Structural Characteristics of Intergovernmental Water Pollution Control Cooperation Networks Using Social Network Analysis and GIS in Yangtze River Delta Urban Agglomeration, China

Abstract

Water pollution exhibits distinct negative externalities, necessitating trans-regional collaborative governance among basin governments. However, few studies have examined the structural features of water pollution collaboration networks among local governments in China from a spatial analysis perspective. This study focuses on 27 central cities in the Yangtze River Delta, collecting 109 policy texts and evidence of cooperative actions on water pollution governance among these cities. By utilizing a combination of social network analysis and GIS spatial analysis, the research visualizes the results and delves into the overall structure and internal features of the network. The results indicate that the density of the water pollution cooperation network is 0.75, suggesting that a relatively stable and closely connected network for collaborative governance of water pollution has been formed. Furthermore, the water pollution cooperation in the Yangtze River Delta exhibits a typical “multi-center” network structure, with Shanghai–Suzhou, Nanjing, and Hangzhou as the core, forming three city clusters with tighter cooperation. Suzhou, Shanghai, and Jiaxing have the highest degree of centrality, which are 51, 46, and 44, respectively. The analysis of degree centrality reveals that cities with higher levels of economic development or those that serve as provincial capitals often play a leading role in the cooperation network. The study also observes that adjacent cities or local governments closer to the core cities are more likely to establish cooperative relationships; this phenomenon is not limited by provincial administrative boundaries.

1. Introduction

Environmental quality problems in densely populated areas have become increasingly prominent with the acceleration of industrialization and urbanization [1,2]. Water resources, indispensable for human societal development, have their quality and sustainability directly tied to human health [3]. However, with the progression of industrialization and urbanization, water pollution issues are becoming more severe, particularly in China’s large city agglomerations and rapidly urbanizing regions [4]. The Yangtze River Delta, recognized as a highly prosperous and rapidly urbanizing area in China, confronts a range of environmental obstacles, with particular emphasis on water-related concerns [5]. These challenges have evolved into crucial impediments that curtail regional development. In this context, the importance of deeply studying and resolving water pollution issues is evident [4,6].

Water environments, positioned between public and private goods, are quasi-public goods with typical negative externalities. In contrast to soil and solid waste, water possesses fluid properties that lead to the diffusion and transportation of pollutants as it flows [7]. Contaminants from upstream areas can potentially cross administrative boundaries, further polluting water in other regions [8]. This gives rise to the transboundary nature of water pollution [9]. Local governments play a central role in governing water pollution within their areas. Their responsibilities include establishing legislation and policies, monitoring and assessing water environments, and managing and enforcing environmental regulations [10]. However, issues such as conflicts of responsibilities and powers, conflicting interests, and information barriers between different local authorities may hinder cooperation among regions, leading to the spread of cross-regional pollution, wasteful investment, and mutual blame [11]. In recent years, numerous cross-border pollution disputes have emerged in China, including the pollution of the Songhua River and Taihu Lake, causing serious impacts on production, livelihoods, and social stability [12,13]. In order to address these disputes, many local governments have sought practical solutions to promote the establishment and improvement of cooperative governance mechanisms for cross-regional pollution [12,14]. For instance, the “Regulations on the Prevention and Control of Pollution in the Taihu Lake Basin” clarify the enforcement of laws across several provinces, mechanisms for cross-administrative litigation, and cross-regional jurisdiction. The “Special Plan for Cross-Border Water Protection in the Yangtze River Delta Ecological Green Integrated Development Demonstration Zone” establishes common criteria and institutional frameworks for joint protection and collaborative governance of key cross-border water bodies in the demonstration zone. Artificial administrative boundaries cannot impede the spread of regional environmental issues, and the governance of water pollution is not solely the concern of individual cities. In the face of complex regional environmental governance challenges, it requires collaborative efforts among governments across regions [15,16]. Thus, the establishment of a cooperative governance mechanism involving multiple cities, departments, and regions is a necessary strategy for addressing water pollution issues [17,18]. This is particularly pertinent for the Yangtze River Delta, an area bustling with economic development and facing severe ecological pollution. As one of China’s largest, most cooperative, and mature city clusters, this region plays a key demonstrative role in China’s integrated regional development and is a pioneer in exploring cross-border environmental governance in China [17]. However, the current water environment governance model does not meet the needs of such a large city cluster. It is imperative to promptly develop a comprehensive inter-governmental policy system for environmental governance and foster a proactive mechanism for pollution control. This will serve to strengthen regional collaboration in addressing water pollution challenges [19,20]. In 2021, the Chinese government issued the “Yangtze River Delta Regional Ecological Environmental Protection Plan”. This plan focuses on tackling prominent ecological and environmental concerns shared by Shanghai, Jiangsu, Zhejiang, and Anhui. Its primary goals are promoting green development, preserving ecological spaces, and ensuring joint efforts in controlling cross-border pollution. The plan indicates the need for collaborative promotion of basin water environment management [21]. However, in terms of global city cluster competition, regionally integrated development, and water environmental protection, the Yangtze River Delta region also faces several prominent issues, such as unsteady improvement in water environmental quality, cross-border environmental problems, implementation of ecological compensation, and the need to perfect the mechanisms and means for the collaborative promotion of ecological, environmental protection.

To tackle complex challenges such as environmental governance that go beyond the capacities of individual governmental departments or regional administrative management, local governments have been progressively establishing informal cooperative relations or formal contractual partnerships. These partnerships are often formalized through the signing of cooperation agreements, including various framework agreements, declarations of cooperation, opinions, and joint actions [22,23,24]. These intergovernmental collaborations include both vertical cooperation within administrative hierarchies and horizontal cooperation between governments or departments at the same level [25,26,27]. The cooperation agreements and actions depict regional cooperative relationships, providing intangible social benefits for cooperating local governments. In turn, these actions are interconnected, forming a network of cooperation among local governments. The cooperation network embeds individuals and departments from different regions into collective actions by local governments, forming a network structure of regional collaboration [26,27]. In recent years, network research has drawn wide attention from public management and policy scholars in the field of public events such as environmental governance. Many existing studies focus on the structure, motives, and performance of networks [27,28,29,30]. For example, Fliervoet et al. (2016) abstracted a cooperative network among central governments, local governments, farmers’ associations, and other stakeholders and discussed the cooperation governance in the floodplain management of the Rhine Delta in the Netherlands [31]. Guan et al. (2022) investigated the social network structure and function of coastal wetland conservation collaboration in the Yellow River Delta region from the perspective of stakeholders such as provincial governments and municipal governments [32]. Liu et al. (2020) evaluated the efficiency of water environmental governance in the Yangtze River Delta region of China from the perspective of diverse collaboration among the government, businesses, and the public [33]. However, existing studies on pollution control from a network perspective have predominantly focused on cooperative networks among different stakeholders, such as the government and residents. There has been relatively less attention given to the horizontal cooperation relationships among local governments, who serve as the implementers of specific policies.

Municipal governments in China occupy a middle position in the government administrative hierarchy. They are responsible not only for implementing relevant policies under the macro guidance of the central and provincial governments but also for providing guidance to grassroots governments and functional departments [32,34,35]. Therefore, it is critical to review water pollution control cooperation among municipal governments. This research focuses on the prefecture level as the research unit. The aim is to comprehensively understand and promote inter-regional government collaboration. Suo et al. (2023) addressed the issue of cross-boundary collaboration in air pollution control. They specifically examined the impact of governance boundaries and the interactive relationships among prefecture-level governments. Their research focused on the intergovernmental cooperation network for air governance in the Yangtze River Delta region of China. They discovered that cities within the same administrative or policy boundary are more likely to engage in collaboration [17]. Li (2023) collected actual cooperative behaviors among municipal-level governments, utilizing sources such as published policies on coordinated air pollution control, intergovernmental agreements, and news reports. Through this, they analyzed the overall structure, internal characteristics, and evolving trends of the collaborative network for regional air pollution control in the Beijing–Tianjin–Hebei and surrounding areas [34]. Chang et al. (2022), using social network analysis, utilized haze pollution data from 284 city-level administrative units in China to identify 10 regions with close collaborative efforts in pollution control [26]. Previous studies have already focused on the role of city-level governments in promoting cross-regional collaboration in pollution control and have revealed the characteristics, structure, and influencing factors of collaborative networks among major city clusters in China. However, relevant studies mainly focus on the field of air pollution, with few findings on the collaborative achievements of municipal governments in water pollution. Furthermore, as Su and Yu (2019) pointed out, due to the simultaneous spatial effects and complex network structure characteristics of pollution control, it is difficult to depict this phenomenon using only social network analysis or traditional econometric models [36]. To address these shortcomings, this study constructs a water pollution cooperation network among municipal governments in the Yangtze River Delta region based on regional cooperation policies. We apply social network analysis and GIS spatial analysis methods to describe the structure and spatial characteristics of the urban agglomeration water pollution regional cooperation network. The research results are of great significance for deeply understanding the characteristics and mechanisms of water pollution regional collaboration in the Yangtze River Delta urban agglomeration.

In summary, revealing the structure of intergovernmental water pollution governance cooperation networks is key to understanding the behavior of governments in cross-regional collaborative water pollution governance [30,31,34]. Particularly for an economically developed, densely populated urban agglomeration like the Yangtze River Delta in China, a thorough understanding of the structure and features of its water pollution governance network can promote cross-regional collaboration and coordination to achieve environmental protection and sustainable development goals. What are the network characteristics of collaboration actions among local governments in the Yangtze River Delta region regarding water pollution prevention and control? Which cities are at the core of this network? Are there multiple sub-regional collaboration networks? If so, what differences exist between these different subgroups of collaboration? By studying and answering these questions, we can gain a deeper understanding of the underlying mechanisms of the collaboration network for water pollution prevention and control in the Yangtze River Delta region. This will contribute to promoting cooperation and coordination across regions, ultimately achieving environmental protection and sustainable development goals. Therefore, this study aims to conduct a case study on the water pollution governance network of central cities in the Yangtze River Delta urban agglomeration, explore the patterns and characteristics of intergovernmental cooperation networks, and reveal their structural features and interaction modes among key participants. This study utilizes GIS spatial analysis techniques to create inter-city collaboration maps that incorporate geographic information. These maps are used to identify spatial patterns within intergovernmental water pollution collaboration networks. By doing so, the study enhances result visibility, aiding decision-making processes. Additionally, it uncovers how the relative geographic locations and administrative boundaries of cities influence the collaborative network of water pollution prevention and control in the YRD region. The research results will provide a decision-making basis for the Yangtze River Delta and other similar urban agglomerations, promote the collaborative cooperation of cross-regional water environment governance, and advance sustainable development and the construction of ecological civilization.

2. Materials and Methods

2.1. Study Area

The Yangtze River Delta (YRD) urban agglomeration, located at the geographical center of East Asia and a vital hub on the East Asian flight path of the West Pacific, serves as a significant intersection for the Belt and Road Initiative and the Yangtze River Economic Belt. In recent years, as the pace of integration in the YRD accelerates, an inter-regional, intergovernmental model of joint environmental governance is gradually taking shape. From the adoption of the “Yangtze River Delta Regional Environmental Cooperation Initiative” in 2004 to the joint issuance of the “Implementation Plan for Ecological Monitoring of the Yangtze River Delta Eco-Green Integrated Development Demonstration Zone” by three provinces and one city in 2022, more than 20 multi-level ecological collaborative governance documents or initiatives have been intensively launched. Especially since the promotion of YRD integration to the national strategic level in November 2018, the YRD regional cooperation and coordination mechanism has been continually refined from a “three-level operation” to a joint office operation. The “Outline for the Integrated Development of the Yangtze River Delta Region”, issued in December 2019, explicitly proposes to solidly advance water pollution prevention and control, water ecosystem restoration, and water resource protection, promoting a noticeable improvement in the water quality of transboundary water bodies. It calls for a joint formulation of targeted remediation schemes for key transboundary water bodies such as the Yangtze River, Tai Lake, Chao Lake, Taipu River, and Dianshan Lake, and a comprehensive strengthening of cooperation in water pollution control. Under the principle of regional joint protection and governance, policy measures are formulated, the ecological compensation system is implemented, regional interests are coordinated, and water pollution management issues within the YRD are regularly discussed and continuously resolved.

The research area chosen for this paper is the central region of China’s YRD, which includes Shanghai, seven cities in Jiangsu province (Nanjing, Suzhou, Wuxi, Changzhou, Nantong, Yangzhou, Zhenjiang, Yancheng, and Tai’zhou), nine cities in Zhejiang province (Hangzhou, Ningbo, Wenzhou, Huzhou, Jiaxing, Shaoxing, Jinhua, Zhoushan, Taizhou), and eight cities in Anhui province (Hefei, Wuhu, Ma’anshan, Tongling, Anqing, Chuzhou, Chizhou, and Xuancheng). The central YRD region encompasses about two-thirds of the total cities in the YRD and represents the main area of YRD regional integrated cooperation. In 2022, these 27 cities, occupying only 2.3% of the nation’s land area, gathered around 12.5% of the population and generated 20.2% of the Gross Domestic Product. This makes them some of China’s most dynamic, open, and populous regions, as well as crucial areas for implementing the country’s current regional coordinated development strategy. This paper focuses on the central YRD as the research subject. The administrative division map of the central YRD, as shown in Figure 1, is drawn using geographic information software based on the coordinates of each city.

2.2. Method

This research integrates social network analysis methods with GIS spatial analysis to investigate the structural characteristics of the intergovernmental collaboration network for water pollution control in the YRD region from the perspectives of collaborative networks and geographical space.

In accordance with social network analysis theory, society consists of networks, with every individual embedded within these networks. Social network analysis (SNA) is a systematic approach used to examine “relational data” and is characterized by its emphasis on the interconnections between individuals or entities. Metrics such as centrality, network density, and cohesive subgroups can quantitatively depict vital structural features within the network [34]. The subject of this study is the collaborative relationships between governments; hence, the main body of research will revolve around the techniques and measurement tools of SNA, interpreting the collaborative network structure among local governments.

In spatial terms, entities in close proximity are more likely to be tightly interconnected. Policies and actions for collaborative pollution governance between regions exhibit spatial correlation. Neighboring cities are typically more inclined to collaborate, with their methods and preferences for collaboration often aligning [37,38,39]. However, most studies on intergovernmental collaboration do not specifically address the spatial proximity in pollution control collaboration [36]. This study applies GIS technology, predominantly using ArcGIS software, leveraging the integrated thinking of geographic analysis and the advantage of a multi-factor overlay to provide thinking and decision-making support. On the foundation of constructing a collaboration network with real government cooperation agreement data, it collects administrative border and natural feature border data. Coupled with the centrality analysis and cluster analysis results from SNA, it performs analysis on the regional collaboration for water pollution control and produces thematic collaboration maps. These clearly display the collaboration networks between regions, contributing to a profound understanding of the characteristics and mechanisms of water pollution regional collaboration within the YRD urban agglomeration.

2.3. Data Collection and Processing

Network data for this study were primarily obtained through retrieval from relevant government websites and manual identification. In China, government authorities at the municipal level and above, as well as their subordinate departments, typically have official websites. These governmental websites publish authoritative, transparent, and accurate information on various aspects such as social, economic, and environmental policies, regulations, data, and notifications. The information records government actions and serves as a basis for analyzing the collaborative behaviors of different regional governments in water pollution control. However, governmental websites store a vast amount of information, requiring effective screening methods. Search engines play a crucial role in filtering government information, as intelligent search algorithms can quickly identify relevant results related to search keywords. In this study, the search engine used is Baidu, China’s largest Chinese search engine. Through this website, information links published on governmental websites can be quickly indexed using keywords. Local official government websites and environmental bureaus were used as the search databases, conducting data retrieval, filtering, and transformation. Python 3.9, UCINET 6.0, and ArcGIS 10.8 software were employed for data processing throughout the process. The data collection and processing mainly went through the following three stages.

The first stage was the collection of data on relevant governmental documents and actions. After multiple attempts, to ensure the maximum amount of relevant information is gathered, this study sets the search criteria on Baidu as follows: if an article contains both the terms “water pollution” or “water environment” and “signing” or “collaboration” or “cooperation”, and the search scope is limited to the target websites. The target websites selected for this study include the official websites of 27 municipal people’s governments and the ecological and environmental bureaus in the respective regions. For example, when searching for information about Shanghai, the selected data will be from the websites “shanghai.gov.cn” or “sthj.sh.gov.cn”, accessed on 11 May 2023. All searchable results will be stored. Stored content includes titles, dates, actual links, abstracts, etc., for subsequent searching and organization. Subsequently, each link leading to a governmental website was followed, and webpage text content was stored for later reference and information filtering.

The second stage involved automated data processing and screening. In terms of automated data processing, the initial step was to remove redundant data and those irrelevant to the research theme, deleting entries with the same titles or those involving project bidding and winning; this was followed by city categorization and counting. Names of the 27 cities were selected from the site text, and the occurrence of each city name was tallied to indicate whether the data involved two or more action subjects so as to swiftly filter out useful information on local government collaboration. Given that each government unit has different habits in policy language use, for more precise information retrieval, the final stage required the author’s personal manual filtering. This involved reading site texts involving two or more cities, excluding information such as notifications issued by the central government, forwarded notifications from the State Council or the Ministry of Environment, etc. Evidence of joint water pollution control by two cities was filtered out, mainly including policy texts (such as signed agreements) and action texts (joint meetings and joint actions). Eventually, 109 entries of local government collaborative water pollution control data were obtained. The third stage consisted of collaboration data calculation and thematic mapping. On the basis of the regional collaboration policy database, the research used the theories and methods of SNA to construct the regional collaboration network for water pollution control in the central urban agglomeration of the YRD. Centrality was used to calculate degree centrality, betweenness centrality, and closeness centrality. Additionally, this study identified clusters of cities with high collaboration intensity in water pollution control using the CONCOR cohesive subgroup algorithm from SNA, with the number of collaborations between cities as weights. Identifying key cooperative subgroups and revealing features of water pollution collaboration among subgroups is a crucial step to comprehensively understanding the structure of intergovernmental policy collaboration in water pollution control in the YRD region. Combining algorithmic results and expert knowledge, this study divided the YRD water pollution collaboration governance groups into three categories: “Shanghai–Suzhou Leading City Group”, “Hangzhou Leading City Group”, and “Nanjing Leading City Group”. In combination with text data, it analyzed the cooperation rules and internal structure among the three major collaboration groups, providing important references for regional pollution control in the YRD and cross-river/cross-regional water pollution prevention in mega-city clusters. The process of data collection and processing is shown in Figure 2. SNA is mainly employed to calculate centrality, network density, core-periphery analysis, and cluster analysis. GIS serves as a visualization method and analytical perspective. By inputting the results of SNA into GIS software, the network structure of government collaboration in the Yangtze River Delta region can be presented more clearly. Additionally, GIS can reveal the role of spatial distance or spatial relationships in regional cooperation. Moreover, as the YRD region involves multiple administrative levels, the combination of SNA and GIS can also highlight these administrative hierarchies more prominently.

3. Results

3.1. Overall Characteristics of the Collaboration Network

In order to gain a comprehensive understanding and observation of the degree of collaboration among local governments in the YRD region for water pollution collaborative governance, this study, based on the weighted collaboration frequency matrix data, uses UCINET 6.0 software in conjunction with ArcGIS 10.8 software to draw a visualized network map. As shown in Figure 3, the thickness of the lines between cities represents the closeness of the connections, while the node size indicates the degree of participation. The same color denotes cohesive subgroups identified by the CONCOR algorithm.

From a macro perspective, the collaboration network for water pollution collaborative governance in the YRD region is on a large scale. Notably, places like Shanghai, Suzhou, and Jiaxing have collaborated more than ten times, while local governments in Hangzhou, Shaoxing, Ningbo, Changzhou, Suzhou, Wuxi, Nanjing, Chuzhou, and Ma’anshan have also launched joint inspections and agreements for joint water pollution collaborative governance five times or more. This suggests that all parts of the central YRD region are actively responding to the “Guiding Opinions on Establishing a Cross-Provincial River Basin Upstream and Downstream Sudden Water Pollution Incident Joint Prevention and Control Mechanism” issued in 2022. The actors are participating actively in water pollution, with the concept of collaboration starting to germinate and now entering the formal implementation phase.

As can be seen from the figure, the water pollution control collaboration network in the YRD is divided into four groups. The first group includes Suzhou, Shanghai, Jiaxing, Wuxi, Changzhou, Huzhou, and Nantong. The second group includes Nanjing, Ma’anshan, Chuzhou, Zhenjiang, Yangzhou, and Xuancheng. The third group includes Hangzhou, Shaoxing, Ningbo, Jinhua, and Zhoushan. The fourth group consists of Wuhu, Hefei, and Tongling. Taizhou and Wenzhou, located in the southern part of the study area, rarely participate in the regional collaboration governance network and are isolated individuals within the entire network. A possible explanation is that Wenzhou only joined the integration in 2019, and among the 27 cities, it is geographically only adjacent to Taizhou. From the information found on Taizhou’s government website, it appears that Taizhou focuses more on controlling water pollution within its jurisdiction and has achieved good results.

Within the overall regional structure, network density effectively reflects the denseness of relationship distribution within the network. It denotes the ratio of the number of existing relationships to the potential number of relationships within the network. Generally, the network density ranges between 0 and 1, with a higher value indicating a more tightly knit network. A higher network density helps reduce coordination costs in transactions and contract risks, facilitates the establishment of trust, and improves communication and exchange. According to calculations, the network density of local government collaboration for water pollution control in the central YRD region is 0.75. This indicates that local governments in the region have close ties in water pollution control, and the local government water pollution control collaboration network is open and inclusive. Members within the network are actively engaging in exchanges with external parties. They have a good foundation for urban collaboration networks, suggesting that the YRD provides a favorable field for study.

3.2. Core-Periphery Structure Analysis

The core-periphery analysis is used to reveal the strategic position and importance of nodes within the entire social network system. Whether a node holds a core or peripheral position in a social network primarily depends on its coreness. If a node has a high coreness, it suggests a higher strategic position within the network, being located in the network’s core area. Conversely, it implies that the node is situated at the network’s periphery.

This study calculates the coreness of the nodal cities by constructing a binary core-periphery model, dividing cities into core and periphery categories. The results indicate that the intercity collaboration network for water pollution collaborative governance in the YRD region exhibits a clear core-periphery structure. The core cities include Shanghai, Nanjing, Nantong, Jiaxing, Ningbo, Changzhou, Wuxi, Hangzhou, Huzhou, Shaoxing, Suzhou, and Zhenjiang. In contrast, the peripheral cities are Hefei, Anqing, Xuzhou, Yangzhou, Xuancheng, Chizhou, Tai’zhou, Chuzhou, Yancheng, Zhoushan, Wuhu, Jinhua, Tongling, Ma’anshan, Taizhou, and Wenzhou. The cities in the core area collaborate more closely, while the peripheral cities are weaker in their cross-jurisdictional collaborative efforts toward water pollution. The core-periphery structure is shown in

Table 1. Core/periphery structure of cooperative water pollution governance.

City Status	Name
Core	Shanghai, Nanjing, Nantong, Jiaxing, Ningbo, Changzhou, Wuxi, Hangzhou, Huzhou, Shaoxing, Suzhou, and Zhenjiang
Periphery	Hefei, Anqing, Yangzhou, Xuancheng, Chizhou, Tai’zhou, Chuzhou, Yancheng, Zhoushan, Wuhu, Jinhua, Tongling, Ma’anshan, Taizhou, and Wenzhou

.

3.3. Characteristics of City Centrality

Centrality analysis focuses on nodes, exploring their importance, relative position, and association within the entire network. Common measurement indicators include degree centrality, betweenness centrality, and closeness centrality. Each city’s centrality within the YRD region’s water pollution cooperation network is presented in

Table 2. The node centrality of cooperative water pollution governance.

City	Degree	Closeness	Betweenness
Suzhou	51	42	9.027
Shanghai	46	41	41.186
Jiaxing	44	44	6.359
Wuxi	35	42	9.027
Nanjing	32	42	53.739
Hangzhou	30	41	28.668
Changzhou	27	48	2.789
Ma’anshan	22	51	1.461
Ningbo	20	44	7.64
Chuzhou	20	48	10.844
Xuancheng	19	41	29.475
Huzhou	19	49	1.87
Shaoxing	18	51	6.61
Zhenjiang	18	49	4.583
Wuhu	17	48	10.844
Nantong	14	44	53.048
Yangzhou	13	47	16.23
Hefei	13	57	3.407
Tongling	9	63	14.017
Zhoushan	8	53	0
Jinhua	6	64	0
Anqing	4	57	20.177
Chizhou	3	72	0
Tai’zhou	2	66	24
Yancheng	1	90	0

.

In this study, degree centrality is used to measure a city’s central position in the water pollution collaboration network of the city cluster, indicating the size of responsibilities a city undertakes. As seen from the table, the degree centrality values in this study range from 1 to 51, with Suzhou having the highest degree centrality. This suggests that Suzhou has the strongest influence and holds a central position in water pollution control among the cities in YRD. Shanghai follows closely, with Jiaxing, Wuxi, Nanjing, and Hangzhou also having a high degree of centrality, indicating substantial influence in water pollution control collaborations. Conversely, Yancheng, Taizhou, Chizhou, and Anqing have a lower degree of centrality, all below 5, suggesting that these cities are relatively isolated in the water pollution collaboration network.

Closeness centrality measures the average shortest distance from a city to all other cities. The smaller the value, the closer it is to the center of the network and the greater its influence. From the table, we can see that the closeness centrality values range from 41 to 90. Shanghai and Hangzhou have the lowest closeness centrality. This implies that in the collaborative efforts for water pollution control, the speed of information and resource exchange between Shanghai, Hangzhou, and other cities may be the fastest, placing them at the core. Jiaxing, Changzhou, and Ma’anshan also have relatively low closeness centrality, suggesting that they have advantageous positions within the network and play significant roles in information and resource dissemination. Conversely, Yancheng has the highest closeness centrality, meaning it has the furthest average distance from all other cities. Thus, information and resource exchange are slower. Similarly, cities like Tai’zhou, Chizhou, and Anqing have higher closeness centrality, indicating their relatively peripheral positions within the network.

Betweenness centrality represents the frequency with which a city appears in the shortest paths across the overall network. In matters of water pollution collaboration, cities with high betweenness centrality can play crucial roles in coordinating collaboration, resource allocation, and information sharing among other cities. In this study, Nanjing has the highest betweenness centrality, implying that it acts as a “bridge” or “intermediary” in network interactions among the YRD city cluster. This role gives Nanjing greater influence and control over the flow of information within the network. Additionally, Nantong’s value is quite high, suggesting that Nantong may also play an important “intermediary” role in collaborations. As can be observed from Figure 3, although Nantong does not have many direct contacts with other cities, it is the only city connected to Taizhou, which in turn is the only city connected to Yancheng. Nantong can extend the water pollution cooperation network to the northern region. Both the degree and betweenness centrality of Shanghai is high, indicating Shanghai’s potentially crucial position in the regional collaboration network. Not only does it have many direct cooperative relations, but it also plays an essential role in connecting other cities and promoting inter-city collaborations. Shanghai is a vital node within the region, likely exerting significant influence on the region’s water pollution collaboration.

3.4. Sub-Network Structures of City Clusters Collaboration

In the previous section, we have identified four cohesive subgroups based on the overall network of intergovernmental collaboration. Combining actual cooperative behaviors of local governments with expert knowledge, the third and fourth city clusters were merged, together with the previous two, forming the three primary groups discussed in this research: Shanghai–Suzhou radiating city cluster, Nanjing radiating city cluster, and Hangzhou radiating city cluster. To understand the specific features of intergovernmental collaboration within each subgroup more accurately, only cities participating within each group are retained to draw the internal collaboration network diagram.

3.4.1. Shanghai–Suzhou Radiating City Cluster

The Shanghai–Suzhou radiating city cluster is comprised of three provincial-level administrative units: Shanghai Municipality (a provincial-level administrative unit), Zhejiang Province, and Jiangsu Province. Specifically, Zhejiang Province includes two cities, Jiaxing and Huzhou, while Jiangsu Province includes four cities, Suzhou, Wuxi, Changzhou, and Nantong. As illustrated in Figure 4, the size of the nodes represents the level of participation of each city in water pollution action: the larger the node, the higher the level of participation. The thickness of the lines represents the degree of cooperation between the two connected parties: the closer the relationship, the thicker the line. The highest frequency of cooperation is 14 times, and the lowest is once. Suzhou’s collaboration with Wuxi, Shanghai, and Jiaxing is the tightest, all exceeding 10 times. Suzhou’s cooperation with Wuxi and Changzhou also occurred more than five times. In contrast, Nantong and Huzhou participate less frequently in transboundary water pollution control, with fewer than two instances each. In this network structure, it is clear that Suzhou, located geographically in the middle, plays a vital role in the Shanghai–Suzhou radiating city cluster. It participates in water pollution cooperation within the Suzhou–Wuxi–Changzhou region and transboundary water pollution governance in the YRD integration demonstration area. The structure of regional cooperative governance in the Suzhou cooperative city cluster combines bilateral relationships and multilateral relationships, including Shanghai–Suzhou, Wuxi–Jiaxing bilateral relations, and Jiaxing–Suzhou–Shanghai, Suzhou–Wuxi–Changzhou, Suzhou–Wuxi–Changzhou–Jiaxing–Huzhou multilateral relations.

3.4.2. Nanjing Radiating City Cluster

The Nanjing radiating city cluster is comprised of two provincial-level administrative units: Jiangsu Province and Anhui Province. Specifically, Jiangsu Province includes six cities, Nanjing, Zhenjiang, Ma’anshan, Yangzhou, Chuzhou, and Changzhou, while Anhui Province includes four cities, Wuhu, Hefei, Xuancheng, and Tongling. As shown in Figure 5, Nanjing, as the center of the network, radiates its influence as a mega-city, leading the surrounding cities to water pollution governance collaboration. The highest frequency of cooperation in the region is six times, and the lowest is one. Nanjing’s collaborative actions with Chuzhou and Ma’anshan, Ma’anshan’s cooperation with Chuzhou and Wuhu, and Chuzhou’s cooperation with Hefei all occurred more than five times. Xuancheng’s cooperation with other cities is once. The cities in the Nanjing radiating city cluster maintain relatively close connections, with Nanjing fully utilizing its role as a provincial capital city to jointly promote cross-boundary water pollution governance with surrounding cities through joint inspections and agreement signings. The structure of cooperative governance in the region includes both bilateral relationships, such as Nanjing–Ma’anshan, Nanjing–Chuzhou, Hefei–Tongling, and Wuhu–Xuancheng, and multilateral relationships such as Nanjing–Zhenjiang–Yangzhou.

3.4.3. Hangzhou Radiating City Cluster

The Hangzhou radiating city cluster includes seven cities in Zhejiang Province: Hangzhou, Shaoxing, Ningbo, Jinhua, Jiaxing, Huzhou, and Zhoushan. As shown in Figure 6, Hangzhou, as the core city, leads the surrounding cities in water pollution cooperation. The highest frequency of cooperation is six times, and the lowest is once. Hangzhou’s collaboration with Shaoxing, Jinhua, Jiaxing, and Ningbo occurs more than four times, whereas Zhoushan’s cooperation with other cities mainly occurs once. In this collaboration network, the structure of regional cooperative governance still combines bilateral relationships and multilateral relationships, with bilateral relationships like Hangzhou–Ningbo, Hangzhou–Shaoxing, and Hangzhou–Jinhua, and multilateral relationships like Hangzhou–Shaoxing–Ningbo.

4. Conclusions and Discussion

This study focuses on the 27 central cities in the Yangtze River Delta, China. By collecting information from official websites, we obtained relevant policies, agreements, and texts concerning the local government’s efforts in water pollution control. Using a combined approach of SNA and spatial analysis, we visualized and analyzed the collaborative network for water pollution prevention and control. UCINET 6.0 software and ArcGIS 10.8 software were employed for the visualization and output of the results. We analyzed the structural characteristics and interactive patterns of local governments in water pollution collaborative actions from three levels: the macro network, the meso sub-network, and the micro-node centrality. By doing so, the study aims to address the questions posed at the beginning of the article. Theoretically, this paper examines the current state of cooperative governance of water pollution in China, investigates the cooperative relationships between governments, and analyzes the characteristics of cooperative networks in the field of water pollution prevention and control. It is a beneficial complement to intergovernmental collaborative studies on water pollution control and enriches the research content of public administration theory and intergovernmental relations theory in China. This study utilizes GIS spatial analysis techniques to create inter-city collaboration maps that incorporate geographic information. These maps are used to identify spatial patterns within intergovernmental water pollution collaboration networks. With this, the study enhances result visibility, aiding decision-making processes. Additionally, it uncovers how the relative geographic locations and administrative boundaries of cities influence the collaborative network of water pollution prevention and control in the YRD region. The findings provide valuable insights for promoting trans-regional water environment governance collaboration in YRD and other similar urban agglomerations, fostering sustainable development and ecological civilization construction. The research results are as follows:

Firstly, on a macro level, the network density of government cooperation in water pollution governance is 0.75. This indicates that a relatively stable and closely connected network has formed among governments in the area, which encourages long-term cooperation among local governments in water environmental governance. These cities, driven by regional interests, have initiated actions such as joint meetings and joint inspections of rivers and lakes, breaking the administrative boundaries between different government bodies. Facing environmental issues, governments are no longer acting in isolation but evaluating and addressing regional water pollution problems from a holistic perspective. They plan and implement joint water pollution plans and collaborate on the development and execution of common framework agreements.

Secondly, on a meso level, the study identifies three closely connected groups of cooperating cities in the intercity network, namely Shanghai–Suzhou, Nanjing, and Hangzhou, which drive the surrounding cities to carry out transboundary water pollution control cooperation. The Shanghai–Suzhou urban agglomeration has seen active participation from cities in the administrative regions of the Shanghai Municipality, Jiangsu Province, and Zhejiang Province. In the Nanjing urban agglomeration, Anhui and Jiangsu have also been extensively involved. This indicates that the collaborative network for water pollution control in the Yangtze River Delta exhibits a “multi-center” structure, surpassing the limitations of provincial administrative boundaries. This finding expands upon the assumption that cities within the same administrative jurisdiction are more likely to collaborate in urban pollution control [17]. Through the use of GIS spatial mapping, the “multi-center” network structure is clearly illustrated, suggesting that the relative positioning of cities may be a contributing factor to inter-provincial collaboration. This discovery not only addresses the initial research questions but also reveals the underlying mechanisms of the collaborative network for water pollution prevention and control in the Yangtze River Delta. It aids in deciphering the coordination and cooperative behaviors among local governments and provides valuable insights for promoting inter-regional cooperation. In the Suzhou-radiating city group, a Yangtze River Delta Ecological Green Integration Demonstration Area has been established in the region, and many new rules have been revised around the construction and development of the demonstration area. In the Nanjing-radiating city group, the establishment of the “Nanjing Metropolitan Circle Water Professional Cooperation Framework Agreement” has led to numerous collaborations between cities. In the Hangzhou-radiating city group, Hangzhou continues to cooperate with surrounding areas, completing the “Hangzhou Metropolitan Circle Ecological Environmental Protection Co-Inspection Plan”. Furthermore, Hangzhou, Shaoxing, and Jinhua have also collaborated multiple times, with the counties under their jurisdiction signing the “Horizontal Ecological Compensation Agreement for the Puyang River Basin”. Similar to the findings of Suo et al. (2023), we discovered a high degree of overlap between the urban agglomerations involved in water pollution control collaboration and metropolitan areas. This suggests that collaboration in water pollution control, as well as in air pollution control, is closely intertwined with institutional promotion and local actions [17]. The collaborative efforts and cooperative practices within these closely connected urban agglomerations can serve as valuable references and inspirations for other cities.

Thirdly, on a micro level, we find that cities with higher levels of economic development or provincial capital tend to occupy a central position in the cooperation network and take the lead in water pollution control actions. Suzhou also has the strongest influence; it is known from the data that Nanjing and Hangzhou also play an important role in the network. This suggests that capital cities, due to their inherent advantages, have a stronger driving and radiating ability than non-provincial capital cities and have a deeper understanding of the concept of cooperation. It is worth noting that Hefei, the administrative center of Anhui Province, lags behind some prefecture-level cities in terms of regional influence, such as Ma’anshan and Changzhou. One possible reason for this could be Hefei’s geographical location on the periphery of the 27 central cities. Additionally, the presence of the closely knit Nanjing metropolitan area in the surrounding vicinity may have contributed to the limited realization of Hefei’s potential as a provincial capital. These hold significant implications for the integrated water pollution prevention and control in the YRD regions. On the one hand, by learning from the successful experiences of core cities in water pollution control, other cities can better fulfill their roles and responsibilities. On the other hand, policymakers and decision-makers should prioritize the participation and leadership of core cities, supporting and enhancing their capacity for water pollution prevention and control. This will drive the integration and coordinated development of water environmental protection across the entire YRD region.

Finally, the study finds that governments that are closer to neighboring cities or closer to central cities are more likely to establish cooperation. This finding is consistent with existing research that suggests the existence of spatial correlation effects in regional pollution control. It is consistent with the notion that cities in closer proximity are more likely to cooperate [17,34,36]. On the one hand, governments choose to cooperate with neighboring cities to expand the scope of governance, which can reduce social costs and improve the economic benefits of water pollution control. On the other hand, proximity between cities can help reduce communication costs, improve the convenience of resource acquisition, increase the likelihood of interaction between government officials, and thus promote the establishment of bilateral cooperative relationships.

Due to geographical reasons, Suzhou is more centrally located and participates in joint governance actions in the Suzhou–Wuxi–Changzhou area and the YRD integration demonstration area, making its position in the network superior to Shanghai. Although Hefei is the capital city, it plays a relatively weak role partly because it is located in the western part of the study area, which leads to fewer neighboring cities. Cities like Wenzhou, Tai’zhou, Yancheng, and Taizhou, which are further away from the central cities and have weaker agglomeration capabilities, become isolated entities in the cooperation network. In smaller regional areas, the proximity of geography has facilitated the establishment of cooperative mechanisms and comprehensive governance projects to effectively manage cross-border rivers and channels. This is evident in the case of the five cities of Suzhou, Wuxi, Changzhou, Jiaxing, and Huzhou, as well as the three cities of Hangzhou, Shaoxing, and Ningbo, and the three cities of Ningbo, Zhenjiang, and Yangzhou. These regions have successfully implemented collaborative efforts to address the challenges posed by cross-border water bodies. Therefore, the study finds geographical adjacency is a basic element in forming a regional water pollution control cooperation network. The closer the geographical locations of the two cities in bilateral cooperation, the greater the possibility of cooperation. This provides a reference for the prioritization of intergovernmental cooperation, highlighting that in situations with limited resources, the choice of collaboration should consider both geographical proximity and cooperative potential. This can assist policymakers and decision-makers in more targeted selection of partners and optimization of collaborative models, ultimately achieving more efficient coordinated governance of water pollution.

Our study illustrates the necessity of combining SNA and GIS to reveal the network structure of intergovernmental cooperation in pollution prevention. This method not only clearly visualizes the structure of inter-governmental cooperation but also enriches the perspective of social network analysis and provides more dimensions for revealing inter-regional cooperative relationships. However, one limitation is that the use of the method presupposes access to a large amount of basic data, especially geographic data. This may pose challenges for some developing nations and countries with limited data openness. In further research, the combination of SNA and GIS can be used to enrich the data sources, analytical perspectives, and visualization methods of network research from more dimensions and to reveal the formation mechanisms of regional cooperation. For instance, we can unveil the interplay between spatial correlations and social relationships by integrating multidimensional geographic data such as distance and land use patterns. This can help us better understand phenomena related to governmental collaboration. We can also analyze the spatial patterns of regional cooperation using cluster analysis and spatial association rule mining in GIS. This allows us to uncover the structure and patterns of the collaborative network in geospatial space. This combination can provide richer means of data analysis and visualization, provide more accurate and comprehensive information for researchers and decision-makers, and help in-depth research and practical application of regional cooperation mechanisms.