Impacts of high speed railways on accessibility transformation and tourism change - The case of China
Qian Zhao1, Liangping Hong2, Xiangbai Wu3, Yulan Mao4
Abstract: From the cases in Asia and Europe, high speed railways (HSRs) play a significant role in transportation network and tourism destination development. China is undergoing a complicated urban transformation after the extensive development of HSRs in recent years. This paper has a double aim: first, evaluate the impact of HSRs on accessibility in China, by reducing time distance between places and modifying their relative location, and then explore the relationship of HSR network development and tourism change in the linked urban nodes. We build a national rail network covering 31 urban nodes (27 provinces and 4 municipalities) in China, and compare the situations in the years of 2006, 2009 and 2010, in order to analyse the changes by HSRs from a national point of view. It is hypothesized that HSRs will certainly improve the accessibility of urban nodes, but will also have a negative effect on some nodes. A weighted average accessibility indicator is used for measurement. This measure identifies the spatial distribution of accessibility in China, emphasizing the HSRs effects, and locates accessibility changes at the national level. HSRs authorize a reduction in transportation time and costs and can be a tool for enhancing tourism industries. According to the statistic in China, many cities experienced a growth in tourism after the introduction of HSRs. Thus many researchers figured out this phenomenon in a qualitative way, this paper aims to analyse this relationship from the quantitative aspect.
1PhD Candidate, School of Architecture and Urban Planning, Huazhong University of Science and Technology, South 4 Teaching Building, Luoyu Road 1037, Wuhan, Hubei, China, 430070 Email: [email protected] 2 Professor, School of Architecture and Urban Planning, Huazhong University of Science and Technology, South 4 Teaching Building, Luoyu Road 1037, Wuhan, Hubei, China, 430070 Email: [email protected] 3 PhD Candidate, College of Oceanography and Environmental Science, Xiamen University, 218 Yingxue Building, Xiamen University, 422 South Siming Road, Fujian, China, 361005. Email: [email protected] 4 Faculty of Materials Science and Engineering, Wuhan University of Technology, Luoshi Road 122, Wuhan, Hubei, China, 430074 A regression analysis is carried out to make of the relationship of tourism and HSRs. We use the accessibility indicator and number of HSR linkages to represent HSRs. On the basis of the maximum radius of train travelling time we calculate by applying the distance formula, we divide the 31 nodes into “High HSR Nodes” and “Low HSR Nodes”, and the result shows “High HSR Nodes” are related to tourism revenues growth. According to the number of HSRs linkages, we divide the 31 nodes into three types: “High, Low and No HSR Linkages”, and the result shows “High HSR linkages” have a relationship with the tourism revenues. However deviations to this are possible. HSRs would encourage the development of tourism industries overall; hence contribute to the regional economic structure formation.
Keywords: high speed railways; rail network; tourism; accessibility indicator; China
1 Introduction
High speed railways (HSRs), operated at a speed of 200 km/h or higher, bring a major improvement in the accessibility level of rail networks, and modify the relative distance of different places (Gutierrez et al, 1996). There are several well-known HSR systems all over the world, for example Shinkansen in Japan and Train à Grande Vitesse (TGV) in France. Now the Chinese HSR network is at an early stage of its development with a limited number of lines, but many new lines have been planned in order to complete a full HSRs grid in China. The rest of national rail transportation network is taken up by ordinary trains at lower speeds. However, the overall competitive strength of the transportation network has increased because of the great reduction of travel time among cities linked by the operation of HSRs. Transportation infrastructure and economic development go hand in hand (Gutierrez et al, 1996). Each new breakthrough in transport technology has encouraged tourists to travel further, faster, cheaper, and more comfortably. An efficient transport system exerts a profound effect on tourism development (Prideaux, 2000). Advanced transportation technologies such as HSRs can have a big influence on tourism industries, because HSRs is more convenient and comfortable to reach destinations and creates much new domestic and foreign tourism (Wang et al, 2010). It has shown that new HSRs with their intermetropolitan passengers help cities to experience a strong growth “in urban tourism and the staging of congresses, scientific meetings, seminars and suchlike” (Urena et al, 2009). If inefficient transportation systems limit the range of travels, visitors will look for other destinations instead (Masson and Petiot, 2009). This paper is divided into four parts. Section 2 uses an accessibility indicator to get the accessibility changes in 2006, 2009 and 2010. The impact of HSRs on tourism revenue in terms of the accessibility indicator and number of HSR linkages is discussed in Section 3. Findings are summarized in Section 4.
2 Accessibility transformation
2.1 Accessibility indicator According to the “XINHUANEWS” [18], China now possesses the longest miles of HSRs in the world and plans to create a national HSR network which can improve the accessibility levels of linked cities, bringing them closer to each other in terms of travel times. In general, accessibility is a measurement of location on a surface relative to suitable destinations, adjusted for the characteristics of transport network or networks linking points on that surface (Vickerman, 1974). Selecting a proper indicator is very important for measuring the accessibility. In fact, results could be different, depending on the indicator we choose. As there are a wide variety of models to measure the accessibility (see, for example, Hansen, 1959; Vickerman, 1974; Gutierrez, 2001), this paper adopts the weighted average accessibility indicator described in Gutierrez et al (1996) and modified by one of the authors, Zhao, as shown below. n Tij(t) GDPj(t) j1 A (1) i(t) n GDPj(t) j1 where Ai(t) = the accessibility indicator of origin “i” in time “t”; Tij(t) = the travel time through the ordinary rail or HSRs between the origin “i” and the destination “j” (in minutes) in time “t”; GDPj(t) = the gross domestic product of the destination “j” in time “t”; “i” subscripts refer to the origin; “j” subscripts refer to the destination; “t” subscripts refer to the time. In this paper, this indicator is more suitable than those of economic potential. First, this paper aims to indicate the accessibility changes brought by HSRs from the locational rather than the economic point of view. Second, short distances contribute heavily and long distances contribute little in the economic potential indicator, while there is no distance decay in the weighted average accessibility indicator. This study aims to create a Chinese-wide transportation network and it is reasonable to consider the effect of long distances instead of short distances. Although “in order to demonstrate the pure rail effect, the GDP economic activity measure in the indicator was fixed” (Gutierrez et al, 1996), we use different GDP from different times, because accessibility depends both on a variation of the access times and the economic weights of regions. China is a rapidly developing country and has great economic gap between different regions. HSRs will bring different economic attraction and growth potential in developing and developed regions and modify their relative location. Also, because of their normally close ties, the GDP of the province as a whole rather than the GDP of its network transportation node is used in the calculations.
2.2 Network building and accessibility of the 31 nodes calculation in China Until now, there have been six national campaigns called “Railway Speed Up” that enhanced the speed of national railways in China. When the sixth “Railway Speed Up” Campaign was commenced on April 18th, 2007, the first HSR service from Shanghai to Suzhou was put into use. After that, several China HSR trainsets entered into operation. According to the Ministry of Railway’s “Mid-to-Long Term Railway Network Plan” (revised in 2008), the national HSRs grid is composed of 8 HSR corridors, among which four running north-south and four going east-west, and has a total of 16,000 km in 2020. By 2008, the mileage of HSRs in operation totalled 649 km [19], 2830 km in 2009 [20], and with a huge development the mileage rose to 8358 km in 2010 [21]. Because of this, the travel times of rails in 2006, 2009 and 2010 are used for analysing the accessibility changes. Although some researches have already analysed the accessibility changes by HSRs in China, they just focused on one specific HSR line, such as Jinghu HSR or Wuguang HSR (see, for example, Jiang et al, 2010). By building a national rail transport network, this paper tries to analyse the accessibility changes by HSRs from a national point of view. There are 27 provinces and 4 municipalities (excluding Hong Kong, Macau and Taiwan) in China, and this paper includes all 31 for analysis, because the municipalities and provincial capitals are not only political and economic centres, but also major transport nodes where important dimensions of the local and regional transportation infrastructures are located in China. In the railway network covering the 31 nodes, only 21 had HSRs service in the year of 2010 and 10 did not, fig. 1. Because the HSR network hasn’t covered whole China, we adopt the HSR data for the accessibility indicator if there is a HSR line passing by a node, or adopt the minimal time for regular trains if there is no HSR.
Figure 1: Distribution of HSR nodes in 2010.
The GDP data of 31 provinces and municipalities in 2006, 2009 and 2010 were obtained from the Chinese National Bureau of Statistics database [13]. In the rail network, the travel times of different years from one point of origin to any other node are the minimal minutes obtained by the new train schedule software called “China Railway Timetable”. That software is developed by the Ministry of Railway Transport and Railway Publishing House. In the data collection, the origins and destinations used in this paper are the 31 nodes in rail transportation network as mentioned before in China1.
1 The travel times between every two nodes among those 31 nodes in 2006, 2009 and 2010 are obtained from different “China Railway Timetable” versions in different times; each version lasts for several months, and some railways may have little changes during these months. When we encounter the situation that a new line was inaugurated during a year, we use the mean of travel time which is the average of all travel times for before and after the change. Taking Hewu HSR for example, this line came into service in April 2009 and the travel time from Hefei to Wuhan was 125 minutes, while the previous time was 451 minutes, so the mean travel time is 207 minutes in 2009. Transfer times which exist for indirect lines, refer to the time required in the intermediate stops to transfer to another train or station: 300 minutes is used as the average transfer time in transit. This includes not only the time in waiting for the next train, but also the time when the user may change stations. For example, many railways to Tianjin need to transfer at Beijing and go to a different station to continue the trip. The procedure of calculating the accessibility indicator in a Matlab is rather simple. The 31 urban agglomerations in China are represented by nodes on the rail network, with the following attributes: name, GDP, travel time, direct line or indirect. Then the accessibility indicator for each node was obtained applying eqn (1), as shown in Table 1. This accessibility indicator reflects not just the average of times between that node and the other 30 nodes, but also the economic value of node by using the provincial GDP. When the accessibility indicator of one node becomes smaller, the accessibility of that node improves.
Table 1: Accessibility indicators of the 31 nodes. Region Node 2006 2009 2010 Difference percent Accessibility Accessibility Accessibility 2009-2010 2006-2010 in minutes in minutes in minutes Huabei Beijing 812.90 727.34 725.81 0.21 10.71 Tianjin 952.07 896.70 904.39 -0.86 5.01 Shijiazhuang 888.77 801.36 780.70 2.58 12.16 Taiyuan 1403.20 1124.87 1067.29 5.12 23.94 Huhehaote 1681.62 1583.10 1583.13 0.00 5.86 Dongbei Shenyang 1585.00 1221.83 1201.92 1.63 24.17 Changchun 1540.89 1496.46 1503.15 -0.45 2.45 Harbin 1700.03 1645.42 1656.97 -0.70 2.53 Huadong Shanghai 1058.69 841.50 789.98 6.12 25.38 Nanjing 1064.04 902.42 854.65 5.29 19.68 Hangzhou 1160.31 984.87 985.12 -0.03 15.10 Hefei 1095.16 820.33 805.93 1.76 26.41 Fuzhou 1696.59 1504.31 1448.18 3.73 14.64 Nanchang 982.13 900.78 883.05 1.97 10.09 Ji’nan 1094.51 925.85 927.49 -0.18 15.26 Zhongnan Zhengzhou 787.79 704.71 693.61 1.58 11.95 Wuhan 857.50 702.26 628.36 10.52 26.72 Changsha 998.68 849.82 787.75 7.30 21.12 Guangzhou 1453.62 1312.71 1287.36 1.93 11.44 Nanning 1685.06 1695.91 1682.60 0.78 0.15 Haikou 2284.04 1834.28 1818.60 0.85 20.38 Xi’nan Chongqing 1733.56 1482.14 1525.54 -2.93 12.00 Chengdu 1960.84 1718.31 1716.29 0.12 12.47 Guiyang 1775.03 1585.10 1543.75 2.61 13.03 Kunming 2338.82 2176.69 2102.19 3.42 10.12 Lasa 3184.96 2970.10 2983.03 -0.44 6.34 Xibei Xi’an 1192.92 1029.33 1022.47 0.67 14.29 Lanzhou 1448.11 1355.29 1349.10 0.46 6.84 Xining 1647.16 1517.15 1519.70 -0.17 7.74 Yinchuan 1897.36 1897.53 1886.68 0.57 0.56 Wulumuqi 3017.45 2780.60 2776.87 0.13 7.97
As Table 1 shows, from 2006 to 2010, the accessibility indicator of each node has been produced and the accessibility has been greatly improved by the operation of HSRs. The node with the highest change is Wuhan with 26.72%, Hefei with 26.41%, Shanghai with 25.38%, then Shenyang with 24.17%, Taiyuan with 23.94% and Changsha with 21.12%. In contrast, nodes in the northwest and southwest parts have little improvement, like Nanning with 0.15%, Lasa with 6.84%, Lanzhou with 6.84%. But in overall, the HSRs have improved the competitive strength of rail system in China, making travels more convenient. Calculations of relative differences in the accessibility indicator between 2009 and 2010 offer somewhat different results. The HSR network has increased some places accessibility by substantially reducing the travel times. The node with the highest change is Wuhan with 10.52%, then Changsha with 7.30% and Shanghai with 6.12%. However, Table 1 also shows that the introduction of HSRs has had some negative effects where the accessibilities of ten nodes were slightly reduced from 2009 to 2010. A common reason for this is that when high speed and ordinary trains use the same tracks, the ordinary trains may need to stop at the passing stations in order to ensure that the high speed trains have the priority to run on time. In regional terms, the data indicates the most accessible areas of China stretch from the eastern region to the central region. The more peripheral nodes in terms of accessibility are in the northwest and southwest. The HSRs improve the accessibility level overall, but it indeed aggravate the transportation network gap among eastern, central and western regions. Nowadays there are great differences in speeds within the railway network in China, from 200 – 300 km/h or greater on the HSR lines to probably 60 km/h or less in some regions such as those in the northwest and southwest.
3 Relationship of HSRs and tourism industry
Although the Chinese HSR network has been operated for only a short time, there are several evidences of a positive effect on a region’s tourism industry. Based on “What’s On Xiamen” [22], the brand-new HSR between Fuzhou and Xiamen had caused a reported 30% increase in tourism to Xiamen. The Zhengxi HSR also helped boost tourism in Xi’an. According to the Tianjin Tourism Market Research Report 2010 [23], Jingjin intercity HSR has a contribution of 35% to the growth rate of tourism. As many researchers figured out that HSRs can improve the development of tourism in a qualitative way, this paper aims to analyse this relationship from the quantitative aspect, using the case of China. In this section, we drawing upon cases in Japan and France, explore the relationship of HSRs and tourism. Then we use a regression analysis to explore the relationship of the accessibility improved by HSRs, the number of HSR linkages in provincial capitals and tourism revenue in provinces of the 31 nodes in China as mentioned above for analysis, not only because they represent for tourists transport centres, but also they are historical and cultural cities and have substantial variation in supply of cultural activities such as exhibitions, like the 2010 EXPO in Shanghai, theatres and competitive sports events. Additionally they have excellent regional transit systems so that they can serve the travellers who want to visit other places in that province better. We used the accessibility indicator and number of HSR linkages to represent HSRs. Though the accessibility indicators calculated in previous parts in Table 1 integrate the economic weights of regions and thus partially the tourist income, the tourist income is just a small proportion of the total GDP. Taking Hubei for example, the total GDP is 1580.6 billion CNY and the tourism revenue is 146.1 billion CNY, so they can reflect accessibility change by HSRs. The number of HSR linkages also contributes a lot on the effect of HSRs. If there are more lines passing through a node, there are more nodes could affect it and vice versa. There are several factors in the formation of the tourism industry including tourist market, tourism resources, transportation, facilities, traffic location, related industries and government policy, and HSRs have a great impact on these elements. After the completion of HSRs, the links of attractions among tourist destinations will be strengthened; the travel cost including travel time and money will be reduced; the number of passenger trips will increase and the cooperation among different industries in different areas will be closer. All these phenomena are mainly due to the enhancement of transportation condition. Because the temporal and spatial distance is shortened by HSRs, the tourism economic links among the areas along the line have been strengthened.
3.1 International empirical cases The introduction of HSRs significantly decreases both the travel time and the price to access the tourism destinations and can be a tool for the development of tourism destinations by improving accessibility (Masson and Petiot, 2009). A number of existed studies in various parts of the world have assessed the impact of HSR development on tourism. The Japanese Shinkansen, which is the first HSR system in the world, shows HSRs can encourage the number of sightseers and increase tourism. After the first Shinkansen was launched in Japan, the proportion for tourism rose from 15% to 25% between 1964 and 1975 (Melibaeva et al, 2010). After the HSRs inauguration in 1975, the number of tourists coming from other places increased in Shinkansen nodes, and the tourism activities of the hinterland in Japan also experienced an increase (Okabe, 1979). According to a questionnaire survey on whether the passengers would choose a trip or not if the Kyushu Shinkansen in Japan had not been commenced, 17.8% responders replied that they would not choose the trip or that they would have selected an alternative destination. As a result, the establishment of Kyushu Shinkansen brought in new travel demand (Tanaka and Monji, 2010). If the HSRs are just passing by but are not stopping at any cities, these places may experience a decrease in tourists. Taking Onomichi City for example, which has notable old temples, shrines and is used as a ferry port connecting Honshu and Shikoku, had as many as 1,764,000 tourists in 1964 and declined to 1,605,000 in 1975, during the inauguration of the Shinkansen (Okabe, 1979). According to Lee (2007), the Kyushu Shinkansen, which opened in March, 2004, has resulted in a rapid rise on D.I base which is the company ratio of positive effect, and the travel services increases from 0% to 43%, with the fastest growth of all departments. Another example is found in France. After the commencement of TGV between Pairs and Lyon, Lyon experienced a significant increase in tourism business (Melibaeva et al, 2010). Because of the great increase in ridership, there were new travel packages for users of the TGV, and this was also a function of increased promotion by areas along the TGV (Sands, 1993). The Abbaye of Fontenay, just 5 km away from Perrache TGV station, experienced a tourists’ rise by nearly 40% in three years (Bonnafous, 1987). In 1989, the opening of the Atlantic TGV put Le Mans only 55 minutes from Paris, leading to tourism development projects, and the tourism industry in Tours, which is located in the Loire valley, also had an important increase (Masson and Petiot, 2009).
3.2 Tourism revenue The data of tourism revenue of every province including both the domestic and foreign tourism revenue in 2006, 2009 and 2010 (Table 2) were obtained from the Statistical Communiqué of each province and municipality in 2006, 2009 and 2010 [14]. The provincial capital is the node used for the analysis of HSRs, but the tourism of province as a whole is used as the measure, because there is a chain reaction of tourism from the provincial capital to the whole province. Table 2: Tourism revenues of the 31 nodes Region Provincial 2006 Revenue 2009 Revenue 2010 Revenue Difference percent Capital in billion in billion in billion 2009-2010 2006-2010 CNY CNY CNY Huabei Beijing 180.4 244.2 276.8 13.3 53.5 Tianjin 64.6 103.1 120.0 16.4 85.7 Shijiazhuang 49.0 71.0 91.5 28.8 86.6 Taiyuan 42.8 89.3 108.4 21.4 152.9 Huhehaote 28.0 61.1 73.3 19.8 161.9 Dongbei Shenyang 97.1 222.5 268.7 20.8 176.9 Changchun 27.6 58.1 73.3 26.1 165.5 Harbin 35.1 65.0 88.3 35.9 151.7 Huadong Shanghai 173.7 234.4 340.0 45.1 95.7 Nanjing 228.4 379.6 468.5 23.4 105.1 Hangzhou 169.0 264.4 331.3 25.3 96.0 Hefei 41.2 90.9 115.1 26.6 179.7 Fuzhou 81.1 113.3 133.7 18.1 65.0 Nanchang 39.1 67.6 81.8 21.1 109.3 Ji’nan 129.6 245.2 305.9 24.7 136.1 Zhongnan Zhengzhou 104.0 198.5 229.5 15.6 120.7 Wuhan 54.0 100.4 146.1 45.4 170.6 Changsha 58.8 109.9 142.6 29.7 142.5 Guangzhou 212.5 306.8 380.4 24.0 79.0 Nanning 36.6 70.1 95.3 35.9 160.2 Haikou 14.1 21.2 25.8 21.5 82.2 Xi’nan Chongqing 34.6 70.3 91.8 30.6 165.2 Chengdu 98.0 147.2 188.6 28.1 92.5 Guiyang 38.7 80.5 106.1 31.8 174.2 Kunming 50.0 81.1 100.7 24.1 101.4 Lasa 2.8 5.6 6.9 23.8 150.1 Xibei Xi’an 41.9 76.8 98.4 28.1 135.0 Lanzhou 8.0 19.3 23.7 22.8 195.5 Xining 3.6 6.0 7.1 18.4 98.9 Yinchuan 2.6 5.3 6.8 27.9 164.1 Wulumuqi 16.0 18.5 28.1 51.6 75.6
3.3 Relationship of accessibility indicators and tourism revenues 3.3.1 Train travel related to distance Preferences for various modes of transportation differ with distance in travel. As train is suitable to service the travel of middle to long distance, it is important to identify the travel distance for train. Li et al (2005) claimed train is the main means of transport for a travel with a straight distance between 251 and 1000 km (Table 3), and they applied eqn (2) to convert straight distance into transport distance.
Table 3: Traffic methods related to distance. Straight distance (km) 0-100 101-250 251-500 501-1000 1001-1500 >1500 Major mode Auto Auto Train Train Airplane Airplane Secondary mode Train Auto Airplane Train Train
D2 D1 (2) 1.31 where D1 = straight distance; D2= transport distance. After the implementation of the fifth “Railway Speed Up” Campaign on April 18th, 2004, the national average travel speed of train was 65.7 km/h. Maximum straight distance of 1000 km is calculated as equal in time to 1196 minutes. The lower point for preferred train travel is 251 km, we calculate to be equal to 300 minutes. The calculation of straight distance preferred transfers to time distance is based on the method applied by Li et al (2005), using an average speed among the period of time for eqn (2). Though the speed rose to 70.18 km/h after the sixth “Railway Speed Up” Campaign on April 18th, 2007, we use the year 2005 to determine a speed of rail between 2004 and 2007 for calculating. We calculate that currently when the travel time is between 300 minutes and 1196 minutes, trains are the major vehicle for tourists. Under this assumption we can use 1196 minutes as the maximum accessibility radius of travel mainly depending on trains. When the accessibilities of some nodes are very poor, visitors will choose airplane instead of rail. So the reduction of train travel time has a major effect on the passenger trips in the accessibility indicator of cities suitable for trains. As the upper choice value of train travel is 1196 minutes, we choose the cases in which the time is less than 1200 minutes to analyse.
3.3.2 Relationship of accessibility indicators and tourism revenues The accessibility indicators of some nodes are higher than 1196 minutes, so we divide the 31 nodes into 2 types. One is “High HSR Nodes”, which have accessibility indicator lower than 1196 minutes; the other one is “Low HSR Nodes”, which higher than 1196 minutes. The accessibility indicator of Taiyuan was 1403 minutes in 2006, but reduced to 1124 minutes in 2009, and Shenyang was 1221 and 1201 minutes in 2009 and 2010, and they are quite near the maximum travel time. Taking all the factors into consideration, we use the regression analysis to explore the relationship of tourism revenues and accessibility indicators of “High HSR Nodes” including Taiyuan and Shenyang.
Figure 2: Relationship between the changes of accessibility indicator and tourism revenue in "High HSR Nodes" from 2009 to 2010.
Figure 3: Relationship between the changes of accessibility indicator and tourism revenue in "High HSR Nodes" from 2006 to 2010.
China is a developing country with the urban economy growing rapidly, and the tourism business growing at a steady rate, so the tourism revenue of every province experiences a growth even if the accessibility has declined. Fig. 2 and fig. 3 reveal that, during the period examined, the changes of accessibility indicators of the nodes which are “High HSR Nodes” were related to tourism revenues growth. The accessibility change has an approximate liner regression with the increase of tourism revenue.
3.4 Relationship of numbers of HSR linkages and tourism revenues 3.4.1 Number of HSR linkages Table 4 shows the numbers of HSR linkages in 31 nodes according to the operation HSR lines in 2010 in China. Taking Changsha for example, there are 3 HSR lines in total located there, which are Wuguang HSR, Changsha to Shanghai line and Changsha to Nanchang line, so the number of HSR linkage of Changsha is 3. These HSR lines were finished between 2007 and 2010.
Table 4: Number of HSR linkages in 31 nodes in 2010. Provincial capital Number of HSR linkages in 2010 Shanghai 7 Nanjing, Zhengzhou 6 Beijing, Hangzhou 5 Shenyang, Wuhan 4 Changsha, Ji'nan, Nanchang, 3 Taiyuan,Tianjin Changchun, Harbin, Hefei, Shijiazhuang 2 Chengdu, Chongqing, Fuzhou, Guangzhou, 1 Xi'an Wulumuqi, Yinchuang, Xining, Lanzhou, 0 Lasa, Kunming, Guiyang, Haikou, Nanning, Huhehaote 3.4.2 Relationship of numbers of HSR linkages and tourism revenues The HSR network in China covers 21 nodes from the total 31 nodes in 2010. We divided these 31 nodes into 3 types. The first type is “High HSR Linkages”, which is the number of HSR linkages is from 3 to 7; another is “Low HSR Linkages”, which is the number of HSR linkages is above 0 and beyond 3; the last one is “No HSR Linkages”, which is no HSR line passing by.
Figure 4: Relationship of number of HSR linkages and tourism revenue in 2010.
We can see from Fig. 4 that the tourism revenues of “No HSR Linkages” are general lower than the nodes which have HSRs passing by. The numbers of “High HSR Linkages” experience an approximate liner regression with the tourism revenues. The numbers of “High HSR linkages” have a relationship with the tourism revenues, while there is no relationship between the numbers of HSR linkages and tourism revenues in the nodes that are “Low HSR Linkages” and “No HSR Linkages”.
3.5 Discussion According to Fig. 2, we note that along with the improvement of accessibility indicators, most of the “High HSR Nodes” are experiencing a regular growth; however there are some nodes experiencing a decline or lack of growth in development of tourism comparing to the accessibility changes than others. There can be both positive and negative effects of HSRs on regional tourism. Thus the core destination along HSRs may become more attractive and the number of visitors goes up while the periphery nodes may have a decrease in tourism. This effect was projected by Mason and Petiot (2009) to be a likely result in their analysis of the region of Barcelona in Spain that has a stronger tourism potential than that of Perpignan in France with the implementation of the South European HSR in 2009. From the situation in China, it seems that along the HSRs, one region will be easier to benefit from the operation of HSRs for tourist business development than another region, while both two regions are experiencing an increase in tourism. For example, the Zhengxi HSR - the first HSR line connecting the central and the western regions in China, was inaugurated in February, 2010, which shortened the travel time from 6 hours to 2 hours, and this line connects two famous tourist cities: Zhengzhou and Xi’an. However, there is an imbalance tourism growth percentage of these two regions. Xi’an is the most important node in the western region and has larger attraction potential for tourists for its long history and fame, especially for foreigners, such as the Terracotta Warriors. It received a maximum increase of inbound tourist arrivals in 2010 and its absolute growth of tourism revenue from 2009 to 2010 almost equals to Zhengzhou. Zhengzhou has a larger base of tourism revenue and GDP than Xi’an, so the relative growth of tourism revenue of Zhengzhou is smaller than Xi’an. Although this is the only HSR route passing through Xi’an and its accessibility indicator doesn’t change a lot, it has a substantial positive impact on tourism. The Hewu HSR came into service in April 2009, linking the east and the central regions, and shortened the travel time between Hefei and Wuhan from 10 hours to less than 2 hours. Because the GDP and base of tourism revenue of Hefei is lower than Wuhan, the attraction of Wuhan for tourists is greater than Hefei and the growth rate of Wuhan tourist is higher than Hefei from 2009 to 2010. According to Fig.3, the value of the coefficient of determination (R2) from 2006 to 2010 is lower than the one from 2009 to 2010. This is because there are other factors affecting the tourism business during this long period, like economic, political and social factors, and HSRs were in the nascent stage in the first years then with a huge development in the years of 2009 and 2010. According to Fig.4, along with the number of HSR linkages increasing, the tourism revenue approximately increases. Deviations to this are possible. Nanjing (Jiangsu) is an example. Although it is ranked high with 6 linkages, its tourism revenue is disproportionately high. This is because Jiangsu Province has rich tourism resources and deep cultural heritage, and it has always been recognized as “tourism province”. In this way, Jiangsu Province experiences the greatest tourism revenue increase with the second highest HSR linkage number. The accessibility indicators of most “Low HSR Linkages” and “No HSR Linkages” are beyond the maximum radius of train travelling time, and just three nodes of Xi’an, Shijiazhuang and Hefei are exceptions. The numbers of HSR linkages of these nodes have no relationship with tourism revenues. Nevertheless, the accessibility indicators of most “High HSR Linkages” are all within the time radius of train travelling time. The numbers of HSR linkages of these nodes have a strong relationship with tourism industry.
3.6 Limitations Although the accessibility plays a significant role, there are other factors can also explain the increase of tourism. For instance, we know about the experience that HSRs are firstly implanted between large economic nodes and it is possible that these lines are implanted between nodes with great potential tourist. Besides, the local transportation is also very important. After the inauguration of Wuguang HSR, there were so many tourists travelling from Guangdong to Wuhan which caused a lot of traffic jams, and that had a negative influence on tourism.
4 Conclusions
The tourism revenue growth has a relationship with the accessibility indicator's change in convenient transportation nodes. Moreover, there is a strong relationship between the growth of tourism revenues and operation in nodes with higher numbers of HSR links. We can reach the conclusion that the HSRs constitute a powerful tool to facilitate the development of tourism business. For our future study of tourism and HSRs, other factors need be brought into consideration. Due to the short time period of research, mid- to long-term analyses will be added to determine the long-term impact. To do this, there should be an analysis of the imbalance among the tourist potentialities of different regions along the HSRs in China. We should not only consider the varying importance of the different region like the core or the periphery, but also consider the base income and GDP of different regions, because there are great economic gaps among the eastern, central and western regions in China. References
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