A method and apparatus for carbon emission scheduling of energy-consuming enterprises in urban areas

Abstract

This invention discloses a method and apparatus for carbon emission scheduling of energy-consuming enterprises within a city, belonging to the field of carbon emission scheduling. The method comprises: first, determining the total carbon emission budget for the city and allocating a basic budget, with the remaining portion serving as an incentive budget; next, quantifying the historical carbon emission responsibility of enterprises using the Shapley value method and assessing their emission reduction potential using the entropy value method; then, establishing a fairness and efficiency optimization model to solve for the optimal allocation weights, accurately allocating the incentive budget to each enterprise; finally, combining the basic budget and the incentive budget to complete the carbon emission budget allocation for enterprises within the city, guiding each carbon emission unit in carbon emission scheduling. Therefore, by implementing this invention, the problem of existing technologies being unable to improve carbon emission scheduling efficiency while ensuring fairness in carbon emission scheduling can be solved.

Description

Technical Field

[0001] This invention relates to the field of carbon emission scheduling, and more particularly to a carbon emission scheduling method and apparatus for energy-consuming enterprises in urban areas. Background Technology

[0002] In the field of carbon emission scheduling, carbon emission scheduling refers to the process of decomposing the total carbon emission control targets set by a country or region to lower-level administrative regions, key industries, and even specific enterprises through a scientific and dynamic allocation mechanism, and conducting real-time monitoring, evaluation, and adjustment during implementation. This mechanism, as the core management tool for achieving total carbon emission control, is essentially a dynamic resource optimization allocation activity under the constraint of limited carbon emission space.

[0003] In existing technologies, the mainstream carbon emission scheduling methods mainly include three categories: First, scheduling methods based on historical emissions, which allocate initial quotas proportionally according to the historical emission levels of enterprises or regions; second, scheduling methods based on industry benchmarks, which set unified standards for allocation by referring to advanced values ​​of energy efficiency or emission intensity in the same industry; and third, decomposition methods based on consultation or administrative orders, which determine scheduling plans through top-down administrative coordination.

[0004] Because the historical emissions method essentially solidifies historical emission patterns, it penalizes entities that took early emission reduction measures while rewarding high-emission entities, severely impacting the fairness of emission control. While the baseline method considers technological differences to some extent, it fails to fully reflect the heterogeneity of different enterprises in terms of industrial structure, energy structure, technological level, and emission reduction potential. Furthermore, the determination of baseline values ​​is often controversial and lacks dynamic adaptability. The negotiated decomposition method lacks transparent and objective mathematical model support, making the control process highly susceptible to subjective factors, resulting in insufficient predictability and scientific rigor in the control results. Therefore, existing technologies mainly suffer from the following problems: First, insufficient fairness, failing to fully consider the differences in historical responsibility, development stage, and resource endowment among different entities, making it difficult to balance the interests of all parties during dynamic control. Second, low efficiency, lacking accurate assessment and real-time response mechanisms for the emission reduction potential and costs of entities, failing to effectively incentivize advanced entities and spur on laggards, thus affecting overall emission reduction efficiency. Third, lack of flexibility, making it difficult to dynamically optimize and adjust according to actual conditions. These problems severely restrict the full effectiveness of carbon emission control policies, necessitating the establishment of a new control methodology system that balances fairness and efficiency. Summary of the Invention

[0005] This invention provides a carbon emission scheduling method and apparatus for energy-consuming enterprises in urban areas, which can solve the problem that existing technologies cannot improve the efficiency of carbon emission scheduling while ensuring the fairness of carbon emission scheduling.

[0006] In a first aspect, embodiments of the present invention provide a carbon emission scheduling method for energy-consuming enterprises in a city, comprising: Obtain the total carbon emission budget within the city and the basic carbon emission budget corresponding to each energy-consuming enterprise, and obtain the incentive budget to be allocated based on the total carbon emission budget and the basic carbon emission budget corresponding to each energy-consuming enterprise; Historical carbon emission data for each energy-consuming enterprise in the city is obtained, and the Shapley value is calculated using the Shapley value calculation formula based on the historical carbon emission data for each energy-consuming enterprise. Based on the historical carbon emission data of each energy-consuming enterprise, the entropy value of each energy-consuming enterprise is calculated using the entropy calculation formula. Based on the historical carbon emission data, Shapley value, entropy value and weight decision variables of each energy-consuming enterprise, a carbon emission fairness and efficiency optimization model is established, and the carbon emission fairness and efficiency optimization model is solved to obtain the allocation weight of each energy-consuming enterprise. Then, based on the allocation weight of each energy-consuming enterprise and the incentive budget to be allocated, the carbon emission incentive budget of each energy-consuming enterprise is determined. Carbon emission budgets are allocated to each energy-consuming enterprise in the city based on its basic carbon emission budget and its incentive carbon emission budget, so that each carbon emission unit within each energy-consuming enterprise can schedule carbon emissions.

[0007] This application's embodiments achieve structured management and dynamic scheduling of carbon emission budgets by constructing a two-tiered allocation mechanism combining basic and incentive budgets. First, while ensuring the basic production needs of energy-consuming enterprises, incentive budget space is reserved to provide policy flexibility for subsequent precise regulation. Second, the Shapley value method is used to systematically evaluate the historical marginal contribution of enterprises in different cooperation combinations, objectively quantifying their historical responsibility and ensuring fairness in the responsibility allocation process. Simultaneously, an entropy value method is used to construct a multi-dimensional evaluation system to dynamically measure the current energy efficiency level and future emission reduction potential of enterprises, identifying those truly requiring key incentives. Based on this, a multi-objective optimization model is established, incorporating historical responsibility and emission reduction potential into a unified decision-making framework to obtain the optimal allocation weights for each enterprise. This ensures that the allocation of incentive budgets reflects both the polluter-pays principle and reinforces the orientation of benefiting high-efficiency enterprises. The final allocation scheme ensures production stability through the basic budget, promotes emission reduction transformation through the incentive budget, and directly transmits the budget allocation results to each carbon emission unit within the enterprise, achieving effective integration from macro-budget allocation to micro-emission scheduling, forming a complete closed loop of budget, allocation, and scheduling management, and significantly improving the overall effectiveness of the carbon emission control system.

[0008] As a preferred example of the first aspect, the acquisition of the total carbon emission budget within the city and the basic carbon emission budget corresponding to each energy-consuming enterprise specifically includes: Obtain carbon emission data and urban development data for the city, and calculate the total carbon emission budget for the city based on the carbon emission data and urban development data using the total carbon emission budget formula. Data on energy-consuming enterprises in various industries within the city are obtained, and the carbon emission basic budget is calculated based on the data of each energy-consuming enterprise using the carbon emission basic budget formula.

[0009] In this preferred example, a complete budget accounting system is established by clearly defining the calculation methods for the total carbon emissions budget and the basic budget. This hierarchical accounting method makes the entire budget allocation process highly transparent and operable, with clear sources and justifications for budgets at all levels. This facilitates government oversight and provides enterprises with stable expectations, making it easier for them to formulate medium- and long-term emission reduction plans.

[0010] As a preferred example of the first aspect, the step of calculating the Shapley value for each energy-consuming enterprise based on its historical carbon emission data using the Shapley value calculation formula is as follows: Based on the energy-consuming enterprises, a subset is obtained. Then, according to the Shapley value calculation formula, the enterprise subset, and the historical carbon emission data corresponding to each energy-consuming enterprise, the Shapley value corresponding to each energy-consuming enterprise is obtained by sequentially traversing each energy-consuming enterprise. During each traversal, the marginal contribution value corresponding to the energy-consuming enterprise and each subset of the enterprise subset is calculated based on the Shapley value calculation formula, the enterprise subset, and the historical carbon emission data corresponding to the energy-consuming enterprise being traversed. The weighted summation of each marginal contribution value is then performed to obtain the Shapley value corresponding to the energy-consuming enterprise being traversed.

[0011] In this preferred example, the calculation process of the Shapley value is described in detail, achieving precise quantification of historical carbon emission responsibility. This method considers the marginal contribution of enterprises to regional carbon emissions when they join different subsets, and obtains the final Shapley value through a weighted average, accurately reflecting each enterprise's relative responsibility in historical emissions. Compared with the traditional method of simply allocating responsibility according to the proportion of historical emissions, this method fully considers the synergistic effects and emission correlations among enterprises, avoiding the one-sidedness of responsibility determination. Especially for enterprise clusters with close industrial chain linkages, it can identify key emission nodes, making the allocation of responsibility more consistent with reality.

[0012] As a preferred example of the first aspect, the entropy value for each energy-consuming enterprise is calculated using an entropy calculation formula based on the historical carbon emission data of each enterprise, specifically as follows: Based on the preset set of evaluation indicators and the entropy calculation formula, each of the energy-consuming enterprises is traversed sequentially to obtain the entropy value corresponding to each of the energy-consuming enterprises. During each traversal, based on the preset evaluation index set and the entropy calculation formula, the information entropy and index weight of each evaluation index in the preset evaluation index set for the energy-consuming enterprise being traversed are calculated, and the entropy value corresponding to the energy-consuming enterprise being traversed is obtained based on the information entropy and index weight of each energy-consuming enterprise being traversed.

[0013] In this preferred example, a multi-dimensional dynamic assessment of an enterprise's emission reduction potential is achieved by establishing a complete entropy calculation process. This method constructs an evaluation system encompassing multiple indicators, including energy efficiency levels and technological upgrade potential. The weights of each indicator are objectively determined using information entropy theory, avoiding the arbitrariness of subjective weighting. Data standardization ensures the comparability of indicators with different dimensions, while the calculation of the comprehensive entropy value fully reflects the enterprise's overall emission reduction potential. Compared to traditional single-indicator assessments, this multi-indicator comprehensive assessment method more accurately identifies the enterprise's true emission reduction potential, providing a reliable basis for efficiency-first incentive allocation.

[0014] As a preferred example of the first aspect, the establishment of a carbon emission fairness and efficiency optimization model based on the historical carbon emission data, Shapley value, entropy value, and weighted decision variables of each energy-consuming enterprise is specifically as follows: Based on the historical carbon emission data, Shapley value, entropy value, and weighted decision variables of each energy-consuming enterprise, a fair efficiency objective function is established with the goal of maximizing the weighted sum of the Shapley value weight and the entropy value weight. Based on the historical carbon emission data, Shapley value, entropy value and weight decision variables of each energy-consuming enterprise, establish the following constraints: non-negativity of weights, sum of weights, upper and lower limits of weights, proportional correlation of Shapley value, minimum entropy and smoothing of historical weights. The carbon emission fairness and efficiency optimization model is established based on the fair efficiency objective function, the non-negativity constraint of the weights, the sum of the weights constraint, the upper and lower limits constraint of the weights, the proportional correlation constraint of the Shapley value, the minimum entropy constraint, and the historical weight smoothing constraint.

[0015] In this preferred example, a balance between fairness and efficiency is achieved by constructing a carbon emission fairness-efficiency optimization model. This model aims to maximize the weighted sum of Shapley values ​​and entropy values, considering both historical responsibility and emission reduction potential. The weights are flexibly adjusted to adapt to policy needs at different stages of development. Rich constraints ensure the feasibility of the allocation scheme: upper and lower limits on weights prevent excessive skewness; Shapley value proportional constraints maintain the correspondence between responsibility and rights; historical weight smoothing constraints ensure policy continuity; and minimum entropy constraints maintain system diversity. This optimization design makes the final allocation scheme both punitive and incentive-based, achieving a balance between fairness and efficiency in carbon emission budget allocation.

[0016] Secondly, the present invention provides a carbon emission scheduling device for energy-consuming enterprises in a city, comprising: a data acquisition module, a first calculation module, a second calculation module, a third calculation module, and a budget allocation module; The data acquisition module is used to acquire the total carbon emission budget within the city and the basic carbon emission budget corresponding to each energy-consuming enterprise, and to obtain the incentive budget to be allocated based on the total carbon emission budget and the basic carbon emission budget corresponding to each energy-consuming enterprise. The first calculation module is used to obtain historical carbon emission data corresponding to each energy-consuming enterprise in the city, and to calculate the Shapley value corresponding to each energy-consuming enterprise based on the historical carbon emission data corresponding to each energy-consuming enterprise using the Shapley value calculation formula. The second calculation module is used to calculate the entropy value of each energy-consuming enterprise based on the historical carbon emission data of each enterprise using the entropy calculation formula. The third calculation module is used to establish a carbon emission fairness and efficiency optimization model based on the historical carbon emission data, Shapley value, entropy value and weight decision variables of each energy-consuming enterprise, and solve the carbon emission fairness and efficiency optimization model to obtain the allocation weight of each energy-consuming enterprise. Then, based on the allocation weight of each energy-consuming enterprise and the incentive budget to be allocated, the carbon emission incentive budget of each energy-consuming enterprise is determined. The budget allocation module is used to allocate carbon emission budgets to each energy-consuming enterprise in the city based on the basic carbon emission budget and the carbon emission incentive budget corresponding to each energy-consuming enterprise, so as to enable each carbon emission unit within each energy-consuming enterprise to schedule carbon emissions.

[0017] As a preferred example of the second aspect, the data acquisition module includes a first acquisition unit and a second acquisition unit; The first acquisition unit is used to acquire carbon emission data and urban development data of the city, and calculate the total carbon emission budget of the city based on the carbon emission data and urban development data using the total carbon emission budget formula. The second acquisition unit is used to acquire data on energy-consuming enterprises in various industries within the city, and to calculate the carbon emission basic budget for each energy-consuming enterprise based on the carbon emission basic budget formula.

[0018] As a preferred example of the second aspect, the first computing module includes a first computing subunit and a second computing subunit; The first calculation subunit is used to divide the energy-consuming enterprises into subsets to obtain enterprise subsets, and to sequentially traverse each energy-consuming enterprise according to the Shapley value calculation formula, the enterprise subsets and the historical carbon emission data corresponding to each energy-consuming enterprise to obtain the Shapley value corresponding to each energy-consuming enterprise. The second calculation subunit is used to calculate the marginal contribution value corresponding to each subset of the energy-consuming enterprise and the enterprise subset according to the Shapley value calculation formula, the enterprise subset and the historical carbon emission data corresponding to the energy-consuming enterprise being traversed at each traversal, and to obtain the Shapley value corresponding to the energy-consuming enterprise being traversed at the current traversal by performing a weighted summation based on each marginal contribution value.

[0019] As a preferred example of the second aspect, the second computing module includes a third computing subunit and a fourth computing subunit; The third calculation subunit is used to sequentially traverse each of the energy-consuming enterprises according to the preset evaluation index set and the entropy value calculation formula to obtain the entropy value corresponding to each of the energy-consuming enterprises. The fourth calculation subunit is used to calculate the information entropy and index weight of each evaluation index corresponding to the energy-consuming enterprise in the preset evaluation index set during each traversal, based on the preset evaluation index set and the entropy value calculation formula, and to obtain the entropy value corresponding to the energy-consuming enterprise in the current traversal based on the information entropy and index weight of each energy-consuming enterprise in the current traversal.

[0020] As a preferred example of the second aspect, the third calculation module includes a fifth calculation subunit, a sixth calculation subunit, and a seventh calculation subunit; The fifth calculation subunit is used to establish a fair efficiency objective function based on the historical carbon emission data, Shapley value, entropy value and weight decision variables corresponding to each energy-consuming enterprise, with the optimization objective of maximizing the weighted sum of Shapley value weight and entropy value weight; The sixth calculation subunit is used to establish weight non-negativity constraints, weight sum constraints, weight upper and lower limit constraints, Shapley value proportional correlation constraints, minimum entropy constraints, and historical weight smoothing constraints based on the historical carbon emission data, Shapley value, entropy value, and weight decision variables corresponding to each energy-consuming enterprise. The seventh calculation subunit is used to establish the carbon emission fairness and efficiency optimization model based on the fairness and efficiency objective function, the weight non-negativity constraint, the weight sum constraint, the weight upper and lower limit constraints, the Shapley value proportional correlation constraint, the minimum entropy constraint, and the historical weight smoothing constraint.

[0021] In summary, this application's embodiments achieve structured management and dynamic scheduling of carbon emission budgets by constructing a two-tiered allocation mechanism combining basic and incentive budgets. First, while ensuring the basic production needs of energy-consuming enterprises, incentive budget space is reserved to provide policy flexibility for subsequent precise regulation. Second, the Shapley value method is used to systematically evaluate the historical marginal contribution of enterprises in different cooperation combinations, objectively quantifying their historical responsibility and ensuring fairness in the responsibility allocation process. Simultaneously, the entropy value method is used to construct a multi-dimensional evaluation system to dynamically measure the current energy efficiency level and future emission reduction potential of enterprises, identifying those truly requiring key incentives. Based on this, a multi-objective optimization model is established, incorporating historical responsibility and emission reduction potential into a unified decision-making framework to obtain the optimal allocation weights for each enterprise. This ensures that the allocation of incentive budgets reflects both the polluter-pays principle and reinforces the orientation of benefiting high-efficiency enterprises. The final allocation scheme ensures production stability through the basic budget, promotes emission reduction transformation through the incentive budget, and directly transmits the budget allocation results to each carbon emission unit within the enterprise, achieving effective integration from macro-budget allocation to micro-emission scheduling, forming a complete closed loop of budget, allocation, and scheduling management, and significantly improving the overall effectiveness of the carbon emission control system.

[0022] Another embodiment of the present invention provides a terminal device, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the steps of the carbon emission scheduling method for energy-consuming enterprises in urban areas as described in the present invention.

[0023] Another embodiment of the present invention provides a computer-readable storage medium item, including: a stored computer program, which, when the computer program is running, controls the device where the computer-readable storage medium is located to perform the steps of the carbon emission scheduling method for energy-consuming enterprises in urban areas as described in the present invention. Attached Figure Description

[0024] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0025] Figure 1 A flowchart illustrating an embodiment of a carbon emission scheduling method for energy-consuming enterprises in a city provided by the present invention; Figure 2 This is a module structure diagram of an embodiment of a carbon emission scheduling device for energy-consuming enterprises in a city, provided by the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0027] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0028] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0029] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0030] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0031] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0032] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0033] Example 1 See Figure 1 To address the problem in existing technologies of simultaneously ensuring fairness and improving efficiency in carbon emission scheduling, an embodiment of the present invention provides a carbon emission scheduling method for energy-consuming enterprises in a municipal area, comprising: S1. Obtain the total carbon emission budget within the city and the basic carbon emission budget corresponding to each energy-consuming enterprise, and obtain the incentive budget to be allocated based on the total carbon emission budget and the basic carbon emission budget corresponding to each energy-consuming enterprise.

[0034] In some embodiments of this application, obtaining the total carbon emission budget within the city and the basic carbon emission budget corresponding to each energy-consuming enterprise specifically involves: Obtain carbon emission data and urban development data for the city, and calculate the total carbon emission budget for the city based on the carbon emission data and urban development data using the total carbon emission budget formula. Data on energy-consuming enterprises in various industries within the city are obtained, and the carbon emission basic budget is calculated based on the data of each energy-consuming enterprise using the carbon emission basic budget formula.

[0035] It should be noted that energy-consuming enterprises are those whose annual energy consumption is no less than 1,000 tons of standard coal.

[0036] In some embodiments of this application, the total carbon emission budget formula may be as follows: In this context, the subscripts 0 and t represent the base period and period t, respectively. Carbon emission intensity per unit of GDP in the base period (tons of CO2 / 10,000 yuan); The city's carbon emission intensity reduction rate (%) The base period regional GDP (in ten thousand yuan); The average annual GDP growth rate (%) for the region during the forecast period; For the prediction period (years); The target city's total carbon emission budget for period t (tons of CO2).

[0037] In some embodiments of this application, the carbon emission basic budget formula may be as follows: in, Base period industry Carbon emission intensity benchmark (tons of CO2 / ton of standard coal); For the industry Carbon intensity reduction targets; For the industry Energy-consuming enterprises Energy consumption in the base period (tons of standard coal). For the industry Energy-consuming enterprises The allocated base budget for carbon emissions in period t (tons of CO2).

[0038] In some embodiments of this application, the step of obtaining the incentive budget to be allocated based on the total carbon emission budget and the basic carbon emission budget corresponding to each of the energy-consuming enterprises can be calculated using the following formula: in, For the incentive budget to be allocated, Total budget for carbon emissions.

[0039] S2. Obtain historical carbon emission data for each energy-consuming enterprise in the city, and calculate the Shapley value for each energy-consuming enterprise using the Shapley value calculation formula based on the historical carbon emission data for each energy-consuming enterprise.

[0040] In some embodiments of this application, the step of calculating the Shapley value for each energy-consuming enterprise based on its historical carbon emission data using the Shapley value calculation formula is specifically as follows: Based on the energy-consuming enterprises, a subset is obtained. Then, according to the Shapley value calculation formula, the enterprise subset, and the historical carbon emission data corresponding to each energy-consuming enterprise, the Shapley value corresponding to each energy-consuming enterprise is obtained by sequentially traversing each energy-consuming enterprise. During each traversal, the marginal contribution value corresponding to the energy-consuming enterprise and each subset of the enterprise subset is calculated based on the Shapley value calculation formula, the enterprise subset, and the historical carbon emission data corresponding to the energy-consuming enterprise being traversed. The weighted summation of each marginal contribution value is then performed to obtain the Shapley value corresponding to the energy-consuming enterprise being traversed.

[0041] In some embodiments of this application, the Shapley value calculation formula may be as follows: in, S represents the number of energy-consuming enterprises in the region, where S is the number excluding energy-consuming enterprises. Other enterprise collections, This refers to the company's historical carbon emissions within the region; For energy-consuming enterprises The Shapley value.

[0042] S3. Based on the historical carbon emission data of each energy-consuming enterprise, the entropy value of each energy-consuming enterprise is calculated using the entropy calculation formula.

[0043] In some embodiments of this application, the step of calculating the entropy value corresponding to each energy-consuming enterprise using the entropy calculation formula based on the historical carbon emission data of each enterprise is specifically as follows: Based on the preset set of evaluation indicators and the entropy calculation formula, each of the energy-consuming enterprises is traversed sequentially to obtain the entropy value corresponding to each of the energy-consuming enterprises. During each traversal, based on the preset evaluation index set and the entropy calculation formula, the information entropy and index weight of each evaluation index in the preset evaluation index set for the energy-consuming enterprise being traversed are calculated, and the entropy value corresponding to the energy-consuming enterprise being traversed is obtained based on the information entropy and index weight of each energy-consuming enterprise being traversed.

[0044] Specifically, the entropy value corresponding to each of the energy-consuming enterprises can be calculated using the following preferred method: An assessment system comprising multiple emission reduction potential evaluation indicators is constructed. The indicator data is standardized, the entropy weight of each indicator is calculated, and finally, a weighted average is obtained to obtain the comprehensive entropy value representing the overall emission reduction potential of the enterprise. The core calculation steps and formulas are as follows: ① There are n energy-intensive enterprises in the region. Establish m evaluation indicators to form the original data matrix. ; ② Calculate the first using the following formula The first evaluation indicator Weight of each enterprise: in, For the i-th sample, the raw data on the j-th evaluation metric. Let j be the minimum value of the j-th evaluation index among all samples. Let j be the maximum value of the j-th evaluation index among all samples. For the standardized data of the i-th sample on the j-th evaluation index, is the standardized value percentage of the i-th sample on the j-th evaluation indicator, and n is the total number of samples (corresponding to the total number of energy-consuming enterprises in the document).

[0045] ③ The calculation of the first term is determined by the following formula. Entropy value of an individual enterprise: in, Let the information entropy of the j-th evaluation index be . Let be the entropy value of the i-th sample (the i-th energy-consuming enterprise).

[0046] S4. Based on the historical carbon emission data, Shapley value, entropy value and weight decision variables of each energy-consuming enterprise, establish a carbon emission fairness and efficiency optimization model, solve the carbon emission fairness and efficiency optimization model, obtain the allocation weight of each energy-consuming enterprise, and then determine the carbon emission incentive budget of each energy-consuming enterprise based on the allocation weight of each energy-consuming enterprise and the incentive budget to be allocated.

[0047] In some embodiments of this application, the step of establishing a carbon emission fairness and efficiency optimization model based on the historical carbon emission data, Shapley value, entropy value, and weighted decision variables corresponding to each energy-consuming enterprise specifically includes: Based on the historical carbon emission data, Shapley value, entropy value, and weighted decision variables of each energy-consuming enterprise, a fair efficiency objective function is established with the goal of maximizing the weighted sum of the Shapley value weight and the entropy value weight. Based on the historical carbon emission data, Shapley value, entropy value and weight decision variables of each energy-consuming enterprise, establish the following constraints: non-negativity of weights, sum of weights, upper and lower limits of weights, proportional correlation of Shapley value, minimum entropy and smoothing of historical weights. The carbon emission fairness and efficiency optimization model is established based on the fair efficiency objective function, the non-negativity constraint of the weights, the sum of the weights constraint, the upper and lower limits constraint of the weights, the proportional correlation constraint of the Shapley value, the minimum entropy constraint, and the historical weight smoothing constraint.

[0048] Specifically, the establishment and solution process of the carbon emission fair efficiency optimization model can be described as follows: The objective of this carbon emission fairness-efficiency optimization model is to maximize the weighted sum of Shapley value weights and entropy value weights. A linear weighting method and optimization algorithm are used to find the Pareto optimal solution set, and the decision-maker then makes a final allocation decision. The objective function and constraints of the model are as follows: ① Objective function ②Constraints Non-negativity constraint on weights: Weight sum constraint: , Weight upper and lower bound constraints: , Shapley value scaling constraint: , Minimum entropy constraint: , Historical weight smoothing constraint: , in, The total number of energy-consuming enterprises; Energy-consuming enterprises ; For the first The optimal allocation weights for each enterprise; For the first Shapley value for each enterprise; This is the coefficient for the trade-off between fairness and efficiency in the objective function (reference value is 0.5). and Assign weights to the optimal values ​​respectively The lower and upper limits; and These are the lower and upper limits of the Shapley value scaling factor, respectively; This is the minimum entropy value allowed by the system. For the first Historical weighting of each enterprise; This represents the maximum allowable fluctuation range for the weight (reference value is 0.05).

[0049] S5. Based on the basic carbon emission budget and the carbon emission incentive budget corresponding to each energy-consuming enterprise in the city, carbon emission budgets are allocated to each energy-consuming enterprise in the city so that each carbon emission unit within each energy-consuming enterprise can schedule carbon emissions.

[0050] Specifically, the allocation of carbon emission budgets to each enterprise in the city based on the basic carbon emission budget and the carbon emission incentive budget corresponding to each energy-consuming enterprise in the city can be implemented through the following preferred methods: First, the incentive budget to be allocated is distributed to each energy-consuming enterprise, and the specific calculation is as follows: in, The total carbon emission budget for the target city (tons of CO2); For energy-consuming enterprises during the t period The allocated basic budget for carbon emissions (tons of CO2); For energy-consuming enterprises The optimal weight allocation; For energy-consuming enterprises during the t period The allocated carbon emission incentive budget (tons of CO2).

[0051] Next, energy-consuming enterprises The formula for calculating the final carbon emissions budget allocated in period t is as follows: in, For energy-consuming enterprises The carbon emissions budget that is finally allocated in period t.

[0052] In summary, this application's embodiments achieve structured management and dynamic scheduling of carbon emission budgets by constructing a two-tiered allocation mechanism combining basic and incentive budgets. First, while ensuring the basic production needs of energy-consuming enterprises, incentive budget space is reserved to provide policy flexibility for subsequent precise regulation. Second, the Shapley value method is used to systematically evaluate the historical marginal contribution of enterprises in different cooperation combinations, objectively quantifying their historical responsibility and ensuring fairness in the responsibility allocation process. Simultaneously, the entropy value method is used to construct a multi-dimensional evaluation system to dynamically measure the current energy efficiency level and future emission reduction potential of enterprises, identifying those truly requiring key incentives. Based on this, a multi-objective optimization model is established, incorporating historical responsibility and emission reduction potential into a unified decision-making framework to obtain the optimal allocation weights for each enterprise. This ensures that the allocation of incentive budgets reflects both the polluter-pays principle and reinforces the orientation of benefiting high-efficiency enterprises. The final allocation scheme ensures production stability through the basic budget, promotes emission reduction transformation through the incentive budget, and directly transmits the budget allocation results to each carbon emission unit within the enterprise, achieving effective integration from macro-budget allocation to micro-emission scheduling, forming a complete closed loop of budget, allocation, and scheduling management, and significantly improving the overall effectiveness of the carbon emission control system.

[0053] Example 2 like Figure 2 As shown, based on the above method embodiments, corresponding device embodiments are provided; An embodiment of the present invention provides a carbon emission scheduling device for energy-consuming enterprises in a city, comprising: a data acquisition module 21, a first calculation module 22, a second calculation module 23, a third calculation module 24, and a budget allocation module 25; The data acquisition module 21 is used to acquire the total carbon emission budget within the city and the basic carbon emission budget corresponding to each energy-consuming enterprise, and to obtain the incentive budget to be allocated based on the total carbon emission budget and the basic carbon emission budget corresponding to each energy-consuming enterprise. The first calculation module 22 is used to obtain the historical carbon emission data of each energy-consuming enterprise in the city, and to calculate the Shapley value of each energy-consuming enterprise based on the historical carbon emission data of each energy-consuming enterprise using the Shapley value calculation formula. The second calculation module 23 is used to calculate the entropy value of each energy-consuming enterprise based on the historical carbon emission data of each energy-consuming enterprise using the entropy calculation formula. The third calculation module 24 is used to establish a carbon emission fairness and efficiency optimization model based on the historical carbon emission data, Shapley value, entropy value and weight decision variables of each energy-consuming enterprise, and solve the carbon emission fairness and efficiency optimization model to obtain the allocation weight of each energy-consuming enterprise. Then, based on the allocation weight of each energy-consuming enterprise and the incentive budget to be allocated, the carbon emission incentive budget of each energy-consuming enterprise is determined. The budget allocation module 25 is used to allocate carbon emission budgets to each energy-consuming enterprise in the city based on the basic carbon emission budget and the carbon emission incentive budget corresponding to each energy-consuming enterprise, so as to enable each carbon emission unit within each energy-consuming enterprise to schedule carbon emissions.

[0054] In some embodiments of this application, the data acquisition module 21 includes a first acquisition unit and a second acquisition unit; The first acquisition unit is used to acquire carbon emission data and urban development data of the city, and calculate the total carbon emission budget of the city based on the carbon emission data and urban development data using the total carbon emission budget formula. The second acquisition unit is used to acquire data on energy-consuming enterprises in various industries within the city, and to calculate the carbon emission basic budget for each energy-consuming enterprise based on the carbon emission basic budget formula.

[0055] In some embodiments of this application, the first computing module 22 includes a first computing subunit and a second computing subunit; The first calculation subunit is used to divide the energy-consuming enterprises into subsets to obtain enterprise subsets, and to sequentially traverse each energy-consuming enterprise according to the Shapley value calculation formula, the enterprise subsets and the historical carbon emission data corresponding to each energy-consuming enterprise to obtain the Shapley value corresponding to each energy-consuming enterprise. The second calculation subunit is used to calculate the marginal contribution value corresponding to each subset of the energy-consuming enterprise and the enterprise subset according to the Shapley value calculation formula, the enterprise subset and the historical carbon emission data corresponding to the energy-consuming enterprise being traversed at each traversal, and to obtain the Shapley value corresponding to the energy-consuming enterprise being traversed at the current traversal by performing a weighted summation based on each marginal contribution value.

[0056] In some embodiments of this application, the second computing module 23 includes a third computing subunit and a fourth computing subunit; The third calculation subunit is used to sequentially traverse each of the energy-consuming enterprises according to the preset evaluation index set and the entropy value calculation formula to obtain the entropy value corresponding to each of the energy-consuming enterprises. The fourth calculation subunit is used to calculate the information entropy and index weight of each evaluation index corresponding to the energy-consuming enterprise in the preset evaluation index set during each traversal, based on the preset evaluation index set and the entropy value calculation formula, and to obtain the entropy value corresponding to the energy-consuming enterprise in the current traversal based on the information entropy and index weight of each energy-consuming enterprise in the current traversal.

[0057] In some embodiments of this application, the third computing module 24 includes a fifth computing subunit, a sixth computing subunit, and a seventh computing subunit; The fifth calculation subunit is used to establish a fair efficiency objective function based on the historical carbon emission data, Shapley value, entropy value and weight decision variables corresponding to each energy-consuming enterprise, with the optimization objective of maximizing the weighted sum of Shapley value weight and entropy value weight; The sixth calculation subunit is used to establish weight non-negativity constraints, weight sum constraints, weight upper and lower limit constraints, Shapley value proportional correlation constraints, minimum entropy constraints, and historical weight smoothing constraints based on the historical carbon emission data, Shapley value, entropy value, and weight decision variables corresponding to each energy-consuming enterprise. The seventh calculation subunit is used to establish the carbon emission fairness and efficiency optimization model based on the fairness and efficiency objective function, the weight non-negativity constraint, the weight sum constraint, the weight upper and lower limit constraints, the Shapley value proportional correlation constraint, the minimum entropy constraint, and the historical weight smoothing constraint.

[0058] In summary, this application's embodiments achieve structured management and dynamic scheduling of carbon emission budgets by constructing a two-tiered allocation mechanism combining basic and incentive budgets. First, while ensuring the basic production needs of energy-consuming enterprises, incentive budget space is reserved to provide policy flexibility for subsequent precise regulation. Second, the Shapley value method is used to systematically evaluate the historical marginal contribution of enterprises in different cooperation combinations, objectively quantifying their historical responsibility and ensuring fairness in the responsibility allocation process. Simultaneously, the entropy value method is used to construct a multi-dimensional evaluation system to dynamically measure the current energy efficiency level and future emission reduction potential of enterprises, identifying those truly requiring key incentives. Based on this, a multi-objective optimization model is established, incorporating historical responsibility and emission reduction potential into a unified decision-making framework to obtain the optimal allocation weights for each enterprise. This ensures that the allocation of incentive budgets reflects both the polluter-pays principle and reinforces the orientation of benefiting high-efficiency enterprises. The final allocation scheme ensures production stability through the basic budget, promotes emission reduction transformation through the incentive budget, and directly transmits the budget allocation results to each carbon emission unit within the enterprise, achieving effective integration from macro-budget allocation to micro-emission scheduling, forming a complete closed loop of budget, allocation, and scheduling management, and significantly improving the overall effectiveness of the carbon emission control system.

[0059] It is understood that the above-described device embodiments correspond to the method embodiments of the present invention, and can realize the carbon emission scheduling method for energy-consuming enterprises in urban areas provided by any of the above-described method embodiments of the present invention.

[0060] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0061] Example 3 Based on the above embodiments of the carbon emission scheduling method for energy-consuming enterprises in urban areas, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the carbon emission scheduling method for energy-consuming enterprises in urban areas according to any embodiment of the present invention.

[0062] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0063] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0064] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0065] Example 4 Based on the above-described method embodiments, another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the carbon emission scheduling method for energy-consuming enterprises in urban areas as described in any of the above-described method embodiments of the present invention.

[0066] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0067] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A carbon emission scheduling method for energy-consuming enterprises in a city, characterized in that, include: Obtain the total carbon emission budget within the city and the basic carbon emission budget corresponding to each energy-consuming enterprise, and obtain the incentive budget to be allocated based on the total carbon emission budget and the basic carbon emission budget corresponding to each energy-consuming enterprise; Historical carbon emission data for each energy-consuming enterprise in the city is obtained, and the Shapley value is calculated using the Shapley value calculation formula based on the historical carbon emission data for each energy-consuming enterprise. Based on the historical carbon emission data of each energy-consuming enterprise, the entropy value of each energy-consuming enterprise is calculated using the entropy calculation formula. Based on the historical carbon emission data, Shapley value, entropy value and weight decision variables of each energy-consuming enterprise, a carbon emission fairness and efficiency optimization model is established, and the carbon emission fairness and efficiency optimization model is solved to obtain the allocation weight of each energy-consuming enterprise. Then, based on the allocation weight of each energy-consuming enterprise and the incentive budget to be allocated, the carbon emission incentive budget of each energy-consuming enterprise is determined. Carbon emission budgets are allocated to each energy-consuming enterprise in the city based on its basic carbon emission budget and its incentive carbon emission budget, so that each carbon emission unit within each energy-consuming enterprise can schedule carbon emissions.

2. The carbon emission scheduling method for energy-consuming enterprises in a city as described in claim 1, characterized in that, The process of obtaining the total carbon emission budget within the city and the basic carbon emission budget for each energy-consuming enterprise specifically involves: Obtain carbon emission data and urban development data for the city, and calculate the total carbon emission budget for the city based on the carbon emission data and urban development data using the total carbon emission budget formula. Data on energy-consuming enterprises in various industries within the city are obtained, and the carbon emission basic budget is calculated based on the data of each energy-consuming enterprise using the carbon emission basic budget formula.

3. The carbon emission scheduling method for energy-consuming enterprises in a city as described in claim 1, characterized in that, The Shapley value for each energy-consuming enterprise is calculated using the Shapley value calculation formula based on the historical carbon emission data of each enterprise. Specifically: Based on the energy-consuming enterprises, a subset is obtained. Then, according to the Shapley value calculation formula, the enterprise subset, and the historical carbon emission data corresponding to each energy-consuming enterprise, the Shapley value corresponding to each energy-consuming enterprise is obtained by sequentially traversing each energy-consuming enterprise. During each traversal, the marginal contribution value corresponding to the energy-consuming enterprise and each subset of the enterprise subset is calculated based on the Shapley value calculation formula, the enterprise subset, and the historical carbon emission data corresponding to the energy-consuming enterprise being traversed. The weighted summation of each marginal contribution value is then performed to obtain the Shapley value corresponding to the energy-consuming enterprise being traversed.

4. A carbon emission scheduling method for energy-consuming enterprises in a city as described in claim 1, characterized in that, The entropy value for each energy-consuming enterprise is calculated using the entropy calculation formula based on its historical carbon emission data. Specifically: Based on the preset set of evaluation indicators and the entropy calculation formula, each of the energy-consuming enterprises is traversed sequentially to obtain the entropy value corresponding to each of the energy-consuming enterprises. During each traversal, based on the preset evaluation index set and the entropy calculation formula, the information entropy and index weight of each evaluation index in the preset evaluation index set for the energy-consuming enterprise being traversed are calculated, and the entropy value corresponding to the energy-consuming enterprise being traversed is obtained based on the information entropy and index weight of each energy-consuming enterprise being traversed.

5. A carbon emission scheduling method for energy-consuming enterprises in a city as described in claim 1, characterized in that, The carbon emission fairness and efficiency optimization model is established based on the historical carbon emission data, Shapley value, entropy value, and weighted decision variables of each energy-consuming enterprise. Specifically: Based on the historical carbon emission data, Shapley value, entropy value, and weighted decision variables of each energy-consuming enterprise, a fair efficiency objective function is established with the goal of maximizing the weighted sum of the Shapley value weight and the entropy value weight. Based on the historical carbon emission data, Shapley value, entropy value and weight decision variables of each energy-consuming enterprise, establish the following constraints: non-negativity of weights, sum of weights, upper and lower limits of weights, proportional correlation of Shapley value, minimum entropy and smoothing of historical weights. The carbon emission fairness and efficiency optimization model is established based on the fair efficiency objective function, the non-negativity constraint of the weights, the sum of the weights constraint, the upper and lower limits constraint of the weights, the proportional correlation constraint of the Shapley value, the minimum entropy constraint, and the historical weight smoothing constraint.

6. A carbon emission dispatching device for energy-consuming enterprises in a city, characterized in that, include: The system includes a data acquisition module, a first calculation module, a second calculation module, a third calculation module, and a budget allocation module. The data acquisition module is used to acquire the total carbon emission budget within the city and the basic carbon emission budget corresponding to each energy-consuming enterprise, and to obtain the incentive budget to be allocated based on the total carbon emission budget and the basic carbon emission budget corresponding to each energy-consuming enterprise. The first calculation module is used to obtain historical carbon emission data corresponding to each energy-consuming enterprise in the city, and to calculate the Shapley value corresponding to each energy-consuming enterprise based on the historical carbon emission data corresponding to each energy-consuming enterprise using the Shapley value calculation formula. The second calculation module is used to calculate the entropy value of each energy-consuming enterprise based on the historical carbon emission data of each enterprise using the entropy calculation formula. The third calculation module is used to establish a carbon emission fairness and efficiency optimization model based on the historical carbon emission data, Shapley value, entropy value and weight decision variables of each energy-consuming enterprise, and solve the carbon emission fairness and efficiency optimization model to obtain the allocation weight of each energy-consuming enterprise. Then, based on the allocation weight of each energy-consuming enterprise and the incentive budget to be allocated, the carbon emission incentive budget of each energy-consuming enterprise is determined. The budget allocation module is used to allocate carbon emission budgets to each energy-consuming enterprise in the city based on the basic carbon emission budget and the carbon emission incentive budget corresponding to each energy-consuming enterprise, so as to enable each carbon emission unit within each energy-consuming enterprise to schedule carbon emissions.

7. A carbon emission dispatching device for energy-consuming enterprises in a city as described in claim 6, characterized in that, The data acquisition module includes a first acquisition unit and a second acquisition unit; The first acquisition unit is used to acquire carbon emission data and urban development data of the city, and calculate the total carbon emission budget of the city based on the carbon emission data and urban development data using the total carbon emission budget formula. The second acquisition unit is used to acquire data on energy-consuming enterprises in various industries within the city, and to calculate the carbon emission basic budget for each energy-consuming enterprise based on the carbon emission basic budget formula.

8. A carbon emission dispatching device for energy-consuming enterprises in a city as described in claim 6, characterized in that, The first computing module includes a first computing subunit and a second computing subunit; The first calculation subunit is used to divide the energy-consuming enterprises into subsets to obtain enterprise subsets, and to sequentially traverse each energy-consuming enterprise according to the Shapley value calculation formula, the enterprise subsets and the historical carbon emission data corresponding to each energy-consuming enterprise to obtain the Shapley value corresponding to each energy-consuming enterprise. The second calculation subunit is used to calculate the marginal contribution value corresponding to each subset of the energy-consuming enterprise and the enterprise subset according to the Shapley value calculation formula, the enterprise subset and the historical carbon emission data corresponding to the energy-consuming enterprise being traversed at each traversal, and to obtain the Shapley value corresponding to the energy-consuming enterprise being traversed at the current traversal by performing a weighted summation based on each marginal contribution value.

9. A carbon emission dispatching device for energy-consuming enterprises in a city as described in claim 6, characterized in that, The second computing module includes a third computing subunit and a fourth computing subunit; The third calculation subunit is used to sequentially traverse each of the energy-consuming enterprises according to the preset evaluation index set and the entropy value calculation formula to obtain the entropy value corresponding to each of the energy-consuming enterprises. The fourth calculation subunit is used to calculate the information entropy and index weight of each evaluation index corresponding to the energy-consuming enterprise in the preset evaluation index set during each traversal, based on the preset evaluation index set and the entropy value calculation formula, and to obtain the entropy value corresponding to the energy-consuming enterprise in the current traversal based on the information entropy and index weight of each energy-consuming enterprise in the current traversal.

10. A carbon emission dispatching device for energy-consuming enterprises in a city as described in claim 6, characterized in that, The third calculation module includes a fifth calculation subunit, a sixth calculation subunit, and a seventh calculation subunit; The fifth calculation subunit is used to establish a fair efficiency objective function based on the historical carbon emission data, Shapley value, entropy value and weight decision variables corresponding to each energy-consuming enterprise, with the optimization objective of maximizing the weighted sum of Shapley value weight and entropy value weight; The sixth calculation subunit is used to establish weight non-negativity constraints, weight sum constraints, weight upper and lower limit constraints, Shapley value proportional correlation constraints, minimum entropy constraints, and historical weight smoothing constraints based on the historical carbon emission data, Shapley value, entropy value, and weight decision variables corresponding to each energy-consuming enterprise. The seventh calculation subunit is used to establish the carbon emission fairness and efficiency optimization model based on the fairness and efficiency objective function, the weight non-negativity constraint, the weight sum constraint, the weight upper and lower limit constraints, the Shapley value proportional correlation constraint, the minimum entropy constraint, and the historical weight smoothing constraint.

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