1. Introduction
Global climate change poses a serious threat to the ecological environment [1,2]. In 2019, China’s carbon emissions amounted to 9.876 billion tons [3]. Based on the background that resources and the environment are increasingly becoming hard constraints, it is urgent to promote the transition from economic growth to high-quality development. The green total factor productivity (GTFP) is a comprehensive indicator whose orientation is significant for driving quality economic development [4]. At the same time, the opening and development of the China–Europe Railway Express (CRE) has provided an endogenous impetus to promote a sustainable economic growth model. Do cities use CRE to enhance GTFP? What are the internal mechanisms and heterogeneity characteristics of CRE that affect GTFP? The objective evaluation of the economic impact of the CRE from the perspective of GTFP is important for promoting high-quality economic development.
CRE is an important transportation mode to promote economic and trade exchanges between China and European countries under the Belt and Road Initiative [5]. The research findings related to this paper mainly include the following two aspects. First, the impact of transportation infrastructure on green economic growth. Early studies mainly focused on the economic impacts of transportation infrastructure. For example, Aschauer [6] confirmed that core infrastructure, such as highways and airports, can significantly promote economic growth. Vickerman [7] further confirmed the positive impact of high-speed trains on economic growth. Chen and Hall [8] found that cities with high-speed rail are more likely to take advantage of development opportunities. Chen [9] pointed out that cities along the route have obviously benefited from the economic impact of the opening of high-speed rail. By using data from developing countries, Devarajan et al. [10] found that transportation infrastructure had a negative impact on economic growth. Hall [11] pointed out that the opening of high-speed rail lines has a negative impact on non-central peripheral cities. With global environmental problems becoming increasingly evident, the environmental impact of transportation infrastructure has received much attention in the academic community. One viewpoint is that transportation infrastructure significantly reduces pollution. For example, Dalkic et al. [12] found that high-speed transportation consumes less fossil energy and promotes carbon emission reduction. Luo et al. [13] pointed out that improving transportation infrastructure conditions can help reduce energy consumption. Yu et al. [14] showed that high-speed transportation promotes green development through industrial transformation. Zhou and Zhang [15] found at the micro-enterprise level that opening high-speed trains can improve the environmental efficiency of enterprises. Another view is that transportation infrastructure has a negative impact on the ecological environment. Shu et al. [16] believe that the construction of transportation facilities consumes much energy and generates exhaust gases and wastewater. Kaewunruen et al. [17] took the Beijing–Shanghai high-speed rail line in China as an example to confirm the negative impact of high-speed rail construction on the environment.
The second study looks at the economic impact of the opening of the CRE. For ASEAN countries, the Belt and Road Initiative is a promising trade facilitation mechanism [18]. As an important link under the Belt and Road Initiative, the CRE has improved the convenience of transporting goods from China to the European market [19]. On the one hand, the construction of CRE will promote regional economic growth and the development of international trade [20,21,22]. On the other hand, CRE has promoted the development of innovation-oriented enterprises while increasing the total factor productivity of the opened cities [23,24].
In summary, the existing literature mainly focuses on high-speed transportation to study the economic benefits of transportation infrastructure. There is little literature discussing the impact of transportation infrastructure on GTFP [25]. Currently, many publications focus on the economic impact of CRE, but no research has focused on the impact of CRE on GTFP, which is a research area for this paper.
This paper considers the opening of the CRE as a quasi-natural experiment and examines in detail the impact of the CRE on GTFP. The main contributions of this paper include the following: First, this paper systematically examines the impact of the opening of CRE on GTFP. From the perspective of sustainable development, the paper broadens the research horizon of the economic impact of the opening of CRE. Second, taking the opening of CRE as a quasi-natural experiment, this paper adopts a difference-in-differences model to study the overall effect of the opening of CRE on the GTFP. The paper also examines the mechanism from various aspects, which enriches the empirical research in this field. Third, this paper examines the conditions under which the opening of the CRE affects GTFP. These will provide a theoretical reference point for the planning of train routes and the rational use of green industries.
The remainder of this paper is organized as follows: Section 2 analyzes the direct influence and mechanism of CRE on GTFP, raising the research hypothesis. Section 3 demonstrates the model identification and data description. Section 4 reports the results of baseline regression, robustness test and heterogeneity analysis. Section 5 examines different mechanisms of action. Section 6 further analyzes the influence of CRE on GTFP under the background of high-quality development. Finally, we provide conclusions and recommendations.
2. Theoretical Mechanism and Research Hypothesis
2.1. Direct Impact of CRE on GTFP
Since the first trains (Yuxin Europe International Railway) in March 2011, CRE has maintained a healthy development trend, connecting 51 cities in 15 European countries. On the one hand, CRE has attracted a large number of goods suitable for railway transportation with one-third of the sea transportation time and one-fifth of the air price. CRE optimizes the division of labor in sea, land and air logistics, which breaks geographical barriers between regions, improves the transportation capacity of inland cities and supports the rapid flow of factors [26], thus increasing the market potential. Table 1 shows the types of goods opened for urban distribution have gradually shifted toward the high end. For example, Lenovo and TCL have become the main customers of Rong–Europe, and the main cargo transported by Yi-Xin–Europe has also expanded from small commodities such as bags and daily necessities to handicrafts, electronic products, etc. The trains between China and Europe have improved the efficiency of high-end resource flow and had a significant impact on technological innovation and, ultimately, on green full-factor productivity. On the other hand, the rapid development of CRE requires strong policy support, and the government systems around the country have responded positively by adopting a variety of financial measures as well as tax incentives to support the development of CRE. The increase in financing has significantly contributed to carbon emission reduction [27], and taxable measures have significant impacts on the production process of a firm [28]. As a result, cities that have CRE have more advantages in policy and fund support, providing financial support as well as tax incentives for promoting the development of green industries and reducing emission, thus improving GTFP.
Under other conditions, the opening of China–Europe trains has significantly increased the productivity of the whole element.
2.2. Mechanism of CRE Opening on GTFP
First, the opening of CRE has improved the GTFP by increasing the level of technological innovation. Based on the theory of “center movement” [29], the opening of China–Europe trains have attracted more innovative elements to gather with comparative advantages, which further strengthens the innovation ability of central cities. At the same time, in the face of the high market thresholds of developed countries, enterprises participating in the trains between China and Europe will raise the level of innovation and meet the needs of trade. The improvement of enterprise innovation has enriched the “quantity” and “quality” of innovation elements, and the opening of China–Europe trains has also reduced the cost of collecting innovation elements toward central cities. Existing studies have proved that the technological progress caused by innovation is the main driving force for green all-factor productivity [30]. The improvement of the technological level can not only improve the efficiency of resource utilization but also greatly promote the quality of green all-factor [31]. It can be seen that the innovation level caused by the opening of China–Europe trains is a strong motivation for promoting the productivity of the overall green element.
Second, the opening of CRE has improved the GTFP by upgrading the industry. The improvement of the industrial structure depends not only on the optimized configuration of high-end elements such as technology and capital [32] but also on the rapid development of international trade [33]. On the one hand, the opening of China–Europe trains has reduced the transportation cost of innovation elements and created conditions for industrial upgrading. On the other hand, China–Europe trains have promoted the vigorous development of the relatively good industry by strengthening trade exchanges and supporting the optimized configuration of production factors in the industry [34]. The upgrading of industry promotes the efficient flow of production factors, which contributes to the improvement of full-factor productivity [35]. At the same time, the optimization of industrial structure has gradually led to low-carbon development in traditional industries with high energy consumption and high pollution, which reduces resource consumption and pollution, thereby increasing green full-factor productivity [36]. Based on the above analysis, the trains between China and Europe have opened up through the improvement of industrial structure to promote the improvement of green full-factor productivity.
Third, the opening of CRE has improved the GTFP by promoting financial development. The opening of China–Europe trains has brought opportunities to “go out” inland, and large financial institutions and functional departments have also actively responded. For example, in 2013, CITIC Bank Chongqing Branch extended a 300 million yuan loan for the construction of the Chongqing China–Europe Train Logistics Park. The Xi’an Branch of the central bank and other departments jointly proposed 13 financial foreign exchange measures to promote the development of digital finance in “Chang’an”. It can be seen that rich financial methods meet the financing needs of CRE and improve the financial development level. Financial development is important in promoting the productivity of the full green factor. First, a developed financial system can ensure the financing needs of low-emission departments [37,38], thereby increasing the GTFP. Second, developed financial markets can effectively eliminate information asymmetry and enable enterprises to obtain low-cost funds for environmental protection projects [39], thus promoting the improvement of GTFP. CRE helps to improve the level of regional financial development and provides the necessary financial support for improving green factor productivity.
Based on the above analysis, this paper systematically identifies the specific path of the CRE on the GTFP (Figure 1) and proposes the following research assumptions:
The opening of CRE is mainly based on technological innovation, industrial upgrading and financial development to improve GTFP.
3. Research Design
3.1. Sample Selection and Data Sources
This paper selects 284 prefecture-level cities in China from 2005 to 2019 as the research sample, excluding the samples that were abolished or established at the prefecture-level city level during the sample period (such as Chaohu City, Sansha City, etc.). The data sources of this paper are from the “China Urban Statistics Yearbook”, “China Regional Economic Statistics Yearbook” and statistical yearbooks of provinces and regions over the years. The data of CRE are derived from the information disclosed in the “Construction Development Plan of China Railway Express (2016–2020)” and the websites of the Ministry of Commerce and China Railway Container Transportation Company. In order to avoid the influence of extreme outliers on empirical results, all continuous variables are Winsorized by 1% before and after.
3.2. Identification Strategy and Model Building
CRE opening is provided with the characteristics of time-varying in order to test the average processing effect of this time-space staggered event on the GTFP; this paper constructs a multi-period difference in differences model to analyze.
(1)
where Gtfpi,t represents GTFP, with subscripts i and t, respectively, representing the enterprise and year. Treat is the virtual variable of the treatment group. The value is 1 if it is a prefecture-level city where the CRE is operating; otherwise, it is 0. Post is a time virtual variable, and the year in which CRE is operating is 1; otherwise, it is 0. Controls is a series of control variables, city is the fixed effect of the city, year is the fixed effect of the year, and ε is the random interference term. The interaction coefficient (Treati × Postt) is the core of this paper, reflecting the difference between the treatment group and the control group.3.2.1. Explained Variable: Green Total Factor Productivity
The explanatory variable in this paper is GTFP. SBM (slacks-based measure) with unexpected output established by Tone [40] can overcome the disadvantages of traditional Data Envelopment Analysis (DEA). The GML (global Malmquist–Luenberger) model built by Oh [41] can take the sum of each period of research data as a reference so that it is transferable. Therefore, this paper adopts the SBM-GML model to calculate the GTFP of 284 prefecture-level cities in China from 2005 to 2019. The relevant indicators are selected as follows:
Input indicators: (1) Capital input: The actual capital stock of prefecture-level municipalities calculated by the “perpetual inventory method” is calculated as follows: , the base period capital stock is the actual capital stock converted at constant prices in 2004, and the depreciation rate δ is set at 9.6%. The depreciation rate is estimated to be 9.6% based on the weighted average of 1952–2000 for construction and installation projects, equipment and equipment purchases, and other fixed assets [42]. The new capital stock is calculated using total new fixed asset investment converted to real terms using the investment price index. (2) Labour input: the sum of the number of employees in urban units and the number of private individuals at the end of the year is used as the measure. (3) Energy input: the total energy consumption of prefecture-level cities is used as the measure.
Output indicator: (1) Expected output: actual GDP of each prefecture-level city based on 2004. (2) Unexpected output: emissions of industrial wastewater, industrial sulfur dioxide and industrial soot.
3.2.2. Explanatory Variable: Opening of China–Europe Railway Express
The core explanatory variables in this paper are based on whether to open the CRE in the 2005–2019 sample, considering that the CRE, whether it is “point-to-point” direct or “transit”, will have an economic effect on urban development. Therefore, both the origin and the route cities were used as the cities for the baseline model treatment group. The corresponding policy dummy variable Treat takes the value of 1; otherwise, it is 0. Since prefecture-level cities have multiple lines, the first line operation time was selected as the opening time of CRE. As part of CRE is opened at the end of the year, the short-term economic effect may not emerge. In this paper, the time variable was set according to the actual opening date. If the opening time of the CRE was October of the year and before, Post was assigned a value of 1 at the beginning of the year, and if it was after October, Post was assigned a value of 1 with a lag of one year.
3.2.3. Control Variable
This paper selected the following control variables: (1) Regional economic development level (gdp), measured by the natural logarithm of GDP. (2) Infrastructure construction (infra), measured by the ratio of fixed asset investment to GDP. (3) Degree of opening-up (open), measured by the ratio of actual utilization of foreign capital to GDP. (4) Government support (gov), measured by the ratio of fiscal expenditure to GDP. (5) Regional R&D level (rds), measured by the ratio of expenditure of scientific research to GDP.(6) Level of informatization (internet), measured by the number of users of broadband access to urban Internet. (7) Population density (density), measured by the year-end population after logarithmization. Table 2 shows descriptive statistics for the main variables.
4. Results and Analysis
4.1. Baseline Regression
Table 3 reports the results of the baseline estimation, with only the interaction term included in (1) and the city-fixed effects, year-fixed effects and the interaction term between city- and year-fixed effects. Columns (2) and (3) are listed as the regression results for the GTFP after the introduction of control variables. Column (2) only adds the two-way fixed effect between city and year. Column (3) adds the interaction between city and year fixed effect on this basis. The results illustrate that whether the control variables are included or not, the estimated coefficient of GTFP is significantly positive at the 5% level in terms of the opening of CRE, indicating that the opening of freight trains plays a significant role in promoting the improvement of GTFP in prefecture-level cities. Among the results, the coefficient of Treat × Post in column (3) is 0.0072, which means the GTFP increases by about 0.122% (0.0052 × 0.2351 × 100% = 0.169%), accounting for 0.0423 (0.00169/0.0289 × 100% = 5.86%) of the standard deviation of GTFP. This manifests that the impact of the opening of CRE on the GTFP cannot be ignored. The baseline regression results show that as an important transport infrastructure of the Belt and Road Initiative, CRE is conducive to improving urban GTFP.
4.2. Robustness Test
4.2.1. Parallel Trend Test
For DID model analysis, a parallel trend test is required. If there is no exogenous impact from the CRE, the changing trend of GTFP of the treatment group and control group should be basically the same. In order to cope with the time-varying situation of the CRE, the parallel trend test was carried out using the event research method of Beck et al. [43] for reference. The model was set as follows:
(2)
The superscript m < 0 of Treat×Post indicates the m year before the treatment group opening the CRE, m > 0 refers to the m year after the CRE is opened by the treatment group, m = 0 refers to the year when the city opens CRE, and other variables are set in the same model (1). Specifically, m = −5 indicates the first 5 years or above in which the CRE is opened in the city, and m = 3 indicates the third year or above in which the CRE is opened in the city. This paper takes the reference group one year before the opening of the CRE to avoid the influence of multicollinearity by removing m = −1 in the process. Figure 2 displays the parallel trend test results of CRE opening and GTFP at a 95% confidence interval. It is found that the estimation coefficients of Treat×Post are not significant before the opening of the CRE (m = −5, …, −2) and significantly positive, at least at a 10% level after the CRE is opened (m = 3, 2, 1). Under this circumstance, Figure 2 illustrates that the treatment and control groups met the parallel trend hypothesis regardless of exogenous shocks opened by the CRE.
4.2.2. Placebo Test
In order to ensure the net effect of GTFP comes only from the impact of the opening of CRE, the placebo test was adopted to exclude possible effects such as random factors and missing variables. By referring to the random sampling method of Li et al. [44], the shock to GTFP from the opening of the CRE was made random (formed by a computer) and estimated 1000 times iteratively according to the baseline regression model (1). Finally, the kernel density distribution of the estimated values of the core explanatory variable Treat × Post coefficient was plotted (see Figure 3). It was found that the estimated value of Treat × Post coefficient after randomization was concentrated around 0, while the baseline regression coefficient in column (3) of Table 3 was much different from the overall distribution, which reveals that the effect of CRE on the GTFP is not affected by the unobservable factors. The baseline regression conclusion in this paper is robust.
4.2.3. Excluding the Impact of Other Policies
CRE belongs to the scope of infrastructure construction. Whereas some of the cities where the freight trains operate are also provided with well-developed transport networks, these transport infrastructures may also influence the spatial movement of factors of production, which in turn may make a difference on GTFP. This paper chose to exclude the traffic infrastructure of high-speed railways and constructed a difference-in-differences variable (Hsr) based on high-speed rail opening and incorporated it into model (1) for regression. Furthermore, when evaluating the impact of CRE on GTFP, it will certainly be disturbed by other policies. The paper selected the free trade area policy for testing and included Trade, a dummy variable for free trade area construction, in the baseline regression model. The results are shown in columns (1) and (2) of Table 4. The core interpretative variable, Treat × Post coefficient, is still significantly positive, which suggests that after eliminating the influence of high-speed railway opening and free trade area policy, the opening of CRE still promotes the improvement of GTFP.
4.2.4. PSM-DID
In order to control the selectivity bias caused by the non-randomness of CRE, this paper rebuilt the treatment group by using the propensity score matching method (PSM) to evaluate the impact of the opening of the CRE on the productivity of green total factors. Firstly, we adopted the logit model, then selected regional economic development level (gdp), infrastructure construction (infra), open degree (open), government support (gov), regional research and development level (rds), the level of informatization (internet) and population density (density) as covariates to estimate the probability of a city operating CRE. Subsequently, the prefecture-level cities that are closer in propensity score to the cities that launched CRE were selected as the treatment group from the cities that have not launched CRE, matched according to the radius matching, as well as re-run the regression based on the matched data to further test the effect of the launch of CRE on GTFP. Column (3) of Table 4 displays that the results obtained from the re-estimation of the newly generated samples after PSM are consistent with the baseline regression, which again demonstrates the robustness of the baseline conclusion.
4.2.5. Endogenous Discussion: Instrumental Variables Analysis
As the opening city of CRE is not the result of random choice, the policy variable (Treat) has potential endogeneity, which will lead to an error of estimation result. This paper adopted the ancient “Silk Road” domestic passing areas as instrumental variables to carry out endogenous control. On the one hand, CRE is an important traffic facility for the construction of the Belt and Road, which overlaps with the route area of the “Silk Road” and has a strong correlation. On the other hand, GTFP is not affected by preexisting historical facts. The ancient “Silk Road” cannot directly influence the current GTFP but can only do so by acting on the opening of the China–European Railway Express, thus satisfying the requirement of exogeneity. Since IV varies only with individuals and not with time, in order to ensure that the instrumental variable is variable, this paper introduced a time trend term (trend) and constructs an interaction term between the time trend term and IV (IV × Trend) as the final instrumental variable for the opening of CRE. Table 4 Columns (4) and (5) report the regression results of instrument variables in two stages. The regression results in column (4) display that the F value of the first stage is much larger than the empirical value 10. Moreover, the probability value (p-value) corresponding to the Wald statistic and LM statistic is less than 1%, which excludes weak instrument variables and unrecognizable problems. The regression coefficient of the interaction term IV × Trend is significant at the 1% level, indicating that there is a high correlation between the opening of CRE and the ancient “Silk Road” route regions. In column (5), the Treat × Post coefficient is significantly positive, which indicates that the baseline conclusion of this paper is still robust after controlling the possible endogeneity.
4.3. Heterogeneity Analysis
4.3.1. Geographic Location Heterogeneity Test
China’s regional economic development has shown a new trend of “obvious regional economic differentiation and a further southward shift of the national economic centre of gravity”, the widening gap between the north and the south has gradually become a new concern for unbalanced regional development. This paper divided the sample data into southern and northern regions. The grouping test results are shown in Table 5. The interaction coefficient of column (1) was not significant, and the coefficient of the interaction item in column (2) was significantly positive at a 10% level, revealing that the opening of the CRE only significantly improves the GTFP in the northern region. The possible reason is that compared to the northern region, the southern cities have abundant economic resources, coupled with their own geographical characteristics, making the southern region more advantageous in innovation, green development and openness [45]. According to the law of diminishing marginal utility, the marginal benefit of the opening of CRE on GTFP in southern China is low. However, the economic development level of northern cities is relatively low, and the opening of CRE promotes the gathering of innovation factors and promotes industrial upgrading, which helps to play an important role in promoting GTFP. In addition, based on the current development status of CRE, the three main Railway Borders (Erenhot, Manzhouli, Alashankou) are all located in the northern region [46]. Furthermore, in recent years, CRE has developed rapidly in the northern region, especially the Changanhao in Xi’an, which has achieved rapid growth since 2017. The goods such as automobiles and auto parts from Europe to Xi’an are mainly imported using the CRE [47]. This also leads to the significant promotion effect of CRE on the openness level, financial development and agglomeration of innovative factors in northern cities, thereby significantly improving the GTFP.
4.3.2. Heterogeneity Test of Policy Support
As an important carrier of promoting trade exchanges between China and the countries along the line during the construction of the Belt and Road, it is worth considering whether the effect of CRE on GTFP is influenced by the policy support of the Belt and Road. This paper adopts the “Policy Communication” index of each province in the “Belt and Road Big Data Report” to measure the level of government policy support for “the Belt and Road”, classifying prefecture-level cities in these ten provinces as the group with higher policy support and other cities as the group with lower policy support. Table 5 reports the regression results of grouping. The coefficient of the interaction item in column (3) is not significant, and the coefficient of the interaction item in column (4) is significant at a 5% level, indicating that in the region with low policy support of “the Belt and Road”, the opening of CRE promotes GTFP more significantly. This is because that cities with low policy support are less open to the outside world, and most of them are inland areas with low development levels. CRE is an important platform for inland areas to open up to the outside world, whose opening has built a bridge between these areas and countries along the Belt and Road, providing important opportunities for these inland areas to attract foreign investment and trade development. Combined with national policy support, the opening of CRE has mainly played its role in regions with low policy support, so its promotion effect on green total factor productivity is more obvious in these regions. Moreover, from the perspective of the development of CRE, a total of 89 cities in this sample have opened CRE. However, only 30 of them are located in areas with high policy support, and the remaining 59 are located in areas with low support, which leads to the promoting effect of CRE on GTFP in these regions being more obvious.
4.3.3. Heterogeneity Test of GTFP
Considering that the difference of GTFP in different cities may affect the development of the CRE effect, this paper makes a regression test on the opening of CRE and GTFP and investigates the heterogeneity effect of CRE on different cities with different GTFP levels using three fractions of 25%, 50% and 75%. The results are shown in column (5)–(7) of Table 5. The coefficient of the interaction term Treat×Post is positive but not significant at the 25% and 50% fractions and is significantly positive at the 75% fraction, indicating that when the urban GTFP is low, the impact effect of the opening of the CRE is not obvious. As the GTFP of the city increases, the impact effect of the opening of CRE is significant, which indicates that CRE has a certain strengthening effect on the GTFP of the city.
5. Mechanism Test of CRE Affecting GTFP
In order to comprehensively grasp the relationship between the opening of CRE and the GTFP, it is necessary to discuss the internal mechanism. The theoretical analysis shows that the promotion of GTFP is realized mainly by improving the regional innovation level, promoting industrial structure upgrading and promoting financial development. In order to test this mechanism, the following mediation model is constructed for testing on the basis of model (1):
(3)
(4)
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MV represents the intermediary variable, including the regional innovation level, industrial structure upgrading index and financial development level. Other variables are selected the same as those in the preceding article. In model (3), the influence effect ɡ1 of the opening of the CRE on the intermediate variable is shown.
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If the coefficient of ɡ1 is significantly positive, it indicates that CRE has a significant promotional effect on the intermediate variable. In Model (4), θ1 represents the indirect effect produced by the intermediary variable. If the coefficient of θ1 is significantly positive, it indicates that this mediating variable is an important channel through which the opening of the CRE increases GTFP.
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First, the opening of CRE is conducive to improving the flow of factors, enhancing regional innovation ability, and thus promoting the improvement of urban GTFP. This paper adopts the number of patents granted per prefecture-level city to measure the level of urban innovation (patent). The results are shown in Table 6. The interaction item Treat × Post coefficient in column (1) is significantly positive, indicating that the opening of CRE has a significant positive effect on the regional innovation level. The coefficient of patent in column (2) is significantly positive at the 10% level, which indicates that CRE promotes urban GTFP by improving the regional innovation level.
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Secondly, the opening of CRE provides convenience for the clustering of human capital, knowledge, information and technology, strengthens the inflow trend of element resources into the open cities, creates conditions for the optimization and upgrades industrial structure, which helps to improve the urban GTFP. This paper constructs the industrial structure advanced index (isu) to measure industrial upgrading. The specific calculation formula is isu = regional tertiary value added/(primary value added + secondary value added). The results of column (3) in Table 6 show that the interaction item Treat × Post coefficient is significantly positive at a 5% level, indicating that CRE significantly promotes industrial upgrading. In column (4), the isu coefficient is significantly positive at a 1% level, indicating that industrial upgrading is an important influence channel for CRE to improve the GTFP.
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Thirdly, the opening of CRE promotes the financial system to improve service strength and scale, meets the financial demand of the opening area and provides necessary financial support for improving the GTFP. This paper measures the level of financial development as the ratio of total year-end deposits and loans of financial institutions to GDP. The regression results are shown in Columns (5) and (6) of Table 6. The regression coefficient of column (5) is significantly positive, indicating that CRE significantly improves the urban financial development level. The coefficient of finance in column (6) is significantly positive at the 10% level, indicating that the increased level of financial development resulting from the opening of CRE contributes to the improvement of GTFP in the opening city.
6. The Impact on GTFP under the High-Quality Development of CRE
CRE has maintained a good development trend since its official operation in 2011. The number of transportation and the value of transported goods has continued to rise, which has become an important support for a stable and smooth international supply chain. In the event that the COVID-19 epidemic has hindered air transportation and maritime transportation, the opening of CRE ensures the smooth operation of international logistics transportation and plays an active role in the stable development of China’s economy. With the construction of the Belt and Road Initiative entering a new stage of development, it is of great significance to promote the high-quality development of CRE. In this case, this paper further discusses the relationship between CRE and GTFP under the background of high-quality development.
Transportation cost optimization: CRE is often in a trade surplus at the beginning of the operation, resulting in high transportation costs. The main reason is that the number of return trips of the Express is far less than the outbound quantity of the Express. At the same time, the added value of returned goods is low. In December 2018, at the Third Plenary Meeting of the Transport Coordination Committee of CRE, the “High Quality Development Evaluation Index of CRE” was formulated. It proposed that the return ratio mainly reflected the import and export cargo flow of the Express and the utilization of transportation equipment. In this case, it was an important indicator to evaluate the optimization degree of the organization and transportation cost of the return cargo source. Moreover, as a railway transport corridor, increasing the density of “pairs” (to prevent one-way “empty” trains) and return cargo, improving the return ratio and promoting a better balance between departures and returns has been the key to reducing operating costs for the CRE. Since 2014, the proportion of Express returns has increased yearly, and the ratio between the number of returns and the number of departures reached 81.768% in 2019. The return ratio (number of return /number of outbound) is used as the transportation cost proxy variable (Cost), and the interaction term is Treat × Post × Cost. The test results in column (1) of Table 7 show that the coefficient of Treat × Post × Cost is significantly positive at a 5% level, indicating that the optimization of transportation cost enhances the promotion of the GTFP of the CRE.
Multi-modal transport development: Multi-modal transport is a process of integrating freight transport through effective coordination between two or more modes of transportation. At the beginning of the opening period, CRE competed with other transportation modes by virtue of its own advantages, such as high safety and short transportation time, but this was contrary to the original intention of the construction of CRE. As a matter of fact, compared to a single mode of transport, the rational use of a combination of inter-modal transport modes can save resources and promote transport efficiency. At present, CRE’s own freight capacity has been boosted by the release of capacity in sea, air and land transport, and government departments are actively exploring new modes of “multi-modal transport” to promote the high-quality development of CRE logistics and transport. (“The Opinions on Promoting the High-Quality Development of Logistics to Facilitate the Formation of a Strong Domestic Market National Development and Reform Commission Economic and Trade (2019) No. 352” proposes to “accelerate the construction of a public information platform for multi-modal transport as well as promote the effective matching of cargo sources with capacity resources such as road, rail, water and air” and “Develop ‘one-stop’ multi-modal transport service products by relying on the national logistics hub network, and accelerate the realization of the ‘one-order system’ for multi-modal container transport”.) This paper chooses the logarithmicized traffic volume of road, air and sea to measure the transport capacity of different transportation modes and constructs the interaction items Treat × Post × road, Treat × Post × air and Treat × Post × sea. The results in columns (2)–(4) of Table 7 show that the combination of CRE and different modes of transport still significantly improves the GTFP, thus confirming the importance of multi-modal transport in promoting the high quality of the CRE.
Sustainable development: Government subsidies for CRE significantly reduce transportation costs and provide important support for the increase in the number of transportation and transportation scale of CRE [5]. However, continuous dependence on subsidies is not a long-term solution. The CRE subsidy mechanism distorts the market resource allocation law and is not conducive to the normal operation of CRE marketization [48]. In recent years, relevant departments have explored and implemented a series of measures to promote the marketization of CRE, as evidenced by the fact that the unified brand of CRE in June 2016. If the GTFP still increases after the unified branding, it indicates that the Express has sustainable development prospects. Considering that there are eight cities where CRE originated on the day of the unified branding (Chongqing, Chengdu, Zhengzhou, Wuhan, Changsha, Suzhou, Dongguan and Yiwu), the paper sets these eight cities as the treatment group and the rest as the control group. The Treat×Post coefficient in column (5) of Table 7 is significantly positive, manifesting that the market-oriented operation has effectively promoted the sustainable development of CRE and contributed to the improvement of GTFP.
This paper takes high-quality development as the starting point and finds that the continuous optimization of transportation costs measured by opening density, integration with other transportation modes and sustainable development can strengthen the effect of CRE on GTFP. The reason is that transportation cost optimization, the combination of different transportation modes and market-oriented development trends can release the transportation potential of CRE, which provides broad space for promoting the development of green industries and achieving green transformation through the opening of CRE. Therefore, this paper, based on the analysis of high-quality development dimensions such as opening density and integration into different transportation modes, substantively strengthens the basic conclusion that CRE can enhance the GTFP.
7. Conclusions and Recommendations
In this paper, the opening of CRE is regarded as a quasi-natural experiment, and the economic impact of the opening of CRE is studied from the perspective of GTFP. The study found that the CRE had a positive impact on GTFP and that the promotion had significant heterogeneity characteristics. The mechanism test results show that the opening of CRE mainly affects GTFP through channels such as regional innovation, industrial upgrading and financial development. Further analysis shows that transportation cost optimization, multi-modal transportation and sustainable development have enhanced the positive impact of CRE on GTFP.
In a review of the impacts of CRE on GTFP, this article provides the following policy recommendations:
The Chinese government should further promote the normalization of the operation of the CRE. On the basis of the existing cities that have been opened for the CRE, more cities should be included in the list of CRE. Moreover, the government should strengthen the brand building and promotion of China Europe freight trains, adhere to market-oriented international trade mechanisms, seize development opportunities such as cross-border e-commerce, actively organize return freight sources and achieve continuous optimization of transportation costs for China Europe freight trains. Chinese local governments should adhere to market orientation to promote the sustainable development of CRE. It is necessary to strengthen multi-modal transportation, build a number of large-scale logistics centers with multi-modal transportation functions and realize the effective connection between CRE and various modes of transportation.
With the help of the green effects of CRE, governments of countries along the route should strengthen investment in green projects and prioritize approval or implementation of green projects such as energy conservation, emission reduction and ecological protection. Moreover, based on the green open channel provided by the CRE, the governments should continuously improve the environmental governance system and cooperation network and strengthen the construction of environmental cooperation mechanisms and platforms among countries along the route, thus further promoting the coordinated development of green trade and green investment.
Considering that CRE has become a stable channel for transporting green products, future research should be devoted to the analysis of what are the advantages of CRE in terms of green product transportation compared to other transportation channels and through the green effect of CRE, whether the carbon neutral of countries along the Belt and Road can be further realized.
Conceptualization, X.W.; methodology, J.L. (Jiaojiao Li); software, J.S.; validation, J.L. (Jianxu Liu); formal analysis, J.L. (Jia Li); investigation, J.L. (Jianxu Liu); resources, J.S.; data curation, X.W.; writing—original draft preparation, J.L. (Jiaojiao Li); writing—review and editing, S.S.; visualization, J.L. (Jianxu Liu); supervision, X.W.; funding acquisition, S.S. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Not applicable.
All data can be obtained by email from the corresponding author.
The Faculty of Econometrics at Shandong University of Finance and Economics.
The authors declare no conflict of interest.
Footnotes
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Changes in goods transported in CRE.
| Date | Routes | Cargo Types in the Year of Opening | Cargo Types in 2019 | Cities |
|---|---|---|---|---|
| 9 March 2011 | Yu-Xin–Europe | Telegram Products | Laptop, machinery, |
Chongqing |
| 24 October 2012 | Han-Xin–Europe | Laptop, other goods | Electronic products, |
Wuhan |
| 26 Aprial 2013 | Rong–Europe | Electronic products, |
Electronic products, auto parts, red wine, etc. | Chengdu |
| 18 July 2013 | Zheng–Europe | Tires, high-grade clothing, recreational articles, crafts, etc. | Light Textile, mechanical and electronic Products | Zhengzhou |
| 29 September 2013 | Su-Man–Europe | LCD screen and power board | Electronic products, machinery, clothing, |
Suzhou |
| 18 November 2014 | Yi-Xin–Europe | Small commodities such as bags and daily necessities | Electronic products, crafts, drinks, toys | Jinhua |
Source: It is sorted out according to the websites of each train operation platform and China “Belt and Road”.
Descriptive statistics of variables.
| Symbols | Mean | Standard Deviation | Median | Max |
|---|---|---|---|---|
| Gtfp | 1.0096 | 0.0289 | 1.0095 | 1.0590 |
| Treat × Post | 0.0587 | 0.2351 | 0.0000 | 1.0000 |
| gdp | 16.0273 | 1.0414 | 15.9957 | 18.7923 |
| infra | 0.6921 | 0.5123 | 0.6094 | 3.5823 |
| open | 10.7842 | 3.4635 | 11.4856 | 15.5384 |
| gov | 0.1891 | 0.1522 | 0.1472 | 1.0565 |
| rds | 0.0020 | 0.0023 | 0.0014 | 0.0145 |
| internet | 0.6291 | 0.9231 | 0.2920 | 5.3900 |
| density | 5.8640 | 0.6769 | 5.9162 | 7.1955 |
Baseline regression results for GTFP for the opening of CRE.
| (1) | (2) | (3) | |
|---|---|---|---|
| Treat × Post | 0.0049 ** | 0.0069 ** | 0.0072 ** |
| (0.0025) | (0.0032) | (0.0032) | |
| gdp | 0.0012 | 0.0013 | |
| (0.0033) | (0.0033) | ||
| infra | −0.0023 | −0.0022 | |
| (0.0022) | (0.0022) | ||
| open | −0.0006 * | −0.0006 ** | |
| (0.0003) | (0.0003) | ||
| gov | 0.0084 | 0.0086 | |
| (0.0080) | (0.0080) | ||
| rds | 0.6815 * | 0.6969 * | |
| (0.3780) | (0.3788) | ||
| internet | −0.0011 | −0.0011 | |
| (0.0010) | (0.0011) | ||
| density | −0.0028 | −0.0028 | |
| (0.0035) | (0.0035) | ||
| city | Yes | Yes | Yes |
| year | Yes | Yes | Yes |
| city × year | Yes | No | Yes |
| N | 4260 | 3954 | 3954 |
| R 2 | 0.060 | 0.068 | 0.070 |
Note: Standard errors in parentheses are clustered at the city level; ** and * denote significance levels at 5% and 10%, respectively.
Robustness test: PSM-DID, exclusion of other policy and instrumental variables method.
| (1) | (2) | (3) | (4) | (5) | |
|---|---|---|---|---|---|
| Gtfp | Gtfp | Gtfp | Treat × Post | Gtfp | |
| Trade | −0.0026 | ||||
| (0.0024) | |||||
| Hsr | 0.0012 | ||||
| (0.0018) | |||||
| IV × Trend | 0.8717 *** | ||||
| (0.0226) | |||||
| Treat × Post | 0.0068 ** | 0.0072 ** | 0.0086 ** | 0.0117 * | |
| (0.0033) | (0.0032) | (0.0034) | (0.0064) | ||
| gdp | 0.0011 | 0.0013 | 0.0018 | −0.0078 | 0.0011 |
| (0.0033) | (0.0033) | (0.0046) | (0.0368) | (0.0033) | |
| infra | −0.0024 | −0.0023 | −0.0002 | −0.0040 | −0.0022 |
| (0.0022) | (0.0022) | (0.0028) | (0.0228) | (0.0022) | |
| open | −0.0006 ** | −0.0006 ** | −0.0006 | −0.0012 | −0.0006 * |
| (0.0003) | (0.0003) | (0.0004) | (0.0014) | (0.0003) | |
| gov | 0.0086 | 0.0090 | 0.0038 | −0.0062 | 0.0085 |
| (0.0079) | (0.0079) | (0.0094) | (0.0718) | (0.0079) | |
| rds | 0.7130 * | 0.6824 * | 0.4494 | −5.6123 * | 0.7128 * |
| (0.3788) | (0.3794) | (0.4188) | (3.3681) | (0.3779) | |
| internet | −0.0010 | −0.0011 | −0.0009 | 0.0524 *** | −0.0014 |
| (0.0011) | (0.0011) | (0.0012) | (0.0153) | (0.0011) | |
| density | −0.0028 | −0.0029 | −0.0031 | 0.0242 | −0.0030 |
| (0.0035) | (0.0036) | (0.0040) | (0.0150) | (0.0036) | |
| city | Yes | Yes | Yes | Yes | Yes |
| year | Yes | Yes | Yes | Yes | Yes |
| city × year | Yes | Yes | Yes | Yes | Yes |
| N | 3741 | 3741 | 2674 | 3741 | 3741 |
| R2 | 0.074 | 0.074 | 0.106 | 0.590 | 0.004 |
| First-stage F-value | 1703.99 | ||||
| Kleibergen-Paap LM | 70.881 | ||||
| Kleibergen-Paap Wald | 1703.991 |
Note: Standard errors in parentheses are clustered at the city level; ***, ** and * denote significance levels at 1%, 5% and 10%, respectively.
Heterogeneity analysis.
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | |
|---|---|---|---|---|---|---|---|
| South | North | High Level | Low Level | 25% | 50% | 75% | |
| Treat × Post | 0.0075 | 0.0070 * | 0.0052 | 0.0081 ** | 0.0054 | 0.0039 | 0.0072 * |
| (0.0054) | (0.0042) | (0.0058) | (0.0039) | (0.0036) | (0.0042) | (0.0037) | |
| gdp | −0.0002 | 0.0006 | −0.0057 | 0.0049 | 0.0015 | 0.0003 | 0.0005 |
| (0.0052) | (0.0041) | (0.0055) | (0.0039) | (0.0011) | (0.0013) | (0.0011) | |
| infra | −0.0006 | −0.0038 | 0.0001 | −0.0037 | 0.0008 | 0.0002 | −0.0019 |
| (0.0033) | (0.0030) | (0.0036) | (0.0029) | (0.0025) | (0.0030) | (0.0026) | |
| open | −0.0006 | −0.0006 | 0.0002 | −0.0009 *** | −0.0008 ** | −0.0004 | 0.0038 |
| (0.0004) | (0.0005) | (0.0005) | (0.0004) | (0.0004) | (0.0004) | (0.0039) | |
| gov | 0.0099 | 0.0053 | −0.0166 | 0.0239 ** | 0.0149 * | 0.0063 | 0.0053 |
| (0.0113) | (0.0114) | (0.0127) | (0.0116) | (0.0086) | (0.0101) | (0.0090) | |
| rds | 0.6393 | 0.5964 | −0.0144 | 0.8777 * | 0.1598 | 1.2056 * | 0.8260 ** |
| (0.4937) | (0.6250) | (0.6656) | (0.4775) | (0.4028) | (0.4737) | (0.4210) | |
| internet | −0.0016 | −0.0002 | −0.0008 | −0.0020 | −0.0003 | 0.0001 | −0.0002 |
| (0.0013) | (0.0018) | (0.0014) | (0.0018) | (0.0006) | (0.0007) | (0.0007) | |
| density | −0.0003 | −0.0113 * | −0.0081 * | 0.0043 | 0.0009 | −0.0015 | −0.0026 |
| (0.0045) | (0.0066) | (0.0043) | (0.0050) | (0.0012) | (0.0014) | (0.0013) | |
| city | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| year | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| city × year | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| N | 2002 | 1739 | 1278 | 2463 | 3741 | 3741 | 3741 |
| R 2 | 0.077 | 0.085 | 0.087 | 0.080 | 0.010 | 0.007 | 0.008 |
Note: Standard errors in parentheses are clustered at the city level; ***, ** and * denote significance levels at 1%, 5% and 10%, respectively.
Mechanism test of impact of CRE on GTFP.
| (1) | (2) | (3) | (4) | (5) | (6) | |
|---|---|---|---|---|---|---|
| Patent | Gtfp | isu | Gtfp | Finance | Gtfp | |
| Treat × Post | 0.0522 ** | 0.0672 ** | 0.2587 *** | |||
| (0.0210) | (0.0277) | (0.0876) | ||||
| patent | 0.0005 * | |||||
| (0.0003) | ||||||
| isu | 0.0079 *** | |||||
| (0.0027) | ||||||
| finance | 0.0020 * | |||||
| (0.0012) | ||||||
| gdp | −0.0679 ** | 0.0005 | −0.1600 *** | 0.0008 | −1.4113 *** | 0.0043 |
| (0.0283) | (0.0007) | (0.0425) | (0.0033) | (0.1355) | (0.0037) | |
| infra | −0.0462 *** | −0.0012 | −0.0208 | −0.0021 | 0.3624 *** | −0.0031 |
| (0.0146) | (0.0017) | (0.0246) | (0.0022) | (0.0990) | (0.0022) | |
| open | 0.0014 | −0.0004 | 0.0007 | −0.0006 ** | −0.0053 | −0.0006 ** |
| (0.0011) | (0.0003) | (0.0014) | (0.0003) | (0.0047) | (0.0003) | |
| gov | −0.1291 *** | 0.0080 | 0.0195 | 0.0073 | 3.0767 *** | 0.0023 |
| (0.0462) | (0.0053) | (0.0825) | (0.0078) | (0.5024) | (0.0086) | |
| rds | 17.4330 *** | 0.4942 * | 2.3231 | 0.6545 * | 39.6800 *** | 0.5860 |
| (3.8322) | (0.2835) | (4.6169) | (0.3841) | (11.5350) | (0.3801) | |
| internet | −0.0022 | −0.0001 | −0.0032 | 0.0007 | 0.0110 | 0.0005 |
| (0.0014) | (0.0004) | (0.0024) | (0.0005) | (0.0084) | (0.0005) | |
| density | −0.0942 *** | −0.0010 | 0.0177 | −0.0031 | 0.0095 | −0.0027 |
| (0.0322) | (0.0008) | (0.0129) | (0.0035) | (0.0467) | (0.0035) | |
| city | Yes | Yes | Yes | Yes | Yes | Yes |
| year | Yes | Yes | Yes | Yes | Yes | Yes |
| city × year | Yes | Yes | Yes | Yes | Yes | Yes |
| N | 3740 | 3740 | 3733 | 3733 | 3741 | 3741 |
| R 2 | 0.703 | 0.007 | 0.827 | 0.075 | 0.916 | 0.073 |
Note: Standard errors in parentheses are clustered at the city level; ***, ** and * denote significance levels at 1%, 5% and 10%, respectively.
Test on relationship between CRE and GTFP under high-quality development.
| (1) | (2) | (3) | (4) | (5) | |
|---|---|---|---|---|---|
| Transportation Cost Optimization | Multi-Modal Transport | Sustainable Development | |||
| Treat × Post × Return | 0.0121 ** | ||||
| (0.0053) | |||||
| Treat × Post × road | 0.0007 ** | ||||
| (0.0003) | |||||
| Treat × Post × air | 0.0002 *** | ||||
| (0.0000) | |||||
| Treat × Post × sea | 0.0012 ** | ||||
| (0.0005) | |||||
| Treat × Post | 0.0087 *** | ||||
| (0.0033) | |||||
| gdp | 0.0010 | 0.0010 | 0.0013 | 0.0014 | 0.0011 |
| (0.0033) | (0.0033) | (0.0033) | (0.0033) | (0.0033) | |
| infra | −0.0023 | −0.0023 | −0.0024 | −0.0021 | −0.0022 |
| (0.0022) | (0.0022) | (0.0022) | (0.0022) | (0.0022) | |
| open | −0.0006 ** | −0.0006 ** | −0.0006 ** | −0.0006 ** | −0.0006 ** |
| (0.0003) | (0.0003) | (0.0003) | (0.0003) | (0.0003) | |
| gov | 0.0088 | 0.0086 | 0.0088 | 0.0083 | 0.0089 |
| (0.0080) | (0.0079) | (0.0080) | (0.0080) | (0.0079) | |
| rds | 0.6831 * | 0.6850 * | 0.6759 * | 0.6633 * | 0.6706 * |
| (0.3817) | (0.3815) | (0.3813) | (0.3802) | (0.3814) | |
| internet | 0.0006 | 0.0006 | 0.0005 | 0.0006 | 0.0006 |
| (0.0005) | (0.0005) | (0.0005) | (0.0005) | (0.0005) | |
| density | −0.0030 | −0.0030 | −0.0026 | −0.0029 | −0.0030 |
| (0.0035) | (0.0035) | (0.0035) | (0.0035) | (0.0035) | |
| city | Yes | Yes | Yes | Yes | Yes |
| year | Yes | Yes | Yes | Yes | Yes |
| city × year | Yes | Yes | Yes | Yes | Yes |
| N | 3741 | 3741 | 3741 | 3741 | 3741 |
| R 2 | 0.074 | 0.074 | 0.073 | 0.074 | 0.074 |
Note: Standard errors in parentheses are clustered at the city level; ***, ** and * denote significance levels at 1%, 5% and 10%, respectively.
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; Songsak Sriboonchitta 3 1 School of Economics, Shandong University of Finance and Economics, Jinan 250014, China;
2 School of Economics, Shandong Normal University, Jinan 250014, China;
3 Faculty of Economics, Chiang Mai University, Chiang Mai 50200, Thailand;