1. Introduction
High-speed rail (HSR) has been developed in many countries and regions, beginning in Japan with the opening of the Tokaido Shinkansen in 1964. The Shinkansen brought a paradigm shift in the flow of business and tourist passengers to the 500 km-long metropolis, resulting in remarkable economic development and changes in the social environment.
The benefits of HSRs are discussed from various aspects and perspectives. In the planning and construction phase of the HSR, economic multiplier effects will be generated in the short term through the construction of the HSR and its surrounding facilities and through investment and demand inducement in related industries in anticipation of the opening of the HSR. Subsequently, during the operational phase, direct benefits will accrue to travelers through time savings and improved comfort when traveling between cities along the HSR in the short term. On the other hand, indirect benefits may contribute to medium- and long-term impacts associated with the stimulation of business-to-business communication and industry interaction as well as an increase in service supply and gross regional product (GRP).
As an example of an HSR project, the Mumbai–Ahmedabad High-Speed Rail (MAHSR) connecting Mumbai and Ahmedabad (
Figure 1) is currently under construction. The MAHSR is the first HSR in India, which is an emerging country with remarkable economic growth. This project is being heralded as a milestone in the establishment of a nationwide HSR network planned in India. Mumbai is the economic and financial center of India as well as the capital city of Maharashtra State, and Ahmedabad is the largest city in Gujarat State. The Mumbai–Ahmedabad route will take approximately six hours by conventional rail, and the fastest MAHSR trains will stop only at Surat and Vadodara, with a planned travel time of approximately two hours [
1].
The geographical layout of the metropolitan areas of Mumbai, Surat, and Ahmedabad along the MAHSR is similar to that of Tokyo, Nagoya, and Osaka in Japan, with the total population of the districts (administrative divisions under Indian states) being approximately 50 million [
3] (
Table 1). This is much larger than the population of approximately 30 million [
4] in the area along the Tokaido Shinkansen line in 1960 before the Shinkansen began operations.
From the perspective of an industrial structure, comparing the composition of employees by industry between the MAHSR corridor region in 2011 [
5] (
Figure 2a) and the Tokaido Shinkansen corridor region in 1960 [
6] (
Figure 2b), it can be seen that they have similar proportions for each industrial sector at the primary to tertiary levels. Additionally, comparing the flow of expenditure among industries and services between India in 2007 [
7] and Japan in 1960 [
8] by referring to the input–output (I/O) tables, similarities can be seen, e.g., in the proportion of expenditure flowing into manufacturing (
Figure 3a,b).
Thus, what changes will be seen in the industrial interactions when the HSR is introduced in India and the passenger travel times between cities are reduced? In this study, the regional economic impacts as a part of the indirect benefits were analyzed by the model combining input–output (I/O) and spatial interaction, using the MAHSR corridor as a case study.
This approach allows the examination of interregional disparity that occurs with I/O interaction and distance impedance between the regions, and it is expected that this method will help the discussion for improvement in terms of economic development considering interregional equity in various emerging countries in the future.
2. Literature Review
The regional economic impacts of HSR have been discussed in a number of studies in recent years (e.g., Vickerman [
9], Blanquart and Koning [
10], and Chen et al. [
11]). Chen and Silva [
12] analyzed the long-term impact of Spain’s HSR network on employment and GDP using a panel structural equation modeling (SEM) formulation. Li et al. [
13] investigated the redistribution of economic activities in the Yangtze River Delta region in China using the geographical network weighted regression model, and they showed that HSR drew an inflow into second-tier cities alongside the HSR for investment activities. Cheng et al. [
14] examined changes in accessibility and provided evidence for the changes in specialization for the main cities and their hinterlands in China and Europe and then concluded that the processes of convergence and divergence vary by the stage of economic development. Vickerman [
15] investigated the impact on the intermediate areas between major metropolitan areas along the network of northwest Europe and revealed that both levels of service and potential economic impacts were much less pronounced in these intermediate areas. The above discussions can be concluded in various ways depending on the social environment of each country and region including population distribution, urban structure, geographical conditions, and economic stage. Much discussion is still needed to establish analytical methods, and continued study and improvement are desirable.
The conventional cost–benefit analysis (CBA), which has been applied to many transportation projects including HSR, targets the direct benefits for users. However, in recent years, this concept has been reconsidered, and the need to capture wider economic impacts (WEI) as indirect benefits for non-users has been discussed. WEI is categorized into three types: induced investment, employment effects, and productivity impacts [
16]. Among them, employment effects are related to changes in commuting behavior, mainly for intra-urban transit, while induced investment and productivity impacts are particularly related also to connectivity between metropolitan areas through intercity transportation such as HSR.
Regarding productivity impacts, Deng [
17] summarized the various related literature estimating the contribution of transport infrastructure to productivity and economic growth. Rice et al. [
18] found a robust relationship between productivity and proximity to economic mass. Chèze and Nègre [
19] applied the UK agglomeration effects assessment method to the Bretagne Pays de Loire high-speed line in France and explored the possibility of assessing agglomeration and productivity gains. Thanh and Derrible [
20] analyzed productivity and agglomeration changes and the wider economic impacts of an HSR project in Vietnam by measuring effective employment density. However, these studies did not take into account the I/O relationships among industries, i.e., the affinities and interactions among industries as expressed by input coefficients. We tried to combine the I/O interaction with productivity impact evaluation in this study.
In terms of ‘passenger or freight’, changes in the accessibility of passenger flow by HSR will assist in the development of a knowledge economy (Chen and Hall [
21]) and regional innovation (Komikado et al. [
22]) among multiple cities; in other words, it will bring communication effects related to the establishment of a new business or the promotion of existing business. These effects have different characteristics from those brought about by changes in freight transportation along with the construction of highways and roads. Yi and Kim [
23] analyzed the spatial economic impacts of road and railway accessibility levels on manufacturing output in South Korea and revealed that there is not a substitutive but a complementary relationship between the two transportation modes. Related to the above, SCGE models have been applied to many transportation projects as the benefit analysis method at the microeconomic level (e.g., Tavasszy et al. [
24], Koike et al. [
25]); however, those are not necessarily versatile, because detailed statistical data, such as interregional input–output tables, are required.
As an alternative approach, the correlation between the industrial linkage and the actual changes in the number of employees in each industry, before and after the opening of the Nagano Shinkansen in Japan, was examined by Han, Hayashi, et al. [
26], using a regression model that incorporates accessibility into the interdependence of inter–industrial transactions and consumption demand obtained from the I/O table. This is an extension of Nakamura, Hayashi, et al. [
27] and Miyamoto et al. [
28], which developed a model to predict industrial and commercial locations for a large metropolitan area in Japan, taking into account the economic distance between regions. This approach will be employed by the model in this study. In the abovementioned literature, the number of employees was applied as the explained variable, but since labor productivity actually differs from one industry to another, in this study, the production value was applied to represent productivity.
5. Conclusions
This study evaluated the regional economic impacts as a part of the indirect benefits brought by the Mumbai–Ahmedabad High-Speed Rail (MAHSR), which is currently under construction. In order to estimate the impacts, we modeled the I/O–spatial interaction by combining the inter–industrial transaction shown on the I/O table with the geospatial distance decay of economic mass through passenger transportation.
The results showed the economic impacts on each zone and each industry along the MAHSR corridor as a relative distribution. In addition, the increase rate of annual GRP averaged among all industries and zones was estimated as 1.6%~1.8% (variation by distance decay parameter set in this study). In this estimation, it was assumed that the production value of each industry in each city would increase by completely accepting the newly induced demand coming in. However, if a scenario in which industrial location and labor flow freely among cities is assumed, industrial concentration in larger metropolitan areas, as well as interregional disparity, may occur, which means a straw effect. In order to maintain the economy of each city from being absorbed into that of the larger metropolitan areas, it will be important to develop a unique industrial structure.
As described above, the unique feature of this approach is that it is possible to evaluate the geographic distributions and interregional disparity of economic impacts by combining the industrial I/O relationships with the changes in passenger accessibility associated with a large-scale transportation project such as HSR. In addition, this method can be applied to various countries and regions where detailed I/O statistical data, such as interregional I/O tables, are difficult to obtain, as well as various transportation project evaluations taking into account interregional equity.
For future work, the statistical data used should be updated, and the model should be applied to multiple cases in other HSR corridor regions in India and abroad and validated based on empirical evidence. In addition, it will be possible to estimate a wide range of effects of HSR by combining impacts on the regional economy with QOL improvement as impacts on personal well-being, which can be estimated by the ‘QOL accessibility method’ (Hayashi et al. [
39]).