You are currently viewing a new version of our website. To view the old version click .
MDPIMDPI
Search all articles
Sustainability
Download PDF
  • Article
  • Open Access

21 October 2019

Impact of High-Speed Rail on Cultural Tourism Development: The Experience of the Spanish Museums and Monuments

,
,
,
and
1
Departamento de Ingeniería del Transporte, Territorio y Urbanismo, ETSI Caminos, Canales y Puertos, Universidad Politécnica de Madrid, 28040 Madrid, Spain
2
Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, 80125 Naples, Italy
*
Author to whom correspondence should be addressed.
Sustainability2019, 11(20), 5845;https://doi.org/10.3390/su11205845
This article belongs to the Special Issue High Speed Rail and Tourism
Version Notes
Order Reprints

Abstract

Although transport infrastructure is one of the prominent factors that make Spain a major tourist destination, the international literature has revealed that the opening of new High-Speed Rail (HSR) lines is not sufficient by itself in order to increase tourist outputs in the new connected destinations. Nevertheless, the roles played by different characteristics of both travelers and destinations are of interest but they still remain not sufficiently explored. This paper focuses on the role played by HSR in fostering cultural tourism by applying a fixed-effect econometric model to a panel database (1988–2017). The database includes the number of tourists to some of the major Spanish cultural centers. The results show different impacts based on the regions, the characteristics of the museums, and the expected tourists. For destinations with previous cultural attraction, a positive effect is more evident. Moreover, the centrality of the HSR station as an enabling factor as well as the growth of the tourist market to the surrounding municipalities has been detected. Another interesting result is related to the cooperation effect between HSR and air transport, which encourages the arrivals of foreign tourists. These findings should help planners to develop policies that optimize tourist revenues by exploiting the potential of HSR development in the future.

1. Introduction

In the past few decades, Spain has massively invested in the expansion of the High-Speed Rail network despite the recent economic crisis and criticism for its low or even negative profitability [1,2]. This was primarily due to its high construction and operating costs and also due to the low number of travelers, which turned out to be lower than predicted in previous studies [3]. As a result, Spain has a High-Speed Rail (HSR) network of more than 2800 km in operation (see www.uic.org) and thus becomes the first country in Europe and the third worldwide with a HSR network this long, only behind Japan with 3041 km and far behind China with more than 31,000 km. The Spanish HSR network has a radial structure and connects Madrid, which is in the center of the country, with the peripheral regions of the east and the south, whereas new sections to Portugal and to the northern coast are still being planned or constructed. The construction order of the Spanish lines, has often been criticized for being the product of political decisions by the different governments of the country rather than issues of economic profitability or integration of the connected regions [4].
Tourism is considered as one of the key contributing sectors of the Spanish economy. It is the top export sector of the country, with a contribution of 11.7% in the Spanish gross domestic product (GDP) and 12.8% in creating employment in 2017 [5]. It not only generates a direct impact on the national economy but also produces a significant effect on the rest of the sectors, with an estimated linkage effect of 1.68 [6]. Furthermore, it has been observed that the contribution of this sector in the Spanish economy has been constantly increasing since 2014, performing well during economic growth and with lower die-off rates during the economic crisis. The growth of this sector is not only because of the recovery of the consumption expenditure of households, but it is primarily due to the increase of foreign tourism revenues, which is attributed to the low crude oil prices, the growing importance of business travel, and the increasing security risks in some Mediterranean tourist destinations that compete with Spanish ones.
Spain is one of the most visited countries worldwide with more than 80 million tourists, which is the second rank in terms of foreign arrivals as well as incomes, following the United States, which has a larger size, comparable to the whole European Union [7]. Its advantageous position in Southern Europe, with its almost 6000 km of seashores, gastronomy, and cultural heritage along with the modern infrastructure and moderate pricing are considered the primary contributing factors for this remarkable performance. Bringing new tourists and raising their high rate of fidelity to Spanish destinations is a priority in all the national regions by public and private stakeholders that strive to attract as many tourists as possible and increase their expenditure and its positive impact on the national economy. In this context, although sun and beach destinations are still considered the preferred ones, cultural tourism is promoted by authorities due to its by higher expenses and less seasonal outputs [8].
The cultural tourism of museums and monuments emerges as one of the pillars of cultural tourism. The growing interest runs alongside the process of transformation of these landmarks that are now often conceived as places of attraction of masses for their exhibitions and architecture. Indeed, these cultural sites are viewed with new interest nowadays, attracting many tourists who consider these places not just as a small, minority resource, but a mass consumption product.
When the nationality of tourists to the Spanish cultural sites is analyzed, different behaviors appear. Although cultural tourism is not the main reason for travelling, cultural activities are amongst the preferred traits by national travelers. Thus, 64.6% of domestic travelers reported to have benefited from visiting museums or monuments [9], while cultural reasons are declared to be the main ones for only 4.4% of the trips [10]. As Table 1 shows, the different motivations result in different preferred destinations. The special importance of cultural tourism is intuited in some regions (Madrid and Andalucía), while a sun and beach leisure profile appears in others coastal ones (Cataluña, Comunidad Valenciana, Islas Canarias, and Islas Baleares). Concerning the expense incurred per trip, destinations with higher cultural attractiveness also seem to result in higher incomes, with Madrid (388.8 €/trip), Asturias (360.7 €/trip), and La Rioja (360.3 €/trip) at the top of the ranking. [11].
Table 1. Number of domestic trips 2017. National Statistics Institute (INE).
Foreign visitors also exhibit different interests. Although 16.8% of foreign tourists reported that “cultural motivations” was the main incentive when selecting a tourist destination [12], the predominance of coastal destinations is more evident. Therefore, though domestic visitors prefer both the coastal and inland regions, with Andalucía, Cataluña, Comunidad Valenciana, and Castilla y León as preferred regions of destination, contributing 18.5%, 14.3%, 10.5%, and 9.9% share, respectively, only coastal “sun and beach” destinations show higher ranking for foreign tourists, with Cataluña, Canarias, Baleares, and Andalucía contributing 23.3%, 17.4%, 16.9%, and 14.1% shares, respectively [13], as can be seen in Table 2. However, cultural resources are also appreciated and recognized as an important factor in the choice of Spain as a destination by foreign tourists [14].
Table 2. Total number of foreign visitors to the different Spanish regions. National Statistics Institute.
Although the availability of an efficient transport plays an essential role in the destination development as well as in tourism choices [15,16,17], in the growth of cultural tourism, the specific role that HSR can play has been underestimated in the current literature. Nevertheless, since most of the museums are located, equally close to HSR stations and to the city centers, HSR fulfils the desirable a priori conditions that can foster an increase in the number of tourists.
This paper is organized as follows. The literature on cultural tourism and HSR is reviewed in Section 2. Section 3 presents the data, main characteristics, and model equations. The results and findings are presented in Section 4. Finally, the conclusions are summarized in Section 5.
By analyzing the growth of the number of tourists to museums and cultural monuments in several Spanish regions, this is the first study that tries to shed some light on whether HSR has an impact on cultural tourism. Three econometric models have been formulated wherein the existence of an HSR station, its different location, and the distance to the museums and the possible interaction with air transportation are considered as important factors.

2. Literature Review

The literature shows that rail infrastructure is a prominent factor in tourism competitiveness [18]. HSR is considered to be not only a comfortable, safe, flexible, and environmentally sustainable [19] mode of transport, but also a fast one. Given that tourists have time constraints that they intend to optimize [20], the advantage of HSR includes providing more time at a destination (though sometimes at the expenses of the trip cost) [21]. In this context, there is a consensus among researchers that the introduction of HSR increases accessibility to destinations and increments previous attractions, maximizes pre-existing infrastructure, or even serves as a change agent for the implementation of new other policies. In the Spanish case, the distance between most Spanish cities is in the range in which HSR competes with advantage against the alternative modes (car and air). As a consequence, in order to boost new opportunities for tourism in the connected destinations, HSR seems most promising.
The impacts of HSR on tourism have been reported in previous studies [22,23] and are as follows: Opening of new tourist markets, increase in the number of tourists, increase in occupancy rates, decrease in the overstays in long terms although there is an increase in short terms, and, finally, development of urban tourism. As a result, a general positive effect is generally observed [24] although detailed studies report important differences in the behavior of tourists and different impacts on the destinations that are being studied.
The existing literature is summarized in Table 3, wherein the cited studies have been ordered by year of publication. Moreover, the countries and, when applicable, the region studied, selected type of approach, specification of the variable under study, and main findings are also included.
Table 3. Existing literature concerning the impact of High-Speed Rail (HSR) on tourism.
The literature shows that although the increase of attraction ranges provided for touristic nodes are generally detected, benefits and their persistence are very dependent on the previous features of these touristic nodes. For some of them, negative factors such as distance or prices are determined to be moderated by other motivations such as broadening cultural knowledge or discovering new places [55]. In the Spanish case, the existence of different tourist targets in the connected cities, such as urban business tourism (Barcelona, Madrid, Sevilla, and Zaragoza), cultural tourism (Cordoba and Segovia), or sun and beach destinations (Alicante, Tarragona, and Málaga), make HSR the most convenient mode of transportation, especially when it can advantageously compete with the private car. This fact is more evident in intermediate cities along the HSR corridors, that often benefit from a large number of services ending not just in these intermediate cities, but also in cities further away in the same line.
The location of HSR stations with reference to the cities centers also provides a higher or lesser access to the general HSR network. Effective time reductions become more apparent when stations are placed downtown, where access to efficient municipal public transportation and the proximity to the final destination maximize utility for users. In this context, these are the most accepted outcomes, which usually depend on the size of the city. Major urban areas tend to reinforce a central location. In such cases, the station is often linked with larger scale strategies, such as the reinforcement of the image of the city. For example, in Zaragoza, the “Delicias” station was the part of a wider plan in order to modernize the city for organizing the 2008 World Exhibition. In small or medium sized cities, the planning of stations significantly depends on the budget criteria of the infrastructure builder, and city edge and peripheral stations are the most common [56], although, their utility for tourists is drastically reduced. The Spanish HSR network and the relative positions of the Spanish stations with respect to the cities they are located in are shown in Figure 1.
Figure 1. HSR network (2018) and the situation of the Spanish HSR stations.
According to the literature, with regard to the competition between HSR and alternative modes of transport, a primary substitution effect and a later positive impact is expected for national tourists who use mainly private cars as a transportation mode. In the case of foreign tourists, for which airplane is preferred, the substitution effect is more difficult to define. Nevertheless, the possibility of secondary internal trips by these travelers, which could not have been possible otherwise, has been detected in the previous study [51] and may generate positive impacts on the revenues and employment in these secondary connected cities. Considering these factors, HSR acts as a feeder to hub airports [57] and its increased demand compensates the possible decrease in the number of passengers that air services may suffer.

3. The Methodology

Considering that museums and cultural resources are one of the pillars on which cultural tourism is based, the number of tourists who visit these museums and cultural resources is considered as a proxy for evaluating the growth of cultural tourism markets in HSR-connected destinations. Therefore, a search in the main databases at the national or the regional level was conducted and the data on the visitors to 64 cultural landmarks in 11 Spanish provinces were collected. The resulting data base included the most visited museums, such as “Museo Reina Sofía” and “Museo del Prado”. The advantage of the resulting database is that it is not aggregated by provinces or regions of destination and in consequence will provide more detailed information.
The econometric model was designed following on Lim’s classical demand model [58], which has been widely applied in the previous studies concerning the relationship between HSR and tourism [38,44,48,52,53]. The advantage of this econometric approach lies in its ability to analyze caudal effects [59], in line with the intended purpose presented in this paper.
It follows
D T i j = f Y j , T C i j , R P i j , E R i j , Q F i j
where
  • DTij represents the tourist demand between “i” and “j” where “i” is destination and “j” is the origin. In this study, the yearly number of tourists who visit museums and monuments (“Visitit” variable) is considered as the indicator of its demand. Data was obtained from the following agencies: State museums and monuments: www.patrimonionacional.es and www.mcu.es; regional and municipal ownership museums and monuments: www.gencat.cat for museums in Cataluña and www.juntadeandalucia.es for museums in Andalucía; municipal ownership museums and monuments: www.madrid.org for museums in Madrid, www.barcelona.cat for museums in Barcelona, and www.jcyl.es for museums in Castilla and Leon; and private ownership museums: www.cabildocatedraldecordoba.es and www.valencia.es.
  • Yj represents the incomes in the origin “j”. The average Spanish GDP per capita deduced from the Spanish National Statistics Institute (INE) is used to measure it, and is available at www.ine.es. The GDP of the rest of the main countries of origin, primarily European, has not been included in the model because a serial correlation was found between them.
  • TCij is the travel cost from origin “j” to destination “i”. Its effect could not be measured as the database does not provide the country or the region of origin of tourists.
  • RPij is the relative cost, that measures the likely cost of goods to tourists at the destination (such as accommodation, local transportation, food, and entertainment). The difference in the DGP per capita between the European Union (source of most of the travelers) and Spain is represented by the variable “Dif Gdp”, which provides a proxy to the perceived difference of level of prices between these originating countries and the destination.
  • The currency exchange rate is represented by ERij. As Euro is the legal currency in both the destination and the majority of the origin countries, this variable was not included.
  • QFi stands for the quality factors in destination “i”. The size of the population at the province of destination “POP” and the lagged variable “Visitt–1” have been considered.
  • The policy variables that have been separately considered are as follows: “Hsrt” is a dummy variable that takes a value of 1 if a High-Speed Rail is available at the province of destination, otherwise it takes a value of 0; “Centrt”, “Cedget”, and “Peripht” are dummy variables that take a value of 1 if HSR is available at the province of destination and the station location in the city is central, city edged, or peripheral, respectively; “ST Dist” are dummy variables that take a value of 0 if HSR is not available at the province of destination, otherwise it takes the value of the distance in kilometers “as the crow flies” from the HSR station to the museum considered.
Three different models with the following specifications have been developed:
Model 1:
ln V i s i t i , t = β 1 × ln V i s i t i , t − 1 + β 2 × ln   P o p t + β 3 × ln   Gdp t +   β 4 × Dif   Gdp t + β 5 × Hsr   t +   a i
Model 2:
ln V i s i t i , t = β 1 × ln V i s i t i , t − 1 + β 2 × ln   P o p t + β 3 × ln   Gdp t +   β 4 × Did   Gdp t + β 5 × Centr   t + β 6 × Cedge   t + β 7 × Periph   t +   a i
Model 3:
ln V i s i t i , t = β 1 × ln V i s i t i , t − 1 + β 2 × ln P o p t + β 3 × ln   Gdp t +   β 4 × Dif   Gdp t + β 5 × ST   Dist   t +   a i
where ai represents the error term, that includes the time-invariant province-specific effect and the individual mean-zero random error, respectively.
Moreover, a preliminary collinearity analysis was carried out resulting in no undesired correlation between the variables in any of the models. It could be argued that an endogenous biasing of the results could emerge as a consequence of the construction order of the different lines. Nevertheless, the fact that tourist activity has not been one of the main drivers of HSR investment decisions, makes Spain a good case study due to the “absence of this kind of endogeneity” [48].
In order to provide as much diverse and detailed information as possible, a disaggregate level of demand analysis is carried out. Thus, at a regional scale, models are separately and successively applied to the complete database. As a result, the regions of Madrid, Cataluña, Andalucía, and Castilla (gathering Castilla and Leon and Castilla-La Mancha) have been considered. Hence, all three models are also applied to cities that benefit from a HUB airport. Finally, the analysis has been separately performed for museums located in the same and different municipalities than the HSR station.
The museums and monuments considered in the database, including the region and province they are located, are presented in Table 4. As the Madrid HSR station has been considered the most suitable HSR gateway, the museums in Badajoz, Burgos, Cáceres, and Cantabria have been assigned to the Region of Madrid.
Table 4. Descriptive statistics for the considered museums.
In order to determine the most suitable econometric technique (generalized least squares (GLS), GLS with fixed effects, or GLS with random effects), a preliminary analysis was carried out. The Breusche-Pagan Lagrange multiplier test combined with the Hausman test confirms that the fixed-effect model is the most suitable technique and provides a robust estimation.
Stata/SE 12.0 is used to execute the models and the results are shown in Table 5, Table 6 and Table 7.
Table 5. Results for fixed effect panel regression. Models 1. (****), (***), (**), and (*) denote significant levels at 1%, 5%, 10%, and 20%, respectively. Numbers in parentheses indicate p-value.
Table 6. Results for fixed effect panel regression. Models 2. (****), (***), (**), and (*) denote significant levels at 1%, 5%, 10%, and 20%, respectively. Numbers in parentheses indicate p-value.
Table 7. Results for fixed effect panel regression. Models 3. (****), (***), (**), and (*) denote significant levels at 1%, 5%, 10%, and 20%, respectively. Numbers in parentheses indicate p-value.

4. Results

In almost all models, a strong significance of the lagged variable “Visitt–1” is observed, which confirms the previous studies [38,52]. Thus, on average, 77% of the visits can be explained by previous experiences, in line with the repetition rate of tourists of 80.2% found for the Spanish sector [10]. The population at the province level does not show a clear effect when the whole database is considered. When the regional scale is adopted, it is only in Andalucía region where a significant positive coefficient of 1.85 is observed. An expected different impact is observed in the provinces with and without hub airports. The accessibility in destination drastically increases and the relative importance of domestic tourists (and especially the inhabitants of the same province) reduces where a hub airport is available. Consequently, it is only in “no hub” airport provinces models where a positive effect of the province population is observed.
In terms of the economic variables, the GDP per capita of the Spanish population is relevant for the complete database. This is an expected result confirming that it is people with higher incomes, who benefit from a greater amount of leisure time and, normally, have greater cultural concerns [60,61]. But simultaneously, regional differences have also been observed. When only the museums in Cataluña or Castilla La Mancha and Castilla y Leon are considered, no clear influence is shown; however, when only Madrid’s or even Andalucía’s database are considered, a clear positive influence appears. No influence of the “Dif Gdp” variable is observed in any of the models except in those that consider museums only in Andalucía. The negative sign of the coefficient indicates that when the relative purchasing power of tourists increases, they tend to visit alternative destinations. This result was already verified in other Spanish “sun and beach” destinations studies [62] that claim that “the higher the number of beds in medium and high-quality accommodation establishments, the lower the number of beach trips generated by a location”.
Concerning the policy variables, the primary objective of this study, and when the whole database is considered, the presence of a high speed rail connection, represented by the variable “HSR” seems to be relevant in Model 1 with a low significance level of 13.6%, with the expected positive sign and with an elasticity of 0.041. Hence, the commissioning of a new HSR connection shows signs of increasing the number of tourists to museums with a modest rate of 0.04% in that province. Moreover, significant differences are observed when the analysis is made regionally. Though the HSR does not induce an impact in Cataluña or Castilla, the variable is considered to be strongly significant for the case of Madrid and Andalucía with elasticities around 0.2% and 2%, respectively. However, differences are also observed when databases with provinces including hub airports are considered. The existence of HSR shows only signs of significance, (although with a low significance rate of 15.8%) in provinces including hub airports with an elasticity of 0.05%. On the contrary, no influence is observed in provinces without these hub airports. This suggests that the attraction of the city and the impact of HSR may increase by the presence of this alternative mode of transportation and that a mutual feedback is developed when a cooperation is established with airline passengers that can use the HSR for secondary trips and thus may increase the access range to other Spanish regions.
In the analysis of Model 2, which evaluates the location of the stations, evidence of the influence of HSR is observed, but only in the provinces with a central station with an average impact of 0.053%, which is similar to the value obtained in Model 1. This result conforms to the previous finding that “central locations generally benefit from better efficiency for tourism trips” [53]. Moreover, no effect is observed when the HSR station is in either the city edge or in a peripheral situation. Surprisingly, the effect of HSR in Barcelona, the main HSR station in Cataluña, is not found to be significant even though its central position makes it an a priori excellent push factor for the increase in the number of tourists. Only when the central location of the station is considered, the presence of a hub airport seems to induce a relevant impact, although the low significance value of 15.8% must be noticed.
Model 3, which evaluates the influence of the distance to HSR, the policy variable, “ST Dist”, shows that there is a low-value overall inverse relation between the distance from the HSR stations to the museums and the number of the tourists. This effect is observed when the museums are considered only in the Madrid, Andalucía, and Castilla regions. Similar to Model 2, no effect is observed for museums in the Cataluña region. Moreover, the impacts are found to be significant in the three models only for the museums located in a different municipality (but in the same province) than the HSR station when databases that include museums in the same or different municipalities are considered separately. This result completes the outcomes of Model 3 and indicates that the existence of an HSR link can also increase the potential of Spanish museums located farther from the station that especially benefits the widening of tourist markets and the increase in the amount of time off available at destinations. When the existence of a hub airport is examined, the negative impact of distance from the HSR station is shown, although it is especially significant when the hub airport is not available. Consequently, it can be concluded that the development of integrated transportation plans in the cities where intermodal links are required, can result in an effective increase in the cultural tourism market in connected destinations.
In addition, the nationality of tourists visiting museums and cultural resources seems to be an important variable which would require further investigation. The analysis of outcomes of databases with and without hub airports at destinations (and considering that the existence of these hub airports is a well-studied relevant factor in promoting foreign tourists [57]) provide primary indications that the increase in accessibility provided by the joint effects of these two modes of transportation results in a higher number of foreign tourists.
With regard to national tourists and considering that database do not show the nationality of visitors, the models do not provide sufficient information. A first approach to this question can lie in analyzing the comparative evolution in the number of tourists to museums and the national population connected by HSR in every HSR node with cultural attractiveness. Figure 2, Figure 3 and Figure 4 graphically show the evolution of the number of tourists to some museums and monuments in the main cities of the regions of Madrid, Cataluña, and Andalucía, and for the same time periods, the number of served population. In order to exclude tourists inhabiting in the same city as the cultural resource, which is already represented by the “Pop” variable in the previous models, residents in the cities where the museums are located have not been considered as “served.” In order to coincide with the extreme values in the database, the scale has been adjusted in the two “y”-axes to the ranges of both series. The lineal tendency lines are shown and provide an indication about the time effect on both series.
Figure 2. Number of tourists per year to main museums in Madrid versus population served by HSR to Madrid.
Figure 3. Number of tourists per year to main museums in Barcelona versus population served by HSR to Barcelona.
Figure 4. Number of tourists per year to main museums in Andalucía (Sevilla and Cordoba) versus population served by HSR.
A linear model including the number of tourists as a dependent variable has also been designed for these main museums in order to complement previous results. The time trend has been included as independent variables. In order to make it independent from the possible influence of the served population, as shown in the figures, the population of the cities where the museums are located is not required to be included. Table 8, Table 9 and Table 10 present the results.
Table 8. Visitors to main museums in Madrid. (****), (***), (**), and (*) denote the significance levels at 1%, 5%, 10%, and 20%, respectively. The numbers in parentheses indicate p-value.
Table 9. Visitors to main museums in Barcelona. (****), (***), (**), and (*) denote significant levels at 1%, 5%, 10%, and 20%, respectively. Numbers in parentheses indicate p-value.
Table 10. Visitors to main museums in Sevilla and Cordoba. (****), (***), (**), and (*) denote significant levels at 1%, 5%, 10%, and 20%, respectively. Numbers in parentheses indicate p-value.
Moreover, the results also suggest different influences depending on the considered destination. The connected population seems to be an influencing factor only in some museums in Andalucía, whereas in other museums the impact is not found to be significant. Furthermore, none of the analyzed museums in Barcelona seem to reflect any influence of potential domestic travelers by HSR. However, a weak influence seems to appear with regard to the museums in Andalucía although not always with the expected sign. Finally, it is concluded that an increase in the HSR network does not seem to result in a general growth in the affluence of domestic tourists, hence further research is required in this regard.

5. Conclusions

The effects of HSR on cultural tourism are a subject of interest though the scarcity of bibliography shows that sufficient attention has not been paid in this regard up to now. Although some analytic studies using the econometric models have already emerged dealing with the influence of HSR on tourism, some of them even with a meritorious high level of disaggregation, the specific impact on the cultural tourism, and, more specifically, on museums and monuments, remain unexplored up to now. Hence, the higher returns and greater positive effects on society by the tourist industry, and in an outstanding way by cultural tourism, make it an appropriate field of research that should receive special attention.
This paper provides a deeper insight into this interesting subject for the first time by implementing an econometric model and taking into consideration the number of tourists of 64 museums and monuments in 25 Spanish municipalities, using a validated methodology which could be suitable in other countries with a HSR network and cultural attractiveness. Although the previous studies focused on general tourism in Spain, little or no influence of HSR was detected, the results in this paper show signs that this mode of transportation can play a positive role in the reinforcement of cultural tourism. This study also highlights the different roles that HSR has played in the Spanish cultural tourist markets of museums and monuments, with a significant increase in the number of tourists in some regions (Madrid and Andalucía) while these outcomes are not significant in other regions (Cataluña and Castilla). A doubly controversial effect of distance to the HSR stations is also observed. While little or no effect is detected in museums located in the same municipality, museums located in a different municipality receive an appreciable significant increase in the number of tourists. This is interpreted as an indication that HSR increases the action radius of tourists surely due to the gain in available time at a destination and reinforces hidden potentialities of further museums. Moreover, the central position of the HSR stations is also detected as a significant beneficial factor and suggests that the location of the HSR with respect to the city is also an important factor in addition to the mere connection to the HSR network. With regard to the nationality of tourists, a need for the integration of HSR and air transportation is perceived in order to favor the affluence of foreign tourists. With regard to domestic tourists, the analysis of the evolution in the number of tourists and the expansion of the network report that HSR may be, in some cases, considered a determinant factor in increasing the tourist demand.
Hence, some considerations about these finding should be addressed despite the above results. First, the limitation of the database should be recognized. Although it has been created with official data from public entities, the presence of unexpected gaps in the series and the small range of time that the HSR has been operating in some destinations limits the number of registers in the database and affects the conditions of the performance of models. On the other hand, the distance from the foreign originating countries to a cultural destination is considered to be beyond the range in which HSR reveals itself as the most competitive mode of transportation. Moreover, the actual limited connection between European and Spanish HSR networks is also considered to be a deterrent against the use of HSR by foreign tourists. Future research should focus on improving the performance of models by using a more complete and detailed database so that further and more accurate information could be concluded.
In conclusion, and although the debate of the impulse given to cultural tourism by HSR remains open, the results reveal that HSR can become a relevant instrument to aid planners and authorities in the promotion of national cultural sites which may yield economic growth returns and improvements in the profitability of HSR investments

Author Contributions

Conceptualisation, J.L.C., M.E.L.-L., R.A. and B.G.; methodology, J.L.C. and B.G.; software, J.L.C., validation, F.P., M.E.L.-L., and R.A.; formal analysis, J.L.C., M.E.L.-L., R.A.; investigation, J.L.C.; resources, J.L.C.; data curation, J.L.C.; writing—original draft preparation, J.L.C.; writing—review and editing, J.L.C. and F.P.; visualization, F.P.; supervision, F.P., M.E.L.-L. and R.A.; project administration, J.L.C. All authors approved the final version.

Funding

This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Betancor, O.; Llobet, G. Contabilidad Financiera y Social de la Alta Velocidad. Estudios Sobre la Economía Española 2015/08; Fedea: Madrid, Spain, 2015. [Google Scholar]
  2. Coto-Millán, P.; Inglada, V.; Rey, B. Effects of network economies in high-speed rail: The Spanish case. Ann. Reg. Sci. 2007, 41, 911–925. [Google Scholar] [CrossRef]
  3. Flyvbjerg, B.; Holm, M.K.S.; Buhl, S.L. How (In)accurate Are Demand Forecasts in Public Works Projects?: The Case of Transportation. J. Am. Plan. Assoc. 2005, 71, 131–146. [Google Scholar] [CrossRef]
  4. Guirao, B.; Campa, J.L. A methodology for prioritising HSR corridors: From U.S. theory to Spanish practice. J. Transp. Geogr. 2014, 35, 95–106. [Google Scholar] [CrossRef]
  5. Instituto Nacional de Estadística (INE). Cuenta Satélite del Turismo en España; INE: Madrid, Spain, 2018. [Google Scholar]
  6. Gutiérrez-Domènech, M. Dossier: El turismo, sector de futuro. In Departamento de Economía Europea, Área de Estudios y Análisis Económico; La Caixa: Valencia, Spain, 2014. [Google Scholar]
  7. UNWTO. Tourist Highligts, 2017 ed.; UNWTO: Madrid, Spain, 2018. [Google Scholar]
  8. Vizcaíno, M.L. Evolución del turismo en España. El turismo cultural. Int. J. Sci. Manag. Tour. 2015, 4, 75–95. [Google Scholar]
  9. Spanish Institute for Tourist Studies IET. Movimientos Turísticos de los Españoles (Familitur); 2012 Anual Report; Instituto de Turismo de España: Madrid, Spain, 2013. [Google Scholar]
  10. Instituto Nacional de Estadística (INE). Resident Tourism Survey; INE: Madrid, Spain, 2019. [Google Scholar]
  11. Spanish Culture and Sports Ministry. Cultural Culture Database (CULTURABase); Spanish Culture and Sports Ministry: Madrid, Spain, 2019.
  12. Spanish Institute for Tourist Studies IET. Encuesta de Movimientos Turísticos en Fronteras (Frontur); 2012 Anual Report; Instituto de Turismo de España: Madrid, Spain, 2013. [Google Scholar]
  13. Instituto Nacional de Estadística (INE). Encuesta de Movimientos Turísticos en Fronteras (Frontur); INE: Madrid, Spain, 2019. [Google Scholar]
  14. World Economic Forum. The Travel & Tourism Competitiveness. Report 2017. Paving the Way for a More Sustainable and Inclusive Future; World Economic Forum: Geneva, Italy, 2018. [Google Scholar]
  15. Chew, J. Transport and tourism in the year 2000. Tour. Manag. 1987, 8, 83–85. [Google Scholar] [CrossRef]
  16. Khadaroo, J.; Seetanah, B. Transport infrastructure and tourism development. Ann. Tour. Res. 2007, 34, 1021–1032. [Google Scholar] [CrossRef]
  17. Prideaux, B. The role of the transport system in destination development. Tour. Manag. 2000, 21, 53–63. [Google Scholar] [CrossRef]
  18. Khan, S.A.R.; Qianli, D.; SongBo, W.; Zaman, K.; Zhang, Y. Travel and tourism competitiveness index: The impact of air transportation, railways transportation, travel and transport services on international inbound and outbound tourism. J. Air Transp. Manag. 2017, 58, 125–134. [Google Scholar] [CrossRef]
  19. European Union. A European High-Speed Rail Network: Not a Reality but an Ineffective Partchwork; 2018 Special Report Nº19; European Union: Brussels, Belgium, 2018. [Google Scholar]
  20. Rugg, D. The Choice of Journey Destination: A Theoretical and Empirical Analysis. Rev. Econ. Stat. 1973, 55, 64. [Google Scholar] [CrossRef]
  21. Shyr, O.; Chao, C.; Huang, C. Impacts of new transportation systems on tourism behavior: The experience of high-speed rail. Int. J. Transp. Econ. 2015, 42, 89–110. [Google Scholar]
  22. Delaplace, M.; Pagliara, F.; Perrin, J. Does High Speed Rail services influence tourists’ choice? Some concerns from Paris and Roma and other linked cities. In Paper Presented at the 53rd Congress of the European Regional Science Association: “Regional Integration: Europe, the Mediterranean and the World Economy”; RePEc: Palermo, Italy, 2013. [Google Scholar]
  23. Wang, X.; Huang, S.; Zou, T.; Yan, H. Effects of the high speed rail network on China’s regional tourism development. Tour. Manag. Perspect. 2012, 1, 34–38. [Google Scholar] [CrossRef]
  24. 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, 472. [Google Scholar] [CrossRef]
  25. Guirao, B.; Soler, F. Impacts of the new high speed rail services on small tourist cities: The case of Toledo (Spain). WIT Trans. Ecol. Environ. 2009, 117, 465–473. [Google Scholar]
  26. 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, 611–617. [Google Scholar] [CrossRef]
  27. Bazin, S.; Beckerich, C.; Delaplace, M. Desserte ferroviaire à grande vitesse, activation des ressources spécifiques et développement du tourisme: Le cas de l’agglomération rémoise. Belgeo 2010, 1–2, 65–78. [Google Scholar] [CrossRef]
  28. Wang, W.-C.; Chou, L.-S.; Wu, C.-C. Impacts of new transportation technology on tourism-related industries—The Taiwan High Speed Rail. World Leis. J. 2010, 52, 14–19. [Google Scholar] [CrossRef]
  29. Bazin, S.; Beckerich, C.; Delaplace, M. High-Speed railway, service innovations and urban and business development. Econ. Manag. Tour. Trends Recent Dev. 2011. Universidade Luisiada Editora, Collecçao Manuais. Hal-02079165. [Google Scholar]
  30. Albalate, D.; Bel, G. High-Speed Rail: Lessons for Policy Makers from Experiences Abroad. Public Adm. Rev. 2012, 72, 336–349. [Google Scholar] [CrossRef]
  31. Chen, Z.; Haynes, K.E. Tourism Industry and High Speed Rail—Is There a Linkage: Evidence from China’s High Speed Rail Development. SSRN Electron. J. 2012. [Google Scholar] [CrossRef]
  32. Bazin, S.; Beckerich, C.; Delaplace, M. Desserte TGV et villes petites et moyennes. Une illustration par le cas du tourisme à Arras, Auray, Charleville-Mézières et Saverne. Les Cahiers Sci. Transp. AFITL 2013, 63, 33–61. [Google Scholar]
  33. Delaplace, M.; Pagliara, F.; La Pietra, A. Does high-speed rail affect destination choice for tourism purpose? Disneyland Paris and Futuroscope case studies. Belgeo 2016, 3, 1–23. [Google Scholar]
  34. 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] [CrossRef]
  35. Yan, Y.Q.; Ye, B.H.; Zhang, H.Q. Assessing the Impacts of the High-Speed Train on Tourism Demand in China. Tour. Econ. 2014, 20, 157–169. [Google Scholar] [CrossRef]
  36. Sánchez Ollero, J.L.; García Pozo, A.; Marchante Mera, A.J. Una aproximación al impacto socioeconómico de la alta velocidad ferroviaria en Andalucía. Bol. Asoc. Geogr. Esp. 2014, 64, 341–356. [Google Scholar] [CrossRef]
  37. 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 Pac. J. Tour. Res. 2014, 19, 890–912. [Google Scholar] [CrossRef]
  38. Chen, Z.; Haynes, K.E. Impact of high-speed rail on international tourism demand in China. Appl. Econ. Lett. 2015, 22, 57–60. [Google Scholar] [CrossRef]
  39. 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] [CrossRef]
  40. Albalate, D.; Fageda, X. High speed rail and tourism: Empirical evidence from Spain. Transp. Res. Part A Policy Pract. 2016, 85, 174–185. [Google Scholar] [CrossRef]
  41. Campa, J.L.; López-Lambas, M.E.; Guirao, B. High speed rail effects on tourism: Spanish empirical evidence derived from China’s modelling experience. J. Transp. Geogr. 2016, 57, 44–54. [Google Scholar] [CrossRef]
  42. Guirao, B.; Campa, J. Cross Effects between High Speed Rail Lines and Tourism: Looking for Empirical Evidence Using the Spanish Case Study. Transp. Res. Procedia 2016, 14, 392–401. [Google Scholar] [CrossRef]
  43. Hiramatsu, T. Unequal regional impacts of high speed rail on the tourism industry: A simulation analysis of the effects of Kyushu Shinkansen. Transportation 2016, 45, 677–701. [Google Scholar] [CrossRef]
  44. Kurihara, T.; Wu, L. The Impact of High Speed Rail on Tourism Development: A Case Study of Japan. Open Transp. J. 2016, 10, 35–44. [Google Scholar] [CrossRef]
  45. Ortuño, A.; Bautista, D.; Fernández-Aracil, P.; Fernández, G.; Sánchez Galiano, J. HSR profile in sun and beach destinations: The case of Alicante (Spain). Open Transp. J. 2016, 10, 97–107. [Google Scholar]
  46. Saladié, Ò.; Clavé, S.A.; Gutiérrez, A. Measuring the influence of the Camp de Tarragona high-speed rail station on first-time and repeat tourists visiting a coastal destination. Belgeo 2016, 3. Available online: https://belgeo.revues.org/17889 (accessed on 10 May 2019).
  47. Wang, D.; Niu, Y.; Alan, A.L.; Chen, T.; Wang, L.; Lu, L. HSR mechanisms and effects on the spatial structure of regional tourism in China. J. Geogr. Sci. 2016, 26, 1725–1753. [Google Scholar] [CrossRef]
  48. Albalate, D.; Campos, J.; Jiménez, J.L. Tourism and high speed rail in Spain: Does the AVE increase local visitors? Ann. Tour. Res. 2017, 65, 71–82. [Google Scholar] [CrossRef]
  49. Gutiérrez, A.; Ortuño, A. High speed rail and coastal tourism: Identifying passenger profiles and travel behavior. PLoS ONE 2017, 12, e0179682. [Google Scholar] [CrossRef]
  50. Pagliara, F.; Mauriello, F.; Garofalo, A. Exploring the interdependences between High Speed Rail systems and tourism: Some evidence from Italy. Transp. Res. Part A Policy Pract. 2017, 106, 300–308. [Google Scholar] [CrossRef]
  51. Wang, D.; Niu, Y.; Sun, F.; Qian, J.; Li, F. Evolution and spatial characteristics of tourism field strength of cities linked by high-speed rail (HSR) network in China. J. Geogr. Sci. 2017, 27, 835–856. [Google Scholar] [CrossRef]
  52. Campa, J.L.; Arce, R.; López-Lambas, M.E.; Guirao, B. Can HSR improve the mobility of international tourists visiting Spain? Territorial evidence derived from the Spanish experience. J. Transp. Geogr. 2018, 73, 94–107. [Google Scholar] [CrossRef]
  53. Gao, Y.; Su, W.; Wang, K. Does high-speed rail boost tourism growth? New evidence from China. Tour. Manag. 2019, 72, 220–231. [Google Scholar] [CrossRef]
  54. Moyano, A.; Rivas, A.; Coronado, J.M. Business and tourism high-speed rail same-day trips: Factors influencing the efficiency of high-speed rail links for Spanish cities. Eur. Plan. Stud. 2019, 27, 533–554. [Google Scholar] [CrossRef]
  55. Nicolau, J.L.; Mas, F.J. The influence of distance and prices on the choice of tourist destinations: The moderating role of motivations. Tour. Manag. 2006, 27, 982–996. [Google Scholar] [CrossRef]
  56. Garmendia, M.; Ribalaygua, C.; Ureña, J.M. High speed rail: Implication for cities. Cities 2012, 29, S26–S31. [Google Scholar] [CrossRef]
  57. Albalate, D.; Bel, G.; Fageda, X. Competition and cooperation between high-speed rail and air transportation services in Europe. J. Transp. Geogr. 2015, 42, 166–174. [Google Scholar] [CrossRef]
  58. Lim, C. Review of international tourism demand models. Ann. Tour. Res. 1997, 24, 835–849. [Google Scholar] [CrossRef]
  59. Song, H.; Li, G. Tourism demand modelling and forecasting—A review of recent research. Tour. Manag. 2008, 29, 203–220. [Google Scholar] [CrossRef]
  60. Kim, H.; Cheng, C.-K.; O’Leary, J.T. Understanding participation patterns and trends in tourism cultural attractions. Tour. Manag. 2007, 28, 1366–1371. [Google Scholar] [CrossRef]
  61. Prince, D.R. Factors influencing museum visits: An empirical evaluation of audience selection. Mus. Manag. Curatorship 1990, 9, 149–168. [Google Scholar] [CrossRef]
  62. Bujosa, A.; Riera, A.; Pons, P.J. Sun-and-beach tourism and the importance of intra-destination movements in mature destinations. Tour. Geogr. 2015, 17, 780–794. [Google Scholar] [CrossRef]

Article Metrics

Citations

Article Access Statistics

Journal Statistics
Multiple requests from the same IP address are counted as one view.
Download PDF
Importance of the Stakeholders’ Interaction: Comparative, Longitudinal Study of Two City Logistics Initiatives
Previous
Biodiversity Impact of Green Roofs and Constructed Wetlands as Progressive Eco-Technologies in Urban Areas
Next