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Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

39227480
70762
10.1038/s41598-024-70762-3
Article
Assessing the impact of urban road transport development on haze pollution in the Yangtze River Delta region
Tao Jing ZOE110@163.com

1
Zameer Hashim 2
Song Haohao 2
1 https://ror.org/05em1gq62 grid.469528.4 0000 0000 8745 3862 School of Business, Jinling Institute of Technology, Nanjing, 211169 Jiangsu China
2 https://ror.org/01scyh794 grid.64938.30 0000 0000 9558 9911 College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106 Jiangsu China
3 9 2024
3 9 2024
2024
14 205205 4 2024
21 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
The aim of this paper is to explore whether and how urban road transport (URT) development affects haze pollution. One of the innovations of this paper is that URT development is measured by road accessibility with novel digital elevation model datasets, which have been used by few scholars. The endogenous problem caused by revere causality issue in the relationship between URT development and haze pollution is also considered. Based on the panel data of prefecture-level cities of Yangtze River Delta (YRD) region in China from 2011 to 2018, this paper uses long-lagged values of URT development as the instrumental variable, employing the two-stage least squares (2SLS) method. The study shows that URT development leads to an increase of haze pollution. Moreover, mechanism tests based on moderating and mediating models support the finding that decreasing haze pollution resulted from better connection effects, while rising agglomeration effects tend to bring about increasing haze pollution, and the latter effect is larger in magnitude than the former. Current URT development may have long-term negative consequences for livability of YRD cities, and urban decision makers should reconsider the effectiveness of the current road transport investment and construction.

Keywords

Urban road transport development
Road accessibility
Urban connection
Urban agglomeration
Haze pollution
Subject terms

Environmental sciences
Environmental social sciences
issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

Road transportation is one of the major sources of urban air pollution1. In most of Asia cities, air quality was reported to be unhealthy2. One of the main contributors to a city’s air pollution is transport sector. It has consumed around 28% of the total energy3, which actively lead to greenhouse gas (GHG) emissions and poor air quality for urban populations to enjoy. This growth in transport emissions is especially daunting in China due to the rapid growth in road transport and motor vehicle usages4. Road transport accounts for about 85% or more of transport sector energy consumption and polluted emissions and motor vehicle emissions has become the dominant source of air pollutants, such as PM10, and PM2.55–7.

For mitigating urban air pollution, the Chinese government is usually concerned with actively promote urban road transport (URT) development. The argument relies on the simple logic that the relative poor road transport supply but high traffic demand generally lead to severe traffic congestion on road within cities, and congestion is usually recognized as a main catalyst for urban air pollution8. Thus, URT development (such as, road transport capacity expansion and new roads construction et al.) has been widely applied to manage congestion and control air pollution all over the world9. In theory, taking Eastern Massachusetts as an example, study also suggests that the road transport capacity promotion could result in a significantly reduction in PM2.510.

The establishment or improvement of links between consumers and producers by URT development brings about a range of positive externalities. It is referred to as internal connection effect. The benefits of better connecting due to the roads improvement affords have long been recognized in the current studies11. However, URT development may also generate concentrated travel flows because it could attract a large number of people and firms flow or mitigate into the city that forms in urban agglomeration, which leads to negative externalities. Urban agglomeration with a high population and activity density in cities has been recognized as a main contributor to result in increasing traffic congestion8. Therefore, the net effect of URT development on air pollution depends on the off-setting effects. However, there is still a lack of research design to explore the casual evidence of above two.

The major challenges in estimating the impact of URT development on air pollution are as following. First, URT development measure issue. Previous literature measures URT development based on either urban road areas, highway density, traffic volume, or passenger-kilometers from the perspective of transport supply or demand11–14, but few study focuses on measuring URT development based on novel digital elevation model (DEM) datasets15. DEM data provides detailed topographical information, enabling the precise calculation of the slope and curvature of roads, and other paths, which can affect accessibility. Second, reverse causality issue. Governments tend to improve road transport supply and land-use adjustment in urban areas, where many severe traffic congestion and air pollution exist, bringing about self-selection bias and revere causality issue for empirical analysis. Third, identifying the mediating effect issue. The effect of URT development on air pollution through internal connection effect is difficult to identify because roads improvement may also increase agglomeration scale by attracting firms and populations.

Based on above concerns, this paper tries to contribute to the current literature three sides. First, contrary to Sun et al.16, this paper measures URT development by using DEM datasets. It makes us adopt a spatial approach to measure the level of URT development at a geographic cost raster perspective. Second, contrary to Schio et al.17, after controlling high speed railway (HSR) network expansion and other variables, this paper employs instrument variable (IV) method to address the reverse causality between URT development and haze pollution with long-lagged values of URT development as the instrumental variable. Third, contrary to Borck et al.18, moderating effect and mediating effect models are used to assess two mechanisms of internal connection effect and urban agglomeration effect, to obtain results that are cleaner.

The YRD region in China is selected as an especially illustrative case study for the following reasons. First, since the reform and opening up in China, the higher levels of urbanization and industrialization in the YRD region have attracted numerous people flow into this area, promoting it to be one of the most densely populated area. Second, YRD cities take form of two specific characteristics of urban spatial structure, this structure within cities can be either polycentric, with concentrated development in suburbs, such as Shanghai, Nanjing and Hangzhou19,20, or monocentric, with economic activities concentrate in one main center (i.e., Hefei)21. Third, YRD region suffers severe environmental problems, in which the annual-mean PM2.5 was much higher than the level 2 standard value22.

We begin by reviewing the measurement of URT development and their impacts on air pollution. Methodology, variables and data are then described. The following section discusses empirical results. We conclude with policy implications and research limitations at last.

Literature review

Measurement of URT development

The current supply side URT studies are mainly limited to three aspects: road transport infrastructure, road transport capacity, and road transport performance. Road transport infrastructure measures are concerned with infrastructure investment and construction that reflects the levels of road transport infrastructure supply. An example of the former is Achour and Belloumi23, which uses the per capita road infrastructure expressed in kilometers, as the proxy variable of road transport infrastructure. The latter indicators usually includes the total length of the roads, the highway density, or the road area11,24.

Another measure of URT development is urban road capacity. Recently, Tennøy and Hagen presents a case study for the analysis of the effects of urban road capacity expansion, taking two Norwegian cities as examples25. Expanded lanes and upgraded interchanges is used to as urban road transport capacity measures to assess the traffic-inducing impacts of capacity expansion. Findings of interest is traffic-inducing land-use development is stronger, as a result of capacity expansions of the road construction. However, this capacity indicator cannot reflect the links in the urban road transport network.

For addressing above issue, travel time or vehicle travelled miles (VTM) are used to evaluate the level of urban road transport service and performance26. It is concerned with a specific interval time or distance to travel a trip between a series of given origins and destinations (OD) pairs. The above current road transport measures provide us unique insights for assessing different consequences related to the urban road transport development. However, none of the above measure addresses the issue of adequacy of urban road transport supply to accommodate demand27. These measures may provide important information for efficient traffic flow, capacity expansion, and other related studies to promote urban road transport development. Recently, Sahitya and Prasad introduce road accessibility as one of the most important performance measure to evaluate the quality of a road transport service28. It is defined as the ability of shifting of people from origins to their destinations. However, a comprehensive analysis of the road accessibility was not provided.

Definition and evaluate of road accessibility

In the classic literature related to the conception of road accessibility, Hansen defines it as potential opportunities for interaction29. Later, the definition of accessibility is considered to be composed of two sides30. One is the land-use side which refers to the distance between different locations, the other is the transport side which refers to the ability of a transportation network to provide a low cost way of reaching valued destinations21. Based on these two papers, Da Schio et al. conceptualizes accessibility as interaction potential which includes transport side and land-use side17.

Conventional road accessibility measures use population and employment densities to represent the proximity of different locations31. For providing a better representation of spatial proximity, researchers use improved measures, such as market potential and effective density based on physical distance to measure road accessibility32. However, these measures cannot account for the road transport network improvement on urban internal connection. For overcoming this issue, Melo et al. use road accessibility type measures based on travel times deriving from actual road networks33. Although travel time is a determinant for transport mode choice and route, Cui and Levinson however point out this measurement neglects the internal cost factor of road transport network15.

Based on these excellent studies, in this paper, road accessibility is defined as people’s ability to reach destinations from different origins to destinations under the context of urban area15. This ability is influenced by two factors that can affect the quality of internal connection. One is the road supply side which refers to intra-city road transport infrastructure, that reduce the generalised travel cost of the trips between OD pairs; the other is traffic demand side in terms of land-use which determines the amount of opportunities for people to reach34. Road accessibility measure in this paper adopts a least cost distance weighted sum of opportunities (e.g. population density) for measuring road accessibility by using DEM datasets.

The impact of URT development on air pollution

A large number of papers have emerged estimating the impact of road development on vehicle miles traveled (VMT), road traffic, traffic efficiency, traffic congestion, road safety, road energy demand, CO2 emissions and air pollution35,36. Most papers support that URT improvement increases more transport-related emissions and air pollution37.

The other strand of literature has sought to estimate the impact of land-use development, such as population or employment density on commuting distance, VMT, traffic congestion, transport-related CO2 emissions and air pollution38,39. However, there is no consensus on the relationship between population density and air pollution40.

The majority of the conventional literature on the negative externality effect of road accessibility provides evidence at the city level, exploring VMT and traffic congestion produced by private vehicles due to road density, road length, road network expansion41–43. A recent wave of empirical study has sought to estimate the impact of road accessibility on air pollution. Higgins et al. suggest that households value high levels of accessibility in locations that do not experience the corresponding environmental costs from traffic using empirical estimates of accessibility to employment with calibrated impedance functions44. Li and Zhou show that traffic accessibility, which is measured by road density, shows little correlation with urban air quality based on the spatial auto-regressive model45. In research of Yuan et al.46, road density is used to measure the accessibility of urban traffic by using OSM road data, and the finding indicates that increasing road accessibility results in more traffic and air pollution.

The mechanism of the impact of URT development on air pollution

The unclear impacts of URT development on air pollution is not surprising because it depends on the relative power of two effects, internal connection and urban agglomeration, which may exert opposite directions.

The former effect refers to the interactions between its existing commuters and their destinations. The convenience of travel to jobs, services and other places of interest for them has been observed as response to road accessibility47. The current literature highlights better connection derived from enhancement of URT is likely to decrease traffic congestion and air pollution6. The motor vehicles spend less time on the high quality road at a higher speed as better conection, leading to decrease fuel consumption and promote fuel efficiency and thus an decrease in automotive exhaust emissions.

Besides URT improvement affects connection of the existing people and economic activities of cities, it also contributes employment growth and attract more people and firms11. From this perspective, the urban agglomeration effect means the concentration of people and economic activities with supply in intra-city road transport, which is likely to increase congestion and its related air pollution48. Baum-Snow et al. also find that enhancement of URT shifts populations and economic activities who concentrate in city centers to suburbs in the Chinese cities49. This would increase air pollution as increasing scale effect of urban agglomeration due to rising travel demand for road but actually limited road capacity50.

In addition, URT development may lead to higher level of population density, as more people are willing to live in areas with convenient transportation. The frequency of vehicle usage is likely to increase, which may result in deteriorating urban air quality. These results suggest that better connection may be offset by increased size and density. The identifying assumption is that, URT development would bring about a deterioration of urban air quality, especially PM2.5, because the scale effects of agglomeration and densely distribution effects may offset the better connection effect. Based on above analysis, Hypothesis 1 is proposed:

H1

Higher level of URT development tends to result in higher level of haze pollution.

The identifying assumption is that, high levels of URT development decrease air pollution in terms of PM2.5 concentration through better internal connection and it increases the whole urban agglomeration (i.e., incumbent agglomeration plus the extra agglomeration that roads attract) and subsequently increases PM2.5 pollution. The focus is on these two effects because this is an important part of agglomeration theory and because it is a common theme in transport and land-use policy due to its relation with people’s choices on where to live and work and traffic congestion51. Thus, Hypothesis 2, 3, and 4 are proposed:

H2

Higher level of URT development tends to bring about less haze pollution through better internal connection effect.

H3

Higher level of URT development tends to bring about higher level of haze pollution through increasing urban agglomeration effect.

H4

The scale effect of urban agglomeration is higher than the internal connection effect.

In addition, we want to look at the distribution of economic activities that derive from URT development and at the links with urban air pollution of the YRD region. In this context, we propose that URT development leads to high levels of density, and thus an increase in urban haze pollution. Therefore, Hypothesis 5 is proposed:

H5

URT enhancement increases urban haze pollution through population density effects.

Methodology, variables and data

Based on the panel data of prefecture-level cities in YRD region of China from 2009–2018, this paper uses panel data model to estimate the effects of URT development on haze pollution, and then employs moderating effect and mediating effect models to test the mechanism of URT development through internal connection and urban agglomeration, respectively.

Panel data model

This paper tests the effect of URT development on haze pollution in terms of PM2.5 concentration. Considering both individual and year effects in each city and different year, the panel data model is set as following Eq. (1).1 POLLUit=α0+α1rdacit+α2Xit+λi+μt+ξit

where POLLU denotes the level of haze pollution represented by PM2.5 concentration, i refers to city,t denotes year, rdac represents the level of URT development, Xit refers to control variables which may affect urban haze pollution, λi refers to city effect, μt refers to year specific effects, ξit refers to the residual error term.

Moderating effect model

To test the effect of URT development on haze pollution through internal connection, the moderating effect model is introduced. This paper uses an exogenous variation in population here because urban agglomeration might adapt to high level of URT development. An interaction term is added to above panel data model as following Eq. (2).2 POLLUit=β0+β1rdacit+β2uragit+β3rdacit×uragit+β4Xit+λi+μt+ξit

where the term of rdac×urag refers to the interaction of URT development and urban agglomeration. However, the coefficient of the interaction (i.e., β3) may not be identified correctly if population responses to the change in URT development because the linear relationship between URT development and urban agglomeration. For addressing this issue, following by Gerritse and Arribas-Bel12, the main empirical strategy in this paper is to isolate exogenous variable in population. We assume that the urban agglomeration consists of a given urban agglomeration (denoted by uragitf) and the relative urban agglomeration adaptations to URT development to satisfy the spatial equilibrium of urban agglomeration (denoted by uragite) as the following Eq. (3).3 uragit=uragitf+uragite

Incorporating Eq. (3) into Eq. (2), the equation could be written as following Eq. (4).4 POLLUit=β0+β1rdacit+β2uragitf+β3rdacit×uragitf+β2uragite+β3rdacit×uragite+β4Xit+λi+μt+ξit

It is should be noted that an OLS regression might provide biased estimation on β1 and β3. According to Gerritse and Arribas-Bel12, the main strategy to address this issue is to treat the term in brackets as the residual error.5 POLLUit=β0+β1rdacit+β2uragitf+β3rdacit×uragitf+β4Xit+λi+μt+ψit

Based on above Eq. (5), an 2SLS method with an exogenous instrument for urban agglomeration that is through uragitf can identify the effect of road accessibility as the URT development on urban agglomeration through attracting industry and people is not used for identification here12.

Mediating effect model

Following by Wang et al.52, the mediating effect model is employed for estimating the scale effects of urban agglomeration on the relationship between URT development and haze pollution. Two equations are setting as following:6 uragit=ϕ0+ϕ1rdacit+ϕ2Xit+λi+μt+ξit

7 POLLUit=η0+η1rdacit+η2uragit+η3Xit+λi+μt+ξit

The mediating role of urban agglomeration is identified by the following two steps. First, the coefficient ϕ1 in Eq. (6) is investigated. If the coefficient ϕ1 is significant, it refers to independent variable: URT development represented by rdac can affect middle variable: urban agglomeration represented by urag. Second, two coefficients: η1 and η2 in Eq. (7) are investigated. If η1 and η2 are significant, it refers to the independent variable: URT development represented by rdac can directly affect dependent variable: haze pollution represented by POLLU, as well as indirectly affect it through middle variable: urban agglomeration represented by urag. If η1 is not significant but η2 is significant, it refers to independent variable: URT development represented by rdac can indirectly affect dependent variable: haze pollution represented by POLLU through middle variable: urban agglomeration represented by urag. All variables in this paper included are log-linearised.

Variables and data source

Dependent variable

Haze pollution (PM2.5 concentration): Haze pollution consists of particulate matter (PM) in terms of PM10 and PM2.5, which refer to PM < 10 µm and PM < 2.5 µm, respectively. Compared with PM10, NOx, SO2 and O3, this paper employs PM2.5 concentration as a proxy variable for haze pollution due to the harmful effect of PM2.5 exposure on human health in both long and short terms53.

Independent variable

URT development: Road accessibility measures the role of URT development on actual proximity and is defined in Eq. (8) as the number of opportunities and road network distance which can reflect the quality of connection and interaction of origins and destinations pairs.8 rdacit=πSitkcdit/EDitEDit

where EDit refers to employment density at year t and in city i, k denotes time band, Sit denotes average car speed on road at year t and in city i, cdit refers to the least cost distance, EDit refers to Euclidean distance. Unfortunately, due to we do not have the micro-data on speeds and time at the city level, Sitk is defined as Euclidean distance as the following Eq. (9).9 Sitk=23areaitπ

Instrument variable

Long-lagged URT development: In the case of reverse causality, the concern is that more air pollution may push industry and people flow out of the city, thus further decreasing urban agglomeration. The main strategy adopted in the previous literature is to employ IV estimators. According to previous literature, the most common IVs used in the road accessibility literature are geological and geographical variables and the rank order of population size36. We follow Combes et al.54 and use long-lagged values of URT development. The rationale is that there is strong association between long-lagged values of URT development and current URT development, but there is no relation between long-lagged values of URT development and haze pollution other than through the value of the current URT development.

Moderating and mediating variables

Moderating variable (historical population: denoted by hp): It is important to use a right instrument for urban agglomeration as the variation in population that responded to better URT and may bring about a biased estimation. For an instrument, we require exogenous variable in population that does not response to the current URT development. According to Gerritse and Arribas-Bel12, we use the population for YRD cities in 1998 as an instrument for urban agglomeration. The population size in 1998 is instrumented for urban agglomeration as danwei which was the urban socio-spatial foundation of China during the planned economy period no longer allocated housing to danwei’s employees after the housing reform of 199855.

Mediating variables (urban agglomeration (denoted by urag) and population density (denoted by jjm)): In previous literature, the proxy for urban agglomeration includes the Spatial Gini coefficient, Theil index, and output density indicators. To proxy for urban agglomeration, we follow Li et al. who used location entropy index, which is calculated by the ratio of industrial output in city i to the total output in YRD region56. Population density is calculated by the ratio of total population number to the total land size in a city.

Control variables

As for control variables, following by Li et al.57 and Sun et al.58, this paper introduces weather variables, industrial structure, and HSR network. First, weather conditions significantly impact the physical and chemical behaviors of air pollutants, making weather variables (such as precipitation and temperature) important control variables. Precipitation effectively removes particulate matter from the air through wet deposition processes. Heavy and frequent rain can significantly reduce the concentrations of PM2.559. Temperature also affects the vertical mixing of atmosphere, which in turn influences the dispersion of pollutants. High temperatures can facilitate the dispersion of pollutants. Therefore, the average annual precipitation (denoted by jsl) and temperature (denoted by qw) are included to control for city-level differences in natural environment.

Second, the industrial structure significantly impacts air pollution. High-pollution industries such as manufacturing and energy sectors, often emit large amounts of pollutants, significantly affecting air quality. Industrial activities are a major source of urban air pollution. Different industries have significant variations in the emission levels of air pollutants, making it important to consider the impact of industrial structure when analyzing air pollution. Therefore, the ration of the output value of secondary industry to the output value of service industry is employed to measure industrial structure.

Third, the expansion of high-speed rail networks can influence urban traffic patterns and air quality through multiple pathways. The construction and operation of high-speed rail provide a fast and efficient long-distance transportation mode, which may reduce reliance on private cars and road transport, thereby alleviating urban road traffic congestion and reducing road traffic-related pollutant emissions. HSR network expansion is defined as an inverse distance-weighted HSR network density as following Eq. (10).10 bdzjmt=∑n∈Zt1Dismnt2

where m and n refers to HSR stations (m≠n). Zt denotes the set of existing HSR stations at year t. The HSR network expansion measure increases with the number of HSR lines.

Data source

Urban haze pollution comes from the average concentration data of YRD cities released by China City Statistical Yearbook (2009–2019). Data on URT development are derived from Rivermap which provides DEM, RN and POI data formatted as a GIS shapefile. Combined with DEM data of YRD cities, the road network and shopping POI data allows us to measure the accumulated least cost distance along the paths by using the ArcGIS spatial analyst tool. The raster cost method is used to figure out the least cost distance of YRD cities. Figure 1 shows that DEM, RN and POI data in Nanjing and Hefei. Weather variables, industrial structure and banking penetration data were from China City Statistical Yearbook (2009–2019). HSR (High speed rail) lines and stations data were from the Ministry of Railways of the People’s Republic of China, and the distance of two stations in different cities were from Baidu Map.Fig. 1 DEM, road network, and POI data in Nanjing and Hefei.

Results

Results of effect of urban road transport development on haze pollution

This section presents the main findings of the two regression models that analyze the effect of URT development on haze pollution. The findings obtained from the non-IV and IV models by employing OLS and 2SLS methods are presented in Table 1. Specifically, columns (1)–(5) in Table 1 shows the results for the models based on OLS methods by incorporating all control variables included in this paper in sequence, while column (6) of the table presents the findings of 2SLS method. We first discuss the results in columns (1)–(5). The discussion focuses on the preferred models selected using model comparison.Table 1 Effect of URT development on haze pollution.

Variable	PM2.5	
OLS(1)	OLS(2)	OLS(3)	OLS(4)	OLS(5)	2SLS(6)	
rdac	 − 0.016*

(− 1.73)

	 − 0.009

(− 1.20)

	 − 0.007

(− 0.85)

	 − 0.004

(− 0.38)

	 − 0.003

(− 0.27)

	0.033***

(2.87)

	
jsl		 − 0.0002***

(− 4.05)

	 − 0.0002***

(− 3.39)

	 − 0.0002***

(− 3.28)

	 − 0.0002***

(− 3.34)

	 − 0.0003***

(− 6.32)

	
qw			 − 0.140***

(− 4.39)

	 − 0.133***

(− 4.07)

	 − 0.118***

(− 3.53)

	0.033

(0.87)

	
es				0.076

(1.03)

	0.058

(0.74)

	0.082

(0.92)

	
bdzj					 − 0.034

(− 0.88)

	 − 0.119***

(− 3.76)

	
ci	No	No	No	No	No	No	
year	No	No	No	No	No	yes	
R2	0.0326	0.1534	0.2745	0.2830	0.2897	0.5881	
N	96	96	96	96	96	81	
Hausman statistic

(p value)

						10.61

(0.059)

	
LM statistic

(p value)

						34.311

(0.000)

	
C-D-Wald F statistic

(p value)

						54.381

(10%, 16.38)

	
***p < 0.01, **p < 0.05, *p < 0.1.

Column (1) shows an OLS regression of the log haze pollution on log of URT development. It shows that URT development is significant in explaining urban haze pollution, but the coefficient of road accessibility is negative which is contrary to above conjecture of the effect of URT development on haze pollution. After we incorporating control variables which include precipitation, temperature, industrial structure and HSR network expansion variables into OLS regression model in sequence (i.e., column (2)–(5)), the coefficient of road accessibility is negative but is not significant, suggesting no role of URT development. The coefficient of road accessibility obtained from the preferred model 2SLS method, which includes all control variables, is 0.033 (p < 1%). It indicates that increasing road accessibility 10% increases haze pollution by 0.33%, all else equal. This result supports H1—namely, higher level of URT development tends to bring about higher level of haze pollution. Correcting for reverse causality based on lag instrument for URT development appears to have a large impact on the effect of URT development, which reverses the symbol of coefficient of road accessibility. As a measure of instrument relevance, this paper reports the Hausman test, Lagrange multiplier-LM test and C-D-Wald F test, which are robust to the fact that the instruments are correlated among themselves.

Robustness checks

As PM2.5 concentration across different cities may be spatially dependent, estimates effects of URT development on haze pollution based on 2SLS models may lead to biased results. Therefore, this paper constructs spatial regressions models for panel data to explore the validity of above results. We present results based on the spatial Durbin model (SDM). A adjacency weight matrix W1 is used for spatial autocorrelation test. Specifically, W1 represents the relationship between adjacent geographical units: if two cities are adjacent, it refers to 1; if not, it refers to 0. In addition, following by these papers11,17, the new road accessibility index is defined in Eq. (11).11 glmit=gzitDisikk′t2

where k refers to the center of a city i, t is year, gz is the weight index measured by wage of city i, dis is the travel time within a city. The values of global Moran’s I in 2008–2018 are between 0.4 and 0.8 and pass the significant test. The results of spatial regression is statistically similar to the above results of regressions as shown in columns (1)–(3) of Table 2. Indirect effects (namely, spatial spillover effects) illustrate how road accessibility in adjacent areas affect local urban haze pollution, while total effects (combining direct and indirect effects) highlight the average impact on urban haze pollution. In this paper, both direct and indirect effects of glm contribute significantly to promote urban haze pollution with W1. However, due to the variables without logarithm transformation employed in spatial models, the values of coefficients are higher than that of logarithm functions.Table 2 Results of robustness checks.

Variable	PM2.5	kql	
Total effect (1)	Direct effect (2)	Indirect effect (3)	GMM (4)	
glm	61.116**

(2.02)

	41.370**

(1.99)

	19.745*

(1.67)

		
rdac				 − 0.026*

(− 1.83)

	
Control variables	Yes	Yes	Yes	No	
ci	Yes	Yes	Yes	No	
year	Yes	Yes	Yes	No	
R2	 0.5398				
Arellano−Bond test				 − 0.590

(0.555)

	
N	 133			133	
rho

(p value)

	 1.972

(0.000)

				
lgt_theta	 15.114

(0.000)

				
***p < 0.01, **p < 0.05, *p < 0.1.

The Difference Generalized Method of Moments method (DIF-GMM) was used to assess the robustness of the finding of our baseline estimation method. The paper used the DIF-GMM approach to prevent any potential problem of endogeneity, since the approach is more resilient to the problem of high correlation among predictor and error term. Moreover, urban air pollution indicator is substituted for annual “The number of days with better air quality”, which is obtained from China City Statistical Yearbook. Logically, after using the DIF-GMM technique, the intensity of independent variable coefficient declined. As such, the statistic indicates that URT development leads to a significant reduction in urban air quality, with coefficient of − 0.026.

Mediating effects

Table 3 shows the regressions explaining haze pollution from URT development and the middle variable which includes urban agglomeration based on moderating effect model. The estimating equations are the empirical forms as shown in Eqs. (5), (6) and (7).Table 3 Results of mediating effects.

Variable	PM2.5	urag	PM2.5	jjm	PM2.5	
2SLS (1)	2SLS (2)	2SLS (3)	Sobel (4)	Sobel (5)	
rdac	 − 0.013**

(− 2.25)

	 − 0.034*

(− 1.79)

	 − 0.057***

(− 2.87)

	0.222***

(6.574)

	 − 0.024**

(− 2.29)

	
urag	0.838***

(2.85)

		 − 0.603***

(− 2.76)

			
rdac*urag	 − 0.021***

(− 3.46)

					
jjm					0.054*

(1.90)

	
Control variables	Yes	Yes	Yes	No	No	
IV	hp	hp	hp	–	–	
ci	–	Yes	Yes	No	No	
year	Yes	Yes	Yes	No	No	
R2	0.5471	0.4478	0.516	0.366	0.071	
N	86	65	65	77	77	
Sobel Z					0.012*	
Hausman statistic

(p value)

	5.36

(0.069)

					
LM statistic

(p value)

	17.738

(0.000)

					
C-D-Wald F statistic

(p value)

	10.134

(10%, 7.03)

					
***p < 0.01, **p < 0.05, *p < 0.1.

The first column shows an moderating effect model of the log haze pollution on logs of URT development, urban agglomeration and their interaction with historical population instrument for urban agglomeration. The instrument is the log of 1998 population number of YRD cities, and that variable interacted with road accessibility. It shows that URT development and urban agglomeration are significant in explaining urban haze pollution. The interaction coefficient is significant too, suggesting the moderating role of URT development.

The coefficient of the interaction cannot be interpreted in isolation, because it is an interacted coefficient, which shows that the scale effects vary with URT development. The coefficient of road accessibility shows that increasing road accessibility decreases haze pollution, suggesting that URT development brings about better air quality. The coefficient of the interaction of road accessibility and urban agglomeration with historical population instrument shows that increasing URT development has a considerable and significant effect on agglomeration’s polluted effect. Namely, road accessibility does moderate internal effects: cities with high level of URT have substantially larger environmental benefits from better connection.

But the differences in benefits may be not sizable. After we process the road accessibility data of each year into two types: the 25th and 50th percentiles, and match it with the existing cross-section, the same above IV method is used to assess the differences, we find that: for the road accessibility at the 25th percentile, the haze pollution-to-internal connection elasticity is − 0.7% (p = 0.09), while at the 50th percentile, the elasticity is − 0.7% (p = 0.09). YRD cities with a higher road accessibility have less agglomeration’s polluted effect confirms the hypothesis that higher level of URT development tends to lead to better internal connection and subsequently less haze pollution. This result supports H2. We also report the Hausman test, Lagrange multiplier-LM test and C-D-Wald F test, which are robust to the fact that the instruments are correlated among themselves.

For testing the scale effect of urban agglomeration, we provide a mediating effect model estimate of the whole urban agglomeration (i.e., incumbent population plus the extra population that URT enhancement attract) effect. The second and third column in Table 3 show that the estimate of the scale effect of the whole urban agglomeration. The coefficient estimates of indirect of urban agglomeration is around 2% ([(− 0.034)*(− 0.603)], implying that the indirect of the scale effect of the whole urban agglomeration is significant. It indicates that URT development increases haze pollution through increasing urban agglomeration size effect because higher level of URT development can also attract industry and people. This result supports H3—namely, URT development tends to bring about higher level of haze pollution through increasing scale effect of urban agglomeration.

The p value of Sobel test is 0.032 (< 0.05), indicating the result of the mediating effect model is robust. For further testing the robustness of above result, we also report the _bs_2 test, and confidence interval is [− 0.098768, − 0.0156648], which does not include 0, suggesting that the scale effect of the whole agglomeration effect is not biased.

From above findings of the moderating effect and mediating effect models, we can conclude that improving URT produces a significant effect through increased internal connectivity for the existing people and economic activities which already concentrate in the city, but the effect is lower than the scale effect of urban agglomeration because higher URT development also attracts industry and people and increase urban agglomeration. This result supports H4—namely, the scale effect of urban agglomeration resulted from URT development is higher than the internal connection effect.

We also report mechanism test of urban population density based on Sobel test. As findings of columns (4) and (5) in Table 3, if we do not include control variables to estimate the marginal effects, result in column (4) suggests that road accessibility resulted in an increase in the population density, with a coefficient value of 0.222; in column (5), the effect of population density on urban haze pollution is positive, with coefficient of 0.054. These findings suggest that road accessibility can indirectly and significantly increases urban haze pollution by enhancing urban population density. This result supports H5.

Conclusions and policy implications

This paper takes a first step towards a better understanding of the relationship between URT development and haze pollution for a sample of YRD cities by using DEM datasets. The results show that cities with such URT development have higher level of PM2.5 concentration. Our results square with evidence on urban air pollution suggested by Da Schio et al.17 who suggests that high levels of URT is associated with high levels of pollution based on data for the Brussels Capital Region. The finding of moderating effect model shows that haze pollution reduction resulted from better internal connection effect. However, the evidence of mediating effect model supports that scale effect of urban agglomeration increases haze pollution. Moreover, the scale effect of urban agglomeration is larger in magnitude than the internal connection effect.

This paper also has a couple of limitations. First, other urban characteristics, such as public transport vehicles or car ownership, could be included as control variables at the city level. Second, gridded population data could help better measurement of road accessibility and its relation with haze pollution. Third, this paper was not distinguished from different types of roads (such as highways and primary roads), and this may affect the conclusions of this paper. In the future, for obtaining robust findings, we would like to investigate the impact of arterial roads, collector roads, and local roads on urban air pollution based on a more comprehensive highway datasets.

Acknowledgements

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author contributions

J.T. and H.Z. wrote the main manuscript text and H.H.S. prepared Fig. 1. All authors reviewed the manuscript.

Data availability

Sequence data that support the findings of this study have been deposited in the https://tjj.nanjing.gov.cn/bmfw/njtjnj/ and https://cnki.nbsti.net/CSYDMirror/trade/Yearbook/Single/N2021020039?z = Z030, et al.

Competing interests

The authors declare no competing interests.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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