Can high-speed rail reduce environmental pollution? Evidence from China

Journal of Cleaner Production 239 (2019) 118135

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Journal of Cleaner Production journal homepage: www.elsevier.com/locate/jclepro

Can high-speed rail reduce environmental pollution? Evidence from China Xuehui Yang a, b, Shanlang Lin a, *, Yan Li c, Minghua He a, b a

School of Economics and Management, Tongji University, Shanghai, 200092, China School of Business, Jinggangshan University, Ji'an, Jiangxi, 343009, China c Party School, Yongfeng County, Ji'an, Jiangxi, 331500, China b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 28 May 2019 Received in revised form 15 July 2019 Accepted 21 August 2019 Available online 22 August 2019

Using balanced panel data of 285 prefecture-level cities in China from 2003 to 2013, this paper studies the impact of High-speed rail (HSR) on environmental pollution. The results by Difference-in-Difference (DID) method show that HSR significantly reduces environmental pollution by 7.35% in China. In addition, we use Propensity Score Matching and DID (PSM-DID) method, as well as the instrumental variable method to deal with the endogenous problem, and we find that the results remain robust. HSR will not automatically reduce environmental pollution, but through the technical effect, allocation effect and substitution effect, which are the main factors to reduce environmental pollution. In addition, we also make further robustness tests for the conclusions of this paper. Further analysis shows that in cities with larger city scale, higher level of economic development, more abundant human capital or stricter environmental supervision, the pollution reduction effect of HSR is more significant. This paper provides some policy implications for improving city environmental quality and building an environment-friendly society. © 2019 Elsevier Ltd. All rights reserved.

Handling editor: Giovanni Baiocchi Keywords: High-speed rail (HSR) Environmental pollution Innovation effect Allocation effect Substitution effect

1. Introduction Since the reform and opening up in 1978, China's economy has experienced rapid growth, with an average annual GDP growth of 9.52%1 from 1978 to 2018. However, China has been accompanied by serious environmental pollution. In 2017, air quality in 338 cities of China reached the standard, accounting for 29.3%, and failed to meet the standard, accounting for 70.7% (Zhou, 2019). In the 2018 world Environmental Performance Index (EPI),2 China ranks 120th, with 180 countries and regions participating. China's EPI has been in a relatively backward position. It can be seen that China's environmental pollution is still very serious. However, the primary contributor to severe environmental quality is industrial emissions. According to the 2016 China Environmental Statistical Yearbook, industrial SO2 emissions accounted for 83.47% of the national total emissions, industrial NOx emissions

* Corresponding author. 20th Floor, Tongji Building A, Siping Road, 1500, Shanghai, China. E-mail address: [email protected] (S. Lin). 1 The data source: http://data.stats.gov.cn/easyquery.htm?cn¼C01. 2 The data source: http://epi.yale.edu. https://doi.org/10.1016/j.jclepro.2019.118135 0959-6526/© 2019 Elsevier Ltd. All rights reserved.

accounted for 63.77%, industrial smoke emissions accounted for 80.14%, industrial waste water accounts for 27.13%. Although the proportion of industrial wastewater discharge is lower than that of domestic sewage, the proportion of direct discharge of industrial wastewater into the environment is as high as 72.47%. It can be seen that industrial emission reduction is the focus of achieving emission reduction goals. Facing the increasingly severe environmental situation and the key to reduce emissions, the Chinese government has clearly proposed in the 13th Five-Year Plan that by 2020, the water consumption per unit of GDP will be reduced by 23%, energy consumption per unit of GDP will be reduced by 15%, CO2 emissions per unit of GDP will be reduced by 18%, and total SO2 emissions will be reduced by 15%.3 At present, reducing pollution and improving environmental quality has become a basic task of city development and construction. HSR is a transportation tool involving resource utilization and energy consumption, and also an important part of building a resource-saving and environment-friendly society. In recent years, China's HSR has developed rapidly. In April 2007, China's first HSR

3

The data source: http://www.12371.cn/special/sswgh/wen/#yi.

2

X. Yang et al. / Journal of Cleaner Production 239 (2019) 118135

opened. By the end of 2018, China had 29,000 km of HSR in operation, ranking first in the world. The number of prefecture-level cities with HSR has grown from more than 50 in 2007 to more than 210 in 2018, accounting for more than 60 percent of China's prefecture-level cities. The distribution of China's HSR cities is shown in Fig. 1. Under the background of rapid development of China's HSR and increasingly severe environmental constraints, it is of great theoretical value and practical significance to study the impact of HSR on environmental pollution. However, is there a correlation between HSR and environmental pollution? We have drawn a graph of the relationship between HSR and industrial enterprises' emission per unit of GDP, as shown in Fig. 2. It can be seen that from 2003 to 2013, the number of cities with HSR gradually increased, while the emission per unit of industrial GDP declined as a whole. Of course, for further determination of the relationship between HSR and environmental pollution, mechanism analysis and strict econometric test are required. The research perspective of this paper is different from the previous research. From a novel perspective we reexamine the impact of HSR on environmental pollution. It is found that HSR can help reduce environmental pollution through causing technological innovation effect, resource allocation effect and industrial structure substitution effect. Specifically, the possible contributions of this paper are as follows. First, it could be the first time to study the impact of HSR on environmental pollution from the perspective of innovation effect, allocation effect and substitution effect, which is very rare in previous studies, enriching relevant literature on environmental pollution related to HSR and other means of transportation. Second, DID was used to evaluate the impact of HSR on environmental pollution, and then the PSM-DID method was used for further test, enriching the literature on empirical measurement. Third, it provides a new reference for the construction of an environment-friendly society from the aspects of city innovative development, optimization of resource allocation efficiency and adjustment of industrial structure. The rest of this paper is arranged as follows. The second part is a brief review of the literature, reviewed the previous research focus, pointed out the lack of research. The third part analyzes the mechanism and puts forward the research hypothesis. The fourth part is the research design, proposes the interpretation framework, sets up DID and PSM-DID regression model, and briefly explains the data. The fifth part is the empirical results and analysis of DID and PSM-DID, as well as the endogenous treatment of instrumental variable method. In addition, the mediating mechanism of HSR affecting environmental pollution are tested. The sixth part makes

Fig. 1. Distribution map of cities with or without HSR.

some further robustness test. The seventh part has carried on the city characteristic heterogeneity analysis. The last part is brief conclusions and discussions. 2. Brief review of the literature This paper mainly reviews two kinds of literature. One is the existing economic literature on environmental pollution. The other category is research on HSR and environmental pollution. A lot of economic literature mainly studies environmental pollution problems from the following aspects. Study the relationship between environmental pollution and economic development. For example, Grossman et al. (1991) first proposed the hypothesis of Environmental Kuznets Curve (EKC Curve), and believed that there was an “inverted U-shaped” relationship between pollution emission and economic development. The relationship between industrial agglomeration and environmental pollution is studied from the perspective of industrial agglomeration. But the conclusions are inconsistent. DE Leeuw et al. (2001) analyzed the data of 200 EU cities and believed that industrial agglomeration aggravated air pollution. Verhoef and Nijkamp (2002) adopted the spatial equilibrium model and found that the industrial distribution aggravated the environmental pollution in the agglomeration area. However, Zeng and Zhao (2009) believed that industrial agglomeration could reduce environmental pollution by building a spatial economic growth model. From the perspective of urbanization or urban scale, scholars have studied the relationship between urbanization and environmental pollution, but they came to a different conclusion. Henderson (1974) believed that with the increase of city population density, external uneconomy, traffic congestion and environmental pollution become prominent. Sadorsky (2014) also found a positive correlation between urbanization and carbon emissions from 39 years' data of 16 emerging countries. Satterthwaite (1997) studied the environmental problems in developing countries such as Asia, Africa and Latin America, and found that environmental pollution presented a mitigation trend in large cities, while environmental problems were most serious in small cities. Chen et al. (2008) found that compact cities are conducive to pollution reduction and emission reduction through research on data of 45 core cities in China. From the perspectives of industrial structure, trade and corruption, some literatures studied the impact of environmental pollution. Vukina et al. (1999) and Dinda (2004) argue that as the industrial structure changes from agriculture to industry, the pollution level gradually increases, and when the “post-industrialization” period enters, the energy-intensive industry gradually transforms to the knowledge-intensive industry and service industry, the environmental pollution level decreases. Grossman and Krueger (1991) put forward the “scale effect”, “technology effect” and “composition effect” of the environment, believing that free trade may increase the total amount of pollution emissions in the process of promoting the expansion of economic activity scale, or it may be conducive to pollution reduction because of the technological progress and structural change in favor of “clean” production brought by trade. Cole (2007) believes that corruption causes the increase of pollution level, possibly because institutional factors affect the government's efforts in environmental protection. Study environmental pollution from the perspective of foreign direct investment (FDI). The conclusions are not consistent. One view is the “pollution paradise” hypothesis (Dean, 2002), which holds that in order to develop economy and to attract foreign investment, developing countries relax environmental control requirements, and FDI worsens regional environmental quality, thus becoming the “pollution paradise” of developed countries. The

X. Yang et al. / Journal of Cleaner Production 239 (2019) 118135

3

140

Emission per unit of GDP

0.016 100

0.014 85

0.012 0.01 0.008

104

110

57

121 120 100 80

66

60

0.006

40

0.004

20

0.002 0 0 0 0 0 2003 2004 2005 2006 2007

2008

2009

2010

2011

2012

0 2013

Number of cities with HSR

0.02 0.018

Year Year Industrial wastewater discharge (1,000 tons/10,000 yuan) Industrial SO2 emissions (tons / 10,000 yuan) Industrial smoke emission (tons/10,000 yuan) Number of cities with HSR

Fig. 2. The number of cities with HSR and industrial pollutant emissions per unit of GDP.

other is the “spillover effect” hypothesis (Liang, 2006), in which foreign businessmen bring in clean production technology,thus contributing to the environmental improvement. In addition, some scholars have summarized the mechanism of environmental pollution. Brock and Taylor (2004) divided city environmental pollution into “scale effect”, “technology effect” and “structure effect”, which were considered as the three factors related to environmental pollution. Crenshaw and Jenkins (1996), Gouldson and Murphy (1997), Mol and Spaargaren (2000) believe that environmental pollution can be effectively reduced through “technological innovation”, “city agglomeration” and “industrial structure transformation". Another category of literature closely related to our research is the impact of HSR on environmental pollution. The existing literature rarely studies this topic, mainly from three aspects. First, HSR is studied from the perspective of high efficiency and low carbon. On a per-passenger kilometer basis, the energy consumption ratio for HSR, cars and aircraft is 1:2.4:2.8 (Gines et al., 2012). HSR has the lowest energy consumption. HSR, cars and planes emit 4 kg, 14 kg and 17 kg of carbon dioxide per 100 people per km,4 respectively. HSR can produce the lowest carbon dioxide emissions, thus contributing to environmental protection (Dobruszkes, 2011). Song et al. (2016) studied the impact of railway transportation on environmental efficiency and believed that railway is the most effective and environmentally friendly mode of transportation. Therefore, it was suggested that the government should shift more investment from road to develop a more complex HSR network. Barbosa (2018) believes that HSR has the characteristics of high mobility, reliability, safety and passenger comfort, as well as large volume, reduced congestion and reduced environmental costs. Second, evaluate HSR from the perspective of nonenvironmental. For example, materials for HSR construction, such

4

The data source: www.uic.org/highspeed.

as concrete and steel, are produced from industries with high energy consumption and high pollution. In the production process, a large amount of waste water, waste gas and solid waste are generated, which will temporarily damage the ecology along the railway and cause vibration, noise pollution and electromagnetic radiation interference to nearby residents. Nepal (2013) studied the environmental impact of HSR in Australia, believing that the construction of HSR and its system components will bring about energy consumption, greenhouse gas emissions, air pollution and land occupation. However, Nepal (2013) believes that the indirect environmental benefits of HSR may outweigh the negative environmental impacts. Third, we found that many local cities in China have established HSR new zones, hoping to drive local economic development with the opening of HSR. In the industrial planning of these HSR new zones, local governments pay more attention to the development of green and environment-friendly technologies and healthy industries. Lu (2016) investigated and analyzed the industrial structure of the HSR new zones in typical Chinese cities, and found that the industrial structure was dominated by the tertiary industry such as commercial service industry, while the secondary industry with high pollution accounted for less. This supports the argument that the opening of HSR is beneficial to environmental protection. To sum up, there are many factors influencing environmental pollution and they are also complicated. We argues that the influencing factors of environmental pollution are mainly manifested in the use of production technologies, the improvement of resource utilization efficiency and the adjustment and substitution of industrial structure, namely, the effect of technological innovation, the effect of resource allocation and the effect of industrial structure substitution. In addition, previous studies mainly focus on the direct effects of HSR on the environment, but ignore the more important indirect effects of HSR. Further, this paper finds that HSR has the mechanism characteristics of reducing environmental pollution, that is, HSR reduces environmental pollution through inducing innovation effect, allocation effect and substitution effect.

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X. Yang et al. / Journal of Cleaner Production 239 (2019) 118135

This paper takes these as the starting point to study the impact of HSR on environmental pollution. The mechanism analysis of the three effects will be further discussed in the next part of this paper. 3. Mechanism analysis Different from previous studies, this paper studies the impact of HSR on environmental pollution from the perspective of indirect effect. The study shows that HSR reduces the emission of pollutants in enterprises through three major effects – innovation effect, allocation effect and substitution effect – so as to reduce environmental pollution. The detailed analysis of the three effects is as follows. First, HSR can reduce enterprises' pollution emissions through technological innovation effect. On the one hand, HSR improves accessibility, greatly reduces the time and space distance between different regions, and promotes face-to-face communication between people and the overflow of knowledge and technology. Because of the strong localization of knowledge spillovers (Glaeser and Mare, 2001), cities with high human capital tend to have higher productivity (Moretti, 2004). Although the rapid development of communication network has reduced the cost of long-distance communication, it can't completely replace face-to-face communication between people (Catalini et al., 2016). Therefore, HSR makes the spillover of knowledge and technology over long distances a reality. The HSR provides convenient conditions for talent and technology cooperation and exchange among enterprises, scientific research institutes and institutions in different regions. HSR speeds up the flow of people and the increase of passenger flow (Lin, 2017). Convenient communication and cooperation are conducive to the innovation activities of the society and enterprises (Acemoglu et al., 2016, Wang et al., 2018). Okabe (1976) investigated the commercial activities of the cities along the Shinkansen line in Japan and found that the number of face-to-face knowledge and technology exchange activities such as business service, R&D and education in the cities along the Shinkansen line increased significantly after the connection of the HSR. Dong et al. (2018) found that HSR has a significant effect on academic innovation output. The mechanism is that HSR shorten travel time, knowledge and ideas can travel farther and faster, highly skilled people can benefit from face-toface communication, and the productivity of co-authors will increase, thus promoting the output of academic innovation. On the other hand, technological innovation and improvement are conducive to improving the efficiency of resource utilization, reducing pollution emissions and reducing environmental pollution. Grossman and Krueger (1991) believe that technological progress can theoretically produce positive effects on the environment. Levinson (2009) studied the impact of technological progress in American manufacturing on the environment, and found that technological progress led to a 39% reduction of SO2 emissions in the United States between 1987 and 2001. Second, HSR can reduce enterprises' pollution emissions through resource allocation effect. The HSR has shortened the travel time. On the one hand, it enables enterprises to get closer to the sales market more quickly and conveniently, understand the market demand, and produce marketable products, which is conducive to improving the efficiency of resource utilization and allocation, reducing resource waste, and thus reducing pollutant emission. Bosetti et al. (2006) believed that technological progress could achieve energy conservation and emission reduction. Hsieh and Klenow (2009) believed that resource mismatch mainly reflected the utilization efficiency of resources. The more serious the resource mismatch is, the lower the utilization efficiency of resources will be, and the more negative

impact it will have on enterprise productivity. On the other hand, HSR makes the market of production factors such as labor force, resources and technology and enterprises more accessible to each other. Expand the scope of the choice of human capital, technology and material capital. It is conducive to obtaining various highquality and cheap production factors, optimizing the investment mix, improving the efficiency of factor allocation, and reducing the pollution emission of enterprises. However, the mismatch of enterprise resources would have an important negative impact on enterprise efficiency, resulting in higher energy consumption per unit of output and aggravating enterprise environmental pollution (Ryzhenkov, 2016). Third, HSR reduces enterprise pollution through the substitution effect of industrial structure. The HSR improves the degree of service industry agglomeration in cities (Pol, 2003) and promotes the development of emerging industries. HSR has reduced the proportion of the secondary industry, which is the main source of environmental pollution, and increased the proportion of the tertiary industry with low pollution. Through the adjustment, upgrading and replacement of industrial structure, the pollutant emission of enterprises has been reduced. Chen and Hall (2011) believe that HSR brings unprecedented space-time effect, which is conducive to face-to-face communication, knowledge creation and business communication, and promotes the development of knowledge-based industry and industrial structure transformation. In the process of resource mobility among industries, improving the utilization rate of resources, deepening the degree of resource recycling, and reducing the consumption of natural resources per unit of output can reduce production pollution. This is the reason why industrial structure is closely related to environmental pollution (Pasche, 2002; Lindmark, 2002). Pol (2003) pointed out that the HSR reduced transportation costs, extended travel distance and promoted the development of city service industry. Cole and Elliott (2003) defined the industrial structure upgrade as the adjustment from the polluting industry to the clean industry, and found that this adjustment is helpful to reduce the pollution emission per unit of GDP. Liu and Li (2017) found that the opening of HSR is conducive to the adjustment of industrial structure. Compared with non-HSR cities, the proportion of the secondary industry in HSR cities decreased by nearly 10%, while that of the tertiary industry increased by nearly 9%, which proves the substitution effect of HSR in the industrial structure between the secondary industry and the tertiary industry. To sum up, the following hypotheses are proposed in this paper. The three effects of HSR, namely, innovation effect, allocation effect and substitution effect, are proposed. The three effects are adopted to reduce the pollutant emission of enterprises and thus reduce environmental pollution. Hypothesis 1. HSR speeds up information, knowledge and technology spillover, which is conducive to enterprise innovation and technology optimization and upgrading, and changes the production mode through the effect of technological innovation to improve production efficiency and reduce city environmental pollution. Hypothesis 2. HSR improves market competition, promotes effective utilization of production factors, improves resource allocation efficiency and utilization efficiency, and reduces pollutant emission and environmental pollution through resource allocation effect. Hypothesis 3. HSR accelerates the flow of production factors, reduces the proportion of high pollution in the secondary industry, and increases the proportion of low pollution in the tertiary

X. Yang et al. / Journal of Cleaner Production 239 (2019) 118135

industry. Through the substitution effect of industrial structure, pollutant emissions of enterprises are reduced, and environmental pollution is reduced.

5

Therefore, the baseline regression model built in this paper based on DID method is

Pollutioncit ¼ d0 þ d1 HSRct þ dr Xit þ Zc þ yeart þ εcit 4. Study design 4.1. Explain the framework As mentioned above, HSR can shorten travel time, promote personnel exchanges, speed up information and knowledge spillover, and improve accessibility, which is conducive to technology spillover, resource allocation efficiency and industrial restructuring. HSR will lead to three effects – innovation effect, allocation effect and substitution effect, which will ultimately reduce pollutant emissions and thus reduce environmental pollution. See Fig. 3 for the detailed explanation frame diagram. 4.2. Model This paper regards HSR as a quasi-natural experiment. In the research on policy effect, DID method can effectively analyze the “policy effect”, so it is widely used in the evaluation of policy effect. To adopt this method, it is necessary to construct grouping variable du and time dummy variable dt. The data range of this paper is from 2003 to 2013. Since 2007, Chinese cities have gradually opened HSR. In this paper, cities with HSR service from 2007 to 2013 were set as the treatment group, and cities without HSR service from 2007 to 2013 were set as the control group. If they belong to the treatment group, du ¼ 1; otherwise, du ¼ 0. If a city does not open HSR in a certain year, then dt ¼ 0, otherwise dt ¼ 1. According to DID method, the interaction term dudt is the core of attention, which describes the “policy treatment effect” of HSR. Define the HSR ≡ dudt.

However, it is noted that the connection of HSR may not be a completely random event, because the construction of HSR was initially mainly to connect large and medium-sized cities. At the same time, polluting enterprises may choose sites in cities with or without HSR based on their own conditions. Hence, there may be sample selection bias. In order to solve the problem of sample selection bias, Propensity Score Matching (PSM) was used in this study. DID can better deal with endogenous problems. Therefore, PSM-DID was used in this paper for further estimation test, in order to obtain more robust conclusions. The specific steps are as follows. First, the PSM method was used to find the control group with the closest characteristics to the treatment group from the control group. Second, DID regression was performed between the treatment group and the control group after matching. The PSM-DID regression model is as follows.

PollutionPSM cit ¼ d0 þ d1 HSRct þ dr Xit þ Zc þ yeart þ εcit

Promote the communication

Accelerate technology overflow

Innovation effect

(2)

In the regression equations (1) and (2), Pollutioncit , PollutionPSM cit respectively represent the environmental pollution indicators without PSM and with PSM, and adopt the SO2 emission per unit output of the region as the proxy variable of environmental pollution. The HSR in the model represents dudt; X is a set of control variables; Z represents the regional fixed effect, year represents the time fixed effect, εcit is the error term and d0 , d1 , dr is the parameter to be estimated. Control variable selection and indicator measurement are described below. Population density (ln_popd). Considering the large difference in

HSR

Compress spacetime distance

(1)

Accelerate the information and knowledge overflow

Improve the efficiency of the allocation of resource

Allocation effect

Reduce emissions of pollutants

Reduce environmental pollution Fig. 3. Diagram of interpretation.

Improve accessibility

Adjust industrial structure

Substitution effect

6

X. Yang et al. / Journal of Cleaner Production 239 (2019) 118135

administrative area and population size among cities, the absolute number of population adopted is not scientifically comparable. Therefore, the population density, that is, the number of people per unit area in an administrative region, is adopted to represent the impact of population on environmental pollution, and the population density is logarithmically processed. Industrial structure (ln_erch_gdp). The secondary industry is the main source of environmental pollution, and the different industrial structures in different cities have different impacts on environmental pollution. This paper uses the proportion of the secondary industry in GDP to reflect the impact of the industrial structure on environmental pollution. Openness level (ln_fdi_gdp). The degree of opening to the outside world represented by foreign direct investment (FDI) is a basic factor to be considered in environmental pollution. Previous studies have shown that there are two kinds of views on the impact of FDI on environmental pollution. Keller and Levinson (2002) argued that in order to develop economy and relax environmental control, developing countries attract foreign investment, which worsens regional environmental quality and thus becomes a “pollution paradise” of developed countries (List and Co, 2000). Another view holds that new technologies provided by FDI are conducive to improving environmental quality and have spillover effects (Wang and Jin, 2002). This paper uses the proportion of FDI in GDP to reflect the impact of opening-up on environmental pollution. Economic level (ln_rjgdp). The level of economic development is expressed as the logarithm of GDP per capita. Existing research shows that there are many relations between environmental pollution and economic development level. The classical Environmental Kuznets Curve (EKC) hypothesis holds that environmental quality and economic growth show an inverted U-shaped trend. However, existing studies have shown that U, N and inverted N curves may be present (Grossman and Krueger, 1995). Shao et al.(2011) and other literatures were used to further investigate the U-shaped and N-shaped relationship between environmental pollution and economic growth by using the primary, secondary and tertiary terms of per capita GDP. Using Shao's method (Shao et al., 2011), we considered quadratic and tertiary terms of per capita GDP (ln_rjgdp_2, ln_rjgdp_3) in the regression model. Scientific and technological (ln_sci). Scientific and technological level is undoubtedly an important means of environmental governance. The higher the scientific and technological level is, the better the resource utilization efficiency will be and the lower the pollution discharge will be. This paper uses the logarithm of the number of people employed in city science and technology to represent the level of science and technology. Education conditions (ln_edu). Education is the foundation of economic development and technological innovation, which affects the national quality level. This paper uses the logarithm of the number of employees in the education industry to measure their education level. Human capital (ln_hum). The treatment of environmental pollution cannot be separated from the participation of workers with higher education level. It is easier for highly educated workers or highly skilled workers to master advanced technology, which is more conducive to the emission reduction and treatment of environmental pollution. The logarithm of the number of college students was used to represent the impact of human capital on environmental pollution. Traffic conditions (ln_roadp, ln_airp, ln_waterp). This paper adopts the logarithm of city road passenger volume, air passenger volume and waterway passenger volume to reflect the city traffic situation. Information development (ln_internet). The monitoring and

control of environmental pollution cannot be separated from the supporting role of information technology based on the development of the Internet. This paper uses the logarithm of the number of Internet users as a proxy variable for the level of information development. In addition, in the part of mechanism testing and analysis, the number of patents granted per capita is used to reflect the innovation effect, and the total factor productivity measured by Olley and Pakes (1996) is used to represent the allocation effect, and the ratio of the output value of the tertiary industry to that of the secondary industry is used to represent the substitution effect. 4.3. Data The data used in this paper were mainly from the Chinese Cities Statistical Yearbook from 2003 to 2013. The data missing in some years were supplemented by the method of average growth rate, and the balance panel data of 3135 samples of 285 cities above prefecture-level in 11 years from 2003 to 2013 were finally obtained. HSR data are mainly from China Railway Yearbook (2003e2013), 12306.com5 website, etc., and the opening time of HSR in each city is collected and sorted by hand. The number of patents granted comes from the National Intellectual Property Administration, PRC. Descriptive statistics of the main variables involved in the research analysis are shown in Table 1. 5. The empirical analysis 5.1. Analysis of benchmark regression results based on DID method In this paper, DID method was used to evaluate the impact of HSR on environmental pollution. In Table 2, model (1)e(5) carried out stepwise regression by gradually increasing control variables, and the explained variable was sulfur dioxide (SO2) emission per unit output value. All the estimated results show that with or without the addition of control variables, the coefficient of HSR is significantly negative, indicating that the HSR has significantly reduced environmental pollution by 7.35% in the model (5) of Table 2. 5.2. Parallel trend test of DID method The presupposition of DID estimation is that the development trend of the treatment group and the control group is parallel, otherwise the estimation will have bias. In order to verify the parallel trend, refer to the method of Lin (2017) and add the leads and lags of the initial connection dummy of HSR. The regression equation is as follows.

Pollutioncit ¼ d0 þ

3 X

dm FirstHSRc;tm þ

m¼1

3 X

dn FirstHSRc;tþn

n¼0

þ dr Xit þ Zc þ yeart þ εcit (3) where FirstHSR is a dummy variable indicating whether a city c is first connected to the HSR network in year t. It switches to 1 only if the HSR line connecting city c is opened in year t. FirstHSRc;tm is its m-th lead, and FirstHSRc;tþn is its n-th lag. Controlling for leads allows me to examine the pre-HSR effects of future railways as a

5 We have some data from 12306.com, the official website of China Railway Corporation, which publishes information about the opening time and cities of high-speed railway lines.

X. Yang et al. / Journal of Cleaner Production 239 (2019) 118135

7

Table 1 Descriptive statistics for major variables. Variables (“ln” stands for logarithm) Explained variables ln_gyfs ln_gyeyhl ln_gyyc ln_rjgyfs ln_rjgyeyhl ln_rjgyyc ln_gyfs_gdp ln_gyeyhl_gdp ln_gyyc_gdp Explanatory variables HSR ln_popd ln_erch_gdp ln_fdi_gdp ln_rjgdp ln_sci ln_edu ln_hum ln_roadp ln_airp ln_waterp ln_internet Intervening variables rjzhuanli ln_op tidxy

Definitions

Obs.

Mean

S.D.

Min

Max

Wastewater emissions (10,000 tons) SO2 emissions(tons) Smoke emission (tons) Per capita wastewater discharge (tons/people) SO2 emissions Per capita (tons/10,000 people) Smoke emission per capita (tons/10,000 people) Wastewater emissions per unit of GDP (tons/yuan) SO2 emissions per unit of GDP (tons/10,000 yuan) Smoke emission per unit of GDP (tons/10,000 yuan)

3135 3135 3135 3135 3135 3135 3135 3135 3135

8.4146 10.5652 9.7249 2.5723 4.7228 3.8825 7.3156 5.1650 6.0053

1.0935 1.1577 1.1298 0.9606 1.1703 1.1492 0.9174 1.1664 1.2459

2.8332 1.0986 3.5264 1.1665 2.9566 1.8918 11.5166 14.0346 12.1682

11.4215 13.4345 15.4582 6.2783 7.9362 9.4056 3.5431 1.4368 0.8597

HSR dummy variable Population density(10,000 people/km2 ) Industrial structure(%) Degree of Openness(%) Economic level(yuan/people) Scientific and technological (10,000 people) Education conditions (10,000 people) Human capital (people) Road traffic conditions (10,000 people) Air traffic conditions (10,000 people) Waterway traffic conditions (10,000 people) Information development (households)

3135 3135 3135 3135 3135 3135 3135 3135 3135 3135 3135 3135

0.2108 5.7101 3.8650 6.6588 9.8878 1.0262 1.3604 9.8720 8.5372 5.3305 2.0517 12.1631

0.4080 0.9095 0.2497 1.8817 0.8591 1.1422 0.7283 2.2039 0.9679 6.4731 2.2091 1.2268

0.0000 1.5476 2.7537 15.8971 7.5450 4.6052 2.5257 0.0000 4.4067 0.0000 0.0000 5.4661

1.0000 7.8867 4.5105 3.0922 13.0541 4.0885 3.8308 13.7984 12.5657 19.4957 7.9230 17.7617

Innovation effect (piece/10,000 people) Allocation effect Substitution effect

3135 3135 3135

4.0641 3.1206 1.0982

13.7179 0.4938 0.1393

0.0039 0.7102 0.7099

226.5572 5.0741 2.0985

Table 2 The baseline regression results of DID method. Variables Pollution HSR

(1)

(2)

(3)

(4)

(5)

0.1042*** (0.0353)

0.0897** (0.0356) 0.0211 (0.0748) 0.3196*** (0.1090) 0.0008 (0.0114)

0.0697** (0.0351) 0.0505 (0.0728) 0.4871*** (0.1281) 0.0041 (0.0111) 5.2260* (2.6745) 0.5478** (0.2666) 0.0156* (0.0089)

0.0722** (0.0352) 0.0539 (0.0727) 0.5575*** (0.1293) 0.0051 (0.0111) 5.2573* (2.7102) 0.5479** (0.2707) 0.0155* (0.0090) 0.0695* (0.0374) 0.3213*** (0.1046) 0.0226* (0.0119)

YES YES 3135 0.464

YES YES 3135 0.465

YES YES 3135 0.497

YES YES 3135 0.499

0.0735** (0.0353) 0.0508 (0.0729) 0.5665*** (0.1299) 0.0047 (0.0111) 5.4283** (2.7404) 0.5654** (0.2737) 0.0161* (0.0091) 0.0709* (0.0375) 0.3209*** (0.1056) 0.0227* (0.0120) 0.0054 (0.0248) 0.0017 (0.0052) 0.0128 (0.0137) 0.0086 (0.0245) YES YES 3135 0.499

ln_popd ln_erch_gdp ln_fdi_gdp ln_rjgdp ln_rjgdp_2 ln_rjgdp_3 ln_sci ln_edu ln_hum ln_roadp ln_airp ln_waterp ln_internet Time fixed City fixed N adj. R2

Note: *, **, and *** represent 10%, 5%, and 1% levels of statistical significance, respectively. Standard errors are reported in parentheses. Due to space limitation, constant term coefficient and standard error are not reported.

placebo test and helps to disentangle anticipatory effects from actual connection effects. The leads are used to test whether there is an expected reduction in environmental pollution before the opening of the HSR, which is equivalent to the placebo test. The lags are used to test the follow-up effect of the HSR on environmental pollution after its opening. The test coefficients of parallel trends are shown in Fig. 4 t-1, t-2 and t-3 represent the first year, 2 years and 3 years before the first HSR was launched. t represents the year when the first HSR was connected. t þ1, tþ2 and tþ3 represent the first year, 2 years and 3 years after the first HSR was connected. It can be seen from Fig. 4 that the regression coefficient of HSR was not significant in the three years before the first HSR was opened. After the first opening of the HSR, the regression coefficient of the HSR is significantly negative at the 10% confidence level in the first year, and is significantly negative at the 5% confidence

Fig. 4. Parallel trend test coefficient diagram.

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X. Yang et al. / Journal of Cleaner Production 239 (2019) 118135

level in the second and third years, indicating that the DID parallel trend assumption is satisfied. 5.3. Further verification based on PSM-DID method In order to deal with the sample selection bias caused by the non-randomness of HSR opening, PSM-DID method is further applied in this paper for further robustness test. The PSM method was used to find the control group for the treatment group. The specific steps are as follows. First, the treatment group and the control group in each of the 7 years from 2007 to 2013 were obtained. The samples were matched year by year. Second, grouping variables and characteristic variables are used as matching data. Third, use logit model to estimate propensity score. Fourth, due to the small amount of data, “1 nearest neighbor matching” is adopted, and juxtaposition is not allowed during matching. The matching characteristic variables are as follows: population density, industrial structure, opening to the outside world, economic growth, science and technology level, education level, human capital, road development status, aviation development status, waterway development status and information development level. In order to ensure the reliability of matching results, the common value test and matching balance test need to be carried out for 7 years. Taking 2007 as an example, as shown in Fig. 5, the core density map of propensity score before and after matching was compared. It can be seen that the propensity score of samples in the treatment group and the control group overlapped, which was consistent with the common tendency hypothesis. After matching, the distribution trend of the two groups of samples tends to be consistent. Judging whether the matching process is effective needs to meet two conditions. First, the changes in t statistics and characteristic variables no longer show significant differences after matching. Second, the absolute value of the standard deviation should be less than 20% (Rosenbaum and Rubin, 1985). Taking 2007 as an example, it can be seen from Table 3 that the standard deviation after matching is within 20%, and the change of t statistic and the concomitant probability of t-test indicate that the characteristic variables of the two groups of samples after matching are no longer significantly different. Therefore, in 7 years, the two groups of variables were processed by PSM, and the matching met the balance test.

(a) Before PSM

Table 4 reports DID regression results after PSM treatment. Model (1) only examines the impact of HSR opening. Model (2) controls the time effect on the basis of model (1). Model (3) adds control variables on the basis of model (1). In model (4), control variables are added, and time fixed effect and city fixed effect are controlled at the same time. From model (1) to model (4) of Table 4, it can be seen that there is no substantial change in the sign and significance of HSR, both of which show that the HSR reduces environmental pollution by 9.26%, which is basically consistent with the results of the benchmark regression. The results of PSM-DID estimation are not significantly different from the previous DID estimation results, which supports the empirical conclusion of this paper, namely, the pollution reduction effect of HSR is very significant. 5.4. Endogenous treatment According to the previous analysis, HSR has an inhibitory effect on environmental pollution, and there are many factors affecting environmental pollution, which may lead to endogenous problems due to the omission of other factors that cannot be observed. Therefore, this paper adopts instrumental variable method for endogenous treatment, in order to get a more robust estimation. Instrumental variable method is a common method to identify the impact of traffic infrastructure. Literature research and analysis show that the instrumental variables of transportation infrastructure are as follows. First, climatic characteristics, such as the number of landslides (Zhang and Zhang et al., 2018). Second, geographic information, such as geographic slope (Duflo and Pande, 2007). Third, historical information, such as post in ancient Ming dynasty of China (Zhang and Wang et al., 2018), passenger volume in historical year (Liu and Li, 2017). Fourth, planning plans and planning texts, such as historical plans (Duranton and Turner, 2011, 2012) or historical planning information (Michaels, 2008). This paper uses geographic information – average altitude of cities – to construct the instrumental variable of HSR. Qualified instrumental variables need to meet the two conditions of correlation and exogenous. First, correlation. The average altitude of a city belongs to geographical information, which generally does not change with time. The higher the altitude is, the more unfavorable

(b)

Fig. 5. Comparison of kernel density distribution before and after PSM (taking 2007 as an example).

X. Yang et al. / Journal of Cleaner Production 239 (2019) 118135

9

Table 3 Balance test results of characteristic variables (taking 2007 as an example). Variable

ln_popd ln_erch_gdp ln_fdi_gdp ln_rjgdp ln_sci ln_edu ln_hum ln_roadp ln_airp ln_waterp ln_internet

Unmatched Matched

Mean Treated

Control

%bias

U M U M U M U M U M U M U M U M U M U M U M

6.1185 5.9787 3.8977 3.8890 5.9233 6.2184 9.9721 9.6933 0.7133 1.0662 1.5878 1.4478 10.5920 10.1850 8.7815 8.5472 5.4911 3.9866 2.3566 2.1060 12.3940 11.9810

5.3086 5.9538 3.8278 3.9123 7.2086 6.1556 9.5386 9.7936 1.4642 1.1500 1.1243 1.2903 9.1375 9.9070 8.1164 8.4044 4.7226 3.9125 1.6766 2.4696 11.4900 11.8790

Table 4 PSM-DID regression results. Variable Pollution HSR Variable control Time fixed City fixed N adj. R2

(1)

(2)

(3)

(4)

0.9896*** (0.0421) NO NO YES 2829 0.086

0.0728* (0.0389) NO YES YES 2829 0.467

0.1499*** (0.0380) YES NO YES 2829 0.469

0.0926** (0.0382) YES YES YES 2829 0.501

Note: *, **, and *** represent 10%, 5%, and 1% levels of statistical significance, respectively. Standard errors are reported in parentheses. Due to space limitation, constant term coefficient and standard error are not reported.

it is for the construction of HSR. The altitude is negatively correlated with the opening of HSR, meeting the correlation condition. Second, exogenous. Altitude is an exogenous geographical variable and generally does not directly affect environmental pollution. Therefore, it is appropriate to adopt the average altitude as the instrumental variable of HSR. Considering that the altitude does not change with time, this paper selects the last year of the sample, namely 2013, for regression of instrumental variables.

Table 5 Regression results of instrumental variables. Variable

(1)

First stage

Pollution

HSR 0.0002*** (0.0001)

high HSR Variable control N adj. R2 F statistic

(2)

2SLS

4.2196*** (1.2079) YES 285 1.499

YES 285 0.305 16.332

Note: *, **, and *** represent 10%, 5%, and 1% levels of statistical significance, respectively. Standard errors are reported in parentheses. Due to space limitation, constant term coefficient and standard error are not reported.

99.8 3.1 28 9.4 79.3 3.9 62.2 14.4 71.6 8 68.6 23.3 69.4 13.2 80.7 17.3 12 1.2 31.1 16.6 97.7 11

% reduct |bias|

96.9 66.5 95.1 76.9 88.8 66.0 80.9 78.5 90.4 46.5 88.7

t-test

p>|t|

8.38 0.28 2.35 0.76 6.64 0.42 5.25 1.23 6.06 0.67 5.79 1.93 5.83 1.06 6.80 1.44 1.02 0.09 2.63 1.23 8.28 1.02

0.000 0.779 0.019 0.447 0.000 0.677 0.000 0.220 0.000 0.502 0.000 0.055 0.000 0.290 0.000 0.151 0.309 0.928 0.009 0.222 0.000 0.308

Table 5 presents the regression results of instrumental variables, and the first column of Table 5 shows that the regression results of instrumental variables are consistent with the baseline regression results, that is, the opening of HSR significantly reduces environmental pollution. Column 2 is the one-stage regression results of the HSR on the altitude, and the results show that the HSR was negatively correlated with the altitude. Kleibergen-paap rank Wald F statistic value was 16.332, over 10, indicating the hypothesis of the weak tool variable rejection. Hence, the instrumental variable is valid.

5.5. Test on the mediating mechanism of HSR affecting environmental pollution Through DID regression and further tests based on PSM-DID, it is shown that HSR has emission reduction effect on environmental pollution. So what is the mechanism of HSR behind the reduction of environmental pollution? As explained in the theoretical part above, HSR leads to innovation effect, allocation effect and substitution effect, and HSR reduces environmental pollution through these three effects. To verify these three effects, refer to Baron and Kenny (1986). For each effect test, a three-step method was used to verify the mechanism. The first step is to make a regression between HSR and environmental pollution level. If the coefficient of HSR is significant, it indicates that HSR has an impact on environmental pollution. In the second step, the HSR and the three effects are respectively regression. If the coefficient of HSR is significant, it indicates that the HSR brings about the three effects. In the third step, both HSR and each effect are regression with the level of environmental pollution. If the coefficient of HSR becomes insignificant, it indicates that there is a complete mediating effect. If the coefficient of HSR is still significant, it indicates that the mediating effect is significant. The model for verifying the mediating effect mechanism of the above three-step method is as follows. Step 1: examine the impact of HSR on environmental pollution:

Pollutioncit ¼ d0 þ d1 HSRct þ dr Xit þ Zc þ yeart þ εcit

(4)

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X. Yang et al. / Journal of Cleaner Production 239 (2019) 118135

Step 2: test the impact of HSR on the three effects:

6.1. Placebo test

Patentcit ðln opcit ; tidxycit Þ ¼ d0 þ d1 HSRct þ dr Xit þ Zc þ yeart þ εcit (5) Step 3: put the HSR and the three effects into the regression equation respectively:

Pollutioncit ¼ d0 þ d1 HSRct þ d2 Patentcit ðln opcit ; tidxycit Þ þ dr Xit þ Zc þ yeart þ εcit (6) Where, as in the baseline regression, “Pollution” represents environmental pollution and is replaced by SO2 emissions per unit of output value. “Patent” represents the innovation effect, which is replaced by the number of per capita patent authorizations in cities. “Ln_op” represents the allocation effect and is replaced by the total factor productivity measured by the OP method. “Tidxy” shows the substitution effect by the ratio of the output value of the tertiary industry to that of the secondary industry. It can be seen from Table 6 model (2) that HSR has a significant negative impact on environmental pollution, and the first step of mechanism test is verified. From Table 6 model (1), (4) and (6), it can be seen that the regression coefficients of HSR are all significantly positive, indicating that HSR significantly promotes the three effects. As can be seen from Table 6 models (3), (5) and (7), the innovation effect, allocation effect and substitution effect all significantly inhibit environmental pollution, and the coefficient of HSR is still significantly negative, so the mechanism test is verified in the third step. At this point, the mechanism of HSR to reduce environmental pollution has been fully verified.

Placebo test was performed by counterfactual method. It is assumed that the opening time of HSR is advanced by 5 years, 4 years and 3 years to construct the pseudo-opening time of HSR, which is represented by F5_HSR, F4_HSR and F3_HSR respectively. If the coefficient of HSR is not significant, it indicates that the reduction of environmental pollution is indeed caused by the HSR; otherwise, it is caused by other unobservable factors, and the conclusion is not robust. The regression results are shown in Table 7. It can be seen in Table 7 that with or without the addition of control variables, the HSR coefficient is inconsistent with the baseline regression, indicating that the HSR does cause a decrease in the level of environmental pollution and enhances the robustness of the baseline regression results. 6.2. Replace environmental pollution variables In above the benchmark return, this paper adopted the SO2 emissions per unit of output as proxy variable of pollution of the environment, based on the availability of data, using industrial wastewater, industrial soot emissions and industrial SO2 emissions (three indexes respectively using absolute emissions, per capita emissions, emissions per unit of output) for robustness test. The regression results are shown in Table 8. After the replacement of environmental pollution indicators, the coefficient of HSR is still significantly negative, and the sign and significance of HSR do

Table 7 Placebo test regression results. Variable Pollution F5_HSR

6. Further robustness tests

(1)

(2)

(4)

(6)

0.0236 (0.0390) NO YES YES 3135 0.462

0.0408 (0.0381) YES YES YES 3135 0.493

0.0242 0.0057 (0.0440) (0.0430)

F3_HSR Variable control Time fixed City fixed N adj. R2

(5)

0.0918* 0.0460 (0.0487) (0.0479)

F4_HSR

In order to further test the robustness of the benchmark regression results, this paper makes further robustness tests from the following aspects. Placebo test was conducted by counterfactual method. The explained variable, namely the substitution variable of environmental pollution, is adopted. Consider the delay characteristics of HSR. The samples of cities affected by the “Two Controlled Areas"(TCA)were removed. The samples of cities directly under the central government and special zones are excluded. Consider the impact of time bandwidth.

(3)

NO YES YES 3135 0.463

YES YES YES 3135 0.493

NO YES YES 3135 0.462

YES YES YES 3135 0.492

Note: *, **, and *** represent 10%, 5%, and 1% levels of statistical significance, respectively. Standard errors are reported in parentheses. Due to space limitation, constant term coefficient and standard error are not reported.

Table 6 Test of the mediating effect mechanism of HSR on environmental pollution. Variable

(1)

(2)

(3)

(4)

(5)

(6)

(7)

Innovation Effect

Pollution

Pollution

Allocation Effect

Pollution

Substitution Effect

Pollution

HSR

3.5525*** (0.5274)

0.1087*** (0.0349)

0.0757** (0.0348) 0.0093*** (0.0012)

0.0276* (0.0153)

0.1066*** (0.0348)

0.0144*** (0.0028)

0.1164*** (0.0349)

YES YES YES 3135 0.156

1.3450*** (0.2311) YES YES YES 3135 0.491

Innovation Effect

0.1466*** (0.0434)

Allocation Effect Substitution Effect Variable control Time fixed City fixed N adj. R2

YES YES YES 3135 0.214

YES YES YES 3135 0.494

YES YES YES 3135 0.504

YES YES YES 3135 0.683

YES YES YES 3135 0.496

Note: *, **, and *** represent 10%, 5%, and 1% levels of statistical significance, respectively. Standard errors are reported in parentheses. Due to space limitation, constant term coefficient and standard error are not reported.

X. Yang et al. / Journal of Cleaner Production 239 (2019) 118135

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Table 8 Regression results after the replacement of explanatory variables. Variable

HSR Variable control Time fixed City fixed N adj. R2

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

ln_gyfs

ln_gyeyhl

ln_gyyc

ln_rjgyfs

ln_rjgyeyhl

ln_rjgyyc

ln_gyfs_gdp

ln_gyyc_gdp

0.0574** (0.0265) YES YES YES 3135 0.058

0.1066*** (0.0348) YES YES YES 3135 0.004

0.1130*** (0.0394) YES YES YES 3135 0.024

0.0595** (0.0266) YES YES YES 3135 0.070

0.1087*** (0.0349) YES YES YES 3135 0.002

0.1150*** (0.0395) YES YES YES 3135 0.017

0.0595** (0.0266) YES YES YES 3135 0.590

0.1150*** (0.0395) YES YES YES 3135 0.411

Note: *, **, and *** represent 10%, 5%, and 1% levels of statistical significance, respectively. Standard errors are reported in parentheses. Due to space limitation, constant term coefficient and standard error are not reported.

not change with different indicators, indicating that the emission reduction effect of HSR on environmental pollution is estimated to be robust.

significantly negative after the deletion of the sample in the TCA, indicating that the HSR does have a significant effect on the reduction of environmental pollution, which further supports the robustness of the benchmark regression results.

6.3. Investigate the lag effect of high speed rail Considering that the opening time of HSR in some cities is in the second half of the year or the end of the year, the role of HSR may not be reflected yet. Therefore, we conduct a regression study on the lag phase of HSR to investigate its delay characteristics. The regression results in the first column of Table 9 show that the coefficient of the HSR lag term is significantly negative, indicating that the impact of HSR on environmental pollution does have delay characteristics. 6.4. Exclude other interfering environmental policy factors In the process of estimating the impact of HSR on environmental pollution, it is inevitable to be influenced by other environmental policy factors, so that the estimation effect will be biased. In order to solve the possible problems, this paper searched for other policy events related to environmental protection during the research period. This paper finds that in 1998, the State Council of China passed the approval on issues related to acid rain control areas and sulfur dioxide (SO2) pollution control areas, and set 158 cities as “Two Controlled Areas "(TCA), namely, acid rain control areas and sulfur dioxide (SO2) control areas. The approval set targets for sulfur dioxide (SO2) emissions and acid rain PH in the TCA by 2000 and 2010, as well as measures for their control. Therefore, the significant impact of HSR on environmental pollution may be mainly caused by the effect of the TCA policy. In order to eliminate this concern, we have deleted the samples involving the TCA in order to further investigate the impact of HSR on environmental pollution. Column 2 of Table 9 shows that the HSR coefficient is still

6.5. Exclude the possible impact of the city's administrative hierarchy Considering the large difference in the administrative levels of cities in the study sample, most of the cities in the sample are ordinary prefecture-level cities, but also include provincial capitals, sub-provincial cities, special economic zones, cities under separate planning and municipalities directly under the central government. Other factors that may influence environmental policy may exist due to differences in administrative levels or economic policies. Therefore, this may affect our estimation results. To this end, we deleted these special city samples, and the regression results are shown in column 3 of Table 9. We found that the coefficient of HSR was still significantly negative. This indicates that the reduction of environmental pollution by HSR is significant, which supports the robustness of the analysis conclusion. 6.6. Consider the effect of sample time bandwidth In order to further identify whether the impact of HSR on environmental pollution changes significantly with the length of the sample time span, we adjust the time bandwidth. The time bandwidth of the benchmark regression samples was from 2003 to 2013, and we set the sample time bandwidth as from 2004 to 2013, 2005 to 2013, and 2005 to 2012, etc. The regression results are shown in Table 9, column 4e6. The coefficient of HSR is still significantly negative, and the coefficient sign and significance have not changed substantially, indicating that the estimated results in this paper are robust.

Table 9 Regression results of other robustness tests. Variable

(1)

(2)

(3)

(4)

(5)

(6)

A phase lag 0.0866** (0.0366)

Non-“TCA”

Ordinary prefecture-level cities

2004e2013

2005e2013

2005e2012

0.1275** (0.0648) YES YES YES 1397 0.412

0.0661* (0.0387) YES YES YES 2717 0.477

0.0920** (0.0364) YES YES YES 2850 0.483

0.0654* (0.0383) YES YES YES 2565 0.469

0.0898*** (0.0337) YES YES YES 2280 0.480

Pollution L1_HSR HSR Variable control Time fixed City fixed N adj. R2

YES YES YES 3135 0.493

Note: *, **, and *** represent 10%, 5%, and 1% levels of statistical significance, respectively. Standard errors are reported in parentheses. Due to space limitation, constant term coefficient and standard error are not reported.

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X. Yang et al. / Journal of Cleaner Production 239 (2019) 118135

7. Heterogeneity analysis of the reduction of environmental pollution by HSR Next, this paper further investigates the heterogeneity of HSR in reducing city pollution from the aspects of city scale, economic development, human capital and environmental policies. 7.1. Heterogeneity of city size The previous analysis shows that HSR can significantly reduce environmental pollution. Then, for different cities, does the pollution reduction effect of HSR exist? Is there heterogeneity? On the one hand, large cities have agglomeration effect, and the allocation and utilization efficiency of resources are higher, which is conducive to reducing environmental pollution. On the other hand, large cities are prone to produce crowding effect, urban diseases and environmental pollution problems. However, the HSR is conducive to the overflow of information and technology, the improvement of the utilization efficiency of resources, the upgrading and transfer of industries, and thus the suppression of environmental pollution. Therefore, this paper believes that HSR can help strengthen the agglomeration effect of city scale and reduce the congestion effect. In other words, the larger the city scale is, the more significant the pollution reduction effect will be. In order to verify the analysis results, we used population size and population density to represent the city size. This paper uses the method of Cai et al. (2016) for reference and adopts Difference-in-Difference-in-Difference (DDD) method to estimate the heterogeneity of city size. We divided cities into two groups according to population size (highpop) or population density (highpopd). For cities with larger than average population size or density, highpop (or highpopd) ¼ 1, otherwise highpop (or highpopd) ¼ 0. The estimation model is as follows.

7.2. The heterogeneity of city economic level and human capital Enterprises cannot reduce pollution and discharge without the support of capital and human capital. The more developed the economy is, the more abundant the capital will be for the treatment of environmental pollution, such as technological upgrading, improvement of resource allocation and utilization efficiency, so as to reduce environmental pollution. Similarly, cities with abundant human capital can provide intellectual guarantee for environmental pollution control, for example, it is easier to master advanced pollution reduction technology, improve the efficiency of resource utilization, thus reducing environmental pollution and so on. Due to the three effects of HSR, it can be predicted that in cities with developed economy and abundant human capital, the pollution reduction effect of HSR will be more significant. To verify the above conclusions, DDD method was also used for verification. The grouping variables of economic level and human capital are highrjgdp and highrlzb respectively. For cities with higher than average economic level and average human capital, set highrjgdp ¼ 1 and highrlzb ¼ 1, otherwise it will be 0. The regression model is as follows.

Pollutioncit ¼ d0 þ d1 HSRct  highrjgdpðor highrlzbÞ þ dr Xit þ Zc þ yeart þ εcit (8) The regression results are shown in columns 3 and 4 of Table 10. The coefficient of HSRhighrjgdp is significantly negative at the confidence level of 1%.The coefficient of the HSRhighrlzb is the same. This indicates that the more developed the economy or the more abundant human capital, the more significant the pollution reduction effect of urban HSR. Therefore, the reduction of environmental pollution by HSR reflects the heterogeneity of economic level and human capital.

Pollutioncit ¼ d0 þ d1 HSRct  highpopðor highpopdÞ þ dr Xit þ Zc þ yeart þ εcit (7) The regression results are shown in column 1 and column 2 of Table 10. It can be seen that the coefficient of the HSRhighpop and HSRhighpopd is significantly negative, that is, the larger the population size or population density is, the more significant the pollution reduction effect of HSR will be. Therefore, the pollution reduction effect of HSR has the heterogeneity of city size.

7.3. The heterogeneity of the “Two Controlled Areas” environmental policy In the previous robustness test, we found that the pollution reduction effect of HSR is still significant in the urban samples of non-" Two Controlled Areas "(TCA). It can be predicted that in the TCA cities, due to the requirements of environmental protection policies, the power to reduce pollution is stronger. It is more likely to make full use of advanced emission reduction technology,

Table 10 Regression results of heterogeneity analysis. Variable Pollution HSRhighpop

(1)

(2)

(3)

(4)

0.1113*** (0.0420) 0.0848** (0.0375)

HSRhighpopd

0.1011*** (0.0365)

HSRhighrjgdp

0.1339*** (0.0373)

HSRhighrlzb HSRTCA Variable control Time fixed City fixed N adj. R2

(5)

YES YES YES 3135 0.494

YES YES YES 3135 0.493

YES YES YES 3135 0.495

YES YES YES 3135 0.494

0.1065*** (0.0403) YES YES YES 3135 0.494

Note: *, **, and *** represent 10%, 5%, and 1% levels of statistical significance, respectively. Standard errors are reported in parentheses. Due to space limitation, constant term coefficient and standard error are not reported.

X. Yang et al. / Journal of Cleaner Production 239 (2019) 118135

improve the utilization efficiency of resources, strengthen the monitoring of high-polluting enterprises, or carry out industrial upgrading, transfer and other measures, so as to reduce environmental pollution. Therefore, the three effects caused by HSR should be more significant in reducing environmental pollution in TCA cities. For this test, the (DDD) method was also used. Set the grouping variable as TCA. If a city belongs to the TCA, then set TCA ¼ 1, otherwise, TCA ¼ 0. The regression model is as follows.

Pollutioncit ¼ d0 þ d1 HSRct  TCA þ dr Xit þ Zc þ yeart þ εcit

(9)

As can be seen from column 5 of Table 10, the coefficient of the HSRTCA is significantly negative at the confidence level of 1%. This shows that the effect of HSR on the reduction of environmental pollution is more significant in the TCA cities, and the analysis has been verified. Therefore, the reduction of environmental pollution by HSR reflects the heterogeneity of environmental policies in the TCA.

13

strengthen the proportion of technology-intensive industries, give priority to supporting the development of low-consumption and environment-friendly industries, and encourage the transformation of traditional industries into environment-friendly ones. Fourth, there is still a need to strengthen environmental supervision and governance, and improve the efficiency of environmental law enforcement. Finally, HSR should give priority to the cities with good economic development foundation and strong human capital. The local governments should make full use of the advantages of high efficiency, green and environmental protection of HSR, promote the improvement of urban environmental quality and build an environment-friendly China. This article also has the insufficiency. At the time of writing, China has built over 29,000 km of HSR. Due to the availability and validity of relevant data, we have not collected the latest data for empirical analysis. Moreover, due to data limitations, we have not studied the direct effect of HSR replacing other means of transportation on reducing environmental pollution. It is foreseeable that in the near future, the impact of HSR on environmental pollution will be further examined in similar studies.

8. Conclusions and discussions Author disclosure statement Based on the panel data of 285 cities above prefecture-level in China from 2003 to 2013, DID was adopted to study the impact of HSR on environmental pollution. The research results showed that the connection of HSR significantly reduced environmental pollution by 7.35%. In order to avoid endogenous problems caused by sample selection bias and variable omission as far as possible, PSMDID method was adopted in this paper for further test, and twostage regression endogenous treatment was conducted with the average altitude of the city as the instrumental variable. The conclusion that HSR can reduce environmental pollution is still true. Mechanism analysis shows that HSR reduces environmental pollution through innovation effect, allocation effect and substitution effect. Through the robustness test of the placebo test, the substitution of explained variables, the consideration of HSR lag, the elimination of environmental policy impacts in the “Two Controlled Areas”, the exclusion of city samples in the municipalities and special zones, and the consideration of time and bandwidth, it was found that the basic conclusion was still valid, which enhanced the reliability of the conclusion. Heterogeneity analysis shows that cities with larger size, higher level of economic development, richer human capital or stricter environmental supervision have more significant pollution reduction effect. This study has the following policy implications. First, HSR will not automatically reduce environmental pollution, it is necessary to rely on advanced science and environmental protection technology to reduce pollution. Therefore, local governments should make full use of the advantages of HSR in knowledge, information and technology spillover, seize development opportunities, build innovation and development platforms, and increase technological innovation cooperation and achievement transformation among universities, scientific research institutes and enterprises, so as to build beautiful cities. Second, the government should be committed to maintaining a fair and orderly market environment, promoting the effective flow of resources and improving the efficiency of resource allocation. Therefore, governments should gradually weaken institutional restrictions on household registration, housing and medical care, promote the rapid and effective flow of talents, capital and other economic factors, improve the efficiency of resource utilization, and reduce pollution and discharge. Third, great support should be offered to the development of environmental protection industries and promote industrial transformation and upgrading. On the basis of vigorously developing environmental protection technologies, local governments should

None. No competing financial exist None. Acknowledgements The authors gratefully acknowledge the financial support from the National Planning Office of Philosophy and Social Science of China (Grant no. 13BJY091), National Natural Science Foundation of China (Grant no. 71773083). References Acemoglu, D., Moscona, J., Robinson, J.A., 2016. State capacity and American technology: evidence from the nineteenth century. Am. Econ. Rev. 106 (5), 61e67. Barbosa, F.C., 2018. High speed rail technology: increased mobility with efficient capacity allocation and improved environmental performance. In: 2018 Joint Rail Conference. American Society of Mechanical Engineers. V001T04A002V001T04A002. Baron, R.M., Kenny, D.A., 1986. The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J. Personal. Soc. Psychol. 51 (6), 1173e1182. Bosetti, V., Carraro, C., Galeotti, M., et al., 2006. WITCH a world induced technical change hybrid model. Energy J. 13e37. Brock, W.A., Taylor, M.S., 2004. Economic Growth and the Environment_ A Review of Theory and Empirics. NBER Working Paper. w10854. Cai, X., Lu, Y., Wu, M., et al., 2016. Does environmental regulation drive away inbound foreign direct investment? Evidence from a quasi-natural experiment in China. J. Dev. Econ. 123, 73e85. Catalini, C., Fons-Rosen, C., Gaule, P., 2016. Did cheaper flights change the direction of science? No. 9897. IZA Discussion Papers. Chen, C.L., Hall, P., 2011. The impacts of HSR on British economic geography: a study of the UK's InterCity 125/225 and its effects. J. Transp. Geogr. 19 (4), 689e704. Chen, H., Jia, B., Lau, S.S.Y., 2008. Sustainable urban form for Chinese compact cities: challenges of a rapid urbanized economy. Habitat Int. 32, 28e40. Cole, M.A., Elliott, R.J.R., 2003. Determining the tradeeenvironment composition effect: the role of capital, labor and environmental regulations. J. Environ. Econ. Manag. 46 (3), 363e383. Cole, M.A., 2007. Corruption, income and the environment: an empirical analysis. Ecol. Econ. 62 (3e4), 637e647. Crenshaw, E.M., Jenkins, J.C., 1996. Social structure and global climate change: sociological propositions concerning the greenhouse effect. Sociol. Focus 29 (4), 341e358. Dean, J.M., 2002. Does trade liberalization harm the environment? A new test. Can. conomique. 35 (4), 819e842. J. Econ./Revue canadienne d'e Dinda, S., 2004. Environmental Kuznets curve hypothesis: a survey. Ecol. Econ. 49 (4), 431e455. Dobruszkes, F., 2011. HSR and air transport competition in Western Europe: a

14

X. Yang et al. / Journal of Cleaner Production 239 (2019) 118135

supply-oriented perspective. Transp. Policy 18 (6), 870e879. Dong, X., Zheng, S., Kahn, M.E., 2018. The Role of Transportation Speed in Facilitating High Skilled Teamwork. National Bureau of Economic Research. Duflo, E., Pande, R., 2007. Dams. Q. J. Econ. 122 (2), 601e646. Duranton, G., Turner, M.A., 2011. The fundamental law of road congestion: evidence from US cities. Am. Econ. Rev. 101 (6), 2616e2652. Duranton, G., Turner, M.A., 2012. Urban growth and transportation. Rev. Econ. Stud. 79 (4), 1407e1440. DE Leeuw, F.A.A.M., Moussiopoulos, N., Sahm, P., et al., 2001. Urban air quality in larger conurbations in the European Union. Environ. Model. Softw 16 (4), 399e414. Gines, de Rus, et al., 2012. Economic Analysis of High Speed Rail in Europe. Fundacion BBVA. Glaeser, E.L., Mare, D.C., 2001. Cities and skills. J. Labor Econ. 19 (2), 316e342. Gouldson, A., Murphy, J., 1997. Ecological modernisation: restructuring industrial economies. Political Q. 68 (B), 74e86. Grossman, G.M., Krueger, A.B., 1995. Economic growth and the environment. Q. J. Econ. 110 (2), 353e377. Grossman, G.M., Krueger, A.B., 1991. Environmental impacts of a North American free trade agreement. Soc. Sci. Electron. Publ. 8 (2), 223e250. Henderson, J.V., 1974. Optimum city size: the external diseconomy question. J. Political Econ. 82 (2), 373e388. Hsieh, C.T., Klenow, P.J., 2009. Misallocation and manufacturing TFP in China and India. Q. J. Econ. 124 (4), 1403e1448. Keller, W., Levinson, A., 2002. Pollution abatement costs and foreign direct investment inflows to US states. Rev. Econ. Stat. 84 (4), 691e703. Levinson, A., 2009. Technology, international trade, and pollution from US manufacturing. Am. Econ. Rev. 99 (5), 2177e2192. Liang, F.H., 2006. Does Foreign Direct Investment Harm the Host Country's Environment? Evidence from China. Haas School of Business. Lin, Y., 2017. Travel costs and urban specialization patterns: evidence from China's high speed railway system. J. Urban Econ. 98, 98e123. Lindmark, M., 2002. An EKC-pattern in historical perspective: carbon dioxide emissions, technology, fuel prices and growth in Sweden 1870e1997. Ecol. Econ. 42 (1e2), 333e347. List, J.A., Co, C.Y., 2000. The effects of environmental regulations on foreign direct investment. J. Environ. Econ. Manag. 40 (1), 1e20. Liu, Y., Li, Y., 2017. HSR and city economic growth in China. J. Inancial Res. 60 (11), 18e33 (in Chinese). Lu, D., 2016. High-speed Rail Metro Planning Strategy. Hefei University of Technology (in Chinese). Michaels, G., 2008. The effect of trade on the demand for skill: evidence from the interstate highway system. Rev. Econ. Stat. 90 (4), 683e701. Mol, A.P.J., Spaargaren, G., 2000. Ecological modernisation theory in debate: a review. Environ. Pol. 9 (1), 17e49. Moretti, E., 2004. Human capital externalities in cities. Handb. Reg. Urban Econ. 4,

2243e2291. Nepal, K.P., 2013. Environmental Impacts of Proposed High Speed Rail in Australia. Solutions to Environmental Challenges through Innovations in Research, pp. 456e474. Okabe, S., 1976. 9. Impact of the sanyo shinkansen on local communities. Shinkansen HSR Netw. Jpn. 105e129. Olley, G.S., Pakes, A., 1996. The dynamics of productivity in the telecommunications equipment industry. Econometrica 64 (6), 1263e1297. Pasche, M., 2002. Technical progress, structural change, and the environmental Kuznets curve. Ecol. Econ. 42 (3), 381e389. Pol, P.M.J., 2003. The Economic Impact of the HSR on Urban Regions. 43rd Congress of the European Regional Science Association. Rosenbaum, P.R., Rubin, D.B., 1985. Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. Am. Statistician 39 (1), 33e38. Ryzhenkov, M., 2016. Resource misallocation and manufacturing productivity: the case of Ukraine. J. Comp. Econ. 44 (1), 41e55. Sadorsky, P., 2014. The effect of urbanization on CO2 emissions in emerging economics. Energy Econ. 41, 147e153. Satterthwaite, D., 1997. Environmental transformations in cities as they get larger, wealthier and better managed. Geogr. J. 163 (2), 216e224. Shao, S., Yang, L., Yu, M., et al., 2011. Estimation, characteristics, and determinants of energy-related industrial CO2 emissions in Shanghai (China), 1994e2009. Energy Policy 39 (10), 6476e6494. Song, M., Zhang, G., Zeng, W., et al., 2016. Railway transportation and environmental efficiency in China. Transp. Res. D Transp. Environ. 48, 488e498. Verhoef, E.T., Nijkamp, P., 2002. Externalities in urban sustainability environmental versus localization-type Agglomeration externalities in a general spatial equilibrium model of a single-sector monocentric industrial city. Ecol. Econ. 40 (2), 157e179. Vukina, T., Beghin, J.C., Solakoglu, E.G., 1999. Transition to markets and the environment: effects of the change in the composition of manufacturing output. Environ. Dev. Econ. 4 (4), 582e598. Wang, H., Jin, Y., 2002. Industrial Ownership and Environmental Performance: Evidence from China. The World Bank. No. 2936. Wang, X., Xie, Z., Zhang, X., et al., 2018. Roads to innovation: firm-level evidence from people's Republic of China (PRC). China Econ. Rev. 49, 154e170. Zeng, D.Z., Zhao, L., 2009. Pollution havens and industrial agglomeration. J. Environ. Econ. Manag. 58 (2), 141e153. Zhang, R., Zhang, X., Dai, R., 2018a. Infrastructure and enterprise productivity: a perspective of market expansion and foreign competition. Manag. World 34 (1), 96e110 (in Chinese). Zhang, X., Wang, X., Wan, G., Sun, F., 2018b. A unified framework of road Infrastructure's growth effect. Econ. Res. J. 1, 50e64 (in Chinese). Zhou, H., 2019. Ecological civilization construction in the past 40 years of reform and opening up. China Dev. Rev. 205 (1), 7e12 (in Chinese).