Cross-regional pollution compensation method based on multi-dimensional ecological bearing capacity and game theory

Abstract

The invention discloses a cross-regional pollution compensation method based on a multi-dimensional ecological bearing capacity and a game theory, and relates to the technical field of environmental economics and ecological compensation crossing, and the method comprises the following steps: 1, constructing a dynamic ecological bearing index CEI (i, t), and fusing the service supply capability, pollution absorption capability and social economic pressure of a regional ecological system to obtain a dynamic ecological bearing index CEI (i, t); the regional ecological environment bearing level is quantified; and step 2, establishing a pollution compensation responsibility distribution model based on the CEI (i, t) and the game theory, and determining the pollution responsibility ratio and the compensation amount of each region. The dynamic quantification of the ecological bearing capacity is realized, and the limitation of a traditional evaluation mode is broken through; the compensation mechanism is more fairness and scientificity, and the system acceptability is improved.

Description

Technical Field

[0001] This invention relates to the interdisciplinary field of environmental economics and ecological compensation, and in particular to a cross-regional pollution compensation method based on multidimensional ecological carrying capacity and game theory. Background Technology

[0002] With the continuous advancement of ecological civilization construction, the ecological environment damage compensation mechanism has gradually become an important component of the environmental governance system. Especially against the backdrop of increasingly prominent cross-regional ecological environment pollution problems, constructing a scientific, fair, and efficient ecological compensation and pollution compensation mechanism has become a crucial issue that urgently needs to be addressed.

[0003] Currently, although some research and engineering practices have attempted to combine ecological value assessment, pollution monitoring, and compensation mechanisms, the following three technical bottlenecks still exist: Existing technologies often use a single indicator (such as pollution intensity per unit of GDP, ecosystem service value, etc.) to calculate the basis for compensation, but fail to incorporate the region's "pollution capacity" into the ecological carrying capacity assessment system. This makes it difficult to fully reflect the region's actual capacity to withstand pollution and its environmental capacity, resulting in a mismatch between the basis for compensation and ecological risks. Summary of the Invention

[0004] The purpose of this invention is to provide a cross-regional pollution compensation method based on multidimensional ecological carrying capacity and game theory, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention adopts the following technical solution, comprising the following steps: Step 1: Constructing a dynamic ecological carrying capacity index CEI(i,t), which quantifies the regional ecological environment carrying capacity by integrating the regional ecosystem service supply capacity, pollution absorption capacity, and socio-economic pressure; Step 2: Establishing a pollution compensation liability allocation model based on the CEI(i,t) and game theory to determine the pollution liability ratio and compensation amount for each region; Step 3: Automatically transferring the compensation amount through a blockchain smart contract to achieve full automation of the cross-regional pollution compensation process.

[0006] The formula for calculating CEI(i,t) in step one is as follows: ; Wherein, CEI(i,t) is the ecological carrying capacity index of region i at time t; S_{eco}(i,t) is the ecosystem service supply capacity of region i at time t; S_{base} is the baseline value of ecosystem service supply capacity; C_{poll}(i,t) is the pollution absorption capacity of region i at time t; C_{max} is the maximum value of pollution absorption capacity; P_{soc}(i,t) is the socioeconomic pressure of region i at time t; P_{ref} is the reference value of socioeconomic pressure; α, β, and γ are weighting coefficients, and are dynamically calibrated using the entropy method.

[0007] The ecosystem service supply capacity S_{eco}(i,t) includes water supply and carbon sink, and is obtained by referring to the E(i) calculation method in CN118153817A; the pollution absorption capacity C_{poll}(i,t) is calculated based on the water / soil environmental capacity model, expanding the GEP accounting scope of CN117974400A; the socio-economic pressure P_{soc}(i,t) is the pollution emissions per unit of GDP.

[0008] The liability allocation function of the pollution compensation liability allocation model described in step two is: ; Where Liability(i) is the proportion of pollution responsibility of region i; Poll_{emit}(i) is the actual pollution load emitted by region i; Poll_{std} is the standard pollution carrying capacity value per unit CEI; [\cdot]^+ is a non-negative function, that is, when the value in the parentheses is negative, it takes 0, and when it is positive, it takes its original value; n is the total number of regions involved.

[0009] The formula for calculating the compensation amount mentioned in step two is as follows: ; Where Comp (i→j) is the amount of compensation paid by region i to region j; D_{ij} is the pollution transmission coefficient, calculated based on a water flow model, wind field model, or gravity model; K_{dam}(j) is the ecological loss value of the compensated region j; a and b are empirical coefficients used to adjust the weights of ecological loss and payment capacity factors; GDP_i is the gross domestic product of region i; and GDP_j is the gross domestic product of region j.

[0010] The ecological loss value K_{dam}(j) is calculated by weighted average based on the assessment system established by the reduction in species abundance, water eutrophication index and farmland yield reduction rate in the compensated area j.

[0011] In step one, when constructing the dynamic ecological carrying capacity index CEI(i,t), a multi-source heterogeneous data stream is accessed for real-time dynamic updates. This multi-source heterogeneous data stream includes: Satellite remote sensing data is used for monitoring ecosystem service functions; Ground-based IoT sensor network data provides real-time feedback on changes in pollutants; Data from government statistical platforms is used to supplement socioeconomic parameters.

[0012] In step three, the blockchain smart contract is deployed on the Ethereum or Hyperledger Fabric blockchain network, and its execution flow is as follows: When the CEI (i,t) exceedance event is triggered, on-chain contamination source tracing and confirmation are performed; The blockchain wallets in the pollution liability areas are used to transfer compensation funds and restoration funds to the compensated areas. Record the results of the implementation and issue regulatory reports.

[0013] Systems applied to cross-regional pollution compensation methods: The system includes: The IoT monitoring layer deploys a multi-source sensor network to collect real-time data related to ecosystem service supply capacity, pollution absorption capacity, and socio-economic pressures. The blockchain execution layer has a built-in smart contract that automatically triggers a compensation process when CEI(i,t) exceeds a set threshold, calculating and executing the fund transfer according to the formulas described in claims 4 and 5. The EOD application layer provides API interfaces to connect to the regional ecological restoration project management system, enabling the coordinated management of compensation funds and ecological restoration projects.

[0014] Compared with existing technologies, this invention has significant advantages and beneficial effects. It realizes the dynamic quantification of ecological carrying capacity, breaking through the limitations of traditional assessment methods; it makes the compensation mechanism more equitable and scientific, and improves the acceptance of the system; it automates the pollution compensation process through smart contracts, significantly improving the efficiency of policy response; at the same time, the entire process is deployed on a blockchain platform, enhancing the transparency and tamper resistance of the system.

[0015] To more clearly illustrate the structural features and effects of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the cross-regional pollution compensation method based on multidimensional ecological carrying capacity and game theory, as proposed in this invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0018] It should be noted that when a component is said to be "fixed to" another component, it can be directly attached to the other component or there may be an intervening component. When a component is said to be "connected to" another component, it can be directly connected to the other component or there may be an intervening component. The terms "vertical," "horizontal," "left," "right," and similar expressions used in this document are for illustrative purposes only.

[0019] Please see Figure 1 This invention provides a cross-regional pollution compensation method based on multidimensional ecological carrying capacity and game theory, comprising the following steps: Step 1: Constructing a dynamic ecological carrying capacity index CEI(i,t), which quantifies the regional ecological environment carrying capacity by integrating regional ecosystem service supply capacity, pollution absorption capacity, and socio-economic pressure; Step 2: Establishing a pollution compensation liability allocation model based on the CEI(i,t) and game theory to determine the pollution liability ratio and compensation amount for each region; Step 3: Automatically transferring the compensation amount through a blockchain smart contract to achieve full-process automation of cross-regional pollution compensation. Step one emphasizes the construction of a dynamic ecological carrying capacity index (CEI(i,t)). The key is to integrate three dimensions: ecosystem service supply capacity, pollution absorption capacity, and socio-economic pressure. The aim is to comprehensively and quantitatively reflect the actual carrying capacity of the regional ecological environment and provide a scientific basis for subsequent responsibility allocation.

[0020] Step two demonstrates the establishment of a pollution compensation liability allocation model based on the previously constructed CEI (i,t) and game theory. This model clarifies the proportion of responsibility each region should bear in a pollution incident and the amount of compensation to be paid, reflecting the scientific and fair nature of liability allocation.

[0021] Step three introduces blockchain smart contract technology to realize the automatic transfer of compensation funds. The core of this step is to solve the problems of difficulty and low efficiency in cross-regional compensation execution, so as to automate and standardize the entire compensation process and form a complete closed loop.

[0022] The formula for calculating CEI(i,t) in step one is as follows: ; Wherein, CEI(i,t) is the ecological carrying capacity index of region i at time t; S_{eco}(i,t) is the ecosystem service supply capacity of region i at time t; S_{base} is the baseline value of ecosystem service supply capacity; C_{poll}(i,t) is the pollution absorption capacity of region i at time t; C_{max} is the maximum value of pollution absorption capacity; P_{soc}(i,t) is the socioeconomic pressure of region i at time t; P_{ref} is the reference value of socioeconomic pressure; α, β, and γ are weighting coefficients, and are dynamically calibrated using the entropy method.

[0023] The ecosystem service supply capacity S_{eco}(i,t) includes water supply and carbon sink, and is obtained by referring to the E(i) calculation method in CN118153817A; the pollution absorption capacity C_{poll}(i,t) is calculated based on the water / soil environmental capacity model, expanding the GEP accounting scope of CN117974400A; the socio-economic pressure P_{soc}(i,t) is the pollution emissions per unit of GDP.

[0024] The liability allocation function of the pollution compensation liability allocation model described in step two is: ; Where Liability(i) is the proportion of pollution responsibility of region i; Poll_{emit}(i) is the actual pollution load emitted by region i; Poll_{std} is the standard pollution carrying capacity value per unit CEI; [\cdot]^+ is a non-negative function, that is, when the value in the parentheses is negative, it takes 0, and when it is positive, it takes its original value; n is the total number of regions involved; Ecosystem service provision capacity S_{eco}(i,t) includes water supply and carbon sink, and specifies the calculation method of E(i) with reference to CN118153817A to ensure the standardization and operability of the calculation of this indicator.

[0025] The pollution absorption capacity C_{poll}(i,t) is calculated based on the water / soil environmental capacity model, and the scope of GEP accounting in CN117974400A is expanded to more comprehensively reflect the region's ability to absorb pollutants.

[0026] Socioeconomic pressure P_{soc}(i,t) is defined as pollution emissions per unit of GDP, which links economic development with pollution emissions and reflects the pressure of socioeconomic activities on the ecological environment. The core of this function is to calculate the pollution liability ratio (i) of each region. The numerator is the difference (non-negative) between the actual pollution load emitted by region i and the standard pollution carrying capacity value of the region based on CEI (i,t), and the denominator is the sum of the above differences for all involved regions.

[0027] This calculation method ensures that only areas with excessive pollution emissions are assigned responsibility, and the degree of responsibility is related to the extent of exceeding the standards. This avoids unreasonable concentration of responsibility and reflects the principles of "whoever exceeds the standards bears the responsibility" and "the more the standards are exceeded, the greater the responsibility."

[0028] The formula for calculating the compensation amount mentioned in step two is as follows: ; Where Comp (i→j) is the compensation amount paid by region i to region j; D_{ij} is the pollution transmission coefficient, calculated based on a water flow model, wind field model, or gravity model; K_{dam}(j) is the ecological loss value of the compensated region j; a and b are empirical coefficients used to adjust the weights of ecological loss and payment capacity factors; GDP_i is the gross domestic product of region i; GDP_j is the gross domestic product of region j. The compensation amount Comp (i→j) is jointly determined by the liability ratio (i) of region i, the pollution transmission coefficient D_{ij}, and the ecological loss and payment capacity adjustment term; D_{ij} is calculated based on water flow models, wind field models, or gravity models, reflecting the intensity of pollutant transport from region i to region j, so that the compensation amount matches the scope and degree of pollution impact; The introduction of ecological loss value K_{dam}(j) and regional GDP ratio takes into account both the actual situation of ecological loss and the region's ability to pay, reflecting a balance between fairness and efficiency. The empirical coefficients a and b are used to adjust the weights of these two factors.

[0029] The ecological loss value K_{dam}(j) is calculated by weighted average based on the assessment system established by the reduction in species abundance, water eutrophication index and farmland yield reduction rate in the compensated area j.

[0030] In step one, when constructing the dynamic ecological carrying capacity index CEI(i,t), a multi-source heterogeneous data stream is accessed for real-time dynamic updates. This multi-source heterogeneous data stream includes: Satellite remote sensing data is used for monitoring ecosystem service functions; Ground-based IoT sensor network data provides real-time feedback on changes in pollutants; Data from government statistical platforms is used to supplement socioeconomic parameters.

[0031] In step three, the blockchain smart contract is deployed on the Ethereum or Hyperledger Fabric blockchain network, and its execution flow is as follows: When the CEI (i,t) exceedance event is triggered, on-chain contamination source tracing and confirmation are performed; The blockchain wallets in the pollution liability areas are used to transfer compensation funds and restoration funds to the compensated areas. Record the results of the implementation and issue regulatory reports.

[0032] Systems applied to cross-regional pollution compensation methods: The system includes: The IoT monitoring layer deploys a multi-source sensor network to collect real-time data related to ecosystem service supply capacity, pollution absorption capacity, and socio-economic pressures. The blockchain execution layer has a built-in smart contract that automatically triggers a compensation process when CEI(i,t) exceeds a set threshold, calculating and executing the fund transfer according to the formulas described in claims 4 and 5. The EOD application layer provides API interfaces to connect to the regional ecological restoration project management system, enabling the coordinated management of compensation funds and ecological restoration projects.

[0033] Phase 1: Pilot Deployment of EOD Construct a carbon-water dual-source traceability network: carbon source traceability uses satellite remote sensing (such as Landsat-8) and ground sensor fusion inversion; water pollution source traceability is based on the construction of a migration model based on pollutant quantum module.

[0034] Establish a data layer: Connect to the ecological environment cloud platform to obtain various parameters required for CEI calculation in real time, providing data support for subsequent ecological carrying capacity assessment.

[0035] Phase 2: Simulation of the compensation model Game Theory Solution: The empirical coefficients a and b in the compensation calculation formula are optimized using the Nash Equilibrium algorithm. The objective function is to maximize the total utility of each region. The constraint is that the total compensation amount does not exceed the product of the total pollution amount and the tax rate, i.e., max∑i=1nUi(Comp)st∑Comp≤Polltotal⋅Taxrate.

[0036] Smart contract development: Smart contract development is based on Hyperledger Fabric. The compensation trigger threshold is set to CEI (i,t)>1.2. When the regional ecological carrying capacity index is detected to exceed this threshold, the compensation process is automatically started.

[0037] Phase 3: Cross-regional expansion By dynamically updating the weight coefficients (α, β, γ) in the calculation formula of the ecological carrying capacity index CEI(i,t) through reinforcement learning, the model can adapt to the ecological characteristics of different EOD projects, thereby improving the applicability and accuracy of the method in different cross-regional scenarios.

[0038] In summary, this approach achieves dynamic quantification of ecological carrying capacity, breaking through the limitations of traditional assessment methods; it makes the compensation mechanism more equitable and scientific, increasing the acceptance of the system; it automates the pollution compensation process through smart contracts, significantly improving policy response efficiency; and the entire process is deployed on a blockchain platform, enhancing the system's transparency and tamper resistance.

[0039] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A cross-regional pollution compensation method based on multidimensional ecological carrying capacity and game theory, characterized in that: The process includes the following steps: Step 1: Construct a dynamic ecological carrying capacity index CEI(i,t), which quantifies the regional ecological carrying capacity by integrating the regional ecosystem service supply capacity, pollution absorption capacity, and socio-economic pressure; Step 2: Establish a pollution compensation liability allocation model based on the CEI(i,t) and game theory to determine the proportion of pollution liability and compensation amount for each region; Step 3: Automatically transfer the compensation amount through a blockchain smart contract to achieve full automation of the cross-regional pollution compensation process.

2. The cross-regional pollution compensation method based on multidimensional ecological carrying capacity and game theory as described in claim 1, characterized in that: The formula for calculating CEI(i,t) in step one is as follows: ； Wherein, CEI(i,t) is the ecological carrying capacity index of region i at time t; S_{eco}(i,t) is the ecosystem service supply capacity of region i at time t; S_{base} is the baseline value of ecosystem service supply capacity; C_{poll}(i,t) is the pollution absorption capacity of region i at time t; C_{max} is the maximum value of pollution absorption capacity; P_{soc}(i,t) is the socioeconomic pressure of region i at time t; P_{ref} is the reference value of socioeconomic pressure; α, β, and γ are weighting coefficients, and are dynamically calibrated using the entropy method.

3. The cross-regional pollution compensation method based on multidimensional ecological carrying capacity and game theory as described in claim 1, characterized in that: The ecosystem service supply capacity S_{eco}(i,t) includes water supply and carbon sink, and is obtained by referring to the E(i) calculation method in CN118153817A; the pollution absorption capacity C_{poll}(i,t) is calculated based on the water / soil environmental capacity model, expanding the GEP accounting scope of CN117974400A; the socio-economic pressure P_{soc}(i,t) is the pollution emissions per unit of GDP.

4. The cross-regional pollution compensation method based on multidimensional ecological carrying capacity and game theory as described in claim 1, characterized in that: The liability allocation function of the pollution compensation liability allocation model described in step two is: ； Where Liability(i) is the proportion of pollution responsibility of region i; Poll_{emit}(i) is the actual pollution load emitted by region i; Poll_{std} is the standard pollution carrying capacity value per unit CEI; [\cdot]^+ is a non-negative function, that is, when the value in the parentheses is negative, it takes 0, and when it is positive, it takes its original value; n is the total number of regions involved.

5. The cross-regional pollution compensation method based on multidimensional ecological carrying capacity and game theory as described in claim 1, characterized in that: The formula for calculating the compensation amount mentioned in step two is as follows: ； Where Comp (i→j) is the amount of compensation paid by region i to region j; D_{ij} is the pollution transmission coefficient, calculated based on a water flow model, wind field model, or gravity model; K_{dam}(j) is the ecological loss value of the compensated region j; a and b are empirical coefficients used to adjust the weights of ecological loss and payment capacity factors; GDP_i is the gross domestic product of region i; and GDP_j is the gross domestic product of region j.

6. The cross-regional pollution compensation method based on multidimensional ecological carrying capacity and game theory as described in claim 1, characterized in that: The ecological loss value K_{dam}(j) is calculated by weighted average based on the assessment system established by the reduction in species abundance, water eutrophication index and farmland yield reduction rate in the compensated area j.

7. The cross-regional pollution compensation method based on multidimensional ecological carrying capacity and game theory as described in claim 1, characterized in that: In step one, when constructing the dynamic ecological carrying capacity index CEI(i,t), a multi-source heterogeneous data stream is accessed for real-time dynamic updates. This multi-source heterogeneous data stream includes: Satellite remote sensing data is used for monitoring ecosystem service functions; Ground-based IoT sensor network data provides real-time feedback on changes in pollutants; Data from government statistical platforms is used to supplement socioeconomic parameters.

8. The cross-regional pollution compensation method based on multidimensional ecological carrying capacity and game theory as described in claim 1, characterized in that: In step three, the blockchain smart contract is deployed on the Ethereum or Hyperledger Fabric blockchain network, and its execution flow is as follows: When the CEI (i,t) exceedance event is triggered, on-chain contamination source tracing and confirmation are performed; Automatically invoke the liability allocation function and compensation calculation formula as described in claims 4 and 5; The blockchain wallets in the pollution liability areas are used to transfer compensation funds and restoration funds to the compensated areas. Record the results of the implementation and issue regulatory reports.

9. The cross-regional pollution compensation method based on multidimensional ecological carrying capacity and game theory according to any one of claims 1-8, applied to a system for cross-regional pollution compensation, characterized in that: The system includes: The IoT monitoring layer deploys a multi-source sensor network to collect real-time data related to ecosystem service supply capacity, pollution absorption capacity, and socio-economic pressures. The blockchain execution layer has a built-in smart contract that automatically triggers a compensation process when CEI(i,t) exceeds a set threshold, calculating and executing the fund transfer according to the formulas described in claims 4 and 5. The EOD application layer provides API interfaces to connect to the regional ecological restoration project management system, enabling the coordinated management of compensation funds and ecological restoration projects.

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