The Impacts of High-speed Rail on Sustainable Economic Development: Evidence from the Central Part of China

Abstract

To give full play to the role of high-speed rail (HSR) in promoting sustainable economic development, the models that can calculate and describe the impacts of HSR on the sustainable economy are required. However, little attention has been paid so far to building such models. To address this, the paper puts forward the definition of sustainable economic development, especially that of China. Based on the definition, the paper proposes the indicators of sustainable economic development for China from industry and labor force perspectives. Compared with the previous literature, these indicators take into account the behavior of enterprises and labor forces as individuals rather than as groups, which is more explanatory. HSR, as the main factor to improve the level of transportation technology and accessibility, is incorporated into the analytical framework. Then, we build decision-making behavior models of single enterprise and labor force under market equilibrium to get the relationship between HSR and indicators. Compared with the previous literature, the methods within the paper not only fully consider the interests of enterprises and labor forces, but also considers the interaction between industries and the trade of commodities in different cities/regions. The evidence from the central part of China shows that the model can effectively reveal the relationship between HSR and indicators. The paper gives new perspectives to study the relationship between HSR and sustainable economic development. Based on the findings herein, we offer recommendations for how HSR can promote sustainable economic development effectively.

1. Introduction

Over the last decades, “sustainable development” has become the latest development catchphrase. Policy makers and people in all countries have been making continuous efforts to attain a sustainable society. Griggs et al. [1] came up with six sustainable development goals: Thriving lives and livelihoods, sustainable food security, sustainable water security, universal clean energy, healthy and productive ecosystems, and governance for sustainable societies. More importantly, they emphasize that none of this is possible without changes to the economic playing field. So, all countries have been seeking a sustainable economic development path over the past few years. Sustainable economic development is conducive to promoting the unity of ecological, economic, and social benefits, ensuring the sustainable, stable, and healthy development of national economies. Economic development is shifted from focusing on immediate and partial interests to long-term and overall interests, and from being material resources-driven to information resources-driven, which is particularly important for developing countries with large populations, natural resource shortages, and backward economic foundation and scientific and technological levels. Only by ensuring sustainable economic development can developing countries achieve a virtuous circle of society and economy. In fact, the integration of transport and land use planning is widely recognized as essential to the achievement of sustainable development [2]. Here, we focus on transportation, especially high-speed rail (HSR).

Transportation, as one of the essential elements to improve output level and production efficiency, plays an important role in the development of economy and society [3]. Transportation, which is becoming the main focus of urban space strategy, can make regular activities spread from the center city to other areas [4]. It is called “the creator and destroyer of the city” by Clark [5]. A city without an effective transportation system will not see growth; the improvement of transportation is usually considered as the precursor to economic growth [6]. Since 1964 when Japan built the first high-speed rail in Honshu Island, the Shinkansen of Hokkaido, many countries in the world have begun to build HSRs. The arrival of HSR gives new meaning to transportation, and is considered to bring “the second railway era” [7]. Time and space have experienced unprecedented shrinkage, giving birth to a “shrinking continent” [8]. The construction of HSR has promoted the flow of inter-regional factors, eliminated the geographical separation between regions, and met the requirements of sustainable economic and social development [9]. Today, with the continuous advancement of globalization and knowledge economy, the advent of HSR has shown unprecedented space-time effects and brought about new economic growth [10]. It is a new bright spot and new opportunity for the current stage of economic development in various countries.

A review of the literature on the concept of sustainable development indicates, however, a lack of consistency in its interpretation. More important, its current formulation by the mainstream of sustainable development thinking contains significant weaknesses which include confusion about the role of economic growth and about the concepts of sustainability and participation [11]. Actually, there are more than 100 definitions of the sustainable development concept, but the one in Our Common Future proposed by the World Commission on Environment and Development is most widely accepted and influential [12]. The report points out that sustainable development is a development mode that can meet the needs of contemporary people without compromising the ability of future generations to meet their needs. From an economic perspective, Barbier defines sustainable development as "maximizing the net benefits of economic development while maintaining the quality of natural resources and the services they provide." In this paper, we believe that sustainable economic development is a long-term and active development model. It intends to achieve harmony between economy, nature, and society by ensuring rapid economic growth while continuously improving the quality of economic development. It requires us to continuously change the mode of economic growth, optimize the industrial structure, improve scientific and technological innovation capabilities, improve the quality of workers, and balance economic development between regions, which is especially true for China with a large population, relatively inadequate per capita resources, high employment pressure, and a prominent ecological environment.

In 1994, the government of China incorporated sustainable development strategies into China’s long-term economic and social development planning for the first time in the “White Paper on China’s Population, Environment, and Development in the 21st Century” [13]. Soon after, the 15th National Congress of the Communist Party of China in 1997 identified the sustainable development strategy as a strategy that must be implemented in China’s modernization. Further, “continuously enhance the ability of sustainable development” was taken as one of the goals of building a well-off society in the 16th National Congress of the Communist Party of China in 2002. As an important part of the “Scientific Development Concept,” the concept of sustainability has played an important role in guiding China’s economic development. China started building a national HSR network in 2004. In 2008, “four vertical and four horizontal” HSR network was proposed which intended to build a passenger transport system focusing on eight lines by 2020 [14]. Further, in order to further exert the supporting role of the transportation aorta to the economic upgrading, an “eight vertical and eight horizontal” HSR network plan was proposed in 2016. The railway network is mainly based on the eight vertical passages along the coast, Beijing-Shanghai, and the eight horizontal passages along the land bridge and river. In the planning, the HSR network is used to guarantee the improvement of sustainable economic development capacity [15]. The length of HSR in China will reach 35,000 kilometers by the end of 2019, which is more than two-thirds of the world’s total HSR mileage. China will become the country with the longest HSR mileage, the highest transportation density, and the most complicated network operation scenario in the world.

Therefore, the purpose of this paper is to provide a new way and more perspectives to study the impacts of HSR on sustainable economic development. The paper intends to explore how HSR drives regional sustainable economic growth. Based on the research, the paper committed to putting forward advice regarding the role of HSR in promoting coordinated and sustainable development of the economy and contributing to the limited existing literature. According to the definition of sustainable economic development in the paper, we believe that optimization of economic structure, improvement of labor force quality, reduction of regional economic level differences, and improvement of the environment are the most important signals for China to achieve sustainable economic development—since the fourth aspect has been widely studied in the literature, this paper pays attention to the other three aspects. The transfer and agglomeration of industries between cities/regions can adjust and optimize the industrial structure. Moreover, the spillover effect can effectively promote the development of surrounding areas and reduce the development differences between regions and cities. In addition, it is the spatial movement of labor that has brought about changes in the structure of urban/regional labor, high-tech talent brought by the movement bring vitality to the economic development of cities/regions, which can make the economic development sustainable. So, this paper describes the impacts of HSR on sustainable economic development from the perspective of industry and labor force.

However, it is difficult to estimate the impacts of new transportation infrastructure on population and economic activity [16]. To address this, the paper puts forward the definition of sustainable economic development, especially that of China. Based on the definition, the paper proposes the indicators of sustainable economic development for China from industry and labor force perspectives. Compared with the previous literature, these indicators take into account the behavior of enterprises and labor forces as individuals rather than as groups, which is more explanatory. Furthermore, some indicators are rarely studied in the literature, let alone the relationship with HSR. Then, the paper builds decision-making behavior models with HSR variable of single enterprise and labor force under market equilibrium to calculate and describe the impacts of HSR on the sustainable economy. In the models, HSR is converted into an indicator to improve the level of transportation technology and accessibility. Through the solution of the model, the relationship between HSR and indicators can be found. Compared with the previous literature, the methods within the paper are built based on the behaviors and decisions of enterprises and labor forces, which not only fully consider the interests of enterprises and labor, but also considers the interaction between industries and the trade of commodities in different cities/regions. The evidence from the central part of China proves the validity of the models, which can contribute to the limited existing literature.

The remainder of the paper is organized as follows. The literature related to our research is discussed in Section 2. Section 3 proposes some indicators for sustainable economic development and establishes theoretical models including the HSR variable from the perspective of industry and labor force to study the relationship between HSR and the indicators. Section 4 uses the data of the central part of China to verify the validity of the models. The conclusions, recommendations, and limitations of the study are discussed in Section 5.

2. Literature Review

The formal formulation of sustainable development first appeared in the “World Conservation Strategy” proposed in 1980. Since then, in 1987, the concept was further refined in the report “Our Common Future” published by the World Commission on Environment and Development. In June 1992, the “Rio Declaration on Environment and Development,” “Agenda 21,” and other documents with sustainable development as the core were adopted in the United Nations Conference on Environment and Development in Rio de Janeiro. Sustainable development has become the guiding principle for the development of countries around the world.

The economic system is treated as closed and nature is treated as an immutable objective existence in traditional economic theory. According to this mode, economic development will be constrained by the natural environment for a long time, which is undoubtedly unsustainable. The concept of sustainable development adjusts traditional concepts with new ideas, emphasizing that the economy should maximize the net profit under the premise of protecting the environment itself and the services it provides [17]. Economic development is the material foundation of the country, and the fundamental guarantee for the coordinated development of population, resources, environment, and economy [18]. Ensuring sustainable economic growth is a goal that more countries should take it into account in an attempt to reconcile the economic needs with those of the environment [19]. Beckerman [20] pointed out that economic growth will eventually bring sufficient funding for environmental protection. Similarly, Barbier [21] confirmed that the national income at inflection point of environmental improvement is higher than that at the current level. Encouraging economic development does not mean opposing economic development and environmental protection. Sustainable growth, as a necessary condition for economic development [22], requires that economic growth must rely on technological progress to improve the quality of economic development.

But how can we promote sustainable economic development? Strengthen the institutional foundations for macroeconomic stability, improve the competitiveness of productive activities, increase the quality and coverage of education and health, improve national modernization, promote regional integration, and activities in these areas will pay particular attention to the environmental dimension, in order to ensure the sustainability of economic growth [23]. Sustainable economic growth is one of the two overall goals of the World Bank. In view of the current challenges, it has prepared a new set of strategies that take on board the cross-cutting nature of the environmental dimension, such as programs that depend on developing consensus, reforms to policies, and technical and financial assistance actions [24]. Feng and Yu [25] proved that marketization, government governance, and legal environment play significant roles in promoting sustainable economic development based on China’s inter-provincial panel data from 1999–2009. Fernando [26] did a study of the sustainable economic development in China, pointing out that due to the emergence of a consumer society in China, individual behaviors are increasingly a source of environmental problems and a key component of efficient and long-lasting solutions. Accordingly, Chinese policymakers should recognize the environmental significance of individual behaviors and look beyond traditional policy tools.

Transportation, which the other economic pillars such as industry, finance, and tourism are highly dependent on, plays an important role in sustainable economic development. The construction of HSR will create favorable conditions for sustainable development, facilitate the rapid flow of production factors and industrial transfer, and optimize resource allocation with greatly improving the effective supply of railway transportation capacity, which will bring new changes and opportunities to regional economic development along the route [27]. As a typical representative of advanced productivity in the field of transportation, HSR is the product of integrated innovation of modern science and technology. The traditional transportation structure and ‘economic geography’ have been changed with the emergence of HSR, which has a profound impact on the development and progress of society [28]. It meets the requirements that sustainable economic development should rely on scientific and technological progress. Meanwhile, cities play a key role in sustainable development. And the growth of cities must be managed in ways that support and drive economic development, and achieve social cohesion and environmental sustainability [29]. HSR connects the production centers and the surrounding areas. The space-time compression effect brought by it significantly reduces the resistance of industrial agglomeration with lowering the transportation and trade costs between cities [30], which is of great significance to the formation of a modern industrial system [31]. The construction of HSR makes the flow of knowledge and intelligence factors between regions more efficient by increasing the ‘knowledge radiation’ effect of central cities on surrounding cities [32], thus driving the development of the knowledge economy, and reducing the path dependence of regional transportation networks [33]. Coordinated regional development is of great significance to sustainable economic development. The spillover effect brought by HSR has greatly promoted the development of cities and regions along the main corridor (axis). It will create a new type of area or corridor with high inter-regional accessibility by making the whole region more integrated in economy, society, and culture [34]. Although this policy may lead to more extreme spatial development models [35], the examples of China and the EU tell us that HSR does promote the improvement of competitiveness and economic cohesion [36]. Obviously, the integration between HSRs and conventional railways is an inevitable requirement for urban development [37]. By improving transportation technology, the innovation, production, and management of companies can be separated, which can improve corporate production efficiency and interdependence with the city [38]. In addition, D’Alfonso et al. [39] studied the impact of air transport and HSR competition on the environment; they indicated that the introduction of HSR may have a net negative effect on the environment, since it may result in additional demand. Further, they conduct a simulation study based on the London–Paris market; it proved that HSR’s introduction has increased LAP (the local air particular matter), but has decreased GHG (greenhouse gas) emissions [40].

Some of the literature has studied the economic promotion effect of HSR from industry and labor markets. Studies in this area include the impacts of HSR on commuting and wages in urban labor [41,42], the impacts of HSR on population growth and population welfare [43,44,45,46], and the promotion impacts of HSR on industrial agglomeration and industrial development [47,48,49]. However, these studies focus more on the specific economic results of HSR on industry, employment, and wage other than the impact paths. In other words, these studies focus more on results than processes.

Therefore, this paper focuses on the ways that HSR impacts on sustainable economy. The theoretical models including HSR variable under market equilibrium conditions are established. In the models, enterprises and labor force are taken as the main participants of HSR’s effects on sustainable economic growth. Their income and cost are fully considered when studying their decision-making behaviors.

3. Methodology

Enterprises and labor forces are the main participants of economic creation. This paper discusses how HSR affects sustainable economic growth from the perspective of industry and labor force. Here, we build models under the condition of market equilibrium to analyze the decision-making behaviors of single enterprise and labor force. Then, the relationship between HSR and economic participants can be found. Compared with the previous literature, the methods within this paper not only fully consider the interests of enterprises and labor forces, but also take into account the interaction between industries and the trade of commodities in different cities/regions.

3.1. Indicators of Sustainable Economic Development

Here, we give our own understanding of sustainable economic development. This paper believes that sustainable economic development is to realize the harmony of economy, nature, and society by ensuring rapid economic growth and improving the quality of economic development, in which economic development is the foundation, economic quality improvement is the key point, and the harmony of economy, nature, and society is the goal. It should be a long-term and dynamic development mode. Therefore, changing the mode of economic growth, improving scientific and technological innovation capabilities, increasing employment rates, and balancing regional economic development are the main ways to achieve sustainable economic development. These ways are also the concerns of this paper, because they are particularly important for China at this stage of economic transformation.

It is clearly pointed out that optimizing the industrial structure is the fundamental way to speed up the transformation of economic development mode in the report of the 18th National Congress of China. If the industrial structure cannot be adjusted reasonably, it will seriously restrict the realization of sustainable economic development. Meanwhile, optimizing the industrial structure is also an important support to promote the coordinated development of the region. Therefore, it is true that if a region or a city wants to change its economic development mode or reduce the economic differences with other regions or cities, it must have industrial attraction. Assume that the plane coordinate system set of cities is $\Theta$, the location coordinate of the central city is ($x_0,y_0$), other cities are (x, y), and (x, y)$ϵ\Theta$. This paper chooses three indicators to study the impacts of HSR on sustainable economic development from industry perspective, which include the land demand $l_i\left(x,y\right)$ of enterprise i for city (x, y), the labor demand $l_i\left(x,y\right)$ of enterprise i for city (x, y), and the willingness cost $p_{i cost}$ of enterprise i for locating in city (x, y). If the construction of HSR has a positive effect on the above indicators, it shows that HSR is beneficial to the adjustment and optimization of the industrial structure of cities along the line. The industrial agglomeration coefficient or the proportion of industry to GDP (Gross Domestic Product) is often used to explain the industrial migration in the literature, which is not specific enough; and the enterprises’ own factors are not considered, which is also not reasonable. The indicators in this paper focus on the enterprises’ own behaviors, which are more specific and provide stronger explanation.

The improvement of labor employment rate and labor quality is required not only to improve the national education level, but also to promote the movement of labor forces between regions. The spatial structure of wages should be changed to increase the urban/regional attractiveness to high-tech and high-level talents. This paper selects two indicators to study the impacts of HSR on sustainable economic development from labor force perspective, which include the wage level $w_i\left(x,y\right)$ of the labor force i in city (x, y) and the effective labor supply $N_i\left(x,y\right)$ of the labor force i provided to city (x, y). The two indicators can not only state the influence of HSR accurately, but also take into account the personal behavior and willingness of the labor force. At the same time, they consider the interaction between the city and the labor force. It is more persuasive than only studying the impact of wage level in the literature.

3.2. Construction of Model from Industry Perspective

Hypothesis (H1).

The market is in equilibrium; the city’s tax on enterprises is included in the land cost.

HSR has greatly optimized the daily accessibility of cities [50], and the space-time compression brought by it can promote the economic and social development of a country on multiple spatial scales, including cities, regions, and even HSR stations [51]. So, we take HSR as the factor to improve the level of accessibility to incorporate into the model. Let $acce\left(x-x^{\prime },y-y^{\prime }\right)$ is the accessibility between cities. We believe that the value of $acce\left(x-x^{\prime },y-y^{\prime }\right)$ of cities with HSR is significantly bigger than that without HSR.

For 0 <$\alpha$<1, 0 <$\beta$< 1, 0 <$\gamma$< 1, α + β +$\gamma$= 1, the decision-making behavior of enterprise i can be expressed as follows (Equation (1)):

$$\begin{gathered} Max{U}_1=l_i{\left(x,y\right)}^{\alpha }{n}_i{\left(x,y\right)}^{\beta }A{E}_i{\left(x,y\right)}^{\gamma } \\ s.t.p_t\left(x,y\right)l_i\left(x,y\right)+p_c\left(x,y\right)n_i\left(x,y\right)+f\left(acce\left(x-x_0,y-y_0\right)\right)\le e_i \end{gathered}$$

(1)

where α is the elasticity coefficient of land demand, β is the elasticity coefficient of labor demand, and γ is the elasticity coefficient of industrial relevance.$e_i$ is the income of the enterprise i in city (x, y), $p_c\left(x,y\right)$ is the unit labor cost, $p_t\left(x,y\right)$ is the unit land cost. Moreover, $p_t\left(x,y\right)$ contains the tax rate that enterprises need to pay for production transactions in city (x, y). $f\left(acce\left(x-x_0,y-y_0\right)\right)$ represents the trade cost of the enterprises, which can be measured by Equation (2).$A{E}_i\left(x,y\right)$ represents the relevance between industries in different regions, which can be described by Equation (3).

$$f\left(acce\left(x-x_0,y-y_0\right)\right)=\left[1+h\left(x-x_0,y-y_0\right)\right]e^{-\theta d}$$

(2)

where $\theta$ is the elasticity coefficient of distance, and 0 <$\theta$< 1. The value of $h\left(x-x_0,y-y_0\right)$ is determined based on whether the two cities are connected by HSR. If there is no railway between the two cities, it is 0. If there is a railway but no HSR, it is 1. If there is an HSR, it is 2; d is the distance between the two cities.

$$A{E}_i\left(x,y\right)=({\omega }_1{\xi }_i^{\left(x,y\right)}+{\omega }_2{{\displaystyle \sum }}_{\left(x^{\prime },y^{\prime }\right)ϵ\Theta \&\left(x^{\prime },y^{\prime }\right)\ne \left(x,y\right)}{\xi }_i^{\left(x^{\prime },y^{\prime }\right)})\left[1+h\left(x-x_0,y-y_0\right)\right]e^{-\theta d}$$

(3)

where different industrial influences are used to indicate industry types. ${\xi }_i^{\left(x,y\right)}$ represents the influence coefficient of industry i in city (x, y), which can be expressed by Equation (4). ${\xi }_i^{\left(x^{\prime },y^{\prime }\right)}$ represents the influence coefficient of industry i of all cities except city (x, y), which can be expressed by Equation (5). In Equations (4) and (5), ${\omega }_1$ and ${\omega }_2$ represent the weight of different influences, in which $b_{ji}^{\left(x^{\prime },y^{\prime }\right)\left(\mathrm{x},\mathrm{y}\right)}$ is the Leontief inverse matrix coefficient of industry j in city $\left(x^{\prime },y^{\prime }\right)$ for industry i in city (x, y), m is the number of cities, n is the number of industries.

$${\xi }_i^{\left(x,y\right)}=\frac{{{\displaystyle \sum }}_{\left(x,y\right)}{{\displaystyle \sum }}_i{b}_{ji}^{\left(x^{\prime },y^{\prime }\right)\left(x,y\right)}}{\frac{1}{m\times n}{{\displaystyle \sum }}_{\left(x,y\right)}{{\displaystyle \sum }}_S{{\displaystyle \sum }}_i{{\displaystyle \sum }}_j{b}_{ji}^{\left(x^{\prime },y^{\prime }\right)\left(x,y\right)}}$$

(4)

$${\xi }_i^{\left(x^{\prime },y^{\prime }\right)}=\frac{{{\displaystyle \sum }}_{\left(x,y\right)\left(\left(x,y\right)\ne \left(x^{\prime },y^{\prime }\right)\right)}{{\displaystyle \sum }}_j{b}_{ji}^{\left(x^{\prime },y^{\prime }\right)\left(x,y\right)}}{\frac{1}{n}{{\displaystyle \sum }}_{\left(x,y\right)\left(\left(x,y\right)\ne \left(x^{\prime },y^{\prime }\right)\right)}{{\displaystyle \sum }}_i{{\displaystyle \sum }}_j{b}_{ji}^{\left(x^{\prime },y^{\prime }\right)\left(x,y\right)}}$$

(5)

So, the optimal value of $l_i\left(x,y\right)$ and $n_i\left(x,y\right)$ can be got (Equation (6)) by solving Equation (1).

$$\{\begin{array}{c}{l}_i{\left(x,y\right)}^{*}=\frac{\alpha }{\alpha +\beta }\frac{e_i-\left[1+h\left(x-x_0,y-y_0\right)\right]e^{-\theta d}}{p_t\left(x,y\right)}\\ n_i{\left(x,y\right)}^{*}=\frac{\beta }{\alpha +\beta }\frac{e_i-\left[1+h\left(x-x_0,y-y_0\right)\right]e^{-\theta d}}{p_c\left(x,y\right)}\end{array}$$

(6)

Moreover, we define $U_1$ in the optimized state is W. With Equations (1) and (6), the indicator $p_{i cost}$, the cost that enterprises are willing to pay can be determined by Equation (7).

$$\begin{gathered} p_{i cost}=p_t{\left(x,y\right)}^{\prime }{}^{\alpha }{p}_c{\left(x,y\right)}^{\prime }{}^{\beta } \\ =\frac{1}{W}{(\frac{\alpha }{\alpha +\beta })}^{\alpha }{(\frac{\beta }{\alpha +\beta })}^{\beta }{\left\{e_i-\left[1+h\left(x-x_0,y-y_0\right)\right]e^{-\theta d}\right\}}^{\alpha +\beta }A{E}_i{\left(x,y\right)}^{\gamma } \end{gathered}$$

(7)

where $p_t{\left(x,y\right)}^{\prime }$ and $p_c{\left(x,y\right)}^{\prime }$ are the largest land cost and labor cost that enterprises are willing to pay for city (x, y).

3.3. Construction of Model from Labor Force Perspective

Hypothesis (H2).

The urban labor force can flow freely between regions; the labor that they provided to producers has non-negative effect; the transportation cost is assumed to have the form of iceberg cost; the market is in equilibrium.

HSR is a typical representative of advanced productivity in the field of transportation [28], it can effectively improve the transportation technology level $Trans\left(x,y\right)$. Let $Trans\left(x,y\right)$= t(x, y) + T(x, y), where T(x, y) is the transportation technology level of city(x, y) before building HSR, and t(x, y) is the level that HSR improves. If the city does not build HSR, t(x, y) = 0, otherwise, t(x, y) = 1. And, 0 <$Trans\left(x,y\right)$< 2.

For 0 <${\alpha }^{\prime }$< 1, 0 <${\beta }^{\prime }$< 1, 0 <${\gamma }^{\prime }$< 1, 0 <$\delta$< 1, ${\alpha }^{\prime }+{\beta }^{\prime }+{\gamma }^{\prime }+\delta$= 1. The decision of labor force i can be determined by Equation (8).

$$\begin{gathered} Max{U}_2=w_i{\left(x,y\right)}^{{\alpha }^{\prime }}{r}_i{\left(x,y\right)}^{{\beta }^{\prime }}{p}^m{\left(x,y\right)}^{{\gamma }^{\prime }}{p}^s{\left(x,y\right)}^{\delta } \\ \mathrm{s}.\mathrm{t}.w_i\left(x,y\right)N_i\left(x,y\right)+r_i\left(x,y\right)K_i\left(x,y\right)-C_i\left(x,y\right)>0 \end{gathered}$$

(8)

where $K_i\left(x,y\right)$ is the physical capital held by labor force i, and $r_i\left(x,y\right)$ is the actual return on capital. ${\alpha }^{\prime }$ is the elasticity coefficient of labor income, ${\beta }^{\prime }$ is the elasticity coefficient of capital income, and ${\gamma }^{\prime }$ is the elasticity coefficient of tradable goods consumption, $\delta$ is the elasticity coefficient of non-tradable goods consumption. $C_i\left(x,y\right)$ is the the consumption of labor force i in city (x,y), which can be expressed as Equation (9).

$$C_i\left(x,y\right)=a{C}_i^m\left(x,y\right)p^m\left(x,y\right)+\left(1-a\right)C_i^s\left(x,y\right)p^s\left(x,y\right)$$

(9)

where $a$ is the percent of income will be used for the consumption of tradable goods, and 0 <$a$< 1. $C_i^m\left(x,y\right)$ is the consumption of tradable goods, $C_i^s\left(x,y\right)$ is the consumption of non-tradable goods, $p^m\left(x,y\right)$ is the the price of tradable goods, $p^s\left(x,y\right)$ is the the price of non-tradable goods.

For 0 < b < 1, $\sigma$> 1, $C_i^m\left(x,y\right)$ can be defined as Equation (10).

$$C_i^m\left(x,y\right)={\displaystyle \sum }_j{[b^{\frac{1}{\sigma }}{C}_i^j{\left(x,y\right)}^{1-\frac{1}{\sigma }}+{\left(1-b\right)}^{\frac{1}{\sigma }}{\displaystyle \sum }_{\left(x^{\prime },y^{\prime }\right)\in \Theta {{\displaystyle \cup }}^{ }\left(x^{\prime },y^{\prime }\right)\ne \left(x,y\right)}{C}_i^j{\left(x^{\prime },y^{\prime }\right)}^{1-\frac{1}{\sigma }}]}^{\frac{\sigma }{\sigma -1}}$$

(10)

where $C_i^j\left(x,y\right)$ is the consumption of tradable goods j, $b$ is the proportion of local products consumption demand of labor force, and $\sigma$ is the alternative elasticity of tradable products in different cities.

Moreover, we consume that the consumption of tradable goods j in local and other cities is assumed to be the same. In this sense, Equation (10) can be expressed as Equation (11).

$$C_i^m\left(x,y\right)={{\displaystyle \sum }}_j{\left[B{C}_i^j{\left(x,y\right)}^{1-\frac{1}{\sigma }}\right]}^{\frac{\sigma }{\sigma -1}}$$

(11)

where B=$b^{\frac{1}{\sigma }}+{\left(1-b\right)}^{\frac{1}{\sigma }}$.

The price of products that city ($x^{\prime },y^{\prime }$) transported to city ($x,y$) is mainly related to the FOB (Free on Board) price of products in city ($x^{\prime },y^{\prime }$) and the transportation cost. For only $\frac{1}{4}Trans\left(x,y\right)Trans\left(x^{\prime },y^{\prime }\right)$ part of per unit product that produced by city $\left(x^{\prime },y^{\prime }\right)$ can reach city $\left(x,y\right)$, the remaining part ($1-\frac{1}{4}Trans\left(x,y\right)Trans\left(x^{\prime },y^{\prime }\right)$) is the transportation cost. In order to transport one unit of product to city ($x,y$), the quantity of product transported to city ($x,y$) should be ($2-\frac{1}{4}Trans\left(x,y\right)Trans\left(x^{\prime },y^{\prime }\right)$). Then, the price of tradable goods in city $\left(x,y\right)$ can be expressed as follows (Equation (12)):

$$P^m\left(x,y\right)={\{A_j{\displaystyle \sum }_{\left(x^{\prime },y^{\prime }\right)\in \Theta }{\left[p\left(x^{\prime },y^{\prime }\right)\left(2-\frac{1}{4} Trans\left(x,y\right) Trans\left(x^{\prime },y^{\prime }\right)\right)\right]}^{1-\sigma }\}}^{\frac{1}{1-\sigma }}$$

(12)

where $A_j$ is the type of products that produced by city ($x^{\prime },y^{\prime }$).

Moreover,$P^m\left(x,y\right)$ can be described as Equation (13) to facilitate the analysis.

$$p^m\left(x,y\right)=\frac{\sigma }{\sigma -1}\frac{1}{p\left(x^{\prime },y^{\prime }\right)\left(2-\frac{1}{4} Trans\left(x,y\right) Trans\left(x^{\prime },y^{\prime }\right)\right)}$$

(13)

Assume that the per-capita income is h (x,y), the total labor force of city (x,y) is L(x,y). In equilibrium, the price of non-tradable goods can be expressed as follows (Equation (14)).

$$p^s\left(x,y\right)=\left(1-a\right)\frac{h\left(x,y\right)L\left(x,y\right)}{C_i^s\left(x,y\right) }$$

(14)

So, the optimal value of $w_i\left(x,y\right)$ and $r_i\left(x,y\right)$ can be found (Equation (15)) by solving Equation (8).

$$\{\begin{array}{c}{w}_i{\left(x,y\right)}^{*}=\frac{{\alpha }^{\prime }}{{\alpha }^{\prime }+{\beta }^{\prime }}\frac{C_i\left(x,y\right)}{N_i\left(x,y\right)}\\ r_i{\left(x,y\right)}^{*}=\frac{{\beta }^{\prime }}{{\alpha }^{\prime }+{\beta }^{\prime }}\frac{C_i\left(x,y\right)}{K_i\left(x,y\right)}\end{array}$$

(15)

Further, we define $C_i^m\left(\mathrm{x},\mathrm{y}\right)p^m\left(x,y\right)=C_i^s\left(x,y\right)p^s\left(x,y\right)$, the optimized state of ${\mathrm{U}}_2$ is $W^{\prime }$, then, the effective labor supply can be expressed as Equation(16).

$$N_i\left(x,y\right)={\left\{\frac{{\frac{\sigma }{\sigma -1}}^{\left(1-\delta \right)}{(C_i^m)}^{({\alpha }^{\text{'}}+{\beta }^{\prime })}{\left[\left(1-a\right)\frac{h\left(x,y\right)L\left(x,y\right)}{C_i^s\left(x,y\right)}\right]}^{\delta }{{\alpha }^{\prime }}^{{\alpha }^{\prime }}{{\beta }^{\prime }}^{{\beta }^{\prime }}}{W^{\prime }{\left({\alpha }^{\prime }+{\beta }^{\prime }\right)}^{\left({\alpha }^{\prime }+{\beta }^{\prime }\right)}{K}_i^{{\beta }^{\prime }}{[p\left(x^{\prime },y^{\prime }\right)\left(2-\frac{1}{4} Trans\left(x,y\right) Trans\left(x^{\prime },y^{\prime }\right)\right)]}^{\left(1-\delta \right)}}\right\}}^{\frac{1}{{\alpha }^{\prime }}}$$

(16)

Moreover, we define $M^{\prime }={\left\{\frac{{\frac{\sigma }{\sigma -1}}^{\left(1-\delta \right)}{C}_i^m{}^{({\alpha }^{\prime }+{\beta }^{\prime })}{\left[\left(1-a\right)\frac{h\left(x,y\right)}{C_i^s\left(x,y\right) }\right]}^{\delta }{{\alpha }^{\prime }}^{{\alpha }^{\prime }}{{\beta }^{\prime }}^{{\beta }^{\prime }}}{W^{\prime }{\left({\alpha }^{\prime }+{\beta }^{\prime }\right)}^{\left({\alpha }^{\prime }+{\beta }^{\prime }\right)}{K}_i^{{\beta }^{\prime }}}\right\}}^{\frac{1}{{\alpha }^{\prime }}}$, then, Equation(16) can be simplified to Equation(17).

$$N_i\left(x,y\right)=\frac{M^{\prime }L{\left(x,y\right)}^{\delta }}{{[p\left(x^{\prime },y^{\prime }\right)\left(2-\frac{1}{4} Trans\left(x,y\right) Trans\left(x^{\prime },y^{\prime }\right)\right)]}^{\left(1-\delta \right)/{\alpha }^{\prime }}}$$

(17)

Therefore, $w_i\left(x,y\right)$ can be expressed as Equation (18).

$$w_i\left(x,y\right)=\frac{{\alpha }^{\prime }}{{\alpha }^{\prime }+{\beta }^{\prime }}\frac{\sigma C_i^m\left(x,y\right)}{(\sigma -1)M^{\prime }L{\left(x,y\right)}^{\delta }{[p\left(x^{\prime },y^{\prime }\right)\left(2-\frac{1}{4} Trans\left(x,y\right) Trans\left(x^{\prime },y^{\prime }\right)\right)]}^{\delta /{\alpha }^{\prime }}}$$

(18)

4. Results and Analysis

4.1. High-speed Rail (HSR) and Industry

The literature in studying the relationship between HSR and industries pays more attention to the result of migration and concentration of industries brought by HSR, but the reasons are ignored. In addition, what the literature usually studies is a trend of industrial groups, without considering the specific behaviors of the enterprise. Meanwhile, due to the trade between cities and regions, ripple effect is generated between industries, and the effect has been more obvious with the construction of HSR networks. However, it is often overlooked in literature. These problems are solved within the models in this paper. This paper fully considers the interests of enterprises to analyze their decisions, and constructs decision-making behavior models (Equation (1)). Here, HSR variable is converted into an accessibility indicator within the model to measure the trade cost (Equation (2)) and the correlation effect between industries (Equation (3)). The relationship between HSR and labor demand (Equation (6)), land demand (Equation (6)), and the maximum cost that enterprises are willing to pay for cities (Equation (7)) can be found through solving decision-making behavior models. The results reveal the ways in which HSR promotes industrial transfer and agglomeration, and provide a basis for the promotion of HSR to the sustainable economic development from the industry perspective. In addition, the results take into account the impacts of different enterprise scales and industrial types on HSR economic effects, which is also ignored by the literature.

Based on the data of industrial revenues of different scales in the China Statistical Yearbook 2018, the business revenues of large-scale enterprises, medium-scale enterprises, and small-scale enterprises are determined to be 4.8 billion yuan, 500 million yuan, and 13 million yuan. Here, Mathematica is used to be the analysis tool. And, the values of the coordinates in the figure only represent the change trend of the indicators. Little attention has been paid to the cost that enterprises are willing to pay for cities, let alone their relationships with HSR. Here, decision-making behavior models of enterprises are built to get the relation between the two, in which the enterprise scale, the trade cost, and the industrial correlation effect are all considered. Let α = β = 0.4, γ = 0.2, $\theta$= 0.5, W is any positive number. Figure 1 shows the impact of HSR on the maximum willingness cost under different enterprise scales. We ignore the impact of industry type here, which will be analyzed separately below. It can be easily seen that, regardless of the enterprise size, HSR will increase the maximum cost that enterprises are willing to pay, and the construction of HSR will reduce the probability of the willingness cost reduction with the increase of distance. Compared with large-scale and medium-scale enterprises, the impact of HSR on the maximum willing cost of small-scale enterprises is obvious only at the small distance. For small-scale enterprises, due to the limitations of enterprise size and capital, cost, especially transportation cost, is what they care about most. In order to reduce the cost as much as possible, small-scale enterprises are more inclined to build factories in places with a small distance from the main production place. At this time, the construction of HSR can increase the willingness cost of the enterprise, and the transportation cost of HSR may be too high for them. For medium-scale enterprises and large-scale enterprises, they tend to transfer to cities with lower land cost and labor cost. So, urban traffic construction has a greater impact on them.

Further, this paper also takes account of the range of HSR impact to make the analysis more complete. Figure 2 gives the result of the HSR’s effect on the maximum willing cost under the same enterprise scale. In the figure, different types of lines represent different HSR construction situations, and the construction level of HSR increases from left to right. It is obvious that for all enterprises, the higher the HSR construction level, the higher the labor cost and land cost that enterprises are willing to pay. However, the distance range of impact of HSR on the willing cost is different at different enterprise scales. The influence of HSR on large-scale enterprises has the widest range of distance, but the range is smallest for small-scale enterprises. For large-scale enterprises, the construction of HSR begins to have an impact on the willing cost when the distance is greater than 2, but it is 3 for small-scale enterprises. For medium-scale enterprises, two intersections can be seen in Figure 2. The impact of the ordinary railway is more obvious when the distance is less than 1.5, HSR cannot increase the willingness to pay in this situation, but it is higher than that of no railway. However, when the distance is less than 0.4, the cost that the enterprises are willing to pay for the cities without railway is still higher than that for the cities with HSR.

According to Equation (7), the industrial type is also a factor to influence the HSR’s effect on maximum willing cost. Different from the study on industry classification in previous literature, we use the difference of industrial influence to classify industries which can be determined by Equation (4) and Equation (5); the result can be seen in

Table 1. Classification of industrial type.

Type of Influence	Industry	$\mathbf{Average}\mathbf{Value}\mathbf{of}{\mathit{\xi }}_{\mathit{i}}^{\left(\mathit{x},\mathit{y}\right)}$	$\mathbf{Average}\mathbf{Value}\mathbf{of}{\mathit{\xi }}_{\mathit{i}}^{\left({\mathit{x}}^{\prime },{\mathit{y}}^{\prime }\right)}$
Weak-Influence	F001	0.8615	0.7134
	F002
	F013
	F016
	F017
Medium-Influence	F003	1.0559	0.8541
	F005
	F006
	F008
	F014
Strong-Influence	F004	1.1157	1.3089
	F007
	F009
	F010
	F011
	F012
	F015

Note: Agriculture(F001), Mining(F002), Manufacture of Foods and Tobacco(F003), Manufacture of Textile, Wearing(F004), Manufacture of Wood and Furniture(F005), Manufacture of Paper and Stationery, Printing(F006), Chemical Industry(F007), Manufacture of Nonmetallic(F008), Manufacture and Processing of Metals and Metal Products(F009), Manufacture of Machinery and Equipment(F010), Manufacture of Transportation Equipment(F011), Manufacture of Electrical Machinery and Electronic Communication Equipment(F012), Other Manufacture(F013), Production and Supply of Electric Power, Heat Power and Water(F014), Construction(F015), Commerce and Transportation (F016), Other Services(F017).

. The way to classify industries here fully takes into account the correlation between industries in different cities and regions from both internal and external. For the HSR network connecting cities and regions, the classification method in the paper is more reasonable. It is assumed that the weight of different influences is the same, that is ${\omega }_1$=${\omega }_2$= 0.5. We take medium-scale enterprises as an example to analyze the impact of industry types on HSR’s effect on maximum willing cost. Figure 3 gives the result that such impact of the industry type is not obvious. However, it still can be found that the higher the influence of the industry, the higher the cost the enterprises are willing to pay.

Compared with the literature in studying the effect of HSR on the land and labor demand, firstly, the result within this paper provides a stronger explanation by studying from the perspective of the enterprise’s own self. Secondly, the effect of enterprise size on the role of HSR is also considered. The relationship between land demand, labor demand, and HSR is highly similar according to Equation (6). Figure 4 takes the change of land demand as an example to reveal the relationship between HSR and the two demands. It can be easily seen that, with the exception of small-scale enterprises, the emergence of HSR slows down the demand reduction caused by the increase of cost to a certain extent, which can reduce cost concerns for decision makers. It can be found that the demand for small-scale enterprises remains unchanged within a certain cost range, and the demand is very small. For small-scale enterprises, cost is the most important factor. If the cost is too high to bear, the land and labor demand may not increase with the improvement of transportation technology.

The above results fully prove that the construction of HSR can significantly improve the urban land demand, labor demand, and the cost that enterprises are willing to pay for the city. HSR brings the optimization of industrial structure and the reduction of regional economic differences by promoting the industry transfer and agglomeration, which provides a driving force for sustainable economic development.

4.2. HSR and Labor Force

The impacts of HSR on wage and labor commutes have been widely studied in the literature. However, more attention has been paid to the proof of positive impact of HSR on wage and labor commutes instead of the impact path. Moreover, what the literature usually studies is group behaviors of labor force, without considering their own individual behaviors. In addition, the income and consumption of labor force are not brought into one research framework within the models in the literature. For the construction of HSR networks, the economic exchange between cities and regions is important to be considered, and can affect the price setting of tradable goods. But it is rarely mentioned in the literature. These problems are solved in the models of this paper. This paper constructs decision-making behavior models of labor force (Equation (8)). Here, HSR variable is converted into an indicator that can improve the traffic technology level within the model to measure the consumption of tradable goods (Equation (11)), the transportation cost and the price of products (Equation (13) and Equation (14)). The relationship between HSR and the effective labor supply (Equation (17)), and the actual wage (Equation (18)) can be got with solving decision-making behavior models. The results fully explain how HSR brings the increase in the effective labor and wage level, and provides a basis for the promotion of HSR to the sustainable economic development from the labor perspective. In addition, the results take into account the impacts of different city scales on the HSR economic effects, which are also ignored within the previous literature.

It is assumed that the traffic technology level of city (x, y) and city ($x^{\prime },y^{\prime }$) is the same before building HSR; that is T(x,y) = T($x^{\prime },y^{\prime }$). Let ${\alpha }^{\prime }={\beta }^{\prime }={\gamma }^{\prime }=\delta$= 0.25, $\sigma$= 2, $C_i^m(x,y$) and $M^{\prime }$ is a positive number

Compared with the literature studying the effect of HSR on wage, the results in this paper are based on the individual decisions of laborers, which is more accurate. Moreover, the results take into account not only the HSR variable, but also the city size and commodity prices, which is more complete. It reveals that wage is closely related to the price, total labor force, and the transportation technology level based on Equation (18). Figure 5 shows the impact of HSR on wage with ignorance of the total labor force, which will be studied separately. It is obvious that the improvement of traffic technology level brought by HSR makes the increase in wage higher than that in price, resulting in the multiplier effect of price on wage. The higher the level of transportation technology, the greater the increase in wage. It can be easily found that when HSRs pass through both two places ($\mathrm{t}\left(\mathrm{x},\mathrm{y}\right)=\mathrm{t}\left(x^{\prime },y^{\prime }\right)=1$), the overall increase in wage levels is the largest. In order to analyze the impact of HSR on the wage increase more directly, we compare wages at four different levels of transportation technology (Figure 6). It can be found that the wages in the cities with HSR are significantly higher than that of the cities without HSR. Moreover, the higher the level of transportation technology, the larger the overall slope of the three-dimensional map, indicating that the increase in wage is greater that in price. The wage growth effect brought by HSR is becoming more and more significant.

Little attention has been paid to the impact of city scale on wage, let alone to the HSR’s wage effects, however, it is an issue worthy of discussion. The standard of city scale is based on the resident population. The greater the resident population, the larger the total labor force. Here, the scale of the city is judged by the size of the total labor force L (x,y). The larger L (x,y) is, the larger the city scale is. Figure 7 shows the simulation diagram of the impact of scale and transportation technology level on wage. It can be found that the scale of the city does not have a significant impact on the wage effect of the transportation technology level brought by HSR. However, it can still be found that the wage effect of HSR is more obvious in larger cities. The three-dimensional map shows a trend of rising to the right, that is, under the same city scale, the higher the HSR construction level the city possessed, the higher the wage level. The emergence of HSR has changed the situation in which wage levels will decline due to the increase in the labor force.

The result can be seen in Figure 8 more clearly. In the figure, different types of lines represent different HSR construction situations, and the construction level of HSR increases from left to right. It can be seen that under the same city scale, the construction of HSR has significantly increased the wage of the city; and, the larger the city scale, the more obvious the wage effect of HSR. With the construction of HSR, the wage curve is gradually flattened and the slope is gradually reduced; that is, the value of wage/city scale shows a downward trend. It is the truth that HSR avoids the result that the actual wage of labor force may drop too much due to the increase of labor population.

The effective supply of labor is of great significance to the economic development, but little literature focuses on it. Therefore, this paper takes the improvement of the effective labor supply as an important indicator for the realization of sustainable economic development, and uses the decision-making model of labor force to get the relationship between the effective labor supply and HSR, so as to fill the research gap. Based on Equation (17), Figure 9 shows the relationship between HSR construction and effective labor supply. It can be seen that the improvement of traffic technology level brought by HSR construction raises the level of effective labor supply significantly, especially when urban consumption is high. It also shows that perhaps the higher the city’s economic level, the greater the impact of HSR on the effective labor supply. It can be seen more clearly in Figure 10. In the figure, different types of lines represent different HSR construction situations, and the construction level of HSR increases from left to right. Under the same level of price, the higher the HSR construction level is, the bigger the effective labor supply is. Labor, just like other commodities, must ensure quality while reducing the cost price when it supplies. On the one hand, the emergence of HSR can expand the scope of labor supply and ensure the matching of high-quality labor with market demand. On the other hand, HSR can reduce labor cost by reducing space-time cost. Therefore, the emergence of HSR can greatly improve the effective labor supply of a city.

The above results fully verify that the construction of HSR significantly improves the wage level and the effective labor supply. HSR brings about the labor flow among regions by changing the employment and wage space. To a certain extent, it balanced the quality of labor force among regions and reduced the regional development differences. At the same time, the economy can obtain long-term development vitality with the inflow of high-tech and high-level talents.

5. Conclusions and Discussion

Network efficiency and spatial equity issues are two crucial elements and goals that must be considered in the decision-making process of HSR construction planning [52], and they are also the key to achieving the role of HSR in promoting sustainable economic development. To achieve the goals, a model that can calculate and describe the impacts of HSR on the sustainable economy is required. The relations of dependence between HSR and sustainable economic development have been widely studied in the literature. However, little attention has been paid to how HSR specifically promotes sustainable economic development. The interaction between HSR and some indicators of sustainable development is completely ignored. Some studies have even constructed indicators for sustainable economic development, but these indicators are more biased towards group behaviors without taking into account the behaviors of economic individuals. Although some attempts have been made to construct a model to explore the impacts of HSR on the industry and labor market, they do not further point out the effects on the sustainable economic development. Moreover, the interaction between different urban/regional economic factors has been ignored in the model.

While many studies are focusing on the environmental part of sustainable economic development, we focus on the economic side. Based on the understanding of sustainable economic development, this paper puts forward the indicators of sustainable economic development for China from industry and labor force perspectives. Under market equilibrium, decision-making behavior models of single enterprise and labor force with HSR variable are constructed to get the relationship between HSR and indicators, and explain how the HSR promotes sustainable economic development. To fully consider the interaction between industries and the trade of commodities in different cities and regions, the industrial relevance and price relevance are defined within the model in the paper. The evidence from the research shows that the models effectively reveal the mechanism between HSR and indicators, and proves the role of HSR in promoting sustainable economic development, which fills this research gap.

Research shows that the construction of HSR can significantly improve the urban land demand, labor demand, and the cost that enterprises are willing to pay for the city. The emergence of HSR has increased the attractiveness of cities/regions to enterprises, and accelerated the migration of enterprises to bring industrial agglomeration. The optimization of urban/regional industrial structures and the enhancement of the economic spillover of central cities to surrounding cities brought by HSR can effectively help the realization of sustainable economic development. Moreover, HSR improves the wage level and the effective labor supply. Moreover, the construction of HSR has changed the spatial distribution of labor force, and accelerated the flow of labor factors between cities and regions. Some labor forces, especially high-tech talents, began to migrate to underdeveloped areas, whose created value can bring long-term vitality to urban development and make the economy sustainable.

In addition, the research reveals that the improvement of the transportation technology level and accessibility is of great significance to urban economic growth and regional coordinated economic development. The improvement of the level of transportation technology and accessibility brought by HSR slows down the trend of excessive increase in trade cost as distance increases, which can increase the cost that enterprises are willing to pay for land and labor. Subsequently, the migration and agglomeration of industries brought by HSR will provide opportunities to the development of cities, especially to underdeveloped cities. And, the economic radiation of the central city to the surrounding cities will be greatly enhanced. Furthermore, the improvement of transportation technology level has significantly improved the effective labor supply level, and produced a multiplier effect of price on wage. It is resulting in the migration of labor, including some high-tech talents, particularly to non-central cities, which ensures that non-central cities can also enjoy the dividends brought by such talent. The regional economic development mode has been changed from single-pole development to multi-pole joint development with the establishment of HSR. Based on these, the economic development model can be transformed, the development differences between regions can be reduced, the vitality for the development can be injected into the cities, all these can make the economy achieve long-term and sustainable development.

First of all, this study strongly suggested that the selection of HSR stations should fully consider the economic location and geographical location of cities. We should not only pay attention to the development of the central developed cities, but also to the development of surrounding underdeveloped cities. Secondly, the construction of HSR is a costly project, and it will take a long time to recover costs. The government should speed up the establishment of commercial circles centered on HSR stations, and accelerate the rise of the commercial circle centered on HSR stations in the urban commercial landscape; the stimulation of its development potential is the key to effectively exerting the role of HSR in driving the urban economy. Thirdly, a modern industrial cluster model centered on the “HSR economy” along the HSR line should be created. For the HSR to have the characteristics of high timeliness and high radiation, the role of HSR as an economic belt channel should be given full play. The agglomeration effect of HSR should be strengthened to accelerate the development of advanced manufacturing and modern service industries along the HSR line. The coordinated development effect of “1 + 1 > 2” for joint development should be supported to build a modern industrial cluster. Fourth, improve the supporting infrastructure and policies for the development of the "HSR economy." Improved supporting infrastructure, more favorable business policies, more attractive talent policies, and convenient transportation brought by HSR can all improve the competitiveness of cities. The arrival of investment and high-tech talents can inject vitality and innovation into the economy. Finally, foster a new economic axis with HSR as the channel and accelerate the formation of new economic growth poles. The layout of HSR must continuously promote the region to extend longitudinally, speed up the construction of HSR between regions and cities, cultivate new economic axes. The economic development should be from a point-like direction to a band-like or sheet-like, based on the new axes to form a new spatial pattern of economic growth. Only in this way can HSR promote the sustainable development of economy more effectively.

Lastly, some further research is required from this paper. In order to facilitate the research, this paper makes some hypotheses when discussing the decision-making behaviors of enterprises and labors. It is necessary to do some further studies under the relaxation of hypothesis. Moreover, the paper does not take into account the market interactions between HSR and other transport modes. Since the interactions between HSR and other transport modes would also affect the sustainable development of the economy, it is necessary to do some further studies.