In order to objectively evaluate the operation effect of the Sustainable Supply Chain (SSC) collaborative governance of Water Diversion Projects (WDPs), this paper, from the perspectives of SSC and collaborative governance, and according to the existing modes of SSC collaborative governance of WDPs, constructs the collaborative management mechanism of related subjects of WDPs, and then constructs the evaluation model of the collaborative governance capacity of WDPs by using the DEMATEL (Decision-Making Trial and Evaluation Laboratory)-ANP (Analytic Network Process) method. Specifically, in this paper, the indexes at various levels are selected through literature mining and text analysis, and the mutual influence degrees among index are determined with the help of DEMATEL, so that the causality diagram can be drawn. On this basis, we obtain the global weight and the mixed weight of each index by using the ANP method and SD (Super Decisions) software, thus determining the relative importance of each index. The study finds that with regard to the SSC collaborative governance capability of WDPs, the highest requirement is made on Information Collaboration Capability (ICC) which also provides the foundational guarantee. The proportion carried by Subject Participation and Collaboration Capability (SPCC) is the lowest, however, its mixed weight is 1.9077, second only to ICC. Both Water Quantity Collaboration Capability (WQCC) and Water Quality Assurance Coordination Capacity (WQACC) account for a high percentage, which is consistent with the actual situation. The study provides a capability evaluation index system for the SSC collaborative governance of WDPs, enriches the theory of SSC collaborative governance of WDPs, and provides decision-making support for the operation and management of WDPs.

  • The evaluation model of large-scale project collaborative governance is provided.

  • It provides a solution to judge the relationship between model indicators, which makes up for the shortcomings of indicator independence.

  • It provides an idea for the evaluation of collaborative governance capacity of water transfer projects.

China is facing the growing problem of uneven spatial distribution of water resources which can be effectively alleviated through building Water Diversion Projects (WDPs). These projects continuously provide clean water for the receiving areas, which injects a strong momentum into the sustainable development of social economy of these areas. According to the Ministry of Water Resources, as of May 2021, nearly 6 years after the middle route of the South-to-North WDP went into operation, it has transferred over 40 billion cubic meters of water, and has directly benefited over 120 million people in four provinces/municipalities along the route. However, we have noticed during field visits and surveys that these trans-regional WDPs are faced with many challenges in water resource regulation and governance while changing the regional water shortage situation and solving the contradiction between supply and demand of water resources, and these challenges are given as follows: (1) WDPs are usually huge projects involving numerous stakeholders, which makes it difficult to coordinate the interests. (2) The current operation and management mechanism cannot meet the need for the sound operation of the projects. In view of these problems, a large number of scholars have introduced the idea of supply chain management into the WDPs. Among these scholars, the team at Hohai University led by Wang Huimin has achieved fruitful results as the first in China to demonstrate the feasibility of applying the theory and methods of supply chain management to the operation and management of the South-to-North WDP (Wang et al., 2004). Then, Li Zhanguo et al., when studying the South-to-North WDP, identified the environmental benefits and social benefits contained in the project, constructed the structural model of the Sustainable Supply Chain (SSC) in the WDP with the SSC idea, and verified its rationality (Li et al., 2021).

Further studies reveal that WDPs contain many governance subjects as usually so many stakeholders are involved in WDPS. Cooperation and collaboration among subjects are needed in the operation and management of WDPs. Scholars often adopt the theory and the idea of collaborative governance when dealing with similar topics. For example, Ge Liting constructed the water pollution prevention and control model of the Luanhe-Tianjin WDP from the perspective of collaborative governance (Ge, 2018). Meanwhile, targeting the organizational obstacles faced by major engineering technology innovations, Feng Jing et al. put forward a relevant collaborative governance framework, and verified the significance of collaborative governance for large-scale engineering technology innovations by taking the island tunnel project of Hong Kong-Zhuhai-Macao Bridge as the research object (Feng et al., 2020). Then, Ma Xiaodong applied the idea of collaborative governance to disaster emergency management from the perspective of government, market and social cooperation, which provided an important reference for emergency management in China (Ma, 2021). To sum up, the theory and idea of collaborative governance can provide an important approach to solving the management problems of major projects. Since it was systematically expounded by Herman Haken, the synergetic theory has been applied to multiple fields such as natural science and social science (Chen et al., 2018). However, at present, the definition of collaborative governance is rather vague, and studies on the evaluation of collaborative governance capacity are scarce. In this paper, we attempt to build a government-dominated WDP SSC collaborative governance system with the participation of companies and the public, and put forward an evaluation model of collaborative governance capability.

At present, there are many established methods for capability evaluation, such as Analytic Hierarchy Process (AHP), Analytic Network Process (ANP), Balanced Score Card (BSC) and Decision-Making Trial and Evaluation Laboratory (DEMATEL). According to the literature, DEMATEL is considered as an effective method for the identification of cause–effect chain components, which has the advantage of solving multi-factor influence and complex interweaving problems. On the basis of investigation and research, experts repeatedly judge and demonstrate, and analyze and calculate the matrix, so as to identify the influence relationship between various factors. Then, the key factors are found through the interdependence between factors and the visual structure model (Si et al., 2018). Scholars have applied these established methods to different objects. For example, when evaluating the capability of lean production, Soroush Avakh Darestani et al. identified the internal relationships among elements through DEMATEL survey and based on BSC, and analyzed the data by using Super Decision and VisualPromethee (Darestani & Shamami, 2019); Ehsan Pourjavad et al. used the DEMATEL and Mamdani fuzzy inference system model to evaluate SSC Management (SSCM) (Pourjavad & Shahin, 2018). On the other hand, some other scholars pointed out the shortcomings of the relevant methods when using them. When evaluating the risks of overseas railway projects, Wang Jinglue found that AHP could only simulate static and one-way decision-making units (Wang et al., 2018). However, in practice, decision-making units are interconnected, as are different decision-making levels, forming a network structure. Therefore, Saaty improved the AHP method and proposed the ANP method to solve similar problems (Saaty & Saaty, 1996). For example, Liu et al. constructed an ANP model to analyze the factor indexes and weights affecting the risk to military personnel by considering the internal dependency of the index network (Liu & Li 2021). Tian et al. used the ANP method to classify the risk levels of the crisis warning indexes (Tian et al., 2021). It is thus evident that the ANP method has strong applicability in dealing with the interrelated problems between different decision units and between decision-making levels. At the same time, the causality between decision-making units also needs to be considered. And the DEMATEL method can be used in describing the multi-dimensional network structure and the complex causality between networks. Therefore, we attempt to combine ANP and DEMATEL in this study and creatively apply them to WDPs to build an evaluation model of the SSC collaborative governance capability in WDPs. Specifically, by using the DEMATEL method, we can quantify the mutual influence relationships and influence degrees between the indexes, fulfill the correlation analysis and importance comparison between the evaluation indexes of collaborative governance capacity, and obtain the weights based on the network layer correlation between indexes by using ANP, thus making the weights more reasonable.

In summary, in the literature at home and abroad, in-depth research has been made on the idea, theory and application of collaborative management, which has good reference value. Relevant scholars’ research on capacity evaluation methods also tends to become mature, which constitutes an important reference for the evaluation of the capacity of SSC collaborative governance of WDPs. However, we believe that there is still room for further improvement. One problem identified by us is that the definition of collaborative governance is relatively vague, and the studies on the evaluation of collaborative governance capacity are scarce. Moreover, availing ourselves of the advantage of ANP in dealing with the problem concerning the interconnection between decision units and between different decision-making levels, we innovatively combine it with the DEMATEL method, and take into account the causal relationship between decision-making units as well. And for the first time, we applied the combined method to WDPs to solve the problem concerning collaborative governance evaluation of SSC of WDPs, with the aim of providing reference and theoretical support for the operation and management practice of large WDPs and enriching the theory related to collaborative governance of WDPs.

In this study, in response to the problems concerning the collaborative governance of SSC of WDPs, we put forward the evaluation index system of collaborative governance, and quantify the influence relationship among Water Quantity Coordination Capacity (WQCC), Water Quality Assurance Coordination Capacity (WQACC), Subject Participation and Collaboration Capability (SPCC) and Information Collaboration Capability (ICC) while considering practical conditions. And then, the relative importance of indexes at all levels is solved by using ANP. The thesis is mainly comprised of the following parts:

  • (1)

    Based on the importance of quantity, quality, participants and information and the necessity of governance system evaluation, we analyzed the WDP SSC collaborative governance model of SSC of WDPs, including governance status and subject collaborative governance model.

  • (2)

    We selected the secondary indexes contained in each primary index through literature mining and text analysis.

  • (3)

    Based on the cognition that there are influencing relationships between various indexes, we quantified the mutual influence degree of each primary index using DEMATEL, gave the mutual influence relationship of the secondary indexes according to primary indexes, and then calculated the global weights and the mixed weights of the indexes at each level by using ANP.

Current situation of SSC governance in WDPs

As national strategic projects, the construction and optimization of WDPs need to take both the environmental and the social benefits of water-receiving areas into account. After reviewing existing studies by other scholars, we noticed that quite a few studies had applied the idea of supply chain to WDPs. In view of WDPs’ multiple goals-economic benefits, environmental benefits and social benefits, based on the existing studies of other scholars, we attempt to apply the SSC idea to a WDP in this study. The WDP is regarded as a complex SSC system concerning multiple basins, multiple water sources, multiple subjects and multiple objectives and emphasizing economic benefits, environmental benefits and social benefits at the same time (see Figure 1; Li et al., 2021).
Fig. 1

Sustainable supply chain structure chart of a Water Diversion Project.

Fig. 1

Sustainable supply chain structure chart of a Water Diversion Project.

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Collaborative governance model of SSC subjects of WDPs

The SSC of WDPs is a large-scale and complex water resource system with multiple basins, multiple water sources, multiple subjects and multiple objectives. It has intensive intersections with the society and has the feature of quasi-public goods. Classified and sorted out by participating organization and individual, it mainly consists of government functional departments, companies and the public. The collaboration mechanism among the participants is shown in Figure 2 (Li et al., 2021).
Fig. 2

Government–company–public tripartite coordination mechanism.

Fig. 2

Government–company–public tripartite coordination mechanism.

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Index selection

When evaluating and studying the relevant capability, according to different objectives and standards we hold for the evaluation of target subjects, we can divide the performance into result performance and behavior performance. Result performance takes management results as the criteria for evaluating performance, while behavior performance takes management behaviors. In the actual capability evaluation systems, behaviors and results are closely tied and are not readily separated (Li et al., 2016). Therefore, we include both result performance and behavior performance when building the capability evaluation index system to ensure the integrity of the performance evaluation system of relevant subjects.

Collaborative governance of WDPs covers many aspects, levels and perspectives, and it is difficult to measure its capability with one single index. The implementation and construction of WDPs take a long time to plan and design. WDPs bear upon the overall strategic blueprint of the country, solve the problem of water shortage in many areas and ensure the sustainable development of regional economy. At present, scholars mainly hold two viewpoints when studying the key to the success or failure of WDPs: (1) Water quality determinism, that is, water quality is the key to the success or failure of WDPs (Meng & Zhao, 2007; Zhang, 2009), a viewpoint which has been followed and supported by the press (Wang et al., 2012; Zhang, 2014). These scholars mainly take the South-to-North Water Diversion as the research object to support their views. (2) Water quantity determinism, that is, water quantity is the key to the success or failure of WDPs (Chen, 2016), and sufficient water quantity can accelerate the adjustment of industrial structure (Mu et al., 2007). Therefore, water quantity and water quality are the factors that need to be focused on and studied in this paper. At the same time, this study is carried out from the perspective of SSC. However, when studying relevant literature, we noticed that while there were only a limited number of studies specially carried out on SSC collaboration, many existing achievements of research on supply chain collaboration could provide us with important references. For instance, many of the above scholars believe that the flow of information plays a very important role in supply chains (Zhou & Zhao, 2008; Zhang & Peng, 2016; Zhang & Shao, 2019), and maintaining efficient information transmission is an important prerequisite for supply chain coordination. For example, Zhang Qi et al. studied the information collaboration in the context of smart campus, and believed that evaluation of information subjects, namely the participants, the receiving subjects and information subject evaluation, could have an important impact on the synergy (Zhang & Shao, 2019). To sum up, this paper intends to construct the index system from the aspects such as WQCC, WQACC, SPCC and ICC. The secondary indexes are as follows:

  • (1)Water Quantity Collaboration Capability

Adequate water quantity is the prerequisite for the existence of WDPs. Therefore, in this study, we take water quantity as the primary consideration when studying collaboration capability. WDPs can effectively alleviate the contradiction between supply and demand of water resources among regions and allocate water resources reasonably. It can also improve the water supply conditions in the water-receiving areas and promote the coordinated development of various regions along the route (Peng, 2018). Meanwhile, WDPs can improve the investment environment, inject a strong momentum into the economic development of the water-receiving areas and bring remarkable economic benefits (Yang, 2007). Among all the WDPs, the South-to-North WDP has transferred 40 billion cubic meters of water to the north. If a calculation is made based on the standard that an average not 57.2 cubic meters of water is required for 10,000 yuan of GDP, we can find that the South-to-North WDP has provided high-quality resource support to nearly 7 trillion yuan of GDP growth in northern China (Yue & Hu, 2021). Moreover, in order to make WDPs operate effectively and continuously in the long run, the proportion of water consumption in different industries also needs to be considered. Therefore, we tend to adopt indexes such as Water Consumption per 10,000 yuan of GDP (Hu & Tang, 2017), Water Transfer Compliance Rate, Proportion of Industrial Water Consumption and Water Transfer Efficiency.

  • (2)Water Quality Assurance Coordination Capacity

WDPs lose their meaning in the absence of good water quality. When studying the South-to-North WDP, many scholars believe that water quality is the key to the success or failure of the projects (Chen, 2016). Many WDPs can not only meet the needs of production and life and generate economic benefits, but also have a great impact on the watersheds and regional ecological environment along the route. The water quality of water-receiving areas can also be greatly improved. For example, after the water transferred from the middle route of the South-to-North WDP entered Beijing, the hardness of tap water in Beijing was reduced from 380 mg to 120–130 mg/L. The improvement of water quality plays a great role in people's health and social stability (Peng, 2018). Water quality improvement can also enhance people's sense of happiness although it is certainly not the sole factor for the enhancement. Given the hysteretic nature of environmental benefits, behavioral performance is adopted more in the evaluation of WQACC. Based on the research of Hu Yiyuan (Hu & Tang, 2017) and Peng Bo (Peng et al., 2017), we selected Ecological Protection Investment, Water Quality Compliance Rate, Ecological Construction Investment, Enterprise Sewage Treatment Investment and Sewage Discharge as the evaluation indexes.

  • (3)Subject Participation and Collaboration Capability

The operation and management of WDPs involves many stakeholders, including relevant governmental departments, companies and the public. These stakeholders participate in the operation and management of a WDP to various extents according to their different appeals. WDPs improve the per capita water resources in the water-receiving areas by adjusting the spatial distribution of water resources. While meeting people's need for drinking water, they ensure local industrial and domestic water consumption, alleviate the competition for water among regions, and reduce the work difficulty of government personnel to a great extent. Meanwhile, WDPs have improved the water quality in water-receiving areas and therefore have been well received by the residents in those areas. The improvement of water quality has improved people's health, and enhanced people's sense of happiness water use satisfaction (Wei & Wang, 2019). Based on the research of Zhang (2016) and Wang (2006), we selected Satisfaction on Government Personnel, Water Use Satisfaction, Public Happiness Index, Villagers’ Collective Events and Public Participation in this paper as indexes to evaluate the subject participation capability of the collaborative governance of WDPs.

  • (4)Information Collaboration Capability

WDPs are generally long-distance and large-scale systematic projects. Their operation and management involve the balance of water use between water source areas and water-receiving areas, between upper reaches and lower reaches, and between left banks and right banks, and involve multi-objective resource management (Gao et al., 2018). Meanwhile, they involve complicated interest relationship and have a high requirement for information coordination along the route. Modern information devices are needed for collecting and sharing relevant hydrological information, and emergencies must be dealt with and handled rapidly. Based on the study carried out by Wang (2013), and in view of the characteristics of WDPs, we selected indexes such as Comprehensiveness of Hydrological Information Collection, Timeliness of Information Transmission, Degree of Information Sharing and Speed of Emergency Coordination and Response in this study. Among the indexes, information storage is a must-do every day. The integrity of index collection represents the comprehensiveness of hydrological information collection. The degree of smoothness of work handover represents the timeliness of information transmission. On the premise that there is an information sharing platform, the frequency and quantity of relevant personnel participating in information sharing is represented by the Degree of Information Sharing. And the efficiency of dealing with emergencies reflects the Speed of Emergency Coordination and Response.

According to the above principles and basis for index construction, we build the evaluation index system of collaborative governance capability as shown in Table 1.

Table 1

Evaluation index table for SSC collaborative governance capability of WDPs.

Primary indexesSecondary indexes
Water Quantity Coordination Capacity (WQCC)
S1 
A1: Water consumption per 10,000 yuan GDP 
A2: Compliance rate of water regulation 
A3: Proportion of industrial water consumption 
A4: Water transfer efficiency 
Water Quality Assurance Coordination Capacity (WQACC)
S2 
B1: Ecological protection investment 
B2: Water quality compliance rate 
B3: Ecological construction investment 
B4: Enterprise sewage treatment investment 
B5: Sewage discharge 
Subject Participation and Collaboration Capability (SPCC)
S3 
C1: Satisfaction on government personnel 
C2: Water use satisfaction 
C3: Public happiness index 
C4: Villagers’ collective events 
C5: Public participation in the project 
Information Collaboration Capability (ICC)
S4 
D1: Comprehensive collection of hydrological information 
D2: Timeliness of information transmission 
D3: Degree of information sharing 
D4: Speed of emergency coordination and response 
Primary indexesSecondary indexes
Water Quantity Coordination Capacity (WQCC)
S1 
A1: Water consumption per 10,000 yuan GDP 
A2: Compliance rate of water regulation 
A3: Proportion of industrial water consumption 
A4: Water transfer efficiency 
Water Quality Assurance Coordination Capacity (WQACC)
S2 
B1: Ecological protection investment 
B2: Water quality compliance rate 
B3: Ecological construction investment 
B4: Enterprise sewage treatment investment 
B5: Sewage discharge 
Subject Participation and Collaboration Capability (SPCC)
S3 
C1: Satisfaction on government personnel 
C2: Water use satisfaction 
C3: Public happiness index 
C4: Villagers’ collective events 
C5: Public participation in the project 
Information Collaboration Capability (ICC)
S4 
D1: Comprehensive collection of hydrological information 
D2: Timeliness of information transmission 
D3: Degree of information sharing 
D4: Speed of emergency coordination and response 

The above indexes used in this study in evaluating SSC collaborative governance capability of WDPs are comprised of four primary indexes, including WQCC, WQACC, SPCC and ICC and 18 secondary indexes. Although the indexes exist independently, they interact and affect one another.

Construction of the evaluation model

In this paper, we construct a comprehensive evaluation model based on DEMATEL-ANP, as shown in Figure 3, to evaluate and analyze the SSC collaborative governance capability of WDPs. The DEMATEL method is adopted to obtain the Comprehensive Index Influence Matrix T. Then, the threshold is set according to experts’ suggestion. After removing the connections with small correlations, we determine the judgment matrix in ANP. The mixed weights of indexes are determined by using both DEMATEL and ANP. Finally, the indexes are sorted according to the mixed weights, and then the performance analysis is carried out.
Fig. 3

Performance evaluation model of collaborative management.

Fig. 3

Performance evaluation model of collaborative management.

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We further analyze the performance evaluation model. DEMATEL, as a decision-making method, is mainly used in judging the degrees of influence and the degrees of being influenced among factors, and analyzing the logical relationship internally among indexes. And through calculating centrality degrees and causality degrees, DEMATEL helps to construct the causality diagram to clarify the network connection of each capability index and its position and role in the whole collaborative management capability, but it cannot be used in calculating the corresponding weights of indexes. However, we can carry out the index importance calculation by using the ANP method to establish a network structure. As opposed to AHP, ANP takes into account the feature that factors are not independent of one another and solves the limitation incurred as AHP can only consider independent factors. The network structure of the evaluation indexes of the SSC collaboration capacity of WDPs constructed by using this method takes into account the influence relationship among the indexes, therefore, it is necessary to study what kind of influence relationships exist between the indexes before constructing the network. By using the DEMATEL method, we can well analyze the logical relationships among indexes, so as to get the influence relationships among indexes, and provide a basis for constructing the ANP network for the performance evaluation of SSC collaboration management of WDPs.

Using DEMATEL to determine the causality between indexes

DEMATEL is a systematic analysis method using graph theory and matrix tools to solve complex system problems. Through the relationships of influencing and being influenced between various elements and the direct influence matrix, the cause degree and the centrality degree can be determined, and therefore, the cause and effect diagram of primary indexes are plotted, which lays a foundation for ANP to determine the relationship among indexes at various levels.
  • (1)
    Calculate direct influence matrix M. WDP experts, managerial personnel along the South-to-North WDP route and experts in related fields use the Delphi method to judge the interaction relationship between indexes and determine the scores. By collecting the scores given by experts and using the arithmetic mean method on the score results, we obtain the direct relationship matrix M as shown below (Duan et al., 2019).
    formula
  • (2)
    Comprehensive influence matrix T. Each element in the direct influence matrix M is multiplied by the minimum value λ which is the inverse of the sum of the elements in each row of the matrix, and the normalized influence matrix Ms is obtained. The self-multiplication of the normalized influence matrix represents the indirect influence added between factors, and we add all the indirect influences together to obtain the comprehensive influence matrix T. In this process, the calculation is mainly carried out by using MATLAB software to write DEMATEL-related programs, and the formulas are shown in the following, i.e.
    formula
    (1)
    formula
    (2)
    formula
    (3)
    formula
    formula
  • (3)
    Cause and effect diagram. We set the threshold as 1.4200 according to experts’ suggestions, then remove the correlations below the threshold and turn the figures into zeros, thereby determining the final comprehensive influence matrix T′. The final comprehensive influence matrix is analyzed. The sum of the elements in each row of the matrix represents the influence degree, which indicates the degree of the direct or indirect influence of the factor on other factors, and the sum of the elements in each column of the matrix represents the degree of being influenced, which indicates the factor's degree of being influenced by other factors directly or indirectly. The sum of degree of influence and the degree of being influenced represents the centrality degree, which indicates the role played by this element in the system. The differences we get by subtracting the centrality degree from the influence degree are the cause degree, in which the differences greater than zero are the cause factors, while the differences less than zero are the result factors. The results are shown in Table 2. We draw the Cartesian coordinate causality diagram of the cause degree-centrality degree of the primary indexes with the help of EXCEL software, as shown in Figure 4, to reflect the causality.
    formula
Fig. 4

Cause and effect diagram.

Fig. 4

Cause and effect diagram.

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Table 2

Cause degree and centrality degree table.

S1S2S3S4Influence degreeCentrality degreeCause degree
S1 0.0000 1.4452 1.5197 1.7516 4.7165 9.5488 −0.1158 
S2 1.4431 0.0000 0.0000 1.6616 3.1047 7.6078 −1.3984 
S3 1.8598 1.6245 1.4295 1.9626 6.8764 9.8256 3.9272 
S4 1.5294 1.4334 0.0000 1.4545 4.4173 11.2476 −2.4130 
Degree of being influenced 4.8323 4.5031 2.9492 6.8303    
S1S2S3S4Influence degreeCentrality degreeCause degree
S1 0.0000 1.4452 1.5197 1.7516 4.7165 9.5488 −0.1158 
S2 1.4431 0.0000 0.0000 1.6616 3.1047 7.6078 −1.3984 
S3 1.8598 1.6245 1.4295 1.9626 6.8764 9.8256 3.9272 
S4 1.5294 1.4334 0.0000 1.4545 4.4173 11.2476 −2.4130 
Degree of being influenced 4.8323 4.5031 2.9492 6.8303    

Determine weights of the index system based on ANP and calculate mixed weights

Based on the cause and effect diagram, the weight of each index is determined by using ANP with the help of the SD software.
  • (1)

    Build the ANP network. According to Figure 4 – Cause and effect diagram and Table 1, we draw the ANP network and ANP model of the evaluation system of SSC collaborative governance capacity of WDPs, as shown in Figures 5 and 6. The ANP network diagram in Figure 5 includes target layer and network layer. And the internal relationships in the network layer are determined by the causal relationship between the primary indexes. In addition, Figure 6 is drawn with the aid of SD on the basis of the final comprehensive influence matrix T′.

  • (2)
    Establish the judgment matrix and form the hypermatrix Wij. This study contains 4 primary indexes and 18 secondary indexes. According to the final influence matrix and causality diagram, we set the pairwise judgment matrix for primary indexes under the upper left sub-criterion and the pairwise judgment matrix for secondary indexes under the upper left sub-criterion for indexes with connections. The results of the pairwise judgment matrix questionnaire filled in by water transfer experts are entered into SD software and the consistency is verified. When the consistency coefficient is below 0.1, we can accept the judgment matrix. Thus, the normalized eigenvectors wi1(jk), wi2(jk), … , win(jk) are found, and the hypermatrix Wij is obtained.
    formula
  • (3)

    Construct the limit hypermatrix. After entering the above data, we can view the unweighted hypermatrix, the weighted hypermatrix and the limit hypermatrix in the SD software (There is not enough space here to show the matrices. Please contact the author if you need them).

  • (4)

    Determine global weights. The global weights of the indexes can be generated by using the [Prioritie] command in the SD software, as shown in Table 3.

Fig. 5

ANP network for performance evaluation of SSC collaborative management of WDPs.

Fig. 5

ANP network for performance evaluation of SSC collaborative management of WDPs.

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Fig. 6

ANP model.

Table 3

Global weight table.

Primary indexesWeight of primary indexesSecondary indexesLocal weight of secondary indexesGlobal weight of secondary indexes
Water Quantity Collaboration Capability (WQCC)
S1 
0.286779 A1: Water consumption per 10,000 yuan GDP 0.21300 0.061085 
A2: Compliance rate of water regulation 0.21518 0.061710 
A3: Proportion of industrial water consumption 0.23624 0.067748 
A4: Water transfer efficiency 0.33558 0.096236 
Water Quality Assurance Coordination Capacity (WQACC)
S2 
0.127859 B1: Ecological protection investment 0.32354 0.041367 
B2: Water quality compliance rate 0.16081 0.020561 
B3: Ecological construction investment 0.33895 0.043338 
B4: Enterprise sewage treatment investment 0.11471 0.014667 
B5: Sewage discharge 0.06199 0.007926 
Subject Participation and Collaboration Capability (SPCC)
S3 
0.038097 C1: Satisfaction on government personnel 0.15403 0.005868 
C2: Water use satisfaction 0.11544 0.004398 
C3: Public happiness index 0.29790 0.011349 
C4: Villagers’ collective events 0.28233 0.010756 
C5: Public participation in the project 0.15030 0.005726 
Information Collaboration Capability (ICC)
S4 
0.547267 D1: Comprehensive collection of hydrological information 0.27929 0.152844 
D2: Timeliness of information transmission 0.14842 0.081224 
D3: Degree of information sharing 0.29336 0.160544 
D4: Emergency coordination and response speed 0.27894 0.152655 
Primary indexesWeight of primary indexesSecondary indexesLocal weight of secondary indexesGlobal weight of secondary indexes
Water Quantity Collaboration Capability (WQCC)
S1 
0.286779 A1: Water consumption per 10,000 yuan GDP 0.21300 0.061085 
A2: Compliance rate of water regulation 0.21518 0.061710 
A3: Proportion of industrial water consumption 0.23624 0.067748 
A4: Water transfer efficiency 0.33558 0.096236 
Water Quality Assurance Coordination Capacity (WQACC)
S2 
0.127859 B1: Ecological protection investment 0.32354 0.041367 
B2: Water quality compliance rate 0.16081 0.020561 
B3: Ecological construction investment 0.33895 0.043338 
B4: Enterprise sewage treatment investment 0.11471 0.014667 
B5: Sewage discharge 0.06199 0.007926 
Subject Participation and Collaboration Capability (SPCC)
S3 
0.038097 C1: Satisfaction on government personnel 0.15403 0.005868 
C2: Water use satisfaction 0.11544 0.004398 
C3: Public happiness index 0.29790 0.011349 
C4: Villagers’ collective events 0.28233 0.010756 
C5: Public participation in the project 0.15030 0.005726 
Information Collaboration Capability (ICC)
S4 
0.547267 D1: Comprehensive collection of hydrological information 0.27929 0.152844 
D2: Timeliness of information transmission 0.14842 0.081224 
D3: Degree of information sharing 0.29336 0.160544 
D4: Emergency coordination and response speed 0.27894 0.152655 

It can be seen from Table 3 that, among all the indexes covered by Water Quantity Coordination Capacity (S1), Water Transfer Efficiency (A4) accounts for the highest proportion. That's because water transfer efficiency is the premise to meet Industrial Water Consumption (A3) and ensure the timely Compliance of Water Regulation (A2). And the local weight results are in line with the practical conditions; among the indexes covered by WQACC (S2), Ecological Construction Investment (B3), for example, the construction of shelter forest, represents the initial investment of Water Quality Assurance and lays the foundation for subsequent Ecological Protection Investment (B1). Water Quality Compliance rate (B2) is the direct index of Water Quality Assurance Coordination, while Enterprise Sewage Treatment Investment (B4) and Sewage Discharge (B5) are the indirect indexes of Water Quality Assurance. The weight calculations are in line with the actual situation; among the indexes covered by Subject Participation and Coordination Capability (S3), Public Happiness Index (C3) and Villagers’ Collective Events (C4) reflect the social contribution made by WDPs, which is consistent with the public welfare nature of WDPs. Public Participation in the Project (C5) is an intuitive representation of Subject Participation and Coordination Capability. Satisfaction on Government Personnel (C1) and Water Use Satisfaction (C2) are the indirect embodiment of the collaborative governance effect, and, among the indexes covered by Information Coordination Capability (S4), the Degree of Information Sharing (D3) and Comprehensiveness of Hydrological Information Collection (D1) provide quality assurance for the effects of Information Transmission Timeliness (D2) and Emergency Coordination and Response Speed (D4). And the weight calculations are in line with practical conditions.

  • (5)

    Calculate the mixed weights

The mixed weight reflects the weight and influence degree of each element, and the rankings of elements can be obtained by sorting them. The mixed weights are obtained and sorted according to Formula (4), as shown in Table 4.
formula
(4)
Table 4

Ranking of mixed weights and global weights of primary indexes.

Primary indexMixed weightGlobal weight
WQACC-S2 1.6484 0.127859 
WQCC-S1 1.8879 0.286779 
SPCC-S3 1.9077 0.038097 
ICC-S4 2.0189 0.547267 
Primary indexMixed weightGlobal weight
WQACC-S2 1.6484 0.127859 
WQCC-S1 1.8879 0.286779 
SPCC-S3 1.9077 0.038097 
ICC-S4 2.0189 0.547267 

Conclusion

  • (1)

    As the global weight of ICC accounts for the highest proportion, it is evident that for the SSC collaborative governance capability of WDPs, the highest requirement is made on ICC. Compared with traditional supply chain governance capability, SSC collaborative governance capability focuses more on coordination capability and takes information coordination as the basic guarantee capability.

  • (2)

    The global weight of SPCC accounts for the lowest proportion, but its mixed weight is 1.9077, second only to the ICC which has the highest mixed weight. This indicates that although SPCC has less impact on the collaborative governance ability of SSC of WDPs, it has a relatively big impact on other primary indexes, such as WQACC, WQCC and ICC.

  • (3)

    The global weight of WQCC accounts for a relatively high proportion which is close to that of WQACC, and this is a manifestation of the high requirement made on WQCC by SSC coordination governance capacity of WDPs. The requirement made on WQACC is just lower than that on WQCC, and the two complement each other, which is consistent with the actual situation and enhances the reliability of the results.

Suggestions

(1) In the process of SSC coordination governance in WDPs, in order to fully reflect that ICC is the basic guarantee for WQACC, WQCC and SPCC, it is necessary for feedback on indexes such as the comprehensiveness of hydrological information collection, the timeliness of information transmission, the degree of information sharing and the speed of emergency coordination and response to be given regularly.

(2) While considering Water Quantity, Water Quality Assurance and Information Coordination, we need to pay special attention to the coordination of participants as well. We can tell from Conclusion (2) that although SPCC is not the key point of SSC collaborative governance of WDPs, it can affect Water Quantity, Water Quality Assurance and Information Collaboration Capability and can be internalized as a main influence factor.

(3) We can tell from Conclusion (3) that the coordinated governance of SSC of WDPs is inseparable from the generally recognized two factors which are Water Quantity and Water Quality Assurance. In addition to Information Coordination Capability which serves as the basic guarantee part and Participant Coordination which serves as the internalized influence part, the supporting part which covers two factors, i.e. water quantity and water quality assurance are also included in coordinated governance of SSC of WDPs. The secondary indexes of the two factors are the internal representations supporting the SSC collaborative governance of WDPs, which also should be emphasized.

Therefore, the sequence of various capabilities of SSC collaborative governance capability in WDPs by the degree of emphasis that should be put on them is: ICC–SPCC–WQCC–WQACC.

First of all, it is worth noting that when selecting the indexes in this paper, we focus more on the uniqueness of the research objects and are somewhat subjective, therefore in future research, we will continue to improve the index system so that it can follow principles such as being objective-oriented, being complete, being operable, being independent, being salient and being dynamic, namely the ‘O-C-W-I-S-D’ principle.

  • (1)

    In the future, we will continue to improve the index system in light of the management scope of China South-to-North Water Diversion Corporation Limited. Especially, it can be further refined from the perspectives of ecological compensation, water pollution control and water regulation.

  • (2)

    We will further collect relative data to conduct empirical tests for the index system in tandem with China South-to-North Water Diversion Corporation Limited and put forward a more feasible index system, so as to provide a relevant reference for the management of South-to-North Water Diversion and give play to the practical value of the index system.

This study was supported financially by the National Natural Science Foundation of China (grant number: 71974056) and by the Science and Technology Innovation Talent Support Plan of Colleges and Universities in Henan Province (grant number: 2021-CX-005).

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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