A Method and System for Carbon Asset Potential Analysis and Assessment Based on Multi-Indicator Comprehensive Evaluation
By using a multi-indicator comprehensive evaluation method, correction factors are generated to correct historical emission data of carbon assets, peak and trough periods are identified, adjustment parameters are set, and emission values are dynamically corrected. This solves the problem of insufficient accuracy in carbon asset assessment in existing technologies and achieves more accurate and reliable assessment results.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CCCC HIGHWAY CONSULTANTS CO LTD
- Filing Date
- 2026-04-21
- Publication Date
- 2026-07-17
AI Technical Summary
Existing carbon asset valuation methods rely on static, single-dimensional emissions data, ignoring dynamic variables. This results in insufficient accuracy and foresight in the valuation results, affecting market credibility.
By using a multi-indicator comprehensive evaluation method, correction factors are generated, historical emission data are corrected, peak and trough periods are identified, adjustment parameters are set, correction factors are calculated, emission values are dynamically corrected, and the evaluation level is determined.
It improves the accuracy and robustness of carbon asset assessment, ensures that the assessment results are based on comprehensive analysis, provides dynamic characteristics, avoids interference from isolated outliers, and enhances the objectivity and reliability of the assessment results.
Smart Images
Figure CN122089366B_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of carbon asset assessment technology, specifically relating to a method and system for carbon asset potential analysis and assessment based on multi-indicator comprehensive evaluation. Background Technology
[0002] Carbon assets are a core market-based tool that combines environmental benefits with economic value. Fair and accurate valuation of carbon assets can not only maintain the stable operation of the carbon trading market, but also serve as a basis for enterprises to formulate sustainable development plans and conduct macroeconomic regulation.
[0003] However, existing technologies rely on static, single-dimensional emission data when assessing the value of carbon assets. Actual carbon dioxide emissions fluctuate significantly across different production cycles, seasons, and market environments. Assessing based solely on total emissions data at a single point in time can easily lead to overestimation or underestimation of carbon asset value, affecting the accuracy of the assessment results and market credibility.
[0004] Furthermore, factors such as corporate technological upgrades, the implementation of energy-saving measures, changes in relevant environmental protection policies, and price fluctuations in the carbon trading market all affect the future value of carbon assets. Existing assessment models often ignore these dynamic variables, leading to biased and unforeseen assessment results.
[0005] To address the aforementioned issues, this invention provides a method and system for analyzing and evaluating the potential of carbon assets based on a comprehensive assessment of multiple indicators. Summary of the Invention
[0006] The purpose of this invention is to provide a method and system for analyzing and evaluating the potential of carbon assets based on a comprehensive evaluation of multiple indicators, which can dynamically evaluate carbon assets and obtain their true value.
[0007] To achieve the above objectives, the present invention adopts the following technical solution:
[0008] The carbon asset potential analysis and assessment method based on multi-indicator comprehensive evaluation includes the following steps:
[0009] Based on historical emissions data of carbon assets, a correction factor is generated;
[0010] Historical emission data were corrected using a correction factor to obtain the corrected emission values;
[0011] And based on the corrected emissions values, determine the assessment level of carbon assets;
[0012] The process of generating correction factors based on historical emissions data of carbon assets includes: identifying peak and trough periods in the time series data formed by historical emissions data; determining adjustment parameters based on the analysis of peak and trough periods; and calculating correction factors based on the adjustment parameters.
[0013] The process of determining the assessment level of carbon assets based on the corrected emission values includes: comparing the corrected emission values with preset judgment thresholds to calculate the deviation; and determining the assessment level according to the preset threshold range to which the deviation belongs, wherein the preset threshold range corresponds one-to-one with multiple assessment levels.
[0014] Preferably, before generating the correction factor based on historical emissions data of carbon assets, the method further includes:
[0015] Acquire historical emission data from multiple carbon assets, summarize and clean the historical emission data to obtain the data to be processed;
[0016] And arrange the data to be processed in chronological order to form time series data.
[0017] Preferably, in the time series data formed from historical emission data, identifying peak periods and trough periods includes:
[0018] In time series data, identify the peak and trough points of emissions, where the peak point is the local maximum emission point within a preset neighborhood, and the trough point is the local minimum emission point within a preset neighborhood.
[0019] Based on the peak point, a continuous annual period including the peak point is defined as the peak period;
[0020] And based on the trough point, a continuous annual period including the trough point is defined as the trough period.
[0021] Preferably, based on the analysis of the peak and trough periods, the adjustment parameters are determined as follows:
[0022] Calculate the global emission mean of time series data;
[0023] For each peak period, if its average emission value is greater than the global average emission value, and the emission amount at the peak point is greater than the average emission value within that peak period, then the corresponding adjustment parameter is set as a positive adjustment parameter; otherwise, it is set as a negative adjustment parameter.
[0024] Furthermore, for each trough period, if its average emission is less than or equal to the global average emission, the corresponding adjustment parameter is set as a positive adjustment parameter; otherwise, it is set as a negative adjustment parameter.
[0025] Preferably, the correction factor calculated based on the adjustment parameters includes:
[0026] Identify the point of maximum deviation from the global emission mean in the time series data;
[0027] Using the maximum deviation point as a benchmark, and based on the adjustment parameters associated with that maximum deviation point, the time period to be judged is determined;
[0028] If there are no other local extreme points besides the maximum deviation point within the time period to be determined, the adjustment parameter will be directly used as the correction factor.
[0029] If there are other local extreme points within the time period to be determined, the adjustment parameter is weighted according to the preset weight of the adjustment parameter, the preset weight of each other local extreme point, and the fluctuation intensity of the other local extreme points to generate a correction factor.
[0030] Preferably, the assessment grades of the carbon assets include:
[0031] Low carbon asset rating, lower carbon asset rating, normal carbon asset rating, higher carbon asset rating, and high carbon asset rating.
[0032] This invention also discloses a carbon asset potential analysis and assessment system based on multi-indicator comprehensive evaluation, used to implement the above-mentioned carbon asset potential analysis and assessment method based on multi-indicator comprehensive evaluation, including:
[0033] The data acquisition module is used to acquire historical emissions data for carbon assets;
[0034] The correction factor generation module is used to analyze the fluctuation characteristics in historical emission data obtained by the data acquisition module and generate correction factors.
[0035] It also includes a rating assessment module, which uses correction factors to correct historical emission data, obtain corrected emission values, and determine the rating of carbon assets based on the corrected emission values.
[0036] Preferably, the correction factor generation module is specifically used for:
[0037] Identify the peaks and troughs in the time series data corresponding to historical emissions data;
[0038] Based on the analysis of peak and trough periods, the adjustment parameters are determined;
[0039] And the correction factor is calculated based on the adjustment parameters.
[0040] Preferably, the calculation method of the correction factor based on the adjustment parameters depends on whether there are other local extreme points within the time period to be determined based on the adjustment parameters.
[0041] Preferably, the rating assessment module is specifically used for:
[0042] Compare the corrected emission values with the preset judgment threshold, and calculate the deviation.
[0043] And the evaluation level is determined based on the preset threshold range to which the deviation belongs, wherein the preset threshold range corresponds one-to-one with multiple evaluation levels.
[0044] Beneficial effects
[0045] 1. This invention identifies peak and trough points in time series data to define peak and trough periods, compares the average emissions within a period with the global average emissions, determines whether the fluctuation is positive or negative, and sets corresponding adjustment parameters. This allows for qualitative classification and quantitative characterization of historical fluctuations, identifying their existence and revealing their specific nature. This provides dynamic characteristic basis for subsequent carbon asset potential analysis and assessment, thereby improving the accuracy of the assessment results.
[0046] 2. This invention uses the maximum deviation point in time series data as a benchmark to determine the time period to be judged. When there are other local extreme points within this time period, the adjustment parameters are weighted by combining preset weights with the fluctuation intensity of each local extreme point to generate a correction factor. This avoids deviations caused by correcting based solely on a single maximum deviation point. By comprehensively considering the overall fluctuation pattern in the neighborhood of the maximum deviation point, the correction factor is adaptively adjusted, avoiding excessive interference of isolated outliers on the assessment results. This improves the robustness of carbon asset potential analysis and assessment and prevents misjudgment of carbon asset potential.
[0047] 3. This invention cleans and times-series historical emission data, and dynamically corrects the data by setting adjustment parameters and generating correction factors. Based on the comparison between the corrected emission value and the preset judgment threshold, the evaluation level is determined in combination with the preset threshold range. By combining data preprocessing, dynamic feature extraction, adaptive correction and graded evaluation, this invention ensures that the evaluation results are based on a comprehensive analysis of historical emission data, thereby guaranteeing the objectivity and reliability of the evaluation level. Attached Figure Description
[0048] Figure 1 This is a flowchart of the method of the present invention;
[0049] Figure 2 This is a system module diagram of the present invention. Detailed Implementation
[0050] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
[0051] Example 1
[0052] See Figure 1 This embodiment discloses a carbon asset potential analysis and assessment method based on multi-indicator comprehensive evaluation, which specifically includes the following steps:
[0053] S1. Perform data acquisition and cleaning steps;
[0054] By using standardized data interfaces or batch import methods, historical emission data of one or more carbon assets over a historical period can be obtained from a specific production facility, enterprise, or region.
[0055] Historical emissions data includes timestamps and values of carbon dioxide equivalents. These data from different monitoring points or recording systems are aggregated to generate an initial dataset containing all records.
[0056] Data cleaning is performed on the initial dataset, which includes two aspects:
[0057] Based on the preset data format specifications, invalid data records that do not conform to the format, have empty values, or exceed the reasonable physical range are identified and removed. The reasonable physical range refers to the emissions that far exceed the maximum production capacity of the equipment. The preferred data format specifications require that the data be non-negative values and that the timestamp format be uniform.
[0058] By comparing key fields in the records, such as timestamps and emission values, duplicate data records with identical content are detected and removed.
[0059] After the above two data cleaning operations, a clean and well-organized set of data to be processed is obtained, laying the foundation for subsequent time series analysis.
[0060] S2. Construct and divide the time series;
[0061] The data to be processed is arranged strictly according to the order of timestamps to form carbon emission time series data with time as the axis and emissions as the value.
[0062] A dynamic segmentation method based on the inherent patterns in the data is adopted to determine the annual time period, so as to objectively define comparable analytical periods. Specifically:
[0063] For each data point in the time series data, calculate the emission difference between the data point and its corresponding point after shifting backward by a preset time period on the time axis, and record the difference as the offset value of the current data point. The preset time period is preferably 12 months. After arranging the offset values of each data point in the original time series data in chronological order, an offset value sequence corresponding to the original time series data is generated.
[0064] Scan the entire offset value sequence, find and locate the two local peaks with the largest values, and define the adjacent time positions of these two local peaks in the original time series data as first-level classification nodes;
[0065] The time range between two adjacent primary classification nodes is defined as an annual time period, representing a complete emission behavior cycle. This allows for the division of years based on the periodic fluctuations of emission data itself, which is more accurate than using a fixed calendar year division to capture the complete emission behavior cycle.
[0066] S3. After dividing the time periods into each year, it is necessary to extract the fluctuation characteristics within each year period.
[0067] In the entire time series data, the peak and trough points of carbon asset emissions are identified by a sliding window mechanism. The peak point is defined as the data point with the largest emission value in a preset neighborhood before and after it. The preset neighborhood is preferably 15 data points before and after it.
[0068] Correspondingly, the trough point is defined as the data point with the smallest emission value within its preset neighborhood.
[0069] Furthermore, in this embodiment, one or more secondary classification nodes are inserted within the annual time period defined by two primary classification nodes, for example, the midpoint or quarterly point of the annual time period is set as a secondary classification node;
[0070] By defining the time range between two adjacent secondary classification nodes, or the time range between a primary classification node and an adjacent secondary classification node, as a subclassification segment, an annual time period can be decomposed into multiple more analytically significant semi-annual or quarterly subclassification segments to capture more subtle emission change dynamics.
[0071] S4. Based on the identified peaks and troughs, determine the nature of the wave and set the corresponding adjustment parameters. Specific steps include:
[0072] Calculate the arithmetic mean of emissions from all data points in the entire carbon emission time series data, and determine this value as the global emission mean, which serves as a baseline for assessing the overall emission level.
[0073] Each identified peak point is used as the core, and the complete annual time period in which it falls is set as the peak period;
[0074] Taking each trough as the core, the complete annual time period in which it falls is set as the trough period, and for each peak period, the average emission during that peak period is calculated.
[0075] If the average emission during the peak period is greater than the global average emission, and the emission value at the peak point is also greater than the average emission during the peak period, then the peak period is marked as a positive fluctuation, and its corresponding adjustment parameter is set to a preset positive value, such as +1, which is a positive adjustment parameter.
[0076] If the average emission during the peak period is greater than the global average emission, but the emission at the peak point is less than or equal to the average emission during the peak period, then the peak period is marked as a reverse fluctuation, and its corresponding adjustment parameter is set to a preset negative value, such as -1, which is a negative adjustment parameter.
[0077] For each trough period, calculate the average emissions during that trough period. If the average emissions during that trough period are greater than the global average emissions, then mark that trough period as a reverse fluctuation and set its corresponding adjustment parameter as a negative adjustment parameter.
[0078] If the average emissions during the trough period are less than or equal to the global average emissions, the trough period is marked as a positive fluctuation, and its corresponding adjustment parameter is set as a positive adjustment parameter.
[0079] Furthermore, after determining the peak period, the average emission value within each sub-category or annual time period covered by the peak period can be calculated. The average emission value within the time period is compared with the average emission value within the peak period. If the absolute value of the difference between the two is greater than a preset offset threshold, the corresponding time period is determined as the average deviation time period, which is used to indicate abnormal internal fluctuations. The offset threshold is a preset value used to determine whether the average emission level of a time period deviates significantly from the overall average level of its respective peak period.
[0080] For a peak period, the average emissions during that period are greater than the global average emissions, and the peak value is also greater than the average emissions during that period; for a trough period, the average emissions during that period are less than or equal to the global average emissions.
[0081] For a peak period, the average emissions during that period are greater than the global average emissions, but the peak value is less than or equal to the average emissions during that period; for a trough period, the average emissions during that period are greater than the global average emissions.
[0082] S5. Based on historical fluctuation characteristics, generate correction factors to adjust future assessments dynamically and finely, allowing for dynamic adjustments to the initial judgment.
[0083] The correction factor is a dynamic calculation factor used to correct the annual total emissions value. Its value can be obtained directly from the adjustment parameter or generated by weighted summation of the fluctuation intensity of the adjustment parameter and other local extreme points in the time period to be determined.
[0084] In the entire time series data, by calculating the absolute value of the difference between the emissions at each data point and the previously calculated global emission mean, the data point with the largest difference is identified and defined as the maximum deviation point. Using the time point of the maximum deviation point as the baseline time point, the direction and duration of the analysis window are determined based on the adjustment parameters set for that period. Specifically:
[0085] If the adjustment parameter is a positive adjustment parameter, then a preset first duration will be extended from the reference time point in the positive direction of the time axis (future);
[0086] If the adjustment parameter is a negative adjustment parameter, then a preset second duration is extended from the reference time point in the negative direction (past) of the time axis. The time range covered by this extended duration is defined as the time period to be determined.
[0087] Within the defined time period to be determined, check whether there are other local extreme points besides the maximum deviation point, i.e., other peaks or troughs:
[0088] If no other local extreme points exist within the time period to be determined, it indicates that the fluctuation pattern during that period is simple, and the previously set adjustment parameters are directly used as the final correction factor. Conversely, if one or more other local extreme points exist, a weighted calculation is required to generate the correction factor. The calculation process for generating the correction factor using weighted calculation is as follows:
[0089] Calculate the fluctuation intensity of each other local extreme point. The fluctuation intensity can be specifically calculated as the absolute value of the difference between the emission of the local extreme point and the average emission of the data points before and after it with a preset number of data points.
[0090] Assign a preset weight to the initial adjustment parameters and the fluctuation intensity of each other local extreme point;
[0091] The adjustment parameter is multiplied by its corresponding first preset weight, and the fluctuation intensity of each other local extreme point is multiplied by its corresponding second preset weight. All the product results are then summed algebraically to generate a correction factor that integrates the fluctuation characteristics of multiple points.
[0092] Its value is either taken directly from the adjustment parameter, or generated by weighted summation of the fluctuation intensity of the adjustment parameter and other local extreme points within the time period to be determined.
[0093] S6. Based on the generated correction factors, complete the potential assessment and rating determination of carbon assets. Specific steps include:
[0094] Using the correction factor generated in the previous step, the total carbon asset emissions for each annual period are corrected to obtain the corrected emissions. The specific correction calculation method is as follows:
[0095] Corrected emissions = Total carbon asset emissions over the annual period × (1 + correction factor);
[0096] Each corrected emission value is compared with a preset benchmark emission level to calculate the deviation, wherein the preset benchmark emission level is preferably the industry average level or the policy target value;
[0097] Multiple assessment levels are provided in advance, along with a preset threshold range corresponding to each assessment level. By determining which preset threshold range the calculated deviation falls into, the corresponding assessment level can be determined.
[0098] Furthermore, the assessment rating can be specifically divided into five levels: low carbon asset rating, lower carbon asset rating, normal carbon asset rating, higher carbon asset rating, and high carbon asset rating.
[0099] The performance of the carbon asset over the corresponding time period is marked with the determined assessment level, and the assessment level is output as the final assessment result.
[0100] Example 2
[0101] See Figure 2 This embodiment discloses a carbon asset potential analysis and assessment system based on multi-indicator comprehensive evaluation, used to implement the above-mentioned carbon asset potential analysis and assessment method based on multi-indicator comprehensive evaluation, including:
[0102] The data acquisition module is configured to perform initial data preparation for carbon asset potential analysis and assessment, as follows:
[0103] Obtain historical emissions data for one or more carbon assets. Historical emissions data can be obtained from various sources, such as publicly released sustainability reports by companies, statistics from relevant regulatory authorities, or databases of third-party carbon verification agencies.
[0104] After obtaining the raw data, i.e. historical emission data, the data is aggregated and cleaned to integrate data from different sources and in different formats, and to process missing values, invalid data records or duplicate data records to obtain standardized data to be processed.
[0105] The data to be processed is arranged in chronological order to form the time series data required for subsequent analysis. This time series data forms the basis for assessing the historical performance of carbon assets.
[0106] The correction factor generation module is configured to analyze the volatility characteristics in the time series data provided by the data acquisition module and generate correction factors for correcting historical data. The specific workflow is as follows:
[0107] Identify peak and trough periods in time series data; specifically, traverse the time series data to identify peak and trough points in emissions.
[0108] The peak point is defined as the local maximum emission point within a preset neighborhood, which is preferably a time window of one year before and after.
[0109] The trough point is the local minimum emission point within the preset neighborhood. After identifying all peak points and trough points, a continuous annual time period including each peak point is set as the peak period, based on each peak point.
[0110] Similarly, based on each trough point, a continuous annual time period including that trough point is defined as the trough period.
[0111] Based on the analysis of peak and trough periods, adjustment parameters are determined, including:
[0112] Calculate the global emission mean for the entire time series data;
[0113] For each identified peak period, it is determined whether the average emission during that period is greater than the global average emission, and whether the emission at the peak point is greater than the average emission during that peak period. If both conditions are met, the adjustment parameter corresponding to that peak period is set as a positive adjustment parameter to be used for subsequent suppression of the abnormally high point; otherwise, it is set as a negative adjustment parameter.
[0114] Furthermore, for each trough period, if the average emission during that trough period is less than or equal to the global average emission, the corresponding adjustment parameter is set as a positive adjustment parameter to compensate for abnormally low points; otherwise, it is set as a negative adjustment parameter.
[0115] The final correction factor is calculated based on the adjustment parameters, specifically:
[0116] In the entire time series data, identify the point with the largest absolute value of the difference from the global emission mean, i.e., the point of maximum deviation;
[0117] Using the maximum deviation point as a benchmark, and based on the adjustment parameter (positive or negative) associated with the maximum deviation point, a time period to be determined is identified;
[0118] Check whether there are other local extreme points besides the maximum deviation point within this time period to be determined. If not, it indicates that the fluctuation is isolated, and the adjustment parameter associated with the maximum deviation point is directly used as the correction factor. Conversely, if there are other local extreme points, it indicates that the fluctuation is complex or interconnected. Then, according to the preset weight of the adjustment parameter, the preset weight of each other local extreme point, and the fluctuation intensity of other local extreme points, the adjustment parameter is weighted and calculated to generate a comprehensive correction factor.
[0119] The rating assessment module is configured to utilize the output of the correction factor generation module to complete the final assessment of carbon assets. Its workflow is as follows:
[0120] The total emissions of carbon assets for each annual period are corrected using correction factors calculated by the correction factor generation module, resulting in corrected emissions values. These corrected emissions values are used to smooth or correct extreme fluctuations in historical data, making the assessment results more reflective of the long-term stability potential of carbon assets.
[0121] Based on the corrected emissions values, the assessment level of carbon assets is determined, specifically:
[0122] The corrected emission value is compared with a preset judgment threshold, and the deviation between the two is calculated. The preset judgment threshold is an industry benchmark value, a policy target value, or a historical average value.
[0123] Based on the preset threshold range to which the deviation belongs, the final assessment level is determined. For example, the preset threshold range corresponds one-to-one with multiple assessment levels, including: low carbon asset level, lower carbon asset level, normal carbon asset level, higher carbon asset level, and high carbon asset level. The final assessment level is output as the result of this analysis.
[0124] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from it. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.
Claims
1. A carbon asset potential analysis and assessment method based on multi-indicator comprehensive evaluation, characterized in that, Includes the following steps: Based on historical emissions data of carbon assets, a correction factor is generated; Historical emission data were corrected using a correction factor to obtain the corrected emission values; And based on the corrected emissions values, determine the assessment level of carbon assets; The process of generating correction factors based on historical emissions data of carbon assets includes: identifying peak and trough periods in the time series data formed by historical emissions data; determining adjustment parameters based on the analysis of peak and trough periods; and calculating correction factors based on the adjustment parameters. The process of determining the assessment level of carbon assets based on the corrected emission values includes: comparing the corrected emission values with preset judgment thresholds to calculate the deviation; and determining the assessment level according to the preset threshold range to which the deviation belongs, wherein the preset threshold range corresponds one-to-one with multiple assessment levels. Based on the analysis of peak and trough periods, the adjustment parameters are determined to include: Calculate the global emission mean of time series data; For each peak period, if its average emission value is greater than the global average emission value, and the emission amount at the peak point is greater than the average emission value within that peak period, then the corresponding adjustment parameter is set as a positive adjustment parameter; otherwise, it is set as a negative adjustment parameter. Furthermore, for each trough period, if its average emission is less than or equal to the global average emission, the corresponding adjustment parameter is set as a positive adjustment parameter; otherwise, it is set as a negative adjustment parameter.
2. The carbon asset potential analysis and assessment method based on multi-indicator comprehensive evaluation according to claim 1, characterized in that, Before generating the correction factor based on historical emissions data from carbon assets, the method further includes: Acquire historical emission data from multiple carbon assets, summarize and clean the historical emission data to obtain the data to be processed; And arrange the data to be processed in chronological order to form time series data.
3. The carbon asset potential analysis and assessment method based on multi-indicator comprehensive evaluation according to claim 1, characterized in that, In time series data formed from historical emissions data, peak periods and trough periods were identified, including: In time series data, identify the peak and trough points of emissions, where the peak point is the local maximum emission point within a preset neighborhood, and the trough point is the local minimum emission point within a preset neighborhood. Based on the peak point, a continuous annual period including the peak point is defined as the peak period; And based on the trough point, a continuous annual period including the trough point is defined as the trough period.
4. The carbon asset potential analysis and assessment method based on multi-indicator comprehensive evaluation according to claim 1, characterized in that, Based on the adjustment parameters, the correction factors calculated include: Identify the point of maximum deviation from the global emission mean in the time series data; Using the maximum deviation point as a benchmark, and based on the adjustment parameters associated with that maximum deviation point, the time period to be judged is determined; If there are no other local extreme points besides the maximum deviation point within the time period to be determined, the adjustment parameter will be directly used as the correction factor. If there are other local extreme points within the time period to be determined, the adjustment parameter is weighted according to the preset weight of the adjustment parameter, the preset weight of each other local extreme point, and the fluctuation intensity of the other local extreme points to generate a correction factor.
5. The carbon asset potential analysis and assessment method based on multi-indicator comprehensive evaluation according to claim 1, characterized in that, The assessment grades of the carbon assets include: Low carbon asset rating, lower carbon asset rating, normal carbon asset rating, higher carbon asset rating, and high carbon asset rating.
6. A carbon asset potential analysis and assessment system based on multi-indicator comprehensive evaluation, used to implement the carbon asset potential analysis and assessment method based on multi-indicator comprehensive evaluation as described in any one of claims 1-5, characterized in that, include: The data acquisition module is used to acquire historical emissions data for carbon assets; The correction factor generation module is used to analyze the fluctuation characteristics in historical emission data obtained by the data acquisition module and generate correction factors. And a rating assessment module, which is used to correct historical emission data with correction factors to obtain corrected emission values, and to determine the rating of carbon assets based on the corrected emission values; Specifically, the correction factor generation module is used to: identify the peak and trough periods in the time series data corresponding to historical emission data; determine the adjustment parameters based on the analysis of the peak and trough periods; and calculate the correction factor based on the adjustment parameters. The calculation method of the correction factor obtained from the adjustment parameters depends on whether there are other local extreme points within the time period to be determined based on the adjustment parameters.
7. The carbon asset potential analysis and assessment system based on multi-indicator comprehensive evaluation according to claim 6, characterized in that, The rating assessment module is specifically used for: Compare the corrected emission values with the preset judgment threshold, and calculate the deviation. And the evaluation level is determined based on the preset threshold range to which the deviation belongs, wherein the preset threshold range corresponds one-to-one with multiple evaluation levels.