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  • Published: 26 November 2023

Impact of industrial robots on environmental pollution: evidence from China

  • Yanfang Liu 1  

Scientific Reports volume  13 , Article number:  20769 ( 2023 ) Cite this article

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  • Environmental sciences
  • Environmental social sciences

The application of industrial robots is considered a significant factor affecting environmental pollution. Selecting industrial wastewater discharge, industrial SO 2 emissions and industrial soot emissions as the evaluation indicators of environmental pollution, this paper uses the panel data model and mediation effect model to empirically examine the impact of industrial robots on environmental pollution and its mechanisms. The conclusions are as follows: (1) Industrial robots can significantly reduce environmental pollution. (2) Industrial robots can reduce environmental pollution by improving the level of green technology innovation and optimizing the structure of employment skills. (3) With the increase in emissions of industrial wastewater, industrial SO 2 , and industrial dust, the impacts generated by industrial robots are exhibiting trends of a “W” shape, gradual intensification, and progressive weakening. (4) Regarding regional heterogeneity, industrial robots in the eastern region have the greatest negative impact on environmental pollution, followed by the central region, and the western region has the least negative impact on environmental pollution. Regarding time heterogeneity, the emission reduction effect of industrial robots after 2013 is greater than that before 2013. Based on the above conclusions, this paper suggests that the Chinese government and enterprises should increase investment in the robot industry. Using industrial robots to drive innovation in green technology and optimize employment skill structures, reducing environmental pollution.

Introduction

Since the reform and opening up, China’s rapid economic growth has created a world-renowned “economic growth miracle” 1 . With the rapid economic growth, China’s environmental pollution problem is becoming more and more serious 2 . According to the “ Global Environmental Performance Index Report ” released by Yale University in the United States in 2022, China’s environmental performance index scores 28.4 points, ranking 160th out of 180 participating countries. The aggravation of environmental pollution not only affects residents’ health 3 , but also affects the efficiency of economic operation 4 . According to calculation of the General Administration of Environmental Protection, the World Bank and the Chinese Academy of Sciences, China’s annual losses caused by environmental pollution account for about 10% of GDP. Exploring the factors that affect environmental pollution and seeking ways to reduce environmental pollution are conducive to the development of economy within the scope of environment.

Industrial robots are machines that can be automatically controlled, repeatedly programmed, and multi-purpose 5 . They replace the low-skilled labor force engaged in procedural work 6 , reducing the raw materials required for manual operation. Industrial robots improve the clean technology level and energy efficiency of coal combustion, reducing pollutant emissions in front-end production. Industrial robots also monitor the energy consumption and sewage discharge in the production process in real time. The excessive discharge behavior of enterprises in the production process is regulated, reducing the emission of pollutants in the end treatment. Based on the selection and coding of literature (Appendix A ), this paper uses the meta-analysis method to compare the impacts of multiple factors such as economics, population, technology, and policy on environmental pollution. As shown in Table 1 , compared to other factors, industrial robots demonstrate greater advantages in reducing environmental pollution. There is a lack of research on the relationship between industrial robots and environmental pollution in China. With the advent of artificial intelligence era, China’s industrial robot industry has developed rapidly. According to data released by the International Federation of Robotics (IFR), from 1999 to 2019, China’s industrial robot ownership and installation shows an increasing trend year by year (Fig.  1 ). In 2013 and 2016, China’s industrial robot installation (36,560) and ownership (349,470) exceeds Japan for the first time, becoming the world’s largest country in terms of installation and ownership of industrial robots. Whether the application of industrial robots in China contributes to the reduction of environmental pollution? What is the mechanism of the impact of China’s industrial robots on environmental pollution? Researching this issue is crucial for filling the gaps in existing research and providing a reference for other countries to achieve emission reduction driven by robots.

figure 1

Industrial robot installations in the world’s top five industrial robot markets from 1999 to 2019.

Based on the above analysis, this paper innovatively incorporates industrial robots and environmental pollution into a unified framework. Based on the panel data of 30 provinces in China from 2006 to 2019, this paper uses the ordinary panel model and mediating effect model to empirically test the impact of industrial robots on China’s environmental pollution and its transmission channels. The panel quantile model is used to empirically analyze the heterogeneous impact of industrial robots on environmental pollution under different environmental pollution levels.

Literature review

A large number of scholars have begun to study the problem of environmental pollution. Its research content mainly includes two aspects: The measurement of environmental pollution and its influencing factors. Regarding the measurement, some scholars have used SO 2 emissions 7 , industrial soot emissions 8 and PM2.5 concentration 9 and other single indicators to measure the degree of environmental pollution. The single indicator cannot fully and scientifically reflect the degree of environmental pollution. To make up for this defect, some scholars have included industrial SO 2 emissions, industrial wastewater discharge and industrial soot emissions into the environmental pollution evaluation system, and used the entropy method to measure environmental pollution level 10 . This method ignores the different characteristics and temporal and spatial trends of different pollutants, which makes the analysis one-sided. Regarding the influencing factors, economic factors such as economic development level 11 , foreign direct investment 12 and income 13 , population factors such as population size 14 and urbanization level 15 , energy consumption 16 all have an impact on environmental pollution. Specifically, economic development and technological innovation can effectively reduce environmental pollution 17 . The expansion of population size can aggravate environmental pollution. Income inequality can reduce environmental pollution, but higher income inequality may aggravate environmental pollution 18 . There are “pollution heaven hypothesis” and “pollution halo hypothesis” between foreign direct investment and environmental pollution 19 . Technological factors also have a non-negligible impact on environmental pollution 20 .

With continuous deepening of research, scholars have begun to focus on the impact of automation technology, especially industrial robot technology, on the environment. Ghobakhloo et al. 21 theoretically analyzed the impact of industrial robots on energy sustainability, contending that the application of industrial robots could foster sustainable development of energy. Using data from multiple countries, a few scholars have empirically analyzed the effect of industrial robots on environmental pollution (Table 2 ). Luan et al. 22 used panel data from 73 countries between 1993 and 2019 to empirically analyze the impact of industrial robots on air pollution, finding that the use of industrial robots intensifies environmental pollution. Using panel data from 66 countries from 1993 to 2018, Wang et al. 23 analyzed the impact of industrial robots on carbon intensity and found that industrial robots can reduce carbon intensity. On the basis of analyzing the overall impact of industrial robots on environmental pollution, some scholars conducted in-depth exploration of its mechanism. Based on data from 72 countries between 1993 and 2019, Chen et al. 5 explored the impact of industrial robots on the ecological footprint, discovering that industrial robots can reduce the ecological footprint through time saving effect, green employment effect and energy upgrading effect. Using panel data from 35 countries between 1993 and 2017, Li et al. 24 empirically examined the carbon emission reduction effect of industrial robots, finding that industrial robots can effectively reduce carbon emissions by increasing green total factor productivity and reducing energy intensity. Although the above studies have successfully estimated the overall impact of industrial robots on environmental pollution and its mechanisms, they have not fully considered the role of technological progress, labor structure and other factors in the relationship between the two. These studies all chose data from multiple countries as research samples and lack research on the relationship between industrial robots and environmental pollution in China, an emerging country.

The above literature provides inspiration for this study, but there are still shortcomings in the following aspects: Firstly, there is a lack of research on the relationship between industrial robots and environmental pollution in emerging countries. There are significant differences between emerging and developed countries in terms of institutional background and the degree of environmental pollution. As a representative emerging country, research on the relationship between industrial robots and environmental pollution in China can provide reliable references for other emerging countries. Secondly, theoretically, the study of the impact of industrial robots on environmental pollution is still in its initial stage. There are few studies that deeply explore its impact mechanism, and there is a lack of analysis of the role of technological progress and labor structure in the relationship between the two.

The innovations of this paper are as follows: (1) In terms of sample selection, this paper selects panel data from 30 provinces in China from 2006 to 2019 as research samples to explore the relationship between industrial robots and environmental pollution in China, providing references for other emerging countries to improve environmental quality using industrial robots. (2) In terms of theory, this paper is not limited to revealing the superficial relationship between industrial robots and environmental pollution. it starts from a new perspective and provides an in-depth analysis of how industrial robots affect environmental pollution through employment skill structure and green technology innovation. This not only enriches research in the fields of industrial robots and the environment, but is also of great significance in guiding the direction of industrial policy and technology research and development.

Theoretical analysis and hypothesis

Industrial robots and environmental pollution.

As shown in Fig.  2 , the impact of industrial robots on environmental pollution is mainly reflected in two aspects: Front-end production and end treatment. In front-end production, industrial robots enable artificial substitution effects 25 . Manual operation is replaced by machine operation, reducing the raw materials needed for manual operation. Through the specific program setting of industrial robots, clean energy is applied to industrial production 26 . The use of traditional fuels such as coal and oil is reduced. In terms of end treatment, the traditional pollutant concentration tester only measures a single type of pollutant. Its data cannot be obtained in time. It is easy to cause pollution incidents. Industrial robots can measure a variety of pollutants, and have the function of remote unmanned operation and warning. It reflects the pollution situation in time, reducing the probability of pollution incidents. The use of robots can upgrade sewage treatment equipment and improve the accuracy of pollution treatment, reducing pollutant emissions. Based on the above analysis, this paper proposes hypothesis 1.

figure 2

The impact of industrial robots on environmental pollution.

Hypothesis 1

The use of industrial robots can reduce environmental pollution.

Mediating effect of green technology innovation

Industrial robots can affect environmental pollution by promoting green technology innovation. The transmission path of “industrial robots-green technology innovation-environmental pollution” is formed. Industrial robots are the materialization of technological progress in the field of enterprise R&D. Its impact on green technology innovation is mainly manifested in the following two aspects: Firstly, industrial robots classify known knowledge, which helps enterprises to integrate internal and external knowledge 27 . The development of green technology innovation activities of enterprises is promoted. Secondly, enterprises can simulate existing green technologies through industrial robots. The shortcomings of green technology in each link are found. Based on this, enterprises can improve and perfect green technology in a targeted manner. Industrial robots can collect and organize data, which enables enterprises to predict production costs and raw material consumption. Excessive procurement by enterprises can occupy working capital. Inventory backlog leads to warehousing, logistics and other expenses, increasing storage costs 28 . Forecasting the consumption of raw materials allows enterprises to purchase precisely, preventing over-procurement and inventory backlog, thereby reducing the use of working capital and storage costs 29 . The production cost of enterprises is reduced. Enterprises have more funds for green technology research and development.

The continuous innovation of green technology is helpful to solve the problem of environmental pollution. Firstly, green technology innovation helps use resources better 30 , lowers dependence on old energy, and reduces environmental damage. Secondly, green technology innovation promotes the greening of enterprises in manufacturing, sales and after-sales 31 . The emission of pollutants in production process is reduced. Finally, green technology innovation improves the advantages of enterprises in market competition 32 . The production possibility curve expands outward, which encourages enterprises to carry out intensive production. Based on the above analysis, this paper proposes hypothesis 2.

Hypothesis 2

Industrial robots can reduce environmental pollution through green technology innovation.

Mediating effect of employment skill structure

Industrial robots can affect environmental pollution through employment skill structure. The transmission path of “industrial robots-employment skill structure-environmental pollution” is formed. Industrial robots have substitution effect and creation effect on the labor force, improving the employment skill structure. Regarding the substitution effect, enterprises use industrial robots to complete simple and repetitive tasks to improve production efficiency, which crowds out low-skilled labor 6 . Regarding the creation effect, industrial robots create a demand for new job roles that matches automation, such as robot engineers, data analysts, machine repairers, which increases the number of highly skilled labor 33 . The reduction of low-skilled labor and increase of high-skilled labor improve employment skill structure.

High-skilled labor is reflected in the level of education 34 . Its essence is to have a higher level of skills and environmental awareness, which is the key to reducing environmental pollution. Compared with low-skilled labor, high-skilled labor has stronger ability to acquire knowledge and understand skills, which improves the efficiency of cleaning equipment and promotes emission reduction. The interaction and communication between highly skilled labor is also crucial for emission reduction. The excessive wage gap between employees brings high communication costs, which hinders the exchange of knowledge and technology between different employees. The increase in the proportion of high-skilled labor can solve this problem and improve the production efficiency of enterprises 35 . The improvement of production efficiency enables more investment in emission reduction research, decreasing pollutant emissions. Based on the above analysis, this paper proposes hypothesis 3.

Hypothesis 3

Industrial robots can reduce environmental pollution by optimizing employment skills structure.

Model construction and variable selection

Model construction, panel data model.

The panel data model is a significant statistical method, first introduced by Mundlak 36 . Subsequently, numerous scholars have used this model to examine the baseline relationships between core explanatory variables and explained variables 37 . To test the impact of industrial robots on environmental pollution, this paper sets the following panel data model:

In formula ( 1 ), Y it is the explained variable, indicating the degree of environmental pollution in region i in year t . IR it is the core explanatory variable, indicating the installation density of industrial robots in region i in year t . X it is a series of control variables, including economic development level (GDP), urbanization level (URB), industrial structure (EC), government intervention (GOV) and environmental regulation (ER). \(\lambda i\) is the regional factor. \(\varphi t\) is the time factor. \(\varepsilon it\) is the disturbance term.

Mediating effect model

To test the transmission mechanism of industrial robots affecting environmental pollution, this paper sets the following mediating effect model:

In formula ( 2 ), M is the mediating variable, which mainly includes green technology innovation and employment skill structure. Formula ( 2 ) measures the impact of industrial robots on mediating variables. Formula ( 3 ) measures the impact of intermediary variables on environmental pollution. According to the principle of mediating effect 38 , the direct effect \(\theta 1\) , mediating effect \(\beta 1 \times \theta 2\) and total effect \(\alpha 1\) satisfy \(\alpha 1 = \theta 1 + \beta 1 \times \theta 2\) .

Panel quantile model

The panel quantile model was first proposed by Koenke and Bassett 39 . It is mainly used to analyze the impact of core explanatory variables on the explained variables under different quantiles 40 . To empirically test the heterogeneous impact of industrial robots on environmental pollution under different levels of environmental pollution, this paper sets the following panel quantile model:

In formula ( 4 ), \(\tau\) represents the quantile value. \(\gamma 1\) reflects the difference in the impact of industrial robots on environmental pollution at different quantiles. \(\gamma 2\) indicates the different effects of control variables at different quantiles.

Variable selection

Explained variable.

The explained variable is environmental pollution. Considering the timeliness and availability of data, this paper selects industrial wastewater discharge, industrial SO 2 emissions and industrial soot emissions as indicators of environmental pollution.

Explanatory variable

According to production theory, industrial robots can enhance production efficiency 41 . Efficient production implies reduced energy wastage, which in turn decreases the emission of pollutants. Industrial robots can upgrade pollution control equipment, heightening the precision in pollution treatment and reducing pollutant discharge. Referring to Acemoglu and Restrepo 25 , this paper selects the installation density of industrial robots as a measure. The specific formula is as follows:

In formula ( 5 ), Labor ji is the number of labor force in industry j in region i . IR jt is the stock of industrial robot use in industry j in the year t .

Mediating variable

Green technology innovation. Industrial robots can increase the demand for highly-skilled labor 42 , subsequently influencing green technology innovation. Compared to ordinary labor, highly-skilled labor possesses a richer knowledge base and technological learning capability, improving the level of green technology innovation. Green technology innovation can improve energy efficiency 43 , reducing pollution generated by energy consumption. The measurement methods of green technology innovation mainly include three kinds: The first method is to use simple technology invention patents as measurement indicators. Some of technical invention patents are not applied to the production process of enterprise, they cannot fully reflect the level of technological innovation. The second method is to use green product innovation and green process innovation as measurement indicators. The third method is to use the number of green patent applications or authorizations as a measure 44 . This paper selects the number of green patent applications as a measure of green technology innovation.

Employment skill structure. The use of industrial robots reduces the demand for labor performing simple repetitive tasks and increases the need for engineers, technicians, and other specialized skilled personnel, improving the employment skill structure 45 . Compared to ordinary workers, highly-skilled laborers typically have a stronger environmental awareness 46 . Such environmental consciousness may influence corporate decisions, prompting companies to adopt eco-friendly production methods, thus reducing environmental pollution. There are two main methods to measure the structure of employment skills: One is to use the proportion of employees with college degree or above in the total number of employees as a measure. The other is to use the proportion of researchers as a measure. The educational level can better reflect the skill differences of workers. This paper uses the first method to measure the employment skill structure.

Control variable

Economic development level. According to the EKC hypothesis 47 , in the initial stage of economic development, economic development mainly depends on input of production factors, which aggravates environmental pollution. With the continuous development of economy, people begin to put forward higher requirements for environmental quality. The government also begins to adopt more stringent policies to control environmental pollution, which can reduce the level of environmental pollution. According to Liu and Lin 48 , This paper uses per capita GDP to measure economic development level.

Urbanization level. The improvement of urbanization level has both positive and negative effects on pollution. Urbanization can improve the agglomeration effect of cities. The improvement of agglomeration effect can not only promote the sharing of public resources such as infrastructure, health care, but also facilitate the centralized treatment of pollution. The efficiency of environmental governance is improved 49 . The acceleration of urbanization can increase the demand for housing, home appliances and private cars, which increases pollutant emissions 50 . This paper uses the proportion of urban population to total population to measure the level of urbanization.

Industrial structure. Industrial structure is one of the key factors that determine the quality of a country’s environmental conditions 51 . The increase in the proportion of capital and technology-intensive industries can effectively improve resource utilization efficiency and improve resource waste 52 . This paper selects the ratio of the added value of the tertiary industry to the secondary industry to measure industrial structure.

Government intervention. Government intervention mainly affects environmental pollution from the following two aspects: Firstly, the government can give high-tech, energy-saving and consumption-reducing enterprises relevant preferential policies, which promotes the development of emission reduction technologies for these enterprises 53 . Secondly, the government strengthens environmental regulation by increasing investment in environmental law enforcement funds, thus forcing enterprises to save energy and reduce emissions 54 . This paper selects the proportion of government expenditure in GDP to measure government intervention.

Environmental regulation. The investment in environmental pollution control is conducive to the development of clean and environmental protection technology, optimizing the process flow and improving the green production efficiency of enterprises 55 . Pollutant emissions are reduced. This paper selects the proportion of investment in pollution control to GDP to measure environmental regulation.

Data sources and descriptive statistics

This paper selects the panel data of 30 provinces in China from 2006 to 2019 as the research sample. Among them, the installation data of industrial robots are derived from International Federation of Robotics (IFR). The data of labor force and employees with college degree or above are from China Labor Statistics Yearbook . Other data are from the China Statistical Yearbook . The descriptive statistics of variables are shown in Table 3 . Considering the breadth of application and the reliability of analysis capabilities, this paper uses Stata 16 for regression analysis.

Results analysis

Spatial and temporal characteristics of environmental pollution and industrial robots in china, environmental pollution.

Figure  3 a shows the overall trend of average industrial wastewater discharge in China from 2006 to 2019. From 2006 to 2019, the discharge of industrial wastewater shows a fluctuating downward trend, mainly due to the improvement of wastewater treatment facilities and the improvement of treatment capacity. Figure  3 b shows the changing trend of average industrial wastewater discharge in 30 provinces of China from 2006 to 2019. Industrial wastewater discharge in most provinces has declined. There are also some provinces such as Fujian, Guizhou and Qinghai, which have increased industrial wastewater discharge. Their emission reduction task is very arduous.

figure 3

Industrial wastewater discharge from 2006 to 2019.

Figure  4 a shows the overall trend of average industrial SO 2 emissions in China from 2006 to 2019. From 2006 to 2019, industrial SO 2 emissions shows a fluctuating downward trend, indicating that air pollution control and supervision are effective. Figure  4 b shows the trend of average industrial SO 2 emissions in 30 provinces of China from 2006 to 2019. Similar to industrial wastewater, industrial SO 2 emissions decrease in most provinces.

figure 4

Industrial SO 2 emissions from 2006 to 2019.

Figure  5 a shows the overall trend of average industrial soot emissions in China from 2006 to 2019. Different from industrial wastewater and industrial SO 2 , the emission of industrial soot is increasing year by year. From the perspective of governance investment structure, compared with industrial wastewater and industrial SO 2 , the investment proportion of industrial soot is low. From the perspective of source, industrial soot mainly comes from urban operation, industrial manufacturing and so on. The acceleration of urbanization and the expansion of manufacturing scale have led to an increase in industrial soot emissions. Figure  5 b shows the trend of industrial soot emissions in 30 provinces in China from 2006 to 2019. The industrial soot emissions in most provinces have increased.

figure 5

Industrial soot emissions from 2006 to 2019.

Figure  6 shows the spatial distribution characteristics of industrial wastewater, industrial SO 2 and industrial soot emissions. The three types of pollutant emissions in the central region are the largest, followed by the eastern region, and the three types of pollutant emissions in the western region are the smallest. Due to resource conditions and geographical location, the central region is mainly dominated by heavy industry. The extensive development model of high input and consumption makes its pollutant emissions higher than the eastern and western regions. The eastern region is mainly capital-intensive and technology-intensive industries, which makes its pollutant emissions lower than the central region. Although the leading industry in the western region is heavy industry, its factory production and transportation scale are not large, which produces less pollutants.

figure 6

Spatial distribution characteristics of industrial wastewater, industrial SO 2 and industrial soot.

Industrial robots

Figure  7 a shows the overall trend of installation density of industrial robots in China from 2006 to 2019. From 2006 to 2019, the installation density of industrial robots in China shows an increasing trend year by year. The increase of labor cost and the decrease of industrial robot cost make enterprises use more industrial robots, which has a substitution effect on labor force. The installation density of industrial robots is increased. Figure  7 b shows the trend of installation density of industrial robots in 30 provinces of China from 2006 to 2019. The installation density of industrial robots in most provinces has increased. Among them, the installation density of industrial robots in Guangdong Province has the largest growth rate. The installation density of industrial robots in Heilongjiang Province has the smallest growth rate.

figure 7

Installation density of industrial robots from 2006 to 2019.

Figure  8 shows the spatial distribution characteristics of installation density of industrial robots. The installation density of industrial robots in the eastern region is the largest, followed by the central region, and the installation density of industrial robots in the western region is the smallest. The eastern region is economically developed and attracts lots of talents to gather here, which provides talent support for the development of industrial robots. Advanced technology also leads to the rapid development of industrial robots in the eastern region. The economy of western region is backward, which inhibits the development of industrial robots.

figure 8

Spatial distribution characteristics of industrial robots.

Benchmark regression results

Table 4 reports the estimation results of the ordinary panel model. Among them, the F test and LM test show that the mixed OLS model should not be used. The Hausman test shows that the fixed effect model should be selected in the fixed effect model and random effect model. This paper selects the estimation results of the fixed effect model to explain.

Regarding the core explanatory variable, industrial robots have a significant negative impact on the emissions of industrial wastewater, industrial SO 2 and industrial soot. Specifically, industrial robots have the greatest negative impact on industrial soot emissions, with a coefficient of -0.277 and passing the 1% significance level. The negative impact of industrial robots on industrial wastewater discharge is second, with an estimated coefficient of -0.242, which also passes the 1% significance level. The negative impact of industrial robots on industrial SO 2 emissions is the smallest, with an estimated coefficient of -0.0875 and passing the 10% significant level. Compared with industrial wastewater and SO 2 , industrial robots have some unique advantages in reducing industrial soot emissions. Firstly, in terms of emission sources, industrial soot emissions mainly come from physical processes such as cutting. These processes can be significantly improved through precise control of industrial robots. Industrial SO 2 comes from the combustion process. Industrial wastewater originates from various industrial processes. It is difficult for industrial robots to directly control these processes. Secondly, in terms of source control and terminal treatment, industrial robots can reduce excessive processing and waste of raw materials, thereby controlling industrial soot emissions at the source. For industrial SO 2 and industrial wastewater, industrial robots mainly play a role in terminal treatment. Since the terminal treatment of industrial SO 2 and industrial wastewater often involves complex chemical treatment processes, it is difficult for industrial robot technology to fully participate in these processes. This makes the impact of industrial robots in the field of industrial SO 2 and industrial wastewater more limited than that in the field of industrial soot.

Regarding the control variables, the level of economic development has a significant inhibitory effect on industrial SO 2 emissions. The higher the level of economic development, the stronger the residents’ awareness of environmental protection, which constrains the pollution behavior of enterprises. The government also adopts strict policies to control pollutant emissions. The impact of urbanization level on the discharge of industrial wastewater, industrial SO 2 and industrial soot is significantly negative. The improvement of urbanization level can improve the efficiency of resource sharing and the centralized treatment of pollutants, reducing environmental pollution. The industrial structure significantly reduces industrial SO 2 and industrial soot emissions. The upgrading of industrial structure not only reduces the demand for energy, but also improves the efficiency of resource utilization. The degree of government intervention only significantly reduces the discharge of industrial wastewater. The possible reason is that to promote economic development, the government invests more money in high-yield areas, which crowds out investment in the environmental field. Similar to the degree of government intervention, environmental regulation has a negative impact on industrial wastewater discharge. The government’s environmental governance investment has not given some support to the enterprise’s clean technology research, which makes the pollution control investment not produce good emission reduction effect.

Mediation effect regression results

Green technology innovation.

Table 5 reports the results of intermediary effect model when green technology innovation is used as an intermediary variable. Industrial robots can have a positive impact on green technology innovation. For every 1% increase in the installation density of industrial robots, the level of green technology innovation increases by 0.722%. After adding the green technology innovation, the estimated coefficient of industrial robots has decreased, which shows that the intermediary variable is effective.

In the impact of industrial robots on industrial wastewater discharge, the mediating effect of green technology innovation accounts for 8.17% of the total effect. In the impact of industrial robots on industrial SO 2 emissions, the mediating effect of green technology innovation accounts for 11.8% of the total effect. In the impact of industrial robots on industrial soot emissions, the mediating effect of green technology innovation accounts for 3.72% of the total effect.

Employment skill structure

Table 6 reports the results of intermediary effect model when the employment skill structure is used as an intermediary variable. Industrial robots have a positive impact on the employment skill structure. For every 1% increase in the installation density of industrial robots, the employment skill structure is improved by 0.0837%. Similar to green technology innovation, the intermediary variable of employment skill structure is also effective.

In the impact of industrial robots on industrial wastewater discharge, the mediating effect of employment skill structure accounts for 6.67% of the total effect. In the impact of industrial robots on industrial SO 2 emissions, the mediating effect of employment skill structure accounts for 20.66% of the total effect. In the impact of industrial robots on industrial soot emissions, the mediating effect of employment skill structure accounts for 15.53% of the total effect.

Robustness test and endogeneity problem

Robustness test.

To ensure the robustness of the regression results, this paper tests the robustness by replacing core explanatory variables, shrinking tail and replacing sample. Regarding the replacement of core explanatory variables, in the benchmark regression, the installation density of industrial robots is measured by the stock of industrial robots. Replacing the industrial robot stock with the industrial robot installation quantity, this paper re-measures the industrial robot installation density. Regarding the tail reduction processing, this paper reduces the extreme outliers of all variables in the upper and lower 1% to eliminate the influence of extreme outliers. Regarding the replacement of samples, this paper removes the four municipalities from the sample. The estimation results are shown in Table 7 . Industrial robots still have a significant negative impact on environmental pollution, which confirms the robustness of benchmark regression results.

Endogeneity problem

Logically speaking, although the use of industrial robots can reduce environmental pollution, there may be reverse causality. Enterprises may increase the use of industrial robots to meet emission reduction standards, which increases the use of industrial robots in a region. Due to the existence of reverse causality, there is an endogenous problem that cannot be ignored between industrial robots and environmental pollution.

To solve the impact of endogenous problems on the estimation results, this paper uses the instrumental variable method to estimate. According to the selection criteria of instrumental variables, this paper selects the installation density of industrial robots in the United States as the instrumental variable. The trend of the installation density of industrial robots in the United States during the sample period is similar to that of China, which is consistent with the correlation characteristics of instrumental variables. The application of industrial robots in the United States is rarely affected by China’s economic and social factors, and cannot affect China’s environmental pollution, which is in line with the exogenous characteristics of instrumental variables.

Table 8 reports the estimation results of instrumental variable method. Among them, the column (1) is listed as the first stage regression result. The estimated coefficient of instrumental variable is significantly positive, which is consistent with the correlation. Column (2), column (3) and column (4) of Table 8 are the second stage regression results of industrial wastewater, industrial SO 2 and industrial soot emissions as explanatory variables. The estimated coefficients of industrial robots are significantly negative, which again verifies the hypothesis that industrial robots can reduce environmental pollution. Compared with Table 4 , the absolute value of estimated coefficient of industrial robots is reduced, which indicates that the endogenous problems caused by industrial robots overestimate the emission reduction effect of industrial robots. The test results prove the validity of the instrumental variables.

Panel quantile regression results

Traditional panel data models might obscure the differential impacts of industrial robots at specific pollution levels. To address this issue, this paper uses a panel quantile regression model to empirically analyze the effects of industrial robots across different environmental pollution levels.

Table 9 shows that industrial robots have a negative impact on industrial wastewater discharge. With the increase of the quantile of industrial wastewater discharge, the regression coefficient of industrial robots shows a W-shaped change. Specifically, when the industrial wastewater discharge is in the 0.1 quantile, the regression coefficient of industrial robot is − 0.229, and it passes the 1% significant level. When the industrial wastewater discharge is in the 0.25 quantile, the impact of industrial robots on industrial wastewater discharge is gradually enhanced. Its regression coefficient decreases from − 0.229 to − 0.256. When the industrial wastewater discharge is in the 0.5 quantile, the regression coefficient of industrial robot increases from − 0.256 to − 0.152. When the industrial wastewater discharge is at the 0.75 quantile, the regression coefficient of industrial robot decreases from − 0.152 to − 0.211. When the industrial wastewater discharge is in the 0.9 quantile, the regression coefficient of industrial robot increases from − 0.211 to − 0.188. For every 1% increase in the installation density of industrial robots, the discharge of industrial wastewater is reduced by 0.188%.

When industrial wastewater discharge is at a low percentile, the use of industrial robots can replace traditional production methods, reducing energy waste and wastewater discharge. As industrial wastewater discharge increases, the production process becomes more complex. Industrial robots may be involved in high-pollution, high-emission productions, diminishing the robots’ emission-reducing effects. When industrial wastewater discharge reaches high levels, pressured enterprises seek environmentally friendly production methods and use eco-friendly industrial robots to reduce wastewater discharge. As wastewater discharge continues to rise, enterprises tend to prioritize production efficiency over emission control, weakening the negative impact of industrial robots on wastewater discharge. When wastewater discharge is at a high percentile, enterprises should balance production efficiency and environmental protection needs, by introducing eco-friendly industrial robots to reduce wastewater discharge.

Table 10 shows that with the increase of industrial SO 2 emission quantile level, the negative impact of industrial robots on industrial SO 2 emissions gradually increases. Specifically, when industrial SO 2 emissions are below 0.5 quantile, the impact of industrial robots on industrial SO 2 emissions is not significant. When the industrial SO 2 emissions are above 0.5 quantile, the negative impact of industrial robots on industrial SO 2 emissions gradually appears.

When industrial SO 2 emissions are at a low percentile, the application of industrial robots primarily aims to enhance production efficiency, not to reduce SO 2 emissions. Enterprises should invest in the development of eco-friendly industrial robots, ensuring they are readily available for deployment when a reduction in industrial SO 2 emissions is necessary. As industrial SO 2 emissions continue to rise, both the government and the public pay increasing attention to the issue of SO 2 emissions. To meet stringent environmental standards, enterprises begin to use industrial robots to optimize the production process, reduce reliance on sulfur fuels, and consequently decrease SO 2 emissions. Enterprises should regularly evaluate the emission reduction effectiveness of industrial robots, using the assessment data to upgrade and modify the robots’ emission reduction technologies.

Table 11 shows that with the increase of industrial soot emissions quantile level, the negative impact of industrial robots on industrial soot emissions gradually weakens. Specifically, when industrial soot emissions are below 0.75 quantile, industrial robots have a significant negative impact on industrial soot emissions. This negative effect decreases with the increase of industrial soot emissions. When the industrial soot emissions are above 0.75 quantile, the negative impact of industrial robots on industrial soot emissions gradually disappears.

When industrial soot emissions are at a low percentile, they come from a few sources easily managed by industrial robots. As industrial soot emissions increase, the sources become more diverse and complex, making it harder for industrial robots to control. Even with growing environmental awareness, it may take time to effectively use robots in high-emission production processes and control industrial soot emissions. Enterprises should focus on researching how to better integrate industrial robot technology with production processes that have high soot emission levels. The government should provide financial and technical support to enterprises, assisting them in using industrial robots more effectively for emission reduction.

Figure  9 intuitively reflects the trend of the regression coefficient of industrial robots with the changes of industrial wastewater, industrial SO 2 and industrial soot emissions. Figure  9 a shows that with the increase of industrial wastewater discharge, the regression coefficient of industrial robots shows a W-shaped trend. Figure  9 b shows that with the increase of industrial SO 2 emissions, the regression coefficient of industrial robots gradually decreases. The negative impact of industrial robots on industrial SO 2 emissions is gradually increasing. Figure  9 c shows that with the increase of industrial soot emissions, the regression coefficient of industrial robots shows a gradual increasing trend. The negative impact of industrial robots on industrial soot emissions has gradually weakened. Figure  9 a, b and c confirm the estimation results of Tables 9 , 10 and 11 .

figure 9

Change of quantile regression coefficient.

Heterogeneity analysis

Regional heterogeneity.

This paper divides China into three regions: Eastern, central and western regions according to geographical location. The estimated results are shown in Table 12 . The industrial robots in eastern region have the greatest negative impact on three pollutants, followed by central region, and the industrial robots in western region have the least negative impact on three pollutants. The use of industrial robots in eastern region far exceeds that in central and western regions. The eastern region is far more than central and western regions in terms of human capital, technological innovation and financial support. Compared with central and western regions, the artificial substitution effect, upgrading of sewage treatment equipment and improvement of energy utilization efficiency brought by industrial robots in eastern region are more obvious.

Time heterogeneity

The development of industrial robots is closely related to policy support 56 . In 2013, the Ministry of Industry and Information Technology issued the “ Guiding Opinions on Promoting the Development of Industrial Robot Industry ”. This document proposes: By 2020, 3 to 5 internationally competitive leading enterprises and 8 to 10 supporting industrial clusters are cultivated. In terms of high-end robots, domestic robots account for about 45% of the market share, which provides policy support for the development of industrial robots. Based on this, this paper divides the total sample into two periods: 2006–2012 and 2013–2019, and analyzes the heterogeneous impact of industrial robots on environmental pollution in different periods. The estimation results are shown in Table 13 . Compared with 2006–2012, the emission reduction effect of industrial robots during 2013–2019 is greater.

The use of industrial robots can effectively reduce environmental pollution, which is consistent with hypothesis 1. This is contrary to the findings of Luan et al. 22 , who believed that the use of industrial robots would exacerbate air pollution. The inconsistency in research conclusions may be due to differences in research focus, sample size, and maturity of industrial robot technology. In terms of research focus, this paper mainly focuses on the role of industrial robots in reducing pollutant emissions during industrial production processes. Their research focuses more on the energy consumption caused by the production and use of industrial robots, which could aggravate environmental pollution. In terms of sample size, the sample size of this paper is 30 provinces in China from 2006 to 2019. These regions share consistency in economic development, industrial policies and environmental regulations. Their sample size is 74 countries from 1993 to 2019. These countries cover different geographical, economic and industrial development stages, affecting the combined effect of robots on environmental pollution. In terms of the maturity of industrial robots, the maturity of industrial robot technology has undergone tremendous changes from 1993 to 2019. In the early stages, industrial robot technology was immature, which might cause environmental pollution. In recent years, industrial robot technology has gradually matured, and its operating characteristics have become environmentally friendly. Their impact on environmental pollution has gradually improved. This paper mainly conducts research on the mature stage of industrial robot technology. Their research covers the transition period from immature to mature industrial robot technology. The primary reason that the use of industrial robots can reduce environmental pollution is: The use of industrial robots has a substitution effect on labor force, which reduces the raw materials needed for manual operation. For example, in the industrial spraying of manufacturing industry, the spraying robot can improve the spraying quality and material utilization rate, thereby reducing the waste of raw materials by manual operation. Zhang et al. 57 argued that energy consumption has been the primary source of environmental pollution. Coal is the main energy in China, and the proportion of clean energy is low 58 . In 2022, clean energy such as natural gas, hydropower, wind power and solar power in China accounts for only 25.9% of the total energy consumption, which can cause serious environmental pollution problems. Industrial robots can promote the use of clean energy in industrial production and the upgrading of energy structure 24 . The reduction of raw materials and the upgrading of energy structure can control pollutant emissions in front-end production. On September 1, 2021, the World Economic Forum (WEF) released the report “ Using Artificial Intelligence to Accelerate Energy Transformation ”. The report points out that industrial robots can upgrade pollution monitoring equipment and sewage equipment, which reduces pollutant emissions in end-of-pipe treatment. Ye et al. 59 also share the same viewpoint.

The use of industrial robots can reduce environmental pollution through green technology innovation, which is consistent with hypothesis 2. Industrial robots promote the integration of knowledge, which helps enterprises to carry out green technology innovation activities. Meanwhile, Jung et al. 60 suggested that industrial robots can lower production costs for companies, allowing them to invest in green technology research. The level of green technology innovation is improved. Green technology innovation reduces environmental pollution through the following three aspects: Firstly, the improvement of energy utilization efficiency. China’s utilization efficiency of traditional energy sources such as coal is not high. The report of “ 2013-Global Energy Industry Efficiency Research ” points out that China’s energy utilization rate is only ranked 74th in the world in 2013. Low energy efficiency brings serious environmental pollution problems 61 . Du et al. 62 found that the innovation of green technologies, such as clean coal, can enhance energy efficiency and decrease environmental pollution. Secondly, the production of green products. Green technology innovation accelerates the green and recyclable process of production, thereby reducing the pollutants generated in production process. Thirdly, the improvement of enterprise competitive advantage. Green technology innovation can enable enterprises to gain greater competitive advantage in green development 63 . The supply of environmentally friendly products increases, which not only meets the green consumption needs of consumers, but also reduces the emission of pollutants.

Industrial robots can reduce environmental pollution by optimizing the structure of employment skills, which is consistent with hypothesis 3. Autor et al. 64 contended that industrial robots would replace conventional manual labor positions, reducing the demand for low-skilled labor. Industrial robots represent the development of numerical intelligence. With the continuous development of digital intelligence, the demand for high-skilled labor in enterprises has increased. Koch et al. 65 demonstrated that the use of industrial robots in Spanish manufacturing firms leads to an increase in the number of skilled workers. In February 2020, the Ministry of Human Resources and Social Security, the State Administration of Market Supervision and the National Bureau of Statistics jointly issues 16 new professions such as intelligent manufacturing engineering and technical personnel, industrial Internet engineering and technical personnel, and virtual reality engineering and technical personnel to the society. These new occupations increase the demand for highly skilled labor. The reduction of low-skilled labor and increase of high-skilled labor optimize the structure of employment skills. The optimization of employment skill structure narrows the wage gap between employees, reducing the communication cost of employees. Employees learn and exchange technology with each other, which not only improves the absorption capacity of clean technology. It also improves the production efficiency of enterprises and increases corporate profits, so that enterprises can use more funds for clean technology research and development, thereby reducing environmental pollution.

Conclusions and policy recommendations

Based on the panel data of 30 provinces in China from 2006 to 2019, this paper uses the panel data model and mediating effect model to empirically test the impact of industrial robots on environmental pollution and its transmission mechanism. This paper uses panel quantile model, regional samples and time samples to further analyze the heterogeneous impact of industrial robots on environmental pollution. The conclusions are as follows: (1) Industrial robots can significantly reduce environmental pollution. For every 1% increase in industrial robots, the emissions of industrial wastewater, industrial SO 2 , and industrial dust and smoke decrease by − 0.242%, − 0.0875%, and − 0.277%. This finding is contrary to that of Luan et al. 22 , who argued that the use of industrial robots exacerbates air pollution. The results of this paper provide a contrasting perspective, highlighting the potential value of industrial robots in mitigating environmental pollution. (2) Industrial robots can reduce environmental pollution by improving green technology innovation level and optimizing employment skills structure. In the impact of industrial robots on industrial wastewater discharge, the mediating effect of green technology innovation accounts for 8.17% of total effect. The mediating effect of employment skill structure accounts for 6.67% of total effect. In the impact of industrial robots on industrial SO 2 emissions, the mediating effect of green technology innovation accounts for 11.8% of total effect. The mediating effect of employment skill structure accounts for 20.66% of total effect. In the impact of industrial robots on industrial soot emissions, the mediating effect of green technology innovation accounts for 3.72% of total effect. The mediating effect of employment skill structure accounts for 15.53% of total effect. While Obobisa et al. 66 and Zhang et al. 67 highlighted the role of green technological innovation in addressing environmental pollution. Chiacchio et al. 68 and Dekle 69 focused on the effects of industrial robots on employment. The mediating impact of technology and employment in the context of robots affecting pollution hasn’t been addressed. Our research provides the first in-depth exploration of this crucial intersection. (3) Under different environmental pollution levels, the impact of industrial robots on environmental pollution is different. Among them, with the increase of industrial wastewater discharge, the impact of industrial robots on industrial wastewater discharge shows a “W-shaped” change. With the increase of industrial SO 2 emissions, the negative impact of industrial robots on industrial SO 2 emissions is gradually increasing. On the contrary, with the increase of industrial soot emissions, the negative impact of industrial robots on industrial soot emissions gradually weakens. (4) Industrial robots in different regions and different periods have heterogeneous effects on environmental pollution. Regarding regional heterogeneity, industrial robots in eastern region have the greatest negative impact on environmental pollution, followed by central region, and western region has the least negative impact on environmental pollution. Regarding time heterogeneity, the negative impact of industrial robots on environmental pollution in 2013–2019 is greater than that in 2006–2012. Chen et al. 5 and Li et al. 24 both examined the overarching impact of industrial robots on environmental pollution. They did not consider the varying effects of robots on pollution across different regions and time periods. Breaking away from the limitations of previous holistic approaches, our study offers scholars a deeper understanding of the diverse environmental effects of industrial robots.

According to the above research conclusions, this paper believes that the government and enterprises can promote emission reduction through industrial robots from the following aspects.

Increase the scale of investment in robot industry and promote the development of robot industry. China’s industrial robot ownership ranks first in the world. Its industrial robot installation density is lower than that of developed countries such as the United States, Japan and South Korea. The Chinese government should give some financial support to robot industry and promote the development of robot industry, so as to effectively reduce environmental pollution. The R&D investment of industrial robots should be increased so that they can play a full role in reducing raw material consumption, improving energy efficiency and sewage treatment capacity.

Give full play to the role of industrial robots in promoting green technology innovation. Industrial robots can reduce environmental pollution through green technology innovation. The role of industrial robots in innovation should be highly valued. The advantages of knowledge integration and data processing of industrial robots should be fully utilized. Meanwhile, the government should support high-polluting enterprises that do not have industrial robots from the aspects of capital, talents and technology, so as to open up the channels for these enterprises to develop and improve clean technology by using industrial robots.

Give full play to the role of industrial robots in optimizing employment skills structure. The use of industrial robots can create jobs with higher skill requirements and increase the demand for highly skilled talents. China is relatively short of talents in the field of emerging technologies. The education department should actively build disciplines related to industrial robots to provide talent support for high-skilled positions. Enterprises can also improve the skill level of the existing labor force through on-the-job training and job competition.

Data availability

The datasets used or analyzed during the current study are available from Yanfang Liu on reasonable request.

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Research Article

The impact of intermediate product imports on industrial pollution emissions: Evidence from 30 industries in China

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  • Yuling Mao, 
  • Yizhong Fu, 

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  • Published: October 4, 2023
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Table 1

Open and sustainable development is the theme that underpins a country’s high-quality economic development. This study uses GMM regression, mediation effect test to conduct empirical tests based on the panel data of China’s industrial sectors from 2003 to 2015 to analyze the internal mechanism of the impact of intermediate product imports on China’s industrial pollution emissions. The results show that (1) Intermediate product imports can significantly promote the emission reduction of industrial wastes, including wastewater, waste gas and solid waste. (2) Considering the differences in the level of pollution intensity, this paper classified the sample and found the impact is heterogeneous that for the heavily, moderately, lightly polluted industries, intermediate product imports have different negative impacts on their pollution emissions. (3) Intermediate products imports reduce industrial pollution emissions through import competition effect, variety effect and technology spillover effect, and all of them play a partial mediating role.

Citation: Wan L, Mao Y, Fu Y, Wan X (2023) The impact of intermediate product imports on industrial pollution emissions: Evidence from 30 industries in China. PLoS ONE 18(10): e0292347. https://doi.org/10.1371/journal.pone.0292347

Editor: Liang Zhuang, East China Normal University, CHINA

Received: July 3, 2023; Accepted: September 18, 2023; Published: October 4, 2023

Copyright: © 2023 Wan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The relevant data are available from figshare at https://doi.org/10.6084/m9.figshare.24085026 .

Funding: LW acknowledge support from Fundamental Research Funds for the Central Universities ( http://www.bjfu.edu.cn ) Grant No. 2023SKY04 and Beijing Social Science Fund ( http://www.bjsk.org.cn ) Grants No. 20JJC026. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Since the reform and opening up, China has actively integrated into the global production system and vigorously promoted the liberalization of intermediate trade, which has led to a significant improvement in production efficiency and rapid economic growth. However, due to its extensive economic growth mode and continuously expanding industrialization scale, the negative impact on the environment is gradually becoming prominent. In order to positively respond to global climate change and push forward high-quality and sustainable development of the Chinese economy, China proposed a specific implementation plan for environmental and ecological protection in 2020, resolutely curbing the blind development of high energy consumption and high emission projects. It can be seen that China attaches great importance to environmental pollution issues and has been seeking a green path for harmonious economic development and environmental protection.

China entered a new round of rapid import trade liberalization after being admitted into the WTO at the end of 2001. From 2003 to 2021, the scale of China’s foreign trade imports increased from 0.41 trillion US dollars to 2.69 trillion US dollars, accounting for a proportion of global imports from 4.4% to 11.9%. Meanwhile, according to WTO, the import volume of intermediate goods was 1676 billion US dollars, 65% and 176% higher than the United States (second place) and Germany (third place) respectively. China has become the largest country in intermediate goods trade. With the reform of the liberalization system of intermediate goods trade, the structure of intermediate goods trade is being optimized faster, reflected in the significant growth of key materials, components, and other intermediate goods in the manufacturing supply chain. In this context, the import of intermediate goods can help to resolve the current structural contradictions on China’s supply side, encourage enterprises to allocate resources globally, accelerate the transformation of production methods, and thus become a new path for carbon reduction and pollution reduction.

Regarding trade and environment, numerous studies have constructed theoretical and empirical models of the impact of international trade on environmental quality and conducted further analysis. It is worth mentioning that Johnson et al. [ 1 ] found that once the intermediate products trade accounted for more than 66.7% of total trade, the impact would be equivalent to that of the final products trade. Through a comparative study, He and Hertwich [ 2 ] discovered that intermediate product trade can result in implicit carbon emissions equivalent to final product trade. From the perspective of import structure, the proportion of intermediate goods imported by China reached 67.61% in 1995, and even increased to 75.71% in 2020 [ 3 ], demonstrating that its impact on environmental quality cannot be overstated. Under such a trade scale, what impact will intermediate product imports have on China’s industrial pollution emissions? Will industrial sectors with different levels of pollution be affected differently? What is its impact mechanism? In order to answer the above questions, this study selects 30 industrial sectors in China, deeply explores the impact of intermediate import trade on the pollution emissions of the industrial sectors and further examines the internal mechanism.

Compared to existing research, the contributions of our work are mainly reflected in the following aspects. First, for the research perspective, exploring the role of import trade in industrial pollution emissions based on intermediate goods trade fills the research gap in the intersection of intermediate goods imports and the environment, and provides new empirical evidence for the reduction of carbon emissions under open economic conditions; Second, in terms of research subjects, we utilized data from the Chinese industrial sector level and used GMM regression to examine the impact and mechanism of intermediate product imports on pollution emissions. Considering the heterogeneity of the industrial sector, we then divides it into three sub samples based on the intensity of pollution emissions for detailed exploration, targeting the largest country in intermediate trade as a reference for improving green development in developing countries; Third, as for research content, the competition effect, variety effect, and technology spillover effect of intermediate goods imports are specifically measured to test their indirect mechanism, further supplementing and improving the possible paths of intermediate goods imports affecting industrial pollution, providing important insights for achieving pollution reduction from international trade.

Literature review

Trade and environmental issues have always been a hot topic of concern for academia, so a wealth of literature has been accumulated. It is possible to categorize the research that are closely linked to this paper into two groups: one is literature discussing the relationship between intermediate import trade and environmental pollution. By researching the relationship between trade and environmental contamination, some academics have reached conflicting conclusions, that is, import trade may inhibit environmental pollution or exacerbate pollution; The other is literature that discusses the effects of intermediate goods imports. Most literature found that intermediate goods imports improve total factor productivity, export product quality, and technological level of enterprises through competition, variety and technology spillover effect, which are important channels for developing countries to progress.

The impact of import trade on the environment

In the development of trade environment theory, Grossman and Krueger [ 4 ] pioneered the decomposition of the impact of trade on the environment into scale effect, composition effect, and technique effect, and empirically studied the impact of the North American Free Trade Agreement (NAFTA) on the environment. Based on this analytical framework, free trade will improve environmental quality to some extent, and when income reaches a certain level, the negative impact of scale effect will be surpassed by the positive impact of composition and technique effect. This income level is the critical value represented at the inflection point of the inverted U-shaped Environmental Kuznets Curve (EKC), which reflects the relationship between per capita income and the degree of environmental degradation. The classic theory is still inspiring a lot of empirical research to this day. The results of Murthy and Gambhir [ 5 ] confirmed that India presents an N-type EKC, proving that India has indeed become a pollution paradise for developed countries in the process of economic development. But while Awaworyi [ 6 ] used Australia as a sample for analysis, the results obtained were consistent with the inverted U-shape of traditional EKC.

With the further deepening of international trade, the depth and breadth of research in this field have also been expanded. From the perspective of import trade, Dietzenbacher and Mukhopadhyay [ 7 ] investigated the impact of trade activities between countries on environmental pollution and found that although India’s import and export trade generally exacerbates pollution, the pollution reduction caused by import trade is less than the pollution increase caused by export trade. Yao et al. [ 8 ] studied the energy efficiency of 36 countries worldwide and found that the impact of export value added on energy efficiency is much greater than that of import value added. Chen et al. [ 9 ] estimated the energy intensity of 30 provinces in China from 2005 to 2018 and explored the impact of trade openness and economic growth on China’s energy intensity. They concluded that the role of foreign trade in energy intensity is mainly attributed to export channels, whereas the role of import channels can be ignored. This may be the reason why import trade has not received sufficient attention in relevant research. However, with the development of emerging economies and the extension of global production networks, intermediate trade has come to the foreground owing to its pivotal position in import trade. Through energy use efficiency, Michele and Ketterer [ 10 ] indirectly measured the environmental performance of enterprises and found that the energy use efficiency of intermediate import enterprises was higher than that of enterprises that had never imported before in Indonesia. Zhang [ 11 ] investigated the relationship between intermediate product trade and pollution in the context of endogenous environmental policies. The results showed that developed countries may reduce pollution at the cost of more pollution from developing countries. In other words, the pollution paradise hypothesis also exists in intermediate product trade, which may cause environmental degradation in developing countries. It reflected the academic circle has not yet reached a consistent conclusion on the environmental benefits of intermediate product imports, so relevant research urgently needs to be supplemented.

The effects of intermediate product imports

When studying import trade, intermediate goods, serving as carriers of new knowledge and technology, became an important form of technology diffusion and transfer [ 12 ]. As a form of materialized technology spillover, it also promotes technological progress and enterprise innovation in importing countries. Grossman and Helpman [ 13 ] were the first to introduce the general equilibrium model to analyze the relationship between trade, growth, and technological progress in an open economy, and discussed how trade in intermediate and final goods affects long-term economic growth. Based on this, Coe and Helpman [ 14 ] empirically examined the impact of import trade on international technology spillovers and total factor productivity growth. Their method of weighting the R&D of trading partner countries with import share has become a common practice in subsequent research on import trade as an international technology spillover channel. Schmit [ 15 ] summarized the intermediate import effect as the expansion of intermediate import scale, improvement of intermediate import quality, and spillover of intermediate import technology, which is the learning effect in imports. Amiti et al. [ 16 ] examined the productivity benefits of Indonesia’s import liberalization from the perspective of tariff reduction, believing that reducing tariffs on final products can introduce more intense import competition to improve productivity, while intermediate imports improve productivity through learning effects. Halpern et al. [ 17 ] studied the degree to which total factor productivity is affected by intermediate goods import trade. They pointed out that in the context of trade openness and globalization, intermediate goods import contributes about 30% of the driving force to the growth of total factor productivity.

In addition to the technology spillover effect caused by the embedded technology level of exporting countries in imported intermediate goods, Feenstra [ 18 ] first adopted the measurement method of the net variety change index of imported products, estimated the welfare effect brought by the substitution elasticity between the change in the number of imported varieties and the share of import costs. It was discovered that an increase in the variety of imported products would lower the import price index and improve trade conditions. And the technical method of measuring changes in import varieties through micro trade data laid the foundation for scholars to examine the relationship between imports and corporate behavior from diversified import products. Subsequently, Goldberg et al. [ 19 ] applied this method to measure changes in intermediate import types, which drew that an increase in intermediate import types would promote the improvement of product quality and innovation capabilities of enterprises. This conclusion has also been supported by Liu and Qiu [ 20 ] and Chen and Zhang [ 21 ]. Based on panel data from prefecture level cities in China, Xiang et al. [ 22 ] also asserted that intermediate goods imports contribute to improving urban energy efficiency through technology spillover effect and diversification of intermediate goods types. Besides, Melitz [ 23 ] believes that the domestic market competition brought about by imports enhances the price elasticity of demand for enterprises, which can lead to lower productivity enterprises exiting the market and reduce product prices and markup rates.

In summary, we can see the shortcomings of existing research: First, although a large number of literature has focused on the relationship between trade and environmental pollution, most of them show solicitude for exports and final products. This paper studies from the perspective of intermediate goods imports, filling the gap in this field. Second, research on the practical impact of intermediate goods import trade mainly concentrates on the technology spillover effects on enterprise productivity and export product quality. There is little literature to test whether these mechanisms are the channels through which intermediate goods import affects industrial pollution emissions. Third, existing literature are mostly based on the macro and micro enterprise levels, so there is a lack of research on the heterogeneity of industrial sectors. We divide the industrial sector into three samples based on pollution emission intensity to explore the impact of intermediate goods imports on heterogeneous industrial pollution emissions. This not only adds empirical evidence from the world’s largest developing country to the existing research on intermediate goods imports and environment, but also provides a new perspective for evaluating the effectiveness of China’s intermediate trade liberalization reform afterwards.

Methodology

Through a review of existing research, we summarize the impact pathways of intermediate product imports on industrial pollution emissions as competition effect, variety effect, and technology spillover effect. In the study on the mechanisms that affect environmental pollution, literature mostly considered technical factors into the impact pathways of environmental pollution for empirical testing, and drew conclusions that can affect environmental pollution through technical pathways. We combine the three types of import effects of intermediate goods and uses technological progress as a link to analyze the impact path between intermediate goods imports and industrial pollution emissions.

The rich variety and wide source of intermediate products imported by enterprises indicate that the market for imported products is filled with competition everywhere. Firstly, for intermediate suppliers, the entry of low-cost and high-quality intermediate products into the domestic market will put pressure on domestic manufacturers of similar intermediate products. Downstream enterprises will be more inclined to use imported intermediate products with low cost, which will squeeze the profits of domestic intermediate suppliers. The impact of high-quality products from external sources will result in the elimination of products that do not have quality advantages produced by enterprises themselves from the production process, freeing up various means of production, including machinery and equipment, labor, capital, and other means of production. This production model of reducing peripheral products and focusing on core products can not only enable enterprises to concentrate resources on the production of core products [ 3 ], but also increase one’s own research and development investment to promote technological progress and develop new and more advantageous intermediate products [ 21 ]. Secondly, for the final product manufacturer, when the price of the intermediate products required increases due to the inclusion of advanced foreign technological elements, the production cost and price of the final product will also increase for the manufacturer who produces the corresponding final product, resulting in the manufacturer losing its price competitive advantage in the international market, so as to forcing enterprises to change their operational management methods or improve their production technology to reduce long-term production costs and regain market position [ 24 , 25 ]. Based on this, we conclude that:

  • Hypothesis 1: Intermediate product imports affect technological progress through competitive effect

One phenomenon that cannot be ignored is that the diversification of intermediate goods imports has become a trend. Due to the incomplete substitution of technology and knowledge internalized by different products or product types, the technological progress brought about by the increase in diversity of imported intermediate products will expand exponentially. This paper summarizes the impact of the increase in the varieties of imported intermediate goods on technological progress into three channels. The first is the learning effect. Within the increase in the types of imported intermediate goods, enterprises have come into contact with and use more varieties of key spare parts inputs and advanced production equipment capital goods. High tech intermediate products from developed countries contain advanced processes that can be internalized by enterprises for learning, thereby improving their technological level and innovation capabilities [ 15 ]; The second is the complementary effect, which can easily produce a "the whole is more than the sum of parts" effect by combining different products or types of intermediate products (imported intermediate products with imported intermediate products, imported intermediate products with domestic intermediate products) [ 26 ]. Likewise, considering the large number of intermediate product assembly activities of Chinese enterprises, by importing intermediate products and combining them with the mutual matching model, if enterprises can integrate their own research and development efforts, the process of innovating products will be simpler and faster [ 27 ]; The third is the R&D incentive effect. The increase in import varieties enhances the global resource allocation efficiency of enterprises, improves production efficiency, price markup, and profit income, reduces financial constraints, and encourages enterprises to invest more available funds in long-term independent innovation activities [ 28 ]. Since the higher level of quality or technical components in new products, the expected revenue of enterprises for new products has been increased, which further stimulates the research and development behavior of enterprises. Based on this, we conclude that:

  • Hypothesis 2: Intermediate product imports affect technological progress through variety effect.

As early as the 1980s, some scholars proposed that the spillover effect of technology in import trade is exerted through both horizontal and vertical perspectives, so we also analyzed it according to this logic. Firstly, the horizontal spillover effect mainly refers to the technological connection between intermediate import enterprises and domestic intermediate producers. On the one hand, a large number of foreign intermediate products are integrated into the domestic market, causing import competitive enterprises in the same industry to face fierce market competition. In order to occupy market share and continue to survive and develop, these import enterprises will absorb advanced technologies contained in imported intermediate products through competition and imitation [ 16 ]. On the other hand, market demand pressure forces unskilled workers to continuously improve their own quality and knowledge skills, gradually adapting to high-end production and research and development [ 29 ], stimulating the motivation of domestic suppliers for technological innovation, thereby driving the overall technological upgrading of the country and improving the level of technological innovation of domestic enterprises. Secondly, vertical spillover effect refers to the transmission of advanced production factors contained in imported intermediate goods through the production chain. If the technology of a certain link or industry in the industrial chain improves, other industries in other links will improve their production technology to match the technology, thus forming a vertical spillover of technology. Especially in China’s processing trade, the technology in imported intermediate goods will be transmitted to manufacturers engaged in processing trade, promoting their technological progress, which will inevitably have a demand for technological innovation in their upstream and downstream industries. Therefore, the technological innovation of enterprises has a great promoting effect on the technological innovation of related enterprises. Based on this, we conclude that:

  • Hypothesis 3: Intermediate product imports affect technological progress through technological spillover effect.

Firstly, the technological progress brought by the import of intermediate products will lead to improvements in production efficiency. Technological progress has improved the utilization rate of resources or raw materials in the production process, enabling the same resources or raw materials to produce more products compared to traditional production technologies; Secondly, technological progress will include innovation in green technologies to reduce pollution emissions during product production. In addition, technological progress promotes pollution reduction through green research and development of products, recycling and comprehensive utilization of excess resources or raw materials. The advanced technology of greening and cleaning is used to monitor the entire production process of enterprise products, and to prevent and control pollution emissions in advance, which is more efficient than post prevention and control in reducing pollution emissions. In terms of pollution control, the discharge of industrial pollutants requires enterprises to solve it through technological means. Improving the level of pollution control technology can effectively reduce the discharge of industrial three wastes; Thirdly, technological progress can improve product production and pollution control technologies, achieving the production of green products. The widespread emergence of green products in the market can awaken people’s environmental awareness, making consumers more inclined to choose green concept products during the consumption process, and enhancing people’s understanding of sustainable development. It can be seen that technological progress is an important channel and necessary path for green development. Based on this, we conclude that:

  • Hypothesis 4: The import of intermediate goods affects technological progress through the above three import effects, thereby improving enterprise production efficiency and enhancing green technology innovation, green product research and development, which are carried out to promote pollution reduction.

Empirical model and variables

The empirical model.

literature review on industrial pollution

Dependent variable.

In previous studies, the pollution emission, is generally selected as a variable to measure environmental pollution, mainly including SO 2, CO 2 , industrial waste gas, wastewater, and solid waste. However, this paper draws on the practices of many scholars and selects industrial wastewater, industrial waste gas and industrial solid waste emissions as the dependent variables [ 33 ].

Independent variable.

This paper directly uses the import scale of intermediate goods as the core independent variable, that is, the trade volume of intermediate goods imported from various countries by various industries [ 34 ]. Combined with the above analysis and elaboration, it is believed that the import of intermediate goods has a positive impact on the industrial pollution reduction.

Control variables.

(1) Foreign Direct Investment (FDI). This paper measures the proportion of foreign investment in various industries and investment enterprises in Hong Kong, Macao and Taiwan in total assets of various industries. (2) Environmental Regulation (ERS). The proportion of waste gas and wastewater treatment operating costs in the industrial sales output value is used to measure the industrial wastewater environmental regulation (FSERS) and industrial waste gas environmental regulation (FQERS). The environmental regulation of industrial solid waste (FGERS) is directly measured by the amount of solid waste disposal. They are used as proxy variables for the effect of environmental regulation. Additionally, due to the differences in the production structure of various industries, the gross industrial output value of the industry needs to be adjusted. (3) Industry Size (SIZE). It is expressed by gross industrial output value. (4) R&D investment (RD). Use R&D intensity (RD expenditure/main business income) as an indicator to measure industry R&D investment. (5) The level of economic development (GDP). A country’s economic development level is measured by per capita GDP.

Mediating variables.

(1) Variety effect index

literature review on industrial pollution

(2) Technology spillover effect index

literature review on industrial pollution

The specific variable descriptions and definitions are shown in Table 1 .

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https://doi.org/10.1371/journal.pone.0292347.t001

Due to the lack of China Environmental Statistics Yearbook in 2002, and since 2016, the China Environmental Statistical Yearbook has not counted the emission of industrial wastewater, industrial waste gas and industrial solid waste in each industry, this paper uses the data of China’s industry from 2003 to 2015. The data of industrial wastewater emission, industrial waste gas emission and industrial solid waste emission are mainly obtained from the China Environmental Yearbook and China Environmental Statistical Yearbook from 2004 to 2016. The trade volume of intermediate goods imported by various industries is obtained from the WITS database. GDP per capita data is from the World Bank database. The data of environmental regulation, foreign direct investment and industry scale are from China Statistical Yearbook and China Industrial Economic Yearbook from 2004 to 2016; The R&D investment data of various industries are from the China Science and Technology Statistics Yearbook from 2004 to 2016. In order to eliminate the impact of exchange rate changes on the data, the annual average exchange rate of the year is used to convert the import trade volume of intermediate goods and GDP per capita into statistical data denominated in RMB. Moreover, the natural logarithm treatment of industrial wastewater emission, industrial waste gas emission, industrial solid waste emission, intermediate product imports, industry scale, industry R&D investment and per capita GDP is carried out.

Characteristic facts

This section analyzes the current situation of China’s industrial intermediate import trade from both macro and micro aspects. According to China’s classification of industrial sectors and SITC Rev3 and BEC, with the reference of the correspondence between industry and intermediate products in Sheng [ 34 ], SITC codes of imported intermediate goods in 30 industrial sectors in China were screened. The SITC code is used to obtain product data from the WITS database, and the import of intermediate goods in the industrial sectors is analyzed from the import scale, the structure of imported goods and the structure of the import source country.

The value of intermediate product imports

The overall growth rate of intermediate product imports in the industrial sector showed a downward trend. Fig 1 shows the import value and growth rate of intermediate goods in the industrial sector from 2000 to 2018. Specifically, in 2000, the import value of intermediate goods in the industrial sectors was 170.6878 billion US dollars, and in 2018, it increased to 1,612.195 billion US dollars, which is about 9.45 times that of 2000. During different periods, there are different development characteristics. Due to its successful entry into the WTO in 2001, tariffs have dropped and the scale of intermediate product imports has risen sharply. Its trade growth rate was as high as 30% in 2003, the highest growth rate in recent years. However, because of the financial crisis in 2018, the important role of manufacturing in the national economic development was re recognized by developed economies, so a series of preferential policies and measures were issued to support the return of manufacturing enterprises. And in light of the implementation of these preferential policies and measures, manufacturing multinational companies in developed countries will gradually withdraw their enterprises or factories from China. As demand decreases, the import of intermediate goods will also decrease. Compared with some emerging developing economies, China’s demographic dividend and cost comparative advantages are gradually disappearing. Therefore, a large number of multinational companies will choose countries with advantages in demographic dividend and cost to move into their factories. By 2010, the economy had recovered, and the import trade volume of intermediate goods had grown rapidly, with a trade growth rate of more than 30%, and the growth rate gradually slowed down in the following years. In 2014, due to the impact of commodity prices, the value of trade imports fell, and since 2016, the value of trade has started to rise again. In general, from 2000 to 2018, the import volume of intermediate products in the industrial sectors was volatile.

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Source: data were obtained from the WITS database.

https://doi.org/10.1371/journal.pone.0292347.g001

The structure of intermediate product imports

Drawing on the practice of Wei [ 35 ], intermediate goods are divided into two categories: primary products and manufactured products. Fig 2 shows the proportion of primary products and manufactured products in the imported intermediate products of the industrial sectors from 2000 to 2018. By observing the product import proportion of intermediate products, from 2000 to 2018, manufactured products have always been the largest import variety of intermediate products in China’s industrial sectors. From 2000 to 2008, the import of primary products accounted for less than 10% of intermediate product imports in the industry, but the proportion was increasing. Since 2009, the proportion of primary products has remained around 10%, and reached the highest in recent years in 2012. Primary products mainly include resource-based products. Since the domestic supply of resource-based products is insufficient to meet the demand, it is necessary to increase the import of resource-based intermediate products from abroad.

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https://doi.org/10.1371/journal.pone.0292347.g002

According to Wei [ 35 ] on the structure of intermediate products, this paper divides intermediate products into the following four categories again. The specific classification is shown in Table 2 .

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https://doi.org/10.1371/journal.pone.0292347.t002

Fig 3 reflects the trend of import trade value of intermediate reclassified products from 2000 to 2018. The four lines from top to bottom represent the import trade trend lines of medium-tech industrial products, resource products, low-tech industrial products and high-tech industrial products. It is evident that the trade trends of mid-tech industrial products, resource products and low-tech industrial products are almost the same. Before 2008, the import value of various products was rising at a relatively stable growth rate. The import value decreased from 2008 to 2009 and began to increase after 2009. Until 2014, the trade value fell due to the impact of prices. Since 2016, the trade value has continued to rise. And high-tech industrial products have not changed much in the past decade.

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https://doi.org/10.1371/journal.pone.0292347.g003

Moreover, in Fig 3 , starting from 2000, mid-tech industrial manufactured products have been the most imported among intermediate products. Before 2004, the import scale of low-tech industrial products was higher than that of resource products, but after 2004, the import scale of resource products has always been second only to the import scale of mid-tech industrial products. With the development of the national economy, domestic producers have more and more demand for resources. When domestic resources cannot meet the demand, domestic producers will import foreign resource products, which may be the reason why the scale of imported resource products has been growing and exceeds the import scale of low technology industrial products.

The sources of intermediate product imports

Depending on different levels of economic development, the source countries of China’s intermediate product imports are classified as underdeveloped countries, developing countries and developed countries. As shown in Fig 4 , the main source countries of China’s imports of intermediate goods are developed countries, and the proportion of intermediate products imported from developed countries has always been the largest and fluctuated around 60%, followed by developing countries, accounting for around 40%. Besides, the least proportion is the underdeveloped countries. Most of the top ten import source countries of industrial intermediate goods are developed countries.

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https://doi.org/10.1371/journal.pone.0292347.g004

Fig 5 shows the import trade volume of intermediate products between China’s industrial sectors and developed countries from 2000 to 2018. The developed countries in Fig 5 include Japan, South Korea, the United States, Germany, Australia, and Singapore. These developed countries are also the top 10 import source countries in terms of trade value of intermediate products in the industrial sectors. It can be seen that the overall import of intermediate products in the industrial sectors of my country and these developed countries has shown a growth trend. Among them, from 2000 to 2018, Japan has been the first source of imports of intermediate products in China’s industrial sectors, followed by South Korea, the United States, and Germany. Before 2007, the trade value of intermediate products imported by the industrial sectors from Singapore was more than that of Australia, but Australia surpassed Singapore in 2007.

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https://doi.org/10.1371/journal.pone.0292347.g005

Empirical results

Gmm regression results, regression results and analysis at the full-sample level..

Based on the dynamic panel model, relying on the collected data on the import volume of intermediate goods and industrial pollution in 30 industrial sectors in China from 2003 to 2015, the emissions of industrial wastewater, industrial waste gas and industrial solid waste were regressed and estimated from the whole industry level through the systematic GMM method.

The results correspond to model (1), model (2) and model (3) in Table 3 , respectively. It is not difficult to find through observation that intermediate product imports can effectively promote the emission reduction of three wastes. From models (1), (2) and (3), the impact of the core independent variable, import scale of intermediate goods on the emissions of three wastes in the industrial sectors, is significantly negative.

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https://doi.org/10.1371/journal.pone.0292347.t003

In terms of control variables, (1) Foreign direct investment (FDI) has a significant negative correlation with industrial waste gas (FQ) emissions, and has a significant positive correlation with industrial wastewater (FS) and industrial solid waste (FG) emissions. This can reflect the different directions of foreign direct investment in industrial investment. One is that foreign investors tend to transfer resource mining industries as investment objects to developing countries like China. These industries have high short-term returns but large emissions of industrial wastewater and industrial solid waste. Second, foreign investors can enter industrial sectors by providing capital or technical support to improve the technical level of production processes and eventually reduce the pollution emission of industrial waste gases. (2) The regression coefficient of industry scale (SIZE) with industrial wastewater and industrial solid waste emissions is significantly negative, while industrial waste gas emissions are significantly positive. (3) The effects of three wastes environmental regulations (FSERS, FQERS, FGERS) on the corresponding pollution emissions (FS, FQ, FG) are all negatively correlated, but only the regression coefficients for industrial waste gas and industrial solid waste are obviously. (4) R&D investment (RD) has obvious effect on promoting emissions reduction of three wastes pollution. (5) The relationship between the level of economic development (GDP) and the emission of industrial wastewater, industrial waste gas and industrial solid waste presents a positive U type, an inverted U type and an inverted U type respectively. As can be seen from Table 3 , the regression coefficients of the primary and quadratic terms of all economic development levels are significant. In model (1), the level of economic development and industrial wastewater emission are in a positive U shape, which is inconsistent with the traditional environmental Kuznets curve theory. This may be because it is still on the left side of the inverted U shape, not passing the inflection point of the curve, and is in the rising stage. The level of economic development and industrial waste gas and industrial solid waste shows an inverted U shaped curve, which is in line with the traditional environmental Kuznets curve law, and is now on the right side of U. With the increase of the level of economic development, the emissions of industrial waste gas and industrial solid waste will decrease.

Regression results and analysis at the sub-sample level

For industrial sectors with different pollution levels, intermediate product imports will also have heterogenous effects on them. Refer to the practice of Wang [ 36 ], the 30 industries are divided into heavily, moderately, lightly polluted industries by the intensity of pollution emissions. The regression analysis is conducted on these three subsamples to explore the relationship between intermediate product imports and industrial pollution emissions with different pollution levels.

The estimated results are shown in Tables 4 – 6 . The regression coefficients of intermediate product imports in the heavy pollution industries to pollution emissions are all significantly negative at the level of 1%, which means that intermediate product imports can promote the reduction of industrial pollutants in the heavily polluted industries. The elimination of industrial solid waste emission is the most visible of them. Heavily polluted industries like coal mining, nonferrous metal mining, ferrous metal mining, and smelting generate more industrial solid wastes than other industries. However, the import of intermediate products lowers the mining industry’s mining volume, which to some extent lowers the industrial solid waste emissions. In the estimation of the moderately polluted industries, the impact of intermediate product imports on the emission reduction of the "three wastes" is ranked in descending order, namely industrial waste gas, industrial solid waste, and industrial wastewater. While lightly polluting industries are industrial wastewater, industrial waste gas, and industrial solid waste.

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https://doi.org/10.1371/journal.pone.0292347.t004

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https://doi.org/10.1371/journal.pone.0292347.t005

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https://doi.org/10.1371/journal.pone.0292347.t006

For control variables, (1) The entry of foreign direct investment (FDI) promotes the emission reduction of industrial waste gas in heavily polluted industries, but has a significant effect on increasing the emission of industrial wastewater and industrial solid waste. It can increase the emission of industrial wastewater from moderately polluted industries, but can reduce the emission of other two types of wastes. It aggravates the emission of industrial wastewater and industrial solid waste in lightly polluted industries, and reduces the emission of industrial waste gas. (2) The regression coefficients of industry scale (SIZE) on industrial wastewater and industrial solid waste emissions in heavily polluted industries are significantly negative, and the relationship with industrial waste gas emissions is positively correlated. And it can significantly increase the emission of three wastes in moderately polluted industries. There is a significant positive correlation between industrial wastewater and industrial exhaust emissions in light pollution industries, while there is no significant impact on industrial solid waste emissions. (3) Under environmental regulations (FGERS, FSERS, FQERS), the treatment of industrial wastewater and industrial solid waste in heavily polluted industries is effective, and it can absorb experience while dealing with the current generation to reduce the next industrial wastewater in the next phase. However, the industry does not pay attention to the treatment of industrial exhaust emissions, and because it is "pollution first, treatment later", thus failing to promote emission reduction industrial solid waste in moderately polluted industries The regression coefficient of environmental regulation (FGERS) is negative at the 1% level, while industrial wastewater environmental regulation (FSERS) and industrial waste gas environmental regulation (FQERS) have no significant effect, but the regression coefficients are all negative. It is obvious that the treatment effect of industrial solid waste in moderately polluted industries is effective; the emission of industrial waste gas and industrial solid waste has been effectively treated in lightly polluted industries. (4) The increase in R&D investment (RD) has a significant role in promoting the emissions reduction of three wastes in the three varieties of polluting industries. (5) The level of economic development (GDP) with the emission of industrial wastewater, industrial waste gas and industrial solid waste in heavily and lightly polluted industries show a positive U shape, an inverted U shape and an inverted U shape, respectively. This is also consistent with the results of economic development level and three wastes emissions in the above analysis.

Robustness test

We conducted multiple robustness tests on the regression results of each sample by changing the sample inspection period. Adjusting the research period from 2003–2015 to 2005–2015, using the same regression method, Tables 7 and 8 were obtained.

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https://doi.org/10.1371/journal.pone.0292347.t007

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https://doi.org/10.1371/journal.pone.0292347.t008

Mechanism test

In the theoretical analysis of the mechanism in Methodology, intermediate product imports impact on the pollution emissions of the industrial sectors through the competition effect, variety effect and technology spillover effect. Since the competition effect can be directly measured by the import trade volume, only the variety index of intermediate product imports and the technology spillover index of intermediate product imports are constructed to test. Finally, the three types of import effects are included in the dynamic mediation effect model as mediating variables to explore the indirect impact channels of intermediate product imports on industrial pollution emissions.

Variety effect

Table 9 shows the regression results of indirect impact of intermediate product imports on industrial pollution charges based on the diversity of imports. Model (16), model (17) and model (18) are the results of the dynamic mediation model based on the panel data of industrial wastewater, waste gas, and solid waste, respectively. Among them, the results in (1), (3) and (5) are the impact models of intermediate product imports on the diversity of imported intermediates, and (2), (4) and (6) are the regression models after adding the mediating variable, import variety effect index.

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https://doi.org/10.1371/journal.pone.0292347.t009

Observing the mediating variable and core independent variable in the table, we found that with the data of different pollutants, the regression coefficients of the import variety effect index (DI it ) for one lag period are all significantly positive, and intermediate product imports have a significant effect on the import species. With the expansion of the import scale of intermediate products, the more varieties and quantities of imports brought about by intermediate product imports, the greater the index of import variety effect will be. In the overall regression model with the mediating variables, that is, in the results of columns (2), (4), and (6), the regression coefficients of intermediate product imports on industrial three wastes pollution emissions are still significantly negative. Furthermore, the increase of import category can reduce the emissions of three wastes pollutants in the industrial sectors, indicating that there is a significant partial mediating role.

Technology spillovers effect

Using the technical spillover effect index of imported intermediate products as the mediating variable, the dynamic mediation effect model is regressed. Table 10 reports the regression results. The regression coefficients of the import technology spillover effect index (TEC it ) for one lag period are all significantly positive. Relying on the panel data of different pollutants, the relationship between intermediate product imports and the import technology spillover effect index is positively correlated. After adding the mediating variable, intermediate product imports still play a role in promoting the emissions reduction of three wastes pollution. In addition, the improvement of imported technology spillover index can reduce the emissions of three wastes in the industrial sectors. The regression coefficients of intermediate product imports and technology spillover indicators are both significantly negative, so the technology spillover effect plays a partial intermediary role in the industrial three wastes emissions.

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https://doi.org/10.1371/journal.pone.0292347.t010

Due to the possibility of some outliers in the original data, resulting in differences between the observed data and the actual data, the original sample data is subjected to a "tail reduction" approach, replacing<1% of the values in the sample data with their 1-percentile values and>99% with values in their 99th percentile [ 37 ] to test the robustness of the mediation effect model.

Table 11 shows the robustness test of the regression results of the intermediary model based on the variety effect indicator of intermediate goods imports.

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https://doi.org/10.1371/journal.pone.0292347.t011

Table 12 shows the robustness test of the regression results of the intermediary model based on the intermediate import technology spillover effect indicator. The conclusions obtained are consistent with those obtained from the mediation regression results in the previous section, indicating that the conclusions in the previous section are reliable and effective.

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https://doi.org/10.1371/journal.pone.0292347.t012

Based on the above results, this paper confirms that the import trade of intermediate products can significantly reduce the pollution emission intensity of the three wastes in the industrial sectors. Through the inspection of the control variables, it is found that the R&D investment has an inhibitory effect on the three pollution emissions, indicating that no matter whether the enterprise improves the front-end treatment of the production process or installs the pollution control equipment for the end treatment, a large amount of investment is required. The difference in the impact direction of the three types of pollutants in foreign direct investment reflects that while foreign investors provide advanced clean technologies and production processes, they tend to choose resource mining industries as investment targets to transfer pollution to China, resulting in insignificant inhibition effects. Further, the different results obtained by regressing the sub-samples will help government departments to implement differentiated strategies according to industry heterogeneity, providing new ideas for accelerating the green and low-carbon transformation of manufacturing. Finally, the results of the mechanism test show that the liberalization of intermediate products trade promotes industrial pollution reduction through the competition effect, variety effect and technology spillover effect, which better verifies the theoretical mechanism of this paper, and has certain enlightenment significance for China to promote the reform of intermediate trade system and its own carbon peak in the industrial field.

Conclusion and discussion

This paper studies the impact and mechanism of intermediate product imports on industrial three wastes pollution charges through dynamic regression model and mediation effect model, and draws the following research conclusions:

First, it is found that the emission of three wastes in the industrial sectors is significantly reduced by intermediate product imports. More subtly, the R&D investment reduces the amount of all three pollutants emissions. Foreign direct investment can help cut down on industrial waste gas and solid waste emissions. While only the emission of industrial wastewater is negatively impacted by the size of the industry. The environmental regulation of industrial solid waste can effectively reduce industrial solid waste production, whereas the level of economic development affects the discharge of industrial wastewater, industrial waste gas, and industrial solid waste, exhibiting a positive U-shaped, inverted U-shaped, and inverted U-shaped curve.

Second, for heavily, moderately and lightly polluted industries with different levels of pollution, intermediate product imports have different negative impacts on three industrial pollution emissions. Among them, the import of intermediate products has a greater negative impact on the emission of industrial solid waste in the heavily polluted industries; In the medium and light pollution industries, the import of intermediate products has a greater impact on the reduction of industrial emissions. In the field of control variables, the impact of R&D investment on the emission of different pollutants in industrial sectors with different pollution levels is significantly negative, and the impact of other control variables on the emission of different pollutants in industrial sectors with different pollution levels is different.

Third, a mediation effect model is constructed to test the mechanism. The results show that the import trade of intermediate products reduces the emission intensity of three wastes in the industrial sectors through the competition effect, variety effect and technology spillover effect, which is consistent with the theoretical expectation.

Since intermediate product imports has significantly reduced the pollution emissions of China’s industrial sectors on the whole, continuing to expand intermediate product imports is an important policy measure for improving production conditions and reducing pollution at the source of production. In this regard, this paper proposes the following policy recommendations:

First, expand the import scale of intermediate products and increase the proportion of high-tech intermediate products. In order to deepen the reform of intermediate trade liberalization, we should continue to promote the reform and innovation of free trade pilot zones, optimize the structure while expanding the import scale of intermediate products, increase the import scale of high-tech products in industrial manufactured products, and obtain technology spillovers from them. First, we should reduce the import tariffs of high-tech intermediate products, reduce the import costs of enterprises, and increase the scale of enterprises’ imports of high-tech intermediate products; The second is to provide considerable subsidies to enterprises that import high-tech products, mobilize their enthusiasm, and absorb and learn advanced clean technology and management experience to improve the industrial pollution emission control system.

Second, broaden the channels for obtaining information on foreign intermediate products and promote the diversification of imported intermediate products. This paper has verified that the import of intermediate goods can enhance the diversification of imported products, and an increase in the variety of imported goods can promote pollution emissions in the industrial sector. Therefore, it is necessary to actively promote the transformation of active import trade from intensive marginal to expanding marginal, that is, from quantity to quality. By continuously improving relevant trade policies, deepening bilateral trade cooperation with multiple countries or regions, and accelerating the construction of a global network of high standard free trade zones, the diversity of imported intermediate goods can be enhanced. Then absorb more high-quality embedded knowledge and technology from it, promote the green upgrading of industrial production models, and accelerate the green and low-carbon transformation and high-quality development of the manufacturing industry.

Third, improve the ability to absorb and learn technology, and promote technological progress in the industrial sector. The import of intermediate goods promotes technological progress in the industry through three effective paths, which in turn affects the pollution emissions of the industrial sector. To this end, more preferential policies and financial subsidies should be implemented, while increasing research and development investment within the industry to make it easier for them to absorb and learn technology and management experience from imported intermediate products, improve the industry’s ability to absorb and learn technology, enhance independent innovation and secondary innovation capabilities, and improve production technology to reduce pollution emissions in the industry’s production process. Advanced management awareness has a certain enlightening effect on environmental pollution control to guide the industry’s awareness of environmental protection.

Fourth, import relatively inferior intermediate goods and optimize the allocation of industry factor resources. Implement differentiation strategies based on industry characteristics, and expand the import scale of resource products required for heavily polluting industrial sectors; Mild pollution industries, mainly including high-tech industry departments, should increase the import of high-tech intermediate products, and learn from the technology and management experience accompanying imported intermediate products. Promote digestion, absorption, and reinnovation, build a market-oriented green technology innovation system, implement green technology innovation initiatives, carry out resource efficiency benchmarking and improvement actions for key industries and products, and improve the technological impact and overall resource allocation efficiency of the economy.

Research limitations and prospects

This study focuses on China, a major intermediate goods trading country, demonstrating that intermediate goods imports reduce industrial pollution emissions through import competition effect, variety effect, and technology spillover effect. However, there is still room for improvement: firstly, although it is quite representative to study Chinese industry with its large volume of imported intermediate products, for other country samples, there may be a threshold effect of technology spillovers from imported intermediate products on pollution emissions, which can be considered in future research; secondly, this study preliminarily proves that the variety effect is one of the channels of action. Future research can discuss whether the variety effect of intermediate goods imports on industrial pollution emissions is influenced by the source of the import, in order to make import policies more targeted.

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A Literature Review of the Effects of Energy on Pollution and Health

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This paper reviews recent economic studies that estimate the impacts of energy accidents and energy-related policies and regulations on pollution and health. Using difference-in-differences and regression discontinuity designs, most papers show that energy accidents and consumption significantly increased pollution and had adverse health effects. However, the enforcement of clean energy policies and strict regulations have improved air quality and mitigated the negative effects on health. Hence, future research should focus more on the health effects of clean energy in developing countries.

I. Introduction

Many people live in places where the average air quality is above the World Health Organization’s suggested limits for pollutants. Pollution is currently the most severe environmental problem and public health concern in the world, especially in developing countries. Studies have mostly focused on the causal effect of pollution on health, and numerous economic studies have documented that pollution is severely harmful to human health, in both developed and developing countries.

With rapid industrialization, energy consumption has increased dramatically. As energy raises our quality of life to higher levels, it also puts our health at risk. There is now a growing literature on the impacts of energy accidents and consumption on pollution and health. It finds that pollution is the most relevant channel related to the health impacts of energy. Recent research suggests that the health of pregnant women, children, and infants is at greater risk of being adversely affected by pollution (e.g., Currie & Neidell, 2005; Janke, 2014 ). Many studies suggest that infants are sensitive to the pollution caused by oil spills and coal smoke. In addition, studies are increasingly suggesting that clean energy policies and environmental regulations can abate the negative health effects of energy-related pollution.

This paper reviews the recent literature that studies the effects of energy on pollution and health. The core of this causal evaluation addresses the endogeneity problem. Using exogenous energy accidents and policy experiments and regulations as exogenous events to test causality, the literature is mainly based on difference-in-differences and regression discontinuity designs.

On the one hand, given the population explosion and rapid urbanization, energy consumption and the volume of long-distance energy transnational transportation has increased rapidly, and energy accidents, such as the oil spill in the Gulf of Mexico in 2010 and the nuclear leak of Fukushima Daiichi in Japan, are occurring frequently. Therefore, research has documented the impacts of energy accidents on pollution and human health. In response to public concerns over the increasing pollution threat from energy consumption and accidents, governments can put forward various policies and regulations to mitigate the negative health effects. Therefore, this paper aims to review the recent literature and determine how energy affects pollution and health.

II. Impacts of energy accidents and consumption on pollution and health

Table 1 lists and categorizes selected economic studies on the impacts of energy accidents and consumption on pollution and health. Environmental disasters caused by energy accidents and general energy consumption can cause pollution, and the problem of human exposure to pollution is attracting greater attention. The literature on the health effects of oil pollution has also been growing rapidly. Many recent papers find oil to have a negative impact on pollution and health. Beland and Oloomi (2019) and Marcus (2021) quantify the health impact of petroleum pollution on infant health in the United States by using the 2010 oil spill in the Gulf of Mexico and the leakage of underground storage tanks as exogenous events. Exploiting a difference-in-differences design, Beland and Oloomi (2019) find that the oil spill of 2010 raised concentrations of PM 2.5 , NO 2 , SO 2 , and CO in the affected coastal counties and increased the incidence of low-birthweight and prematurely born infants. Marcus (2021) finds that exposure to a leaking underground storage tank during gestation increases the probability of both low birthweight and preterm birth by 7–8%. In addition, all the studies mentioned above find that prenatal exposure to pollution had a heterogeneous impact on mothers with different individual characteristics (age, race, marital status, etc.). The infants of black, Hispanic, less educated, unmarried, and younger mothers suffer from more pronounced adverse health outcomes (Beland & Oloomi, 2019) . Marcus (2021) also finds that the adoption of preventative technologies mitigated the entire effect of storage tank leak exposure on birthweight, and information increased avoidance and moving among highly educated mothers.

In addition to environmental disasters such as oil spills and leakage, general energy consumption has an impact on air quality and health. In particular, the decline in air quality caused by coal fires and straw burning leads directly to an increase in mortality. Beach and Hanlon (2018) provide the first estimate of the mortality effects of British industrial coal use in 1851–1860. The results indicate that local industrial pollution had a powerful impact on mortality. Raising local industrial coal use by one standard deviation above the mean increased infant mortality by roughly 6–8% and mortality among children under five by 8–15%.

Farmers often burn straw after a harvest, which is the main cause of seasonal air pollution in developing countries. Based on agricultural straw burning satellite data, He et al. (2020) use non-local straw burning as an instrumental variable for air pollution to estimate the impact of straw burning on air pollution and health. The results show that straw burning increased particulate matter pollution and caused people to die from cardiorespiratory diseases. Middle-aged individuals and the elderly in rural areas are more sensitive to such pollution. Furthermore, using a difference-in differences approach, the authors find that China’s recent straw recycling policy has effectively improved the country’s air quality.

III. Impacts of energy policies and regulations on pollution and health

Due to the negative impacts of energy accidents and consumption on pollution and health, more and more energy-related policies and regulations are established by governments to alleviate the negative effects of air quality on health. These policies and regulations provide exogenous shocks for identifying a causal relationship. A growing research literature has begun to focus on how energy policies and energy-related environmental regulations can affect pollution and health. As Table 2 shows, many papers examine the causal relationship between energy policies or regulations and pollution (health), using a difference-in-differences design and regression discontinuity analyses. Imelda (2020) investigates the health impacts of household access to cleaner fuel through a nationwide fuel-switching program. The results suggest that the program led to a significant decline in infant mortality and that fetal exposure to indoor air pollutants is an important channel. Fan et al. (2020) focus on China’s coal-fired winter heating systems, the country with the largest and most expensive energy welfare policies in the developing world. The authors estimate the contemporaneous impact of winter heating on air pollution and health by using regression discontinuity designs and find that such winter heating systems increased the weekly Air Quality Index by 36% and mortality by 14%. Fan et al. (2020) and Imelda (2020) both suggest that the health impact of air pollution can be mitigated by improving socioeconomic conditions.

Based on the fact that coal and gasoline fuels emit a huge number of harmful pollutants, many targeted environmental regulations have been enforced to reduce pollution and improve health welfare. Yang and Chou (2018) find that the shutdown of power plants located upwind of New Jersey reduced the likelihood of having a low-birthweight or preterm infant by 15% and 28%, respectively. Strict command-and-control energy regulation policies have obvious effects. Because of limited financial resources and staff, developing countries are constrained in terms of their regulatory capacity to enforce regulations. Evidence from better developed economies might not be readily generalizable to the developing world. Li et al. (2020) and Zhu and Wang (2021) examine the causal effects of China’s prefecture-level fuel standards and fuel content regulation on air pollution at ports. Their papers bridge the gap by documenting novel empirical evidence and measure the benefits of fuel standards in the world’s largest developing country. Li et al. (2020) find that the enforcement of high-quality gasoline standards significantly reduced average pollution by 12.9%, and improved air quality. Their conclusion demonstrates the importance of fuel quality. Zhu and Wang (2021) show that fuel content regulation at ports immediately reduced major types of pollutants by more than 15%.

IV. Conclusion

To sum up, the economic studies on the impact of energy on pollution and health are becoming richer. Methodologically, these studies typically use difference-in-differences and regression discontinuity designs to evaluate the causal effects of energy accidents, policies, and regulations on pollution and health. Several studies support negative effects on air quality and infant health, and all of these find that energy policies and regulations can reduce regional pollution and improve health welfare, especially in less developed countries and areas. Furthermore. resources and information should target pregnant women to help mitigate poor infant health. The findings can have important implications for countries besides China, including developing ones.

The papers covered in this review contribute to cost–benefit analysis by quantifying the pollution and health effects from energy accidents, policies, and regulations. The largest energy transition projects are being attempted in many developing countries, and further research is needed to fully understand the long-term health effects of clean energy in developing countries.

Is air pollution politics or economics? Evidence from industrial heterogeneity

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  • Published: 07 November 2022
  • Volume 30 , pages 24454–24469, ( 2023 )

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  • Kaihua Wang 1  

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This paper checks the asymmetrical impact of Beijing’s and Shanghai’s air quality (AQ) on cross-industries stock returns (SR) by using the quantile-on-quantile (QQ) regression method. The major empirical findings as shown as followings. There are heterogeneous responses from SR to AQ within the same city. Different links are discovered for Beijing and Shanghai within the same industry. Air pollution does not have political or economic properties for all industries. Our research provides useful contributions compared with past literature. First of all, we distinguish whether air pollution is political or economic. Apart from psychology and physiology, government intervention and economic expectation are also important components in interpreting the influence from AQ to SR. Second, this study adequately considers the heterogeneity of industries. Industries differently react to the identical extrinsic shock, depending on the nature of their industry. Besides, the QQ approach captures quantile-varying relationship between variables, and does not need to consider structural fracture and time lag effects. The practical significance is that investors need to focus on national industrial policies, and avoiding biased decisions in stock market from air pollution.

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Wang, K. Is air pollution politics or economics? Evidence from industrial heterogeneity. Environ Sci Pollut Res 30 , 24454–24469 (2023). https://doi.org/10.1007/s11356-022-23955-0

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Transport Infrastructure, High-Quality Development and Industrial Pollution: Fresh Evidence from China

Xiaole wang.

1 School of Economics and Management, China University of Mining and Technology, Xuzhou 221116, China; moc.361@8891eloaixw (X.W.); nc.ude.tmuc@dl13a73107091st (Y.P.); nc.ude.tmuc@2b50007081bt (Y.L.)

2 School of Business, Jiangsu College of Finance and Accounting, Lianyungang 222061, China

3 Jiangsu Key Laboratory of Coal-Based Greenhouse Gas Control and Utilization, China University of Mining and Technology, Xuzhou 221008, China

Associated Data

Not applicable.

To achieve high-quality development, transport infrastructure will play a crucial role in China’s economic growth, but its damage to the ecological environment has not been paid enough attention. This study was based on panel data for 30 Chinese provinces for the period of 2004–2017. A comprehensive index system for high-quality development based on the new development concept was developed. This high-quality development index used the entropy weight method and integrated transport infrastructure, high-quality development, and industrial pollution into a comprehensive framework, and systematically examined the effects of transport infrastructure and high-quality development on industrial pollution emissions. It was found that transport infrastructure significantly contributed to industrial pollution emissions, and there was a regional heterogeneity and time lag, with high-quality development and industrial pollution having an inverted “U”-shaped relationship. Further analysis showed that transport infrastructure significantly affected high-quality development and industrial pollution through industrial agglomeration, reduced the inhibitory effect on high-quality development by promoting industrial agglomeration, and reduced industrial pollution emissions by promoting industrial agglomeration.

1. Introduction

Over the past 40 years of reform and opening up, China’s economy has continued to grow at a high rate, and the ratio of GDP to global warming potential (GWP) has increased from 2% at the beginning of the reform to 15% currently. During this period China has accounted for more than 30% of the world’s economic growth, and has become the second largest economy in the world [ 1 ]. Alongside China’s rapid economic growth, environmental pollution and the wasteful use of resources have become increasingly serious. According to China’s Ministry of Ecology and Environment, the quality of China’s natural environment has improved in the past three years, but it still faces difficulties and challenges. The most direct and effective way to improve the natural environment is to reduce the intensity of major pollutant emissions, which is a binding goal that has been included in China’s 12th, 13th, and 14th Five-Year Plans. While China’s economy continues to grow, industrial emissions have become an important source of environmental pollution, and a reduction in the intensity of pollution emissions per unit of output is essential to control industrial emissions [ 2 ]. Since the 1990s, the development of China’s transport infrastructure has continued to steadily develop, and road density has been gradually increasing at an average annual rate of 7.3% [ 3 ]. However, ecological damage is closely related to the continuous expansion of transport infrastructure, which has recently received general attention [ 4 ]. Large amounts of industrial emissions, wastewater, and smoke (dust) are generated during the construction and operation of transport infrastructure, and natural resources are also consumed, which have adverse impacts on local ecosystems. It is therefore necessary to consider how the development of transport infrastructure affects industrial pollution emissions and what intrinsic mechanisms are involved.

The deterioration of the natural environment has become an important social issue, and the Chinese government has raised environmental protection to an unprecedented level. This is directly related to the goal of achieving a beautiful China, and has become a major obstacle to China’s sustainable economic development. The 19th National Congress of the Communist Party of China proposed the “five-in-one” concept, including the construction of an ecological civilization, as the overall layout of socialism with Chinese characteristics. A consensus has been reached around the concept of green development as a way to promote the sustainable development of China’s economy, society, and natural environment. As a link between regional economic and social activities, the construction of transport infrastructure has realized the flow of production factors between regions and promoted the formation of regional economic integration, but it has also damaged the natural environment to a certain extent. The report of the 19th National Congress of the Communist Party of China states that China’s economy has shifted from a high-speed growth stage to high-quality development, and it is necessary to continuously strengthen the layout of the transport infrastructure network to achieve the goal of “a strong transportation country”. Studies have shown that the construction of transport infrastructure can increase pollution emissions, thereby inhibiting the quality of economic development and aggravating the deterioration of the natural environment [ 5 ]. In the new era, the Chinese government needs to consider the dialectical relationship between economic growth and transport infrastructure development, steadily promote economic and social transformation, upgrade transport infrastructure and ensure its rational layout, and enhance environmental quality and efficiency. High-quality development is a new development approach that integrates economic quantitative growth and improvements in quality, and is related to both improvements in the quality of the infrastructure network and the construction of an ecological civilization. There is a need to consider the impact of high-quality development on industrial pollution and how the construction of transport infrastructure will affect the quality of economic development. Resolving these issues would enable policy recommendations to be provided to the Chinese government to promote the layout of the transport infrastructure network and improve the natural environment in the new era.

Many researchers have focused on the relationship between transport infrastructure [ 6 , 7 , 8 ], economic quality development, and environmental pollution [ 9 , 10 ], and some valuable conclusions have been drawn. In recent years, China’s rapid economic development has resulted in serious environmental pollution. Transport infrastructure, a key factor supporting economic development, has often been studied for its impact on the quality of economic development and air pollution [ 11 ], but little research has been conducted on its impact on industrial pollution. With the introduction of the new development concept in China, the relationship between high-quality development and haze pollution has also been discussed [ 12 ], but its impact on industrial pollution has rarely been studied. Transport infrastructure development promotes economic development through industrial agglomeration [ 13 ], but its impacts on high-quality economic development and industrial pollution have not been fully studied. Therefore, this study aims to analyze the relationship between the role of transport infrastructure, high-quality development, and industrial pollution, with a focus on the impact of transport infrastructure through industrial agglomeration. This study makes the following four main contributions. First, transport infrastructure, high-quality development, and industrial pollution were integrated into a comprehensive framework to empirically test the effects of transport infrastructure and high-quality development on industrial pollution emissions at the regional level. This would provide a new perspective on how to build an ecological civilization. Second, a system to evaluate high-quality development was constructed based on the new development concept, and the entropy weight method was used to calculate the comprehensive score of high-quality development in each region, while the time-lagged effect of high-quality development on industrial pollution was also examined. Third, using industrial agglomeration as a mediating variable, this study further explored the mechanism by which transport infrastructure influenced high-quality development and industrial pollution. Fourth, given the uneven regional development, the heterogeneity of transport infrastructure and high-quality development on industrial pollution emissions was examined. This study provides practical recommendations not only for China’s economic and social transformation and quality benefits, but also for other countries that are in the process of promoting green transport infrastructure.

The remainder of this paper is structured as follows: Section 2 is a review of the relevant literature; Section 3 introduces the research hypotheses; Section 4 explains the research methodology, variables, and data collection; Section 5 represents the results and discusses the findings; Section 6 summarizes the findings and proposes policy recommendations. The specific idea is shown in Figure 1 .

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Analysis framework of the current study.

2. Literature Review

There is existing literature regarding the relationship between transport infrastructure and environmental pollution, but little research has been conducted on the influence of high-quality development on environmental pollution. There is currently a research focus on the environmental Kuznets curve (EKC), which describes the relationship between economic growth and environmental pollution. These studies have focused only on the single influence of quantitative economic growth on environmental pollution, while ignoring the important relationship between high-quality economic improvements and environmental pollution. Transport infrastructure is not only related to regional economic development, but also has a close link to environmental pollution.

Regarding research on transport infrastructure and environmental pollution, many scholars have demonstrated that transport infrastructure has significant negative impacts on the environment in terms of air pollution [ 6 ], traffic pollution [ 14 ], and carbon emissions [ 15 , 16 ]. One study also found an inverted “U”-shaped relationship between traffic density and urban smog in large and medium-sized cities, and direct emissions were an important channel through which traffic density affected smog formation [ 17 ]. In addition, there is also a close relationship between carbon emissions and energy efficiency. It has been shown that there is an intuitive network structure of carbon emissions across Chinese provinces [ 18 ], and transport infrastructure directly affects energy efficiency [ 19 ]. Although transport infrastructure contributes to regional economic growth and leads to industrial agglomeration, it will inevitably increase energy consumption and affect the natural environment [ 20 ]. Moreover, many researchers have also conducted studies on the environmental effects of transport infrastructure from different perspectives, such as the impact of urban road construction [ 21 ], the speed of transportation vehicles [ 22 ], and the number of vehicles on environmental pollution emissions [ 23 ].

To study the relationship between transport infrastructure and the quality of economic development, researchers have focused on three aspects: the promotional effect of transport infrastructure on economic development, the negative spillover effect on developed cities, and regional heterogeneity. Early research in this area was in the American Scholar , and one empirical study found that transport infrastructure and regional economic development are mutually reinforcing and co-developing [ 24 ]. Some researchers have also studied the performance of firms near high-speed railroads. It was found that on the Shinkansen extension in Japan, the opening of high-speed railroads improved the productivity of firms and increased their profits; thus, promoting economic development along the route [ 25 ]. In addition, transport infrastructure will also have an impact on employment and industrial specialization, which will lead to the development of the regional economy by reducing regional unemployment and increasing the number of employed people in relevant areas [ 26 ]. At the same time, the construction of transport infrastructure also consumes large amounts of resources, which leads to the reallocation of factor resources in upstream and downstream enterprises and expands the scope of labor mobility. This has an important impact on the production costs of enterprises in the region, especially the cost of human capital, which is conducive to improving the factor allocation efficiency of enterprises, and thus promoting economic development [ 27 ]. There are spillover effects of transport infrastructure because it facilitates economic growth and the spatial clustering of economic activities, and there are regional and industrial differences [ 28 ], as well as time-lag effects. With the implementation of China’s “One Belt, One Road” policy, the role of transport infrastructure connectivity has become more prominent [ 29 ], with asymmetric positive effects on countries and strong complementarity with trade facilitation and tariff reduction policies.

Regarding the study of economic development and environmental pollution, some scholars have focused on the EKC curve relationship between economic growth and environmental pollution, while less research has been conducted on how high-quality development affects environmental pollution. Many researchers have verified the existence of an EKC curve, with an inverted “U”-shape relationship between economic development and environmental pollution using data from different countries and regions [ 9 , 30 ], while clarifying the importance of economic and financial development on environmental performance [ 31 ]. In terms of the impact of environmental pollution on economic development, studies have found that environmental pollution significantly reduces urban employment and may also have significant hidden economic costs that inhibit economic development [ 32 ]. Some studies have found that increasing the tax rate on carbon emissions not only achieves a significant reduction in carbon emissions, but also saves operational costs; thus, promoting the coordination of ecological conservation and economic growth [ 33 ]. However, further analysis has found that managing the environment with a policy of licensing pollution through taxation inhibits regional economic development [ 34 ], and the higher the intensity of carbon emissions, the greater the welfare loss [ 10 ]. It has also been found that attracting foreign direct investment enhances capital accumulation, which generates environmental pollution, and when the inflow of foreign direct investment is restricted, economic growth is inhibited; although, environmental quality is improved [ 35 ].

Although there are extensive studies on the relationships between transport infrastructure, economic growth, and environmental pollution, research on the impacts of transport infrastructure and high-quality development on industrial pollution is extremely scarce. There are many factors and complex influential mechanisms affecting the impacts of transport infrastructure and high-quality development on industrial pollution, and there is an urgent need to clarify the internal connections and relationships between them. Such studies will enable the relationship between reform and development and the promotion of high-quality development to be better understood and managed. Therefore, this study developed a theoretical model to analyze the mechanism of the interaction between transport infrastructure, high-quality development, and industrial pollution.

3. Research Hypotheses

Since China’s reform and opening, many regions have embraced the simple idea of “build roads to get rich”, and the construction of transport infrastructure has steadily advanced, ensuring the continuous expansion of road mileage and a steady improvement in road grades, which has effectively boosted and guaranteed China’s continuous rapid economic growth [ 36 , 37 ]. Although the construction of transport infrastructure drives regional economic growth, it also damages the natural environment to some extent [ 3 , 20 ]. Many studies have found that transport infrastructure has an impact on air pollution through changes in transportation modes [ 21 , 26 ], and an increase in road length will lead to more vehicles, which will increase air pollutant emissions [ 28 ]. It has also been found that the improvement of transport infrastructure triggers the inter-regional movement of elements, which has an impact on environmental pollution [ 5 ]. Although many researchers have found that the construction of transport infrastructure has a significant negative impact on environmental pollution (air pollution, carbon emissions, traffic pollution, etc.) [ 6 , 14 , 15 , 16 ], there have been fewer studies on the impact of regional industrial pollution. The improvement in transport infrastructure has accelerated China’s industrialization, and industrialization has driven rapid economic growth, but this has been accompanied by serious industrial pollution [ 38 ]. Analyzing the relationship between industrial pollution and transport infrastructure will help the optimization of transport network layout and the formulation of environmental policies. In addition, given the differences in transport infrastructure construction between regions, this will likely directly determine the impact of transport infrastructure on industrial pollution [ 39 ]. Based on this, the first hypothesis was proposed.

Transport infrastructure construction is significantly and positively correlated with industrial pollution emissions, and has regional heterogeneity .

In recent years, the quality of China’s natural environment has improved, but the situation is still not satisfactory. How to reduce the intensity of industrial pollution emissions and break through the “zero-sum” thinking of high-quality development and environmental protection is an important issue that needs to be resolved in the current economic transformation context to ensure the coordination of economic growth and environmental quality, and thus achieve high-quality economic growth. High-quality development not only helps promote sustainable economic development, but also helps to curb environmental pollution emissions and improve the natural environment. According to the traditional EKC curve theory, after achieving a certain level of economic development, environmental pollution may reduce as the economy continues to develop [ 40 ]. The fundamental purpose of economic development is to achieve high-quality development. The study of the EKC curve relationship between economic development and environmental pollution has been discussed in depth by academics, such as the inverted U-shaped relationship [ 41 ]. Since the concept of high-quality economic development was introduced at the 19th People’s Congress of China, the connotation of economic development has become increasingly clear and its impact on environmental pollution has become an important issue. The core of high-quality economic development lies in changing economic growth dynamics, optimizing industrial structures and upgrading technology, and adhering to high-quality economic development helps to bring into play the structural and technological effects of economic development on environmental pollution [ 12 ]. With the continuous improvement of the quality of economic development, the coupling between industrial structure and economic development will change, and the improvement of transportation infrastructure can play a positive role in promoting industrial agglomeration and development [ 11 ]. Moreover, in order to reduce environmental pollution, the government is more willing to increase R&D investment and introduce advanced production technology to develop the economy, which not only improves the efficiency of resource use, but also effectively increases the reduction in pollution emissions [ 42 ]. Therefore, it is important to realize that the relationship between high-quality development and industrial pollution is similar to the traditional EKC curve. At the same time, it is also important to realize that the problem of unbalanced and insufficient economic and social development in China is extremely prominent [ 43 ], and the impact of high-quality development on industrial pollution may not be the same in different regions. Based on this, the second hypothesis was proposed.

High-quality development and industrial pollution have an inverted “U”-shaped relationship, and have regional heterogeneity .

In the new era of high-quality development, achieving advances in the scale and quality of the transportation network is important to serve and support the sustained, rapid, and healthy development of the economy and society. To improve the natural environment, the following should be addressed: adhere to supply-side reform as the main outcome; reform and innovate as the fundamental driving forces; coordinate development and safety; adhere to the principles of integrated, safe, innovative, efficient, and green development; accelerate the construction of a strong transportation country; and promote the high-quality development of China’s economy. Pollution emissions continuously accumulate during unplanned economic growth and continuously reduce the environmental carrying capacity and ecological quality of society [ 44 ]. Once the accumulated pollutants exceed the self-purification capacity of the environment, there will inevitably be irreversible consequences such as the destruction of ecological balance and deterioration of environmental quality. The construction of transport infrastructure helps to increase regional economic growth, reduce income inequality and energy poverty [ 45 , 46 ], and improve the productivity of enterprises through industrial agglomeration [ 47 , 48 ]. This will promote economic development; although, the impact on the quality of regional economic development is unknown. Many studies have shown that industrial agglomeration is a key channel through which transport infrastructure affects environmental pollution [ 49 , 50 ], but its relationship is uncertain. On the one hand, industrial agglomeration inevitably increases energy consumption, causing damage to the local natural environment [ 20 , 51 ]; on the other hand, industrial agglomeration will improve the level of production technology and management efficiency, improving energy efficiency and effectively alleviating the pressure on the local natural environment [ 43 , 52 ]. Given that the important role of industrial agglomeration on the quality of regional economic development and industrial pollution is uncertain, the third hypothesis is proposed.

Transport infrastructure significantly affects high-quality development and industrial pollution through industrial agglomeration .

The global mechanism analysis is illustrated in Figure 2 .

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Mechanism analysis.

4. Methods, Variables, and Data

4.1. model specification.

To examine the impact of transport infrastructure on industrial pollution emissions in China, the following benchmark regression model was constructed:

where i and t represent province and year, respectively; SO 2 denotes industrial pollution emission intensity; TRA denotes a transport infrastructure variable, whose coefficient β measures the impact of transport infrastructure on industrial pollution emissions and is therefore the core parameter of interest in this study; Z is a series of control variables; γ is the matrix of marginal impact coefficients of control variables; μ i is individual fixed effect; η t is time fixed effect; and ε i t is a random error term. By controlling for double fixed effects, the effects of regional heterogeneity and macro shocks that did not vary with time were eliminated.

On this basis, we were able to explore the heterogeneous impact of high-quality development in the construction of transport infrastructure on industrial pollution reduction in China. This enabled an analysis of the synergistic impact of both transport infrastructure and high-quality development on industrial pollution reduction in China. According to the traditional EKC curve theory, after a certain level of economic development is achieved, environmental pollution may be reduced as the economy continues to develop. Therefore, we added a quadratic high-quality development term ( HQD ) to the model for a regression analysis to investigate whether the relationship between industrial pollution and high-quality development was similar to that described by the traditional EKC curve. Therefore, we used the HQD , a composite indicator of high-quality development in each region, and the transport infrastructure variable TRA and its quadratic term in the study, which were added to Equation (1) to obtain:

where β 1 , β 2 , and β 3 are the core explanatory variable elasticity coefficients, respectively.

Furthermore, this study examined the internal mechanism by which transport infrastructure affects high-quality development and industrial pollution, and constructed a mediating effect model, with industrial agglomeration as a mediating variable, as follows:

where A G G i t denotes the industrial agglomeration level of Chinese province i in year t . The other variables are defined as described previously.

4.2. Variables

4.2.1. transport infrastructure.

Considering that the cost of air transport is extremely high and pipelines are not the main mode of transportation for industry, roads, railroads, and inland waterways were selected as proxy variables for transport infrastructure. Referring to Sun et al. (2019) and Huang et al. (2020), traffic density can be used to measure transport infrastructure, which was expressed as the ratio of the sum of road miles, railroad miles, and inland waterway miles to the land area of each province [ 2 , 5 ].

4.2.2. High-Quality Development

Construction of the indicator system.

As China’s development approaches the new era, high-quality development is development that can meet the growing demand of people for a better life and is an important way to build a beautiful China, while following the new development concept of “innovation, coordination, green, openness, and sharing” [ 53 ]. This five-dimensional new development concept also indicates the direction, ideas, and focus underpinning China’s high-quality development. Based on the five-dimensional new development concept and the views of previous researchers [ 54 ], we argue that the five dimensions of innovation, coordination, sustainability, openness, and sharing should be the characteristics of high-quality development.

First, from the perspective of innovation, based on the fact that innovation is the source of power and potential, high-quality development can promote efficiency improvements through innovation, thus improving the quality of economic development. This study therefore measured three aspects of innovativeness: innovation input, innovation output, and efficiency improvement.

Second, from the perspective of coordination, China’s economic and social development has the problematic feature of uneven and insufficient regional development, which could be effectively solved by improving the efficiency of resource allocation and the rational use of resources, highlighting the importance of coordinated development. Coordination is the key to achieving high-quality regional co-development, and this study therefore measured three aspects of coordination: industrial coordination, urban–rural coordination, and regional coordination.

Third, from the perspective of sustainability, the new era of green development has led to the concept of “green mountains are golden mountains”. The rational use of natural resources is a prerequisite for sustainable economic development. Additionally, stable development can effectively restrict large fluctuations in prices, employment, and polluting industries, and is a tool to keep the national economy stable. Sustainable development is an important way to achieve high-quality development, and this study measured sustainability in terms of both green development and stable development.

Fourth, from the perspective of openness, alongside the implementation of policies such as the “One Belt, One Road” and “Free Trade Zone”, China has established cooperative relationships with many countries, and the level of openness has been continuously improved, which is also a necessary path to achieve high-quality development. This study measured three aspects of openness: foreign investment, foreign capital utilization, and foreign trade.

Finally, from the perspective of sharing, to realize the desire for a better and happier life among both urban and rural residents, to improve their quality of life, to meet their spiritual needs, and to ensure they receive the benefits of high-quality development, shareability is an important judgment criterion. This study measured five aspects of shareability: health, education, income, consumption, and leisure.

Considering the principles of high-quality development based on the new development concept, and by drawing on representative literature [ 12 , 54 ], an indicator system was developed following the principles of scientificity, dynamism, operability, and comprehensiveness. From the five aspects of innovation, coordination, openness, sustainability, and sharing, an evaluation index of China’s high-quality development was constructed that consisted of five primary indicators, 16 secondary indicators, and 39 tertiary indicators. The details are shown in Table 1 .

Indicator system for evaluating quality development in China.

Note: “Positive (Negative)” in the “Attributes” column of the indicator indicates that the measure is a positive (negative) indicator under the set measurement approach, the larger (smaller) the better.

Measurement of the Indicator System

This study developed a comprehensive inter-provincial high-quality development index for China for the period 2004–2017, with the details shown in Table 1 . As can be seen from Table 1 , the attributes of each indicator were different and the data needed to be dimensionless before measurements could be made. First, taking the inverse of the negative indicator and the inverse of the deviation of the moderate indicator, the positive indicator followed the principle of the larger the better. Second, on the basis of the above data processing, this study adopted the practices of previous researchers [ 53 ] and used the entropy weight method to determine a high-quality development index for China. The specific steps were as follows.

First, a comprehensive evaluation system of high-quality development was developed, and each indicator was processed to be dimensionless, with its value recorded as W j . The index level matrix Y = ( x ij ) m × n was established after standardized processing. Second, the same metric was used to quantify the comprehensive indicators of high-quality development, and the value of the j item high-quality development indicator of each province in year i was calculated as W ij . Third, the entropy value of the j item high-quality development indicator e ij was calculated. The larger the e ij , the smaller the variability among the high-quality development indicators and the less important the indicators were, and vice versa. Fourth, the variability d j of the j item high-quality development indicator was calculated. Fifth, the weight of the j item quality development indicator was calculated. Finally, based on the measurement method of the United Nations Human Development Index (HDI), a final comprehensive score of high-quality development was obtained for each province. The specific equations are shown as (6)–(11). The index value was calculated according to Equation (10) after the dimensionless processing of each indicator in the comprehensive evaluation system of high-quality development. The comprehensive score of the high-quality development index ( P ) was also calculated for each province according to Equation (11). The value of p was between 0 and 1, and the closer it was to 1, the higher the quality of development of the province, and vice versa. Using the results of the entropy method, it was possible to compare the overall level of high-quality development of each province and also to observe the historical process of high-quality development in each province.

Based on the data availability, the comprehensive score of the high-quality development index was determined for 30 provinces in China for the period 2004–2017, with Tibet, Hong Kong, Macao, and Taiwan not included. The comprehensive scores of the high-quality development index of each province in selected years are shown in Table 2 . The comprehensive scores of the high-quality development index of the 30 Chinese provinces in 2004, 2009, 2013, and 2017 were mapped and are shown in Figure 3 . There was little change in the comprehensive score of the high-quality development index of each province over the period of 2004–2017. There were obvious differences in the comprehensive score of the high-quality development index among provinces, reflecting the unbalanced regional development throughout China. The index values for developed regions were very similar, with a cluster of high values in the eastern coastal region, and the development index of the less developed central and western regions being relatively low. Therefore, in the future, the government should accelerate the high-quality development of central and western regions, promote coordinated regional development, gradually narrow regional differences, and realize a new pattern of balanced development in eastern, central, and western regions.

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Composite index of high-quality development of Chinese provinces.

Composite index of high-quality development in China, 2004–2017.

4.2.3. Industrial Pollution Emission Intensity

According to Sun et al. (2019), industrial pollution emission intensity mainly refers to pollutant emissions per unit of industrial value added [ 2 ]. Considering the availability of data in each province, in this study, industrial pollution emission intensity was determined by dividing industrial pollution emissions by the value added of the secondary industry, focusing on the impact of transport infrastructure and high-quality development on industrial sulfur dioxide emission intensity (SO 2 ).

4.2.4. Other Variables

Industrial agglomeration (AGG): there are many measures of industrial agglomeration in the existing literature, such as industrial concentration, the locational quotient method (LQM), the Ellison–Glaeser (E-G) index, and the Herfindahl–Hirschman index. Because the dependent variable was industrial pollution, the LQM of the secondary industry was used as a measure of industrial agglomeration [ 5 ]. A G G i t = q s i t / q i t / q s n t / q n t , where A G G i t represents the LQM of the secondary industry in the ith region in year t; q s i t represents the value of the secondary industry in the ith region in year t ; q i t represents the total output value in the ith region in year t ; q s n t represents the value of the secondary industry in all regions of the country in year t ; and q n t represents the national total output value in year t . This index was used to measure the degree of specialization of the local secondary industry. Compared with the national level, the larger the index value, the higher the degree of specialization of the local secondary industry, i.e., the higher the degree of industrial agglomeration.

Control variables: to minimize estimation bias due to omitted variables, reference was made to the existing literature on industrial pollution emissions [ 5 ] and the quality of economic development [ 5 , 55 ], and the following control variables were set. Environmental regulation (ER), which was measured as the amount of investment in environmental management as a proportion of GDP. Income level of workers (INC), which was measured as the per capita income of workers in employment. Technology development level (TEC), which was measured as the expenditure on science and technology. Population density (POP), which was measured as the number of people per unit area. Human capital level (EDU), which was measured as the number of university students per 10,000 people. Real estate (EST), which was measured as the proportion of real estate investment. Economic growth level (PERGDP), which was measured as GDP per capita. Urbanization level (URB), which was measured as the percentage of the population in urban areas. Energy intensity (ENER), which was measured as the contribution of energy consumption to overall GDP.

4.3. Data Sources

Due to the availability of data, this study considered 30 Chinese provinces (excluding Tibet, Hong Kong, Macao, and Taiwan) spanning 14 years from 2004 to 2017, and the research data were obtained from the China Statistical Yearbook, China Industrial Statistical Yearbook, China Environment Yearbook, China Energy Statistical Yearbook, China Labor Statistical Yearbook, China Science and Technology Statistical Yearbook, China Fixed Assets Statistical Yearbook, and the statistical yearbooks of the 30 provinces, as well as the National Bureau of Statistics, China Economic and Social Big Data Research Platform, CENSUS Statistical Database, and Wind Database. The data descriptions and descriptions of the variables referred to above, core explanatory variables, and a series of control variables are given in Table 3 .

Descriptive statistics of the variables.

5. Results and Discussion

5.1. baseline results.

Table 4 shows the baseline results for Equations (1) and (2). We select the appropriate model based on the F-test and the Hausman test, reported in the last two rows. The tests indicate that the fixed effect model is appropriate in most models. Table 4 gives the results of the benchmark regressions for models (1)–(3). The results showed that transport infrastructure was significantly positively associated with industrial pollution and the secondary term of high-quality development was significantly negatively associated with industrial pollution after controlling for a series of province-specific variables as well as fixed time effects. This indicated that transport infrastructure promoted industrial pollution emissions, while the relationship between high-quality development and industrial pollution had an inverted U-shaped relationship, i.e., as high-quality development increased, it initially promoted industrial pollution emissions and then suppressed industrial pollution emissions after reaching an inflection point. This was because in the early stage of high-quality development, the increase in production efficiency of enterprises is accompanied by an increase in related costs, and therefore, enterprises continue to increase the price of products or the production speed and increase the production output of existing products. In the early stage of high-quality development, the environmental standards are low, environmental legislation and regulations are not perfect, and environmental protection technology is weak or even absent, which results in high industrial SO 2 emissions, leading to a further deterioration in environmental quality. When the quality of economic development improves to a certain level, it will lead to further technological improvements and the optimization of production level and industrial structure. At this point enterprise production efficiency is substantially improved, costs are reduced, environmental protection technology matures, and environmental legislation and regulations are perfected; thus, improving resource utilization and environmental quality. From the absolute values of the basic regression coefficients in Table 4 , high-quality development had a much greater value than transport infrastructure, indicating that high-quality development had a strong effect on reducing industrial pollution. From the regression results of the control variables, it was found that the level of scientific and technological development was significantly negatively correlated with industrial pollution emissions, indicating that an improvement in the level of scientific and technological development can effectively suppress industrial pollution emissions. The level of human capital was significantly positively correlated with industrial pollution, indicating that an improvement in the level of human capital can promote industrial pollution emissions.

Transportation infrastructure, high-quality development, and industrial pollution: baseline regression results.

Note: Standard errors are presented in the parentheses. *** indicates p < 0.01. ** indicates p < 0.05. * indicates p < 0.1.

Economic and social development in China is still unbalanced and insufficient, and the regional imbalance is particularly obvious when observed on a line graph of the high-quality development index. The effect of the heterogeneity between transport infrastructure and high-quality development on industrial pollution emissions was further evaluated by defining three major regions: eastern, central, and western regions. Columns (1), (2), and (3) of Table 5 show the regression results of the effects of transport infrastructure and high-quality development on industrial pollution emissions in the three different regions, respectively. The regression results for transport infrastructure showed that transport infrastructure in the eastern region was significantly and positively correlated with industrial pollution emissions, and the estimated coefficients were larger than those of the national sample, indicating that transport infrastructure had a more significant impact on industrial pollution emissions in the eastern region. The traffic infrastructure in the central region was not significantly related to industrial pollution emissions, indicating a lower degree of influence. Transport infrastructure in the western region was significantly and positively correlated with industrial pollution emissions, and the estimated coefficients were larger than the national sample, indicating that transport infrastructure had a more significant impact on industrial pollution emissions in the western region. Thus, H1 was verified.

Regional heterogeneity test.

From the regression results of high-quality development shown in Table 5 , the primary term of high-quality development in the eastern region was significantly positively correlated with industrial pollution emissions, and the secondary term of high-quality development was significantly negatively correlated with industrial pollution emissions, confirming that high-quality development significantly increased industrial pollution emissions in the early stage, and an inflection point occurred after reaching a certain development stage, after which industrial pollution emissions were significantly suppressed. In the central region, the trend of the impact of high-quality development on industrial pollution emissions was similar to that of the east, but it was not significant, indicating a lower degree of impact. In the western region, the primary term of high-quality development was significantly positively correlated with industrial pollution emissions, and the secondary term of high-quality development was significantly negatively correlated with industrial pollution emissions, confirming that high-quality development significantly increased industrial pollution emissions in the early stage, and after reaching a certain stage of development, there was an inflection point after which industrial pollution emissions were significantly suppressed. The degree of influence was higher than that in the eastern and central regions. This was largely because the eastern region is more developed, and the effect of high-quality development on industrial pollution emission reduction was obvious. The central region is in the middle stage of industrialization, with a prominent “late-stage disadvantage” in economic development. Local governments pay more attention to the new normal of economic development at this stage, which is characterized by stronger environmental constraints and a reduction in the economic growth rate [ 56 ]. In this region, high-quality development has not yet significantly affected industrial pollution emission reduction. The western region is more undeveloped and is currently in a stage of vigorous economic development, but the effect of high-quality development on industrial pollution reduction was obvious. Thus, H2 was verified.

5.2. Endogeneity Issues and Instrumental Variables

Although the model results suggested that transport infrastructure can increase industrial pollution emissions, and high-quality development helps promote reductions in industrial pollution emissions, the results may still be biased due to endogeneity problems. On the one hand, the better the effect of industrial pollution emission reduction, the better the quality of the transport infrastructure network and the better the quality of the economy and society will be. There may be a two-way causal relationship between transport infrastructure, high-quality development, and industrial pollution. However, it is difficult to enumerate the individual factors affecting industrial pollution, and there will inevitably be omitted variables. To avoid any bias in the model results caused by endogeneity and omitted variables, reference was made to Shi et al. (2020), and the lagged terms of the indexes of transport infrastructure and high-quality development were used as the instrumental variables of both to correct the bias of the estimation results [ 55 ]. Transport infrastructure and high-quality development had intertemporal effects on industrial pollution, and in the prior period, transport infrastructure and high-quality development indices were not affected by the later period industrial pollution emissions. Therefore, lagging the transport infrastructure and high-quality development indexes could effectively address the two-way causality between them. For this reason, the regression results of the transport infrastructure and high-quality development indices are shown in Table 6 , with one and two lagged periods, respectively. The results showed that the estimated coefficients of transport infrastructure and high-quality development were still statistically significant, which again verified that transport infrastructure could significantly increase industrial pollution emissions, while high-quality development could significantly suppress industrial pollution emissions.

Instrumental variable method (2SLS) estimation results.

Columns (1)–(6) of Table 7 show the estimated results of the impact of transport infrastructure and high-quality development on industrial pollution emissions for different regions with one and two lags and with significant overall lagged effects. Transport infrastructure in the eastern region was significantly and positively correlated with industrial pollution emissions, and the correlation coefficients were larger than those of the national sample, indicating that transport infrastructure had a more significant impact on industrial pollution emissions in the eastern region. The traffic infrastructure in the central region was significantly negatively correlated with industrial pollution emissions, indicating that traffic infrastructure had a lagging effect on industrial pollution. Increasing the construction of traffic infrastructure in the central region would be conducive to the reduction in industrial pollution, probably because the central region is an important hub linking the eastern and western regions. If its transportation network of roads, railroads, and water transport was more extensive, the economic hinterland of the eastern region would be both expanded and improved, which would also improve the accessibility of the western region. The correlation coefficient was larger than that of the national sample, indicating that the most significant impact of transport infrastructure on industrial pollution emissions occurred in the western region. From the regression results for high-quality development, the primary term of high-quality development in the eastern region was significantly positively correlated with industrial pollution emissions, and the secondary term of high-quality development was significantly negatively correlated with industrial pollution emissions. The trend of the impact of high-quality development on industrial pollution emissions in the central region was similar to that in the eastern region, but it was not significant, indicating a lower degree of impact. The primary term of high-quality development in the western region was significantly positively correlated with industrial pollution emissions, and the secondary term of high-quality development was significantly negatively correlated with industrial pollution emissions. This indicated that high-quality development significantly increased industrial pollution emissions in the early stage of development, until eventually an inflection point was reached where industrial pollution emissions were significantly suppressed, and the degree of influence was higher than that in the eastern and central regions.

Regional heterogeneity test: instrumental variable method (2SLS) estimation results.

5.3. Mechanism Identification: Industrial Agglomeration

Previous studies empirically tested the effects of transport infrastructure and high-quality development on industrial pollution emissions. In this study, the mechanism of the impact of transport infrastructure on high-quality development was investigated using a mediating effect model to test whether industrial agglomeration was the transmission path. Table 8 presents the results of the mediating effects model. Column 1 presents the results with industrial agglomeration as the dependent variable, indicating a positive and significant relationship between transport infrastructure and industrial agglomeration (β = 0.000586, p < 0.01). In columns 2 and 3, the coefficient of transport infrastructure can be seen to decrease significantly (dropping from −0.000148 to −0.000143). The coefficient of industrial agglomeration was also significant (β = −0.0852, p < 0.01), which suggests that industrial agglomeration (AGG) plays a role in the association between transport infrastructure and high-quality development. This suggests that industrial agglomeration (AGG) plays a mediating role in the association between transport infrastructure and high-quality development, and TRA reduces the inhibitory effect on high-quality development by promoting industrial agglomeration.

Analysis of the impact mechanism of transportation infrastructure on high-quality development and industrial pollution.

In columns 2 and 3 of Table 8 , the coefficient of transport infrastructure can be seen to decrease significantly (dropping from 0.00183 to 0.00155), and the coefficient of industrial agglomeration was also significant (β = −1.704, p < 0.01). This suggests that industrial agglomeration (AGG) played a mediating role in the association between transport infrastructure construction and industrial pollution emissions, i.e., TRA reduces industrial SO 2 emissions by promoting industrial agglomeration. Thus, H3 was verified.

5.4. Robustness Test

The aforementioned instrumental variables were used as the only core explanatory variables, with a related concern being that the control variables may also have endogeneity problems caused by “reverse causality”. The reliability of the conclusions was further ensured. In addition, tailoring was applied to eliminate the sample outliers, with column 4 in Table 9 showing the results after a 5% tailoring of the sample. The results after eliminating the outliers were basically consistent with the baseline regression findings. Therefore, the robustness of the study findings was considered to be good.

Robustness tests.

6. Conclusions

6.1. conclusions.

China’s ongoing modernization is based on a new development stage, the implementation of a new development concept, and the building of a new development pattern. The Fifth Plenary Session of the 19th CPC Central Committee and the Central Economic Work Conference made important arrangements for ecological protection, continued to promote pollution prevention and control, increased the optimization and adjustment of transportation structure, and promoted public-to-rail, public-to-water, and multimodal transportation. Therefore, promoting high-quality development is an active choice to adapt to the new normal of economic development and is the way to build a modern economic system. Based on China’s new development concept, this study developed a high-quality development index for China that consisted of 5 primary indicators, 16 secondary indicators, and 39 tertiary indicators based on the five aspects of innovation, coordination, openness, sustainability, and sharing. The index used the entropy weight method to measure the comprehensive score of the high-quality development index for 30 Chinese provinces (excluding Tibet, Hong Kong, Macao, and Taiwan). Based on the data for these 30 provinces covering the 14 years from 2004 to 2017, an empirical study was conducted to investigate the mechanism by which transport infrastructure and high-quality development affected industrial pollution emissions, and heterogeneity tests were conducted in three major regions: the eastern, central, and western regions. The regression results revealed several features.

First, the construction of transport infrastructure significantly promoted industrial pollution emissions, and there was a regional heterogeneity and time lag, with a significant positive correlation in the eastern and western regions and a significant negative correlation in the central region.

Second, the relationship between high-quality development and industrial pollution had an inverted “U” shape, and there was both regional heterogeneity and a time lag, with a significant positive correlation in the eastern and western regions, and no significant correlation in the central region.

Third, transport infrastructure significantly affected high-quality development and industrial pollution through industrial agglomeration, and transport infrastructure reduced the inhibitory effect on high-quality development by promoting industrial agglomeration. Transport infrastructure also reduced industrial SO 2 emissions by promoting industrial agglomeration.

Fourth, improvements in the level of scientific and technological development could suppress industrial pollution emissions, while improvements in the level of human capital could promote industrial pollution emissions.

6.2. Policy Recommendations

Based on the research findings, several policy recommendations can be made.

First, environmental factors should be incorporated into the transport infrastructure planning process. The transport infrastructure network should be optimized by region, with a focus on building green transport infrastructure construction to effectively improve the ecological environment. For the eastern region, there should be a focus on the transformation of highways and railroads, as well as the construction of inland waterways, while ensuring a transition from quantity to quality. Clean energy generation should be implemented and a green transportation network should be established to alleviate industrial pollution. For the central region, transportation infrastructure should be seen as a link, playing the role of radiating urban and economic development to the surrounding areas. The construction of green and intelligent transportation network systems should be encouraged, and the coordinated development of the regional economy should be promoted. For the western region, policy support should be increased, the upgrading of transport infrastructure networks should be accelerated, and the construction of high-speed railroads, highways, and other infrastructure is required. The overall outcome should be a clean and efficient transportation system.

Second, high-quality development should be promoted, with a new balance between economic development and environmental quality, which should be maintained by combining high-quality development and environmental protection. Exchanges between the western region and the central and eastern regions should be strengthened to steadily achieve high-quality regional economic development and effectively protect the ecological environment.

Third, industrial agglomeration and the achievement of a reasonable industrial layout should be promoted to establish the regional advantages of the industrial development pattern, encourage enterprises to innovate and upgrade, facilitate green production, and promote the positive role of transport infrastructure on high-quality development and industrial pollution.

Fourth, there is a need to improve the level of scientific and technological development to encourage innovation in industrial pollution reduction. At the same time, there is a need to build green human capital, stimulate a new green energy dynamic, gradually optimize the industrial and economic structure, and improve the natural environment.

6.3. Limitations

Our work has examined the impact of transport infrastructure, and high-quality development on industrial pollution emissions from a fresh perspective and has led to some interesting conclusions, but there is still some work to be done. Firstly, this study is limited to data on SO 2 emissions, and indeed other air pollutants such as fine particulate matter (PM2.5) and nitric oxide (NO), and our work should be further extended to assess emissions of other pollutants to confirm the heterogeneous impact of transport infrastructure and high-quality development. Secondly, this study focuses on the relationship between transport infrastructure and the output of polluting activities within Chinese provinces, while ignoring the spatial spillover and spatial unit autocorrelation of transport infrastructure in other provinces, so the impact of transport infrastructure on pollution emissions between Chinese provinces should be studied in the future.

Funding Statement

This work was supported by the National Natural Science Foundation of China (Grant No. 71974188), Major Program of National Social Science Foundation of China (Grant No. 21ZDA086), Excellent Young Teacher of Jiangsu Qing Lan Project (2022), and Major research project of Jiangsu Key Laboratory of Coal-based Greenhouse Gas Control and Utilization (Grant No. 2020ZDZZ03C).

Author Contributions

Conceptualization, X.W. and F.D.; methodology, Y.P.; software, Y.L.; writing—original draft preparation, X.W.; writing—review and editing, F.D.; supervision, X.W. and F.D. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Informed consent statement, data availability statement, conflicts of interest.

The authors declare that they have no competing interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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