Industrial park enterprise carbon performance evaluation method, system and equipment based on multi-dimensional carbon indexes and medium

Abstract

The invention discloses an industrial park enterprise carbon performance evaluation method, system, equipment and medium based on a multi-dimensional carbon index, and belongs to the technical field of industrial carbon management, and the method comprises the steps: collecting real-time power consumption data of an enterprise in a period and power carbon data of a corresponding time period, clearly defining an evaluation range, determining an evaluation object, and carrying out the evaluation of the enterprise carbon performance. The method comprises the following steps: calculating a core index to obtain a core index original value, carrying out standardization processing on the core index original value, endowing the standardized core index with a corresponding weight according to a preset standard, carrying out calculation through a primary algorithm to obtain an enterprise carbon performance comprehensive score, and dividing enterprise rating according to a comprehensive score interval. According to the method, three core indexes are constructed, and the problems of limitation of a single efficiency index, evaluation time lag and lack of power grid collaboration are solved; through standardization processing and weight distribution, quantitative evaluation of enterprise carbon emission efficiency, load regulation capability and clean energy consumption level is realized, and the defect that a traditional method only pays attention to total amount and static efficiency is overcome.

Description

Technical Field

[0001] This invention relates to the field of industrial carbon management technology, specifically to a method, system, equipment, and medium for evaluating the carbon performance of enterprises in industrial parks based on multi-dimensional carbon indicators. Background Technology

[0002] For carbon emission assessment of enterprises in industrial parks, common methods mainly rely on traditional, periodic carbon emission accounting, such as calculations based on annual or quarterly total energy consumption and fixed emission factors. Evaluation indicators are mostly single total carbon emissions or carbon emissions per unit of output value, focusing on static outcome evaluation, while some may introduce trend analysis, such as carbon emissions per unit of added value.

[0003] Existing technologies suffer from limited evaluation dimensions, relying excessively on absolute quantities or efficiency indicators, failing to comprehensively reflect enterprises' initiative in carbon reduction and their contribution to a cleaner power grid. Secondly, their timeliness is poor; evaluations based on long-term data cannot provide near-real-time guidance, making it difficult to instruct enterprises to adjust their daily electricity consumption. Finally, they lack a collaborative orientation, failing to dynamically link enterprises' electricity consumption behavior with the real-time carbon intensity of the power grid, thus failing to incentivize enterprises to optimize electricity usage periods to collaboratively consume clean energy, thereby missing the enormous potential for systemic carbon reduction through demand-side response. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problems solved by this invention are: how to address the problem of over-reliance on absolute quantities or efficiency indicators, which cannot fully reflect the initiative of enterprises in carbon reduction and their contribution to the clean energy of the power grid; poor timeliness, as evaluations based on long-term periodic data cannot provide near-real-time guidance and are difficult to guide enterprises to make daily production and electricity consumption adjustments; and lack of collaborative guidance, which fails to dynamically link the electricity consumption behavior of enterprises with the real-time carbon intensity of the power grid, and cannot incentivize enterprises to coordinate the consumption of clean energy by optimizing electricity consumption periods, thus missing the huge potential for systemic carbon reduction through demand-side response.

[0006] To address the aforementioned technical problems, this invention provides the following technical solution: a method for evaluating the carbon performance of enterprises in industrial parks based on multi-dimensional carbon indicators, comprising, Collect real-time electricity consumption data and corresponding carbon emissions data of enterprises within the collection period, clarify the scope of evaluation, and design core indicators based on the low-carbon behavior goals of industrial parks. The evaluation object is determined, the original values ​​of the core indicators are obtained by calculating the core indicators, and the original values ​​of the core indicators are standardized. The standardized core indicators are assigned corresponding weights according to preset standards, and the comprehensive carbon performance score of enterprises is calculated through a first-level algorithm. Enterprise ratings are then determined based on the comprehensive score range.

[0007] As a preferred embodiment of the method for evaluating the carbon performance of enterprises in industrial parks based on multi-dimensional carbon indicators described in this invention, the method includes: real-time electricity consumption data of enterprises within the collection period and corresponding electricity carbon data for the time period, with core indicators designed based on the low-carbon behavior goals of the industrial park, including... Real-time electricity consumption data of enterprises during the collection period and corresponding time period carbon data; Based on the real-time electricity consumption data of enterprises within the collected period and the corresponding time period's carbon emissions data, the organizational boundaries and emission source boundaries are clearly defined. Based on organizational boundaries and emission source boundaries, comprehensive carbon efficiency intensity stage variation parameters, low-carbon energy use parameters, and clean energy penetration parameters are designed according to the low-carbon behavior goals of industrial parks.

[0008] As a preferred embodiment of the method for evaluating the carbon performance of enterprises in industrial parks based on multi-dimensional carbon indicators as described in this invention, the step of determining the evaluation object, obtaining the original values ​​of the core indicators by calculating the core indicators, and standardizing the original values ​​of the core indicators includes: The evaluation object is determined based on the phase variation parameters of comprehensive carbon efficiency intensity, low-carbon energy use parameters, and clean energy penetration parameters. The parameters for the phased variation of comprehensive carbon efficiency intensity, low-carbon energy use, and clean energy penetration were calculated separately to obtain the original values ​​of the core indicators. Standardize the original values ​​of the core indicators.

[0009] As a preferred embodiment of the method for evaluating the carbon performance of enterprises in industrial parks based on multi-dimensional carbon indicators described in this invention, the standardized core indicators are assigned corresponding weights according to preset standards, and a comprehensive carbon performance score for the enterprise is calculated using a first-level algorithm. Enterprise ratings are then determined based on the comprehensive score range. The standardized core indicators are assigned corresponding weights according to preset standards; Based on the weighted core indicators, the comprehensive score of corporate carbon performance is calculated through a primary algorithm. Based on the calculated comprehensive carbon performance score, companies are rated according to a preset comprehensive score range.

[0010] As a preferred embodiment of the method for evaluating the carbon performance of enterprises in industrial parks based on multi-dimensional carbon indicators described in this invention, the method of defining organizational boundaries and emission source boundaries based on real-time electricity consumption data of enterprises within a collected period and corresponding time period carbon data includes: The assessment's boundaries include organizational boundaries and emission source boundaries; The main target of the organizational boundary assessment system is the key monitored enterprises within the park, and the assessment results focus on the carbon performance of individual enterprises. The emission source boundaries are based on existing data conditions and include Scope 2 emissions and Scope 1 emissions. Scope 1 emissions are direct carbon emissions from the combustion of fossil fuels, while Scope 2 emissions are indirect carbon emissions from industrial electricity consumption.

[0011] As a preferred embodiment of the method for evaluating the carbon performance of enterprises in industrial parks based on multi-dimensional carbon indicators described in this invention, the calculation of the comprehensive carbon efficiency intensity stage variation parameter, low-carbon energy use parameter, and clean energy penetration parameter includes: The carbon intensity per unit of output is calculated using the following expression: in, Carbon emission intensity per unit of output value This represents the company's total carbon emissions. The total industrial output value of the enterprise; The year-on-year change rate of carbon emission intensity per unit of output is calculated using the following expression: in, The year-on-year change rate of carbon emission intensity per unit output value. This represents the carbon emission intensity per unit of output value in this period. Carbon emission intensity per unit output value in the previous period; The average electricity carbon emission factor for this period is calculated using the following expression: in, This represents the average carbon emission factor for electricity during this period. This is the sum of the company's carbon factor for the current period. This is the sum of the time periods within this cycle; Define the set of low-carbon periods To meet the requirements of this period time period The set of total electricity consumption during low-carbon periods is expressed as follows: in, This represents the total electricity consumption during low-carbon periods. This is a collection of low-carbon periods. In order to be in Electrocarbon factor over time period In order to be in Electricity consumption during a given time period; The expression for calculating the electricity consumption rate during low-carbon periods is: in, Electricity consumption rate during low-carbon periods This represents the total electricity consumption during low-carbon periods. This represents the company's total electricity consumption during this period. Define the cleaning period set as the set that satisfies the requirements of this cycle. time period The collection of clean power hours For set The total number of hours included is expressed as: in, For clean energy hours, Gather for cleaning time. Indicates time period The number of hours included, In order to be in Electrocarbon factor over time period The annual average carbon factor of electricity in the province where the company is located; The formula for calculating clean electricity penetration rate is: in, To improve clean energy penetration, For clean energy hours, This represents the total number of hours within the period.

[0012] This invention establishes quantitative calculation formulas for core indicators and combines dynamic carbon emission data with enterprise time-of-use electricity data to quantify the trend of carbon emission efficiency changes, load regulation capabilities, and clean energy consumption levels of enterprises. This solves the problem that traditional static accounting methods cannot reflect enterprises' proactive carbon reduction behavior.

[0013] As a preferred embodiment of the method for evaluating the carbon performance of enterprises in industrial parks based on multi-dimensional carbon indicators as described in this invention, the comprehensive carbon performance score of the enterprise is calculated through a first-level algorithm based on the weighted core indicators, including: The overall carbon performance score for a company is calculated using the following expression: in, The overall score for a company's carbon performance. The standardized fraction of the year-on-year change rate of carbon emission intensity per unit output. The weight of the year-on-year change rate of carbon emission intensity per unit output value. This is a standardized score representing the electricity consumption rate during low-carbon periods. Weighting of electricity consumption rate during low-carbon periods A standardized score for clean electricity penetration. Weights for clean electricity penetration rate.

[0014] This invention calculates a comprehensive score by weighting the standardized scores of three core indicators with their corresponding weights. It quantifies and integrates multi-dimensional carbon performance into directly comparable values, effectively unifying the evaluation standards for different dimensions such as carbon emission efficiency, electricity consumption period optimization, and clean energy consumption. This solves the application problem of the difficulty in comprehensively comparing the evaluation results of multiple indicators.

[0015] This invention provides a system for evaluating the carbon performance of enterprises in industrial parks based on multi-dimensional carbon indicators.

[0016] To address the aforementioned technical problems, this invention provides the following technical solution: a system for evaluating the carbon performance of enterprises in industrial parks based on multi-dimensional carbon indicators, comprising: a core indicator design module, a core indicator calculation and standardization module, and a comprehensive carbon performance score calculation module for enterprises; The core indicator design module collects real-time electricity consumption data and corresponding carbon emissions data of enterprises within the collection period, clarifies the scope of evaluation, and designs core indicators based on the low-carbon behavior goals of industrial parks. The core indicator calculation and standardization module determines the evaluation object, calculates the core indicators to obtain the original values ​​of the core indicators, and then standardizes the original values ​​of the core indicators. The corporate carbon performance comprehensive score calculation module assigns corresponding weights to standardized core indicators according to preset standards, calculates the corporate carbon performance comprehensive score through a first-level algorithm, and classifies corporate ratings based on the comprehensive score range.

[0017] The present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, characterized in that the processor executes the computer program to implement the steps of the method for evaluating the carbon performance of enterprises in industrial parks based on multi-dimensional carbon indicators.

[0018] The present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of the method for evaluating the carbon performance of enterprises in industrial parks based on multi-dimensional carbon indicators.

[0019] The beneficial effects of this invention are as follows: By introducing dynamic carbon emission data and real-time electricity consumption data, this invention constructs three core indicators to solve the problems of limitations of single efficiency indicators, time lag in evaluation, and lack of grid coordination. Through standardized processing and weight allocation, a comprehensive scoring system is formed to achieve quantitative evaluation of enterprises' carbon emission efficiency, load regulation capacity, and clean energy consumption level. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, 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 the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 The following is an overall flowchart of a method for evaluating the carbon performance of enterprises in industrial parks based on multi-dimensional carbon indicators, provided as an embodiment of the present invention. Detailed Implementation

[0022] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0023] Example 1, referring to Figure 1 This is one embodiment of the present invention, which provides a method for evaluating the carbon performance of enterprises in industrial parks based on multi-dimensional carbon indicators, including: To address the issues of over-reliance on absolute quantities or efficiency indicators, which fail to comprehensively reflect enterprises' initiative in carbon reduction and their contribution to a cleaner power grid; poor timeliness, as evaluations based on long-term data cannot provide near-real-time guidance and are insufficient to guide enterprises in adjusting their daily production electricity consumption; and a lack of collaborative guidance, failing to dynamically link enterprises' electricity consumption behavior with the real-time carbon intensity of the power grid, thus failing to incentivize enterprises to optimize electricity consumption periods to collaboratively absorb clean energy and miss the enormous potential for systemic carbon reduction through demand-side response, this paper proposes a method for evaluating the carbon performance of enterprises in industrial parks based on multi-dimensional carbon indicators.

[0024] Collect real-time electricity consumption data and corresponding carbon emissions data of enterprises within the collection period, clarify the scope of evaluation, and design core indicators based on the low-carbon behavior goals of industrial parks. The evaluation object is determined, the original values ​​of the core indicators are obtained by calculating the core indicators, and the original values ​​of the core indicators are standardized. The standardized core indicators are assigned corresponding weights according to preset standards, and the comprehensive carbon performance score of enterprises is calculated through a first-level algorithm. Enterprise ratings are then determined based on the comprehensive score range.

[0025] Therefore, this invention collects real-time electricity consumption data and dynamic carbon dioxide data to construct core indicators. The standardized indicator values ​​are then integrated into a comprehensive score through a weighted algorithm and classified into ratings, realizing the transformation from static accounting to dynamic evaluation. This effectively solves the problems of time lag and single dimension in traditional methods.

[0026] Example 2, an embodiment of the present invention, provides a method for evaluating the carbon performance of enterprises in industrial parks based on multi-dimensional carbon indicators, based on the previous embodiment, including: In this embodiment of the application, step S1 involves collecting real-time electricity consumption data of enterprises within a given period and corresponding carbon emissions data for that period, clearly defining the scope of the evaluation, and designing core indicators based on the low-carbon behavior targets of the industrial park, including the following steps A1-A3: A1: Real-time electricity consumption data of enterprises within the collection period and corresponding time period carbon data.

[0027] A2: Based on the real-time electricity consumption data of enterprises within the collected period and the corresponding time period's carbon emissions data, clarify the organizational boundaries and emission source boundaries.

[0028] The assessment's boundaries include organizational boundaries and emission source boundaries; The main target of the organizational boundary assessment system is the key monitored enterprises within the park, and the assessment results focus on the carbon performance of individual enterprises. The emission source boundaries are based on existing data conditions and include Scope 2 emissions and Scope 1 emissions. Scope 1 emissions are direct carbon emissions from the combustion of fossil fuels, while Scope 2 emissions are indirect carbon emissions from industrial electricity consumption.

[0029] Scope 1 emissions refer to direct carbon emissions from the combustion of fossil fuels. The carbon emissions of some key enterprises within the park primarily originate from the direct combustion of fossil fuels during their production and operation processes. Considering the significant contribution of these emissions to the overall carbon footprint of some enterprises, Scope 1 emissions are included in the evaluation system to ensure the comprehensiveness of the evaluation results. Enterprises are required to provide their fossil fuel consumption data as required and to calculate emissions based on nationally or industry-recognized emission factors.

[0030] Scope 2 emissions refer to indirect carbon emissions caused by enterprise electricity consumption. Carbon emission calculations rely on two key data points: first, the actual electricity consumption data of users (unit: kWh), requiring data with fine granularity by user and hour to meet different time-sensitive evaluation needs; second, the carbon emission factor, which uses the existing "hourly historical electricity carbon data" (unit: kgCO2e / kWh) in the industrial park. Ideally, the evaluation timeliness should combine real-time electricity consumption data and corresponding time-specific electricity carbon data to achieve near real-time calculation of electricity-related carbon emissions. As an alternative, periodic calculation can be used, employing average electricity carbon data within the evaluation period (e.g., weekly, monthly, quarterly). However, the averaging method (e.g., arithmetic mean) and the factor update frequency must be clearly specified (e.g., monthly updates of the previous month's average electricity carbon factor).

[0031] A3: Based on organizational boundaries and emission source boundaries, design the year-on-year change rate of carbon emission intensity per unit output, electricity consumption rate during low-carbon periods, and clean electricity penetration rate according to the low-carbon behavior goals of industrial parks.

[0032] The year-on-year change rate of carbon emission intensity per unit of output reflects the changing trend of carbon emissions per unit of output for an enterprise and is a core indicator for measuring an enterprise's carbon emission efficiency. The year-on-year change rate directly reflects the improvement or deterioration of an enterprise's carbon emission control over a certain period.

[0033] The reasons for selection are: to reflect efficiency improvement, as carbon emission intensity per unit of output value eliminates the impact of differences in enterprise size compared to total carbon emissions, and can more accurately reflect the improvement in enterprise energy utilization and carbon emission efficiency; and to ensure high data availability, as it can be calculated based on electricity consumption and output data, with a solid data foundation that facilitates promotion and application.

[0034] The low-carbon period electricity consumption rate is quantified as the percentage of a company's total electricity consumption during periods when its electricity carbon data is lower than or equal to the benchmark value set by the park.

[0035] The reasons for selection are: to guide grid coordination and encourage enterprises to prioritize production during periods of low carbon emission intensity, thereby improving the overall low-carbon operation level of the grid; to reflect responsiveness, reflecting the enterprise's ability to regulate load and respond to low-carbon signals from the grid, which is an important indicator for measuring the enterprise's green electricity consumption behavior; and to promote clean energy consumption, encouraging enterprises to proactively adapt to the characteristics of clean energy output from the grid and increase the proportion of new energy consumption.

[0036] Clean electricity penetration measures the percentage of a company’s total electricity usage during periods of extremely low carbon emissions (such as below a certain clean energy threshold). It aims to identify and encourage companies to increase their electricity load during periods of high clean energy availability.

[0037] The reasons for selection are: direct contribution to carbon reduction, as enterprises can directly reduce system carbon emissions by using electricity in the current period, which is the most direct support for the consumption of clean energy; reflecting enterprise initiative, indicating whether enterprises have the ability to identify low-carbon windows and proactively adjust production plans; and contributing to energy transformation, by quantifying enterprises' clean electricity use behavior to promote the optimization of energy structure.

[0038] It should be noted that by collecting real-time electricity consumption data and dynamic carbon emissions data from enterprises, an accounting boundary covering emissions in scope one and scope two was constructed. Three core indicators were designed: the year-on-year change rate of carbon emission intensity per unit output, the electricity consumption rate during low-carbon periods, and the penetration rate of clean electricity. These indicators are used to achieve quantitative evaluation from three dimensions: dynamic changes in carbon emission efficiency, electricity consumption behavior and low-carbon synergy with the power grid, and utilization of clean energy windows. This effectively solves the problems of single dimensions and time lag in traditional methods.

[0039] In this embodiment of the application, step S2 involves determining the evaluation object, calculating the original values ​​of the core indicators to obtain the original values ​​of the core indicators, and standardizing the original values ​​of the core indicators, including the following steps B1-B3: B1: The evaluation object is determined based on the phase variation parameters of comprehensive carbon efficiency intensity, low-carbon energy use parameters, and clean energy penetration parameters.

[0040] B1: Calculate the phase variation parameters of comprehensive carbon efficiency intensity, low-carbon energy use parameters, and clean energy penetration parameters respectively to obtain the original values ​​of core indicators.

[0041] The average electricity carbon emission factor for this period is calculated using the following expression: in, This represents the average carbon emission factor for electricity during this period. This is the sum of the company's carbon factor for the current period. This is the sum of the time periods within this cycle; Define the set of low-carbon periods To meet the requirements of this period time period The set of total electricity consumption during low-carbon periods is expressed as follows: in, This represents the total electricity consumption during low-carbon periods. This is a collection of low-carbon periods. In order to be in Electrocarbon factor over time period In order to be in Electricity consumption during a given time period; The expression for calculating the electricity consumption rate during low-carbon periods is: in, Electricity consumption rate during low-carbon periods This represents the total electricity consumption during low-carbon periods. This represents the company's total electricity consumption during this period. Define the cleaning period set as the set that satisfies the requirements of this cycle. time period The collection of clean power hours For set The total number of hours included is expressed as: in, For clean energy hours, Gather for cleaning time. Indicates time period The number of hours included, In order to be in Electrocarbon factor over time period The annual average carbon factor of electricity in the province where the company is located; The formula for calculating clean electricity penetration rate is: in, To improve clean energy penetration, For clean energy hours, This represents the total number of hours within the period.

[0042] B3: Standardize the original values ​​of core indicators.

[0043] In this embodiment of the application, the calculation of the year-on-year change rate of carbon emission intensity per unit output in step B1 is specifically reflected as follows: The carbon intensity per unit of output is calculated using the following expression: in, Carbon emission intensity per unit of output value This represents the company's total carbon emissions. The total industrial output value of the enterprise; The year-on-year change rate of carbon emission intensity per unit of output is calculated using the following expression: in, The year-on-year change rate of carbon emission intensity per unit output value. This represents the carbon emission intensity per unit of output value in this period. This represents the carbon emission intensity per unit of output value in the previous period.

[0044] In an alternative embodiment, the calculation of the stage change parameter of the comprehensive carbon efficiency intensity in step B1 can also adopt a smooth change rate calculation method based on moving average. For enterprises with large fluctuations in the cycle, the moving average (MA) of the carbon emission intensity per unit output value I can be calculated first, and then the change rate can be calculated based on the smoothed sequence.

[0045] In another alternative embodiment, the calculation of the stage change parameter of the comprehensive carbon efficiency intensity in step B1 can also adopt a trend change rate calculation method based on linear fitting. When the fluctuation of the I value within the cycle is obvious, multiple points within the cycle can be linearly fitted and the slope can be calculated as the change trend, and then it can be converted into a change rate.

[0046] In the embodiment of the present application, in step B1, calculating the stage change parameter of the comprehensive carbon efficiency intensity solves the problem that the traditional method is difficult to intuitively reflect the short-term fluctuations of the enterprise's carbon emission efficiency, and the change in the carbon emission control level within adjacent cycles can be captured through the year-on-year calculation method.

[0047] In the embodiment of the present application, the specific embodiment of the standardization processing of the original value of the core index in step S1 is as follows: Each index is converted into a standardized score of 0-100 through a preset scoring rule. The specific rule is as follows: Standardize the year-on-year change rate of the carbon emission intensity per unit output value: Set the scoring rule: when R ≤ -20%, score 100 points (excessive decline); when -20% < R ≤ -10%, score 90 points; when -10% < R ≤ 0%, score 80 points; when 0% < R ≤ 5%, score 60 points (basically flat); when 5% < R ≤ 10%, score 40 points (slight increase); when 10% < R ≤ 20%, score 20 points; when R > 20%, score 0 points (significant increase).

[0048] Standardize the electricity consumption rate during the low-carbon period: When ≥ 80%, score 100 points; when 60% ≤ < 80%, score 80 points; when 40% ≤ < 60%, score 60 points; when 20% ≤ < 40%, score 40 points; when 0% ≤ < 20%, score 0 points.

[0049] Standardize the electricity consumption rate during the low-carbon period: When ≥ 50%, score 100 points; when 30% ≤ < 50%, score 80 points; when 10% ≤ < 30%, score 60 points; when 5% ≤ < 10%, score 40 points; when 0% ≤ <5% of commentaries received a score of 0.

[0050] In an optional implementation, the standardization of the original values ​​of the core indicators in step S1 can also be performed using a linear normalization standardization method. In scenarios where the indicators and the target have a linear relationship, each indicator is linearly scaled according to its physical value range and mapped to 0–100.

[0051] In another optional implementation, the standardization of the original values ​​of the core indicators in step S1 can also be carried out using a difference scoring method based on the target value or the base year, with the policy target, the enterprise emission reduction target or a certain base year indicator as a reference, and the degree of deviation is mapped to 0-100 points.

[0052] In one optional implementation, step S1 standardizes the original values ​​of the core indicators. By using preset segmented scoring rules, the original values ​​of core indicators with different dimensions and magnitudes are uniformly mapped to standardized percentage scores. This solves the problem of data incomparability caused by differences in units and numerical ranges in multi-indicator evaluation systems, enabling the scoring results to intuitively reflect management effectiveness.

[0053] It should be noted that by constructing precise calculation formulas for the year-on-year change rate of carbon emission intensity per unit output, the electricity consumption rate during low-carbon periods, and the penetration rate of clean electricity, a quantitative assessment of enterprises' carbon emission efficiency, load regulation capacity, and clean energy consumption level can be achieved. By pre-setting scoring rules, the original values ​​of each indicator are standardized into a unified scoring system to ensure the objectivity of the evaluation results.

[0054] In this embodiment of the application, step S3 assigns corresponding weights to the standardized core indicators according to preset standards, and calculates the comprehensive carbon performance score of the enterprise through a first-level algorithm. The enterprise rating is then divided according to the comprehensive score range, including the following steps C1-C3: C1: Assign corresponding weights to the standardized core indicators according to the preset standards.

[0055] Each indicator is assigned a corresponding weight based on its importance in reflecting the overall carbon performance of enterprises and guiding low-carbon behavior (the sum of all weights is 1): The year-on-year change rate of carbon emission intensity per unit of output, R, has a weight of 40%. As a core efficiency indicator, it directly reflects the emission level and trend changes of energy consumption per unit of output and serves as the basis for evaluation.

[0056] Low-carbon electricity consumption The weighting is 30%. This reflects the company's dynamic load response capability and its direct contribution to the low-carbon operation of the power grid, and is a key manifestation of the synergy between carbon reduction and energy transition.

[0057] Clean power penetration The weighting is 30%. It directly measures the actual effectiveness of enterprises' proactive actions to consume clean energy and encourages them to seize the best opportunity to use electricity.

[0058] C2: Based on the weighted core indicators, the comprehensive score of corporate carbon performance is calculated through a first-level algorithm.

[0059] C3: Based on the calculated comprehensive score of corporate carbon performance, corporate ratings are divided according to the preset comprehensive score range.

[0060] In this embodiment of the application, the comprehensive carbon performance score of the enterprise calculated by the first-level algorithm in step S3 is specifically reflected as follows: The overall carbon performance score for a company is calculated using the following expression: in, The overall score for a company's carbon performance. The standardized fraction of the year-on-year change rate of carbon emission intensity per unit output. The weight of the year-on-year change rate of carbon emission intensity per unit output value. This is a standardized score representing the electricity consumption rate during low-carbon periods. Weighting of electricity consumption rate during low-carbon periods A standardized score for clean electricity penetration. Weights for clean electricity penetration rate.

[0061] In an optional implementation, the comprehensive score of corporate carbon performance calculated by the first-level algorithm in step S3 can also be obtained by using a comprehensive score method based on principal component analysis (PCA). The comprehensive principal component score that can represent the overall carbon performance is extracted by PCA and used as the final evaluation.

[0062] In another optional implementation, the comprehensive score of corporate carbon performance calculated by the first-level algorithm in step S3 can also be obtained by a subjective weighted comprehensive evaluation method based on the analytic hierarchy process (AHP). First, a hierarchical structure model is constructed, the weight of each indicator is determined by pairwise comparison, and then a comprehensive score is formed by linear weighting.

[0063] In the implementation of this application, in step S3, the comprehensive score of corporate carbon performance is calculated by a first-level algorithm. By constructing a comprehensive score calculation expression based on a weighted summation model, the standardized multi-dimensional carbon performance indicators are integrated into a single quantitative value. This solves the problem of scattered results in various dimensions and difficulty in forming a unified judgment conclusion in the multi-indicator evaluation system. By introducing configurable weight coefficients, the relative importance of different indicators in the overall evaluation is objectively reflected.

[0064] In this embodiment of the application, step C3, which involves classifying enterprise ratings based on the calculated comprehensive carbon performance score and according to a preset comprehensive score range, is specifically reflected in the following: The overall score ranges from 0 to 100 points. A higher score indicates a better overall carbon performance for the company. The company ratings based on the score range are shown in Table 1. Table 1 Enterprise Rating Standards

[0065] In one optional implementation, step C3, which divides enterprise ratings based on the calculated comprehensive carbon performance score and according to a preset comprehensive score range, can also adopt a rating division method based on cluster analysis. In scenarios with a large number of enterprises and complex score distribution, the comprehensive scores of all enterprises are automatically aggregated into several categories, and the categories are mapped to ratings.

[0066] In another optional implementation, the enterprise rating based on the calculated comprehensive carbon performance score in step C3, according to the preset comprehensive score range, can also adopt the threshold dynamic adjustment method, which dynamically adjusts the rating threshold according to the carbon emission risk or energy structure characteristics of the region / industry where the enterprise is located.

[0067] In this application embodiment, step C3 is based on the calculated comprehensive carbon performance score of the enterprise, and the enterprise rating is divided according to the preset comprehensive score range. By dividing the continuous comprehensive score into four clear levels from excellent to needing improvement, the abstract numerical results are transformed into intuitive performance levels, which solves the problems of inconsistent interpretation of comprehensive scores and ambiguous decision-making basis in management practice.

[0068] In summary, this invention collects real-time electricity consumption data and corresponding time-period carbon data from enterprises, clarifies organizational boundaries and emission source boundaries, and designs three core indicators: the year-on-year change rate of carbon emission intensity per unit output, the electricity consumption rate during low-carbon periods, and the clean electricity penetration rate. These indicators comprehensively evaluate enterprise carbon performance from the perspectives of carbon emission efficiency, load regulation capacity, and clean energy consumption. By standardizing the original values ​​of the indicators and assigning scientific weights, a comprehensive score for enterprise carbon performance is calculated, and ratings are divided according to the score range, thus intuitively quantifying the improvement of carbon emission efficiency in the industry.

[0069] Example 3 is an embodiment of the present invention, which provides a method for evaluating the carbon performance of enterprises in industrial parks based on multi-dimensional carbon indicators.

[0070] S1: Calculation of core carbon indicators for enterprises.

[0071] Taking 18 key enterprises in the park as an example, based on electricity consumption data and carbon dioxide data from June 16 to June 20, 2025, the values ​​of three core indicators for each enterprise were calculated.

[0072] The year-on-year change rate of carbon emission intensity per unit of output is shown in Table 2, the electricity consumption rate during low-carbon periods is shown in Table 3, and the clean electricity penetration rate is shown in Table 4.

[0073] Table 2 Year-on-year change rate of carbon emission intensity per unit of enterprise output

[0074] Table 3. Enterprise Electricity Consumption Rate During Low-Carbon Periods

[0075] Table 4 Clean Energy Penetration Rate of Enterprises

[0076] S2: Standardization of indicator scoring.

[0077] According to the scoring rules, the indicators of each enterprise were standardized, and the results are shown in Table 5.

[0078] Table 5 Standardized Scores of Enterprise Indicators

[0079] S3: Overall score calculation and rating.

[0080] Based on the indicator weight allocation rules, the comprehensive score of each enterprise was calculated and its grade was determined. The results are shown in Table 6.

[0081] Table 6. Enterprise Comprehensive Score and Rating

[0082] Taking the data of company 9 in the park from June 16th to 20th, 2025 as an example. Calculations show that the year-on-year change rate of carbon intensity per unit of output, R, was -43.75% (score 100); the low-carbon electricity consumption rate, P_low-carbon, was 93.86% (score 100); and the clean electricity penetration rate, P_clean, was 60.83% (score 80). The weighted comprehensive score = 100*0.4 + 100*0.3 + 80*0.3 = 94 points. With a comprehensive score of 94 points, the rating is "Excellent".

[0083] Taking the data of company 6 in the park from June 16th to 20th, 2025 as an example. The calculated values ​​are: R = -23.77% (score 100), P = low-carbon 48.53% (score 40), and P = clean 80.00% (score 100). The weighted average score is 100*0.4 + 40*0.3 + 100*0.3 = 82 points. With an average score of 82, the rating is "Excellent".

[0084] Taking the data of company 2 in the park from June 16th to 20th, 2025 as an example. The calculated values ​​are: R = +121.19% (score 0), P = low-carbon = 64.70% (score 80), and P = clean = 0.00% (score 0). The weighted average score is 0*0.4 + 80*0.3 + 0*0.3 = 24 points. The overall score is 24 points, and the rating is "Needs Improvement".

[0085] Example 4 is an embodiment of the present invention. This embodiment provides a system for evaluating the carbon performance of enterprises in industrial parks based on multi-dimensional carbon indicators, including: a core indicator design module, a core indicator calculation and standardization module, and a comprehensive carbon performance score calculation module for enterprises. The core indicator design module collects real-time electricity consumption data and corresponding carbon data of enterprises within the collection period, clarifies the scope of evaluation, and designs core indicators based on the low-carbon behavior goals of industrial parks. The core indicator calculation and standardization module is used to identify the evaluation object, calculate the core indicators to obtain the original values ​​of the core indicators, and then standardize the original values ​​of the core indicators. The Enterprise Carbon Performance Comprehensive Score Calculation Module assigns corresponding weights to standardized core indicators according to preset standards, calculates the comprehensive carbon performance score of enterprises through a primary algorithm, and classifies enterprises into ratings based on the comprehensive score range.

[0086] This embodiment also provides an electronic device applicable to a method for evaluating the carbon performance of enterprises in industrial parks based on multi-dimensional carbon indicators, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the method for evaluating the carbon performance of enterprises in industrial parks based on multi-dimensional carbon indicators as proposed in the above embodiment.

[0087] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements a method for evaluating the carbon performance of enterprises in industrial parks based on multi-dimensional carbon indicators as proposed in the above embodiments.

[0088] The storage medium proposed in this embodiment and the method for evaluating the carbon performance of enterprises in industrial parks based on multi-dimensional carbon indicators proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0089] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0090] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for evaluating carbon performance of enterprises in an industrial park based on multi-dimensional carbon indicators, characterized in that: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps:

2. The method for evaluating carbon performance of enterprises in industrial parks based on multi-dimensional carbon indicators according to claim 1, characterized in that: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps:

3. The method for evaluating carbon performance of enterprises in industrial parks based on multi-dimensional carbon indicators according to claim 2, characterized in that: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps:

4. The method for evaluating carbon performance of enterprises in industrial parks based on multi-dimensional carbon indicators according to claim 3, characterized in that: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps:

5. The method for evaluating carbon performance of enterprises in industrial parks based on multi-dimensional carbon indicators according to claim 4, characterized in that: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps:

6. The method for evaluating carbon performance of enterprises in industrial parks based on multi-dimensional carbon indicators according to claim 5, characterized in that: The method comprises the following steps: The method comprises the following steps: wherein, is the carbon emission intensity per unit of output value, is the total carbon emission of the enterprise, is the total industrial output value of the enterprise; The method comprises the following steps: Wherein, is the unit output carbon emission intensity, and is the unit output carbon emission intensity of the current period, is the unit output carbon emission intensity of the previous period. The method comprises the following steps: wherein, is the average electricity carbon emission factor for the period, is the sum of the electricity carbon factors for the enterprise for the period, is the sum of the periods for the period. Define low carbon time period set For the current cycle to meet The time period Set, the total power consumption expression of low carbon time period is: in, This represents the total electricity consumption during low-carbon periods. This is a collection of low-carbon periods. In order to be in Electrocarbon factor over time period In order to be in Electricity consumption during a given time period; The method comprises the following steps: wherein, is the electricity consumption rate during the low-carbon period, is the total electricity consumption during the low-carbon period, is the total electricity consumption of the enterprise during the period. Define the set of cleaning periods as the set of periods in the current period that satisfy The total number of cleaning power hours is the sum of the hours contained in the set The expression is:​​ wherein, is the clean electricity hours, is the set of clean hours, denotes the hours contained in the hours, is the annual average electricity carbon factor of the province where the enterprise is located; and is the electricity carbon factor of the hours, is the annual average electricity carbon factor of the province where the enterprise is located. The method comprises the following steps: wherein, is clean power penetration, is clean power hours, is total hours in the period.

7. 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8. A system for evaluating carbon performance of enterprises in an industrial park based on multi-dimensional carbon indicators, applying the method for evaluating carbon performance of enterprises in an industrial park based on multi-dimensional carbon indicators according to any one of claims 1-7, characterized in that, It comprises: A core index design module, a core index calculation and standardization module, and an enterprise carbon performance comprehensive score calculation module; The core index design module collects real-time power consumption data of the enterprise in the collection period and corresponding period electric carbon data, clearly defines the scope of evaluation, and designs the core index based on the low-carbon behavior target of the industrial park; The core index calculation and standardization module determines the evaluation object, calculates the core index to obtain the core index original value, and standardizes the core index original value; The enterprise carbon performance comprehensive score calculation module gives the corresponding weight to the standardized core index according to the preset standard, calculates the enterprise carbon performance comprehensive score through a first-order algorithm, and divides the enterprise rating according to the comprehensive score interval. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-8 when the computer program is executed by the processor. The processor executes the computer program to realize the steps of the method for evaluating the carbon performance of an enterprise in an industrial park based on multi-dimensional carbon indicators according to any one of claims 1-7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the method for evaluating the carbon performance of an enterprise in an industrial park based on multi-dimensional carbon indicators according to any one of claims 1-7.

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