gives the regression results with the alternative measures of tourism development, using domestic tourist arrivals per capita, calculated as the rate of the ratio of domestic tourist arrivals to year-end population. Model (2) shows the results for the sample excluding outliers. Model (3) provides the results based on the samples after 2005, and model (4) shows the results with an alternative measures of industrial structure using a comprehensive index of industrial structure (CIIS), where \(CIIS=\frac{Output\;value\;of\;primary\;industry}{GDP}+2\times \frac{Output\;value\;of\;secondary\;industry}{GDP}+3\times \frac{Output\;value\;of\;tertiary\;industry}{GDP}\) . Model (5) displays the results of adding highway passengers to the existing control variables, and model (6) documents the results of considering the impact of airports by introducing a dummy variable for whether the city has a civil aviation airport. All the coefficients of the main explanatory variable HSR were significantly positive in the six models. Regardless of some adjustments to the measurement, regression method, sample selection, and variable controlling, the HSR connection makes positive promotion on local tourism development in China. Hence, the core conclusion of the above analysis has been confirmed and is robust and reliable.

Estimation with PSM-DID

To enhance the cogency of the main conclusion, we used the PSM-DID model to solve the problem of sample selection bias. We obtained the propensity score by a logit regression with the control variables in the benchmark model as the explanatory variables and HSR as the explained variable. HSR and non-HSR cities were matched by their propensity score. Table 7 gives the results. The advantage of the PSM-DID is that it can ameliorate the potential systematic disparities in HSR and non-HSR cities.

In models (1) to (7), the matching data are the cross-sectional data of the control variables in 2003, 2004, 2005, 2006, and 2007; the average value in 2003–2007; and the average value before the connection and opening of the HSR, respectively. The coefficients of HSR were significant at the 5% level and located in the interval (0.0469, 0.1182) in the seven models. Thus, HSR connection and station construction can stimulate domestic tourist arrivals and further enhance local tourism development. These results bolstered the robustness of the core conclusion in the above analysis.

Placebo test

Setting the false opening year of hsr.

To avoid the probable bias from the pre-existing effect, referring to the decision on HSR connection and station construction being closely based on economic and social conditions (Faber 2014 ), we conducted a placebo test (Ma et al. 2021 ) with false HSR opening times by assuming that the opening year of HSR was 2004, 2005, 2006, and 2007, respectively. The corresponding outcomes in Table 8 show insignificant coefficients of false HSR and demonstrate the lack of a preexisting confounding factor that disturbs our core conclusion.

Randomly generated HSR status

To separate the benchmark regression results from “change findings caused by the influences of missing variables,” we referred to Li et al. ( 2016 ) and Jia et al. ( 2021 ) to execute the placebo test by randomly selecting HSR opening cities. First, the same number of cities in the treatment group is randomly selected according to the number of cities with HSR opening each year. Second, with the samples, a dummy treatment variable \({HSR}_{it}^{false}\) is set artificially. Finally, aligned with the benchmark model, regressions are 500 times and 1000 times based on the dummy variable \({HSR}_{it}^{false}\) . Figure  3 shows the distribution of the estimated coefficients and their p -values.

Distribution of estimated coefficients of the falsification test. Notes: a and b show the distribution of estimated coefficients and their p -values derived from 500 and 1000 simulations randomly assigning the HSR status to cities, respectively. The vertical line represents the true estimate from model 2 of Table 3

The estimated coefficients of \({HSR}_{it}^{false}\) were distributed around 0. The means of the coefficients in the 500 and 1000 simulations were − 0.000573 and 0.000148, respectively. Comparatively, the coefficient in the benchmark regression was 0.0425, larger than most coefficients in the simulations. Regarding the distribution of the p -value, in the 500 simulations, only 23 coefficients of \({HSR}_{it}^{false}\) were located in the interval larger than 0.0425, and the corresponding p -value was equal to or less than 0.1. That is, in the 500 simulations, the coefficient of benchmark regression was true 95.4% (1–23/500) of the time. Similarly, in the 1000 simulations, only 53 coefficients of \({HSR}_{it}^{false}\) were in the interval larger than 0.0425, and the corresponding p -value was equal to or less than 0.1, indicating that the coefficient of benchmark regression was true 94.7% (1–53/1000) of the time. The above results implied that the positive link from HSR connection to tourism development avoided the omitted variable bias at the 90% level at least.

Validity and endogeneity of HSR

Validity of parallel trend assumption and dynamic did.

Following Li et al. ( 2016 ) and Dong ( 2018 ), we probed the validity of the parallel trend assumption by event study. The econometric model was constructed as follows:

where \(i\) and \(t\) denote city and year, respectively. The explained variable \({tourism development}_{it}\) was consistent with the “ Empirical framework ” section. \({D}_{it}^{k}\) is a dummy for the “event,” HSR opening. Considering the data interval (2003–2016) and the original year of HSR opening (2008), we documented the opening year of HSR of a city with \({s}_{i}\) ; if \(t-{s}_{i}\le -5\) , \({D}_{it}^{-5}=1\) ; otherwise, \({D}_{it}^{-5}=0\) . Generally, if \(t-{s}_{i}=k\) , \({D}_{it}^{k}=1\) ; otherwise, \({D}_{it}^{k}=0 (\mathrm{k}\in \left[-\mathrm{5,5}\right])\) . With \(t-{s}_{i}\ge 5\) , then \({D}_{it}^{5+}=1\) ; otherwise, \({D}_{it}^{5+}=0\) . The coefficient \({\beta }_{k}\) captures the effect that HSR opening has on local tourism development. Other variables in Eq. ( 2 ) are the same as in Eq. ( 1 ). The regression of Eq. ( 2 ) documents the validity of the parallel trend assumption and the effect of dynamic DID.

From Fig.  4 , the results did not reject the null hypothesis that cities had no systematic difference before HSR opening, thereby verifying the validity of the parallel trend assumption. In the 0 and 1 year of HSR opening, the estimations of \({\beta }_{k}\) were positive, with their 95% confidence intervals at (0.0003, 0.1637) and (0.0009, 0.1773), respectively. Thus, tourism development increased by 0.082 and 0.0891 in the original year and the first year of HSR opening, respectively. From the year 2, the coefficients of \({D}_{it}^{k}\) became positive but non-significant, meaning that the positive effect of HSR weakened and disappeared after 2 years of HSR opening. Firstly, the opening of HSR shows the benefits of substitution and space–time compression on local tourism development. Over time, the substitution effect weakened and the Matthew effect (Siphonage) led to more little cities failing to benefit from HSR opening. As the result, the average effect of HSR on cities’ tourism ended up being negative and undesired. Overall, the conclusion of the dynamic DID was reasonable and acceptable.

Dynamic effect of HSR on tourism. Notes : The graph shows regression coefficients from Eq. ( 2 ) and their 95% confidence intervals. The reference category is “at least five years” prior to HSR connection

Estimation with instrumental variables

After determining the causality between HSR and tourism development, we aimed to discuss the bias from the underlying endogeneity of HSR opening. Referring to Faber ( 2014 ) and Wang et al. ( 2018 ), we constructed an IV based on cities’ slope values. The superiority of slope as an IV is that, as an exogenous geographic variable, slope has no direct nexus with economic variables and considering the cost and difficulty of HSR construction, slope shows a direct relationship with the HSR connection and station construction. Given the invariance of cities’ slopes, we constructed a dummy variable with the product of slope and year as the IV of HSR. The results are presented in Table 9 .

Ten coefficients of the dummy variables were apparently negative in the first stage of the type I models. Only one coefficient was negative and significant in the first stage of the type II models. The first-stage F values were 208.854 and 92.912, respectively, indicating that the slope of cities increased the cost of HSR and had a negative effect on HSR connection. In the second stage of the IV regressions, the coefficient was dramatically positive in the type I model, indicating that HSR connection had a positive effect on cities’ tourism development, consistent with the core conclusion of our study. However, the coefficient in the type II model was positive but non-significant. Considering the F test, it is reasonable to claim that in the IV regressions, HSR promotes local tourism development and adheres to the above results.

Our study focused on the effects from HSR to tourism development and analyzed the city-level data covering 2013–2016. We found that, first, HSR connections had an overall positive effect on cities’ tourism development. Second, there is distinct heterogeneity in the HSR’s effects from multidimensional sights. Specifically, central and western cities, non-resource-based cities, and small cities benefit more from the HSR opening. Finally, cities with and without HSR had no systematic disparity, and HSR connection apparently promoted local tourism development in the 0 and 1 year of HSR opening but tended to fail to promote local tourism in the long term.

Based on the above conclusion, some adjustments are proposed to optimize China’s HSR management and the design of HSR systems. The positive effect of HSR on tourism development should be adequately considered in the strategy and evaluation of HSR connections and the construction of a city to ensure a comprehensive HSR management. In the design of an optimized HSR system, the construction should consider the heterogeneity among the effects of HSR to match local conditions and encourage a catch-up effect. From a long-term perspective, the HSR environment and travel attributes should be improved by connecting the structure of the HSR system with local tourism features to better drive the recovery of tourism in the post-epidemic era.

Data availability

The datasets used or analyzed during the current study are available from the yearbooks or the corresponding author on reasonable request.

Code availability

Not applicable.

Albalate D, Fageda X (2016) High speed rail and tourism: empirical evidence from Spain. Transp Res Part A-Policy Pract 85:174–185

Article   Google Scholar  

Albalate D, Campos J, Jiménez J (2017) Tourism and high speed rail in Spain: does the AVE increase local visitors? Ann Tour Res 65:71–82. https://doi.org/10.1016/j.annals.2017.05.004

Beria P, Grimaldi R, Albalate D, Bel G (2016) Delusions of success: costs and demand of high-speed rail in Italy and Spain. Transp Policy 68:63–79. https://doi.org/10.1016/j.tranpol.2018.03.011

Campa JL, López-Lambas M, Guirao B (2016) High speed rail effects on tourism: Spanish empirical evidence derived from China’s modelling experience. J Transp Geogr 57:44–54. https://doi.org/10.1016/j.jtrangeo.2016.09.012

Cartenì A, Pariota L, Henke I (2017) Hedonic value of high-speed rail services: quantitative analysis of the students’ domestic tourist attractiveness of the main Italian cities. Transp Res Part A-Policy Pract 100:348–365. https://doi.org/10.1016/j.tra.2017.04.018

Castillo-Manzano JI, Castro-Nuño M, López-Valpuesta L, Pedregal-Tercero DJ, Garrido-Michó JM (2018) High speed rail: fast tracking tourism in the EU? Ann Tour Res 71:64–66. https://doi.org/10.1016/j.annals.2018.02.005

Chen Z, Haynes K (2015) Impact of high-speed rail on international tourism demand in China. Appl Econ Lett 22:57–60. https://doi.org/10.1080/13504851.2014.925043

Article   CAS   Google Scholar  

China Railway Maps (2021) By 2020, China keeps the world’s largest high speed rail (HSR) network with a length totaling over 37,900 km (23,550 mi). https://www.travelchinaguide.com/china-trains/high-speed/

Dai X, Xu M, Wang N (2018) The industrial impact of the Beijing-Shanghai high-speed rail. Travel Behav Soc 12:23–29. https://doi.org/10.1016/j.tbs.2018.03.002

Delaplace M, Pagliara F, Perrin J, Mermet S (2014) Can high speed rail foster the choice of destination for tourism purpose. Procedia Soc Behav Sci 111:166–175. https://doi.org/10.1016/j.sbspro.2014.01.049

Deng T, Gan C, Du H, Hu Y, Wang D (2020) Do high speed rail configurations matter to tourist arrivals? Empirical evidence from China’s prefecture-level cities. Res Transp Econ 100952.  https://doi.org/10.1016/j.retrec.2020.100952

Dong XF (2018) High-speed railway and urban sectoral employment in China. Transportation Research Part A: Policy and Practice 116:603–621. https://doi.org/10.1016/j.tra.2018.07.010

Faber B (2014) Trade integration, market size, and industrialization: evidence from China’s national trunk highway system. Rev Econ Stud 81:1046–1070. https://doi.org/10.1093/restud/rdu010

Gao Y, Su W, Wang K (2019) Does high-speed rail boost tourism growth? New evidence from China. Tour Manage 72:220–231. https://doi.org/10.2139/ssrn.3289421

Guirao B, Campa JL (2016) Cross effects between high speed rail lines and tourism: looking for empirical evidence using the Spanish case study. Transp Res Procedia 14:392–401. https://doi.org/10.1016/j.trpro.2016.05.091

Jia R, Shao S, Yang L (2021) High-speed rail and CO2 emissions in urban China: a spatial difference-in-differences approach. Energy Econ 99:105271. https://doi.org/10.1016/j.eneco.2021.105271

Jin S, Yang J, Wang E, Liu J (2020) The influence of high-speed rail on ice–snow tourism in northeastern China. Tour Manage 78:104070. https://doi.org/10.1016/j.tourman.2019.104070

Li N, Chen J, Tsai I, He Q, Chi S, Lin Y, Fu T (2016) Potential impacts of electric vehicles on air quality in Taiwan. Sci Total Environ 566–567:919–928. https://doi.org/10.1016/j.scitotenv.2016.05.105

Li X, Wu Z, Zhao X (2020) Economic effect and its disparity of high speed rail in China: a study of mechanism based on synthesis control method. Transp Policy 99:262–274. https://doi.org/10.1016/j.tranpol.2020.09.003

Li H, Dong X, Jiang Q et al. (2021) Policy analysis for high-speed rail in China: Evolution, evaluation, and expectation. Transport Policy 106:37–53

Lin Y (2017) Travel costs and urban specialization patterns: evidence from China’s high speed railway system. J Urban Econ 98:98–123. https://doi.org/10.1016/j.jue.2016.11.002

Liu L, Zhang M (2018) High-speed rail impacts on travel times, accessibility, and economic productivity: a benchmarking analysis in city-cluster regions of China. J Transp Geogr 73:25–40

Ma L, Niu D, Sun W (2021) Transportation infrastructure and entrepreneurship: evidence from high-speed railway in China. China Econ Rev 65:101577. https://doi.org/10.1016/j.chieco.2020.101577

Matas A, Raymond J, Roig J (2020) Evaluating the impacts of HSR stations on the creation of firms. Transp Policy 99:396–404. https://doi.org/10.1016/j.tranpol.2020.09.010

National Bureau of Statistics of China

Pagliara F, Mauriello F (2020) Modelling the impact of high speed rail on tourists with geographically weighted Poisson regression. Transp Res Part A-Policy Pract 132:780–790. https://doi.org/10.1016/j.tra.2019.12.025

Pagliara F, Pietra A, Gómez JJ, Vassallo JM (2015) High speed rail and the tourism market: evidence from the Madrid case study. Transp Policy 37:187–194. https://doi.org/10.1016/j.tranpol.2014.10.015

Pagliara F, Mauriello F, Garofalo A (2017) Exploring the interdependences between high speed rail systems and tourism: some evidence from Italy. Transp Res Part A-Policy Pract 106:300–308. https://doi.org/10.1016/j.tra.2017.09.022

Wang X, Huang S, Zou T, Yan H (2012) Effects of the high speed rail network on China’s regional tourism development. Tour Manag Perspect 1:34–38. https://doi.org/10.1016/j.tmp.2011.10.001

Wang D, Niu Y, Qian J (2018) Evolution and optimization of China’s urban tourism spatial structure: a high speed rail perspective. Tour Manage 64:218–232. https://doi.org/10.1016/j.tourman.2017.08.010

Xu W, Zhou J, Yang L, Li L (2018) The implications of high-speed rail for Chinese cities: connectivity and accessibility. Transp Res Part A-Policy Pract 116:308–326. https://doi.org/10.1016/j.tra.2018.06.023

Yin P, Lin Z, Prideaux B (2019) The impact of high-speed railway on tourism spatial structures between two adjoining metropolitan cities in China: Beijing and Tianjin. J Transp Geogr 80:102495–102495

Zhang A, Liu L, Liu G (2020) High-speed rail, tourist mobility, and firm value. Econ Model 90:108–116. https://doi.org/10.1016/j.econmod.2020.05.004

Download references

This work was supported by the National Natural Science Foundation of China (Grant No. 71804001, 71703073).

Author information

Authors and affiliations.

School of Finance, Anhui University of Finance and Economics, Bengbu, 233030, China

Xiaoxiao Zhou

School of Economics, Anhui University of Finance and Economics, Bengbu, 233030, China

School of Business, Nanjing Audit University, Nanjing, 211815, China

You can also search for this author in PubMed   Google Scholar

Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Hua Zhang and Siyu Chen. The first draft of the manuscript was written by Xiaoxiao Zhang and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Hua Zhang .

Ethics declarations

Ethics approval and consent to participate, consent for publication, competing interests.

The authors declare no competing interests.

Additional information

Responsible Editor: Philippe Garrigues

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Zhou, X., Chen, S. & Zhang, H. Travel on the road: does China’s high-speed rail promote local tourism?. Environ Sci Pollut Res 30 , 501–514 (2023). https://doi.org/10.1007/s11356-022-22114-9

Download citation

Received : 07 May 2022

Accepted : 15 July 2022

Published : 28 July 2022

Issue Date : January 2023

DOI : https://doi.org/10.1007/s11356-022-22114-9

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• High-speed rail
• Tourism development
• Difference-in-differences
• Find a journal
• Publish with us
• Track your research

Fact Sheet | High Speed Rail Development Worldwide

By richard nunno.

July 19, 2018

Those who travel to other countries may experience high speed rail (HSR) services and wonder why a similar transportation network has not been implemented in the United States. The following fact sheet provides a brief history of international high-speed rail developments and a comparison of the status of HSR deployment around the world, along with a discussion of issues that policymakers and business leaders may want to consider in their long-term planning for future U.S. transportation infrastructure.

While there is no single international standard for high speed rail, new train lines having speeds in excess of 250 kilometers per hour (km/h), or 160 miles per hour (mph), and existing lines in excess of 200 km/h (120 mph) are generally considered to be high speed. Many countries in Europe and Asia have developed high-speed rail for passenger travel, although some systems also offer freight service.

Japan: The Birth of High-Speed Rail

The first high-speed rail system began operations in Japan in 1964, and is known as the Shinkansen , or “bullet train.” Today, Japan has a network of nine high speed rail lines serving 22 of its major cities, stretching across its three main islands, with three more lines in development. It is the busiest high-speed rail service in the world, carrying more than 420,000 passengers on a typical weekday. Its trains travel up to 320 km/h (200 mph), and the railway boasts that, in over 50 years of operation, there have been no passenger fatalities or injuries due to accidents.

Europe: An International High-Speed Network

The next country to make high-speed rail available to the public was France in 1981, with service at 200 km/h (124 mph) between Paris and Lyon. Today, the French high-speed rail network comprises over 2,800 km of Lignes à grande vitesse (LGV), which allows speeds of up to 320 km/h or 200 mph, on which its TGVs ( Trains à grande vitesse ) run. This inter-city high-speed rail service is operated by SNCF, France’s national rail operator. Germany began operation of its Inter-City Express (ICE) high-speed trains through several German cities in 1991. The Eurostar service, connecting Paris to London via the Channel Tunnel, began operation in 1994. Due to France’s early adoption of high-speed rail and its central position between the Iberian Peninsula, the British Isles and Central Europe, most other high-speed rail lines in Europe have been built to the French standards for speeds, voltage and signaling, with the exception of Germany, which built to existing German railway standards.

Over the ensuing years, several European countries have built extensive high-speed rail networks that include several cross-border international links. Tracks are continuously being built and upgraded to international standards, expanding the network. International links between Italy and France, with connections to Switzerland, Austria and Slovenia, are underway. These links all incorporate extensive new tunneling under the Alps. European Union funding was approved in 2015 for the Turin–Lyon high-speed railway (at a cost of €25 billion), which will connect the French and Italian networks, and provide a link with Slovenia.

In 2007, a consortium of European railway operators, Railteam, was formed to coordinate and promote cross-border high-speed rail travel. Developing a trans-European high-speed rail network is a stated goal of the European Union, and most cross-border railway lines receive EU funding.

China: Surpassing the Rest of the World

Due to generous funding from the Chinese government, high-speed rail in China has developed rapidly over the past 15 years. China began planning for its current high-speed rail system in the early 1990s, modeling it after Japan’s Shinkansen system. Chinese high-speed rail service began operation in 2008, running at speeds from 250 km/h to 350 km/h (217 mph) and traveling from Beijing to Tianjin (117 km or 73 miles). China’s HSR network is expected to reach over 38,000 km by 2025, and 45,000 km in the longer term, far more rail lines than in the rest of the world combined. China has imported most of its HSR systems through joint ventures with Japan, Germany, France, and other countries. But in recent years, China has been developing an internal production capability, and is now winning contracts for HSR development in other countries.

After building high-speed rail on conventional tracks, in 2006 China began increasing its budget to build dedicated high-speed rail lines (from $14 billion in 2004 to $88 billion in 2009). Overall, China has dedicated $300 billion to build a 25,000 km HSR network by 2020. Most of the new lines follow the routes of existing trunk lines and are designated for passenger travel only. Several sections of the national grid link cities that had no previous rail connections and will carry a mix of passenger and freight. High-speed trains can generally reach 300–350 km/h (190–220 mph). On mixed-use HSR lines, passenger train service can attain peak speeds of 200–250 km/h (120–160 mph).

China’s most profitable high-speed rail line, reporting 6.6 billion yuan (over $1 billion) in net operational profit in 2015, connects Beijing to Shanghai, two major economic zones. Construction first started on this 1,318 km-line in 2008, and it opened for commercial service in 2011.

Critics both in China and abroad have questioned the necessity of having an expensive high-speed rail system in a largely developing country, where most workers cannot afford to pay a premium for faster travel. In response, the Chinese government argues that high-speed rail:

• Provides a fast, reliable and comfortable means of transporting large numbers of travelers in a densely populated country over long distances and improves economic productivity and competitiveness in the long run by linking labor markets and freeing up older railways to carry freight.
• Stimulated the economy in the short term by creating construction jobs and helping drive demand for construction, steel and cement during the economic downturn in 2008-2009.
• Facilitates cross-city economic integration and promotes the growth of smaller cities by connecting them with larger cities.
• Supports energy independence and environmental sustainability, as electric trains use less energy to transport people and goods on a per unit basis and can draw power from more diverse sources of energy (including renewables) than automobiles and aircraft.
• Fosters an indigenous HSR technology and components industry; Chinese train equipment manufacturers have quickly absorbed foreign technologies (such as Japan’s Shinkansen systems), localized production processes, and begun competing with foreign suppliers in the export market.

The growth of HSR in China has forced domestic airlines to cut airfares and cancel regional flights, especially for flights under 500 km, and some of the shorter inter-city routes were completely terminated. China’s high-speed rail now carries more than twice as many passengers as its domestic airlines.

United States: Lagging Behind but Catching Up?

In the United States, there is not yet a fully high-speed train line, and none are being built except in California. The Acela Express, running between New York and Washington D.C., reaches a top speed of 150mph on limited portions of its route, but its average speed is only about 66 mph. California is in the process of building an HSR system, but the first phase, connecting San Francisco to Los Angeles and Anaheim, is not expected to be completed until 2029 (although some of the infrastructure is already being used). No other state or local jurisdiction has, at this time, allocated the funding to begin construction of high-speed rail. In Texas, studies are being conducted for a “Bullet Train” between Dallas and Houston, and advocates say that construction should begin in a year or so. In Florida, the Brightline service between Miami and Orlando is operational, but with an average speed of 80 mph, it does not meet the minimum speeds to be considered HSR (although plans for increased speeds are underway). In addition, Florida’s governor recently announced another potential HSR line between Orlando and Tampa.

Several reasons can be listed for this disparity between U.S. and foreign HSR developments:

• the lower population density of U.S. cities compared to those in Europe and Asia makes it difficult to give high-speed rail large enough numbers of people to make it economically viable;
• stronger property rights in the United States compared to other countries, which make it difficult for governments to purchase land for new railroads;
• America’s car culture and emphasis on driving (total automotive marketing spending in the United States is about $35 billion per year and climbing);
• the difficulty of shifting to public transit once city/county infrastructure has already been built and been designed for automobile accessibility rather than train stations;
• U.S. long distance railways are mostly owned by freight companies, forcing passenger rail carriers to yield priority to freight trains;
• the greater distance between many U.S. cities allows many transportation needs to be more conveniently served by commercial airlines; and
• political interference by some extremely wealthy individuals who want to suppress interest in railroads to maximize fossil fuel use.

High-Speed Rail by Country

The table below compares countries/economies according to their level of deployment of HSR railways, in order from most development to least, based on data from the International Union of Railways (UIC) and from other sources that provide updated data. A number of other countries are listed as having long-term planning for HSR, but no funding has been allocated to their programs to date. In addition, other sources indicate that some countries have HSR systems in place even though UIC has indicated that they do not.

Long-term Prospects for High-Speed Rail

Economic Viability. Analysts have suggested that some countries may have over-extended their HSR networks, claiming that revenues and profit margins have fallen, and cheap flights and car-sharing services may draw some customers away from rail options. The facts, however, seem to belie these warnings. In China, HSR lines have proven their profitability, and throughout Asia and Europe, HSR is providing a lower cost and shorter travel time alternative to air travel for many of the shorter routes. Advocates argue that by increasing the number of cities that have HSR hubs, the network effect will geometrically multiply the utility of HSR to travelers, and hence will provide long-term economic and lifestyle benefits for all citizens.

It is not clear whether developing HSR between some U.S. cities would stimulate their economies enough to make it sustainable in the long term. The HSR deployments in California will be watched closely by government and business leaders in other U.S. regions, who may make their financing decisions based on the perceived degree of success of California’s HSR. Despite the increases in projected costs, support for high-speed rail among Californians remains high.  

Competition with other technologies. Technologies such as magnetic levitation (maglev) and hyperloop are promising ever faster rail speeds. Maglev is already a proven technology: since 2004, for example, China has operated a maglev train between Shanghai and Pudong International Airport, which can travel up to 430 km/h (270 mph). The line covers 30 km (19 miles) in seven minutes. China is currently one of only three countries (along with Japan and South Korea) that operate a maglev train. Hyperloop systems, which involve propelling trains through sealed tubes that have been emptied of as much air as possible to reduce air resistance, are still on the drawing board.

Maglev and hyperloop systems both require the construction of all new rail lines, which calls into question further investment in more conventional HSR technologies. Nevertheless, advocates point out that HSR is a mature technology, unlike these other rail transport schemes, and so is a much lower risk investment for governments and urban planners. Both maglev and hyperloop are very costly, and pose potential health and safety risks that conventional HSR does not.

HSR advocates further argue that the throughput (in terms of numbers of people moved from place to place for a given investment) provided by high-speed rail far outpaces those provided by highways or airports. In the chart to the left, the US High Speed Rail Association depicts how high-speed rail offers significant time savings compared with flying or driving between downtown San Francisco and downtown Los Angeles in California.

Transportation benefits . Many would argue that economic development should not be the main measure of a transportation system, but that its ability to move people and goods should be the primary consideration. That is how highway and airport projects are evaluated. Every country that builds HSR does so for the high capacity, sustainable mobility it delivers, first and foremost, with economic development and better safety as beneficial side effects.

Energy savings. Reducing the number of cars on roads and highways translates into big energy savings and a reduced demand for oil. According to International Union of Railways (UIC) data, high-speed rail is more than four times as energy efficient as driving in cars and nearly nine times more efficient than flying.  

Environmental considerations. High-speed rail clearly offers a path to lower greenhouse gas emissions than other modes of transportation. If HSR services can entice people out of their cars by offering convenience and speed at a low cost, this would significantly reduce societal energy consumption and carbon emissions. The California High-Speed Rail Authority (CHSRA), for example, estimates that by 2040, California’s HSR system will reduce vehicle miles of travel in the state by 10 million miles each day; over a 58-year period, the system will reduce auto traffic on the state’s highways by over 400 billion miles of travel. In addition, CHSRA estimates that starting in 2030, the state will see a reduction of 93 to 171 flights daily, which translates into improved air quality and improved health, along with the economic benefits of a more energy-efficient transportation system.

In many countries, laws and policies are already in place requiring businesses and consumers to reduce their emissions, and a consensus toward those trends is emerging over time. High-speed rail can offer the triple bottom line (economic, social and environmental sustainability) that many policymakers have called for over the years.

Author: Richard Nunno

Editor: Carol Werner

• high speed rail

$64M grant pushes Houston to Dallas high-speed rail closer to reality, but still far from completion

HOUSTON, Texas (KTRK) -- For decades, we've been hearing about the possibility of a high-speed train that would take passengers from Houston to Dallas.

The video above is from ABC13's 24/7 livestream.

It would be the first of its kind -- a Japanese-style bullet train that could take you from Houston to Dallas in less than an hour and a half.

The federal government has now awarded Amtrak $64 million to move forward with the project.

It's nowhere close to the tens of billions the project is expected to cost, but it is a start.

"It's one of those things that I'll believe it when I see it," Ed Emmett, a fellow of Energy and Transportation at Rice University's Baker Institute for Public Policy, said. "Generally, I roll my eyes because I first was hearing about Texas high-speed rail, I guess, way back in the 1970's."

The proposed route would have one stop, between College Station and Huntsville, to pick up university traffic.

The train would have to go through rural counties, but buying private land to do so has been expensive and complicated in the past.

"You have a lot of property rights people that are upset. High-speed rail can get around that by using eminent domain, but it's going to cost a lot of money and take a lot of time and court battles," Emmett said. "High-speed rail really hasn't gone in anywhere else in the country. The one in California is at least double the cost it was planned."

We can all agree, however, that Texas is growing quickly, and transportation will eventually need to expand, too.

The question for Texans will be which route that expansion takes.

RELATED: History of Texas bullet train project

RELATED: Amtrak-Texas Central agreement revives dormant Houston-Dallas high-speed rail project

For more on this story, follow Pooja Lodhia on Facebook , X and Instagram .

Related Topics

• HIGH SPEED RAIL
• TRANSPORTATION

High Speed Rail

Texas high-speed rail appears back on track with Amtrak agreement

Construction of bullet train from SoCal to Las Vegas set to begin

Texas bullet train faces leadership exodus as land acquisition slows

Texas high-speed rail project's ceo steps down after 6 years, top stories.

Officer opens fire on man resisting arrest who told her to shoot him

Woman murdered by husband who assaulted her 3 times before: Records

Slain deputy, suspect had business disputes over the last year: Friend

Noted Houston pastor Kirbyjon Caldwell released early from prison

Man out on bail for 2008 murder found 16 years later in Mexico

We get a break from rain this weekend, but more is coming next week

Former police chief presses to drop charges in 2022 Uvalde shooting

The 'King' has reentered the building! Astros announce star's return

September 07, 2024 72 °F PBS Passport .st0{fill:#0A145A;} .st1{fill:#5680FF;} .st2{fill:#FFFFFF;} UH Search for: Search MENU CLOSE News & Information Features Hello Houston inDepth Topics Local News Texas Education News Politics Criminal Justice Transportation Energy & Environment Weather Hurricane Tracker All Stories >>> Arts & Culture Arts & Culture Main Classical Music Music Opera & Musical Theater Dance Visual Art Literature Theatre & Film Voices and Verses: A Poem-A-Day Series Awareness Hispanic Heritage Pride Month: Better Together! Asian American Pacific Islander Heritage Black History Women’s History Education Programs & Podcasts Local Programs Party Politics Houston Matters with Craig Cohen I SEE U Texas Standard UH 100 Years of Houston Bauer Business Focus Briefcase Engines of Our Ingenuity Health Matters UH Moment Features Dead and Buried Career Frontier Podcasts Below the Waterlines: Houston After Hurricane Harvey Party Politics Skyline Sessions Encore Houston All Podcasts >> Support Membership Update Payment Method Upgrade Your Monthly Gift Give a Gift Membership Giving Programs Affinity Council Studio Society In Tempore Legacy Society Innovation Fund Volunteers Foundation Board Young Leaders Council Mission Ambassadors Donations Vehicle Donation Giving Opportunities Employee Match Program More Ways to Give Partnerships Corporate Sponsorship About About Us Meet the Team Join the Team Contact Us Ethics and Standards Reports & Financials Press Room Listen Watch Donate Facebook Twitter Instagram YouTube Linkedin Mastodon googletag.cmd.push(function() { googletag.display('div-gpt-ad-1488818411584-0'); }); Transportation

Houston to dallas high-speed rail corridor receives $63.9 million federal grant.

The 240-mile route would take under 90 minutes and travel at a top speed of 204 MPH, according to Amtrak.

Amtrak has received a nearly $64 million grant to continue planning the Texas High-Speed Rail project after several years of stagnation due to the COVID pandemic.

The project — which proposes a less than 90-minute high-speed rail route between Houston and Dallas, with one stop in the Brazos Valley — has been progressing through the early planning and development stages for the past several years as it continues to lobby for support among Texans and representatives alike. According to early concepts of the route, the Houston station would be located at the Northwest Mall site near the interchange of US 290 and Interstate 610.

The $63.9 million grant was awarded last month as part of $153 million in funding made available by the U.S. Department of Transportation's Federal Railroad Administration ( FRA ) in early July. According to FRA, the funds were created as part of the bipartisan Infrastructure Investment and Jobs Act which was passed under the Biden Administration in Nov. 2021. The main goal of the grant money is to "initiate, restore and enhance intercity passenger rail services." This most recent influx of federal money follows on the heels of a $500,000 grant to Amtrak in December 2023.

In August 2023, Amtrak said that it was exploring the possibility of a partnership with Texas Central, the company originally behind the Houston-Dallas corridor concept. Less than a year later, Amtrak's senior vice president and head of high-speed rail development, Andy Byford, announced Amtrak was officially in charge of the project.

"One of the first things Amtrak did in taking over the project was to undertake research to see [if] the demand is still there post-COVID that the same research indicated there was pre-COVID," he said in April during the 2024 Southwestern Rail Conference. "The actual forecast, in terms of the projected ridership, is very strong and that's important because that means you can then make a business case for the capital investment."

It is currently unclear what changes, if any, Amtrak has made to the plans for the Texas High-Speed Rail Corridor, but Byford said during his presentation that the Houston to Dallas route was nearly a perfect candidate.

"You want to have a line that is reasonably easy to construct, that has relatively straightforward topography," he said. "You're looking for routes which maybe have suboptimal alternatives, maybe a very dangerous and overcrowded interstate or overcrowded airports. If you put together all those characteristics and then you figure out which route you would build, there's one that really stands out and that is Dallas to Houston."

As of the April presentation, Byford said Amtrak and the Japanese government have entered a non-binding agreement to move the project forward again. According to Byford, the hope would be to use an N700S Series Shinkansen train from Japan. This would mean the 240-mile route between Houston and Austin could be completed in under 90 minutes at 205 MPH, which would be the fastest average train journey in the world.

"The Shinkansen has a flawless safety record," he said. "It has not had a single chain-cause fatality in its whole operation since 1964 and that's because what you're buying is a system."

If Amtrak can accomplish its ambitious goal, Byford said it could begin a new age of high-speed rails across the U.S.

"If we are successful in putting together that funding package ... then we will be looking to open in the early 2030s and that includes testing, commissioning, trial operations, and everything else," he said. "So, watch this space. There [are] still a lot of big hurdles to overcome, but I really do think that this, if we can pull it off, will be an absolute jewel in Texas's crown."

Amtrak did not immediately respond to a request for comment regarding the new grant, but Byford told the Texas Rail Advocates that the project has now progressed into the final step of the FRA Corridor Identification Program.

However, not everyone is in favor of the proposed high-speed corridor, including the organization ReRoute the Route — which was created by "Texas business and civic leaders" to lobby against the corridor's creation.

Citing the federal budget deficit and the nation's more than $35 trillion in debt, federal affairs advisor to ReRoute the Route, John Sitilides, said the nearly $64 million should be spent on something else.

"Because Texas is not Japan or China or Europe, the Texas Legislature prohibits wasting a single state taxpayer dollar on this boondoggle's severe public hazards," he said in a statement to Houston Public Media. "The White House would better spend that $64 million to build or repair schools, hire hundreds of border patrol agents, or deliver health care to thousands of veterans in need."

Resources like these are made possible by the generosity of our community of donors, foundations, and corporate partners. Join others and make your gift to Houston Public Media today! DONATE

Sign up for our daily weekday newsletter - hello, houston sign up now.

Houston-Dallas high-speed rail line project gets $64 million in funding

J ust months after  Amtrak took over Texas’ bullet train plan, the project was awarded a multi-million-dollar grant. The high-speed rail development connecting Dallas and Houston recently received $63.9 million in funding as part of the Corridor Identification and Development Program. 

The proposed corridor would connect Texas’ largest metropolitan cities by operating a new high-speed rail at “speeds of up to 186 mph or greater, primarily or solely on new, dedicated alignment,” according to the U.S. Department of Transportation’s Federal Railroad Administration. 

In a conversation with Amtrak, president Roger Harris and SVP Andy Byford, senior vice president of high-speed rail programs for Amtrak, talked about what he said made the cities ideal for such a project. 

SWITCHING GEARS: Amtrak is taking the reins of Texas’ bullet train plan. Is a Houston-Dallas rail line in the cards?

“You’ve got a good intermediate stop at college,” Byford said. “You’ve got suboptimal mobile alternatives. You got the I-45 interstate, which (is) very, very crowded and (a) quite dangerous interstate.” 

Byford also said other travel options between Dallas and Houston weren't as efficient as the potential high-speed rail. 

“There’s a lot of people flying between Dallas and Houston,” he said. “But, to be fair, it’s really kind of too short to fly you know by the time you get to the airport and (go) through everything.” 

The Chronicle previously reported on the slow-moving process to bring the concept to life. It’s been a discussion 30 years in the making. Officials estimate the project will cost more than $30 billion. The service would have station stops in Dallas, the Brazos Valley and Houston. With the corridor sponsor, the project enters into  “Step 1 of the program to develop a scope, schedule, and cost estimate for preparing, completing, or documenting its service development plan.”

“What we’re looking to do is introduce the legendary Japanese bullet train — the shinkansen,” Byford said. “People are just going to be blown away when they experience that. I have been lucky enough to go to Japan and ride it and that train will cover the 240 miles at 205 miles per hour. The average speed will be 187 miles per hour, which would be the fastest average speed in the world.” 

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Elsevier - PMC COVID-19 Collection

The impact of high-speed railway on tourism spatial structures between two adjoining metropolitan cities in China: Beijing and Tianjin

a Department of Tourism Management, School of Economics and Management, Beijing Jiaotong University, China

b Durham University Business School, Mill Hill Lane, Durham DH1 3LB, United Kingdom

Bruce Prideaux

c School of Business and Law, Central Queensland University, Australia

This study examines the impact of HSR services on the tourism spatial interactions between Beijing and Tianjin in China. Data were collected from official statistical reports. A method of derivation was developed and several indexes, such as tourism mean center, and tourism standard distance are further applied to measure temporal-spatial changes between the two adjoining cities. The results reveal the dynamic tourism spatial interaction between Beijing and Tianjin has been influenced by a range of factors including population, destination attractiveness, disposable income and income elasticity, changes in the domestic and international spatial structure of tourist flows and how destination management organizations react to the changes. The study has implications for both the research and practice of city transportation and tourism development.

1. Introduction

In recent years China has undergone a period of rapid High Speed Rail (HSR) construction and now has the world's largest HSR network ( Wang et al., 2018 ; Yang et al., 2019 ). By the end of 2017, China's HSR network had grown to 25,000 km ( China Ministry of Transport, 2018 ) and is planned to increase to 30,000 km by 2020 ( China Ministry of Transport, 2017 ). It is expected that the temporal and spatial changes facilitated by the HSR system ( Chen, 2019 ) will both booster domestic tourism flows and generate significant changes in the structure of tourism flows ( Yin et al., 2019 ). Theoretically, the compression of travel time and space induced by short-distance HSR services provides opportunities for adjoining cities to be regarded as the “same city”. In practice, however, adjoining cities tend to operate independently, and each can be expected to adopt strategies that will increase its competitiveness vis à vis the other city. Understanding the current spatial relationship between adjacent cities and the impact potential impact of HSR has important implications in relation to collaboration on tourism policy development and planning.

Previous studies have examined HSR's impacts on regional medium-sized cities in France and Spain ( Bazin et al., 2006 ; Coronado et al., 2013 ; Ureña et al., 2009 ), on metropolitan cities including Madrid, Paris, and Rome ( Delaplace et al., 2014 ; Garmendia et al., 2012 ; Pagliara et al., 2015 ), on the intermediate areas between major metropolitan areas ( Vickerman, 2015 ), and on cities along HSR routes ( Chen and Haynes, 2015 ; Wang et al., 2012 ; Wang et al., 2014b ; Yan et al., 2014 ). Empirical studies conducted in China show that there has been an increase in accessibility of all cities and regions along HSR lines ( Liu and Zhang, 2018 ). Research has also found that regional economic disparity has decreased since the introduction of HSR ( Chen and Haynes, 2017 ), with the exception of a number of central-Eastern cities where there is some evidence that they might gained greater accessibility benefits from HSR than other regions ( Cao et al., 2013 ). HSR promotes wider destination choice, which can create significant changes in the spatial distribution of tourism resources ( Wang et al., 2012 ).

Despite the growing research interest in the effects of HSR, the impact of HSR on the temporal-spatial pattern of tourism flows between adjoining city pairs remain unexplored. This study aims to contribute to the transport and tourism literatures by addressing this gap by assessing the impact of high-speed railway on tourism spatial structures between two adjoining metropolitan cities. We chose Beijing and Tianjin for this study based on the size of the two cities, their adjacent location, well-developed tourism infrastructure, and the length of time that HSR has been operating ( Wang et al., 2018 ). Specifically, this study attempts to understand: a) the impact that HSR can have on the dynamics of tourism spatial interaction between Beijing and Tianjin; b) the changes in spatial structure of both international and domestic tourist flows between the city pair. In addition, we also examine how each city responded to the impacts of HSR in its tourism development and marketing policies, as evidenced by the impact of high-speed rail on tourism spatial structure.

2. Literature review

Transportation is an essential component of tourism infrastructure ( Wang et al., 2018 ). In general, tourism demand is negatively related to distance, i.e. the longer the distance, the smaller the demand. This is the so-called “distance-decay effect” ( Bull, 1991 ). Geographic distance is invariable, but travel time can be reduced with the introduction of new transportation technology thus stimulating tourism demand. In addition, reduced travel time enables tourists to spend more time enjoying tourism activities at a destination. Travel time thus replaces distance as a determinant of tourist demand in the gravity model, widely used to investigate interaction between spaces ( Gu and Pang, 2008 ; Prideaux, 2000 ). The result is time-space compression known as the “time compression effect” of HSR ( Wang et al., 2018 ). The “time compression effect” also provides destinations connected to the HSR network with the opportunity to grow the level of tourist arrivals to that destination ( Zhou and Li, 2018 ).

The opening of a HSR will increase accessibility in general ( Ravazzoli et al., 2017 ) and can will disrupt regional spatial structures ( Wang et al., 2018 ) to the extent that there may be both winner and loser cities ( Fröidh, 2005 ; Wang et al., 2019 ). Chen and Haynes (2015) found significant positive effects of HSR on accessibility as well as economic convergence in several regions of China. Similarly, Liu and Zhang (2018) further confirmed that HSR the increased accessibility and reported a reduction of access disparity within regions but not between regions. Examining the potential HSR in the Piedmont Atlantic Megaregion in the US, Yu and Fan (2018) estimate how HSR will improve the megaregional accessibility but they also predicted an increase of inequality in accessibility. In the UK, Fröidh (2005) suggested that while the building of HSR will disrupt the country's geography, it may not provide significant overall accessibility benefits. Vickerman (2015) for example found that cross-border inter-regional HSR services such as Europe's Trans-European Transport Network initiative has failed to reduce regional disparities in accessibility or to integrate regions across national borders in many regions.

HSR may also generate changes in the spatial distribution of industry ( Chen and Hall, 2011 ). A study by Gimpel (1993) found that France's TGV network has played an important role in changing the socio-economic and spatial patterns in the regions it services. Plassard (1991) observed that a centralizing effect occurred in France where Paris has become the center of the star-shaped TGV network. Masson and Petiot (2009) noted that after the introduction of HSR between Paris and Marseille in 2001 there was an increase in short stay travel to Marseille and as well as a change in travel by specific market sectors such as seniors and international travelers. However, the introduction of HSR does not automatically lead to increased tourist flows. The construction of a HSR line from Perpignan in southern France to Spain generated increased flows of French visitors to Spain but not of French visitors to Perpignan ( Masson and Petiot, 2009 ), because French tourists were more attracted to Spanish cities such as Barcelona than Perpignan.

HSR can also trigger tourism spatial competition between linked cities, as in the case of Perpignan and Barcelona ( Masson and Petiot, 2009 ). In response to the changes brought by HSR, destinations may develop policies to differentiate their tourism appeal through marketing and the introduction of new products ( Chen and Hall, 2011 ; Masson and Petiot, 2009 ). A “structuring effect” occurs where the introduction of a new transport system assists local actors to maximize the utility of pre-existing structures and relationships or encourages policy makers to adopt complimentary policies that utilize HSR as a change agent ( Masson and Petiot, 2009 ).

HSR may also facilite changes in the spatial structure of regional urban tourism, offering favorable conditions for regional tourism cooperation and stimulating integration and aglormeation of urban resources ( Wang et al., 2018 ). For example, Liang (2010) reported a pattern of cooperation among a number of Chinese cities including Guangzhou, Changsha, and Wuhan. Zhou and Li (2018) observed a similar patten with the Wuhan-Guangzhou HSR that has helped optimize the opportunities for tourism co-operation between cities within the Delta area including offering multi-destination itinerates using the savings in time achieved by using HSR. Using economic relation model and spatial analysis of 338 cities across China, Wang et al. (2018) presented a tourism spatial structure with 19 urban agglomerations. Recently, Huang et al. (2019) have shown that the influence of HSR on the urban agglomeration tourism system is increasing.

The development of HSR systems also stimulates inter-city travel ( Hou et al., 2011 ). HSR attracts travelers who previously used other transport modes leading to changes in travel behavior ( Fröidh, 2005 ). Since the opening of Beijing-Tianjin HSR in 2008, inter-city commuting traffic has increased with commuters working in Beijing and living in Tianjin. In this way HSR can influence commuters' space feeling, facilitating a life-style based on inter-city commuting ( Hou et al., 2011 ). Zhang et al. (2013) examined HSR's impact on urban tourism in Nanjing and found that HSR expands tourists' route choice, range and frequency of visits, but tourist stay time may be reduced. However, little is known about the impact of HSR on the change of inter-city tourism spatial structures.

3. Methodology

3.1. research context.

The area around Beijing and Tianjin (the Jingjin Region, Fig. 1 ) has experienced rapid development in recent decades and is one of the most heavily urbanized region in China. Prior to the opening of the Beijing to Tianjin HSR service in 2008 the region suffered significant passenger congestion. Prior to 2007, the average speed of the rail service connecting Beijing and Tianjin was 98–110 km per hour, and the travel time was about 2 h. In April 2007, the average speed was increased to 200 km per hour with a travel time of 69 min. The introduction of HSR services in 2008 led to a decrease in travel time to 34 min. The designed speed of the HSR service is 350 km/h although the commercial speed is limited to 300 km/h during normal service (see Table 1 ).

Beijing-Tianjin HSR.

Types of Trains Service between Beijing and Tianjin.

Construction of the Beijing to Tianjin HSR commenced in 2005 and was completed in August 2008 with a total length of 113.54 km. The line passes through the directly governed city regions of Beijing and Tianjin with no stops (See Fig. 1 ). By the end of the first full 12 months of operation, the Beijing-Tianjin HSR had transported over 18.7 million passengers ( Qi and Wang, 2009 ).

3.2. Research design, unit of analysis

We used a revised Wilson Model ( Li et al., 2012 ), described as Tourism Spatial Interaction (TSI) to measure the tourism spatial interaction between Beijing and Tianjin over the period 2002 to 2017. We applied the first derivative of the TSI versus different factors to compare the impact of a range of factors including population, destination attractiveness, disposable income and income elasticity on tourism spatial interaction. The temporal-spatial structure changes are based on tourism mean center ( Hobbs and Stoops, 2002 ) and tourism standard distance ( Kim, 2000 ). The research region is divided into two units, which equate to the areas of the respective local government administrative boundaries. Data from 2000 through to 2017 on tourist numbers and tourism enterprises in each zone were obtained from annual statistic bulletins and reports published by local tourism administrations and the Ministry of Culture and Tourism of China (Formerly China National Tourism Administration). It should be noted that the tourism data for 2003 was skewed by the fall in passenger traffic during the 2003 Severe Acute Respiratory Syndrome crisis and is treated as unordinary data.

3.2.1. Tourism Spatial Interaction (TSI)

A spatial interaction is a realized movement of people, freight or information between an origin and a destination ( Rodrigue et al., 2016 ). TSI is a key representation of the level of tourism industry development level between tourism origin and destination and is described in gravity models ( Haynes and Fotheringham, 1984 ; Lowe and Moryadas, 1975 ; Roy and Thill, 2004 ; Sen and Smith, 2012 ). Gravity models are often used to explain bilateral tourism movements between two geographic areas ( Morley et al., 2014 ). Empirical support focusing on international tourism can be found in tourism flow analysis ( Keum, 2010 ; Khadaroo and Seetanah, 2008 ) although some inherent defects in the model are still present ( Olsson, 1967 ). Wilson's model ( Wilson, 1967 , Wilson, 1970 ) with exponential deterrence function becomes a possible alternative. Li et al. (2012) presented a revised Wilson's model with three important coefficients basing on traditional regression method based on data from China. The models are:

Where T jk is the tourism spatial interaction between origin j and destination k ； A k presents the attractiveness of destination k ; P j C j α is the tourism demand capacity for destination k from origin j , where P j is the amount of population and C j is the average disposable income in origin j ; r jk is the distance between j and k ; α is the income elasticity index, indicating the degree of change in the amount of demand caused by changes in income； β is the coefficient of spatial damping, which determines the influence of distance on spatial interaction; K is a balancing factor.

Li et al. (2012) evaluated K, α and β . α , understood as income elasticity, was estimated using the traditional regression method, and the result is 0.64. β was estimated using “integral method on tourist amount” (IMTA) ( Li et al., 2012 ), and the result is shown in Table 2 .

Parameterβ.

Given that the distance between Beijing and Tianjin is about 150 km (which is <500 km), β might be a number between 0.04 and 0.02724. Since the purpose of this paper is to estimate the TSIs between the two cities and to judge the trend of TSIs change from the perspective of time, the average of 0.04 and 0.02724 is placed on β , which is 0.0337.

K was estimated using the data of the whole country from 1999 to 2008 in Li et al. (2012) . It is not appropriate to use the number directly given these were calculated from the data for China as a whole. We calculate K basing on the Eq. (2) :

The results of K is shown in Table 3 .

Results of K.

The equation for TSI between Beijing and Tianjin is shown as following:

In China, the quality of a tourist scenic spot (or tourist attraction) is rated using a coding system (AAAAA, AAAA, AAA, AA, and A) with five As designating the highest quality scenic spot. We use the number of A-level scenic spots as the measure of A k . Population size, disposable income and A-level Scenic Spots are published in the Annual Statistical Bulletin of Economic and Social Development for each city. The shortest travel time is chosen as the measure of distance between j and k ，since the physical distance is replaced by temporal distance because of the advancement of transportation technology ( Wang et al., 2014a ) and ‘the shrinking continent’ effect described by ( Spiekermann and Wegener, 2008 ).

The method of derivation is introduced to compare the impact derived from different variables of spatial interaction. The more absolute the value of the derivative is, the higher impact is derived from that variable. Derivation is expressed as follows:

3.2.2. Tourism mean center and standard distance

To examine if there are changes in the tourism spatial structure before and after the operation of HSR, tourism mean center and standard distance are used in this paper. Mean center is used to identify the geographic center for a set of features and the U.S. Census Bureau ( Hobbs and Stoops, 2002 , pp. B-4) defined the indicator as “The point at which an imaginary, flat, weightless, and rigid map of the United States would balance perfectly if weights of identical value were placed on it so that each weight represented the location of one person on the date of the census”. The United States Census Bureau uses mean center to measure changes the population in the US ( Hobbs and Stoops, 2002 ).

As Kim (2000) stated ‘Standard distance is a measure of spatial dispersion, indicating whether an attribute (e.g. population) is widely dispersed with a high standard distance or concentrated’. Because standard distance gauges dispersion around the mean center, it is sensitive to extreme cases ( Kim, 2000 ) and is an appropriate tool for describing dispersion patterns where there are only minor changes underway in the periphery of the area under study.

Mean center and stand distance are calculated as follows:

where, ( x ,  y ) represents the tourism mean center, D is the standard distance. P i is the amount of tourists of the tourism unit i ，and ( x i ,  y i ) is the geographical coordinates of the unit i . R is a constant term to convert the spherical distance to the plane distance which is 111.32.

In this paper, we use the coordinates of the HSR stations as the coordinate for Beijing and Tianjin separately, which are (116.38,39.87) for Beijing South Railway Station and (117.12,39.08) for Tianjin Railway Station. R can be found in the Annual Statistic Bulletin of Economic and Social Development of each city. Geographic Information System Mapping package Arcgis 10.3 is used for processing of data and illustrating results.

4. Findings

4.1. tsis between beijing and tianjin and the influence from different factors.

The tourism spatial interactions between Beijing and Tianjin were examined according to formula (3) , (4) and presented in Table 4 and Fig. 2 . Two major findings can be observed from these results.

Results of TSI and derivation of different factors.

Actual number of 0.15* is 0.152, actual number of 0.15** is 0.146.

Actual number of 0.09* is 0.095, actual number of 0.09** is 0.088.

TSIs between Beijing and Tianjin from 2002 to 2017.

First, TSIs between Beijing and Tianjin have increased greatly over the period of the study and can be attributed to growth in population and disposable income, increased tourist attractions, but most importantly to decreased travel time. The TSI between Beijing to Tianjin is larger than that between Tianjin to Beijing indicating that Beijing is more attractive to Tianjin than the other way round.

Second, the growth of TSIs from 2002 to 2017 can be grouped into three stages. During stage one (2002 to 2006), the curve was quite steady, suggesting that there is little increase of TSIs. Stage 2 occurred between 2007 and 2008 when the TSIs increased dramatically. As previously mentioned, the travel time between Beijing and Tianjin decreased from 120 min to 69 min in 2007 and to 34 min in 2008. We can also observed the correspondingly increases of TSIs in 2007 and 2008 in both directions. After 2008, the growth of TSIs continued at high but different rates. The reduction in travel times led to an increase in K in the TSI equation and in turn lead to increases in TSIs.

The impact of input factors on the spatial effect is shown in Table 4 . From the size of the impact of these factors, we can determine the impact of HSR services on the spatial effects of the two cities.

The results show that the impact of travel time brought about by HSR on TSIs has been the most dramatic. Travel time was the least important factor on TSIs from Beijing to Tianjin before 2005 and its importance was only higher than disposable income in 2006 and 2007 but become the most important factor after 2008.

4.2. Changes in domestic tourists flows' spatial structure

Analysis of the spatial changes in domestic tourist movements between Beijing and Tianjin using mean center and standard distance revealed that there have been three distinct periods of changes in tourist spatial structure between 2000 and 2017. The first period from 2000 to 2008 (see Fig. 3 ) represents the period prior to the opening of the HSR service. During this period, the domestic tourism mean center moved southeastwards towards Tianjin. This occurred because of the “pull” exerted by tourism development in Tianjin and the “push” exerted by the emissiveness in Beijing. The standard distance from the mean center increased from 35.98 in 2000 to 52.16 in 2008, indicating greater tourist dispersion (see Table 5 ). The dispersion is shown in ( Fig. 3 ).

Domestic tourists mean centers from 2000 to 2017.

Tourism mean center and standard distance in Beijing and Tianjin.

In the second period from 2009 to 2011, the tourism mean center moved north-west towards Beijing (see Fig. 3 ). The standard distance away from the mean center declined from 52.16 in 2008 to 50.36in 2011, indicating that tourists began to concentrate in Beijing after 2008. While the 2008 Olympic Games and post-games interest in visiting Olympic sites were likely to be significant factors behind the change of tourist flows spatial structure, the operation of HSR should not be ignored.

In the third period (2012-2017), the tourism mean center along the Jingjin HSR line reversed as it moved back towards Tianjin (see Fig. 3 ). In this period, the standard distance from the mean center increased from 50.59 in 2012 to 56.19 in 2017. The change in both mean center and standard distance is illustrated by the shift in tourist activities and dispersion towards Tianjin during 2012 to 2017.

4.3. Changes in international tourists' flows' spatial structure

In the case of international tourists, with the exception of 2003 and 2010, the mean center moved towards Tianjin from 2000 to 2014 and from 2015 to 2017 (see Fig. 4 ). The standard distance continued to increase during this period, suggesting the emergence of a more pronounced pattern of international tourist's dispersion. The dispersion is shown in Fig. 4 .

International tourists mean centers from 2000 to 2017.

4.4. The difference between domestic and the international spatial structures

The pattern of distribution of international tourists over the study period is different to that exhibited by domestic tourists with the mean center of domestic tourist distribution being closer to Beijing compared to the mean center of international tourists. This pattern suggests that international tourists have a higher propensity to undertake a one-day trip to Tianjin when visiting Beijing. Domestic tourists also began concentrating back to Beijing after the opening of the HSR service and diffused to Tianjin as additional HSR services were introduced. The results indicate that the spatial structure of international tourist in relation to travel to Tianjin remain the same with or without HSR services.

5. Discussion and conclusions

The objective of this study was to examine how HSR influence tourist flows and spatial relationships between two linked city destinations. The findings indicate dynamic patterns of spatial interaction of the two adjoining cities, which has to date not been investigated the transport geography literature.

The findings of this study advance our understanding of the impact that changes in time-space will have on the tourism spatial interactions of two adjoining metropolitan cities. The change brought about by HSR can have a strong impact on the tourism industry in comparison with other input factors, such as disposable income and tourist attractions which have weakened impact on TSIs.

We found that domestic and international tourists exhibited different distribution patterns. The mean center of domestic tourist's distribution was closer to Beijing than that of international tourists. Domestic tourists initially concentrated in Beijing after the opening of HSR services but became more interested in Tianjin after more frequent HSR services were added. This is a new finding that has not been previously reported. In contrast, the spatial structure of international tourist has continued to move towards Tianjin with or without HSR, which is consistent with the findings by Chen and Haynes (2015) and Pagliara et al. (2015) that intercontinental tourists are less likely to be affected by HSR operations.

The study's findings further support Hannam et al.' (2014) argument that the impact of new mobility capabilities such as the HSR can assist in the creation of new tourism infrastructure such as hotels and leisure complexes. Our study shows that the initial decline in domestic tourism numbers from Beijing spurred the Tianjin tourism authorities to develop new attractions to regain the market share that was lost between 2008 and 2011. It also suggests that it is possible to counter the centralizing effect as suggested by Plassard (1991) by either developing new marketing strategies or new infrastructure development, or both.

There were both similarities and differences of the impacts of HSR in Europe and in China. For example, the relationship between the opening of the HSR and the change in the location of the mean center appears to be an example of the operation of the “structuring effect” where firms, in this case Beijing's tourism sector, were able to take advantage of the change and boost business. It is also apparent that in the post 2008 period there was a change in the intensity of competition which in turn influenced the location of tourism firms. This change highlights how the intensity of competition between connected destinations may increase because of the agglomeration effect as described by Masson and Petiot (2009) and Yan et al. (2014) .

The findings of this study also indicate that changes in passenger flows may be reversed if cities develop new or refreshed products and mount successful marketing campaigns. Unlike some of their French counterparts ( Gimpel, 1993 ), neither Beijing nor Tianjin accepted that the changes caused by the HSR were irrevocable and both cities mounted marketing campaigns, and in some cases constructed new infrastructure, to defend their positions.

This research considered the HSR connection between Beijing and Tianjin. Not all factors that may influence the changes of tourism spatial structures between the two adjoining metropolitan cities were analyzed including the effect of expressways linking Beijing and Tianjin and the impact of HSR services in wider region. Other factors not considered include the influence of improvements to the region's entire transportation network and tourism marketing efforts by competing destinations. There is an opportunity to investigate these factors in future research. Moreover, there is also considerable scope to further investigate the effect of time/space compression noted by Plassard (1991) and to build new understandings of the boost that can accrue to local economies.

The latest expansion of the HSR network in the larger Jingjinji Area (Beijing, Tianjin and Hebei Province) provides an additional opportunity to examine the impact of HSR on tourism mobility within the larger region. The expanded HSR network will connect all cities in the Jingjinji Area and is likely to have a huge influence on tourists' motivation, the location of tourism enterprises and tourism spatial structure.

Acknowledgement

This study is partially supported by the Fundamental Funds for Humanities and Social Sciences of Beijing Jiaotong University (2019JBWB002) and Beijing Social Science Fund (18GLB037).

• Bazin S., Beckerich C., Delaplace M., Masson S. L'arrivée de la LGV en Champagne-Ardenne et la nécessaire réorganisation des rapports de proximité les Cahiers Scientifiques des transports. 2006; 49 :51–76. [ Google Scholar ]
• Bull A. Longman Cheshire Pty Ltd.; Melbourne: 1991. The Economics of Travel and Tourism. [ Google Scholar ]
• Cao J., Liu X.C., Wang Y., Li Q. Accessibility impacts of China's high-speed rail network. J. Transp. Geogr. 2013; 28 :12–21. [ Google Scholar ]
• Chen Z. Measuring the regional economic impacts of high-speed rail using a dynamic SCGE model: the case of China. Eur. Plann. Stud. 2019:1–30. [ Google Scholar ]
• Chen C.-L., Hall P. The impacts of high-speed trains on British economic geography: a study of the UK's InterCity 125/225 and its effects. J. Transp. Geogr. 2011; 19 (4):689–704. [ Google Scholar ]
• Chen Z., Haynes K.E. Impact of high-speed rail on international tourism demand in China. Appl. Econ. Lett. 2015; 22 (1):57–60. [ Google Scholar ]
• Chen Z., Haynes K.E. Impact of high-speed rail on regional economic disparity in China. J. Transp. Geogr. 2017; 65 :80–91. [ Google Scholar ]
• China Ministry of Transport. (2017). Report on the Development of Transportation Industries 2017 (in Chinese). Retrieved from http://zizhan.mot.gov.cn/zfxxgk/bnssj/zhghs/201803/t20180329_3005087.html
• China Ministry of Transport. (2018). The 13th Five-Year Plan for the Railways (in Chinese). Retrieved from http://www.mot.gov.cn/zhuanti/shisanwujtysfzgh/guihuawenjian/201712/P020171201297878609466.pdf .
• Coronado J.M., Garmendia M., Moyano A., Ureña J.M. Assessing Spanish HSR network utility for same-day tourism. Recherche Transports Sécurité 2013; 2013 :161–175. [ Google Scholar ]
• Delaplace M., Pagliara F., Perrin J., Mermet S. Can High Speed Rail foster the choice of destination for tourism purpose? Procedia Soc. Behav. Sci. 2014; 111 :166–175. [ Google Scholar ]
• Fröidh O. Market effects of regional high-speed trains on the Svealand line. J. Transp. Geogr. 2005; 13 (4):352–361. [ Google Scholar ]
• Garmendia M., Romero V., Ureña, J. M. d., Coronado, J. M., & Vickerman, R. High-speed rail opportunities around metropolitan regions: Madrid and London. J. Infrastruct. Syst. 2012; 18 (4):305–313. [ Google Scholar ]
• Gimpel W. Paper presented at the High Speed Ground Transportation Systems I: Planning and Engineering. 1993. The effects of HSGT on urban decentralization. [ Google Scholar ]
• Gu, C.-l., & Pang, H.-f. Study on spatial relations of Chinese urban system: gravity model approach. Geogr. Res. 2008; 1 :1–12. [ Google Scholar ]
• Hannam K., Butler G., Paris C.M. Developments and key issues in tourism mobilities. Ann. Tour. Res. 2014; 44 :171–185. [ Google Scholar ]
• Haynes K.E., Fotheringham A.S. Sage; Beverly Hills, CA: 1984. Gravity and Spatial Interaction Models. [ Google Scholar ]
• Hobbs F., Stoops N. vol. 4. US Census Bureau; Washington, DC: 2002. Demographic Trends in the 20th Century. [ Google Scholar ]
• Hou X., Liu S., Zhang W., Hu Z. Characteristics of commuting behaviors between Beijing and Tianjin influenced by high speed train. Econ. Geogr. 2011; 31 (9):1573–1579. [ Google Scholar ]
• Huang T., Xi J.-C., Ge Q.-S. Spatial differentiation and integration optimization of an urban agglomeration tourism system under the influence of high-speed railway network evolution. Appl. Spatial Anal. Policy. 2019; 12 (2):349–376. [ Google Scholar ]
• Keum K. Tourism flows and trade theory: a panel data analysis with the gravity model. Ann. Reg. Sci. 2010; 44 (3):541–557. [ Google Scholar ]
• Khadaroo J., Seetanah B. The role of transport infrastructure in international tourism development: a gravity model approach. Tour. Manag. 2008; 29 (5):831–840. [ Google Scholar ]
• Kim K.S. High-speed rail developments and spatial restructuring: a case study of the capital region in South Korea1. Cities. 2000; 17 (4):251–262. [ Google Scholar ]
• Li S., Wang Z., Zhong Z. Gravity model for tourism spatial interaction: basic form, parameter estimation, and applications. Acta Geograph. Sin. 2012; 67 (4):129–147. [ Google Scholar ]
• Liang X. Discussion on the development opportunity of tourism industry in Hunan province based on the dual location space. Econ. Geogr. 2010; 30 (5):859–864. [ Google Scholar ]
• Liu L., Zhang M. High-speed rail impacts on travel times, accessibility, and economic productivity: a benchmarking analysis in city-cluster regions of China. J. Transp. Geogr. 2018; 73 :25–40. [ Google Scholar ]
• Lowe J.C., Moryadas S. Houghton Mifflin; Boston: 1975. The Geography of Movement. [ Google Scholar ]
• Masson S., Petiot R. Can the high speed rail reinforce tourism attractiveness? The case of the high speed rail between Perpignan (France) and Barcelona (Spain) Technovation. 2009; 29 (9):611–617. [ Google Scholar ]
• Morley C., Rosselló J., Santana-Gallego M. Gravity models for tourism demand: theory and use. Ann. Tour. Res. 2014; 48 :1–10. [ Google Scholar ]
• Olsson G. Central place systems, spatial interaction, and stochastic processes. Papers Regional Sci. Assoc. 1967; 18 (1):13–45. [ Google Scholar ]
• Pagliara F., La Pietra A., Gomez J., Vassallo J.M. High Speed Rail and the tourism market: evidence from the Madrid case study. Transp. Policy. 2015; 37 :187–194. [ Google Scholar ]
• Plassard F. Le train à grande vitesse et le réseau des villes. Transports. 1991; 345 :14–23. [ Google Scholar ]
• Prideaux B. The role of the transport system in destination development. Tour. Manag. 2000; 21 (1):53–63. [ Google Scholar ]
• Qi Z., Wang Y. Annual report for High-speed Railway between Beijing and Tianjin. 2009. http://www.gov.cn/jrzg/2009-08/02/content_1381587.htm Retrieved from.
• Ravazzoli E., Streifeneder T., Cavallaro F. The effects of the planned high-speed rail system on travel times and spatial development in the European Alps. Mt. Res. Dev. 2017; 37 (1):131–140. [ Google Scholar ]
• Rodrigue J.-P., Comtois C., Slack B. Routledge; London: 2016. The Geography of Transport Systems. [ Google Scholar ]
• Roy J.R., Thill J.C. Spatial interaction modelling. Pap. Reg. Sci. 2004; 83 (1):339–361. [ Google Scholar ]
• Sen A., Smith T.E. Springer; Chicago, IL: 2012. Gravity Models of Spatial Interaction Behavior. [ Google Scholar ]
• Spiekermann K., Wegener M. The shrinking continent: Accessibility, competitiveness, and cohesion. In: Faludi A., editor. European Spatial Research and Planning. Lincoln Institute of Land Policy; Cambridge, MA: 2008. pp. 115–140. [ Google Scholar ]
• Ureña J.M., Menerault P., Garmendia M. The high-speed rail challenge for big intermediate cities: a national, regional and local perspective. Cities. 2009; 26 (5):266–279. [ Google Scholar ]
• Vickerman R. High-speed rail and regional development: the case of intermediate stations. J. Transp. Geogr. 2015; 42 :157–165. [ Google Scholar ]
• Wang D., Chen T., Li L., Zhang Y. Enlightenment and research of tourism impact on high-speed rail. Sci. Geogr. Sin. 2012; 32 (3):322–328. [ Google Scholar ]
• Wang J., Jiao J., Jin F. Spatial effects of high-speed rails on interurban economic linkages in China. Acta Geograph. Sin. 2014; 69 (12):1833–1846. [ Google Scholar ]
• Wang D., Qian J., Chen T., Zhao M., Zhang Y. Influence of the high-speed rail on the spatial pattern of regional tourism–taken Beijing–Shanghai high-speed rail of China as example. Asia Pacific J. Tour. Res. 2014; 19 (8):890–912. [ Google Scholar ]
• Wang D., Niu Y., Qian J. Evolution and optimization of China's urban tourism spatial structure: a high speed rail perspective. Tour. Manag. 2018; 64 :218–232. [ Google Scholar ]
• Wang L., Zhang W., Duan X. Speed Increases and Distance Deductions. Applied Spatial Analysis and Policy; China: 2019. Understanding Accessibility Changes from the Development of a High-Speed Rail Network in the Yangtze River Delta; pp. 1–19. [ Google Scholar ]
• Wilson A.G. A statistical theory of spatial distribution models. Transp. Res. 1967; 1 (3):253–269. [ Google Scholar ]
• Wilson A.G. Pion; London: 1970. Entropy in Urban and Regional Models. [ Google Scholar ]
• Yan Y.Q., Zhang H.Q., Ye B.H. Assessing the impacts of the high-speed train on tourism demand in China. Tour. Econ. 2014; 20 (1):157–169. [ Google Scholar ]
• Yang Y., Li D., Li X. Public transport connectivity and Intercity tourist flows. J. Travel Res. 2019; 58 (1):25–41. [ Google Scholar ]
• Yin P., Pagliara F., Wilson A. How does high-speed rail affect tourism? A case study of the capital region of China. Sustainability. 2019; 11 (2):472. [ Google Scholar ]
• Yu M., Fan W. Accessibility impact of future high speed rail corridor on the piedmont Atlantic megaregion. J. Transp. Geogr. 2018; 73 :1–12. [ Google Scholar ]
• Zhang, W.-x., Liu, X.-x., Yang, C.-z., Lyu, G.-w., & Hou, X. Study on the impact of high speed railway on urban tourism: taking Nanjing as an example. Econ. Geogr. 2013; 33 (7):163–168. [ Google Scholar ]
• Zhou B., Li N. The impact of high-speed trains on regional tourism economies: empirical evidence from China. Tour. Econ. 2018; 24 (2):187–203. [ Google Scholar ]

• Bahasa Indonesia
• Slovenščina
• Science & Tech
• Russian Kitchen

A traveler's guide to Novosibirsk, the unofficial capital of Siberia

Trans-Siberian heritage

Residents of Novosibirsk love trains and are proud of the fact that their city played a significant role in the history of the grand Trans-Siberian railway, which spans the breadth of Russia. The railway is such a part of Novosibirsk identity that it is even depicted on the city’s emblem, along with the bridge that crosses the Ob river and two Siberian sables standing on their hind legs.  

In the city, there are as many as five monuments to trains, and an open-air locomotive museum is located in the vicinity of the train station Seyatel’. The museum has more than 100 steam locomotives, diesel locomotives and carriages, reflecting the history of rail transportation in Russia from pre-revolutionary times to the present day. Wondering around the stationary trains and comparing your height with the diameter of the gigantic iron wheels of the first steam locomotives is all very well, but why not climb inside the carriages and see how the nobility once traveled across Russia in pre-revolutionary times? These tours will however need to be booked in advance. The museum opens from 11:00 until 17:00 every day except Mondays. 

Novosibirsk spans both sides of the river Ob. In the early twentieth century, the border of two different timezones passed right through the city which led to a strange situation- morning on the east bank started one hour earlier than on the west bank! The two-kilometer covered metro bridge that crosses the river is considered the longest in the world. Due to the fluctuations in temperature across the year (on average +30 °C to -30 °C), during the summer the metro bridge expands, and in the winter it contracts by half a meter. To counter these effects, the bridge’s supports are equipped with special rollers that allow it to move.   

The cultural center of Siberia

The repertoire of the theatre can be viewed on its official website . The theatre season runs from September to July, and comprises mainly classical performances, like the ballet “The Nutcracker” by Tchaikovsky, Borodin’s opera “Prince Igor” and Verdi’s “La Traviata”.  

The large Siberian sea and ligers

Weekends are best spent at the Novosibirsk zoo . The zoo is known for breeding big cats, although surrounded by controversy, hosts a successful crossing of a tiger and lion, which of course would not otherwise breed in wildlife. Ligers, or exotic cubs of an African lion and Bengal tigress, feel quite comfortable in the Siberian climate and even produce offspring. The zoo is open to visitors year-round, seven days a week, and even has its own free mobile app, Zoo Nsk .

Every year at the beginning of January, the festival of snow culture takes place bringing together artists from across Russia and around the world to participate in a snow sculpting competition. The tradition started in 2000 inspired by the snow festival in Sapporo, Novosibirsk’s twin-city.

Siberian Silicon Valley

Despite the fact that Akademgorodok was built half a century ago in the middle of the uninhabited Siberian taiga, architecturally it was ahead of its time. No trees were destroyed for its construction, and houses were built right in the middle of the forest. A man walking through the woods would seemingly stumble upon these structures. At that time, no one had built anything similar in the world and ecovillages only became fashionable much later.

For residents of the Novosibirsk Akademgorodok is a different world. When you step out the bus or car, you are immediately on one of the hiking paths through the forest, between the scientific buildings and clubs. On a walk through Akademgorodok, it is possible to unexpectedly encounter art-like objects handmade by residents of the city which have been erected as monuments and some monuments fixed up by city authorities. For example, the monument to the laboratory mice, which knits a strand of DNA on to some needles, can be found in the square alongside the Institute of Cytology and Genetics. In Akademgorodok there are many cafes and restaurants, in which it is possible to rest after a long walk. Grab a coffee and go to eat at Traveler’s Coffee , or eat lunch at the grille and bar People’s or Clover .

Winters in the Akademgorodok are slightly colder than in the city, so wrap up. Spring and summer are usually wetter, so waterproof boots are recommended. In the summer the Ob sea provides respite from the heat, so do not forget your swimsuit to go for a dip.

Memento Mori

Among the exhibits of the museum is one dedicated to world funeral culture — hearses, memorial jewellery from the hair of the deceased, samples from a specific photo-genre of  "post mortem", a collection of funeral wear from the Victorian era, deathmasks, statues and monuments. There’s also an impressive collection of coffins. One of them, resembling a fish, was manufactured on a special visit to Novosibirsk by a designer coffin-maker from Africa, Eric Adjetey Anang, who specializes in the production of unusual coffins.

Surprisingly, the crematorium itself does not look at all gloomy in appearance and definitely does not look like infernal scenes from movies, or like crematoriums of other cities that gravitate towards gloomy temple aesthetics. The Novosibirsk crematorium is decorated in “cheerful” orange tones and is surrounded by a park with a children’s playground nearby. A visit to the museum then leaves you with mixed feelings. 

Novosibirsk underground

Tourists from all over the world go down into the Moscow metro to take a ride and a few selfies in the most famous underground museum. The Novosibirsk metro is also quite a museum in itself — it has 13 stations, the most beautiful of which is Gagarinskaya, Sibirskaya and Rechnoy Vokzal.

The ultramodern Gagarinskaya station is like a real cosmos underground. Its technologically themed design includes marble walls with metallic elements, dark blue backlighting and portraits of Yuri Gagarin. The Sibirskaya station looks like an underground treasure trove, decorated by Altai masters craftsmen with mosaics of precious Siberian stones. The Rechnoy Vokzal station is framed with ten glowing stained glass windows depicting the largest cities of Siberia, including Novosibirsk itself, Omsk, Barnaul and others. The platform resembles a big ship sailing on the Ob, from which ancient Siberian cities are visible through its windows.  

How to get there

The easiest way to get to Novosibirsk is by plane with Aeroflot or Novosibirsk airline S7 with one-way tickets from Moscow costing from 200-250 USD. If you decide to take from the train from Moscow, you’ll have to travel approximately a third of the Trans-Siberian Railway. That’s 3,300 kilometers over almost a three-day journey. 

Where to stay

There are many great hotels in Novosibirsk. Amongst the best include a four-star Doubletree hotel by Hilton , which is located near Lenin Square (per room from $200). After renovations and repairs, the congress-hotel Novosibirsk has improved (per room from $100) and is located across from the train station. Less expensive but of a similar standard is the four-star River Park hotel near Rechnoy Vokzal metro station, which costs $80 per night.

All rights reserved by Rossiyskaya Gazeta.

to our newsletter!

Get the week's best stories straight to your inbox

• Krasnoyarsk to Novosibirsk: A journey into Russia’s heartland
• Across Siberia on the cheap: 6 tips for travelers on a budget

This website uses cookies. Click here to find out more.

Texas Is About To Learn That High Speed Rail Ain’t Easy

Recently, the federal government awarded a serious chunk of cash for Amtrak to build a high speed rail corridor between Dallas-Fort Worth and Houston, the state’s two biggest metro areas. The $64 million is supposed to bring previous plans back on track with a shot of funding, but this doesn’t mean Texas will be any faster at getting trains moving.

The California Struggles That Texas Mocked

California’s high-speed rail project, initially pitched as a transformative infrastructure marvel connecting San Francisco to Los Angeles with stops in between, has become emblematic of the state’s struggle with ambitious public works. Since its approval by voters in 2008, with a budget of $33 billion, the project has ballooned in cost, now projected to exceed $100 billion, with significant delays pushing the completion timeline far beyond initial expectations. This saga reflects a confluence of issues that plague large-scale infrastructure projects in the US: unrealistic cost estimates, political interference, and a myriad of technical challenges.

Land and routing has been a big holdup. The project’s route, which was supposed to be straightforward, was altered due to political considerations, leading to a less efficient path through a western stretch of Mojave Desert, adding both cost and complexity. This detour was just one example of how political forces have shaped the project, often prioritizing local interests over efficiency or cost-effectiveness. Such decisions have not only increased the financial burden but also extended the timeline, as each change requires new environmental impact studies, engineering assessments, and community consultations.

Environmental and engineering challenges have been equally formidable. California’s diverse geography, from seismic zones to sinking land in the Central Valley, has necessitated expensive solutions like elevated tracks and extensive tunneling. These adaptations, while necessary, have significantly driven up costs. Moreover, the project’s approach to start construction in the less populated Central Valley, while economically beneficial for the region, has delayed the more complex and costly segments near urban centers like Los Angeles and San Francisco — the very places that are supposed to benefit the most.

Funding has been another critical bottleneck. Despite initial voter approval for a bond measure, the project’s escalating costs have outpaced available funds. Federal support has been inconsistent, and while recent allocations have provided some relief, the gap between what’s needed and what’s available remains vast. This funding shortfall has led to a piecemeal approach, where only segments like Merced to Bakersfield might see completion in the near term, leaving the full vision of a statewide high-speed rail network in doubt.

Conservatives (including Texans) have mocked the project, saying that it’s proof that California’s big government approach to things is responsible for the delays and ballooning costs. For example, Ted Cruz has questioned the whole project’s spending and philosophy , while Greg Abbott has been skeptical about the costs . But, they’ve stayed away from outright mockery due to Texas’ struggles to do a private rail system. Others have been less hesitant:

$11 billion dollar have been spent making California's High Speed Rail, and it has taken around 3 years to complete, per NYP. It is 1,600 feet. pic.twitter.com/1SvnaTaZ3Y — unusual_whales (@unusual_whales) May 11, 2024

Texas Hasn’t Done Anything In A Decade

As I pointed out earlier, Texas politicians have been hesitant to be harsh critics because they know that their own state’s private approach hasn’t worked out, either.

Legal battles, epitomized by the Texas Supreme Court’s decision on eminent domain, reveal a fundamental tension between private development ambitions and property rights, both important to Texas politics. While the court’s ruling in favor of Texas Central’s right to acquire land was seen as a victory, it also highlighted the deep-seated opposition from some landowners and legislators, painting a picture of a state divided over the vision of its future transport landscape.

Financially, the project’s ballooning costs from initial estimates to over $30 billion by 2020, and potentially higher, illustrate a common pitfall in infrastructure: underestimating the complexity and cost of such endeavors. This financial escalation, coupled with the reluctance of private investors and the state’s refusal to fund the project, has left Texas Central in a precarious position, relying on a mix of federal grants, partnerships like that with Amtrak, and international interest, notably from Japan, to keep the dream alive.

Politically, the project has become a battleground, with some seeing it as a step towards a more sustainable future, reducing the reliance on highways and air travel, while others view it through the lens of economic inefficiency or as an unnecessary government overreach. The lack of broad political support, especially at the state level, has been a significant hurdle, with critics arguing that the project’s benefits don’t justify its costs or that it’s a boon for a few at the expense of many.

The recent infusion of federal cash to fund Amtrak’s involvement, along with some support from the Japanese government, seems to have given the project a shot in the arm, but recent reporting indicates that we’re still looking at not turning any shovels of dirt until the 2030s. 70% of the land still needs to be secured, and there’s still significant opposition from rural land owners who will still have political sway despite not being able to stop the forced purchase of their land. Significant business opposition is also in play.

The Sad Truth: It Isn’t Easy Anywhere

While it can be fun to dunk on California, Texas, and maybe even the whole United States for not doing high speed rail yet, the fact is that no country has had an easy time of building high speed rail.

In Japan, the project experienced cost overruns, delays, engineering challenges, and funding woes. When Taiwan (a former Japanese colony that became de facto independent after World War II) adopted the technology, it had the same challenges and setbacks, and was forced to throw away initial plans for a public private partnership.

More recently, China has made a globally famous HSR system, but the costs have not been covered. Only a fraction of the routes pay for themselves, with other lines struggling for upkeep and frequent service. Promises of economic revitalization driven by rural lines have remained largely unfulfilled.

Instead of trying to cast all of these projects as failures, it may be better to just be more realistic about high speed rail. It’s not the kind of project that often becomes profitable, but the benefits of lower emissions and eased congestion on major highways between metro areas can still make them worth the cost in more indirect ways.

Featured image: a map of the proposed route for high speed rail in Texas. Image by Texas Central.

Latest CleanTechnica.TV Videos

CleanTechnica's Comment Policy

Share this story!

Jennifer sensiba.

Jennifer Sensiba is a long time efficient vehicle enthusiast, writer, and photographer. She grew up around a transmission shop, and has been experimenting with vehicle efficiency since she was 16 and drove a Pontiac Fiero. She likes to get off the beaten path in her "Bolt EAV" and any other EVs she can get behind the wheel or handlebars of with her wife and kids. You can find her on Twitter here , Facebook here , and YouTube here .

Jennifer Sensiba has 2065 posts and counting. See all posts by Jennifer Sensiba