This study investigates the green development of the South-to-North Water Diversion Project (SNWDP) by exploring the synergistic reduction of water pollution and carbon emissions. Firstly, a differential game model is constructed to reduce pollution and carbon in the water supply chain. Secondly, the emission reduction strategies of water source and receiving areas under centralized and decentralized decision models are compared and analyzed and a cost-sharing contract is designed to coordinate the supply chain. Finally, numerical analysis is used to compare and analyze the important parameters to draw conclusions. The results show that: (1) Collaboration between water source and receiving areas influences emission reduction efforts, with the highest achieved under the centralized model. (2) The cost-sharing contract improves efforts and addresses ‘free-riding’ in the decentralized model. (3) The SNWDP can achieve a win-win situation in terms of both environmental and economic benefits by promoting the synergy of pollution reduction and carbon reduction.

  • Established a model for synergistic management of low carbon and water pollution control in water supply chain.

  • Investigated the benefits of low carbon and water pollution control in the water supply chain under various decision models.

  • Designed a cost-sharing contract to improve synergies in the water supply chain.

  • Carbon and pollution reduction can improve the economic and environmental benefits of the SNWDP.

The distribution of water resources in China has long been characterized by the spatial feature of ‘abundance in the south and scarcity in the north’. To solve the problem of water shortage in the northern region, the Chinese government has constructed the South-to-North Water Diversion Project (SNWDP) since 2002, which is the world's largest water diversion project. As of December 2022, the project has been in full operation for 8 years, with a cumulative water transfer of 58.6 billion cubic meters. The project benefits more than 40 large and medium-sized cities and more than 280 counties and municipalities along the route, covering a population of more than 150 million (Xiong & Xuan 2022), and continuing to play a comprehensive benefit in society, economy, and ecology. Due to the strategic significance of the SNWDP, the water ecological and environmental problems in the operation of the project have been widely discussed (Zhang et al. 2022), and water quality is one of the most prominent ecological problems of the SNWDP (Gao & Wang 2008). At the same time, the carbon emission and energy problems generated by the SNWDP should not be ignored. For example, up to 2030, the Eastern Route project is expected to transfer 7.3 billion cubic meters of water, consume 1.35 billion kilowatt-hours (kWh) of energy, consume 4.6 million cubic meters (MCM) of virtual water, and emit 0.94 million metric tons of carbon dioxide-equivalent (MtCO2e) carbon (Chen et al. 2019a, 2019b). With China's ‘3060’ dual-carbon target, water environmental management has once again become a key area for reducing pollution and carbon.

The SNWDP is a long-distance, cross-basin, multi-principal super project. On the basis of guaranteeing the allocation and dispatch of water resources, the synergy of promoting pollution reduction and carbon reduction must take into account the rights and interests of all parties concerned. The public welfare attributes of the SNWDP and the complexity of cross-basin water transfer involve a wide range of interests, and all parties want to pay the least cost to obtain the greatest benefits (Feng 2016). According to the experience of trans-regional water pollution control, once the interests of multiple subjects and the division of regional jurisdiction are involved, it is easy to lead to problems such as ‘free-riding’ and ‘tragedy of the commons’ (Kahn et al. 2015). In the inter-basin water transfer project, due to the differences in economic development and resource endowment of the areas along the route, the role played in the water transfer process is also different. The government of the receiving areas only needs to pay less cost to obtain high-quality water resources (Gao et al. 2022), while the government of the water source areas needs to bear the increasing cost of water resources management. Differences in management options and interests between the two sides make cooperation difficult. Therefore, cooperation mechanisms need to be established in the area of water resource allocation and management to promote collaboration and cooperation between the SNWDP water source and receiving areas.

In summary, the synergistic reduction within the SNWDP faces two primary challenges: how to achieve a synergistic reduction of carbon and water pollution in the SNWDP, and how to foster synergistic cooperation between the water source areas and the water receiving areas. Given those, this paper aims to explore the decision-making behavior regarding water pollution and carbon emission control in water resource supply chains under various levels of collaboration. In the following sections, the corresponding literature is first reviewed in the Literature Review; the notation and assumptions for the SNWDP green supply chain model are defined in Modeling and Assumptions; centralized decision, decentralized decision, and decentralized decision models introducing cost-sharing are constructed and analyzed; the comparison and discussion of the analytical results are carried out in Comparative Analysis; numerical and sensitivity analyses are performed and compared in numerical analysis; finally, the findings and contributions of the study were summarized in the discussion.

In the current literature and practice, the theory of supply chain management provides a solid foundation for studying the coordination among multiple stakeholders in the SNWDP and developing appropriate operational mechanisms. Wang et al. (2004) introduced the idea of supply chain management in the SNWDP. Zhang et al. (2004) studied the application of the CAS paradigm in water resources supply chain management, analyzed the complex adaptability of supply chain systems, and provided a theoretical basis for the modeling and simulation of water resources supply chains. On this basis, scholars have conducted research on water inventory control (Wang et al. 2006; Tao 2008), water pricing (Qu et al. 2011), and sustainable supply chain management (Lu et al. 2021).

As the problems of climate change and water ecology deterioration intensify, how to achieve efficient utilization and protection of water resources and sustainable operation and management in inter-basin water transfer projects has become a hot research topic. Green supply chain management provides a theoretical approach to solving the above problems. Chen et al. (2019a, 2019b) studied the coordination and cooperation of the inter-basin water resources supply chain from the perspective of social welfare maximization. They innovatively introduced the water resources green level (Chen & Pei 2018), which is defined as the water quality, water resources efficiency and water environmental impacts of inter-basin water transfer projects with a comprehensive green level. Wang et al. (2022) established a joint pricing and inventory management framework for the green supply chain of water transfer projects on this basis, and analyzed the key influencing factors of green supply chain management of cross-basin water transfer projects.

The current research on the green supply chain of inter-basin water transfer projects mostly considers the green level from the perspective of the water environment or water quality. Fewer studies consider the low carbon level of the SNWDP from a dynamic perspective, with research focusing on green supply chain management. Such as Xia et al. (2018) and others established a differential game model for upstream and downstream joint emission reduction, and investigated the mechanism and effect of upstream and downstream joint emission reduction in the supply chain from a dynamic perspective. Xia et al. (2022) incorporated consumers' low carbon awareness and social preference into a carbon emission reduction differential game model under a long-term perspective and found that improving consumers' low carbon awareness is conducive to reducing carbon emissions. Zhang & Yu (2022) introduced the altruistic behaviors of supply chain members into their decision-making objective function and found that the altruistic behaviors of the supply chain can promote both parties to actively reduce emissions and obtain a higher level of social welfare. Di et al. (2023) proposed a multi-objective sustainable water allocation mechanism based on differential game constraints, which takes into account net carbon emissions and negative effluent values, thus promoting the coordinated and balanced development of economic, social, and ecological values in the watershed.

In short, the existing literature has rarely studied the abatement of water resources green supply chain from the perspective of carbon emission reduction. Furthermore, there is a lack of research considering the synergistic reduction of both carbon emissions and water pollution. Therefore, this paper takes the two-level supply chain composed of the SNWDP water source areas and receiving areas as the research object, considers the greenness of water resources and the level of efforts to reduce pollution and carbon emissions, and constructs the differential evolution process of the supply chain's low carbon level and water pollution control level. The findings of the study can provide a good basis for the SNWDP to be carried out and a decision reference for the subsequent green operation of the SNWDP.

Model overview

In a continuous time , the SNWDP water supply system is generalized as a two-tier supply chain system consisting of a water supplier (S) in the source areas and a water distributor (R) in the receiving areas. Under the unified guidance of the central government, the water resource supplier delivers high-quality southern water to the northern receiving areas through the water transfer project. The supplier assumes the dual responsibility of water quality assurance and low carbon assurance, and water pollution prevention requires strengthening source control to meet water quality standards while reducing carbon emissions and discharges during water abstraction, transfer, and transmission. Downstream distributors need to improve the efficiency of water resource utilization in terms of water conservation and energy saving and reduce carbon emissions from water resource utilization. At the same time, consumers in the receiving areas have green preferences and water quality preferences for water resources, and the greenness of water resources is determined by the low carbon level and the level of water pollution control, and the low carbon level and the water pollution control level of the SNWDP will have a positive effect on the market demand for water resources. The overall structure and operation mechanism of the water resources supply chain of the SNWDP is shown in Figure 1.
Figure 1

Synergistic mechanisms for pollution control and low carbon in the water supply chain of the SNWDP.

Figure 1

Synergistic mechanisms for pollution control and low carbon in the water supply chain of the SNWDP.

Close modal

Parameter assumptions and description

The low carbon efforts of water resource suppliers and distributors positively contribute to the low carbon level of water transfer projects, which is a dynamic process. Drawing on the role of green efforts in low carbon supply chains in influencing the low carbon level of green products (Liang & Futou 2020), the differential evolution process of the low carbon level can be expressed as follows:
formula
(1)
where denotes the low carbon level of the water transfer project at time t, and its practical meaning is the reduction of carbon dioxide emissions. In the initial state, , . and denote the level of low carbon efforts made by the water supplier and distributor at time t. and denote the degree of the influence of the water supplier's and the distributor's low carbon efforts on the low carbon level, respectively. denotes the decay coefficient of low carbon level over time in the absence of upstream and downstream low carbon efforts.
Carbon mitigation strategies can decrease water-related pollutants discharge (Su et al. 2019). Referring to the relationship equation between goodwill level and low carbon level in the goodwill model (Nerlove & Arrow 1962; Liang & Futou 2020), it is assumed that pollution control level have a similar linear relationship with low carbon level:
formula
(2)
where denotes the water pollution control level of the water transfer project at moment t, which can also reflect the level of water quality improvement. In the initial state , ; denotes the pollution reduction effort level of the water source zone district at the moment t; denotes the influence coefficient of the pollution reduction effort level of the water resource supplier on the water pollution control; denotes the coefficient of influence of the low carbon level on the level of pollution control; and denotes the attenuation coefficient of the water pollution control quantity.
The cost of pollution control and low carbon is related to the low carbon efforts and pollution control efforts of water suppliers and distributors, while there is a phenomenon of diminishing marginal efficiency. The green/low carbon cost function is assumed to be a quadratic form, following Bai et al. (2018) and Chen et al. (2020), the cost of low carbon efforts and the cost of pollution control efforts of supply chain members are assumed to be a quadratic form:
formula
(3)
where denotes the level of pollution control and low carbon efforts of suppliers and distributors, respectively; denotes the coefficient of pollution control and carbon reduction costs of supply chain members; and and indicate that the low carbon (pollution reduction) costs increase with the increase of the low carbon (pollution control) efforts, and that the increase tends to be on an upward trend.
According to relevant studies in the field of green supply chain (Ghosh & Shah 2015; Basiri & Heydari 2017), the level of greenness can positively affect the demand, demand function can be assumed to be:
formula
(4)
where denotes the water demand in the receiving water areas and denotes the initial demand without synergistic abatement; denotes the green preference coefficient of consumers in the receiving water areas; and denotes the water quality preference coefficient of consumers.

The market for water resources is cleared without considering the cost of water scarcity and storage. To simplify the calculations, the marginal profit of the supplier is set to and the marginal profit of the distributor is set to . The discount rates are the same and positive.

Centralized decision model (CDM)

Assume that under the supervision of the central government, the water source areas and the water receiving areas reach a binding cooperation agreement to maximize the benefits of the entire water supply chain as the overall goal, and carry out the pollution reduction and carbon reduction of the water supply chain as a synergistically controlled whole, with the decision variables , , and . The CDM can be used as an upper limit of the optimal decision of the supply chain to carry out the analysis of the contractual coordination, so firstly CDM is analyzed (denote CDM by superscript C).

The decision problem for the whole supply chain system under CDM is:
formula
(5)

Due to the difficulty of solving under dynamic parameter conditions, we refer to the treatment in the literature (Jørgensen et al. 2003), which assumes that the parameters in the model are all time-independent constants. In addition, for writing convenience, the time variable t is no longer listed below. The solution results are as follows (see Appendix for their derivations):

  • The equilibrium strategy for optimal decision-making in the supply chain is , , and :
    formula
    (6)
  • The optimal trajectory of the supply chain's low carbon level is:
    formula
    (7)
    where , it represents the stabilized value of the low carbon level of the supply chain, when .
  • The optimal trajectory of the supply chain's pollution control level is:
    formula
    (8)
  • where , it represents the stabilized value of the pollution control level of the supply chain, when .

  • The present value function of total supply chain profit is:
    formula
    (9)
  • where
    formula

Decentralized decision model (DDM)

In this case, water suppliers and distributors make decisions independently of each other, constituting a Nash equilibrium game, where suppliers and distributors determine their optimal low carbon strategies and optimal pollution control strategies from the perspective of maximizing their interests. The decision problem of the suppliers and distributors can be obtained as follows:
formula
(10)
formula
(11)

The equalization results under DDM are as follows (see Appendix for their derivations):

  • The equilibrium strategy for the optimal decisions of suppliers and distributors is , , and :
    formula
    (12)
  • The optimal trajectory of the supply chain's low carbon level is:
    formula
    (13)
  • where , it represents the stabilization value of low carbon level under the DDM, when .

  • The optimal trajectory of the supply chain's pollution control level is:
    formula
    (14)
  • where , it represents the stabilized value of pollution control level under the DDM, when .

  • The present value of profit functions for the supplier in the water source areas and the distributor in the receiving areas are, respectively:
    formula
    (15)
    formula
    (16)
    where
    formula
    formula

Cost-sharing model (CSM)

In the actual operation of the SNWDP to reduce pollution and carbon emissions, the water source areas will pay more efforts to reduce carbon emissions and control water pollution, while the receiving areas only need to invest less to obtain high-quality water, so the distribution of benefits between upstream and downstream is not equal. To address the misalignment of interests up and down the supply chain, the cost-sharing contract is widely used in supply chain coordination and optimization (He et al. 2020). The study of Hou & Wang (2007) shows that the SNWDP receiving water areas share certain costs for water source areas, which can effectively promote the coordination of the water resources supply chain. Therefore, denote as the proportion of low carbon effort cost shared by the distributor of the receiving water district to the supplier at time t, and as the proportion of pollution control effort cost shared by the distributor of the receiving water district to the supplier at time t.

Considered from a dynamic perspective, the decision between water suppliers and distributors regarding low carbon versus pollution control efforts constitutes a Stackelberg differential response model between upstream and downstream (denoted by superscript Y under the CSM). The profit objective functions are, respectively:
formula
(17)
formula
(18)

The equalization results under CSM are as follows (see Appendix for their derivations):

  • The equilibrium strategies for the optimal decisions of suppliers and distributors are , , and , and the optimal cost-sharing ratios are and :
    formula
    (19)
  • The optimal trajectory of the supply chain's low carbon level is:
    formula
    (20)
    where , it represents the stabilization value of low carbon level under the CSM, when .
  • The optimal trajectory of the supply chain's pollution control level is:
    formula
    (21)
    where , it represents the stabilized value of pollution control level under the CSM, when .
  • The present value of profit functions for the supplier in the water source areas and the distributor in the receiving areas are, respectively:
    formula
    (22)
    formula
    (23)
    where
    formula
    formula

Comparative analysis

By comparing and analyzing the optimal parameters under the above three decision scenarios, the following relevant conclusions can be obtained.

When is satisfied, water suppliers and distributors under CSM will invest more in green efforts compared to DDM. The calculation process is as follows:
formula
(24)
formula
(25)
formula
(26)

It can be seen that when , , the distributor's decision is unchanged under the CSM compared with the DDM, and the supplier's low carbon efforts and pollution control efforts are increased; when , , the distributor not only does not share the supplier's costs but also collects some subsidies from the supplier, and the supplier's low-carbon efforts and pollution control efforts are decreased. Therefore, is a necessary condition for the cost-sharing contract to be effective.

The level of low carbon and pollution control is higher in the stabilization case under CSM compared to DDM. The calculation process is as follows:
formula
(27)
formula
(28)

It can be seen that when condition is satisfied for the cost-sharing contract to be effective, the low carbon level and water pollution control level of the water transfer project under the CSM is improved relative to the DDM without cost-sharing. This suggests that the receiving areas distributors can achieve the dual optimization of the water supply chain in terms of carbon emission reduction and water pollution control by incentivizing the water suppliers to improve the level of green efforts through cost-sharing.

Comparison of supply chain benefits available under the three decision models:
formula
(29)
formula
(30)
formula
(31)

It can be seen that when is satisfied, the benefits of both suppliers and distributors are improved after the introduction of the cost-sharing contract. This suggests that the implementation of cost-sharing contracts can foster collaboration between water resource suppliers and distributors, thereby enhancing the benefits for both parties. The supply chain benefits under CDM are greater than those of the two DDMs. This suggests that the higher the level of collaboration between the water resource suppliers and distributors, the higher the environmental and economic benefits of the SNWDP.

In order to verify the conclusions and inference obtained in the previous paper, numerical simulation calculations are carried out for the example of the SNWDP Middle Route Project. Through scientific dispatching, the annual water transfer of the Middle Route Project has reached up to 9 billion cubic meters, and taking this as the baseline to take the base value of water transfer . According to the ‘Notice on the Water Supply Price Policy for the Initial Operation of the Main Project of Phase I of the SNWDP Middle Route Project’ by the National Development and Reform Commission (NDRC), the comprehensive price of water for the middle line project is from 0.18 yuan to 2.33 yuan/m3, and taking into account the income from power generation of the SNWDP, the marginal returns of the supplier and the distributor The marginal returns of suppliers and distributors are taken as and . In the operation of the project, the water resource suppliers will bear more pressure on carbon emission reduction, and the impact coefficients of low carbon efforts of suppliers are taken as and the impact coefficients of the low carbon efforts of distributors are taken as . The water source area needs to safeguard the quality of water and the treatment of water pollution, and the impact coefficient of the efforts of the treatment of pollution is taken as . The cost coefficients are taken as , , and , and the decay rate of low carbon level and water pollution control level are taken as and . The preference coefficients of the receiving water areas are taken as and . The coefficient of influence of low carbon level on the level of pollutant management is taken as . The discount rate is taken as .

Analysis of optimal trajectories for low carbon levels and pollution control levels

Figure 2 shows the numerical simulation of the low carbon levels and pollution control levels of the SNWDP under the three decision models. Each curve in Figure 2(a) represents the trajectory of the supply chain's low carbon level under different decision-making modes. The low carbon level under all three decision models increases and stabilizes over time. The practical significance is that the carbon emission intensity of the project gradually decreases with the green development of the SNWDP and the enhancement of low carbon technology. Each curve in Figure 2(b) represents the trajectory of the supply chain pollutant management level. Unlike the smooth increase of the low carbon levels in Figure 2(a), the pollution control levels in Figure 2(b) show diverse changes. The levels of water pollution control decrease in the first period and then increase rapidly. Its practical significance lies in the fact that the SNWDP promotes the application of clean energy by building large-scale distributed solar power generation facilities in the early stage, which may increase the difficulty of water pollution control to a certain extent. After the completion of the low carbon facilities, their carbon reduction benefits will positively contribute to water pollution control and water quality improvement. The long-term effectiveness of water pollution control has been significantly enhanced.
Figure 2

Optimal trajectories of low carbon and pollution control levels under different decision models: (a) optimal trajectories for low carbon level and (b) optimal trajectory of pollution control level.

Figure 2

Optimal trajectories of low carbon and pollution control levels under different decision models: (a) optimal trajectories for low carbon level and (b) optimal trajectory of pollution control level.

Close modal

By comparing the trajectories in Figure 2(a) and 2(b), the low carbon level and water treatment under CDM are higher on average than that of DDM without cost-sharing, which can significantly improve carbon emission reduction and water pollution control capacity. This is because, in the water supply chain, the primary responsibility for reducing pollution and carbon emissions lies with upstream suppliers, who concurrently sacrifice a portion of development space. The implementation of cost-sharing mechanisms as a form of ecological compensation effectively motivates environmentally sustainable practices in upstream suppliers.

Supply chain profits analysis

Figure 3(a) and 3(b) shows the profits accrued by the supply chain and its members under different decision models. Notably, the aggregate supply chain benefits reach their maximum under CDM, while the overall profit of the supply chain under CSM exceeds that under DDM without cost-sharing. Combined with the trend change of the optimal trajectory in Figure 2, it becomes evident that CDM not only attains the highest level of low carbon and water pollution control but also achieves the highest overall profit for the entire supply chain. This observation underscores that the degree of upstream and downstream synergy within the SNWDP correlates positively with the benefits derived by the supply chain.
Figure 3

Supply chain profit under different decision models: (a) change in total supply chain profit and (b) change in profitability of supply chain members.

Figure 3

Supply chain profit under different decision models: (a) change in total supply chain profit and (b) change in profitability of supply chain members.

Close modal

Introducing a cost-sharing contract facilitates Pareto improvement in supply chain profit, as depicted in Figure 3(b). Subsequent to the implementation of cost-sharing contracts, both water resource suppliers and distributors witness revenue growth. This phenomenon is attributed to the water receiving areas sharing the costs of low carbon initiatives and pollution control with upstream water source areas. This shared responsibility motivates the water source areas to actively reduce pollution and carbon emissions within the SNWDP, subsequently enhancing water quality. The environmental dividends of this collective effort manifest in increased water mobility, ultimately fostering economic growth in both upstream and downstream areas.

Cost factor sensitivity analysis

From Figure 4(a) and 4(b), it can be seen that the cost coefficients of low carbon efforts and increase, the optimal trajectory of low carbon levels exhibits a gradual decline over time. This trend suggests that higher cost coefficients in low carbon efforts correspond to an increase in the cost of carbon abatement and a decrease in the efficiency of carbon abatement. Conversely, a smaller cost coefficient in low carbon efforts leads to a noticeable increase in the optimal trajectory of low carbon levels. As shown in Figure 4(c), the level of pollution control is negatively impacted by the cost coefficient of pollution control efforts. In simpler terms, a higher cost coefficient for pollution control efforts leads to a reduction in the effectiveness of water pollution control. This trend remains consistent across different decision scenarios. Consequently, there is a pressing need for research and development in green technology to mitigate cost coefficients when the SNWDP aims at synergies in pollution reduction and carbon reduction. Alternatively, measures such as government technology subsidies may be instrumental in reducing these costs and fostering sustainable environmental outcomes.
Figure 4

Impact of cost coefficients on low carbon and pollution control level: (a) impact of on low carbon level, (b) impact of on low carbon level, and (c) impact of on pollution control level.

Figure 4

Impact of cost coefficients on low carbon and pollution control level: (a) impact of on low carbon level, (b) impact of on low carbon level, and (c) impact of on pollution control level.

Close modal

Sensitivity analysis of preference coefficients

It can be seen from Figure 5(a) and 5(b) that the optimal trajectories of the low carbon level and the pollution control level gradually increase over time with the increase of the coefficients of the low carbon preference and the water quality preference in different decision models. That is to say, when the market demand for water resources is more sensitive to the level of low carbon and the level of water pollution control (the larger and are), the better the long-term effect of supply chain pollution control and carbon reduction. From the perspective of the water resources market-oriented supply chain, the heightened preferences for low carbon and water quality increase the overall demand for water resources. To maximize supply chain profit, suppliers and distributors are inclined to invest more in low carbon and pollution treatment efforts, thereby enhancing the effectiveness of pollution and carbon reduction. From the perspective of environmental regulation, the higher the low carbon constraints and water quality requirements for the water transfer process, the better the SNWDP's carbon emission reduction and water pollution control.
Figure 5

Impact of preference coefficients on low carbon and pollution control level: (a) impact of low carbon preferences on low carbon level and (b) impact of water quality preferences on pollution control level.

Figure 5

Impact of preference coefficients on low carbon and pollution control level: (a) impact of low carbon preferences on low carbon level and (b) impact of water quality preferences on pollution control level.

Close modal

Firstly, the central government should develop and implement comprehensive policies specifically addressing carbon emissions and water pollution within the SNWDP. This involves setting clear emission reduction targets and water quality standards. Secondly, establish a market-based and diversified ecological compensation mechanism between water source and receiving areas to enhance synergies between upstream and downstream regions. Thirdly, increase research and development efforts in ‘dual-carbon’ technologies. Utilize financial support from public REITs, the National Low Carbon Transformation Fund, the Green and Low Carbon Industry Investment Fund, and other comprehensive funding sources to encourage greater investment in the research and development of green and low carbon technologies. Finally, construct a green management information platform to dynamically monitor water pollution and carbon emissions. This platform should provide decision-making support for green and low carbon development initiatives.

With climate change and ecological degradation pressing concerns, there is an urgent need for water transfer projects to improve water system efficiency and carbon reduction while ensuring water security. Previous research on the SNWDP primarily focused on water quality improvement and pollution management. As the world's largest water transfer endeavor, the SNWDP's carbon reduction trajectory is crucial. This paper integrates green supply chain management theory with SNWDP supply chain research to propose a synergistic emission reduction mechanism. Unlike prior studies, this mechanism considers synergistic cooperation between water source and receiving areas, vital for SNWDP's green and low carbon development.

A differential game model was utilized to explore synergistic emission reduction in SNWDP water source and receiving areas. Findings reveal that the synergy between these areas dictates SNWDP's green level. The CDM achieves the highest green level and benefits, yet actual cooperation between water source and receiving areas is challenging. In contrast, the decentralized decision model (DCM) exhibits the lowest green and low carbon development level, necessitating an enhanced cooperation mechanism. Cost-sharing effectively boosts water resource suppliers' low carbon efforts, carbon emission reduction, water pollution control, and supply chain benefits. This underscores the need for promoting upstream–downstream cooperation through ecological compensation and transfer payments. Hence, the SNWDP's synergistic emission reduction mechanism should refine the diversified eco-compensation mechanism between water source and receiving areas to foster comprehensive cooperation on water resources and economic development.

This work is supported by the National Natural Science Foundation of China (Grant No. 42071278), the Major Project of the National Social Science Foundation of China (Grant No. 19ZDA084), and the Fundamental Research Fund for the Central Universities of China (Grant No. B230207002).

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

The authors declare there is no conflict.

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