Basin discharge right transaction comprehensive cost optimization method

Abstract

The invention discloses a drainage basin emission right transaction comprehensive cost optimization method, which is applied to the technical field of pollutant emission treatment. Comprising the following steps: acquiring the total pollutant allowable discharge amount of each region in a target drainage basin, and historical pollution discharge data and pollution discharge treatment cost of all enterprises; constructing a double-layer emission right transaction comprehensive cost optimization model taking the lowest processing cost in the drainage basin as a target function, wherein the model comprises an upper-layer drainage basin decision model and a lower-layer enterprise decision model; setting an initial pollution discharge right distribution scheme, and carrying out iterative solution to obtain an optimal decision scheme; and outputting an optimal decision scheme, including an emission right distribution scheme, a balanced price, an enterprise decision scheme and a minimum processing cost. According to the method, the double-layer pollution discharge right transaction comprehensive cost optimization model is constructed, the optimal allocation and decision scheme is solved, the overall pollution discharge treatment and pollution discharge right transaction cost minimization in the drainage basin are realized, and the environmental protection target is realized while the pollutant treatment cost is reduced.

Description

Technical Field

[0001] This invention relates to the field of pollutant emission control technology, and more specifically to a method for optimizing the comprehensive cost of watershed pollution discharge rights trading. Background Technology

[0002] Pollution rights trading refers to the exchange of pollution discharge rights among enterprises within a defined area, provided that their total pollutant emissions do not exceed permitted limits. This mechanism allows companies to sell their unused pollution rights to other enterprises that need to increase their emissions, thereby optimizing resource allocation and protecting the environment. This mechanism incentivizes companies to reduce emissions through technological upgrades and cleaner production, thus generating economic returns. Through pollution rights trading, companies receive economic compensation for their environmental protection efforts, promoting pollutant reduction and contributing to ecological improvement.

[0003] While emissions trading can encourage companies to proactively treat pollutants instead of directly discharging them, existing schemes place too much emphasis on minimizing the pollutant treatment costs for individual companies. Emissions trading may lead to uneven distribution of pollutant emissions within a watershed, resulting in pollutant exceedances in specific areas and posing localized environmental risks. Furthermore, trading decisions are often considered from the perspective of individual companies, neglecting the overall pollutant treatment costs within the watershed. This makes it difficult to minimize the total pollutant treatment costs (including emissions trading costs and remediation costs) within the watershed while ensuring environmental safety objectives. Therefore, providing a comprehensive cost optimization method for watershed emissions trading is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0004] In view of this, the present invention provides a method for optimizing the comprehensive cost of watershed pollution rights trading. By constructing a two-layer comprehensive cost optimization model for pollution rights trading and setting the regional emission capacity within the watershed, the method solves the problems of conflict between existing technology market transactions and environmental objectives, and the non-optimal total system cost, thereby minimizing the comprehensive cost of pollution control across the entire watershed.

[0005] To achieve the above objectives, the present invention provides the following technical solution: A method for optimizing the overall cost of watershed pollution rights trading includes the following steps: S1. Obtain the allowable pollutant discharge volume for each region within the target watershed, historical pollution discharge data for all enterprises, and pollution treatment costs. S2. Construct a two-tier comprehensive cost optimization model for pollution rights trading with the objective function of the lowest treatment cost within the watershed, including an upper-level watershed decision-making model and a lower-level enterprise decision-making model; S3. Set an initial pollution discharge rights allocation scheme, iteratively solve the comprehensive cost optimization model of the two-level pollution discharge rights trading, and obtain the optimal decision scheme; S4. Output the optimal decision-making scheme, including the pollution rights allocation scheme, equilibrium price, enterprise decision-making scheme, and minimum treatment cost.

[0006] Optionally, S1 specifically involves: collecting hydrological and water quality data of the target watershed; determining the total allowable discharge of pollutants in the watershed and the allowable discharge of pollutants in each region within the watershed based on the water environment capacity model; and obtaining historical discharge data, pollutant treatment technologies, and their corresponding discharge treatment cost functions for all polluting enterprises within the watershed.

[0007] Optionally, the objective function of the upper-level watershed decision model is: ; In the formula, To reduce processing costs, For the number of enterprises, For the first i The cost of wastewater treatment for individual enterprises For the first i The amount of wastewater treated by each enterprise The price for emissions trading, For the first i The amount of pollution discharge rights held by an individual enterprise after the transaction For the first i The amount of pollution discharge rights allocated to each enterprise This is the loss coefficient for emissions trading.

[0008] Optionally, the constraints of the two-level pollution rights trading comprehensive cost optimization model include total watershed pollutant discharge constraints, regional pollutant discharge constraints, pollution rights balance constraints, pollution rights non-negativity constraints, pollution treatment extreme value constraints, transaction cost extreme value constraints, and initial pollution discharge allocation constraints.

[0009] Optionally, S3 specifically refers to: S31. Set multiple initial pollution discharge rights allocation schemes and initial pollution discharge rights trading prices; S32. Calculate the pollution rights trading volume and pollution treatment volume of each enterprise corresponding to each pollution rights allocation scheme and pollution rights trading price based on the lower-level enterprise decision-making model, until the market reaches the supply and demand balance of pollution rights, and obtain multiple sets of equilibrium prices and decision-making schemes of each enterprise. S33. Input the equilibrium price and the decision-making schemes of each enterprise into the upper-level watershed decision-making model, and calculate the processing cost of the entire watershed under each set of equilibrium prices and the decision-making schemes of each enterprise. S34. Iteratively solve the upper-level watershed decision model, and take the pollution rights allocation scheme with the lowest processing cost as the optimal pollution rights allocation scheme to obtain the corresponding equilibrium price and enterprise decision scheme.

[0010] Optionally, S31 specifically refers to: Define the decision variables of the upper-level watershed decision model, encode the initial pollution discharge rights allocation for each enterprise, set the population size, number of iterations, and crossover and mutation probability of the iterative solution algorithm, input the actual pollution treatment cost calculation function, historical emissions, and pollutant emission limits of each region for all enterprises, and randomly generate multiple initial pollution discharge rights allocation schemes according to preset rules and constraints to form the first generation population.

[0011] Optionally, S32 specifically includes: For each set of pollution discharge rights allocation schemes and pollution discharge rights trading prices, enterprises calculate their optimal pollution discharge treatment volume based on the principle that the treatment cost equals the purchase price of pollution discharge rights. They then calculate the net demand for pollution discharge rights for each enterprise, and finally calculate the total net demand for all enterprises. If the total net demand for all enterprises is greater than 0, it indicates a shortage of pollution discharge rights, and the pollution discharge rights trading price is increased. If the total net demand for all enterprises is less than 0, it indicates a surplus of pollution discharge rights, and the pollution discharge rights trading price is decreased. The pollution discharge rights trading price is iteratively adjusted until the total net demand for all enterprises is within the preset supply and demand balance threshold. The corresponding pollution discharge rights trading price is then taken as the equilibrium price.

[0012] Optionally, S34 specifically includes: Treating a set of equilibrium prices and each firm's decision-making scheme as an individual, and the entire watershed's processing cost as the corresponding fitness function value, the population is sorted non-dominated based on the fitness function values ​​of all individuals. Individuals are randomly selected from the population for crossover and mutation operations, prioritizing those with smaller non-dominated orders. This selection is repeated multiple times to obtain multiple parent individuals for crossover and mutation operations, generating offspring populations. The parent and offspring populations are merged, and the merged population is sorted non-dominated, dividing individuals into different non-dominated layers. Individuals from each non-dominated layer are added to the new population sequentially until the new population size reaches the initial population size. This process of non-dominated sorting, crossover and mutation operations, and new population generation is repeated until the maximum number of iterations is reached. The first layer in the non-dominated layer is taken as the optimal solution set, and the optimal solution is selected from the optimal solution set according to actual needs.

[0013] As can be seen from the above technical solutions, compared with the prior art, the present invention provides a comprehensive cost optimization method for watershed pollution rights trading, which has the following beneficial effects: The present invention sets the total allowable pollutant discharge for each region within the target watershed, ensuring that the final allocation scheme and pollution rights decision scheme can meet the water quality environmental protection goals of the entire watershed and local areas, thus solving the problem of conflict between market transactions and environmental goals in the prior art; The present invention constructs a two-layer comprehensive cost optimization model for pollution rights trading with the lowest treatment cost within the watershed as the objective function, considering the overall pollutant treatment cost within the watershed, avoiding the failure to achieve the goal of minimizing overall costs due to individual enterprise decisions; The present invention can optimize the initial allocation and trading decisions of pollution rights, providing decision support for managers. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0015] Figure 1 This is a flowchart of the comprehensive cost optimization method for watershed pollution discharge rights trading according to the present invention; Figure 2 This is a flowchart of the solution process for the comprehensive cost optimization model of the two-tiered emissions trading system of the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] This invention discloses a method for optimizing the overall cost of watershed pollution discharge rights trading, such as... Figure 1 As shown, it includes the following steps: S1. Obtain the allowable pollutant discharge volume for each region within the target watershed, historical pollution discharge data for all enterprises, and pollution treatment costs. S2. Construct a two-tier comprehensive cost optimization model for pollution rights trading with the objective function of the lowest treatment cost within the watershed, including an upper-level watershed decision-making model and a lower-level enterprise decision-making model; S3. Set an initial pollution discharge rights allocation scheme, iteratively solve the comprehensive cost optimization model of the two-level pollution discharge rights trading, and obtain the optimal decision scheme; S4. Output the optimal decision-making scheme, including the pollution rights allocation scheme, equilibrium price, enterprise decision-making scheme, and minimum treatment cost.

[0018] Furthermore, S1 specifically involves: collecting hydrological and water quality data of the target watershed; determining the total allowable discharge of pollutants in the watershed and the allowable discharge of pollutants in each region within the watershed based on the water environment capacity model; and obtaining historical discharge data, pollutant treatment technologies, and corresponding discharge treatment cost functions of all polluting enterprises within the watershed.

[0019] In this embodiment of the invention, the water environment capacity model is specifically as follows: ; In the formula, The number of regions to be divided in the watershed. The total allowable emissions of pollutants for the target watershed. For the first j Design flow for each region For the first j Water quality target values ​​for each region For the first j Background water quality values ​​for the river section upstream of each region For the first j Pollutant degradation coefficient in each region For the first j The designed water volume for each area.

[0020] Furthermore, the objective function of the upper-level watershed decision-making model is: ; In the formula, To reduce processing costs, For the number of enterprises, For the first i The cost of wastewater treatment for individual enterprises For the first i The amount of wastewater treated by each enterprise The price for emissions trading, For the first i The amount of pollution discharge rights held by an individual enterprise after the transaction For the first i The amount of pollution discharge rights allocated to each enterprise This is the loss coefficient for emissions trading.

[0021] In this embodiment of the invention, pollution treatment cost refers to the construction, operation, and maintenance costs of end-of-pipe treatment facilities invested by an enterprise to reduce pollutant emissions and ensure that its emissions do not exceed the pollution discharge rights it holds. This cost is determined based on the pollutant treatment technology adopted by the enterprise and its corresponding pollution treatment cost function, which is fitted using historical values. The pollution discharge rights trading loss coefficient represents the indirect costs incurred by an enterprise when buying and selling pollution discharge rights in the market, such as commissions and fees paid to the trading platform, the human and time costs incurred in finding trading partners and negotiating prices, and the routine expenses incurred by the enterprise in implementing the pollution discharge rights management system.

[0022] Furthermore, the constraints of the two-tier pollution rights trading comprehensive cost optimization model include total watershed pollutant discharge constraints, regional pollutant discharge constraints, pollution rights balance constraints, pollution rights non-negativity constraints, pollution treatment extreme value constraints, transaction cost extreme value constraints, and initial pollution discharge allocation constraints.

[0023] In this embodiment of the invention, specifically, the total pollutant discharge constraint means that after the transaction, the actual total discharge of all polluting enterprises in the entire basin shall not exceed the total allowable pollutant discharge of the target basin: ; In the formula, For the first i Baseline pollutant emissions for each enterprise; Regional pollutant emission constraints mean that the actual total emissions of polluting enterprises in each region must not exceed the allowable pollutant emission limits for that region. ; In the formula, For the first in the region l Baseline pollutant emissions for individual enterprises For the first in the region l The amount of wastewater treated by each enterprise This represents the total amount of pollutants emitted within the region. The number of businesses in the region; The pollution rights balancing constraint means that all companies in the market must buy and sell an equal number of pollution rights: ; The non-negative constraint on emission rights means that the emission rights ultimately held by a company cannot be negative. ; The extreme value constraint on wastewater treatment capacity indicates that a company's wastewater treatment capacity is limited by technology, funding, and physical conditions. ; In the formula, For enterprises iThe maximum feasible amount of governance under current technological and economic conditions; Transaction cost extreme value constraints mean that transaction prices are restricted: ; In the formula, The minimum allowed transaction price, The maximum allowed transaction price; Initial emission allocation constraints mean that the allocation of initial emission quotas is restricted by industry policies, such as certain types of enterprises receiving higher allocations.

[0024] Furthermore, such as Figure 2 As shown, S3 specifically refers to: S31. Set multiple initial pollution discharge rights allocation schemes and initial pollution discharge rights trading prices; S32. Calculate the pollution rights trading volume and pollution treatment volume of each enterprise corresponding to each pollution rights allocation scheme and pollution rights trading price based on the lower-level enterprise decision-making model, until the market reaches the supply and demand balance of pollution rights, and obtain multiple sets of equilibrium prices and decision-making schemes of each enterprise. S33. Input the equilibrium price and the decision-making schemes of each enterprise into the upper-level watershed decision-making model, and calculate the processing cost of the entire watershed under each set of equilibrium prices and the decision-making schemes of each enterprise. S34. Iteratively solve the upper-level watershed decision model, and take the pollution rights allocation scheme with the lowest processing cost as the optimal pollution rights allocation scheme to obtain the corresponding equilibrium price and enterprise decision scheme.

[0025] Furthermore, S31 specifically refers to: Define the decision variables of the upper-level watershed decision model, encode the initial pollution discharge rights allocation for each enterprise, set the population size, number of iterations, and crossover and mutation probability of the iterative solution algorithm, input the actual pollution treatment cost calculation function, historical emissions, and pollutant emission limits of each region for all enterprises, and randomly generate multiple initial pollution discharge rights allocation schemes according to preset rules and constraints to form the first generation population.

[0026] Furthermore, S32 specifically refers to: For each set of pollution discharge rights allocation schemes and pollution discharge rights trading prices, enterprises calculate their optimal pollution discharge treatment volume based on the principle that the treatment cost equals the purchase price of pollution discharge rights. They then calculate the net demand for pollution discharge rights for each enterprise, and finally calculate the total net demand for all enterprises. If the total net demand for all enterprises is greater than 0, it indicates a shortage of pollution discharge rights, and the pollution discharge rights trading price is increased. If the total net demand for all enterprises is less than 0, it indicates a surplus of pollution discharge rights, and the pollution discharge rights trading price is decreased. The pollution discharge rights trading price is iteratively adjusted until the total net demand for all enterprises is within the preset supply and demand balance threshold. The corresponding pollution discharge rights trading price is then taken as the equilibrium price.

[0027] In this embodiment of the invention, the supply-demand balance threshold is a minimum value. When the absolute value of the total net demand of all enterprises is less than the supply-demand balance threshold, although the total net demand of all enterprises is not zero, the supply and demand of pollution discharge rights can be considered to be in balance. A dichotomy method can be used to determine the new pollution discharge rights trading price.

[0028] Furthermore, S34 specifically refers to: Treating a set of equilibrium prices and each firm's decision-making scheme as an individual, and the entire watershed's processing cost as the corresponding fitness function value, the population is sorted non-dominated based on the fitness function values ​​of all individuals. Individuals are randomly selected from the population for crossover and mutation operations, prioritizing those with smaller non-dominated orders. This selection is repeated multiple times to obtain multiple parent individuals for crossover and mutation operations, generating offspring populations. The parent and offspring populations are merged, and the merged population is sorted non-dominated, dividing individuals into different non-dominated layers. Individuals from each non-dominated layer are added to the new population sequentially until the new population size reaches the initial population size. This process of non-dominated sorting, crossover and mutation operations, and new population generation is repeated until the maximum number of iterations is reached. The first layer in the non-dominated layer is taken as the optimal solution set, and the optimal solution is selected from the optimal solution set according to actual needs.

[0029] Based on the processing cost of all individuals, perform non-dominated sorting, randomly select individuals from the population for crossover and mutation operations to generate offspring populations, merge the parent and offspring populations, perform non-dominated sorting on the merged populations, divide individuals into different non-dominated layers, and add individuals from each non-dominated layer to the new population in turn until the size of the new population reaches the initial population size. Repeat the non-dominated sorting, crossover and mutation operations, and new population generation until the maximum number of iterations is reached, and use the first layer in the non-dominated layer as the optimal solution set.

[0030] In this embodiment of the invention, the non-dominated sorting is specifically as follows: Comparing dominance relationships, for each pair of individuals in the population i and j Check the fitness function value; if the individual i Not worse than j Then it is believed i Dominate j The process iterates through all individuals, identifying those not dominated by any other individual. These individuals form the first non-dominated frontier F1. Individuals in F1 are removed, and from the remaining individuals, another group of individuals not dominated by any other remaining individuals is identified, forming the second non-dominated frontier F2. This process is repeated until all individuals are assigned to a frontier and a corresponding level value. A smaller level value indicates better convergence of the solution.

[0031] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0032] Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for optimizing the overall cost of watershed pollution discharge rights trading, characterized in that, Includes the following steps: S1. Obtain the allowable pollutant discharge volume for each region within the target watershed, historical pollution discharge data for all enterprises, and pollution treatment costs. S2. Construct a two-tier comprehensive cost optimization model for pollution rights trading with the objective function of the lowest treatment cost within the watershed, including an upper-level watershed decision-making model and a lower-level enterprise decision-making model; S3. Set an initial pollution discharge rights allocation scheme, iteratively solve the comprehensive cost optimization model of the two-level pollution discharge rights trading, and obtain the optimal decision scheme; S4. Output the optimal decision-making scheme, including the pollution rights allocation scheme, equilibrium price, enterprise decision-making scheme, and minimum treatment cost.

2. The method for optimizing the comprehensive cost of watershed pollution discharge rights trading according to claim 1, characterized in that, S1 specifically involves: collecting hydrological and water quality data of the target watershed; determining the total allowable discharge of pollutants in the watershed and the allowable discharge of pollutants in each region within the watershed based on the water environment capacity model; and obtaining historical discharge data, pollutant treatment technologies, and corresponding discharge treatment cost functions of all polluting enterprises within the watershed.

3. The method for optimizing the comprehensive cost of watershed pollution discharge rights trading according to claim 1, characterized in that, The objective function of the upper-level watershed decision model is: ； In the formula, To reduce processing costs, For the number of enterprises, For the first i The cost of wastewater treatment for individual enterprises For the first i The amount of wastewater treated by each enterprise The price for emissions trading, For the first i The amount of pollution discharge rights held by an individual enterprise after the transaction For the first i The amount of pollution discharge rights allocated to each enterprise This is the loss coefficient for emissions trading.

4. The method for optimizing the comprehensive cost of watershed pollution discharge rights trading according to claim 1, characterized in that, The constraints of the two-level pollution rights trading comprehensive cost optimization model include total watershed pollutant discharge constraints, regional pollutant discharge constraints, pollution rights balance constraints, pollution rights non-negativity constraints, pollution treatment extreme value constraints, transaction cost extreme value constraints, and initial pollution discharge allocation constraints.

5. The method for optimizing the comprehensive cost of watershed pollution discharge rights trading according to claim 1, characterized in that, S3 specifically refers to: S31. Set multiple initial pollution discharge rights allocation schemes and initial pollution discharge rights trading prices; S32. Calculate the pollution rights trading volume and pollution treatment volume of each enterprise corresponding to each pollution rights allocation scheme and pollution rights trading price based on the lower-level enterprise decision-making model, until the market reaches the supply and demand balance of pollution rights, and obtain multiple sets of equilibrium prices and decision-making schemes of each enterprise. S33. Input the equilibrium price and the decision-making schemes of each enterprise into the upper-level watershed decision-making model, and calculate the processing cost of the entire watershed under each set of equilibrium prices and the decision-making schemes of each enterprise. S34. Iteratively solve the upper-level watershed decision model, and take the pollution rights allocation scheme with the lowest processing cost as the optimal pollution rights allocation scheme to obtain the corresponding equilibrium price and enterprise decision scheme.

6. The method for optimizing the comprehensive cost of watershed pollution discharge rights trading according to claim 5, characterized in that, S31 specifically refers to: Define the decision variables of the upper-level watershed decision model, encode the initial pollution discharge rights allocation for each enterprise, set the population size, number of iterations, and crossover and mutation probability of the iterative solution algorithm, input the actual pollution treatment cost calculation function, historical emissions, and pollutant emission limits of each region for all enterprises, and randomly generate multiple initial pollution discharge rights allocation schemes according to preset rules and constraints to form the first generation population.

7. The method for optimizing the comprehensive cost of watershed pollution discharge rights trading according to claim 5, characterized in that, S32 specifically refers to: For each set of pollution discharge rights allocation schemes and pollution discharge rights trading prices, enterprises calculate their optimal pollution discharge treatment volume based on the principle that the treatment cost equals the purchase price of pollution discharge rights. They then calculate the net demand for pollution discharge rights for each enterprise, and finally calculate the total net demand for all enterprises. If the total net demand for all enterprises is greater than 0, it indicates a shortage of pollution discharge rights, and the pollution discharge rights trading price is increased. If the total net demand for all enterprises is less than 0, it indicates a surplus of pollution discharge rights, and the pollution discharge rights trading price is decreased. The pollution discharge rights trading price is iteratively adjusted until the total net demand for all enterprises is within the preset supply and demand balance threshold. The corresponding pollution discharge rights trading price is then taken as the equilibrium price.

8. The method for optimizing the comprehensive cost of watershed pollution discharge rights trading according to claim 5, characterized in that, S34 specifically refers to: Treating a set of equilibrium prices and each firm's decision-making scheme as an individual, and the entire watershed's processing cost as the corresponding fitness function value, the population is sorted non-dominated based on the fitness function values ​​of all individuals. Individuals are randomly selected from the population for crossover and mutation operations, prioritizing those with smaller non-dominated orders. This selection is repeated multiple times to obtain multiple parent individuals for crossover and mutation operations, generating offspring populations. The parent and offspring populations are merged, and the merged population is sorted non-dominated, dividing individuals into different non-dominated layers. Individuals from each non-dominated layer are added to the new population sequentially until the new population size reaches the initial population size. This process of non-dominated sorting, crossover and mutation operations, and new population generation is repeated until the maximum number of iterations is reached. The first layer in the non-dominated layer is taken as the optimal solution set, and the optimal solution is selected from the optimal solution set according to actual needs.

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