A method and system for collaborative management and potential assessment of internal carbon emission quotas for cross-regional enterprise groups

Abstract

The present disclosure belongs to the technical field of carbon emission management, and particularly relates to a method and system for internal carbon quota collaborative management and potential evaluation of cross-regional enterprise groups. First, by constructing a collaborative optimization objective function containing a marginal net benefit vector and a carbon emission structure vector, an internal carbon quota transaction constraint is introduced, so that the marginal emission reduction cost of each subsidiary company in the group tends to be balanced, and the maximum profit of the group as a whole is realized. On this basis, a collaborative potential evaluation method based on the marginal emission reduction cost variance of each subsidiary company under decentralized decision-making is proposed. By calculating the collaborative potential index, the expected income space of internal carbon quota transaction can be quickly predicted without complex global optimization. The present application effectively solves the problems of carbon cost evaluation distortion and the lack of collaborative decision-making mechanism caused by the differences in regional energy structure of cross-regional enterprise groups, significantly reduces the computational burden of carbon management decision-making, and provides quantifiable decision support for the unified optimization and deployment of group carbon assets.

Description

Technical Field

[0001] This invention relates to the field of carbon emission management, and more particularly to a method for collaborative management and potential assessment of internal carbon quotas for cross-regional enterprise groups. Background Technology

[0002] As global climate change becomes increasingly severe, carbon emissions trading has become one of the core policy tools for countries around the world to promote greenhouse gas emission reduction. Carbon markets internalize the external costs of carbon emissions by setting carbon emission limits and allowing quota trading, thus incentivizing companies to shift to low-carbon production models. Since its launch in 2021, China's national carbon emissions trading system has covered industries such as power and has completed multiple compliance cycles, making it the world's largest carbon market in terms of emissions coverage.

[0003] Existing research primarily focuses on the theoretical framework design of carbon policies, including carbon allowance allocation methods, carbon pricing mechanisms, and trading mechanism design. With the gradual maturation of the carbon market, corporate-level carbon management decisions are receiving increasing attention. Scholars have explored corporate optimization strategies under carbon constraints from different perspectives: some studies examine how companies can leverage digital transformation to address carbon policies from a supply chain perspective; some studies focus on how companies can align their production decisions with consumers' low-carbon preferences; and others have established production optimization models that consider carbon costs, achieving a balance between cost and compliance by adjusting output and carbon trading volume.

[0004] However, existing research faces pressing technical challenges. Firstly, current corporate carbon management models generally assume regional homogeneity of carbon costs, with a severe lack of research on internal carbon synergy management for cross-regional corporate groups. Many studies treat carbon cost as a uniform external parameter, ignoring significant differences in energy structure, industrial structure, and carbon pricing across different regions of China. In fact, there are fundamental differences in the proportion of fossil fuels, the proportion of renewable energy consumption, and the carbon emission factor of the power grid across different regions; furthermore, differences in environmental tax rates between regions lead to substantial differentiation in corporate carbon costs. More importantly, empirical research shows that when faced with carbon policy shocks, some high-carbon-emitting companies transfer carbon reduction pressure by establishing subsidiaries in different locations, further highlighting the significant heterogeneity of carbon costs among subsidiaries within a group. Secondly, the computational efficiency and practicality of existing models are insufficient to meet the real-time decision-making needs of enterprises. Most carbon management optimization models rely on complex data statistics and lengthy model calculations, making them difficult to implement in actual operations for corporate groups that need to quickly assess the differences in carbon costs among subsidiaries and the potential for improving carbon management benefits. Summary of the Invention

[0005] In view of this, this disclosure provides a method and system for collaborative management and potential assessment of internal carbon quotas for cross-regional enterprise groups, which addresses at least the following issues: (1) Existing corporate carbon management models generally regard carbon cost as a homogeneous external parameter, ignoring the significant differences in energy structure, power grid carbon emission factors and environmental tax rates in different regions of China. This leads to a distortion in the carbon cost assessment of cross-regional corporate groups, which cannot truly reflect the heterogeneity of marginal carbon emission revenue of each subsidiary due to regional differences.

[0006] (2) Existing technologies lack a collaborative optimization mechanism for carbon quotas within cross-regional enterprise groups, which makes it impossible to achieve unified allocation of carbon assets within the group under the difference in marginal emission reduction costs, thus missing the opportunity to reduce overall compliance costs and maximize profits through internal collaboration.

[0007] (3) Existing carbon management optimization models mostly rely on complex global iterative solutions, which have low computational efficiency and lack a lightweight assessment method that can quickly quantify the potential for synergistic benefits without precise optimization. This makes it difficult to meet the high-frequency and rapid decision-making needs of corporate groups in their daily operations.

[0008] To this end, the first aspect of this disclosure proposes a method for collaborative management and potential assessment of internal carbon emission quotas for cross-regional enterprise groups. This method includes: acquiring production decision variables, raw material prices, carbon emission coefficients, and government-allocated carbon emission quotas for each subsidiary within the cross-regional enterprise group; constructing a marginal net revenue vector and a carbon emission structure vector for each subsidiary; and establishing a collaborative optimization objective function aimed at maximizing the overall profit of the group. This objective function includes internal carbon quota trading constraints, which allow subsidiaries within the group to autonomously allocate and transfer carbon quotas, thereby balancing the marginal emission reduction costs of each subsidiary through internal carbon quota trading. The method then solves the collaborative optimization objective function to obtain the optimal carbon quota allocation and optimal production decision for each subsidiary. Based on the marginal emission reduction costs of each subsidiary under the optimal production decision, a collaborative potential index is calculated, which is the square root of the variance of the marginal emission reduction costs of each subsidiary. Finally, based on the value of the collaborative potential index, the method determines whether to initiate internal carbon quota collaborative optimization.

[0009] A second aspect of this disclosure provides a computer-readable storage medium storing a computer program that can be loaded by a processor and execute any of the methods of this disclosure.

[0010] The third aspect of this disclosure proposes a collaborative management and potential assessment system for internal carbon emission quotas within a cross-regional enterprise group, comprising: a model building module configured to acquire production decision variables, raw material prices, carbon emission coefficients, and government-allocated carbon emission quotas data for each subsidiary within the cross-regional enterprise group; construct a marginal net revenue vector and a carbon emission structure vector for each subsidiary; and establish a collaborative optimization objective function aimed at maximizing the overall profit of the group. This collaborative optimization objective function includes internal carbon quota trading constraints, which allow subsidiaries within the group to autonomously allocate and transfer carbon quotas, thereby balancing the marginal emission reduction costs of each subsidiary through internal carbon quota trading; a collaborative solution module configured to solve the collaborative optimization objective function to obtain the optimal carbon quota allocation and optimal production decisions for each subsidiary; and a potential assessment module configured to calculate a collaborative potential index based on the marginal emission reduction costs of each subsidiary under the optimal production decisions. This collaborative potential index is the square root of the variance of the marginal emission reduction costs of each subsidiary. Based on the value of the collaborative potential index, a determination is made as to whether to initiate internal carbon quota collaborative optimization.

[0011] Beneficial Technical Effects: By constructing an internal carbon quota collaborative optimization model that incorporates regional carbon cost heterogeneity, this study achieves, for the first time, unified allocation of carbon assets and marginal emission reduction cost equilibrium at the cross-regional enterprise group level. It overcomes the decision-making distortion caused by the homogenization of carbon costs in traditional models, accurately reflecting the carbon cost differences among subsidiaries in different regions and significantly improving the overall group profit. By introducing an internal carbon quota transfer mechanism unconstrained by external carbon market prices, the group can achieve flexible transfer of carbon quotas among subsidiaries without distorting internal resource allocation efficiency. This avoids interference from external market price fluctuations on internal collaborative decision-making, enhancing the autonomy and stability of the group's carbon management. An innovative collaborative potential index D based on the variance of marginal emission reduction costs is proposed. ISP This system establishes a rapid mapping channel from decentralized decision-making to anticipated synergistic benefits, enabling enterprise groups to predict synergistic value with extremely low computational costs without performing complex global optimization solutions. This significantly lowers the threshold and cycle of carbon management decisions, facilitating high-frequency deployment and dynamic monitoring in actual business operations. By setting a dual-threshold judgment mechanism for potential indicators, an intelligent decision-making closed loop of "prediction-triggering-solving-execution" is achieved. This avoids excessive investment of computational resources when synergistic value is insufficient, while ensuring timely optimization when synergistic opportunities are significant, thus balancing system efficiency and economy. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 A schematic diagram of the carbon emission structure of groups and enterprises when ICS is permitted. Detailed Implementation

[0014] The following description, in conjunction with the accompanying drawings, clearly and completely describes how the technical solution of this case is implemented. Obviously, the described embodiments are only a part of the embodiments of this case, and not all of them. Based on the embodiments in this case, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of this application.

[0015] In this document, “illustrative” means “serving as an example, illustration or description”, and any illustration or implementation described herein as “illustrative” should not be construed as a more preferred or advantageous technical solution.

[0016] To keep the drawings concise, only the parts relevant to the invention are shown schematically in each figure, and they do not represent the actual structure of the product. Furthermore, for ease of understanding, some figures show only one or more components with the same structure or function, or only one or more are labeled.

[0017] This invention provides a method for collaborative management and potential assessment of internal carbon allowances for cross-regional enterprise groups. Its core lies in constructing an enterprise carbon cost model that incorporates regional carbon cost heterogeneity, and maximizing overall group profits through internal carbon synergy (ICS). It also provides a rapid assessment method for collaborative potential based on the variance of marginal emission reduction costs. The invention will be further described in detail below with mathematical derivation and specific implementation steps.

[0018] I. Basic Model Building Against the backdrop of global climate change, carbon policies internalize the external costs of carbon emissions through governance incentives, prompting companies to adopt sustainable production and consumption patterns. Early carbon policy research mainly focused on theoretical framework design, including carbon allowance allocation, carbon pricing, and trading mechanisms. With the gradual expansion of carbon governance, companies have begun to pay more attention to the management and optimization of carbon emission allowances. On the one hand, researchers have assessed the impact of emission reduction policies such as allowance allocation and marketing policies on corporate carbon emissions and their transmission channels; on the other hand, companies' proactive behavior under carbon policies has also received attention. Companies are not only concerned with compliance issues but have also begun to reduce carbon emissions and allowance purchase costs by optimizing production processes and improving energy efficiency. Existing research has explored how companies can leverage digital transformation to cope with carbon policies from statistical and supply chain perspectives, and how to reduce carbon market volatility risks by predicting and analyzing carbon market dynamics, or adjust production decisions to adapt to consumers' low-carbon preferences. In terms of building enterprise-level decision-making models, many studies focus on how companies can optimize their carbon-related strategies, such as constructing total cost minimization models that consider carbon costs (including raw material costs, production costs, and carbon costs), or studying the impact of low-carbon technologies and government regulations on corporate carbon emissions and production. These studies have found that companies can achieve a balance between cost reduction and carbon emission compliance by adjusting production volume and carbon trading volume. Other studies have noted differences in decision-making regarding carbon emission management and carbon policy responses across different industries, and have conducted targeted research on specific types of companies, such as the layout of distributed energy storage facilities, production strategies for aluminum electrolysis companies participating in the green certificate-carbon trading joint market, and the analysis of carbon emission patterns and marginal emission reduction costs in the paper industry.

[0019] This invention establishes a corporate carbon cost model that incorporates internal carbon quota compliance, aiming to demonstrate how regional carbon cost heterogeneity constrains production decisions. Production optimization models that focus solely on the carbon costs of individual companies tend to push the marginal revenue per unit of carbon emissions towards zero; however, for subsidiaries within a group, differences in regional energy structures, transportation costs, and production models lead to varying carbon and energy costs among subsidiaries, resulting in heterogeneous marginal revenue per unit of carbon emissions. This invention's model reveals that corporate groups can eliminate the differences in marginal emission revenue among their subsidiaries through internal quota trading, thereby maximizing profits at the group level.

[0020] Appendix Figure 1This is a schematic diagram illustrating the principle of the model of this invention. Two corporate groups and three independent subsidiaries are distributed across three different regions, resulting in differences in energy and raw material costs across these regions. Unlike independent companies that can only trade in the external carbon market, the group's subsidiaries engage in internal carbon allowance trading (ICS). The yellow portion of the diagram represents the division of regions and industries. Within each different region and industry, production enterprises generate different levels of carbon emissions due to differences in the carbon emission factors of raw materials and processes. Products enter the regional product market, generating revenue. Based on this, the blue and green portions represent the two corporate groups. The blue group includes subsidiary 1 from region 1, subsidiary 2 from region 2, and subsidiary 3 from region 3, each generating emissions 1, 2, and 3, respectively. Each subsidiary's carbon emissions must be offset by an equal amount of carbon emission allowances, primarily derived from allocated allowances and allowances purchased by the subsidiary. Similarly, the green group includes subsidiary 4 from region 1 and subsidiary 6 from region 2. In this invention's model, the subsidiaries within the group first utilize ICS to balance the differences in marginal profits from carbon emissions among the subsidiaries. Subsequently, the group engages in external carbon trading in the carbon market to purchase scarce allowances or sell surplus allowances. In contrast, companies without group collaboration can only buy and sell allowances directly in the carbon market through external carbon trading.

[0021] II. Model Construction To facilitate the explanation of the model in this invention, we assume the existence of a corporate group C consisting of multiple subsidiaries, and study its behavioral patterns in the carbon market. Group X consists of N subsidiaries numbered 1, 2, ..., i, ..., N. This invention focuses on subsidiary i as the object of analysis. We assume that subsidiary i has the following raw material quantity: This invention considers the costs involved in achieving that quantity of raw materials. For subsidiaries participating in the carbon market, this invention focuses on their carbon-containing raw materials, primarily including fuels in organic form and carbon-containing raw materials in impurity form.

[0022] Assume the unit price of raw materials is Its carbon emission coefficient is Accordingly, the raw material carbon cost of subsidiary i and carbon emissions The calculation is as follows: (12) (13) in, This indicates the amount of carbon emissions generated by subsidiary i during the processing of raw materials, which is usually related to the quantity of raw materials.

[0023] The carbon allowances that subsidiary i needs to purchase to cover these emissions are given by the following formula: (14) in, This represents the carbon emission allowance allocated to subsidiary i; This represents the carbon allowance trading volume of subsidiary i, with a positive value indicating the purchase of carbon allowances and a negative value indicating the sale of allowances; This indicates the portion of subsidiary i that was penalized for exceeding emission limits.

[0024] Therefore, the cost of purchasing carbon allowances can be expressed as: (15) in, This indicates the current carbon price in the carbon market. This represents the fine a company must pay for each unit of excessive emissions. Based on this, calculate the market revenue of these companies' products. Assume that the output rate (i.e., the ratio of output to raw material input) of any subsidiary i is... Then the output of subsidiary i for: (16) The optimization objective is to maximize net profit. The following optimization model is constructed: (17) in, This refers to the unit price of the product of subsidiary i.

[0025] When the carbon market reaches equilibrium, if subsidiary i achieves its maximum return, the following relationship will be satisfied: (18) Subsequently, group C is considered as a whole. Assume the output of subsidiary i... A series of production decisions , ,…, Decision. The decision vector of subsidiary i is defined as a stack of columns of these decision variables. The overall decision vector X of the entire group can be expressed as: (19) Based on the decision variables, define the group's marginal net revenue vector F and raw material cost matrix. : (20) in, This refers to the marginal net income of subsidiary i.

[0026] Considering the marginal carbon emissions resulting from the production decisions of each subsidiary, the group's carbon emission structure vector C(X) is defined as follows: (twenty one) in, This represents the gradient of carbon emissions of subsidiary i during the production and processing process on the decision variable.

[0027] To characterize the marginal carbon emissions of each subsidiary resulting from changes in production decisions, this vector incorporates internal carbon allowance trading constraints. These constraints include: each subsidiary's carbon emissions consist of the sum of direct carbon emissions from the production process and carbon emissions from raw materials; and the carbon allowances each subsidiary needs to purchase are its total carbon emissions minus the government-allocated allowances and the penalties for exceeding emission limits. These internal carbon allowance trading constraints allow for the autonomous allocation and transfer of carbon allowances among subsidiaries within the group. The internal carbon allowance transfer price is not constrained by external carbon market prices, and the total carbon allowance trading volume of all subsidiaries within the group meets the following requirements: ,in The amount of carbon emission credits purchased by subsidiary i (can be negative, indicating a sale). This represents the actual total carbon emissions of subsidiary i. Carbon emission allowances obtained for subsidiary i. This refers to excess emissions when the carbon emission allowance is insufficient.

[0028] Based on (21), constraint (14) is applied. Replace with: (twenty two) in and Let be an N-dimensional column vector of all 1s. The above formula represents the carbon emission allowance trading volume constraints for each subsidiary. The transaction price for the transfer of carbon allowances within the group is set to zero or allocated uniformly by the group headquarters, and is not affected by fluctuations in the external carbon market.

[0029] The profit optimization model for Group C is as follows: (twenty three) Find the optimal solution for the overall decision vector X of the group. This yields the optimal production and carbon quota allocation schemes for each subsidiary under the collaborative model. Based on the optimal solution... The enterprise group controls the internal transfer of carbon allowances among its subsidiaries and participates in buying or selling transactions in the external carbon market based on the remaining total allowances.

[0030] III. Model Solving When solving the problem, considering only constraint (22), the Lagrange multiplier method is used to transform the objective function into an unconstrained optimization problem, whose Lagrange function is:

[0031] Where τ is a Lagrange multiplier.

[0032] Next, using the Kuhn-Tucker (KKT) conditions, let the Lagrange multipliers... Equal to the external carbon market equilibrium price And to ensure that the unit marginal carbon emission net revenue of each subsidiary within the group satisfies the following equilibrium condition, so that the marginal emission reduction costs of each subsidiary within the group are equal, thereby achieving optimal overall group profit: (25) in Represents the decision variable at the optimal solution. This represents the Lagrange multiplier value corresponding to the optimal solution. The first equation is the gradient condition for the decision variables, the second is the internal carbon price condition, and the third is the original feasibility constraint. From the internal carbon price condition, we can obtain... Substituting this back into the decision gradient condition, we obtain the equilibrium condition under ICS that the group reaches its optimal state: (26) IV. Improved Estimation Methods In practice, quantitatively describing the impact of decisions on output is crucial for groups because the transfer functions connecting decisions to the group's marginal net revenue vector F(X) and carbon emission structure vector C(X) are often non-explicit. Determining these transfer functions requires significant statistical costs; therefore, groups need to determine the criteria for assessing the potential benefits of implementing ICS. To this end, this invention provides a specific method for quantitatively evaluating a group's profit improvement with or without ICS.

[0033] This invention assumes that, in a scenario without ICS, the decision sub-vector of subsidiary i is denoted as... The group decision matrix, composed of the hierarchical decision vectors of these subsidiaries, is denoted as... The square root of the variance is defined as the synergistic potential index. The criterion for judging the extent of profit improvement after adopting ICS is D. ISP The calculation formula is as follows: (27) in, For the marginal emission reduction cost of subsidiary i, This is the sum of the marginal emission reduction costs of each subsidiary.

[0034] To prove this formula, assume that the optimal decision for the group under ICS is: Therefore, the decision vector of each subsidiary i is According to the Lagrangian function (28) of the group's profit optimization model under ICS, its distributed solution... The gradient at is: (28) Note that λ is arbitrary here. Now, let's choose a specific value and set it as the average marginal emission reduction cost under decentralized decision-making. Based on the KKT conditions of the ICS-free optimization model (17), for each subsidiary i, we have: (29) in, This represents the group's average marginal emission reduction cost.

[0035] Substitute (29) into (28), and rewrite the gradient in the form of components of each subsidiary company i: (30) According to the KKT conditions of the optimization model of subsidiary i, we have This holds true for any i ∈ C. Substituting into the above equation, we get: (31) Now, regarding the improvement in earnings after adopting ICS Applying the second-order approximation as its... Taylor expansion at the location: (32) To estimate the first-order term, we use the previously obtained gradient expression. We assume the optimization path direction is approximately aligned with the negative gradient direction, i.e. ,but: (33) Combining (32) and (33), we can deduce that: (34) Where κ is a constant.

[0036] Furthermore, assuming the emission gradient norm of each subsidiary... The differences are not significant, so their average value can be used. Alternatively, the formula for calculating the estimated synergistic gain is: (35) in, It is proportional to the standard deviation of the marginal emission reduction cost.

[0037] In summary, this invention defines the synergy potential index as the square root of the variance (i.e., the variance of marginal emission reduction costs), as a proxy variable for measuring synergy potential: (1) According to the aforementioned synergistic potential indicators The numerical value of the value is used to output the corresponding estimated value of the collaborative gain; wherein, the value of the value is... The higher the value, the greater the expected profit increase potential for the enterprise group by introducing internal carbon allowance trading. This indicator can quickly and quantitatively assess the potential benefits of an enterprise group implementing internal carbon allowance trading without the need for complex global optimization solutions, thereby significantly reducing the data statistics costs and computational burden on the group in carbon management decision-making.

[0038] V. System Implementation Method Based on the above method, this invention also provides a collaborative management system for carbon quotas of an enterprise group, including a model building module, a collaborative solving module, a potential assessment module, and an execution control module.

[0039] The model building module is used to acquire production decision variables (including raw material input, process parameters, etc.), raw material prices, carbon emission coefficients, and carbon quota data allocated by the government for each subsidiary in real time. This module can automatically collect and clean data through standardized interfaces with the enterprise's ERP system and production management system. The marginal net revenue vector, raw material price vector, and group carbon emission structure vector are constructed according to formulas (1)-(23), and the collaborative optimization objective function for maximizing the group's overall profit and the corresponding constraints are generated.

[0040] The collaborative solution module is used to solve the optimization objective function. It uses the Lagrange multiplier method to transform the constrained optimization into an unconstrained problem and uses KKT conditions to solve it, outputting the optimal carbon quota allocation for each subsidiary. And optimal output. This module can be deployed on the central server at the group headquarters and solved using numerical optimization algorithms (such as interior point method or sequential quadratic programming method).

[0041] The potential assessment module is used to calculate the marginal emission reduction cost for each subsidiary under the optimal production decision. Its variance, and output the synergistic potential index D according to formula (36). ISP This module further includes a threshold determination function: when D ISP When the value is below a preset lower threshold, it is determined that internal carbon quota trading should not be initiated at this time, and a control signal to maintain decentralized decision-making is output; when D ISP If the value exceeds the preset upper limit threshold, it is determined to have high collaborative value, the collaborative solving module is forcibly triggered to solve again, and a suggestion to start ICS is output.

[0042] The execution control module generates carbon quota transfer instructions (i.e., internal trading instructions) for each subsidiary based on the optimal solution output by the collaborative solving module, and conducts transaction settlement between the group as a whole and the external carbon market. This module connects with the carbon trading account systems of each subsidiary and the external carbon market trading platform through standardized data interfaces to realize the automatic execution of quota transfers and external buying and selling operations.

[0043] In practical applications, the system can operate according to the following process: First, the data acquisition module periodically obtains the operational data of each subsidiary; the potential assessment module quickly calculates the DISP index; if the DISP is higher than the upper limit threshold, the collaborative solution module is activated for precise optimization; the solution result is issued and executed by the execution control module; the above process is repeated in the next cycle to achieve dynamic closed-loop management.

[0044] It should be understood that the above embodiments are only used to illustrate the embodiments of the present invention, and are not intended to limit the embodiments of the present invention. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the embodiments of the present invention. Therefore, all equivalent technical solutions also fall within the scope of the embodiments of the present invention, and the patent protection scope of the embodiments of the present invention should be defined by the claims.

Claims

1. A method for collaborative management and potential assessment of internal carbon emission quotas for cross-regional enterprise groups, characterized in that, include: Data on production decision variables, raw material prices, carbon emission coefficients, and government-allocated carbon emission quotas for each subsidiary within a cross-regional enterprise group are obtained. Marginal net revenue vectors and carbon emission structure vectors for each subsidiary are constructed, and a collaborative optimization objective function is established with the goal of maximizing the overall profit of the group. The collaborative optimization objective function includes internal carbon quota trading constraints, which allow subsidiaries within the group to autonomously allocate and transfer carbon quotas, so that the marginal emission reduction costs of each subsidiary tend to be balanced through internal carbon quota trading. Solve the aforementioned collaborative optimization objective function to obtain the optimal carbon quota allocation and optimal production decision for each subsidiary; Based on the marginal emission reduction cost of each subsidiary under the optimal production decision, a synergy potential index is calculated, which is the square root of the variance of the marginal emission reduction cost of each subsidiary. Based on the value of the aforementioned synergistic potential index, determine whether to initiate internal carbon quota synergistic optimization.

2. The method according to claim 1, characterized in that, The specific objective function for the collaborative optimization is as follows: The following constraints must be met: （12） （13） （15） （16） （19） （21） （22） in, This is the marginal net revenue vector. Let the raw material price vector be... , It is a column vector of all 1s. The current carbon price in the carbon market, This refers to the carbon emission credit purchase volume of subsidiary i, which belongs to Group C. Fines imposed on entities for exceeding emission limits. Excess emissions due to insufficient carbon emission allowances; This refers to the unit price of raw materials. Carbon emission coefficient, For the raw material carbon cost of subsidiary i, Carbon emissions; This indicates the carbon emissions generated by subsidiary i during the processing of raw materials; Cost of purchasing carbon allowances; N is the total number of subsidiaries; For the output of subsidiary i, For the output rate of subsidiary i, Let X represent the quantity of raw materials for subsidiary i, and let X be the overall decision vector for the group. This is the group's carbon emission structure vector. This represents the gradient of carbon emissions of subsidiary i during the production and processing process on the decision variable.

3. The method according to claim 1, characterized in that, The calculation of synergy potential indicators includes: Among them, D ISP As an indicator of synergistic potential, Let be the decision vector of subsidiary i under decentralized decision-making. For the marginal emission reduction cost of subsidiary i, This represents the sum of the marginal emission reduction costs of all subsidiaries, where N is the total number of subsidiaries.

4. The method according to claim 1, characterized in that, Based on the value of the aforementioned synergy potential index, determine whether to initiate internal carbon quota synergy optimization, including: When the synergy potential index is lower than the preset lower limit threshold, it is determined that it is not appropriate to start internal carbon quota trading at present, and the decentralized decision-making status of each subsidiary is maintained. When the synergy potential index is higher than the preset upper limit threshold, it is determined to have high synergy value, triggering the re-solution of the synergy optimization objective function, and outputting an instruction to start internal carbon quota trading. When the synergistic potential index is between a preset lower threshold and a preset upper threshold, the current state remains unchanged.

5. The method according to claim 1, characterized in that, The objective function of the collaborative optimization is solved by using the Lagrange multiplier method to transform the constrained optimization into an unconstrained problem, and then using the KKT conditions for solution.

6. The method according to claim 1, characterized in that, Based on the optimal solution output by the collaborative solving module, carbon quota transfer instructions are generated for each subsidiary, and the group as a whole conducts transaction settlement with the external carbon market.

7. A computer-readable storage medium, characterized in that: The computer program is stored that can be loaded by a processor and executed according to any one of claims 1 to 4.

8. A collaborative management and potential assessment system for internal carbon emission quotas for cross-regional enterprise groups, characterized in that, include: The model building module is configured to acquire production decision variables, raw material prices, carbon emission coefficients, and government-allocated carbon emission quotas for each subsidiary within a cross-regional enterprise group, construct the marginal net revenue vector and carbon emission structure vector for each subsidiary, and establish a collaborative optimization objective function aimed at maximizing the overall profit of the group. The collaborative optimization objective function includes internal carbon quota trading constraints, which allow subsidiaries within the group to autonomously allocate and transfer carbon quotas, so that the marginal emission reduction costs of each subsidiary tend to be balanced through internal carbon quota trading. The collaborative solution module is configured to solve the collaborative optimization objective function to obtain the optimal carbon quota allocation and optimal production decision for each subsidiary. The potential assessment module is configured to calculate a collaborative potential index based on the marginal emission reduction cost of each subsidiary under the optimal production decision. The collaborative potential index is the square root of the variance of the marginal emission reduction cost of each subsidiary. Based on the value of the collaborative potential index, it is determined whether to initiate internal carbon quota collaborative optimization.

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