Impact of spatial structure of urban agglomerations on PM2.5 pollution：Based on resource misallocation

Abstract

The spatial structure of urban agglomerations affects the economic development and environmental conditions of urban agglomerations. Severe air pollution makes green development an empty talk, and how the spatial structure of urban agglomerations affects air pollution is an urgent problem to be solved in pursuit of high-quality economic development. The panel data of 20 urban agglomerations in China from 2002 to 2017 are used to test how the spatial structure of urban agglomerations affects regional PM2.5 concentration in resource misallocation mediation effect. The empirical results show that (1) most urban agglomerations are polycentric, and only Guanzhong, Poyang Lake Ring, Wuhan and Nanqin Beifang urban agglomerations are monocentric. (2) The spatial structure and PM2.5 concentration have U-shaped characteristics, with the inflection point of centrality equal to 1. To reduce PM2.5 concentration in polycentric urban agglomerations can be achieved by increasing the centrality, while the opposite is true for monocentric urban agglomerations. (3) Improving the resource allocation of urban agglomerations can reduce PM2.5 concentration. The spatial structure of urban agglomerations with a limited degree of centrality can improve the resource allocation of urban agglomerations and further reduce PM2.5 pollution. This also indicates to a certain extent that a reasonable spatial structure of urban agglomerations is conducive to optimizing resource allocation and improving air pollution.

1. Introduction

Since the reform and opening up, China has experienced a rapid urbanization process. The urbanization rate increased from 17.92 in 1978 to 64.72% in 2022. With the rapid development of urbanization, urban agglomerations are also constantly developing and evolving. And it has formed a relatively complete spatial structure system of urban agglomerations nationwide (Fang et al., 2011) [1]. As a form of advanced spatial organization between cities, the role of urban agglomeration in national economic development is becoming more and more obvious. It has gradually become a carrier for China to participate in global competition. However, urban agglomerations have problems such as uncoordinated development, unreasonable spatial organization, and different environmental supervision in different cities (Liu et al., 2020, Sun et al., 2019b) [2,3]. These have led to interregional industrial shifts and changes in air pollution (Zhang et al., 2019, Feng et al., 2020) [4,5]. Air pollution affects human health and causes climate problems (Wu et al., 2020) [6].In recent years, with the increasing degree of air pollution restricting residents' health and economic development. Coordinated treatment of pollutants has also received more and more attention from urban agglomerations. Documents such as the 14th Five-Year Plan for ecological environmental protection and the 2020 Government Work Report also emphasize the importance of “comprehensive management” and “joint prevention and control” from the policy level. Therefore, it is of great practical significance to seek the coordinated promotion of urban agglomeration development and air pollution control.

The spatial structure of urban agglomerations affects their economic performance (Yao and Wu, 2020, Liu and Wen, 2020) [7,8]. A reasonable spatial structure can reduce unit energy consumption and reduce air pollution through technological spillover and economies of scale (Glaeser and Kahn, 2010) [9].However, the inefficiency of production due to excessive agglomeration can exacerbate pollution (Zhang and Wang, 2014) [10].Moreover, the spatial structure can have spillover effects from air pollution emissions. At the same time, a reasonable spatial system layout is the premise of optimizing resource allocation (Ye et al., 2022) [11]. The distortion of factor allocation will have a significant inhibitory effect on the innovation of green technology in local and surrounding cities (Yao et al., 2022) [12], and the level of independent innovation plays a decisive role in environmental pollution (Chen et al., 2019a, Chen et al., 2019b) [13,14], Green technology innovation can reduce air pollution (Akbostancı et al., 2009, Zhu et al., 2018) [15,16].This paper discusses the impact of spatial structure of urban agglomerations on air pollution from the perspective of resource allocation, and provides a new focus for the coordinated development of urban agglomerations and green economy in China.

1.1. Basic definitions

Urban agglomeration is the theme of new urbanization and plays an important role in China's economic and social development. The earliest research on urban agglomerations can be traced back to the British scholar Howard's conception of the combination model of pastoral urban agglomerations in 1898, that is, urban agglomerations composed of pastoral cities. Gottmann proposed the concept of the Megalopolis when he conducted a study of urbanization in the northeast coast of the United States. He believes that in a huge urbanized area, the form of dominance space economy is no longer limited to a single large city or metropolitan area, but is concentrated in several metropolitan areas, which are closely related and form a huge whole in terms of population and economic activities (Gottmann, 1957) [17]. The research on the concept of urban agglomeration in Chinese academia focuses on integrating it with the actual development of local cities. Some scholars suggested unifying the concept of urban agglomeration, and through the continuous discussion of scholars, the similar concept of urban agglomeration became clearer. Yao Shimou et al. made a relatively complete and comprehensive definition of urban agglomeration (Yao et al., 2006) [18]. At present, the broader definition of urban agglomeration in China is proposed by Fang Chuanglin. She believes that urban agglomerations have more than one megacity as the core, and at least three or more large cities as the constituent units. With the help of well-developed infrastructure networks such as transportation and communications, urban agglomerations have formed areas with compact spatial organization, close economic ties, and a high degree of urbanization and integration (Fang et al., 2011) [1].

Under the guidance of agglomeration economy theory, existing research uses single-center or multi-center to characterize the spatial structure of urban agglomerations. It mainly describes whether the factors are distributed in one city or in multiple cities, and to what extent the factors are concentrated in the city (Liu and Wen, 2020) [8]. There are two types of measurement methods. The first type is morphological centrality, which usually uses the order-scale rule, the Pareto index, the first degree formula, and the Mono index to calculate the concentration of the population (or employed population) of the urban agglomeration to represent the spatial structure (Meichang and Bingbing, 2020, Sat, 2018, Sun et al., 2019a) [[19], [20], [21]]. The second category is functional centrality, which determines the degree of connection between urban functions based on the development of information technology. The mainstream view is that the spatial structure of urban agglomerations is monocentric (Sun et al., 2019, Wang et al., 2019) [22,23].

1.2. Problem setting

The spatial structure of urban agglomerations is a network formed by large cities and surrounding small and medium-sized cities in the process of scale expansion and infrastructure connectivity. When agglomeration is not economic, urban agglomerations tend to be polycentric, but when agglomeration is economic, factors are promoted to concentrate in central cities to form a single center (P, 1991b) [24]. Different from a single city, in the regional space composed of multiple cities, air pollution is not only closely related to the production and lifestyle and factor allocation mode of the region, but also affected by the spatial organization structure between cities. The unicenter-multicenter distribution characteristics of the spatial structure, as well as the agglomeration and dispersion states, can have spillover effects on the emission of atmospheric pollutants (Liu et al., 2020) [2]. For example, the London smog incident, which ranks among the top ten environmental pollution events in the world, is related to the spatial structure of the southern elements of the British urban agglomeration that is over-concentrated in London. Reasonable distribution of urban system is an effective prerequisite for optimizing the spatial allocation of factors and reducing air pollution.

Resource allocation changes with the change of the spatial structure of urban agglomerations, and a reasonable spatial structure of urban agglomerations can optimize resource allocation through urban industrial cooperation (Ye et al., 2022) [11]. The effective allocation of labor, capital, information, infrastructure and other resources in cities can increase productivity and reduce unit costs, and promote the elimination of low-end production enterprises (and often high-polluting enterprises). Factors flow to enterprises with high productivity and low pollution, and realize the effective allocation of resources in various industries and industries, thereby reducing the total amount of pollution emissions (Ryzhenkov, 2016) [25]. Resource misallocation adversely affects environmental pollution improvement by hindering technological innovation (Yu et al., 2018) [26]. First, the overallocation of resources makes factor prices underestimated, reduces corporate costs, increases profit margins, and lacks the motivation and pressure of technological innovation. Second, the misallocation of resources leads to the distortion of the factor market, and it is possible to seek rent to obtain excess profits, which also inhibits the independent innovation of enterprises (Zhang et al., 2011) [27]. Third, due to the performance appraisal mechanism centered on economic construction, local governments will discriminate against different enterprises in terms of land transfer and taxation in order to obtain limited “tickets” in the promotion championship (Ma Liang, 2013) [28]. Enterprises with little innovation and high short-term economic benefits are favored by the government. Resource misallocation (especially rent-seeking and information asymmetry) makes enterprises in the low-end industrial chain obtain profits that do not match productivity, preventing the exit of factors from low-productivity enterprises。

Based on the above analysis, this paper proposes the following hypothesis.

Hypothesis 1

The degree of air pollution in urban agglomerations is affected by their spatial structure.

Hypothesis 2

Spatial structure can affect air quality by influencing resource allocation.

1.3. Literature review

Most of the existing studies have explored the causes of air pollution from both natural environment and economic development perspectives.With regard to the natural environment, natural conditions such as wind speed, rainfall and temperature have significant effects on air pollution (Pateraki et al., 2012) [29]. In addition, the level of economic development, urbanization, industrial structure, level of foreign direct investment, and regional environmental regulations also influence the formation and spread of air pollution (Bian et al., 2019, Wang et al., 2021) [30,31]. Among the above factors affecting air pollution, the most relevant to this study is the impact of urbanization on the environment.

In academic studies, there are three main views on the impact of urbanization level on environmental pollution. First, the mainstream view is that the increase in the level of urbanization deteriorates the environment. The inefficient allocation of resources in urbanization is an important cause of pollution [32]. Second, some scholars hold the opposite view, and they believe that the increase in the level of urbanization can alleviate environmental pollution. Charfeddine and Mrabet (2017) argue that regions with high urbanization levels can increase public awareness of environmental protection by means of issuing environmental policies, and strict environmental regulations can improve the level of application of green technologies by enterprises to reduce environmental pollution at the source [33]. Third, a small number of scholars believe that urbanization and environmental pollution are not simply linear, and they have explored a variety of nonlinear relationships between urbanization and pollution such as “U", inverted “U", and “N" (Yang, Renfa, 2015; Zhang, K., 2018; Shao et al., 2019) [[34], [35], [36]]. The reason for the non-linear relationship is that different stages of urbanization have different environmental policies. In the early stages of urbanization, many regions focused on economic growth at the expense of ecological environment, which led to an inverted U-shaped effect of urbanization on carbon emissions. However, when urbanization reaches higher levels, the emphasis on environmental protection leads to a U-shaped relationship between urbanization and carbon emissions (Hashmi et al., 2021) [37].

With the advancement of urbanization, particulate air pollution has become the most serious and common type of air pollution in China, and PM2.5 is one of the most prominent harmful components. Regarding the impact of urban spatial structure on air quality, a large number of studies have found that urban spatial structure is one of the key factors affecting PM2.5 levels (Clark et al., 2011, Ewing et al., 2003) [38,39].Yla.et al. (2019) Based on data from 28 sample cities in China, it is found that polycentric structures increase PM2.5 concentration and per capita greenhouse gas emissions [40], Veneri (2012) reached similar conclusions based on sample from the NUTS-2 region in Italy [41].In contrast, Meichang and Bingbing (2020) found that every 1% increase in polycentricity in cities resulted in a 1.46%–2.67% decrease in PM2.5 concentration [19].The reduction in the total concentration of air pollutants is associated with the redistribution of polluting industries (Sun et al., 2019a) [21]. Domestic scholars also conduct research from the macro (spatial structure indicators such as city size and shape) and micro (land use status, urban spatial layout, etc.). The results show that the area of urban built-up area and the concentration of major pollutants in the air are positively correlated (Li et al., 2013) [42], Urban sprawl increases local PM2.5 concentrations (Qin et al., 2016) [43]. Song Yan et al. (2014) demonstrate that urban development with smart growth can effectively reduce PM2.5 emissions [44]. Sun Shuang et al. (2019) systematically analyzed the air pollution characteristics of Beijing-Tianjin-Hebei and concluded that pollutant concentration was negatively correlated with normalized difference vegetation index value [45].

Existing studies either take the Urbanization as the starting point to study the impact of agglomeration on environmental pollution, or study the impact of individual urban spatial structure on air pollution. Few studies directly analyze the impact of the spatial structure of urban agglomerations on air pollution. The marginal contribution of this paper is as follows. (1).Taking 20 major urban agglomerations in China as research samples, exploring the impact of urban agglomerations on air pollution and effectively supplementing relevant research. (2). Based on the perspective of resource allocation, explore the influence of spatial structure of urban agglomerations on PM2.5 concentration, clarify its mechanism, and empirically supplement related fields. (3) Panel data of 20 urban agglomerations in China from 2002 to 2017 were analyzed using STIRPAT model and mediating effects model to explore the linkages between spatial structure of urban agglomerations, resource misallocation and PM2.5 concentration, and the empirical results provide certain policy recommendations for the development of urban agglomerations and synergistic development of green economy in China.

2. Materials and methods

2.1. Study area and data

Urban agglomerations are important carriers of China's new urbanization war path, and new territorial units for the country to participate in global competition and international division of labor. At present, China's regional spatial governance model is undergoing a major transformation, starting from the original administrative district division management to type-area spatial governance. This paper classifies Chinese urban agglomerations into national, regional, sub-regional and regional levels by drawing on the size and agglomeration of mature urban agglomerations in the world. The scope of urban agglomerations in this paper mainly refers to the 23 urban agglomerations defined in Fang Chuanglin's “China Urban Agglomeration Development Report” published in 2011(Fang et al., 2011). Due to the adjustment of administrative divisions and the fact that some urban data are not readily available or have poor continuity, we mainly study 20 urban agglomerations, mainly focusing on the prefecture-level cities in each urban agglomeration, See Table 1 for details.

Table 1.

The scope of China's urban agglomeration.

urban agglomerations	Cities at the prefecture level and above
ChangZhuTan	Changsha, Zhuzhou, Xiangtan
Lanbai West	Lanzhou, Baiyin, Xining
Qianzhong	Guiyang, Zunyi, Anshun
Jinzhong	Taiyuan, Jinzhong, Yangquan
Central Yunnan	Kunming, Qujing, Yuxi
Yinchuan Plain	Yinchuan, Wuzhong, Shizuishan
Nanqin-North Defence	Nanning, Beihai, Fangchenggang, Qinzhou
Guanzhong	Xi'an, Xianyang, Tongchuan, Baoji, Weinan
Poyang Lake	Nanchang, Jiujiang, Jingdezhen, Shangrao, Yingtan
West Coast	Fuzhou, Xiamen, Zhangzhou, Quanzhou, Putian, Ningde
Wuhan	Wuhan, Huangshi, Ezhou, Xiaogan, Xianning, Huanggang
Hada Chang	Harbin, Daqing, Changchun, Qiqihar, Jilin, Songyuan
Shandong Peninsula	Jinan, Qingdao, Weihai, Yantai, Dongying, Rizhao, Zibo, Weifang
Central Plains	Zhengzhou, Luoyang, Kaifeng, Xinxiang, Jiaozuo, Xuchang, Pingdingshan, Luohe
Pearl River Delta	Guangzhou, Shenzhen, Zhuhai, Foshan, Huizhou, Zhaoqing, Jiangmen, Dongguan, Zhongshan
Beijing-Tianjin-Hebei	Beijing, Tianjin, Tangshan, Langfang, Baoding, Qinhuangdao, Shijiazhuang, Zhangjiakou, Chengde, Cangzhou
Jianghuai	Hefei, Chaohu, Wuhu, Tongling, Maanshan, Chuzhou, Liu'an, Huainan, Chizhou, Anqing, Bengbu
Liaodong Peninsula	Shenyang, Dalian, Anshan, Fushun, Fuxin, Panjin, Dandong, Liaoyang, Tieling, Huludao, Yingkou, Jinzhou
Yangtze River Delta	Shanghai, Suzhou, Wuxi, Changzhou, Nanjing, Zhenjiang, Yangzhou, Taizhou, Nantong, Hangzhou, Jiaxing, Huzhou, Ningbo, Shaoxing, Zhoushan
Chengdu-Chongqing	Chengdu, Chongqing, Deyang, Mianyang, Guangyuan, Yibin, Leshan, Luzhou, Nanchong, Zigong, Dazhou, Meishan, Neijiang, Suining, Guang'an, Ya'an, Ziyang, Bazhong

2.2. Measures of the spatial structure of urban agglomerations

Drawing on the practice of Sun Bundong et al. (2017), the position-scale method is used to measure the spatial structure of urban agglomerations [46].

$${\mathrm{N}}_{\mathrm{i}}={\mathrm{N}}_1/{\mathrm{R}}_{\mathrm{i}}$$ (1)

Perform a logarithmic transformation of equation (1).

$$\ln {\mathrm{N}}_{\mathrm{i}}=\ln {\mathrm{N}}_1-\mathrm{Q}\ln {\mathrm{R}}_{\mathrm{i}}$$ (2)

N represents the city population size, and R represents the ranking of city population size in the city agglomeration. The relevant data comes from the China Statistical Yearbook and the China Cities Statistical Yearbook. Q is the value to be calculated, which measures the centrality of the spatial structure of the urban agglomeration. When Q > 1, it represents a monocentric spatial structure of the urban agglomeration; When Q < 1, it indicates that the urban agglomeration is a polycentric spatial structure.

According to equation (2), Table 2 shows the results of the spatial structure calculation of urban agglomerations. The spatial structure of most urban agglomerations did not change much during the sample period, and only the Jianghuai Urban Agglomeration evolved from monocentric to polycentric in 2011, which may be attributed to the merger of Chaohu Lake into Hefei. 15 urban agglomerations such as Chengdu-Chongqing, Central Yunnan, HaDaChang, etc. show polycentric spatial structure.And Guanzhong City Agglomeration, Poyang Lake City Agglomeration, Nanqin-North Defense City Agglomeration and Wuhan City Agglomeration are monocentric spatial structures. Overall, polycentric spatial structure is predominant in urban agglomerations in China, which is consistent with other scholars' studies (Wang et al., 2019) [47].The formation of polycentricity was made possible by the development of modern transportation and communication technologies. Infrastructure connectivity has strengthened industrial links and resource flows between cities.

Table 2.

Spatial structure of urban agglomerations.

Urban Agglomerations	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017
Chengdu-Chongqing	0.73	0.73	0.73	0.73	0.74	0.74	0.74	0.74	0.75	0.75	0.75	0.75	0.76	0.76	0.79	0.79
Central Yunnan	0.83	0.83	0.85	0.86	0.87	0.87	0.87	0.88	0.82	0.84	0.91	0.91	0.92	0.92	0.92	0.92
Guanzhong	1.02	1.03	1.03	1.04	1.05	1.06	1.06	1.07	1.07	1.07	1.07	1.08	1.09	1.09	1.10	1.17
Hada Chang	0.76	0.75	0.76	0.76	0.76	0.76	0.75	0.75	0.75	0.75	0.75	0.75	0.76	0.75	0.75	0.75
West Coast	0.76	0.75	0.74	0.73	0.72	0.70	0.69	0.68	0.68	0.67	0.67	0.66	0.65	0.65	0.64	0.63
Poyang Lake	1.06	1.09	1.09	1.10	1.10	1.11	1.11	1.10	1.10	1.11	1.10	1.10	1.10	1.11	1.11	1.11
Jianghuai	1.04	1.04	1.02	1.02	1.03	1.03	1.03	1.03	1.03	0.84	0.84	0.84	0.84	0.84	0.62	0.63
Jinzhong	0.78	0.79	0.80	0.82	0.84	0.85	0.85	0.86	0.85	0.85	0.85	0.84	0.85	0.85	0.85	0.85
Beijing-Tianjin-Hebei	0.62	0.62	0.62	0.62	0.63	0.63	0.65	0.63	0.64	0.64	0.64	0.64	0.64	0.64	0.65	0.64
Lanbai West	0.51	0.52	0.52	0.53	0.54	0.54	0.54	0.56	0.53	0.54	0.57	0.67	0.70	0.54	0.54	0.54
Liaodong Peninsula	0.60	0.60	0.60	0.61	0.61	0.61	0.61	0.61	0.61	0.62	0.62	0.62	0.63	0.63	0.63	0.63
Nanqin-North Defence	1.01	1.47	1.50	1.51	1.50	1.50	1.50	1.49	1.47	1.47	1.47	1.47	1.47	1.47	1.47	1.47
Qianzhong	0.96	0.96	0.96	0.96	0.95	0.94	0.93	0.93	0.97	0.93	0.92	0.93	0.92	0.91	0.91	0.90
Shandong Peninsula	0.73	0.73	0.73	0.73	0.73	0.73	0.73	0.73	0.73	0.73	0.73	0.73	0.73	0.73	0.73	0.73
Wuhan	1.02	1.02	1.02	1.04	1.04	1.04	1.04	1.04	1.04	1.03	1.02	1.02	1.02	1.02	1.02	1.02
Yinchuan Plain	0.82	0.82	0.53	0.56	0.57	0.59	0.60	0.62	0.63	0.65	0.69	0.69	0.81	0.75	0.77	0.80
Yangtze River Delta	0.65	0.65	0.66	0.66	0.66	0.66	0.66	0.66	0.67	0.67	0.67	0.67	0.67	0.67	0.67	0.68
Changzhutan	0.68	0.69	0.70	0.71	0.71	0.71	0.72	0.73	0.74	0.74	0.74	0.75	0.76	0.78	0.80	0.82
Central Plains	0.39	0.39	0.39	0.39	0.39	0.40	0.40	0.40	0.50	0.52	0.55	0.49	0.50	0.45	0.45	0.46
Pearl River Delta	0.90	0.88	0.87	0.85	0.84	0.83	0.82	0.81	0.80	0.80	0.79	0.78	0.78	0.77	0.76	0.76

2.3. Measurement of PM2.5 concentration

The change of PM2.5 concentration can reflect the change of air pollution, and it is statistically continuous and the data is publicly available, so this paper uses PM2.5 concentration to reflect the air pollution status of urban agglomerations. PM2.5 concentration data were obtained from the Atmospheric Composition Analysis Group of Dalhousie University from 2002 to 2017. The data were rasterized to obtain the annual average concentration data after matching the vector maps of 20 urban agglomerations.

The “Average” line in Fig. 1 shows that the average PM2.5 concentration in the 20 urban agglomerations during the sample period has an inverted U-shaped characteristic. PM2.5 concentrations were on the rise from 2003 to 2011 and turned to a downward trend from 2012 to 2017, with 2011 as the inflection point. The variation of PM2.5 concentrations in urban agglomerations such as Central Yunnan, Pearl River Delta, and Changzhutan also shows an inverted U-shape, which is comparable to the trend of the “Average” line. The variation of PM2.5 concentrations in urban agglomerations such as Chengdu-Chongqing, Guanzhong, West Coast of the Strait, Poyang Lake, Jinzhong, Beijing-Tianjin-Hebei, Lanbai-West, Nanqin-North Defense, Qianzhong, Wuhan, Yinchuan Plain, and Yangtze River Delta zigzagged up from 2002 to 20011 and kept decreasing from 2012 to 2017. The trend of PM2.5 concentration in the urban agglomerations in Central Yunnan and West Coast of the Strait is flatter and the values are lower than those in other urban agglomerations. This may be related to the fact that the two urban agglomerations are mainly developing service industries such as tourism, with lower pollution emissions than industrial and manufacturing-based cities. There is no obvious pattern of PM2.5 concentration changes in the urban agglomerations of HaDaChang, Jianghuai, Liaodong Peninsula and Shandong Peninsula. In general, PM2.5 concentrations in most urban agglomerations reached an inflection point around 2012, which is related to the increased awareness of environmental protection and the emphasis on green development.

Fig. 1.

Changes in PM2.5 in urban agglomerations.

2.4. Measurement of resource misallocation in urban agglomerations

The efficiency of resource allocation affects air pollution, and the efficiency of resource allocation can be measured by the degree of resource misallocation (Bai and Liu, 2018) [48]. This paper presents a measure of the degree of capital misallocation ${\tau }_{Ki}$ of urban agglomerations i and the degree of labor misallocation of ${\tau }_{Li}$ are measured as follows.

$$\widehat{{\gamma }_{Ki}}=\frac{1}{1+{\tau }_{Ki}},\widehat{{\gamma }_{Li}}=\frac{1}{1+{\tau }_{Li}}$$ (3)

The $\widehat{{\gamma }_{Ki}}$ _and $\widehat{{\gamma }_{Li}}$ represent the relative price distortion coefficients.

$$\widehat{{\gamma }_{Ki}}=\left(\frac{K_i}{K}\right)/\left(\frac{s_i{\beta }_{Ki}}{{\beta }_K}\right),\widehat{{\gamma }_{Li}}=\left(\frac{L_i}{L}\right)/\left(\frac{s_i{\beta }_{Li}}{{\beta }_L}\right)$$ (4)

$s_i=\frac{y_i}{Y}$ represents the share of output $y_i$ of city agglomeration i in the total output Y of all urban agglomerations. ${\beta }_K={\sum }_1^N{s}_i{\beta }_{Ki}$ represents the weighted capital contribution. $\frac{K_i}{K}$ represents the share of capital used by urban agglomeration i in total capital. $\widehat{{\gamma }_{Ki}}$ indicates the degree of capital misallocation.If $\widehat{{\gamma }_{Ki}}>1$ it indicates that the cost of using capital in urban agglomeration i is relatively low and capital is over-allocated; on the contrary, if $\widehat{{\gamma }_{Ki}}<1$ , then it indicates that the urban agglomeration is under-allocated. From equation (3), it can be seen that when the misallocation index ${\tau }_{Ki}$ ＞0 than the under-allocation of resources and ${\tau }_{Ki}$ ＜0 than the over-allocation of resources. (The analysis of labor misallocation is the same as above.) Since resource misallocation is divided into different directions, the total degree of misallocation is recorded as ${\mathrm{M}\mathrm{I}\mathrm{S}=\left|{\tau }_k\right|＋\left|{\tau }_L\right|}_{.}\text{.}$

According to equations (3), (4), it can be seen that to calculate the misallocation index ${\mathrm{\tau }}_{\mathrm{K}\mathrm{i}}$ and ${\mathrm{\tau }}_{\mathrm{L}\mathrm{i}}$ need to calculate the factor output elasticity ${\mathrm{\beta }}_{\mathrm{K}}$ and ${\mathrm{\beta }}_{\mathrm{L}}$ first. Referring to Zhao et al. (2006) [49], the Solow residual method is used to calculate the output elasticity.

$$Y_{it}=A{K_{it}}^{{\beta }_{Ki}}{L_{it}}^{{1-\beta }_{Ki}}$$ (5)

Taking the natural logarithm on both sides of the equation, adding individual effects μi and time effects λt.

$$\ln \left(Y_{it}/L_{it}\right)=lnA+{\beta }_{Ki}\ln \left(K_{it}/L_{it}\right)+u_i+{\lambda }_t+{\epsilon }_{it}$$ (6)

The output variable ($Y_{it}$). The GDP of each urban agglomeration is expressed.

The amount of labor input ($L_{it}$). The average annual number of employees in each urban agglomeration can be expressed.

The amount of capital input ($K_{it}$). Expressed as the fixed capital stock of each urban agglomeration, calculated by the perpetual inventory method (Liu, Changqing et al., 2017) [43]. The formula is：

$$K_t=(I_t+I_{t-1}+I_{t-2})/3+(1-{\delta }_t)K_{t-1}$$ (7)

$K_t$、 $K_{t-1}$ represent the fixed capital stock in periods t, t-1. $I_t$、 $I_{t-1}$、 $I_{t-2}$ represent the total fixed capital formation in periods t, t-1, and t-2, and ${\delta }_t$ represents the depreciation rate. Data are obtained from the China Urban Statistical Yearbook.

According to equations (5), (6), (7), Table 3 shows the degree of capital and labor misallocation by urban agglomerations in the 2017year, and it can be seen that all urban agglomerations in China have different degrees of resource misallocation, and the degree of capital misallocation is higher than labor misallocation. In general, the under-allocation of capital is more serious in Chang-Zhu-Tan and Pearl River Delta urban agglomerations, and the over-allocation of capital in Liaodong Peninsula and West Coast Strait urban aagglomerations. There is a serious labor surplus in Jinzhong and Lanbaixi urban agglomerations, and a relative labor surplus in Changzhutan and Yangtze River Delta urban agglomerations.

Table 3.

Degree of resource misallocation in 2017.

urban agglomerations	${\tau }_k$	${\tau }_L$	urban agglomerations	${\tau }_k$	${\tau }_L$
Liaodong Peninsula	-0.396	-0.022	Yangtze River Delta	-0.003	0.131
West Coast of the Strait	-0.374	0.013	South Qin and North Fang	0.027	-0.195
Shandong Peninsula	-0.296	0.073	Yinchuan Plain	0.028	-0.237
Jianghuai	-0.289	-0.054	Jinzhong	0.124	-0.330
Central Plains	-0.224	0.084	Lanbai West	0.134	-0.409
Around Poyang Lake	-0.217	-0.129	Wuhan	0.221	-0.167
Chengdu and Chongqing	-0.173	-0.186	Beijing-Tianjin-Hebei	0.233	0.052
Central Yunnan	-0.142	-0.179	Qianzhong	0.237	0.014
Hada Chang	-0.117	0.079	Changzhutan	0.398	0.225
Guanzhong	-0.099	-0.109	Pearl River Delta	1.071	-0.003

2.5. Basic model setting

To investigate the influence of the spatial structure of urban agglomerations on PM2.5, the STIRPAT model (T. and A., 1994) [50]was extended and constructed as follows.

$$\ln {PM}_{2.5}={\alpha }_0+{\alpha }_1{Q}_{it}+{\alpha }_2{Q}_{it}^2+{\alpha }_3{MIS}_{it}+{\alpha }_{4}Controls＋{\epsilon }_i$$ (8)

where i and t represent urban agglomerations and years. lnPM2.5 is the logarithm of PM2.5 concentration. Q denotes the spatial structure of urban agglomerations, expressed by the centrality index calculated in the previous section, and Q² is introduced to measure the nonlinear effect of spatial structure on PM2.5. MIS is the mediating variable, indicating the degree of resource misallocation. ε is the stochastic disturbance term. Since PM2.5 concentrations are influenced by several factors, the Control variables are detailed in Table 4.

Table 4.

Definitions of control variables.

variable symbol	variable definition	Explanation of indicators
EG	Economic growth. Expressed in GDP per capita.	Pollution concentration is highly correlated with GDP(Wu et al., 2018) [51]. Sustained economic growth makes the need for high-quality development more urgent, reducing pollution emissions, and the expected sign is negative (Zhao et al., 2018) [52].
IS	Industrial structure. It is expressed by the ratio of the added value of the secondary and tertiary industries.	In the process of industrialization, the secondary industry will bring more serious pollution than the tertiary industry (Zhao et al., 2018) [52]. The larger the ratio, the heavier the pollution, and the expected sign is positive.
ES	Employment structure. It is expressed by the ratio of employees in the secondary and tertiary industries	The larger the ratio, the lower the level of urban development, the less pollution, and the expected sign is negative.
EO	The degree of economic openness. It is expressed in terms of the amount of foreign funds actually utilized per capita.	With the rapid growth of foreign direct investment, PM2.5 pollution in Chinese cities is increasing (Pei et al., 2021) [53]. The expected sign is positive.
EDU	Educational level. Expressed as the number of students enrolled in general institutions of higher learning per 10,000 people。	Education level is related to air quality (Bravo et al., 2022) [54], and a higher level of education means an improvement in the quality of residents, which helps to reduce pollution, and the expected sign is negative.
QL	Quality of life. Expressed in terms of per capita disposable income of urban residents.	Per capita disposable income is one of the factors affecting PM2.5 concentration (Chen et al., 2020) [55]. Higher quality of life leads to more consumption, and increased household consumption leads to increased pollution, and the expected sign is positive.

3. Result

3.1. Full sample regression results

According to equation (8),a stepwise regression method was used to estimate the effect of spatial structure of urban agglomerations on PM2.5 for the full sample, and the results are shown in Table 5. From the regression results, the primary term coefficient of Q is negative and the secondary term coefficient is positive, which is significant at the 1% level, indicating that there is a U-shaped relationship between PM2.5 concentration and spatial structure centrality in urban agglomerations, and hypothesis 1 is verified. As the centrality increases, factors are agglomerationed in the core large cities, promoting productivity and energy efficiency. The increase in productivity implies technological progress and lower unit costs, and enterprises have more capital and technology to reduce emissions and pollution. When the centrality of urban agglomerations increases to a certain degree, the concentration of factors is accompanied by an increase in resource misallocation, which causes enterprises to expand in a disorderly manner and lack incentives for technological innovation, and increases pollution emissions. According to the regression results, it can be concluded that when Q = 1, the PM2.5 concentration achieves the minimum value, in other words, Q = 1 is the inflection point. For polycentric spatial structure, the pollution concentration decreases as Q increases; for monocentric spatial structure, the pollution concentration increases as Q increases. The coefficients of MIS, IS, EO, and QL are positive; the coefficients of EG and ES are negative, which is consistent with expectations. The positive coefficient of EDU is inconsistent with expectations, which may be due to the fact that urban agglomerations with higher education levels tend to have higher industrial development, which increases pollution emissions. This effect is greater than the effect of people's increased environmental awareness on air pollution.

Table 5.

Effects of spatial structure of urban agglomerations on PM2.5

Variables	Coef.	Std. Err.	t	P > t	[95% Conf.Interval]
Q	-2.205***	0.401	-5.490	0.000	[-2.994187,-1.41504]
Q2	1.018***	0.211	4.820	0.000	[0.6019537,1.4331]
MIS	0.524***	0.100	5.220	0.000	[0.3261722,0.7209533]
lnEG	-0.542***	0.076	-7.110	0.000	[-0.6919842,-0.391934]
IS	0.289***	0.066	4.360	0.000	[0.1584204,0.419266]
ES	-0.180***	0.056	-3.210	0.001	[-0.2909114,-0.0696504]
lnEO	0.166***	0.020	8.120	0.000	[0.1258172,0.206299]
lnEDU	0.254***	0.046	5.480	0.000	[0.1631073,0.3457657]
lnQL	0.421***	0.093	4.510	0.000	[0.2370814,0.604496]
_cons	3.720***	0.459	8.100	0.000	[2.816265,4.623524]

Notes：*p < 0.1,**p < 0.05,***p < 0.01，The following tables are the same.

3.2. Robustness test

In order to test the reliability of the results, this paper employs a robustness test by replacing the core explanatory variables. The Herfindahl index (HHI) was first used to measure industrial concentration and one of the common indicators to measure whether the spatial structure of urban agglomerations is monocentric or polycentric, and was later widely introduced into urban economics. In view of its ability to provide a more accurate portrayal of the spatial structure distribution and evolution pattern within urban agglomerations, this paper uses the HHI index to replace Q for stability testing. Its calculation formula is (Yu and Guo, 2021) [56].

$${HHI}_{it}={\sum }_{i=1}^n{\left(\frac{P_{it}}{P}\right)}^2={\sum }_{i=1}^n{\left(S_{it}\right)}^2$$ (9)

$P_{it}$ denotes the population size of urban agglomerations in period t, P denotes the total population, and S_it denotes the proportion of the population of urban agglomerations to the total population in period t and n is the number of urban agglomerations. The HHI is between [1/n, 1], and the closer the index is to 0, the more the urban agglomerations tends to have a polycentric urban structure; the closer the index is to 1, the more the urban agglomerations tends to have a monocentric urban structure. According to equation (9), the effect of HHI on PM2.5 concentration is shown in Table 6. The direction and significance of the coefficients are the same as those in Table 5, verifying the robustness of the effect of spatial structure on PM2.5 concentration. It indicates that hypothesis 1 is valid and robust.

Table 6.

Effect of HHI on PM2.5

Variables	Coef.	Std. Err.	t	P > t	[95% Conf.Interval]
HHI	-6.034***	1.042	-5.790	0	[-8.084363,-3.98377]
HHI2	7.963***	2.048	3.890	0	[3.934304,11.99192]
MIS	0.505***	0.090	5.630	0	[0.3282667,0.680802]
lnEG	-0.555***	0.061	-9.100	0	[-0.6754125,-0.4352272]
lnIS	0.322***	0.059	5.500	0	[0.2070937,0.437571]
ES	-0.154***	0.051	-3.020	0.003	[-0.2549286,-0.0537063]
lnEO	0.030***	0.020	1.460	0.146	[-0.0103181,0.0693673]
lnEDU	0.479***	0.047	10.080	0	[0.3852432,0.5721641]
lnQL	0.462***	0.078	5.910	0	[0.308571,0.6163408]
_cons	2.599***	0.409	6.360	0	[1.794349,3.403734]

3.3. A test of mediating effects based on resource misallocation

The mechanism of the spatial structure of urban agglomerations affecting PM2.5 concentrations was further examined. A mediating effect model is used with resource misallocation as the mediating variable to further test the effect of spatial structure of urban agglomerations on resource misallocation and resource misallocation on PM2.5 concentration. The econometric model was set as follows.

$${MIS}_{it}={\beta }_0+{\beta }_1{Q}_{it}+{\beta }_2{Q}_{it}^2+{\beta }_3{\mathit{INF}}_{Kit}+{\beta }_4{lnEO}_{it}+{\beta }_5{lnEDU}_{it}＋{\epsilon }_i$$ (10)

$$\ln {PM}_{2.5}={\gamma }_0+{\gamma }_1{MIS}_{it}+\gamma {CV}_{it}＋u_i$$ (11)

Considering that there are significant effects of the level of science and technology INF (expressed as a share of GDP for telecoms business), EO and EDU on the allocation of capital and labor in urban agglomerations, those are used as control variables to join the model (10), and ${CV}_{it}$ in equation (11) denotes the control variables involved in Table 4.

The regression results of Eq. (10) are shown in Table 7. for the polycentric spatial structure, the primary term coefficient of Q is positive and the secondary term coefficient is negative, and the effect of spatial structure Q on resource misallocation shows an obvious inverted U-shaped relationship. Initially, economic agglomeration aggravates resource misallocation. Only when spatial agglomeration produces scale and spillover effects will it improve resource misallocation and thus reduce air pollution. For the monocentric spatial structure, the primary term coefficient of Q is negative and the secondary term coefficient is positive, and the effect of spatial structure Q on resource misallocation shows an obvious U-shaped relationship. When the monocenter develops to a certain degree, the continued concentration of resources will lead to inefficient use of resources, increased misallocation, and increased pollution.

Table 7.

Regression results of Q impact MIS.

Polycentric spatial structure
MIS	Coef.		Std. Err.	t	P > t	[95% Conf.Interval]
Q	2.069***		0.769	2.69	0.008	[0.5526547,3.585498]
Q2	-1.423***		0.550	-2.59	0.01	[-2.508983,-0.3389034]
INF	4.658***		0.985	4.73	0.00	[2.717766,6.599561]
lnEO	-0.035***		0.009	-3.79	0.00	[-0.0534157,-0.0168799]
lnEDU	0.110***		0.023	4.64	0.00	[0.063322,0.1568526]
_cons	-0.816***		0.284	-2.87	0.004	[-1.376278,-0.2570155]
Monocentric spatial structure
MIS		Coef.	Std. Err.	t	P > t	[95% Conf.Interval]
Q		-10.055***	4.302	2.34	0.019	[-18.48821,-1.622628]
Q2		3.895***	1.713	2.27	0.023	[0.5364846,7.254582]
INF		4.856**	2.659	1.83	0.068	[-0.3555877,10.06937]
lnEO		-0.011	0.047	0.23	0.821	[-0.1049606,0.0831799]
lnEDU		-0.046	0.073	0.63	0.53	[-0.1911409,0.0984192]
_cons		6.902***	2.567	2.69	0.007	[1.868827,11.93487]

If the spatial structure of urban agglomerations affects PM2.5 concentrations by influencing resource misallocation, then we expect ${\gamma }_1$ to be significant. Table 8 demonstrates the estimation results of equation (11) with a significantly positive estimated coefficient of MIS. This indicates that resource misallocation is an effective mediating variable for the spatial structure of urban agglomerations to affect PM2.5 pollution, and hypothesis 2 is tested.

Table 8.

Intermediary effect test results.

lnPM2.5	Coef.	Std.Err.	t	P > t	[95% Conf.Interval]
MIS	0.429***	0.104	4.11	0.00	[0.2241582,0.6351515]
lnEG	-0.363***	0.068	-5.28	0.00	[-0.4990641,-0.2281672]
IS	0.336***	0.068	4.92	0.00	[0.201882,0.4709454]
ES	-0.175***	0.058	-3	0.003	[-0.2900636,-0.0604099]
lnEO	0.128***	0.020	6.26	0.00	[0.0878174,0.1682853]
lnEDU	0.216***	0.048	4.47	0.00	[0.1210719,0.3113331]
lnQL	0.254***	0.089	2.86	0.005	[0.0793458,0.4302893]
_cons	2.789***	0.452	6.16	0.00	[1.898409,3.679654]

4. Discussion

The results of the empirical analysis in Part 3 reveal the relationship between spatial structure, resource misallocation and PM2 concentration in urban agglomerations. Table 5, Table 6 analyze the effects of spatial structure on PM2.5 concentration using two different calculations of spatial structure of urban agglomerations. Table 7 distinguishes two different cases of monocentric and polycentric spatial structure of urban agglomerations for heterogeneity analysis, and verifies the influence of spatial structure of urban agglomerations on resource misallocation. Table 8 examines the mediating role played by resource misallocation between spatial structure and PM2.5 concentration. Why does this connection exist between the three? This section will discuss the intrinsic linkage between the three.

There is a U-shaped relationship between PM2.5 concentrations and spatial structure centrality in urban agglomerations. This is because PM2.5 concentrations are closely related to demographic factors, economic activities and industrial structure (Xie et al., 2019, Liu et al., 2017) [57,58]. The spatial structure of an urban agglomeration can reflect whether it is in a state of agglomeration economy, and the higher the morphological centrality of the spatial structure (the more it tends to a monocentric structure) indicates a more agglomeration economy (Sun et al., 2019) [22]. Initial spatial agglomeration helps to achieve increasing returns to scale (P, 1991a, Fujita and Thisse, 2013) [59, 60] reducing pollution emissions per unit of output (Lu, M., Feng, H., 2014) [61]. However, excessive agglomeration brings environmental degradation, and industrial agglomeration and increased population density lead to increased atmospheric pollution from both production and living aspects (Wang, Xingjie et al., 2015) [62]. These findings support the conclusion of this paper that there is an optimal level of agglomeration state in the spatial structure of urban agglomerations, and there may be a nonlinear relationship between agglomeration and environmental quality There is a U-shaped relationship between PM2.5 concentration in urban agglomerations and the centrality of spatial structure.

The mediating role played by resource misallocation between spatial structure and PM2.5 concentration. This is because resource allocation is determined by factors such as government support, financial disincentives, administrative barriers to entry, and financing cost constraints, and the spatial structure of urban agglomerations directly affects these factors. The effect of polycentric spatial structure of urban agglomerations on resource misallocation shows an obvious inverted U-shaped relationship. Economic agglomeration initially aggravates resource misallocation, which in turn raises PM2.5 concentration. This is consistent with existing studies that the development of urban agglomerations is initially characterized by severe market segmentation, which significantly exacerbates the misallocation of labor and capital resources, which is also an important factor contributing to environmental pollution (Bian et al., 2019) [30]. Only when scale and spillover effects are generated can resource misallocation be improved, which in turn reduces air pollution. Economic agglomeration can improve environmental pollution through technology spillover effects and reducing the degree of resource misallocation (Zhong and Wei, 2019) [63]. A study by Jiang et al. (2022) also demonstrated that urban agglomeration construction suppresses haze pollution mainly by accelerating industrial agglomeration, promoting technological innovation, and reducing resource misallocation [64]. The impact of monocentric spatially structured urban agglomerations on resource misallocation shows an obvious U-shaped relationship. When monocentric development reaches a certain level, the continued agglomeration of resources leads to inefficient use of resources, increased misallocation, and increased pollution. Urban sprawl leads to inefficient resource use and aggravates ecological and environmental pressure (Wang et al., 2014) [65].

5. Conclusion

Panel data of 20 urban agglomerations in China from 2002 to 2017 were analyzed using the STIRPAT model and mediating effects model to explore the linkages between the spatial structure of urban agglomerations, resource misallocation and PM2.5 concentrations. It was found that (1) the effect of spatial structure of urban agglomerations on PM2.5 concentration showed a significant U-shaped characteristic with an inflection point of Q = 1. The increase of polycentric city centrality could reduce PM2.5 concentration; the opposite was true for monocentric urban agglomerations. The results remain robust after applying the HHI index as a proxy variable for the spatial structure of urban agglomerations. (2) At present, only four urban agglomerations, including Guanzhong, Poyang Lake, Nanqin and Beifang, and Wuhan, are monocentric urban agglomerations. The increase of spatial structure centrality of urban agglomerations positively affects PM2.5 concentration. The PM2.5 concentrations of most other urban agglomerations are still in the stage of improving with the increase of centrality. (3) The results of the mediation effect test show that the spatial structure centrality of urban agglomerations can improve the resource allocation of urban agglomerations and further reduce PM2.5 pollution within a limited interval. For polycentric urban agglomerations, the effect of spatial structure on resource allocation is inverted U-shaped, and only polycentric urban agglomerations with a certain degree of centrality (beyond the inflection point Q = 1) can alleviate resource misallocation and thus improve PM2.5 pollution by increasing centrality. For monocentric urban agglomerations, the influence of spatial structure on resource allocation is U-shaped, and only those urban agglomerations whose centrality reaches the pre-inflection point can alleviate resource misallocation and thus improve PM2.5 pollution by increasing centrality. Based on the research results of this paper, the following policy recommendations are proposed for the orderly development of China's urban agglomeration construction, the improvement of air pollution and resource misallocation, and the realization of high-quality economic development.

Considering the polycentric spatial structure is more conducive to optimizing the resource allocation of urban agglomerations. In order to narrow the gap between cities within urban agglomerations and avoid the increase of PM2.5 concentration due to resource misallocation, the general suggestion of this paper is to further promote the construction of polycentric urban agglomerations.

First, monocentric spatial structure city agglomerations are encouraged to build polycentric spatial structure. Infrastructure is the basis for promoting inter-city functional links and reducing transportation costs. Strengthening the construction of roads, highways and railroads within monocentric city agglomerations can realize the efficient circulation of resources and promote the maximization of positive spillover effects of the spatial structure of city agglomerations. It helps monocentric city agglomerations strengthen the synergistic development of core metropolis and neighboring cities and reduce centrality. By reducing centrality, it improves resource misallocation, directs the flow of resources to neighboring cities, and reduces the degree of pollution.

Second, breaking administrative boundaries and local protectionism, strengthening industrial cooperation within city agglomerations, and promoting the improvement of resource allocation level. The cross-regional flow of technology and talents helps to improve the overall technology level and production efficiency of city agglomerations and reduce pollution. The cities fully integrate the market and each play their own comparative advantages in order to maximize the benefits and bring into play the positive sway of the spatial structure spillover effect of the city agglomerations polycentric on industrial emission reduction.

Thirdly, to develop a collaborative pollution control program within a city agglomeration, the system is an effective guarantee for development. Under the shackles of local performance, urban “tournaments” commonly exist, and cities tend to pursue their own interests to maximize. Seeking synergistic development of city agglomerations requires strong contracts. The governments concerned need to negotiate a division of labor among the cities in the agglomerations based on their comparative advantages to promote urban economic development. A unified environmental policy should be formulated to promote the reduction of air pollution concentration in urban agglomerations.

Statements & declarations

Summary of requirements

The experimental data in this paper are from publicly available databases, and Research not involving Human Participants and Animals so ethics approval is not required for this paper. All authors consent to participate and publish by Heliyon. All data, models, or code generated or used during the study are available from the corresponding author by request.

Author contribution statement

Shijin Wang: Conceived and designed the experiments; Analyzed and interpreted the data; Wrote the paper. Mengya Li: Performed the experiments; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Funding statement

Shijin, Wang was supported by 2022 Young and Middle-aged Academic Leaders Project of the Youth and Blue Project of Jiangsu Universities. Shijin, Wang was supported by 2022 Double Carbon Special Cultivation Project of Jiangsu Normal University. Shijin, Wang was supported by Grant Project of Advantageous Discipline Construction Project of Jiangsu Universities (PAPD). Shijin, Wang was supported by National Natural Science Foundation of China Project (Nos. 72074102).

Data availability statement

Data will be made available on request.

Declaration of interest’s statement

The authors declare no competing interests.