The impact of high-speed rail operations: A case study in Shanghai-Kunming line

Abstract

High-speed railway is an indispensable part of the transportation system. The construction and opening of high-speed railway will contribute to the economic development of cities along the route, but it will also have an adverse impact on peripheral areas. The research of the article is mainly carried out from an empirical point of view, using comparative analysis, regression analysis and other methods to measure and quantitatively describe the impact of high-speed railways on urban and regional spatial development. The study found that the operation of the Shanghai-Kunming high-speed rail had a siphon effect on cities along the Jiangxi Province, leading to unbalanced regional development.

Keywords

High-speed railway regional economy linear regression siphon effect

1 Introduction

On June 30, 2011, Shanghai-Kunming high-speed rail, which links China’s coastal and inland areas, was officially opened for operation, marking our country’s entry into the era of high-speed rail. It is more significant that high-speed railway is a part of the transportation system. In the process of implementing our country’s “the Belt and Road” [4] important national strategy, high-speed railways have developed rapidly. According to the relevant plan of the 2017 edition of the “Medium and Long-term Railway Network Planning”, by 2030, our country will basically realize the “eight vertical and eight horizontal” layout.

The Shanghai-Kunming High-speed Railway is a high-speed railway connecting Shanghai and Kunming in Yunnan Province. It is one of the main passages of the “eight vertical and eight horizontal” high-speed railway. It is the longest east-west line in China with the highest speed registration and the most provinces passing through. The Shanghai-Kunming high-speed railway is one of our country’s 4 trillion yuan economic stimulus plan, with an investment of 280 billion yuan, passing through poverty-stricken areas such as Qinba, Wuling and Liupanshui mountainous areas. The new opportunities will encourage the people in the village to go out of the mountains and seek new development. The operation of high-speed railways will cause economic downturn in small cities [5].

The article analyzes and studies the impact of the Shanghai-Kunming high-speed rail on economic growth along the route from both theoretical and empirical aspects.

Fig. 1

National railway operating mileage

Fig. 2

Railway passenger and freight traffic comparison

2 Literature

In the process of high-speed railway operation, it will promote economic exchanges between industries in different regions and enhance inter-regional exchanges. LI and SU [4] used the DID model to verify that the operation of high-speed rail showed obvious heterogeneity in the economic impact. Large cities have a stronger siphon effect. Begoña Guirao, and Juan Luis Campa [2] analyze the evolution of the impact of high-speed rail on the labor market in commuting relations. The location of high-speed rail stations is a more significant indicator for choosing high-speed rail commuting. SHAO [17] used the difference-in-differences model to study the impact of high-speed rail on urban service industry agglomeration. The results show that high-speed rail has a positive impact on the agglomeration of urban service industries in cities along the rout [20]. YU and LIN [5] found that the decline real GDP per capita was driven by the loss of skilled labor and the significant shrinkage of capital investment.

LIU and ZHANG [13] built a regression model around economic production theory, and the research found that high-speed rail increased the accessibility of all cities and regions. LI and WANG [18] based on the nonlinear time-varying factor model, the convergence of the urban-rural income gap from the perspective of high-speed railways is analyzed [19]. WANG [6] analyzed the panel data of 26 cities in the Yangtze River Delta from 2005 to 2016. MI [14, 20] found that the fixed asset investment in the high-speed rail cities has increased substantially after the operation of the high-speed rail. HOU [15, 21] evaluate tourist cities based on the General Difference Model (GDID) and Dose Response (DR). High-speed rail operations have a positive average impact on the development of the tourism industry [9, 10]. In some cases, high-speed rail services will also supplement multimodal transport [3] with air transport [1, 11]. The impact of high-speed rail on the tourism industry also depended on the destination and travel mode of each tourist [16].

CHEN [8] research found that high-speed rail passengers were mainly young people, who have received higher education and have moderate to higher incomes. GAO [22] used county-level panel data from the Yangtze River Delta in China to study the impact of high-speed rail connections on the local economy [12].

From the domestic and foreign literature, there are two aspects of existing research that need attention. Firstly, from the perspective of research results, the construction and later operation of high-speed rail will have a variety of impacts and impacts on the economic development of cities along the line. The possibility is high, and it may be negative. The other is from the perspective of research methods, few scholars use the theoretical framework of spatial economics for analysis.

3 Theory and influence mechanism analysis

3.1 Overview of the development of spatial economics theory

Spatial economics is also called The New Economic Geography. The development basis of spatial economics is location theory, which mainly studies the allocation of production factors and economic resources in the space field and the spatial location of economic activities. Among them, urban economics, regional economics and transportation demand have a relationship of mutual influence, interaction and coupling development. There are thousands of industrial clusters in China, which have a very obvious structure in line with the spatial economic framework. It is feasible to analyze the impact of high-speed rail on the economic development of cities of China from the perspective of spatial economics.

3.2 Siphon effect

The siphon effect [17] is a concrete manifestation of the effect of urban agglomeration, which refers to large cities attracting investment from surrounding cities by virtue of their powerful attractions, such as capital, location,and charm. The siphon effect will slow down the development of surrounding urban areas [7]. The siphon effect and spillover effect of high-speed railways occur simultaneously under normal circumstances, but it is different between the economic development of each city along the high-speed railway. After two to three cycles, the economic development of each region has reached an equilibrium state basically.

Fig. 3

Schematic diagram of siphon effect.

The impact mechanism of high-speed rail on the regional economy can be analyzed directly and indirectly. On the one hand, the opening of high-speed rail will improve the regional transportation capacity and regional accessibility, on the other hand, after the opening of high-speed rail, resource allocation will be more efficient and social division of labor will be more refined. Therefore, the opening of high-speed rail is conducive to regional economic development and urbanization.

4 Empirical process

The Shanghai-Kunming high-speed railway is one of the “eight vertical and eight horizontal” high-speed railway passages in China’s “Medium and Long-term Railway Network Planning” (2016 edition). The Shanghai-Kunming high-speed railway connects the three economic circles of the upper, middle and lower reaches of the Yangtze River, connecting small and medium-sized cities along the route, forming a transportation economic belt that runs through the upper, middle and lower reaches of the Yangtze River. After opening to traffic, the journey from Shanghai to Kunming will be shortened from more than 20 hours to about 8 hours.

4.1 Data selection

Taking into account the lack of data in some regions, the changes in administrative regions, and the representativeness of selected regions, the article selects Shanghai, Hangzhou, Nanchang, Changsha, Guiyang, and Yunnan as the sample cities. Using 2016 as the time node, select the GDP data of the sample cities from 2012 to 2019. The data used in this article are from the City Statistical Yearbook.

Fig. 4

Shanghai-Kunming high-speed railway map.

4.2 Data processing

After sorting and analyzing the GDP of the provincial capital cities of the Shanghai-Kunming high-speed rail, the output value of each region in 2019 is about twice that of 2012. In 2019, the GDP of Shanghai increased by 500 billion yuan from 2018. The GDP of Hangzhou increased at an average annual rate of 100 billion yuan. The GDP of Guiyang in 2019 is more than three times that of 2012. The GDP of Nanchang and Kunming has been growing steadily year by year.

However, from 2012 to present, The GDP growth rate of each region is declining at an average annual rate of 1%. For example, the GDP of Changsha grew at a rate of 20.3% in 2012, so it is the fastest growing city among the sample cities. However, in 2016, the growth rate of Changsha dropped by 50%, and the growth rate in 2019 was only 8.1%. The growth rate of Kunming in 2014 was 5% lower than that in 2013. Shanghai has been growing steadily at an average rate of 7% in the past 8 years. However, since 2016, the growth rate of various regions has slowed down. This situation is related to the official operation of the Shanghai-Kunming high-speed rail in 2016. In the correlation analysis, SPSS software was used to perform linear regression to calculate the GDP growth rate of each city. Calculated as follows: $Z_{i, j} = \frac{R_{i, j} - R_{i, j - 1}}{R_{i, j - 1}}$

Fig. 5

GDP growth trend of each city.

Z represents the growth rate, R represents a certain economic variable, i represents the city, and j represents the year. We also used the opening of the high-speed rail as the time node, and calculated the GDP growth rate of each city from 2012 to 2014 and 2015 to 2019. As shown in Table 2, we used SPSS to perform linear regression on the data to analyze the two periods GDP changes.

Table 1

GDP of some areas along the Shanghai-Kunming high-speed rail (100 million yuan)

	2012	2013	2014	2015	2016	2017	2018	2019
Shanghai	20558.98	22264.06	24068.20	25659.18	28183.51	30632.99	32679.87	38155.32
Hangzhou	7833.62	8398.58	9206.16	10050.02	11313.72	12603.36	13509.15	15373.00
Nanchang	3031.27	3387.26	3705.51	4011.88	4395.68	4819.76	5274.67	5596.18
Changsha	6479.78	7249.06	7934.66	8630.52	9292.33	10210.10	11003.41	11574.22
Guiyang	1710.30	2085.42	2497.27	2891.16	3157.71	3537.96	3798.45	4039.60
Kunming	3011.14	3415.31	3712.99	3970.00	4300.43	4857.64	5206.90	6485.88

Table 2

Indices of GDP in some regions of the province

Ctiy	2012	2013	2014	2015	2016	2017	2018	2019
Shanghai	7.50%	7.90%	7.10%	7.00%	6.80%	6.90%	6.60%	6.00%
Hangzhou	9.00%	8.00%	8.20%	10.20%	9.60%	8.10%	6.70%	8.00%
Nanchang	12.50%	10.70%	9.80%	9.60%	9.00%	9.00%	8.90%	8.00%
Changsha	20.30%	14.20%	13.10%	11.20%	10.00%	9.80%	8.50%	8.10%
Guiyang	15.9%	16.0%	13.9%	12.5%	11.7%	11.3%	9.9%	7.4%
Kunming	14.20%	12.90%	8.10%	8.10%	8.50%	9.70%	8.40%	6.50%

Table 3

Urban GDP growth rate

City	2008–2013 (g1)	2014–2019 (g2)	Increase (g2-g1)	GDP change trend ranking
Shanghai	–2.266	–2.769	–0.503	3
Hangzhou	0.635	–0.735	–1.37	6
Nanchang	–0.914	–2.191	–1.277	5
Changsha	–0.306	–1.316	–1.01	4
Guiyang	–0.72	–0.631	0.089	1
Kunming	–0.378	–0.696	–0.318	2

4.3 GDP growth impact

When the GDP growth rate is negative number and the slope increases, the GDP growth trend will become more gentle. The bigger the slope, the faster the GDP growth trend declines.

The impact of the Shanghai-Kunming high-speed rail on the GDP growth rate of cities along the line is nearly negative growth in the two stages. From 2012 to 2015, there was only one city——Hangzhou having a GDP growth coefficient of +0.634. According to SPSS data analysis, the differences of the GDP growth slope of Shanghai and Kunming are –0.503 and –0.318 respectively, and the slopes are decreasing. It is indicating that the growth trends of these three cities are decreasing faster. The GDP growth slope differences of Nanchang, Hangzhou and Changsha are respectively –1.37, –1.27, –1.01. The slopes is increasing, and it is indicating that the GDP growth has stabilized. The GDP growth coefficient of Guiyang is 0.089, and the GDP growth trend is accelerating. The reason for the above situation is that the siphon effect of the Shanghai-Kunming high-speed rail plays a major role. Since Hangzhou, Nanchang and Changsha are not provinces at the two ends of the Shanghai-Kunming high-speed railway, their opening has accelerated the development of the larger core cities at both ends of the railway. The opening of high-speed rail may not has ability to balance the gap between the core metropolis and the small and medium-sized cities in remote cities. The high-speed rail has a more obvious siphon effect on the sample cities.

4.4 Siphon effect verification

Through the analysis of the chart, there are obvious differences in the economic development GDP growth rate of the sample city along the Shanghai-Kunming high-speed rail. In order to be more intuitive, the difference in urban GDP growth rate will be averaged,

The average GDP growth rate difference between Hangzhou and Nanchang is –1.05 (near Shanghai), and the average GDP growth rate difference between Changsha and Guiyang is –0.413 (near Kunming). Then, four sets of data from Shanghai, near Shanghai, near Kunming and Kunming were used to make graphs to obtain the revised Kunming mixed high-speed rail impact on GDP growth in cities along the route.

Fig. 6

The GDP growth rate difference before and after the opening of.

Fig. 7

Curve of GDP growth rate of cities along the Shanghai-Kunming high-speed rail after processing.

There is a U-shaped from the graph after processing the average GDP growth rate, and the Shanghai-Kunming high-speed rail has a siphon effect on the sample cities along the route. The difference in GDP growth rates between Shanghai and Kunming is almost twice that of medium-sized cities. It shows that cities at both ends of the Shanghai-Kunming high-speed rail may be attractive to cities in the middle.

5 Conclusions

There are two views of the impact of high-speed rail on urban and regional economic development——both consensus and controversy. This article analyzes the GDP growth rate of cities along the Shanghai-Kunming high-speed rail. At the same time, it tests and verifys that the opening of the Shanghai-Kunming high-speed rail has accelerated the development of core cities at both ends of Shanghai and Kunming. Then, it has also increased the suction to the surrounding fringe cities, causing the GDP growth rate of other cities along the route to slow down, relativing to the GDP growth rate of Shanghai and Kunming. Comparing with Shanghai and Kunming, the GDP growth rate has slowed down. Through data analysis, the economic growth trend of the area passed by the Shanghai-Kunming High-speed Railway is a U-shaped. The opening of high-speed rail has increased the gap between core large cities and marginal small and medium-sized cities. Therefore, the development of high-speed rail should be compatible with regional economic development. In the process of responding to development, the “siphon effect” is a “double-edged sword” that shoud be paid attention to, otherwise it may become an obstacle to regional economic development.

The state should provide finance and taxation, attract talents, and guide relevant industrial policies to establish a great soft environment for the development of the city itself. And it is necessary to retain local talents, funds and technology to the greatest extent, and deeply attract logistics, information flow, and people flow away from the economic highlands. The city clusters with stations along the line will turn the disadvantages of the siphon effect into advantages. In the mid-term of high-speed rail construction, it is necessary to actively eliminate institutional restrictions on public resources, break administrative divisions, and achieve a comprehensive urban effect of medical and educational security. In the long term, it is necessary to rationally plan the industrial links between cities, adjust the industrial structure, and form a reasonable urban structure.

Data availability

The data will be accessible upon request.

Conflicts of interest

The authors declare that they have no conflicts of interest.

Footnotes

Acknowledgments

This work was financially supported by the National Natural Science Foundation of China (51805169, 52062014). This study is also supported by the Natural Science Foundation of Jiangxi Province 20202BABL212009.

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