Regional carbon emission potential analysis and redundant carbon dioxide distribution method and system

Abstract

The invention discloses a regional carbon emission potential analysis and redundant carbon dioxide distribution method and system, and belongs to the technical field of carbon emission reduction potential analysis, and the method comprises the steps: S1, determining a to-be-evaluated region, and constructing a carbon dioxide emission influence factor regression model; s2, determining an input index and an output index of each region in any time period, and constructing an unexpected output super-efficiency SBM-DEA model to obtain a comprehensive efficiency value of each region and redundancy of an expected output index and an unexpected output index; s3, analyzing the carbon emission reduction potential of each region by using the redundancy of the unexpected output indexes; s4, under the constraint of the redundant carbon dioxide distribution objective function with the energy consumption constraint and the equipment health state constraint, searching the redundant carbon dioxide distribution proportion of each enterprise in each region through a GeneticAlgorithm algorithm; and S5, verifying the redundant carbon dioxide distributed by each enterprise, and carrying out necessary adjustment.

Description

Technical Field

[0001] The present invention belongs to the technical field of carbon emission reduction potential analysis, and in particular relates to a method and system for regional carbon emission potential analysis and redundant carbon dioxide allocation. Background Art

[0002] As the global climate change problem becomes increasingly serious, carbon emission reduction has become a core issue of great concern to the international community and governments. Due to the significant differences in economic structure, industrial development level, energy consumption pattern and other aspects between different regions, these factors directly affect the carbon emission efficiency and emission reduction potential of each region. Therefore, how to accurately evaluate the carbon emission efficiency of each region and reasonably allocate emission reduction tasks on this basis is of vital importance to achieving global carbon emission reduction goals.

[0003] Traditional assessment methods often have certain limitations when facing this complex problem. These methods usually cannot fully consider all factors that affect carbon emission efficiency, such as economic development level, industrial structure, energy consumption structure, technology level, etc. In addition, traditional assessment methods are also difficult to cope with the complexity and variability of the environment in practical applications, and it is difficult to find the optimal emission reduction allocation plan to ensure fair and reasonable allocation of emission reduction tasks among different regions. Summary of the invention

[0004] The present invention provides a method and system for regional carbon emission potential analysis and redundant carbon dioxide allocation. Based on SBM-DEA and Genetic Algorithm algorithms, the efficiency differences and emission reduction potentials of various regions under carbon dioxide as an undesirable output constraint are deeply analyzed through the undesirable output super-efficiency SBM-DEA model, and the optimal redundant carbon dioxide allocation scheme is solved under complex constraint conditions using a genetic algorithm.

[0005] To achieve the above-mentioned purpose, the first purpose of the present invention is to provide a method for regional carbon emission potential analysis and redundant carbon dioxide allocation, comprising:

[0006] S1. Determine the area to be evaluated and construct a regression model of factors affecting carbon dioxide emissions in the area to be evaluated;

[0007] S2. Determine the input index and output index of each region in the region to be evaluated within any time period, and use the input index and output index to construct an unexpected output super efficiency SBM-DEA model to obtain the comprehensive efficiency value of each region, as well as the redundancy of the expected output index and the unexpected output index in the input index and output index;

[0008] S3, analyzing the carbon emission reduction potential of each region by using the redundancy of the non-desirable output indicator;

[0009] S4. Under the constraints of the redundant carbon dioxide allocation objective function with energy consumption constraints and equipment health status constraints, the Genetic Algorithm algorithm is used to find the redundant carbon dioxide allocation ratio of each enterprise in each region;

[0010] S5. Verify the redundant CO2 allocated to each enterprise and make necessary adjustments.

[0011] Preferably, the regression model of carbon dioxide emission influencing factors is as follows:

[0012] CDE=α+β1EDL+β2HIP+β3IS+β4ES+β5TL+β6CL;

[0013] Among them, CDE, EDL, HIP, IS, ES, TL and CL represent the carbon dioxide emissions, economic development level, industrial structure, industrial structure, energy structure, technological level, factor endowment and environmental regulation of the region to be evaluated, respectively.

[0014] Preferably, the input indicators are simplified as follows: the economic development level is represented by GDP, the energy structure is represented by the proportion of raw coal consumption to total energy consumption, the industrial structure is represented by the proportion of total heavy industry output value to total industrial output value, the industrial structure is represented by the proportion of the added value of the tertiary industry to GDP, the technological level is represented by the number of patents granted, the factor endowment is represented by the labor-capital ratio, and the environmental regulation is represented by the completed investment amount of industrial waste gas treatment projects;

[0015] The output indicators include undesirable output and expected output, the undesirable output is industrial carbon dioxide emissions, and the expected output is the total industrial output value;

[0016] The undesired output super-efficiency SBM-DEA model is as follows:

[0017] Let X be the input matrix m is the type of input indicator, h is the number of decision-making units; Y g is the expected output matrix, s1 is the type of expected output; Y b is the expected output matrix, s2 is the type of expected output;

[0018] Then the measurement formula of the EEP of the decision-making unit to be evaluated is:

[0019]

[0020] In the formula, ρ * Indicates the point closest to the frontier formed by the unit to be evaluated and other units to be evaluated. - ,s g ,s bStrictly monotonically decreasing, and 0<ρ * ≤1, represents the shortfall of the expected output of the decision-making unit to be evaluated, represents the excess of the undesired output of the decision-making unit to be evaluated, s - represents the input slack variable, s g represents the expected output slack variable, s b represents the undesirable output slack variable, λ is the weight vector, and k represents the evaluated unit.

[0021] Preferably, the carbon emission reduction potential analysis includes three perspectives, namely, the scale of emission reduction that can be reduced, the relative emission reduction potential and the importance of emission reduction, wherein the scale of emission reduction that can be reduced is defined as the absolute value of the industrial carbon dioxide redundancy, indicating the industrial carbon dioxide emissions that can be reduced relative to the existing technical conditions; the relative emission reduction potential is defined as the ratio of the industrial carbon dioxide redundancy in the region that year to the actual emissions in the region that year; the importance of emission reduction is defined as the ratio of the scale of industrial carbon dioxide emission reduction in the region that year to the scale of industrial carbon dioxide emission reduction in all regions that year.

[0022] Preferably, the objective function expression is as follows:

[0023]

[0024] Where n is the number of enterprises in the region that participate in the allocation of redundant carbon dioxide, λ i is the redundant carbon dioxide allocation coefficient of the i-th enterprise in the region, C is the amount of redundant carbon dioxide to be allocated in the region, Allocate an estimated mean of the amount of redundant CO2 to all enterprises in the region, satisfying β is the maximum allocation ratio of redundant carbon dioxide in the region.

[0025] Preferably, the upper limit of the tolerance for redundant carbon dioxide allocated to each enterprise is:

[0026]

[0027] in, is the maximum upper limit of redundant carbon dioxide to be allocated for the i-th enterprise.

[0028] Preferably, the fitness function of the GeneticAlgorithm algorithm is its objective function:

[0029]

[0030] The crossover operation expression of the GeneticAlgorithm algorithm is:

[0031]

[0032] The second object of the present invention is to provide a regional carbon emission potential analysis and redundant carbon dioxide allocation system, comprising:

[0033] A model building module is used to determine the area to be evaluated and to build a regression model of factors affecting carbon dioxide emissions in the area to be evaluated;

[0034] The first analysis module determines the input index and output index of each region in the area to be evaluated within any time period, and uses the input index and output index to construct an unexpected output super efficiency SBM-DEA model to obtain the comprehensive efficiency value of each region, as well as the redundancy of the expected output index and the unexpected output index in the input index and output index;

[0035] The second analysis module uses the redundancy of the undesired output indicator to analyze the carbon emission reduction potential of each region;

[0036] The third analysis module, under the constraints of the redundant carbon dioxide allocation objective function with energy consumption constraints and equipment health status constraints, uses the GeneticAlgorithm algorithm to find the redundant carbon dioxide allocation ratio of each enterprise in each region;

[0037] The adjustment module verifies the redundant CO2 allocated to each enterprise and makes necessary adjustments.

[0038] A third object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-mentioned regional carbon emission potential analysis and redundant carbon dioxide allocation method.

[0039] A fourth object of the present invention is to provide a computer program product, comprising a computer program, which implements the above-mentioned regional carbon emission potential analysis and redundant carbon dioxide allocation method when executed by a processor.

[0040] The advantages and positive effects of this application are:

[0041] The present invention uses a non-expected output super-efficiency SBM-DEA model. Through this model, we can deeply analyze and study the performance of each region in terms of carbon emission efficiency, and reveal the potential emission reduction space in these regions in terms of carbon emissions. Such analysis and research provide a relatively accurate evaluation basis for the formulation of emission reduction plans, so that emission reduction work can be more targeted. At the same time, in order to solve the optimal redundant carbon dioxide allocation plan under complex constraints, the present invention uses the excellent optimization ability of genetic algorithms. Through this algorithm, we can ensure that in the process of achieving emission reduction tasks, a balance between fairness, rationality and efficiency is achieved.

[0042] In the process of analyzing carbon emission efficiency and solving redundant carbon dioxide allocation schemes, the present invention comprehensively considers various multivariate factors that affect carbon emissions, including but not limited to energy consumption structure, industrial structure, technology level, policies and regulations, etc. This comprehensive consideration makes the evaluation results both comprehensive and accurate, providing relatively accurate information support for decision makers, helping them to better formulate scientific and reasonable carbon emission reduction policies and measures.

[0043] In addition, the present invention is not only theoretically advanced in algorithm design, but also has great potential for practical application. Its algorithm is easy to implement and highly operable, which enables the present invention to be widely promoted and applied in regional carbon emission reduction management and policy formulation. Through the application of this algorithm, each region can be effectively guided to formulate a practical carbon emission reduction plan based on its actual situation, thereby providing strong technical support for achieving the goal of controlling total carbon emissions and reducing carbon emission intensity. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0045] Figure 1 A flow chart showing a preferred embodiment of the present invention is shown. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] The present invention is mainly divided into two parts: first, using the non-expected output super-efficiency SBM-DEA model, aiming at the constraint condition of carbon dioxide as non-expected output, the efficiency difference between regions and the potential carbon emission reduction space are deeply analyzed; secondly, on the basis of comprehensive consideration of multiple constraints such as carbon dioxide emissions, economic development level, industrial structure, industrial structure, energy structure, technology level, factor endowment and environmental regulation, the redundant carbon dioxide allocation objective function is constructed, and the complex carbon dioxide allocation model is solved by the efficient Genetic Algorithm algorithm, aiming to generate a distribution ratio that is both practical and optimized. The core points of the present invention are that, on the one hand, the non-expected output super-efficiency SBM-DEA model is used to accurately evaluate the carbon emission efficiency of each region under existing technical conditions and economic activities, thereby revealing its emission reduction potential and improvement space; on the other hand, relying on the excellent global search capability and multi-constraint processing capability of the Genetic Algorithm algorithm, the optimal redundant carbon dioxide allocation scheme is explored in a complex and changeable real environment to ensure that the execution of emission reduction tasks is both fair, reasonable and efficient.

[0048] See also Figure 1 ; The first embodiment, a method for regional carbon emission potential analysis and redundant carbon dioxide allocation, comprises the following steps:

[0049] S1. Determine the assessment area and construct a regression model of factors affecting carbon dioxide emissions

[0050] The first step is to clarify the scope of the region to be evaluated, and build a regression model for the factors affecting carbon dioxide emissions based on historical data and expert knowledge. This model takes into account multiple factors such as economic development level (EDL), industrial structure (HIP), industrial structure (IS), energy structure (ES), technology level (TL), factor endowment (CL) and environmental regulation (REG), aiming to comprehensively and accurately reflect the current status of carbon emissions in various regions.

[0051] S2. Construct the SBM-DEA model of unexpected output super efficiency and calculate the comprehensive efficiency and redundancy

[0052] In any given time period, it is necessary to collect input indicators (covering GDP, energy structure, industrial structure, industrial structure, technological level, factor endowment and environmental regulation, etc.) and output indicators (including expected output such as total industrial output value, and undesirable output such as industrial carbon dioxide emissions) of each region. Subsequently, these indicators are used to construct the undesirable output super-efficiency SBM-DEA model, and the carbon emission efficiency and potential emission reduction capacity of each region are evaluated by calculating the comprehensive efficiency value of each region and the redundancy of input and output indicators.

[0053] S3. Analyze the carbon reduction potential of each region

[0054] Based on the results of the non-expected output super-efficiency SBM-DEA model, we will conduct a detailed analysis of the carbon reduction potential of each region from three dimensions: the scale of emission reduction, relative emission reduction potential, and emission reduction importance. Among them, the scale of emission reduction represents the amount of industrial carbon dioxide emissions that can be reduced in each region under existing technical conditions; the relative emission reduction potential reveals the relative size of the emission reduction potential of each region; and the importance of emission reduction is to evaluate the importance of the role played by each region in the global or regional emission reduction task.

[0055] S4. Solving the redundant CO2 allocation model using genetic algorithm

[0056] In the face of actual conditions such as energy consumption constraints and equipment health constraints, an efficient genetic algorithm will be used to solve the complex carbon dioxide allocation model. With its powerful global search capability and multi-constraint processing capabilities, the genetic algorithm can find the optimal redundant carbon dioxide allocation solution in a complex and changing actual environment, thereby ensuring the fair, reasonable and efficient execution of emission reduction tasks.

[0057] S5. Verify and fine-tune the allocation results

[0058] Finally, the redundant CO2 allocated to each enterprise will be verified to ensure that it is in line with the actual situation and emission reduction targets. If necessary, the allocation plan will be fine-tuned based on the verification results to further optimize the emission reduction effect.

[0059] In order to more deeply understand the technical solution of the present invention, the following non-limiting examples are given:

[0060] The regression model of the factors affecting carbon dioxide emissions is specifically described as follows:

[0061] CDE=α+β1EDL+β2HIP+β3IS+β4ES+β5TL+β6CL;

[0062] Among them, CDE, EDL, HIP, IS, ES, TL and CL represent the carbon dioxide emissions, economic development level, industrial structure, industrial structure, energy structure, technological level, factor endowment and environmental regulation of the region to be evaluated, respectively.

[0063] The input indicators are simplified into the following aspects: the level of economic development is measured by gross domestic product (GDP); the energy structure is represented by the proportion of raw coal consumption in total energy consumption; the industrial structure is reflected by the proportion of total heavy industry output value in total industrial output value; the industrial structure is reflected by the proportion of the added value of the tertiary industry in GDP; the technological level is measured by the number of patents granted; the factor endowment is characterized by the labor-capital ratio; and environmental regulation is measured by the completed investment in industrial waste gas treatment projects.

[0064] The output indicators cover two categories: unexpected output and expected output. Among them, unexpected output specifically refers to industrial carbon dioxide emissions, while expected output refers to total industrial output value.

[0065] The undesired output super-efficiency SBM-DEA model is as follows:

[0066] Let X be the input matrix m is the type of input indicator, h is the number of decision-making units; Y g is the expected output matrix, s1 is the type of expected output; Y b is the expected output matrix, s2 is the type of expected output;

[0067] Then the measurement formula of the EEP of the decision-making unit to be evaluated is:

[0068]

[0069] In the formula, ρ * Indicates the point closest to the frontier formed by the unit to be evaluated and other units to be evaluated. - ,s g ,s b Strictly monotonically decreasing, and 0<ρ * ≤1, represents the shortfall of the expected output of the decision-making unit to be evaluated, represents the excess of the undesired output of the decision-making unit to be evaluated, s - represents the input slack variable, s g represents the expected output slack variable, s b represents the undesirable output slack variable, λ is the weight vector, and k represents the evaluated unit.

[0070] The carbon reduction potential analysis covers three dimensions, namely, the scale of emission reduction, relative emission reduction potential and the importance of emission reduction. Among them, the scale of emission reduction refers to the absolute value of industrial carbon dioxide redundancy, which is used to characterize the potential for industrial carbon dioxide emissions to be reduced under current technical conditions; the relative emission reduction potential refers to the proportion of industrial carbon dioxide redundancy in the region in that year to the actual emissions in that year; the importance of emission reduction is defined based on the proportion of the scale of industrial carbon dioxide emission reduction in the region in that year to the scale of industrial carbon dioxide emission reduction in all regions in that year.

[0071] The objective function expression is as follows:

[0072]

[0073] Where n is the number of enterprises in the region that participate in the allocation of redundant carbon dioxide, λ i is the redundant carbon dioxide allocation coefficient of the i-th enterprise in the region, C is the amount of redundant carbon dioxide to be allocated in the region, Allocate an estimated mean of the amount of redundant CO2 to all enterprises in the region, satisfying β is the maximum allocation ratio of redundant carbon dioxide in the region.

[0074] The upper limit of redundant carbon dioxide tolerance allocated to each enterprise is:

[0075]

[0076] in, is the maximum upper limit of redundant carbon dioxide to be allocated for the i-th enterprise.

[0077] The fitness function of the GeneticAlgorithm algorithm is its objective function:

[0078]

[0079] The crossover operation expression of the GeneticAlgorithm algorithm is:

[0080]

[0081] The second embodiment is a regional carbon emission potential analysis and redundant carbon dioxide allocation system, comprising:

[0082] A model building module determines the area to be evaluated and builds a regression model of factors affecting carbon dioxide emissions in the area to be evaluated;

[0083] The first analysis module determines the input index and output index of each region in the area to be evaluated within any time period, and uses the input index and output index to construct an unexpected output super efficiency SBM-DEA model to obtain the comprehensive efficiency value of each region, as well as the redundancy of the expected output index and the unexpected output index in the input index and output index;

[0084] The second analysis module uses the redundancy of the undesired output indicator to analyze the carbon emission reduction potential of each region;

[0085] The third analysis module uses the Genetic Algorithm algorithm to find the redundant carbon dioxide allocation ratio of each enterprise in each region under the constraints of the redundant carbon dioxide allocation objective function with energy consumption constraints and equipment health status constraints;

[0086] The adjustment module verifies the redundant CO2 allocated to each enterprise and makes necessary adjustments.

[0087] The regression model of carbon dioxide emission influencing factors is as follows:

[0088] CDE=α+β1EDL+β2HIP+β3IS+β4ES+β5TL+β6CL;

[0089] Among them, CDE, EDL, HIP, IS, ES, TL and CL represent the carbon dioxide emissions, economic development level, industrial structure, industrial structure, energy structure, technological level, factor endowment and environmental regulation of the region to be evaluated, respectively.

[0090] The input indicators are simplified into the following items: the level of economic development is represented by GDP, the energy structure is reflected by the proportion of raw coal consumption in the total energy consumption, the industrial structure is measured by the proportion of the total output value of heavy industry in the total industrial output value, the industrial structure is reflected by the proportion of the added value of the tertiary industry in GDP, the technological level is indicated by the number of patents granted, the factor endowment is expressed by the labor-capital ratio, and environmental regulation is reflected by the completed investment in industrial waste gas treatment projects.

[0091] The output indicators include undesirable output and expected output, the undesirable output is industrial carbon dioxide emissions, and the expected output is the total industrial output value;

[0092] The undesired output super-efficiency SBM-DEA model is as follows:

[0093] Let X be the input matrix m is the type of input indicator, h is the number of decision-making units; Y g is the expected output matrix, s1 is the type of expected output; Y b is the expected output matrix, s2 is the type of expected output;

[0094] Then the measurement formula of the EEP of the decision-making unit to be evaluated is:

[0095]

[0096] In the formula, ρ * Indicates the point closest to the frontier formed by the unit to be evaluated and other units to be evaluated. - ,s g ,s b Strictly monotonically decreasing, and 0<ρ * ≤1, represents the shortfall of the expected output of the decision-making unit to be evaluated, represents the excess of the undesired output of the decision-making unit to be evaluated, s - represents the input slack variable, s g represents the expected output slack variable, s b represents the undesirable output slack variable, λ is the weight vector, and k represents the evaluated unit.

[0097] The carbon reduction potential analysis covers three dimensions, including the scale of emission reduction, relative emission reduction potential and emission reduction importance. Among them, the scale of emission reduction refers to the absolute value of industrial carbon dioxide redundancy, which is used to characterize the potential scale of industrial carbon dioxide emissions that can be reduced under existing technical conditions; the relative emission reduction potential refers to the proportion of industrial carbon dioxide redundancy in the region in that year to the actual emissions in the region in that year; the importance of emission reduction is defined as the proportion of the scale of industrial carbon dioxide emission reduction in the region in that year to the total scale of industrial carbon dioxide emission reduction in all regions in that year.

[0098] The objective function expression is as follows:

[0099]

[0100] Where n is the number of enterprises in the region that participate in the allocation of redundant carbon dioxide, λ i is the redundant carbon dioxide allocation coefficient of the i-th enterprise in the region, C is the amount of redundant carbon dioxide to be allocated in the region, Allocate an estimated mean of the amount of redundant CO2 to all enterprises in the region, satisfying β is the maximum allocation ratio of redundant carbon dioxide in the region.

[0101] The upper limit of redundant carbon dioxide tolerance allocated to each enterprise is:

[0102]

[0103] in, is the maximum upper limit of redundant carbon dioxide to be allocated for the i-th enterprise.

[0104] Preferably, the fitness function of the GeneticAlgorithm algorithm is its objective function:

[0105]

[0106] The crossover operation expression of the GeneticAlgorithm algorithm is:

[0107]

[0108] A third embodiment is a computer-readable storage medium storing a computer program, which implements the above-mentioned regional carbon emission potential analysis and redundant carbon dioxide allocation method when executed by a processor.

[0109] A fourth embodiment is a computer program product, comprising a computer program, which implements the above-mentioned regional carbon emission potential analysis and redundant carbon dioxide allocation method when executed by a processor.

[0110] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When the use is implemented in whole or in part in the form of a computer program product, the computer program product includes one or more computer instructions. When the computer program instructions are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL) or wireless (e.g., infrared, wireless, microwave, etc.) mode) to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk SolidState Disk (SSD)), etc.

[0111] The above is only a preferred embodiment of the present invention. It should be pointed out that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for regional carbon emission potential analysis and redundant carbon dioxide allocation, characterized in that: include: S1. Determine the area to be evaluated and construct a regression model of factors affecting carbon dioxide emissions in the area to be evaluated; S2. Determine the input index and output index of each region in the region to be evaluated within any time period, and use the input index and output index to construct an unexpected output super efficiency SBM-DEA model to obtain the comprehensive efficiency value of each region, as well as the redundancy of the expected output index and the unexpected output index in the input index and output index; S3, analyzing the carbon emission reduction potential of each region by using the redundancy of the non-desirable output indicator; S4. Under the constraints of the redundant carbon dioxide allocation objective function with energy consumption constraints and equipment health status constraints, the Genetic Algorithm algorithm is used to find the redundant carbon dioxide allocation ratio of each enterprise in each region; S5. Verify the redundant CO2 allocated to each enterprise and make necessary adjustments.

2. The method for regional carbon emission potential analysis and redundant carbon dioxide allocation according to claim 1, characterized in that: The regression model of carbon dioxide emission influencing factors is as follows: CDE=α+β1EDL+β2HIP+β3IS+β4ES+β5TL+β6CL; Among them, CDE, EDL, HIP, IS, ES, TL and CL represent the carbon dioxide emissions, economic development level, industrial structure, industrial structure, energy structure, technological level, factor endowment and environmental regulation of the region to be evaluated, respectively.

3. The method for regional carbon emission potential analysis and redundant carbon dioxide allocation according to claim 2, characterized in that: The input indicators are simplified as follows: the economic development level is represented by GDP, the energy structure is represented by the proportion of raw coal consumption to total energy consumption, the industrial structure is represented by the proportion of total heavy industry output value to total industrial output value, the industrial structure is represented by the proportion of the added value of the tertiary industry to GDP, the technological level is represented by the number of patents granted, the factor endowment is represented by the labor-capital ratio, and the environmental regulation is represented by the completed investment in industrial waste gas treatment projects; The output indicators include undesirable output and expected output, the undesirable output is industrial carbon dioxide emissions, and the expected output is the total industrial output value; The undesired output super-efficiency SBM-DEA model is as follows: Let X be the input matrix m is the type of input indicator, h is the number of decision-making units; Y g is the expected output matrix, s1 is the type of expected output; Y b is the expected output matrix, s2 is the type of expected output; Then the measurement formula of the EEP of the decision-making unit to be evaluated is: In the formula, ρ * Indicates the point closest to the frontier formed by the unit to be evaluated and other units to be evaluated. - ,s g ,s b Strictly monotonically decreasing, and 0<ρ * ≤1, represents the shortfall of the expected output of the decision-making unit to be evaluated, represents the excess of the undesired output of the decision-making unit to be evaluated, s - represents the input slack variable, s g represents the expected output slack variable, s b represents the undesirable output slack variable, λ is the weight vector, and k represents the evaluated unit.

4. The method for regional carbon emission potential analysis and redundant carbon dioxide allocation according to claim 1, characterized in that: The carbon emission reduction potential analysis includes three aspects, namely, the scale of emission reduction, relative emission reduction potential and emission reduction importance. Among them, the scale of emission reduction is defined as the absolute value of industrial carbon dioxide redundancy, which indicates the industrial carbon dioxide emissions that can be reduced relative to the existing technical conditions; the relative emission reduction potential is defined as the proportion of industrial carbon dioxide redundancy in the region in that year to the actual emissions in the region in that year; the importance of emission reduction is defined as the proportion of the scale of industrial carbon dioxide emission reduction in the region in that year to the scale of industrial carbon dioxide emission reduction in all regions in that year.

5. The method for regional carbon emission potential analysis and redundant carbon dioxide allocation according to claim 1, characterized in that: The objective function expression is as follows: Where n is the number of enterprises in the region that participate in the allocation of redundant carbon dioxide, λ i is the redundant carbon dioxide allocation coefficient of the i-th enterprise in the region, C is the amount of redundant carbon dioxide to be allocated in the region, Allocate an estimated mean of the amount of redundant CO2 to all enterprises in the region, satisfying β is the maximum allocation ratio of redundant carbon dioxide in the region.

6. The method for regional carbon emission potential analysis and redundant carbon dioxide allocation according to claim 1, characterized in that: The upper limit of redundant carbon dioxide tolerance allocated to each enterprise is: in, is the maximum upper limit of redundant carbon dioxide to be allocated for the i-th enterprise.

7. The method for regional carbon emission potential analysis and redundant carbon dioxide allocation according to claim 1, characterized in that: The fitness function of the GeneticAlgorithm algorithm is its objective function: The crossover operation expression of the GeneticAlgorithm algorithm is:

8. A regional carbon emission potential analysis and redundant carbon dioxide allocation system, characterized in that: include: A model building module determines the area to be evaluated and builds a regression model of factors affecting carbon dioxide emissions in the area to be evaluated; The first analysis module determines the input index and output index of each region in the area to be evaluated within any time period, and uses the input index and output index to construct an unexpected output super efficiency SBM-DEA model to obtain the comprehensive efficiency value of each region, as well as the redundancy of the expected output index and the unexpected output index in the input index and output index; The second analysis module uses the redundancy of the undesired output indicator to analyze the carbon emission reduction potential of each region; The third analysis module uses the Genetic Algorithm algorithm to find the redundant carbon dioxide allocation ratio of each enterprise in each region under the constraints of the redundant carbon dioxide allocation objective function with energy consumption constraints and equipment health status constraints; The adjustment module verifies the redundant CO2 allocated to each enterprise and makes necessary adjustments.

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for regional carbon emission potential analysis and redundant carbon dioxide allocation described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the program is executed by a processor, the method for regional carbon emission potential analysis and redundant carbon dioxide allocation as described in any one of claims 1 to 7 is implemented.

Images