Lake type drainage basin pollution discharge configuration double-layer robust optimization method based on ecological compensation

Abstract

The invention relates to the technical field of water conservancy projects, and discloses a lake-type drainage basin pollution discharge configuration double-layer robust optimization method based on ecological compensation, and the method comprises the steps: building a segmented ecological compensation model based on a monitored section water environment quality index; according to the sectional type ecological compensation model, by taking maximization of a pollution discharge configuration Gini coefficient as an upper layer target and minimization of ecological environment cost as a lower layer target, constructing a lake type drainage basin pollution discharge configuration double-layer optimization model; lake water environment capacity uncertainty parameters are introduced into the lake type drainage basin pollution discharge configuration double-layer optimization model, and a lake type drainage basin pollution discharge configuration double-layer robust optimization model is constructed; and converting the double-layer robust optimization model of the pollution discharge configuration of the lake type drainage basin into a single-layer planning model by adopting a KKT condition, and solving to obtain an optimal pollution discharge configuration scheme. According to the invention, accurate configuration of environment resources can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of water conservancy projects, and in particular to a double-layer robust optimization method for sewage discharge configuration in lake-type watersheds based on ecological compensation. Background Art

[0002] Water environment governance in transregional lake basins is crucial for achieving regional ecological balance and sustainable economic development. However, in practice, governance faces challenges such as uneven economic development, significant disparities in governance capacity, and inconsistent commitment to water quality protection. Furthermore, the public good nature of lake water resources, coupled with the blurred boundaries of responsibility between upstream and downstream sectors in intersecting regions, makes it difficult to internalize the positive externalities of pollution control. Investors in governance bear high costs but are unable to reap the benefits of improved environment, while non-investors enjoy environmental gains but avoid sharing in the corresponding costs. Therefore, establishing a coordinated pollution allocation mechanism that balances basin ecological security and equitable economic development, and supporting decision-making in real-world settings through effective mathematical models, has become a pressing scientific challenge. Summary of the Invention

[0003] In view of the above-mentioned deficiencies in the prior art, the present invention provides a two-layer robust optimization method for sewage discharge configuration in lake-type watersheds based on ecological compensation.

[0004] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is: A two-layer robust optimization method for pollution discharge configuration in lake-type watersheds based on ecological compensation includes the following steps: Construct a segmented ecological compensation model based on the water environment quality index of the monitoring section; Based on the segmented ecological compensation model, a two-level optimization model for pollution discharge configuration in lake basins was constructed with maximizing the Gini coefficient of pollution discharge configuration as the upper level goal and minimizing the ecological environmental cost as the lower level goal. The uncertainty parameter of lake water environment capacity is introduced into the two-layer optimization model of sewage discharge configuration in lake-type basins, and a two-layer robust optimization model of sewage discharge configuration in lake-type basins is constructed. The KKT condition is used to transform the two-layer robust optimization model of sewage discharge configuration in lake-type basins into a single-layer planning model, and the optimal sewage discharge configuration scheme is obtained.

[0005] Furthermore, the constructed segmented ecological compensation model is:

[0006]

[0007] in, is the ecological compensation function, Lake sub-areai Pollutants j The concentration of the monitoring section, Lake sub-area i The water environment quality index of the monitoring section, is the unit reward value of ecological compensation, It is the penalty value of ecological compensation unit.

[0008] Furthermore, based on the segmented ecological compensation model, with maximizing the Gini coefficient of pollution discharge configuration as the upper level goal and minimizing the ecological and environmental costs as the lower level goal, a two-level optimization model for pollution discharge configuration in lake-type basins was constructed, including: The upper-level model is constructed with the maximization of the Gini coefficient of pollution allocation as the upper-level goal and the constraints that the pollutant allocation amount of the lake sub-region does not exceed the lake water environment capacity and the pollutant allocation amount of the lake sub-region is greater than the pollutant discharge amount. Based on the segmented ecological compensation model, the lower-level model is constructed with minimizing the ecological environmental cost as the lower-level goal and the constraints that the pollutant emissions of each lake sub-region do not exceed the pollutant allocation, the pollutant emissions of each lake sub-region are greater than the minimum pollutant emission limit, and the concentration of pollutants in the monitoring section of each lake sub-region does not exceed the pollutant concentration limit. A two-layer optimization model for sewage discharge configuration in lake-type basins is constructed based on the upper model and the lower model.

[0009] Furthermore, with maximizing the Gini coefficient of pollution discharge configuration as the upper-level goal, and with the constraints that the pollutant allocation amount in the lake sub-region does not exceed the lake water environment capacity and the pollutant allocation amount in the lake sub-region is greater than the pollutant discharge amount, the upper-level model is constructed, specifically: ; st ; ; in, Min To obtain the minimum function, G Configure the Gini coefficient for sewage discharge, Lake sub-area i pollutants j The distribution amount, Lake sub-area i The average annual environmental economic benefit value is For sub-region l pollutants j The distribution amount, For sub-region h pollutants j The distribution amount, For sub-region lThe average annual environmental economic benefit value is For sub-region h The average annual environmental economic benefit value is is the lake water environment capacity, Lake sub-area i The pollutant treatment rate, Lake sub-area i The average annual pollutant emissions, Lake sub-area i Pollutants j Concentration in the monitoring section.

[0010] Furthermore, based on the segmented ecological compensation model, with minimizing the ecological environmental cost as the lower-level goal, and with the constraints that the pollutant emissions of each lake sub-region do not exceed the pollutant allocation, the pollutant emissions of each lake sub-region are greater than the minimum pollutant emission limit, and the concentration of pollutants in the monitoring section of each lake sub-region does not exceed the pollutant concentration limit, the lower-level model is constructed, specifically as follows: ; st ; ; ; in, For sub-region i environmental costs, Lake sub-area i Pollutants j The cost of emission reduction, is the ecological compensation function, Lake sub-area i Pollutants j Minimum emission limits, For pollutants j concentration limit.

[0011] Furthermore, a two-layer optimization model for lake-type basin sewage discharge configuration is constructed based on the upper and lower models, specifically: .

[0012] Furthermore, the uncertainty parameter of lake water environment capacity is introduced into the two-layer optimization model of sewage discharge configuration in lake-type basins, specifically:

[0013] in, is the lake water environment capacity, is the nominal value of lake water environment, is the constant disturbance term of the lake water environment, is a robust parameter, For pollutants j uncertain parameters.

[0014] Furthermore, the KKT condition is used to transform the two-layer robust optimization model of sewage discharge configuration in lake-type basins into a single-layer planning model, and the optimal sewage discharge configuration scheme is obtained, including: Based on the constraints of the two-layer robust optimization model for sewage discharge configuration in lake-type basins, the Lagrangian function is constructed and the first-order conditions are obtained by solving it. According to the first-order conditions, the two-layer robust optimization model of sewage discharge configuration based on lake-type basins is transformed into a single-layer planning model, and the optimal sewage discharge configuration plan is obtained.

[0015] Furthermore, a Lagrangian function is constructed based on the constraints of the two-layer robust optimization model for sewage discharge configuration in lake-type basins, specifically: ; in, is the Lagrangian function, is the Lagrange multiplier, Lake sub-area i Pollutants j The cost of emission reduction, Lake sub-area i The pollutant treatment rate, Lake sub-area i pollutants j The distribution amount, is the ecological compensation function, Lake sub-area i The average annual pollutant emissions, Lake sub-area i Pollutants j The concentration of the monitoring section, Lake sub-area i Pollutants j Minimum emission limits, For pollutants j concentration limit.

[0016] Furthermore, according to the first-order conditions, the two-layer robust optimization model of sewage discharge configuration based on lake-type basins is transformed into a single-layer planning model, specifically: ; in, Min To obtain the minimum function, G Configure the Gini coefficient for sewage discharge, Lake sub-area i pollutants j The distribution amount, Lake sub-area i The average annual environmental economic benefit value is For sub-region l pollutants j The distribution amount, For sub-region h pollutants j The distribution amount, For sub-region l The average annual environmental economic benefit value is For sub-region h The average annual environmental economic benefit value is is the nominal value of lake water environment, is the constant disturbance term of the lake water environment, is a robust parameter, Lake sub-area i The pollutant treatment rate, Lake sub-area i The average annual pollutant emissions, Lake sub-area i Pollutants j The concentration of the monitoring section, For pollutants j The uncertain parameters, To find the partial derivative of the decision variable, Lake sub-area i Pollutants j Minimum emission limits, For pollutants j concentration limit.

[0017] The present invention has the following beneficial effects: The present invention takes into account practical problems such as information asymmetry and inconsistent interests among various environmental governance entities, and uses a cooperative game method to determine a scientific and reasonable basin ecological compensation price; and based on the uncertainty of the water environment capacity of the lake basin, a two-layer optimization model for the allocation of basin emission rights is established. Through the budget uncertainty set robust optimization method, a stable decision-making plan is obtained to achieve precise allocation of environmental resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a flow chart of a two-layer robust optimization method for pollution discharge configuration in lake basins based on ecological compensation; Figure 2 This is a schematic diagram of the framework for collaborative management of inter-provincial lake water environments; Figure 3 This is a diagram of the price game process of ecological compensation in inter-provincial lake basins. DETAILED DESCRIPTION

[0019] The specific embodiments of the present invention are described below to facilitate understanding of the present invention by those skilled in the art. However, it should be clear that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the appended claims, these changes are obvious, and all inventions and creations utilizing the concepts of the present invention are protected.

[0020] like Figure 1 and Figure 2 As shown, the embodiment of the present invention provides a two-layer robust optimization method for lake-type basin sewage discharge configuration based on ecological compensation, including the following steps S1 to S4: S1. Construct a segmented ecological compensation model based on the water environment quality index of the monitoring section; In an optional embodiment of the present invention, the monitoring section involved in step S1 includes the river entering the lake and the provincial controlled section, and the water environment quality index of the monitoring section is determined according to the water environment quality of the monitoring section. .when When it is less than 1, the water environment quality is good and ecological compensation rewards will be obtained; when When it is greater than 1, the water environment quality is poor and will be punished. is the unit reward value of ecological compensation, It is the penalty value of ecological compensation unit.

[0021] like Figure 3 As shown in the figure, in a multi-party game environment, cooperative game theory allows all parties to reach a mutually beneficial compensation plan through negotiation and contract, thereby promoting the realization of water pollution control goals. Regarding the water environment management of interprovincial lakes, the provinces along the lakes, as the main participants in the game, need to bear the water pollutant reduction in the lake basin. , total reduction Determined, and ; The cost function of water environment governance in each province is assumed to be an increasing function: ,in For the province i The unit cost coefficient of pollution control is: ,in For the province i Unit reduction of ecological benefits; ecological compensation price It is used to reward all participating entities and meet the water environment control goals. Based on the above analysis, the objective function of the entire basin is to maximize benefits: , put it into the function expression, then it becomes: . i , at the optimal reduction At the point where the marginal governance cost is equal to the ecological compensation price, then: , substituted into the cost function: and bring it into , the optimal ecological compensation price is obtained through conversion: .

[0022] The segmented ecological compensation model constructed in this embodiment is:

[0023]

[0024] in, is the ecological compensation function, Lake sub-area i Pollutants j The concentration of the monitoring section, Lake sub-area i The water environment quality index of the monitoring section, is the unit reward value of ecological compensation, It is the penalty value of ecological compensation unit.

[0025] S2. Based on the segmented ecological compensation model, a two-level optimization model for pollution discharge configuration in lake basins was constructed with maximizing the Gini coefficient of pollution discharge configuration as the upper level goal and minimizing the ecological and environmental costs as the lower level goal. In an optional embodiment of the present invention, step S2 constructs a two-level optimization model for pollution discharge configuration in a lake-type basin based on a segmented ecological compensation model, with maximizing the Gini coefficient of pollution discharge configuration as the upper level goal and minimizing the ecological environment cost as the lower level goal, including: The upper-level model is constructed with the maximization of the Gini coefficient of pollution allocation as the upper-level goal and the constraints that the pollutant allocation amount of the lake sub-region does not exceed the lake water environment capacity and the pollutant allocation amount of the lake sub-region is greater than the pollutant discharge amount. Based on the segmented ecological compensation model, the lower-level model is constructed with minimizing the ecological environmental cost as the lower-level goal and the constraints that the pollutant emissions of each lake sub-region do not exceed the pollutant allocation, the pollutant emissions of each lake sub-region are greater than the minimum pollutant emission limit, and the concentration of pollutants in the monitoring section of each lake sub-region does not exceed the pollutant concentration limit. A two-layer optimization model for sewage discharge configuration in lake-type basins is constructed based on the upper model and the lower model.

[0026] This embodiment uses the lake basin management as the upper-level decision maker, with the goal of maximizing the fairness of the allocation of emission rights. The Gini coefficient ranges from 0 to 1. The lower the value, the fairer the resource allocation. It can be effectively used to evaluate the fairness of water pollutant emission rights in the basin system. In the present invention, fairness is mainly reflected in the difference between the economic benefits of each sub-region and the pollutant emission load permit it bears. The smaller the difference, the more reasonable the allocation of emission rights. The present invention proposes a specific model based on the Gini coefficient and uses it as the optimization target. The expression is:

[0027] in, , representing different sub-regions in the same lake basin, with a total of m ; , indicating the types of water pollutants in the basin, and the total number of water pollutants is n ; For lake management in sub-regions water pollutants The distribution amount, For sub-region The average annual environmental economic benefit value.

[0028] This embodiment determines the constraints of the upper model: (1) Water environment quality bottom line control: For lakes, the pollution absorption capacity of water bodies is limited. In order to ensure the sustainable development of lake basins, the pollution discharge rights allocated to sub-regions should not exceed the water environment capacity of the lake. , as shown below:

[0029] (2) Guarantee of basic development needs: In the decision-making process, it is necessary to consider that the actual emission rights of the region are greater than the actual amount of pollution discharged. Each sub-region has emission reduction capacity and can obtain more emission rights by treating water pollutants, as shown in the following formula:

[0030] in, For sub-region The pollutant treatment rate, For sub-region The average annual pollutant emissions, For sub-region Water pollutants Monitor the control of section concentration.

[0031] This example uses the pollution control entity as the lower-level decision maker, with the decision-making goal of minimizing environmental costs. The pollution control entity implements this strategy under the lake ecological compensation mechanism to achieve the optimal annual average concentration at the lowest environmental cost. Ecological environmental costs consist of two parts: pollution reduction costs and ecological compensation fees. Pollution reduction costs are the costs incurred by reducing emissions from various pollution sources through technical means, governance measures, or policy constraints; the purpose of ecological compensation is to balance the contradiction between environmental protection and regional economic development, expressed as:

[0032] in, Sub-area Water pollutants j emission reduction costs.

[0033] This embodiment determines the constraints of the lower model: (1) Constraints on the total amount of pollution emission rights: The actual annual discharge of each lake sub-region must not exceed the pollutant discharge permit it has been allocated. The total amount of pollution emissions is controlled within an acceptable range to prevent excessive discharge of pollutants from a single sub-region, thereby safeguarding the self-purification capacity and ecological balance of the lake. The expression is:

[0034] (2) Meeting minimum demand constraints: Pollution emissions and economic development are interrelated to a certain extent, and pollution emission rights can be regarded as a necessary condition for economic development. In order to ensure basic economic growth, a minimum limit should be set for the total amount of pollutant emissions. , the expression is:

[0035] (3) Water quality index constraints: Pollutant concentration is a key indicator in lake water environment management. The primary function of lake water quality monitoring sections is to detect pollutant concentrations in water bodies in real time. According to the Surface Water Environmental Quality Standard (GB 3838-2002), different types of surface water bodies have clear limit requirements for pollutant concentrations, providing a scientific basis for lake water quality monitoring and management. The expression is:

[0036] In this example, the hierarchical structure of upper and lower decision-making reflects the coordination and interaction between different management levels in the fair allocation of pollution discharge rights in the lake basin. The lake basin management is responsible for formulating the overall pollution discharge rights allocation plan, aiming to maximize the fairness of pollution discharge rights. Each pollution control entity focuses on reducing environmental costs to achieve the optimal annual average pollutant concentration level. The global model is: .

[0037] S3. Introduce the uncertainty parameter of lake water environment capacity into the two-layer optimization model of sewage discharge configuration in lake-type basins, and construct a two-layer robust optimization model of sewage discharge configuration in lake-type basins; In an optional embodiment of the present invention, step S3 is for the uncertainty parameter of lake water environment capacity , its accurate distribution cannot be obtained from known data, and the application of traditional stochastic programming methods in this case is limited. However, by collecting data from previous years, a more accurate interval range can be obtained, thereby modeling uncertainty.

[0038] This embodiment introduces the uncertainty parameter of lake water environment capacity into the two-layer optimization model of sewage discharge configuration in lake-type basins, specifically:

[0039] in, is the lake water environment capacity, , is the nominal value of lake water environment, is the constant disturbance term of the lake water environment, which represents the deviation of the actual capacity from the nominal value; is a robust parameter, For pollutants j uncertain parameters.

[0040] S4. Use KKT conditions to transform the two-layer robust optimization model of lake-type basin sewage discharge configuration into a single-layer planning model to obtain the optimal sewage discharge configuration plan.

[0041] In an optional embodiment of the present invention, step S4 uses KKT conditions to transform the two-layer robust optimization model of lake-type basin sewage discharge configuration into a single-layer programming model to solve the optimal sewage discharge configuration scheme, including: Based on the constraints of the two-layer robust optimization model for sewage discharge configuration in lake-type basins, the Lagrangian function is constructed and the first-order conditions are obtained by solving it. According to the first-order conditions, the two-layer robust optimization model of sewage discharge configuration based on lake-type basins is transformed into a single-layer planning model, and the optimal sewage discharge configuration plan is obtained.

[0042] In this example, for the two-layer structure in the model, in order to simplify the problem and find the optimal solution, the KKT (Karush-Kuhn-Tucker) method can be used to transform the model. KKT conditions are a standard method for solving constrained optimization problems. The constraints in the model are processed by constructing a Lagrangian function, and the optimal solution is finally obtained. The expression is: ; in, is the Lagrangian function, is the Lagrange multiplier, Lake sub-area i Pollutants j The cost of emission reduction, Lake sub-area i The pollutant treatment rate, Lake sub-area i pollutants j The distribution amount, is the ecological compensation function, Lake sub-area i The average annual pollutant emissions, Lake sub-area i Pollutants j The concentration of the monitoring section, Lake sub-area i Pollutants j Minimum emission limits, For pollutants j concentration limit.

[0043] In this embodiment, the variables of the Lagrangian function are By taking partial derivatives and obtaining first-order conditions, and resolving the interdependence between upper- and lower-level decisions, the transformed model is shown below. By using the KKT method to transform and solve the two-layer structure model, we can obtain the optimal emission rights allocation plan, while maximizing the utilization of water environment capacity and minimizing environmental costs.

[0044] The resulting single-level programming model is: ; in, Min To obtain the minimum function, G Configure the Gini coefficient for sewage discharge, Lake sub-area i pollutants j The distribution amount, Lake sub-area i The average annual environmental economic benefit value is For sub-region l pollutants j The distribution amount, For sub-region h pollutants j The distribution amount, For sub-region l The average annual environmental economic benefit value is For sub-region h The average annual environmental economic benefit value is is the nominal value of lake water environment, is the constant disturbance term of the lake water environment, is a robust parameter, Lake sub-area i The pollutant treatment rate, Lake sub-area i The average annual pollutant emissions, Lake sub-area i Pollutants j The concentration of the monitoring section, For pollutants j The uncertain parameters, To find the partial derivative of the decision variable, Lake sub-area i Pollutants j Minimum emission limits, For pollutants j concentration limit.

[0045] The present invention is based on the interest game relationship in water environment governance and quantifies the lake ecological compensation mechanism through a piecewise function mathematical model. Moreover, through the coupling analysis of water quality and waste load, an optimal decision-making method for pollution discharge configuration in lake-type basins is realized.

[0046] The method provided by the present invention is analyzed and explained below with reference to specific examples.

[0047] The present invention adopts COD and NH3-N, which can reflect the water environment status of the Taihu Lake Basin, as the main water pollutant indicators, and takes 2020 as the base year.

[0048] This paper takes the Taihu Lake Basin, a typical inter-provincial lake, as a case study object. Aiming at the problem of coordinated governance of the inter-provincial water environment in the Taihu Lake Basin, it collects relevant data from lakes and monitoring sections, verifies the effectiveness and feasibility of the model, and provides management suggestions for the coordinated governance of inter-provincial lakes.

[0049] Data on the Taihu Lake basin's area, water pollutant emissions, and GDP are derived from the 2022 "Taihu Lake Basin Water Environment Comprehensive Management Master Plan," jointly issued by the National Development and Reform Commission, the Ministry of Natural Resources, the Ministry of Ecology and Environment, and other departments, and calculated in conjunction with the 2013 plan. Pollutant concentration indicators are taken from the "Surface Water Environmental Quality Standard (GB3838-2002)" and are based on the Class IV water quality standard, namely, COD concentration of 30 mg / L and NH3-N concentration of 1.5 mg / L. In the cooperative negotiation process for ecological compensation prices, water pollutant reductions, ecological compensation adjustment coefficients, and ecological benefits of pollutant reductions are derived from the "Guiding Opinions on Promoting the Establishment of an Ecological Protection Compensation Mechanism in the Taihu Lake Basin" and the Taihu Water Conservancy Yearbook, both issued by the Ministry of Ecology and Environment. The penalty value per unit of ecological compensation is set at 10 times the reward. Water pollutant emissions from Taihu Lake are derived from the "Water Resources Bulletin of the Taihu Lake Basin and Southeastern Rivers."

[0050] Based on the collection and collation of basic data of the Taihu Lake Basin, the basic scenario was planned and designed by selecting parameters and using LINGO18.0 as the solution tool for the converted model. The value is 0.1, At this point, the Upper Lake Basin Authority's fairness target value is 0.58, aiming to balance the interests of the provinces and ensure fairness in the allocation of emission rights. Solving the optimization model shows that the differences in the allocation of emission rights among provinces have been reconciled to a certain extent.

[0051] In the initial plan, the COD allocation scheme calculated through the optimization model reduced total emissions by 17% and total NH3-N emissions by 16% compared to emissions in 2020. Specifically, Jiangsu Province was allocated 162,162 tons of COD emission rights and 18,600 tons of NH3-N, Zhejiang Province was allocated 196,833 tons of COD emission rights and 9,490 tons of NH3-N, and Shanghai was allocated 45,714 tons of COD emission rights and 489 tons of NH3-N.

[0052] By optimizing the allocation of pollution discharge rights in the Taihu Lake Basin and conducting a detailed analysis of the underlying objective functions and water pollutant concentration control under a baseline scenario, the study reveals the costs, pollutant concentrations, and ecological compensation values of water pollution control in Jiangsu, Zhejiang, and Shanghai. Zhejiang Province has the highest pollutant concentrations of the three provinces, indicating significant pressure on water pollution control. Shanghai has the lowest pollutant concentrations of the three provinces, indicating good results in water pollution control.

[0053] Zhejiang Province has the highest environmental costs, indicating it has invested the most in water pollution control. Shanghai has the lowest environmental costs, suggesting either high control efficiency or a shift in its industrial structure toward low-pollution sectors. Ecological compensation values reflect the amount each province is willing to pay for water pollution control. All ecological compensation values are positive, indicating that all three regions have chosen to pay pollution fines. Zhejiang Province, with the highest pollutant concentrations, also has the highest ecological compensation values, indicating a willingness to pay higher compensation amounts to promote economic development. This reflects the environmental pressures Zhejiang faces during its rapid economic development and its emphasis on economic development.

[0054] Under the segmented watershed eco-compensation mechanism, provinces face a choice: either be penalized for emitting large amounts of pollutants or be rewarded for reducing them. These decisions directly influence the allocation strategies of higher-level decision-makers, resulting in eight possible scenarios across the three provinces, reflecting the varying strategies adopted by provincial governments in water environmental governance.

[0055] Judging from the changes in COD concentrations in various provinces, there are large fluctuations in the governance effects of Jiangsu and Zhejiang provinces under different scenarios, especially Jiangsu's COD concentration is higher in certain scenarios. Shanghai performs well in most scenarios, especially in pollution control effects in certain scenarios. Each province needs to adjust its governance measures according to specific scenarios, strengthen pollution source control and emission standards to ensure continuous improvement in water quality. Jiangsu Province has a high degree of stability, which means that its fluctuations affected by policies are relatively small. Even under different scenarios, Jiangsu's water quality changes are relatively slow, indicating that Jiangsu has a relatively solid foundation in pollution control and can maintain relatively consistent governance effects under different policy scenarios. Zhejiang Province's water quality concentration fluctuates greatly, indicating that the effectiveness of its water quality control may be greatly affected when policies are adjusted or measures are implemented.

[0056] Zhejiang Province's eco-compensation values showed significant fluctuations overall. Positive scenarios indicate that Zhejiang was able to obtain compensation through emission reductions and water quality improvements in certain circumstances, potentially demonstrating the effectiveness of its governance strategies under specific scenarios. Shanghai's eco-compensation values also showed significant fluctuations, with positive values in multiple scenarios. This suggests that Shanghai's governance measures were able to achieve significant emission reductions, leading to compensation, in most scenarios.

[0057] Zhejiang Province's eco-compensation values showed significant fluctuations overall. Positive scenarios indicate that Zhejiang was able to obtain compensation through emission reductions and water quality improvements in certain circumstances, potentially demonstrating the effectiveness of its governance strategies under specific scenarios. Shanghai's eco-compensation values also showed significant fluctuations, with positive values in multiple scenarios. This suggests that Shanghai's governance measures were able to achieve significant emission reductions, leading to compensation, in most scenarios.

[0058] In order to explore the impact of changes in watershed water environment capacity on the allocation of COD and NH3-N emission rights, this paper sets four scenarios: , representing four different styles of decision makers: low conservative, generally low conservative, generally highly conservative, and highly conservative. When uncertainty increases, pollutant allocation and control are more balanced across regions, reducing the risk of over-reliance on a single emission reduction measure in a region and preventing the failure of a single strategy from leading to the failure of water quality management goals.

[0059] As the robust parameter for water environmental capacity increases, COD emissions in Jiangsu Province increase, from 4,188.73 tons to 5,571.42 tons. This indicates that higher levels of conservatism lead to higher emissions in Jiangsu Province, potentially indicating an increase in emissions under greater uncertainty and pressure. The allocation of COD emission rights in Zhejiang Province fluctuates significantly with the uncertainty of water environmental capacity, from 180,356.25 tons to 111,111.2 tons, after which emissions decrease significantly to 19,683.33 tons. This suggests that Zhejiang Province may have implemented stricter control measures under higher robust values, resulting in reduced emissions. To reduce ammonia nitrogen emissions, Jiangsu Province should further strengthen emission controls under high robust values, especially when faced with greater uncertainty. Existing control measures should be optimized to prevent further increases in emissions. Shanghai should further strengthen ammonia nitrogen emission controls, particularly under high robust values, to ensure effective reductions in the face of uncertainty.

[0060] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0061] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0062] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0063] Specific embodiments are used in the present invention to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core ideas. At the same time, for those skilled in the art, according to the ideas of the present invention, there may be changes in the specific implementation methods and application scopes. In summary, the contents of this specification should not be understood as limiting the present invention.

[0064] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and it should be understood that the scope of protection of the present invention is not limited to such specific descriptions and embodiments. Those skilled in the art can make various other specific variations and combinations based on the technical teachings disclosed in the present invention without departing from the essence of the present invention, and such variations and combinations are still within the scope of protection of the present invention.

Claims

1. A two-layer robust optimization method for lake-type basin sewage discharge configuration based on ecological compensation, characterized by: The following steps are involved: Construct a segmented ecological compensation model based on the water environment quality index of the monitoring section; Based on the segmented ecological compensation model, a two-level optimization model for pollution discharge configuration in lake basins was constructed with maximizing the Gini coefficient of pollution discharge configuration as the upper level goal and minimizing the ecological environmental cost as the lower level goal. The uncertainty parameter of lake water environment capacity is introduced into the two-layer optimization model of sewage discharge configuration in lake-type basins, and a two-layer robust optimization model of sewage discharge configuration in lake-type basins is constructed. The KKT condition is used to transform the two-layer robust optimization model of sewage discharge configuration in lake-type basins into a single-layer planning model, and the optimal sewage discharge configuration scheme is obtained.

2. The double-layer robust optimization method for lake-type river basin sewage discharge configuration based on ecological compensation according to claim 1 is characterized in that: The constructed segmented ecological compensation model is: in, is the ecological compensation function, Lake sub-area i Pollutants j The concentration of the monitoring section, Lake sub-area i The water environment quality index of the monitoring section, is the unit reward value of ecological compensation, It is the penalty value of ecological compensation unit.

3. The double-layer robust optimization method for lake-type river basin sewage discharge configuration based on ecological compensation according to claim 1 is characterized in that: Based on the segmented ecological compensation model, with maximizing the Gini coefficient of pollution discharge configuration as the upper level goal and minimizing the ecological and environmental costs as the lower level goal, a two-level optimization model for pollution discharge configuration in lake-type basins was constructed, including: The upper-level model is constructed with the maximization of the Gini coefficient of pollution allocation as the upper-level goal and the constraints that the pollutant allocation amount of the lake sub-region does not exceed the lake water environment capacity and the pollutant allocation amount of the lake sub-region is greater than the pollutant discharge amount. Based on the segmented ecological compensation model, the lower-level model is constructed with minimizing the ecological environmental cost as the lower-level goal and the constraints that the pollutant emissions of each lake sub-region do not exceed the pollutant allocation, the pollutant emissions of each lake sub-region are greater than the minimum pollutant emission limit, and the concentration of pollutants in the monitoring section of each lake sub-region does not exceed the pollutant concentration limit. A two-layer optimization model for sewage discharge configuration in lake-type basins is constructed based on the upper model and the lower model.

4. The double-layer robust optimization method for lake-type river basin sewage discharge configuration based on ecological compensation according to claim 3 is characterized in that: Taking maximizing the Gini coefficient of pollution discharge configuration as the upper-level goal, and taking the pollutant allocation amount of the lake sub-region not exceeding the lake water environment capacity and the pollutant allocation amount of the lake sub-region being greater than the pollutant discharge as the constraint conditions, the upper-level model is constructed, specifically: ； st ； ； in, Min To obtain the minimum function, G Configure the Gini coefficient for sewage discharge, Lake sub-area i pollutants j The distribution amount, Lake sub-area i The average annual environmental economic benefit value is For sub-region l pollutants j The distribution amount, For sub-region h pollutants j The distribution amount, For sub-region l The average annual environmental economic benefit value is For sub-region h The average annual environmental economic benefit value is is the lake water environment capacity, Lake sub-area i The pollutant treatment rate, Lake sub-area i The average annual pollutant emissions, Lake sub-area i Pollutants j Concentration in the monitoring section.

5. The double-layer robust optimization method for lake-type river basin sewage discharge configuration based on ecological compensation according to claim 4 is characterized in that: According to the segmented ecological compensation model, with minimizing the ecological environmental cost as the lower-level goal, and with the pollutant emissions of each lake sub-region not exceeding the pollutant allocation, the pollutant emissions of each lake sub-region being greater than the minimum pollutant emission limit, and the concentration of pollutants in the monitoring section of each lake sub-region not exceeding the pollutant concentration limit as the constraint conditions, the lower-level model is constructed, specifically as follows: ； st ； ； ； in, For sub-region i environmental costs, Lake sub-area i Pollutants j The cost of emission reduction, is the ecological compensation function, Lake sub-area i Pollutants j Minimum emission limits, For pollutants j concentration limit.

6. The double-layer robust optimization method for lake-type river basin sewage discharge configuration based on ecological compensation according to claim 5 is characterized in that: A two-layer optimization model for sewage discharge configuration in lake basins is constructed based on the upper and lower models, specifically: 。 7. The double-layer robust optimization method for lake-type river basin sewage discharge configuration based on ecological compensation according to claim 1 is characterized in that: The uncertainty parameter of lake water environment capacity is introduced into the two-layer optimization model of sewage discharge configuration in lake-type basins, specifically: in, is the lake water environment capacity, is the nominal value of lake water environment, is the constant disturbance term of the lake water environment, is a robust parameter, For pollutants j uncertain parameters.

8. The double-layer robust optimization method for lake-type river basin sewage discharge configuration based on ecological compensation according to claim 1 is characterized in that: The KKT condition is used to transform the two-layer robust optimization model of sewage discharge configuration in lake basins into a single-layer planning model to obtain the optimal sewage discharge configuration scheme, including: Based on the constraints of the two-layer robust optimization model for sewage discharge configuration in lake-type basins, the Lagrangian function is constructed and the first-order conditions are obtained by solving it. According to the first-order conditions, the two-layer robust optimization model of sewage discharge configuration based on lake-type basins is transformed into a single-layer planning model, and the optimal sewage discharge configuration plan is obtained.

9. The double-layer robust optimization method for lake-type river basin sewage discharge configuration based on ecological compensation according to claim 8 is characterized in that: The Lagrangian function is constructed based on the constraints of the two-layer robust optimization model for sewage discharge configuration in lake-type basins, specifically: ； in, is the Lagrangian function, is the Lagrange multiplier, Lake sub-area i Pollutants j The cost of emission reduction, Lake sub-area i The pollutant treatment rate, Lake sub-area i pollutants j The distribution amount, is the ecological compensation function, Lake sub-area i The average annual pollutant emissions, Lake sub-area i Pollutants j The concentration of the monitoring section, Lake sub-area i Pollutants j Minimum emission limits, For pollutants j concentration limit.

10. The double-layer robust optimization method for lake-type river basin sewage discharge configuration based on ecological compensation according to claim 9 is characterized in that: According to the first-order conditions, the two-layer robust optimization model of sewage discharge configuration based on lake-type basins is transformed into a single-layer planning model, specifically: ； in, Min To obtain the minimum function, G Configure the Gini coefficient for sewage discharge, Lake sub-area i pollutants j The distribution amount, Lake sub-area i The average annual environmental economic benefit value is For sub-region l pollutants j The distribution amount, For sub-region h pollutants j The distribution amount, For sub-region l The average annual environmental economic benefit value is For sub-region h The average annual environmental economic benefit value is is the nominal value of lake water environment, is the constant disturbance term of the lake water environment, is a robust parameter, Lake sub-area i The pollutant treatment rate, Lake sub-area i The average annual pollutant emissions, Lake sub-area i Pollutants j The concentration of the monitoring section, For pollutants j The uncertain parameters, To find the partial derivative of the decision variable, Lake sub-area i Pollutants j Minimum emission limits, For pollutants j concentration limit.

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