# Spatiotemporal Variations of Water Pollution-Intensive Enterprises and Influencing Factors in China’s “Two Control Zone” Policy Using Spatial Statistics and Spatial Autoregressive Models

Authors: Fuqiang Dai; Xia Zhang; Qing Li; Hao Liu
Journal: Discrete Dynamics in Nature and Society
 (2022)

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## Abstract

The management of water environments is an important public policy issue. With accelerated industrialization, China’s water quality has deteriorated to one of the lowest levels in the world. Against this background, analyzing the spatial agglomeration characteristics and driving mechanisms of water pollution-intensive enterprises (WPIEs) can help local governments formulate regulations to better manage pollution by enterprises. This study used nearest-neighbor index analysis, global spatial autocorrelation analysis, hot spot analysis, and spatial regression to investigate spatial distribution changes in WPIEs. Further, a spatial autoregressive (SAR) model was used to examine the effects of enterprise agglomeration and environmental regulation on changes in the quantity of WPIEs in the Yangtze River Economic Belt (YREB) from 1998 to 2012. The results showed that the overall quantity of WPIEs in the YREB grew significantly, though growth in the downstream regions gradually slowed from 2005 to 2012. The distribution of WPIEs showed obvious spatial agglomeration characteristics, gradually expanding from a single-core pattern to a multicore one. Enterprise agglomeration and environmental regulation had scale effects on the spatial distribution of WPIEs, yet the effect of environmental regulation was not significant. However, other factors influencing the spatial distribution of WPIEs are complex, and the influence of the boundary effect was found to be more significant.


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## Body

## 1. Introduction

With the global advancement of industrialization and urbanization, the problem of water pollution is becoming increasingly serious in large cities. It is important, therefore, for global climate governance to develop effective policies to reduce emissions in production processes. In this regard, investigating the spatial agglomeration characteristics and influencing factors of water pollution-intensive enterprises (WPIEs) has great practical significance for controlling high-polluting enterprises.The effects of environmental regulation on water pollution reduction have been extensively investigated in the literature [1–7]. Because of differences in the social, economic, and technological endowments of various cities, different socioeconomic factors have uneven spatiotemporal effects on water pollution [8]. These differences lead to different driving factors of spatiotemporal distribution of WPIEs in different regions. In addition to these socioeconomic factors, water pollution can also be affected by the boundary effect and the different paths of the technical change [9, 10]. Wang et al. [11] used county-level data to investigate the spatiotemporal patterns of water pollution in the upper reaches of the Yangtze River. Uncertainty in the driving factors of spatiotemporal changes in water pollution increases the difficulty of controlling it [12]. It is necessary, therefore, to deepen our understanding of the spatiotemporal change patterns of water pollution and its driving factors to formulate targeted, effective policies [13]. Previous studies of spatiotemporal changes in water pollution have used various multivariate statistical methods to reveal spatiotemporal trends, such as comprising cluster analysis, principal component analysis, factor analysis, and discriminant analysis [14–16]. The effects of environmental regulations on water pollution have become a hot spot in Chinese research [17–19]. Few studies, however, have investigated the complicated relationship between the natural and socioeconomic factors and the spatiotemporal patterns of water pollution. Moreover, few studies have established micro databases with enterprises as research objects. In addition, there are fewer studies on the spatiotemporal distribution of WPIEs from a spatial perspective alone. Compared with the traditional econometric model, the spatial econometric models can effectively solve the spatial correlation and spatial dependence [20]. With the rise of new economic geography, increasing attention has been paid to spatial agglomeration’s effect on the spatial layout of enterprises; there is also renewed interest in the economic space level, which has produced a wide range of studies [21–23]. A review of the literature reveals that few studies have sought to use systematic methods and quantitative data to identify clusters of WPIEs in a given region [24, 25].As the largest industrial base in China, the Yangtze River Economic Zone accounts for 43% of China’s total wastewater discharge and more than half of its total chemical oxygen demand (COD) discharge [26]. Thus, water safety is a critical issue in this area. Aiming to reduce acid rain and sulfur dioxide (SO2) pollution, China enacted the Two Control Zone (TCZ) policy in 1998 [27]. Based on natural conditions, such as weather, topography, and soil, this policy delimits areas that have produced or may produce acid rain or serious SO2 pollution as control areas for one or the other (Figure 1).Figure 1
Geographical location of Two Control Zones in the Yangtze River Economic Belt.This study examined the spatial agglomeration characteristics of WPIEs and the main factors affecting their spatiotemporal patterns. Based on 1998–2012 data for WPIEs in the Yangtze River Economic Belt (YREB), GIS-based spatial statistical methods were used to analyze the spatial agglomeration characteristics of WPIEs, and the key driving factors were identified using a spatial autoregressive (SAR) model. This study enriches the research on the factors affecting the spatial agglomeration of WPIEs in various regions of China while avoiding the limitations caused by ignoring the effects of spatiotemporal lag.


## 2. Materials and Methods

### 2.1. Study Area

The YREB (Figure2) has a total area of 2.1 million km2, covering 11 provinces and municipalities, namely, Shanghai, Jiangsu, Zhejiang, Anhui, Jiangxi, Hubei, Hunan, Chongqing, Sichuan, Yunnan, and Guizhou. The YREB has an estimated population of 600 million inhabitants, with a per capita income of 78.28 thousand Yuan, which is above the national average of 7.4 thousand Yuan. According to topographic features, the study area was divided into three parts: the upper, middle, and lower reaches. The downstream areas include Shanghai, Zhejiang, Jiangsu, and Anhui. The lower reaches of the Yangtze River have experienced the most severe channelization and riverbank construction (e.g., construction of harbors) in the entire basin. There are about 225 million people in the downstream areas, accounting for 37.6% of the YREB, and the GDP is about 21.15 trillion Yuan, accounting for 52% of the YREB. The midstream regions include Hubei, Hunan, and Jiangxi, with 175 million people, accounting for 29.2% of the YREB. The middle reaches have experienced the highest level of sand mining in the entire basin, especially in the areas of the Dongting and Poyang lakes. Sichuan, Chongqing, Guizhou, and Yunnan belong to the upper reaches of the Yangtze River, with 199 million people. The upper reaches have experienced the greatest anthropogenic hydrological modifications as a result of serially built cascade dams, such as the Gezhouba, Three Gorges, Xiangjiaba, and Xiluodu Dams.Figure 2
Location of the Yangtze River Economic Belt in China.


### 2.2. Definition and Data Sources

There is no unified definition of WPIEs. For this study, WPIEs were defined as enterprises that discharge industrial wastewater containing COD, ammonia nitrogen, petroleum, volatile phenol, and other pollutants. Based on the First National Pollution Source Census Bulletin (2007), the following 15 high-pollution industries were selected for this study: papermaking and paper; textiles, agricultural and sideline food processing; chemical raw materials and chemical products manufacturing; beverage manufacturing; food manufacturing; pharmaceutical manufacturing; nonferrous metal mining selection; oil processing, coking, and nuclear fuel processing; leather, fur, feathers, and related products; general equipment manufacturing; ferrous metal mining selection; transportation equipment manufacturing, metal products; and coal mining and washing.The data used in this study were collected from the China Industrial Statistical Yearbooks, China City Statistical Yearbooks, and China Statistical Yearbooks (county-level) from 1998 to 2012. Based on enterprises’ address information, a geocoding service was used to obtain the geographic coordinates of each enterprise, and a spatial database was established for WPIEs in the YREB. Finally, 34,770 WPIEs were selected for 1998; 59,723 for 2005; and 82,412 for 2012.


### 2.3. Methods

The study involved two stages: spatial statistical analysis and SAR analysis. In the first stage, the spatial agglomeration characteristics of WPIEs were explored based on spatial statistical methods, including nearest-neighbor index (NNI), kernel density estimation (KDE), spatial correlation analysis, and hot spot analysis. In the second stage, taking into account economic disparities between different regions in the YREB, SAR models were employed to analyze the factors affecting the spatial agglomeration of WPIEs for the entire area and for typical regions.(1)
Nearest-neighbor index. NNI is expressed as the ratio of the observed mean distance to the expected mean distance to describe the spatial point distribution pattern [28]. The formula is as follows:(1)R=d00.487a/n+0.127a/n,whereR is the NNI, d0=(Σd)/n represents the average distance between points, d is the distance from each point to the nearest point, and a and n denote the study area and the number of samples, respectively. If the index is less than 1, the pattern exhibits clustering; if it is greater than 1, the trend is toward dispersion or competition.(2)
Kernel density estimation. To analyze the spatial clustering of point elements, KDE is often used to reflect the relative degree of concentration of a spatial distribution of elements, mainly estimated with a regular moving quadratic [29]. It is given by the following formula:(2)fx=∑i=1n1h2kx−xih,wheref(x) is the kernel density value at spatial position x, k is a spatial weighting function, h is the distance-attenuation threshold, and n is the number of factors with distance from the positions less than or equal to h.(3)
Global spatial autocorrelation. Exploratory spatial analysis is widely used to clearly test the spatial correlation of economic units. Global Moran’sI measures the spatial correlation between the overall spatial elements of a region. Moran’s I is calculated as follows:(3)I=n∑i=1n∑j=1nwijxi−|x¯xj−x¯∑i=1n∑j=1nwij∑i=1nxj−x¯2,wherewij is the spatial weight matrix. Moran’s I values fall within the range [−1, 1]. Global Moran’s I estimates statistical significance by calculating Moran’s I, z score, and p value. When the z score or p value indicates statistical significance, a positive Moran’s I index value indicates a tendency toward clustering, while a negative Moran’s I value indicates a tendency toward dispersion.(4)
Hot spot analysis. Although the LISA scatterplot corresponding to Moran’sI can also reflect local spatial autocorrelation, the Getis–Ord Gi∗, based on the normal distribution hypothesis test, is more sensitive than LISA, which is based on the random distribution hypothesis test [30]. Getis–Ord Gi∗ can detect whether the regional unit belongs to a high-value cluster (HH) or a low-value cluster (LL). The formula is(4)Gi∗=∑j=1nwijxij−X¯∑j=1nwijS×n∑j=1nwij2−∑j=1nwij2/n−1.Getis–Ord Gi∗ estimates the statistical significance of the index by calculating the Gi∗,z score, and p value. The higher the z score, the closer the high-value (hot spot) clustering; the lower the z score, the closer the low-value (cold spot) clustering.(5)
Spatial econometric model. In this study, the Lagrange multiplier (LM) and likelihood ratio (LR) test were used to determine the construction of SAR models, including the space lag term and spatial error term, and to investigate the factors affecting the spatial distribution of WPIEs. The SAR is given by(5)yit=ρ∑j=1nWijyit+βxit+α+μi+vt+φit,φit=λ∑j=1nWijμit+εit,where yit is the net change of t year in WPIEs in district (county) i, ρ is the coefficient of the space lag term, μi is the regional disturbance term that cannot be observed, vt is the time disturbance term that cannot be observed, and λ is the coefficient of the spatial error term.


## 2.1. Study Area

The YREB (Figure2) has a total area of 2.1 million km2, covering 11 provinces and municipalities, namely, Shanghai, Jiangsu, Zhejiang, Anhui, Jiangxi, Hubei, Hunan, Chongqing, Sichuan, Yunnan, and Guizhou. The YREB has an estimated population of 600 million inhabitants, with a per capita income of 78.28 thousand Yuan, which is above the national average of 7.4 thousand Yuan. According to topographic features, the study area was divided into three parts: the upper, middle, and lower reaches. The downstream areas include Shanghai, Zhejiang, Jiangsu, and Anhui. The lower reaches of the Yangtze River have experienced the most severe channelization and riverbank construction (e.g., construction of harbors) in the entire basin. There are about 225 million people in the downstream areas, accounting for 37.6% of the YREB, and the GDP is about 21.15 trillion Yuan, accounting for 52% of the YREB. The midstream regions include Hubei, Hunan, and Jiangxi, with 175 million people, accounting for 29.2% of the YREB. The middle reaches have experienced the highest level of sand mining in the entire basin, especially in the areas of the Dongting and Poyang lakes. Sichuan, Chongqing, Guizhou, and Yunnan belong to the upper reaches of the Yangtze River, with 199 million people. The upper reaches have experienced the greatest anthropogenic hydrological modifications as a result of serially built cascade dams, such as the Gezhouba, Three Gorges, Xiangjiaba, and Xiluodu Dams.Figure 2
Location of the Yangtze River Economic Belt in China.


## 2.2. Definition and Data Sources

There is no unified definition of WPIEs. For this study, WPIEs were defined as enterprises that discharge industrial wastewater containing COD, ammonia nitrogen, petroleum, volatile phenol, and other pollutants. Based on the First National Pollution Source Census Bulletin (2007), the following 15 high-pollution industries were selected for this study: papermaking and paper; textiles, agricultural and sideline food processing; chemical raw materials and chemical products manufacturing; beverage manufacturing; food manufacturing; pharmaceutical manufacturing; nonferrous metal mining selection; oil processing, coking, and nuclear fuel processing; leather, fur, feathers, and related products; general equipment manufacturing; ferrous metal mining selection; transportation equipment manufacturing, metal products; and coal mining and washing.The data used in this study were collected from the China Industrial Statistical Yearbooks, China City Statistical Yearbooks, and China Statistical Yearbooks (county-level) from 1998 to 2012. Based on enterprises’ address information, a geocoding service was used to obtain the geographic coordinates of each enterprise, and a spatial database was established for WPIEs in the YREB. Finally, 34,770 WPIEs were selected for 1998; 59,723 for 2005; and 82,412 for 2012.


## 2.3. Methods

The study involved two stages: spatial statistical analysis and SAR analysis. In the first stage, the spatial agglomeration characteristics of WPIEs were explored based on spatial statistical methods, including nearest-neighbor index (NNI), kernel density estimation (KDE), spatial correlation analysis, and hot spot analysis. In the second stage, taking into account economic disparities between different regions in the YREB, SAR models were employed to analyze the factors affecting the spatial agglomeration of WPIEs for the entire area and for typical regions.(1)
Nearest-neighbor index. NNI is expressed as the ratio of the observed mean distance to the expected mean distance to describe the spatial point distribution pattern [28]. The formula is as follows:(1)R=d00.487a/n+0.127a/n,whereR is the NNI, d0=(Σd)/n represents the average distance between points, d is the distance from each point to the nearest point, and a and n denote the study area and the number of samples, respectively. If the index is less than 1, the pattern exhibits clustering; if it is greater than 1, the trend is toward dispersion or competition.(2)
Kernel density estimation. To analyze the spatial clustering of point elements, KDE is often used to reflect the relative degree of concentration of a spatial distribution of elements, mainly estimated with a regular moving quadratic [29]. It is given by the following formula:(2)fx=∑i=1n1h2kx−xih,wheref(x) is the kernel density value at spatial position x, k is a spatial weighting function, h is the distance-attenuation threshold, and n is the number of factors with distance from the positions less than or equal to h.(3)
Global spatial autocorrelation. Exploratory spatial analysis is widely used to clearly test the spatial correlation of economic units. Global Moran’sI measures the spatial correlation between the overall spatial elements of a region. Moran’s I is calculated as follows:(3)I=n∑i=1n∑j=1nwijxi−|x¯xj−x¯∑i=1n∑j=1nwij∑i=1nxj−x¯2,wherewij is the spatial weight matrix. Moran’s I values fall within the range [−1, 1]. Global Moran’s I estimates statistical significance by calculating Moran’s I, z score, and p value. When the z score or p value indicates statistical significance, a positive Moran’s I index value indicates a tendency toward clustering, while a negative Moran’s I value indicates a tendency toward dispersion.(4)
Hot spot analysis. Although the LISA scatterplot corresponding to Moran’sI can also reflect local spatial autocorrelation, the Getis–Ord Gi∗, based on the normal distribution hypothesis test, is more sensitive than LISA, which is based on the random distribution hypothesis test [30]. Getis–Ord Gi∗ can detect whether the regional unit belongs to a high-value cluster (HH) or a low-value cluster (LL). The formula is(4)Gi∗=∑j=1nwijxij−X¯∑j=1nwijS×n∑j=1nwij2−∑j=1nwij2/n−1.Getis–Ord Gi∗ estimates the statistical significance of the index by calculating the Gi∗,z score, and p value. The higher the z score, the closer the high-value (hot spot) clustering; the lower the z score, the closer the low-value (cold spot) clustering.(5)
Spatial econometric model. In this study, the Lagrange multiplier (LM) and likelihood ratio (LR) test were used to determine the construction of SAR models, including the space lag term and spatial error term, and to investigate the factors affecting the spatial distribution of WPIEs. The SAR is given by(5)yit=ρ∑j=1nWijyit+βxit+α+μi+vt+φit,φit=λ∑j=1nWijμit+εit,where yit is the net change of t year in WPIEs in district (county) i, ρ is the coefficient of the space lag term, μi is the regional disturbance term that cannot be observed, vt is the time disturbance term that cannot be observed, and λ is the coefficient of the spatial error term.


## 3. Results

### 3.1. Spatial Distribution Pattern of WPIEs

Table1 lists the NNI results for WPIE locations for the three node years. The NNI for all years was less than 1, passing the significance test; this indicates that the spatial distributions of WPIEs had strong agglomeration characteristics. The NNI value in 1998 was less than that in 2005, while the lowest was in 2012. The NNI values in Table 1 reveal that the spatial distribution of WPIEs was generally clustered for each study year, but the intensity of clustering varied slightly in different years.Table 1
Nearest-neighbor index and spatial distribution pattern of water pollution-intensive enterprises locations in the Yangtze River Economic Belt.
YearActual mean distanceExpected mean distanceNNIz valuep valueDistribution pattern1998972.0764935.9120.197-286.8380.000Clustering2005742.9343578.0150.208-370.4460.000Clustering2012577.7213083.4880.187-446.2980.000ClusteringFigure3 shows the KDE results. The distributions of WPIEs in the three node years are characterized by the coexistence of single-center agglomeration and multicenter dispersion. In 1998, WPIEs were mainly distributed in Shanghai and its surrounding areas, while only small scale agglomerations were found in the middle and upper reach areas. From 1998 to 2005, the trend of agglomeration continued to increase, showing a Shanghai–Wuxi–Hangzhou–Ningbo multicore development mode in 2005. In 2012, the scope of agglomeration was further expanded, and a trend of integrated development appeared in the downstream areas.Figure 3
Kernel density estimation of water pollution-intensive enterprises in the Yangtze River Economic Belt.To better understand the spatial distribution patterns of WPIEs in the YREB, KDE was carried out for three typical regions. Figure4 shows the KDE of WPIEs in the upper reaches of the YREB. It can be seen that the agglomeration characteristics were gradually enhanced from 1998 to 2012. However, in the process of industrial agglomeration, Chengdu and Chongqing have always been at the core. This is closely related to the fact that Chengdu and Chongqing have always been the economic centers of Southwest China. Specifically, the spatial agglomeration pattern in the upstream region changed from single-core agglomeration in 1998 to multicore agglomeration in 2005 and then to integration in 2012. Although the agglomeration trend in other regions of the upper reaches also increased significantly, its influence and agglomeration intensity are far less than those of the Chengdu–Chongqing region. Figure 5 shows the development model of spatial agglomeration in the middle reaches. The agglomeration core appeared first in Wuhan. In 2005, Changsha became another agglomeration core aside from Wuhan. By 2012, the agglomeration center had extended, and Nanchang became the third largest core. As shown in Figure 6, the downstream area reflects the evolution of the spatial agglomeration model, from single-core to multicore pattern, as well as to regional integration. In 1998, there was only one agglomeration center in the downstream area (i.e., Shanghai). Then, the number of agglomeration centers increased from one to four in 2005 (i.e., Shanghai, Changzhou, and Wuxi in Jiangsu and Hangzhou in Zhejiang). Finally, these three agglomeration cores expanded to the surrounding areas, presenting a trend of regional integration in 2012. Compared with Figure 6, Figure 5 can better reflect the increase of aggregation nuclei, and Figure 6 shows the expansion of aggregation degree. In summary, the distribution characteristics of spatial agglomeration in the entire region or at the regional scale were consistent with Krugman’s core–periphery model [31].Figure 4
Kernel density estimation of water pollution-intensive enterprises in the upper reaches of the Yangtze River Economic Belt.Figure 5
Kernel density estimation of water pollution-intensive enterprises in the middle reaches of the Yangtze River Economic Belt.Figure 6
Kernel density estimation of water pollution-intensive enterprises in the lower reaches of the Yangtze River Economic Belt.


### 3.2. Spatiotemporal Changes in WPIEs at the County Scale

Figure7 shows the spatial distribution of changes in the number of WPIEs. In general, the values for increases in the number of WPIEs are significantly higher than those of decreases, with a range of 0–50 in most counties of the YREB. The number of WPIEs in the downstream area decreased the most in the Qingpu District of Shanghai, with a decrease of 592. Further, Jiangnan District in Hubei Province was the only one with a reduction of more than 200 in the midstream area. The number of WPIEs in other counties decreased by 51–200 (Figure 7(a)). Figure 7(b) shows 39 counties where the number of WPIEs in the downstream area was greater than 200, and the increase in the number of enterprises in the middle reaches was mostly in the range of 21–50. The number of enterprises in Chengdu and Chongqing increased mostly in the upper reach areas. Even the number of WPIEs in the TCZs has still increased from 1998 to 2005. In Figures 7c and 6d, similar results are seen in the quantitative changes in WPIEs from 2005 to 2012. However, the reduction of WPIEs from 2005 to 2012 was significantly greater than the values from 1998 to 2005. There were regional differences in the increase of WPIEs. Compared to the growth of WPIEs from 1998 to 2005, the upstream and midstream regions had more growth from 2005 to 2012, while the downstream regions had a smaller increase. During the study period, the growth of WPIEs in the TCZs of the upper reaches was not obvious, while other areas in the TCZs showed a considerable increase. The WPIEs were mainly distributed in the TCZs, so the number of WIPEs in the non-TCZs has not changed significantly. Altogether, the increase and decrease were mainly concentrated in the lower reaches, but the increase trend in the upper and middle reaches was significant from 2005 to 2012.Figure 7
Changes in the number of water pollution-intensive enterprises. (a) Decrement (1998–2005). (b) Increment (1998–2005). (c) Decrement (2005–2012). (d) Increment (2005–2012).
(a)(b)(c)(d)On the whole, the absolute number of WPIEs increased rapidly from 1998 to 2005, while the growth of net change from 2005 to 2012 developed in two directions. There was rapid growth in the upstream and midstream regions, while the downstream regions showed a significant slowdown in growth. The growth of net change in upstream and midstream has to do with industrialization process, where the downstream areas benefit from the economic development strategy of coastal areas and the establishment of a series of special economic zones. The Yangtze River Delta has the highest density of river networks in China, with convenient transportation and abundant resources. Therefore, downstream areas can attract more companies than upstream and midstream regions.


### 3.3. County-Scale Spatial Agglomeration Characteristics of WPIEs

Table2 shows Moran’s I value for the total amount and net change of WPIEs in the YREB. The p value of Moran’s I was 0.000, less than 0.01, which indicates that Moran’s I result was statistically significant at the 99% confidence level. This means the WPIEs and the quantity changes in WPIEs in the YREB were not randomly distributed. Moran’s I values were greater than 0.6 and were significant at the 10% level. From 1998 to 2005, the spatial correlation of changes in the total amount of WPIEs increased, with Moran’s I values ranging from 0.682 to 0.795, while the spatial correlation of total amount decreased between 2005 and 2012, with Moran’s I value dropping to 0.677. The trend of spatial correlation related to the net change in WPIEs was the same as the total amount. Moran’s I values reveal a positive spatial correlation between changes in the number of WPIEs. Not only was the distribution of the total number of WPIEs spatially interdependent, but there was also a positive spatial correlation between changes in WPIEs.Table 2
Moran’sI values with spatial autocorrelation estimation.
YearMoran’sIVariancep valuez value19980.682∗∗∗0.0000810.00076.00620050.795∗∗∗0.0000870.00085.38020120.677∗∗∗0.0000880.00072.2111998–2005JS0.631∗∗∗0.0000810.00070.3181998–2005XZ0.781∗∗∗0.0000870.00083.8022005–2012JS0.808∗∗∗0.0000870.00086.8212005–2012XZ0.609∗∗∗0.0000880.00065.057JS indicates a quantity decrease in WPIEs; XZ indicates a quantity increase in WPIEs.∗∗∗Statistically significant as P < 0.001.While global Moran’sI shows the general trend, the Getis–Ord Gi∗ shows the spatial pattern in local neighborhoods [32]. Hot spot analysis is an effective tool for identifying the spatial clustering of WPIEs across regional units. The Getis–Ord Gi∗ results revealed multiple areas of statistically significant high–high clustering in the eastern YREB (mainly located in the Yangtze River Delta) and low–low clustering in the southwestern YREB (Yunnan, Guizhou, and Sichuan Provinces). Figure 8(a) shows the results of a hot spot analysis indicating that the reduction in the number of WPIEs from 1998 to 2005 was concentrated in the Yangtze River Delta, while the cold spots were distributed in the northern parts of Hubei, southeastern Sichuan, northern Guizhou, and southwestern Yunnan. The Getis–Ord Gi∗ results shown in Figure 8(b) reveal an emerging hot spot area for WPIEs in central Hubei Province. In Figure 8(c), the distribution of hot spot and cold spot areas indicates that the number of WPIEs with more reduction was mainly concentrated in Shanghai, southern Jiangsu, and eastern Zhejiang Provinces, while the number of WPIEs with less reduction was more likely to be distributed in the cold spot provinces (Yunnan, southern Sichuan, and Guizhou). Yunnan, southwestern Sichuan, and Guizhou are more prominent in Figure 8(d) than in Figure 8(c). This means the increase in WPIEs is low in these areas but high in the surrounding provinces. Further, there were new hot spot areas in northern Jiangsu and southern Zhejiang.Figure 8
Hot spot analysis of changes in the number of water pollution-intensive enterprises in the Yangtze River Economic Belt. (a) Decrement (1998–2005). (b) Increment (1998–2005). (c) Decrement (2005–2012). (d) Increment (2005–2012).
(a)(b)(c)(d)


### 3.4. Factors Influencing the Location of WPIEs

Here, an empirical study was conducted to test how agglomeration economy and spatial factors influenced the locations of WPIEs in the YREB. A comprehensive analysis of the factors affecting spatial agglomeration was then implemented for both the YREB region and typical regions divided by watersheds. This can help clarify the key factors in the spatial arrangement of WPIEs to propose better pollution management policies.


#### 3.4.1. Variables

The dependent variables were the decreases and new increases in the number of WPIEs for the periods 1998–2005 and 2005–2012, respectively. Enterprise agglomeration and environmental regulation were established as the two core explanatory variables. As seen in previous sections, WPIEs had strong agglomeration characteristics with regard to spatial distribution. Specifically, enterprise agglomeration was measured by the number of WPIEs in each county. In addition, environmental regulation was established as the dummy variable, based on the implementation of the TCZ policy. If WPIEs are located in the TCZs, the value is 1; otherwise, it is 0. Enterprises in the TCZs faced more stringent environmental regulations than other enterprises. Therefore, it is reliable to take the TCZ policy as the measurement standard for environmental regulation. Some controlling factors were also included in the regression models. Enterprise location is also related to transportation facilities and government policies, and the boundary effect also has a certain effect on the location of enterprises [33]. Transportation cost, factory rent, and policy support are usually considered in the site selection of enterprises. Therefore, factors representing the distances between enterprises and infrastructures (e.g., distance from enterprises to main roads, railway stations, ports, and rivers) were included in the models. Generally, land rent will be cheaper with further distance from the regional center and closer distance to the regional boundary. High-polluting enterprises that pursue the maximization of benefit tend to build factories in the suburbs. For this study, the boundary was divided into three levels: provincial boundary, prefecture-level city boundary, and county boundary. Considering that enterprises located in the National Technology Development Zone were more sufficiently developed and received more favorable policy support, a dummy variable was introduced; 1 and 0 indicate whether enterprises were or were not located in the zone, respectively. Table 3 provides more details about the variables.Table 3
Definitions of variables.
CategoryNameDefinitionDependent variablesY1Amount of reduction in WPIEs in each county from 1998 to 2005Y2Amount of increase in WPIEs in each county from 1998 to 2005Y3Amount of reduction in WPIEs in each county from 2005 to 2012Y4Amount of increase in WPIEs in each county from 2005 to 2012Core independent variablesAgglomerationAGGUnit area quantity of polluting enterprises in each district (county)Environmental regulationERLocated in TCZ or notControl variablesBoundary effectBE1Average shortest distance from the location of the enterprise to the boundary of the county-level administrative districtBE2Average shortest distance from the location of the enterprise to the boundary of the prefecture-level city administrative districtBE3Average shortest distance from the location of the enterprise to the boundary of the provincial administrative regionTransportationTR1Average shortest distance from business location to main roadTR2Average shortest distance from business location to train stationTR3Average shortest distance from the location of the enterprise to the port on the Yangtze RiverTR4Average shortest distance from the business location to the riverRiver network densityRNDInland river network density in each district (county)DecentralizationDEAverage shortest distance from the location of the enterprise to the national development zone


#### 3.4.2. Influencing Factors

First, this study tested the effect of the influencing factors on the whole YREB; Table4 shows the results. The influence of AGG was insignificant from 1998 to 2005 but was significantly positive from 2005 to 2012. This means AGG had no significant effect on the quantity of WPIEs from 1998 to 2005; from 2005 to 2012, however, the quantity of companies changed more drastically. The overall effect of ER was not ideal; it is consistent with the results of the changes in the number of WPIEs in the TCZs above. The policies of the TCZs have relatively loose control on WPIEs, so WPIEs have lower environmental costs. Until 2010, the government has set a new emission reduction target for the TCZs. TR1 showed a negative influence on the reduction and the increase of WPIEs, suggesting that the closer the enterprises were to the main road, the less the number of enterprises decreased, and the less the number of enterprises increased A possible explanation could be that a large number of enterprises were located near the main roads, and competition among too many similar enterprises reduced the number of enterprises. Another significant variable is the RND; its effect was positive, and the coefficient was significant at the 5% level. This implies that the greater the density of the river network, the greater the decrease in the number of enterprises. Except for these two variables, all variables were not significant in the overall regression, and the core variables, AGG and ER, were also insignificant. The second column shows that the influence of AGG was positive but insignificant, while the spatial lag coefficient of ER was significantly positive. This implies that the stricter the environmental regulation, the greater the increase in the number of enterprises. This is contrary to the expected effect of environmental regulation [34]. Moreover, TR1 was significantly negative, suggesting that whether the quantity of enterprises increased or decreased, this phenomenon occurred near the main roads from 1998 to 2005. TR2 and TR4 had no significant effect during 1998–2012. The effect of TR3 was complicated, indicating that shipping had a certain effect on changes in the quantity of WPIEs. The effect of RND was complicated as well. Meanwhile, the effect of DE was not ideal. BE1 was significantly positive from 2005 to 2012; this means WPIEs preferred to be located within the boundaries of the county (district). The other factors were all insignificant. There are two possible reasons for this result. On the one hand, other variables really had no effect on the increase or decrease in the number of enterprises. On the other hand, the result could have been determined by the research scale. To eliminate this effect, spatial econometric regression estimation was conducted for the upper, middle, and lower reaches.Table 4
SAR model results for the Yangtze River Economic Belt.
VariablesY1Y2Y3Y4AGG5.100115.557827.6803∗∗∗101.8879∗∗∗ER3.795013.2384∗∗4.83726.6484TR1−0.0002∗∗−0.0002∗−0.0001−0.0002TR2−0.0000−0.0000−0.0000−0.0001TR30.00000.0001∗∗0.0001∗∗∗0.0000TR40.0000−0.00010.0000−0.0000DE−0.0000−0.0001∗∗−0.0000−0.0001BE10.00020.00100.0010∗∗0.0022∗∗∗BE20.0000−0.0001−0.0000−0.0002BE3−0.0000−0.0000−0.00000.0000RND0.0353∗∗−0.0489−0.0353∗−0.0264Cons7.5547−26.6078∗−88.1036∗∗∗−33.4053∗∗∗lambda_cons278.0146∗∗∗631.5244∗∗∗1210.1962∗∗∗480.2941∗∗∗rho_cons898.3984∗∗∗1279.9267∗∗∗801.7008∗∗∗1461.7000∗∗∗sigma2_cons1315.0408∗∗∗4294.8947∗∗∗2046.0307∗∗∗3359.6298∗∗∗AIC10301.8311526.9510864.3311378.95BIC10375.8111600.9210938.4311453.05LR−5135.9170−5748.4737−5417.1643−5674.4757N1024102410331033T statistics in parentheses. ∗p < 0.1, ∗∗p < 0.05, ∗∗∗p < 0.01.The results for the upper, middle, and lower reaches are shown in Tables5 and 6. Columns (1) and (2) in Table 5 show that the estimated coefficients of AGG to the Y1 and Y2 discharges were 12.6535 and 17.8672, respectively, which were statistically significant at the 5% level. However, the estimated coefficients of AGG in the midstream area were only statistically significant to Y1, and in the downstream area, the coefficients of AGG were both insignificant. The reason could be that the benefits brought by enterprise agglomeration were less than those brought by other factors, such as geographic advantage and abundant labor resources. Once the scale of enterprise agglomeration reaches a certain level, it can reduce costs for enterprises and promote technology circulation in agglomeration areas; the benefits of agglomeration will then far exceed the benefits brought by other factors [35]. Enterprises will migrate to places where other enterprises gather to reduce costs for themselves [36]. Compared with the results in Table 5, the estimated coefficients of AGG to Y1 and Y2 in Table 6 increased, and both were statistically significant at the 1% level. This suggests the agglomerations were helpful for accelerating changes in the number of WPIEs from 2005 to 2012. Agglomeration contributed not only to lower production costs but also to a reduction in transaction costs, since proximity translates into lower costs and more opportunities to combine needs and capabilities [37]. Table 5 shows that the coefficient of ER to Y1 passed the significance test in three watersheds, but the coefficients of ER varied in sign, meaning that environmental regulation changed the direction of WPIEs. However, the coefficients of ER were only significantly negative in the downstream area. Generally, environmental regulations did not show the expected effect at this stage. The results show that the implementation of the TCZ policy did not affect changes in the number of WPIEs. During the study period, the TCZ policy was relatively loosely regulated. This result may be explained by the fact that China’s economy was in a stage of rapid development from 1998 to 2012. Rapid industrialization will inevitably result in unpredictable pressures on the environment [35, 38]. In the context of pursuing rapid economic development, to achieve policy goals, local governments will lower environmental standards to protect local polluting enterprises [39].Table 5
SAR model results for typical regions, 1998–2005.
VariablesY1Y2Y1Y2Y1Y2SAC_UPSAC_UPSAC_ MidSAC_ MidSAC_ LowSAC_ LowAGG12.6535∗∗17.8672∗∗19.703036.4423∗∗∗−0.250124.8836ER3.5475∗∗3.3126−9.0187∗∗∗−0.267119.6026∗58.3543∗∗∗TR1−0.0001∗∗∗−0.0001∗∗0.0000−0.0000−0.0003−0.0006TR2−0.0000−0.0000−0.0002∗∗−0.0002∗−0.0003−0.0007TR30.00000.0000−0.00000.0001∗∗−0.0001−0.0001TR40.00000.0000−0.0000−0.00010.0001−0.0000DE0.0000−0.0000−0.0000−0.0002∗∗∗−0.0001−0.0006∗∗∗BE1−0.0003∗−0.00020.0013∗∗∗0.0009∗0.00190.0063∗∗BE20.0002∗∗∗0.0002∗∗0.0001−0.0000−0.0005−0.0011BE30.0000−0.00000.00000.0000−0.00000.0000RND0.0052−0.0521∗∗∗0.0236∗−0.01970.0664−0.0494Cons1.6250−10.590425.1405102.9921∗∗∗55.4476159.3246∗∗lambda_cons409.4796∗∗960.7315∗∗∗218.9568−1034.973∗∗∗61.345218.0435rho_cons1081.4247∗∗∗1707.6785∗∗∗−1031.03921929.5319∗∗∗1009.1295∗∗∗1779.3018∗∗∗sigma2_cons92.1367∗∗∗210.4255∗∗∗439.4054∗∗∗633.4715∗∗∗3562.7771∗∗∗12193.812∗∗∗AIC2940.453270.952907.163029.053417.353794.80BIC3000.133330.632963.773085.673473.263850.71LR−1455.2229−1620.4742−1438.5777−1499.5269−1693.6769−1882.4014N395395322322307307T statistics in parentheses. ∗p < 0.1, ∗∗p < 0.05, ∗∗∗p < 0.01. SAC_UP, SAC_ Mid, and SAC_ Low indicate upstream, midstream, and downstream areas of the YREB, respectively.Table 6
SAR model results for typical regions, 2005–2012.
VariablesY3Y4Y3Y4Y3Y4SAC_UPSAC_UPSAC_ MidSAC_ MidSAC_ LowSAC_ LowAGG31.6672∗∗∗122.0326∗∗∗11.2182∗∗∗70.2626∗∗∗31.0093∗∗∗107.3883∗∗∗ER1.35064.2229-2.43542.142939.0022∗∗∗30.7261∗TR1−0.0001∗∗−0.0001∗∗−0.0000−0.0001−0.0004−0.0002TR2−0.0000−0.0000−0.0001∗∗−0.0004∗∗∗−0.0003−0.0008∗TR30.0000−0.0000∗0.00000.0000−0.0002−0.0002TR4−0.0000−0.0000−0.00000.00000.00020.0002DE−0.0000−0.0000−0.0000−0.0002∗∗∗−0.0003∗−0.0004BE1−0.00000.00020.0006∗∗0.0016∗∗∗0.0040∗∗0.0085∗∗∗BE20.0001∗0.0002∗∗−0.0001−0.0002−0.0006−0.0014BE30.0000−0.00000.00000.00010.00000.0001RND−0.0217∗∗−0.0425∗−0.00950.0070−0.0290−0.0133Cons1.837426.4892∗∗10.571155.0103∗∗63.8191107.4565∗∗lambda_cons495.0056∗∗∗133.0427327.5829−398.6397124.3005−79.6277rho_cons1783.8998∗∗∗2945.6210∗∗∗1095.6989∗∗∗2300.3359∗∗∗1486.5089∗∗∗1833.1115∗∗∗sigma2_cons88.6898∗∗∗452.9930∗∗∗204.8102∗∗∗1082.7912∗∗∗5977.0864∗∗∗8578.0584∗∗∗AIC2973.623619.722686.273233.503578.883687.09BIC3033.533679.632743.033290.253634.783743.00LR−1471.8110−1794.8618−1328.1353−1601.7487−1774.4388−1828.5475N401401325325307307T statistics in parentheses. ∗p < 0.1, ∗∗p < 0.05, ∗∗∗p < 0.01. SAC_UP, SAC_ Mid, and SAC_ Low indicate upstream, midstream, and downstream areas of the YREB, respectively.Aside fromAGG and ER, other factors were investigated, including boundary effect, transportation, river network density, and decentralization. Boundary effects had a greater influence on changes in the number of enterprises. As shown in Tables 5 and 6, the effect of BE was roughly the same. WPIEs in the upstream area preferred to be located within the boundaries of the prefecture-level city; yet, WPIEs in the midstream and downstream areas were inclined to be located within the boundaries of the county. Combining the results in Tables 5 and 6, we can see that WPIEs in the upstream areas were significantly affected by the main roads, those in the midstream areas were significantly affected by ports, and those in the downstream areas were not sensitive to transportation. The coefficient of DE to Y2 was significantly negative in the midstream area in Tables 5 and 6, indicating that the amount of increase in the number of enterprises in national development zones was reduced. Moreover, well-developed river networks played a small role in the site selection of WPIEs in the study area.


## 3.1. Spatial Distribution Pattern of WPIEs

Table1 lists the NNI results for WPIE locations for the three node years. The NNI for all years was less than 1, passing the significance test; this indicates that the spatial distributions of WPIEs had strong agglomeration characteristics. The NNI value in 1998 was less than that in 2005, while the lowest was in 2012. The NNI values in Table 1 reveal that the spatial distribution of WPIEs was generally clustered for each study year, but the intensity of clustering varied slightly in different years.Table 1
Nearest-neighbor index and spatial distribution pattern of water pollution-intensive enterprises locations in the Yangtze River Economic Belt.
YearActual mean distanceExpected mean distanceNNIz valuep valueDistribution pattern1998972.0764935.9120.197-286.8380.000Clustering2005742.9343578.0150.208-370.4460.000Clustering2012577.7213083.4880.187-446.2980.000ClusteringFigure3 shows the KDE results. The distributions of WPIEs in the three node years are characterized by the coexistence of single-center agglomeration and multicenter dispersion. In 1998, WPIEs were mainly distributed in Shanghai and its surrounding areas, while only small scale agglomerations were found in the middle and upper reach areas. From 1998 to 2005, the trend of agglomeration continued to increase, showing a Shanghai–Wuxi–Hangzhou–Ningbo multicore development mode in 2005. In 2012, the scope of agglomeration was further expanded, and a trend of integrated development appeared in the downstream areas.Figure 3
Kernel density estimation of water pollution-intensive enterprises in the Yangtze River Economic Belt.To better understand the spatial distribution patterns of WPIEs in the YREB, KDE was carried out for three typical regions. Figure4 shows the KDE of WPIEs in the upper reaches of the YREB. It can be seen that the agglomeration characteristics were gradually enhanced from 1998 to 2012. However, in the process of industrial agglomeration, Chengdu and Chongqing have always been at the core. This is closely related to the fact that Chengdu and Chongqing have always been the economic centers of Southwest China. Specifically, the spatial agglomeration pattern in the upstream region changed from single-core agglomeration in 1998 to multicore agglomeration in 2005 and then to integration in 2012. Although the agglomeration trend in other regions of the upper reaches also increased significantly, its influence and agglomeration intensity are far less than those of the Chengdu–Chongqing region. Figure 5 shows the development model of spatial agglomeration in the middle reaches. The agglomeration core appeared first in Wuhan. In 2005, Changsha became another agglomeration core aside from Wuhan. By 2012, the agglomeration center had extended, and Nanchang became the third largest core. As shown in Figure 6, the downstream area reflects the evolution of the spatial agglomeration model, from single-core to multicore pattern, as well as to regional integration. In 1998, there was only one agglomeration center in the downstream area (i.e., Shanghai). Then, the number of agglomeration centers increased from one to four in 2005 (i.e., Shanghai, Changzhou, and Wuxi in Jiangsu and Hangzhou in Zhejiang). Finally, these three agglomeration cores expanded to the surrounding areas, presenting a trend of regional integration in 2012. Compared with Figure 6, Figure 5 can better reflect the increase of aggregation nuclei, and Figure 6 shows the expansion of aggregation degree. In summary, the distribution characteristics of spatial agglomeration in the entire region or at the regional scale were consistent with Krugman’s core–periphery model [31].Figure 4
Kernel density estimation of water pollution-intensive enterprises in the upper reaches of the Yangtze River Economic Belt.Figure 5
Kernel density estimation of water pollution-intensive enterprises in the middle reaches of the Yangtze River Economic Belt.Figure 6
Kernel density estimation of water pollution-intensive enterprises in the lower reaches of the Yangtze River Economic Belt.


## 3.2. Spatiotemporal Changes in WPIEs at the County Scale

Figure7 shows the spatial distribution of changes in the number of WPIEs. In general, the values for increases in the number of WPIEs are significantly higher than those of decreases, with a range of 0–50 in most counties of the YREB. The number of WPIEs in the downstream area decreased the most in the Qingpu District of Shanghai, with a decrease of 592. Further, Jiangnan District in Hubei Province was the only one with a reduction of more than 200 in the midstream area. The number of WPIEs in other counties decreased by 51–200 (Figure 7(a)). Figure 7(b) shows 39 counties where the number of WPIEs in the downstream area was greater than 200, and the increase in the number of enterprises in the middle reaches was mostly in the range of 21–50. The number of enterprises in Chengdu and Chongqing increased mostly in the upper reach areas. Even the number of WPIEs in the TCZs has still increased from 1998 to 2005. In Figures 7c and 6d, similar results are seen in the quantitative changes in WPIEs from 2005 to 2012. However, the reduction of WPIEs from 2005 to 2012 was significantly greater than the values from 1998 to 2005. There were regional differences in the increase of WPIEs. Compared to the growth of WPIEs from 1998 to 2005, the upstream and midstream regions had more growth from 2005 to 2012, while the downstream regions had a smaller increase. During the study period, the growth of WPIEs in the TCZs of the upper reaches was not obvious, while other areas in the TCZs showed a considerable increase. The WPIEs were mainly distributed in the TCZs, so the number of WIPEs in the non-TCZs has not changed significantly. Altogether, the increase and decrease were mainly concentrated in the lower reaches, but the increase trend in the upper and middle reaches was significant from 2005 to 2012.Figure 7
Changes in the number of water pollution-intensive enterprises. (a) Decrement (1998–2005). (b) Increment (1998–2005). (c) Decrement (2005–2012). (d) Increment (2005–2012).
(a)(b)(c)(d)On the whole, the absolute number of WPIEs increased rapidly from 1998 to 2005, while the growth of net change from 2005 to 2012 developed in two directions. There was rapid growth in the upstream and midstream regions, while the downstream regions showed a significant slowdown in growth. The growth of net change in upstream and midstream has to do with industrialization process, where the downstream areas benefit from the economic development strategy of coastal areas and the establishment of a series of special economic zones. The Yangtze River Delta has the highest density of river networks in China, with convenient transportation and abundant resources. Therefore, downstream areas can attract more companies than upstream and midstream regions.


## 3.3. County-Scale Spatial Agglomeration Characteristics of WPIEs

Table2 shows Moran’s I value for the total amount and net change of WPIEs in the YREB. The p value of Moran’s I was 0.000, less than 0.01, which indicates that Moran’s I result was statistically significant at the 99% confidence level. This means the WPIEs and the quantity changes in WPIEs in the YREB were not randomly distributed. Moran’s I values were greater than 0.6 and were significant at the 10% level. From 1998 to 2005, the spatial correlation of changes in the total amount of WPIEs increased, with Moran’s I values ranging from 0.682 to 0.795, while the spatial correlation of total amount decreased between 2005 and 2012, with Moran’s I value dropping to 0.677. The trend of spatial correlation related to the net change in WPIEs was the same as the total amount. Moran’s I values reveal a positive spatial correlation between changes in the number of WPIEs. Not only was the distribution of the total number of WPIEs spatially interdependent, but there was also a positive spatial correlation between changes in WPIEs.Table 2
Moran’sI values with spatial autocorrelation estimation.
YearMoran’sIVariancep valuez value19980.682∗∗∗0.0000810.00076.00620050.795∗∗∗0.0000870.00085.38020120.677∗∗∗0.0000880.00072.2111998–2005JS0.631∗∗∗0.0000810.00070.3181998–2005XZ0.781∗∗∗0.0000870.00083.8022005–2012JS0.808∗∗∗0.0000870.00086.8212005–2012XZ0.609∗∗∗0.0000880.00065.057JS indicates a quantity decrease in WPIEs; XZ indicates a quantity increase in WPIEs.∗∗∗Statistically significant as P < 0.001.While global Moran’sI shows the general trend, the Getis–Ord Gi∗ shows the spatial pattern in local neighborhoods [32]. Hot spot analysis is an effective tool for identifying the spatial clustering of WPIEs across regional units. The Getis–Ord Gi∗ results revealed multiple areas of statistically significant high–high clustering in the eastern YREB (mainly located in the Yangtze River Delta) and low–low clustering in the southwestern YREB (Yunnan, Guizhou, and Sichuan Provinces). Figure 8(a) shows the results of a hot spot analysis indicating that the reduction in the number of WPIEs from 1998 to 2005 was concentrated in the Yangtze River Delta, while the cold spots were distributed in the northern parts of Hubei, southeastern Sichuan, northern Guizhou, and southwestern Yunnan. The Getis–Ord Gi∗ results shown in Figure 8(b) reveal an emerging hot spot area for WPIEs in central Hubei Province. In Figure 8(c), the distribution of hot spot and cold spot areas indicates that the number of WPIEs with more reduction was mainly concentrated in Shanghai, southern Jiangsu, and eastern Zhejiang Provinces, while the number of WPIEs with less reduction was more likely to be distributed in the cold spot provinces (Yunnan, southern Sichuan, and Guizhou). Yunnan, southwestern Sichuan, and Guizhou are more prominent in Figure 8(d) than in Figure 8(c). This means the increase in WPIEs is low in these areas but high in the surrounding provinces. Further, there were new hot spot areas in northern Jiangsu and southern Zhejiang.Figure 8
Hot spot analysis of changes in the number of water pollution-intensive enterprises in the Yangtze River Economic Belt. (a) Decrement (1998–2005). (b) Increment (1998–2005). (c) Decrement (2005–2012). (d) Increment (2005–2012).
(a)(b)(c)(d)


## 3.4. Factors Influencing the Location of WPIEs

Here, an empirical study was conducted to test how agglomeration economy and spatial factors influenced the locations of WPIEs in the YREB. A comprehensive analysis of the factors affecting spatial agglomeration was then implemented for both the YREB region and typical regions divided by watersheds. This can help clarify the key factors in the spatial arrangement of WPIEs to propose better pollution management policies.


### 3.4.1. Variables

The dependent variables were the decreases and new increases in the number of WPIEs for the periods 1998–2005 and 2005–2012, respectively. Enterprise agglomeration and environmental regulation were established as the two core explanatory variables. As seen in previous sections, WPIEs had strong agglomeration characteristics with regard to spatial distribution. Specifically, enterprise agglomeration was measured by the number of WPIEs in each county. In addition, environmental regulation was established as the dummy variable, based on the implementation of the TCZ policy. If WPIEs are located in the TCZs, the value is 1; otherwise, it is 0. Enterprises in the TCZs faced more stringent environmental regulations than other enterprises. Therefore, it is reliable to take the TCZ policy as the measurement standard for environmental regulation. Some controlling factors were also included in the regression models. Enterprise location is also related to transportation facilities and government policies, and the boundary effect also has a certain effect on the location of enterprises [33]. Transportation cost, factory rent, and policy support are usually considered in the site selection of enterprises. Therefore, factors representing the distances between enterprises and infrastructures (e.g., distance from enterprises to main roads, railway stations, ports, and rivers) were included in the models. Generally, land rent will be cheaper with further distance from the regional center and closer distance to the regional boundary. High-polluting enterprises that pursue the maximization of benefit tend to build factories in the suburbs. For this study, the boundary was divided into three levels: provincial boundary, prefecture-level city boundary, and county boundary. Considering that enterprises located in the National Technology Development Zone were more sufficiently developed and received more favorable policy support, a dummy variable was introduced; 1 and 0 indicate whether enterprises were or were not located in the zone, respectively. Table 3 provides more details about the variables.Table 3
Definitions of variables.
CategoryNameDefinitionDependent variablesY1Amount of reduction in WPIEs in each county from 1998 to 2005Y2Amount of increase in WPIEs in each county from 1998 to 2005Y3Amount of reduction in WPIEs in each county from 2005 to 2012Y4Amount of increase in WPIEs in each county from 2005 to 2012Core independent variablesAgglomerationAGGUnit area quantity of polluting enterprises in each district (county)Environmental regulationERLocated in TCZ or notControl variablesBoundary effectBE1Average shortest distance from the location of the enterprise to the boundary of the county-level administrative districtBE2Average shortest distance from the location of the enterprise to the boundary of the prefecture-level city administrative districtBE3Average shortest distance from the location of the enterprise to the boundary of the provincial administrative regionTransportationTR1Average shortest distance from business location to main roadTR2Average shortest distance from business location to train stationTR3Average shortest distance from the location of the enterprise to the port on the Yangtze RiverTR4Average shortest distance from the business location to the riverRiver network densityRNDInland river network density in each district (county)DecentralizationDEAverage shortest distance from the location of the enterprise to the national development zone


### 3.4.2. Influencing Factors

First, this study tested the effect of the influencing factors on the whole YREB; Table4 shows the results. The influence of AGG was insignificant from 1998 to 2005 but was significantly positive from 2005 to 2012. This means AGG had no significant effect on the quantity of WPIEs from 1998 to 2005; from 2005 to 2012, however, the quantity of companies changed more drastically. The overall effect of ER was not ideal; it is consistent with the results of the changes in the number of WPIEs in the TCZs above. The policies of the TCZs have relatively loose control on WPIEs, so WPIEs have lower environmental costs. Until 2010, the government has set a new emission reduction target for the TCZs. TR1 showed a negative influence on the reduction and the increase of WPIEs, suggesting that the closer the enterprises were to the main road, the less the number of enterprises decreased, and the less the number of enterprises increased A possible explanation could be that a large number of enterprises were located near the main roads, and competition among too many similar enterprises reduced the number of enterprises. Another significant variable is the RND; its effect was positive, and the coefficient was significant at the 5% level. This implies that the greater the density of the river network, the greater the decrease in the number of enterprises. Except for these two variables, all variables were not significant in the overall regression, and the core variables, AGG and ER, were also insignificant. The second column shows that the influence of AGG was positive but insignificant, while the spatial lag coefficient of ER was significantly positive. This implies that the stricter the environmental regulation, the greater the increase in the number of enterprises. This is contrary to the expected effect of environmental regulation [34]. Moreover, TR1 was significantly negative, suggesting that whether the quantity of enterprises increased or decreased, this phenomenon occurred near the main roads from 1998 to 2005. TR2 and TR4 had no significant effect during 1998–2012. The effect of TR3 was complicated, indicating that shipping had a certain effect on changes in the quantity of WPIEs. The effect of RND was complicated as well. Meanwhile, the effect of DE was not ideal. BE1 was significantly positive from 2005 to 2012; this means WPIEs preferred to be located within the boundaries of the county (district). The other factors were all insignificant. There are two possible reasons for this result. On the one hand, other variables really had no effect on the increase or decrease in the number of enterprises. On the other hand, the result could have been determined by the research scale. To eliminate this effect, spatial econometric regression estimation was conducted for the upper, middle, and lower reaches.Table 4
SAR model results for the Yangtze River Economic Belt.
VariablesY1Y2Y3Y4AGG5.100115.557827.6803∗∗∗101.8879∗∗∗ER3.795013.2384∗∗4.83726.6484TR1−0.0002∗∗−0.0002∗−0.0001−0.0002TR2−0.0000−0.0000−0.0000−0.0001TR30.00000.0001∗∗0.0001∗∗∗0.0000TR40.0000−0.00010.0000−0.0000DE−0.0000−0.0001∗∗−0.0000−0.0001BE10.00020.00100.0010∗∗0.0022∗∗∗BE20.0000−0.0001−0.0000−0.0002BE3−0.0000−0.0000−0.00000.0000RND0.0353∗∗−0.0489−0.0353∗−0.0264Cons7.5547−26.6078∗−88.1036∗∗∗−33.4053∗∗∗lambda_cons278.0146∗∗∗631.5244∗∗∗1210.1962∗∗∗480.2941∗∗∗rho_cons898.3984∗∗∗1279.9267∗∗∗801.7008∗∗∗1461.7000∗∗∗sigma2_cons1315.0408∗∗∗4294.8947∗∗∗2046.0307∗∗∗3359.6298∗∗∗AIC10301.8311526.9510864.3311378.95BIC10375.8111600.9210938.4311453.05LR−5135.9170−5748.4737−5417.1643−5674.4757N1024102410331033T statistics in parentheses. ∗p < 0.1, ∗∗p < 0.05, ∗∗∗p < 0.01.The results for the upper, middle, and lower reaches are shown in Tables5 and 6. Columns (1) and (2) in Table 5 show that the estimated coefficients of AGG to the Y1 and Y2 discharges were 12.6535 and 17.8672, respectively, which were statistically significant at the 5% level. However, the estimated coefficients of AGG in the midstream area were only statistically significant to Y1, and in the downstream area, the coefficients of AGG were both insignificant. The reason could be that the benefits brought by enterprise agglomeration were less than those brought by other factors, such as geographic advantage and abundant labor resources. Once the scale of enterprise agglomeration reaches a certain level, it can reduce costs for enterprises and promote technology circulation in agglomeration areas; the benefits of agglomeration will then far exceed the benefits brought by other factors [35]. Enterprises will migrate to places where other enterprises gather to reduce costs for themselves [36]. Compared with the results in Table 5, the estimated coefficients of AGG to Y1 and Y2 in Table 6 increased, and both were statistically significant at the 1% level. This suggests the agglomerations were helpful for accelerating changes in the number of WPIEs from 2005 to 2012. Agglomeration contributed not only to lower production costs but also to a reduction in transaction costs, since proximity translates into lower costs and more opportunities to combine needs and capabilities [37]. Table 5 shows that the coefficient of ER to Y1 passed the significance test in three watersheds, but the coefficients of ER varied in sign, meaning that environmental regulation changed the direction of WPIEs. However, the coefficients of ER were only significantly negative in the downstream area. Generally, environmental regulations did not show the expected effect at this stage. The results show that the implementation of the TCZ policy did not affect changes in the number of WPIEs. During the study period, the TCZ policy was relatively loosely regulated. This result may be explained by the fact that China’s economy was in a stage of rapid development from 1998 to 2012. Rapid industrialization will inevitably result in unpredictable pressures on the environment [35, 38]. In the context of pursuing rapid economic development, to achieve policy goals, local governments will lower environmental standards to protect local polluting enterprises [39].Table 5
SAR model results for typical regions, 1998–2005.
VariablesY1Y2Y1Y2Y1Y2SAC_UPSAC_UPSAC_ MidSAC_ MidSAC_ LowSAC_ LowAGG12.6535∗∗17.8672∗∗19.703036.4423∗∗∗−0.250124.8836ER3.5475∗∗3.3126−9.0187∗∗∗−0.267119.6026∗58.3543∗∗∗TR1−0.0001∗∗∗−0.0001∗∗0.0000−0.0000−0.0003−0.0006TR2−0.0000−0.0000−0.0002∗∗−0.0002∗−0.0003−0.0007TR30.00000.0000−0.00000.0001∗∗−0.0001−0.0001TR40.00000.0000−0.0000−0.00010.0001−0.0000DE0.0000−0.0000−0.0000−0.0002∗∗∗−0.0001−0.0006∗∗∗BE1−0.0003∗−0.00020.0013∗∗∗0.0009∗0.00190.0063∗∗BE20.0002∗∗∗0.0002∗∗0.0001−0.0000−0.0005−0.0011BE30.0000−0.00000.00000.0000−0.00000.0000RND0.0052−0.0521∗∗∗0.0236∗−0.01970.0664−0.0494Cons1.6250−10.590425.1405102.9921∗∗∗55.4476159.3246∗∗lambda_cons409.4796∗∗960.7315∗∗∗218.9568−1034.973∗∗∗61.345218.0435rho_cons1081.4247∗∗∗1707.6785∗∗∗−1031.03921929.5319∗∗∗1009.1295∗∗∗1779.3018∗∗∗sigma2_cons92.1367∗∗∗210.4255∗∗∗439.4054∗∗∗633.4715∗∗∗3562.7771∗∗∗12193.812∗∗∗AIC2940.453270.952907.163029.053417.353794.80BIC3000.133330.632963.773085.673473.263850.71LR−1455.2229−1620.4742−1438.5777−1499.5269−1693.6769−1882.4014N395395322322307307T statistics in parentheses. ∗p < 0.1, ∗∗p < 0.05, ∗∗∗p < 0.01. SAC_UP, SAC_ Mid, and SAC_ Low indicate upstream, midstream, and downstream areas of the YREB, respectively.Table 6
SAR model results for typical regions, 2005–2012.
VariablesY3Y4Y3Y4Y3Y4SAC_UPSAC_UPSAC_ MidSAC_ MidSAC_ LowSAC_ LowAGG31.6672∗∗∗122.0326∗∗∗11.2182∗∗∗70.2626∗∗∗31.0093∗∗∗107.3883∗∗∗ER1.35064.2229-2.43542.142939.0022∗∗∗30.7261∗TR1−0.0001∗∗−0.0001∗∗−0.0000−0.0001−0.0004−0.0002TR2−0.0000−0.0000−0.0001∗∗−0.0004∗∗∗−0.0003−0.0008∗TR30.0000−0.0000∗0.00000.0000−0.0002−0.0002TR4−0.0000−0.0000−0.00000.00000.00020.0002DE−0.0000−0.0000−0.0000−0.0002∗∗∗−0.0003∗−0.0004BE1−0.00000.00020.0006∗∗0.0016∗∗∗0.0040∗∗0.0085∗∗∗BE20.0001∗0.0002∗∗−0.0001−0.0002−0.0006−0.0014BE30.0000−0.00000.00000.00010.00000.0001RND−0.0217∗∗−0.0425∗−0.00950.0070−0.0290−0.0133Cons1.837426.4892∗∗10.571155.0103∗∗63.8191107.4565∗∗lambda_cons495.0056∗∗∗133.0427327.5829−398.6397124.3005−79.6277rho_cons1783.8998∗∗∗2945.6210∗∗∗1095.6989∗∗∗2300.3359∗∗∗1486.5089∗∗∗1833.1115∗∗∗sigma2_cons88.6898∗∗∗452.9930∗∗∗204.8102∗∗∗1082.7912∗∗∗5977.0864∗∗∗8578.0584∗∗∗AIC2973.623619.722686.273233.503578.883687.09BIC3033.533679.632743.033290.253634.783743.00LR−1471.8110−1794.8618−1328.1353−1601.7487−1774.4388−1828.5475N401401325325307307T statistics in parentheses. ∗p < 0.1, ∗∗p < 0.05, ∗∗∗p < 0.01. SAC_UP, SAC_ Mid, and SAC_ Low indicate upstream, midstream, and downstream areas of the YREB, respectively.Aside fromAGG and ER, other factors were investigated, including boundary effect, transportation, river network density, and decentralization. Boundary effects had a greater influence on changes in the number of enterprises. As shown in Tables 5 and 6, the effect of BE was roughly the same. WPIEs in the upstream area preferred to be located within the boundaries of the prefecture-level city; yet, WPIEs in the midstream and downstream areas were inclined to be located within the boundaries of the county. Combining the results in Tables 5 and 6, we can see that WPIEs in the upstream areas were significantly affected by the main roads, those in the midstream areas were significantly affected by ports, and those in the downstream areas were not sensitive to transportation. The coefficient of DE to Y2 was significantly negative in the midstream area in Tables 5 and 6, indicating that the amount of increase in the number of enterprises in national development zones was reduced. Moreover, well-developed river networks played a small role in the site selection of WPIEs in the study area.


## 3.4.1. Variables

The dependent variables were the decreases and new increases in the number of WPIEs for the periods 1998–2005 and 2005–2012, respectively. Enterprise agglomeration and environmental regulation were established as the two core explanatory variables. As seen in previous sections, WPIEs had strong agglomeration characteristics with regard to spatial distribution. Specifically, enterprise agglomeration was measured by the number of WPIEs in each county. In addition, environmental regulation was established as the dummy variable, based on the implementation of the TCZ policy. If WPIEs are located in the TCZs, the value is 1; otherwise, it is 0. Enterprises in the TCZs faced more stringent environmental regulations than other enterprises. Therefore, it is reliable to take the TCZ policy as the measurement standard for environmental regulation. Some controlling factors were also included in the regression models. Enterprise location is also related to transportation facilities and government policies, and the boundary effect also has a certain effect on the location of enterprises [33]. Transportation cost, factory rent, and policy support are usually considered in the site selection of enterprises. Therefore, factors representing the distances between enterprises and infrastructures (e.g., distance from enterprises to main roads, railway stations, ports, and rivers) were included in the models. Generally, land rent will be cheaper with further distance from the regional center and closer distance to the regional boundary. High-polluting enterprises that pursue the maximization of benefit tend to build factories in the suburbs. For this study, the boundary was divided into three levels: provincial boundary, prefecture-level city boundary, and county boundary. Considering that enterprises located in the National Technology Development Zone were more sufficiently developed and received more favorable policy support, a dummy variable was introduced; 1 and 0 indicate whether enterprises were or were not located in the zone, respectively. Table 3 provides more details about the variables.Table 3
Definitions of variables.
CategoryNameDefinitionDependent variablesY1Amount of reduction in WPIEs in each county from 1998 to 2005Y2Amount of increase in WPIEs in each county from 1998 to 2005Y3Amount of reduction in WPIEs in each county from 2005 to 2012Y4Amount of increase in WPIEs in each county from 2005 to 2012Core independent variablesAgglomerationAGGUnit area quantity of polluting enterprises in each district (county)Environmental regulationERLocated in TCZ or notControl variablesBoundary effectBE1Average shortest distance from the location of the enterprise to the boundary of the county-level administrative districtBE2Average shortest distance from the location of the enterprise to the boundary of the prefecture-level city administrative districtBE3Average shortest distance from the location of the enterprise to the boundary of the provincial administrative regionTransportationTR1Average shortest distance from business location to main roadTR2Average shortest distance from business location to train stationTR3Average shortest distance from the location of the enterprise to the port on the Yangtze RiverTR4Average shortest distance from the business location to the riverRiver network densityRNDInland river network density in each district (county)DecentralizationDEAverage shortest distance from the location of the enterprise to the national development zone


## 3.4.2. Influencing Factors

First, this study tested the effect of the influencing factors on the whole YREB; Table4 shows the results. The influence of AGG was insignificant from 1998 to 2005 but was significantly positive from 2005 to 2012. This means AGG had no significant effect on the quantity of WPIEs from 1998 to 2005; from 2005 to 2012, however, the quantity of companies changed more drastically. The overall effect of ER was not ideal; it is consistent with the results of the changes in the number of WPIEs in the TCZs above. The policies of the TCZs have relatively loose control on WPIEs, so WPIEs have lower environmental costs. Until 2010, the government has set a new emission reduction target for the TCZs. TR1 showed a negative influence on the reduction and the increase of WPIEs, suggesting that the closer the enterprises were to the main road, the less the number of enterprises decreased, and the less the number of enterprises increased A possible explanation could be that a large number of enterprises were located near the main roads, and competition among too many similar enterprises reduced the number of enterprises. Another significant variable is the RND; its effect was positive, and the coefficient was significant at the 5% level. This implies that the greater the density of the river network, the greater the decrease in the number of enterprises. Except for these two variables, all variables were not significant in the overall regression, and the core variables, AGG and ER, were also insignificant. The second column shows that the influence of AGG was positive but insignificant, while the spatial lag coefficient of ER was significantly positive. This implies that the stricter the environmental regulation, the greater the increase in the number of enterprises. This is contrary to the expected effect of environmental regulation [34]. Moreover, TR1 was significantly negative, suggesting that whether the quantity of enterprises increased or decreased, this phenomenon occurred near the main roads from 1998 to 2005. TR2 and TR4 had no significant effect during 1998–2012. The effect of TR3 was complicated, indicating that shipping had a certain effect on changes in the quantity of WPIEs. The effect of RND was complicated as well. Meanwhile, the effect of DE was not ideal. BE1 was significantly positive from 2005 to 2012; this means WPIEs preferred to be located within the boundaries of the county (district). The other factors were all insignificant. There are two possible reasons for this result. On the one hand, other variables really had no effect on the increase or decrease in the number of enterprises. On the other hand, the result could have been determined by the research scale. To eliminate this effect, spatial econometric regression estimation was conducted for the upper, middle, and lower reaches.Table 4
SAR model results for the Yangtze River Economic Belt.
VariablesY1Y2Y3Y4AGG5.100115.557827.6803∗∗∗101.8879∗∗∗ER3.795013.2384∗∗4.83726.6484TR1−0.0002∗∗−0.0002∗−0.0001−0.0002TR2−0.0000−0.0000−0.0000−0.0001TR30.00000.0001∗∗0.0001∗∗∗0.0000TR40.0000−0.00010.0000−0.0000DE−0.0000−0.0001∗∗−0.0000−0.0001BE10.00020.00100.0010∗∗0.0022∗∗∗BE20.0000−0.0001−0.0000−0.0002BE3−0.0000−0.0000−0.00000.0000RND0.0353∗∗−0.0489−0.0353∗−0.0264Cons7.5547−26.6078∗−88.1036∗∗∗−33.4053∗∗∗lambda_cons278.0146∗∗∗631.5244∗∗∗1210.1962∗∗∗480.2941∗∗∗rho_cons898.3984∗∗∗1279.9267∗∗∗801.7008∗∗∗1461.7000∗∗∗sigma2_cons1315.0408∗∗∗4294.8947∗∗∗2046.0307∗∗∗3359.6298∗∗∗AIC10301.8311526.9510864.3311378.95BIC10375.8111600.9210938.4311453.05LR−5135.9170−5748.4737−5417.1643−5674.4757N1024102410331033T statistics in parentheses. ∗p < 0.1, ∗∗p < 0.05, ∗∗∗p < 0.01.The results for the upper, middle, and lower reaches are shown in Tables5 and 6. Columns (1) and (2) in Table 5 show that the estimated coefficients of AGG to the Y1 and Y2 discharges were 12.6535 and 17.8672, respectively, which were statistically significant at the 5% level. However, the estimated coefficients of AGG in the midstream area were only statistically significant to Y1, and in the downstream area, the coefficients of AGG were both insignificant. The reason could be that the benefits brought by enterprise agglomeration were less than those brought by other factors, such as geographic advantage and abundant labor resources. Once the scale of enterprise agglomeration reaches a certain level, it can reduce costs for enterprises and promote technology circulation in agglomeration areas; the benefits of agglomeration will then far exceed the benefits brought by other factors [35]. Enterprises will migrate to places where other enterprises gather to reduce costs for themselves [36]. Compared with the results in Table 5, the estimated coefficients of AGG to Y1 and Y2 in Table 6 increased, and both were statistically significant at the 1% level. This suggests the agglomerations were helpful for accelerating changes in the number of WPIEs from 2005 to 2012. Agglomeration contributed not only to lower production costs but also to a reduction in transaction costs, since proximity translates into lower costs and more opportunities to combine needs and capabilities [37]. Table 5 shows that the coefficient of ER to Y1 passed the significance test in three watersheds, but the coefficients of ER varied in sign, meaning that environmental regulation changed the direction of WPIEs. However, the coefficients of ER were only significantly negative in the downstream area. Generally, environmental regulations did not show the expected effect at this stage. The results show that the implementation of the TCZ policy did not affect changes in the number of WPIEs. During the study period, the TCZ policy was relatively loosely regulated. This result may be explained by the fact that China’s economy was in a stage of rapid development from 1998 to 2012. Rapid industrialization will inevitably result in unpredictable pressures on the environment [35, 38]. In the context of pursuing rapid economic development, to achieve policy goals, local governments will lower environmental standards to protect local polluting enterprises [39].Table 5
SAR model results for typical regions, 1998–2005.
VariablesY1Y2Y1Y2Y1Y2SAC_UPSAC_UPSAC_ MidSAC_ MidSAC_ LowSAC_ LowAGG12.6535∗∗17.8672∗∗19.703036.4423∗∗∗−0.250124.8836ER3.5475∗∗3.3126−9.0187∗∗∗−0.267119.6026∗58.3543∗∗∗TR1−0.0001∗∗∗−0.0001∗∗0.0000−0.0000−0.0003−0.0006TR2−0.0000−0.0000−0.0002∗∗−0.0002∗−0.0003−0.0007TR30.00000.0000−0.00000.0001∗∗−0.0001−0.0001TR40.00000.0000−0.0000−0.00010.0001−0.0000DE0.0000−0.0000−0.0000−0.0002∗∗∗−0.0001−0.0006∗∗∗BE1−0.0003∗−0.00020.0013∗∗∗0.0009∗0.00190.0063∗∗BE20.0002∗∗∗0.0002∗∗0.0001−0.0000−0.0005−0.0011BE30.0000−0.00000.00000.0000−0.00000.0000RND0.0052−0.0521∗∗∗0.0236∗−0.01970.0664−0.0494Cons1.6250−10.590425.1405102.9921∗∗∗55.4476159.3246∗∗lambda_cons409.4796∗∗960.7315∗∗∗218.9568−1034.973∗∗∗61.345218.0435rho_cons1081.4247∗∗∗1707.6785∗∗∗−1031.03921929.5319∗∗∗1009.1295∗∗∗1779.3018∗∗∗sigma2_cons92.1367∗∗∗210.4255∗∗∗439.4054∗∗∗633.4715∗∗∗3562.7771∗∗∗12193.812∗∗∗AIC2940.453270.952907.163029.053417.353794.80BIC3000.133330.632963.773085.673473.263850.71LR−1455.2229−1620.4742−1438.5777−1499.5269−1693.6769−1882.4014N395395322322307307T statistics in parentheses. ∗p < 0.1, ∗∗p < 0.05, ∗∗∗p < 0.01. SAC_UP, SAC_ Mid, and SAC_ Low indicate upstream, midstream, and downstream areas of the YREB, respectively.Table 6
SAR model results for typical regions, 2005–2012.
VariablesY3Y4Y3Y4Y3Y4SAC_UPSAC_UPSAC_ MidSAC_ MidSAC_ LowSAC_ LowAGG31.6672∗∗∗122.0326∗∗∗11.2182∗∗∗70.2626∗∗∗31.0093∗∗∗107.3883∗∗∗ER1.35064.2229-2.43542.142939.0022∗∗∗30.7261∗TR1−0.0001∗∗−0.0001∗∗−0.0000−0.0001−0.0004−0.0002TR2−0.0000−0.0000−0.0001∗∗−0.0004∗∗∗−0.0003−0.0008∗TR30.0000−0.0000∗0.00000.0000−0.0002−0.0002TR4−0.0000−0.0000−0.00000.00000.00020.0002DE−0.0000−0.0000−0.0000−0.0002∗∗∗−0.0003∗−0.0004BE1−0.00000.00020.0006∗∗0.0016∗∗∗0.0040∗∗0.0085∗∗∗BE20.0001∗0.0002∗∗−0.0001−0.0002−0.0006−0.0014BE30.0000−0.00000.00000.00010.00000.0001RND−0.0217∗∗−0.0425∗−0.00950.0070−0.0290−0.0133Cons1.837426.4892∗∗10.571155.0103∗∗63.8191107.4565∗∗lambda_cons495.0056∗∗∗133.0427327.5829−398.6397124.3005−79.6277rho_cons1783.8998∗∗∗2945.6210∗∗∗1095.6989∗∗∗2300.3359∗∗∗1486.5089∗∗∗1833.1115∗∗∗sigma2_cons88.6898∗∗∗452.9930∗∗∗204.8102∗∗∗1082.7912∗∗∗5977.0864∗∗∗8578.0584∗∗∗AIC2973.623619.722686.273233.503578.883687.09BIC3033.533679.632743.033290.253634.783743.00LR−1471.8110−1794.8618−1328.1353−1601.7487−1774.4388−1828.5475N401401325325307307T statistics in parentheses. ∗p < 0.1, ∗∗p < 0.05, ∗∗∗p < 0.01. SAC_UP, SAC_ Mid, and SAC_ Low indicate upstream, midstream, and downstream areas of the YREB, respectively.Aside fromAGG and ER, other factors were investigated, including boundary effect, transportation, river network density, and decentralization. Boundary effects had a greater influence on changes in the number of enterprises. As shown in Tables 5 and 6, the effect of BE was roughly the same. WPIEs in the upstream area preferred to be located within the boundaries of the prefecture-level city; yet, WPIEs in the midstream and downstream areas were inclined to be located within the boundaries of the county. Combining the results in Tables 5 and 6, we can see that WPIEs in the upstream areas were significantly affected by the main roads, those in the midstream areas were significantly affected by ports, and those in the downstream areas were not sensitive to transportation. The coefficient of DE to Y2 was significantly negative in the midstream area in Tables 5 and 6, indicating that the amount of increase in the number of enterprises in national development zones was reduced. Moreover, well-developed river networks played a small role in the site selection of WPIEs in the study area.


## 4. Discussion

Using the framework of “pattern evolution–typical alienation–influencing factors”, this study explored the distribution and effects of WPIEs in the YREB. The results suggested that WPIEs have an inherent motivation to gather together. It has been argued that enterprises in the same sector tended to concentrate geographically because of the benefits derived from external economies [40]. However, the findings of the present study, which covered the whole YREB, did not fully reflect such agglomeration characteristics. Therefore, similar investigations were further carried out on a smaller scale—namely, on the upper, middle, and lower reach areas. The results for these areas, based on spatial analysis and spatial econometric model, were similar to those for the whole economic belt, showing the same evolutionary trend of spatial agglomeration.Most studies emphasized the influence of location on business performance [41]. Since specialized labor and inputs are available locally (e.g., suppliers or manufacturing facilities), production costs can be reduced in agglomerated spaces [30]. The overall and partial regression analysis results showed that the spatial scale affected the results. If heterogeneity analysis was not carried out, the results would often deviate from the actual situation.The agglomeration of WPIEs greatly reduced production costs and played an important role in the spatial distribution of WPIEs. Industrial agglomeration can achieve a centralized consumption of the environment and improve the utilization efficiency of environmental factors [42]. Research on the agglomeration economy of the service industry and manufacturing industry showed that agglomeration was indispensable in the process of urbanization [43]. This study indicated that agglomeration had a significant impact on the spatial layout of WPIEs. The impact of environmental regulation on the location of pollution-intensive enterprises was complex. Empirical studies examining the relationship between environmental regulations and company’s behaviors can be divided into two waves. Studies related to the first wave included Bartik [44], McConnell and Schwab [45], Duffy-Deno [46], Friedman et al. [47], and Levinson [48]. These reports found little systematic evidence in support of the hypothesis that environmental regulations affected capital location decisions. Besides, an empirical research found that environmental regulation may not have a measurable effect on the location of the pig industry; however, it has indeed a strong influence on the sectoral composition of the industry [49]. Studies involved in the second wave argued that there is much stronger evidence in favor of the hypothesis that environmental regulations have made a significant impact on the location of pollution-intensive enterprises. For instance, Henderson [50] revealed that environmental regulation had a significant negative effect on decisions of firms in polluting industries in seven out of ten regression models. However, some other studies have found that the impact of environmental regulation on polluting enterprises was not significant [51–53]. In addition, Xing and Kolstad [54] found that laxity of environmental regulations in the destination countries tended to attract more inflow of pollution-intensive industries (such as chemical and metal industries).In China, according to the “Decisions of the State Council on Several Issues Concerning Environmental Protection”, “Ninth Five Year Plan on National Environmental Protection”, and “Outlines of Objectives in Perspective of the Year 2010”, the TCZ policy was proposed and implemented in 1998. The environmental protection objective was to effectively strengthen the prevention and control of acid rain and sulfur dioxide pollution by 2010. Before 2010, the control target for the TCZs was only a limited target, which can only achieve the purpose of suppressing the increasing trend of acid rain and sulfur dioxide pollution, and cannot reverse the serious situation of water pollution and air pollution. This was a possible reason why the environmental regulation policy did not achieve the expected effect during the study period of this article. This strategy was regarded as “grow first, clean up later”, which means attracting pollution-intensive industries by lax environmental regulation to boost economic growth at first and then driving the pollution-intensive plants out for environmental protection after the economy matures [55].


## 5. Conclusions

This study set out to investigate the quantitative changes of WPIEs in time and space and identify the significant factors influencing the location of WPIEs in the YREB based on the district (county) level data from 1998 to 2012. Spatial statistical methods including NNI, KDE, and spatial correlation analysis were used to explore the evolution of spatial agglomeration of WPIEs. SAR modes were constructed to reveal the influencing mechanisms of spatiotemporal variations of WPIEs.Firstly, WPIEs in the YREB were spatially clustered. The spatial distribution pattern of enterprises gradually evolved from polarization characteristics to multicenter network characteristics. The overall number of enterprises increased year by year, but the downstream areas showed a weakening growth trend from 2005 to 2012. Geographical agglomeration can bring benefits to enterprises. Agglomeration contributes to not only lower production costs but also reduced transaction costs, since proximity translates into lower costs and greater opportunities to combine needs and capabilities. Furthermore, influences of enterprise agglomeration and environmental regulation on the number of WPIEs had a scale effect. Enterprise agglomeration had a more significant effect on changes in the number of WPIEs from 2005 to 2012. The effect of environmental regulation on changes in the number of WPIEs in the two research phases was more complex. The implementation of the TCZ policy did not significantly control the number of WPIEs. Policy implementation, local protection, and economic development background all had an effect on its implementation. Moreover, compared to other control variables, the boundary effect had the most significant influence. Enterprises in the upstream region tended to be located within the boundaries of prefecture-level cities, while enterprises in the midstream and downstream regions preferred the boundaries of districts (counties).The development of the upper reaches of the YREB provides a reference for the development of other regions. When the economy develops to a certain level, the demand for a good environment will force water pollution-intensive enterprises to move out of the central city. This shows that local governments should formulate appropriate policies to coordinate the relationship between economic development and environmental protection. First of all, it is necessary to strengthen environmental protection publicity so that people can completely change the traditional conception of environmental protection. Local governments should integrate environmental protection objectives into local economic development plans and regard the achievement of these objectives as important indicators in the evaluation of local government officials. Secondly, the economic development model should be based on the principle of protecting the environment first. A strategy known as “pollute first, clean up later” should be shifted to a new one aimed at putting prevention first and combining prevention with control. Finally, traffic factors affecting the spatial layout of WPIEs indicate that local governments should strengthen infrastructure construction to attract investment.



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*Source: 2772091-2022-03-04.xml*