A dynamic accounting method for user indirect carbon emission flow considering source load uncertainty

Abstract

The present invention relates to the technical field of low-carbon optimization of power systems, and in particular to a method for dynamic accounting of indirect carbon emission flows of users taking into account uncertainty in source and load. It includes the steps of clarifying the accounting objectives and scope, building an accounting framework, data collection and preprocessing, setting parameters and initial conditions, uncertainty quantification analysis, and indirect carbon emission flow accounting. Among them, the indirect carbon emission flow accounting step is based on the uncertainty analysis results, and performs life cycle assessment and carbon emission data integration to obtain the indirect carbon emission flow and calculate the carbon emission contribution ratio at different stages. This method can more accurately calculate the indirect carbon emissions of users, provide a reliable basis for formulating effective emission reduction policies and measures, and thus promote the goal of reducing carbon emissions.

Description

Technical Field

[0001] The present invention relates to the technical field of low-carbon optimization of power systems, and in particular to a method for dynamically calculating user indirect carbon emission flows taking into account source and load uncertainty. Background Art

[0002] In carbon emission accounting, indirect carbon emissions from users are an important part that cannot be ignored. It covers the carbon emissions generated by multiple stages such as upstream production, transportation and use of products or services throughout their life cycle. However, there are some shortcomings in the current accounting of indirect carbon emissions from users.

[0003] Existing technologies often fail to fully consider the impact of source load uncertainty when calculating indirect carbon emissions from users. Source load uncertainty includes uncertainty in energy supply, uncertainty in user demand, and uncertainty in various parameters. These uncertainties can lead to inaccurate and incomplete carbon emission accounting results.

[0004] For example, in the existing technology, for the upstream production stage, a fixed carbon emission factor is usually used to calculate carbon emissions, but in fact the carbon emission factor may change due to various factors such as raw material quality and production process; in the transportation stage, there may be uncertainties in parameters such as transportation distance and energy efficiency of transportation tools, but the existing technology often does not fully consider and analyze these uncertainties; in the use stage, there may also be uncertainties in the frequency and duration of product use, and the existing technology may not accurately reflect these uncertainties in the calculation.

[0005] In summary, the existing technology has many defects in the accounting of user indirect carbon emissions, and a new method is needed to solve these problems and improve the accuracy and reliability of carbon emissions accounting. Summary of the invention

[0006] In view of the above shortcomings in the prior art, the purpose of the present invention is to provide a method for dynamic accounting of indirect carbon emission flows of users that takes into account the uncertainty of source loads, so as to solve the defects of the prior art that the uncertainty of source loads is not fully considered and the integration and analysis of carbon emission data is insufficient. This method can more accurately calculate indirect carbon emissions of users, provide a reliable basis for formulating effective emission reduction policies and measures, and thus promote the goal of reducing carbon emissions.

[0007] In order to achieve the above objectives, the present invention adopts the following technical solutions:

[0008] A dynamic accounting method for user indirect carbon emission flow considering source load uncertainty includes the following steps:

[0009] S1. Clarify the accounting objectives and scope. Through literature review and industry report analysis, combined with the opinions collected at the stakeholder meeting, the key areas and potential needs of the accounting are clarified, and the ultimate purpose of the accounting is established;

[0010] S2. Build an accounting framework, based on the goals and scopes specified in S1, construct a model of carbon emission flows, and design a database for storing data and parameters;

[0011] S3. Data collection and preprocessing: According to the requirements of the accounting framework, data is collected from statistical yearbooks, enterprise reports, monitoring data, questionnaires and other channels, and the data is cleaned and verified;

[0012] S4. Set parameters and initial conditions, estimate parameters of the carbon emission flow model constructed in S2, and use version control tools to manage changes in parameters and initial conditions;

[0013] S5. Uncertainty quantification analysis: simulation and sensitivity analysis based on the parameters and initial conditions set in S4;

[0014] S6. Indirect carbon emission flow accounting: Based on the results of S5 uncertainty analysis, a life cycle assessment is conducted, and the carbon emission data of each link is integrated to obtain the indirect carbon emission flow. The carbon emission contribution ratio of different stages is calculated based on the obtained indirect carbon emission flow.

[0015] Furthermore, in S2, the method for constructing a carbon emission flow model includes: dividing the user's indirect carbon emissions into upstream production stage, transportation stage, and use stage, etc., and constructing a carbon emission flow model for each stage,

[0016] Method for constructing the carbon emission flow model in the upstream production stage: For the upstream production stage, the activity level is the input of different raw materials, the operating time of production equipment, etc., assuming that two raw materials are used in the production process and , and their input amounts are and , input amount The corresponding carbon emission factor is ; Input The corresponding carbon emission factor is ; At the same time, the production equipment running time is , the carbon emission factor of the equipment is , then the carbon emissions in the upstream production stage for:

[0017] ;

[0018] The method of constructing the carbon emission flow model in the transportation stage: In addition to the transportation volume, the activity level in the transportation stage also considers factors such as transportation distance and energy efficiency of transportation tools; assuming that the transportation distance is , the energy efficiency of transportation is , Carbon emissions during transportation for:

[0019] ;

[0020] in Indicates the transport volume, represents the carbon emission factor of the transportation stage;

[0021] Method for constructing a carbon emission flow model in the use phase: The activity level in the use phase can be determined based on the frequency and duration of use of the product; for:

[0022] ;

[0023] in Indicates the usage time. Indicates the carbon emission factor during the use phase; Indicates frequency of use.

[0024] Furthermore, in S2, the data inventory includes carbon emission source data (such as the consumption of various energy sources and the corresponding carbon emission factors), carbon emission sink data (such as the absorption capacity of forest vegetation), carbon emission flow path data (such as transportation methods and transportation distances in the energy supply chain structure), and parameter data (such as conversion coefficients, etc.).

[0025] Furthermore, in S3, the method for cleaning data is:

[0026] Use statistical methods to determine whether the data exceeds the reasonable range, set the data point , the mean is , the standard deviation is ,like ( k is a constant), it is considered as an outlier and corrected or deleted; for erroneous data, it is corrected by comparing with other reliable data sources. Suppose the data in the enterprise report is , the data from reliable data sources are , the difference rate is , if the difference rate If it exceeds the preset threshold, correction is performed;

[0027] in, ;

[0028] Duplicate data is removed through data deduplication to ensure data uniqueness. , through the hash function ( MD5 ) Calculate the hash value of the data , if the hash values ​​of two data are the same, they are considered as duplicate data and removed;

[0029] The process of calculating the hash value using MD5 is expressed as:

[0030] Assume the data is , the hash value calculation formula is: ,in is the calculated hash value;

[0031] For data d 1” and “ d 2", calculate their MD5 hash values ​​respectively:

[0032] ;

[0033] ;

[0034] if , then the two data are considered to be duplicated.

[0035] Furthermore, in S3, the method for verifying data is:

[0036] Energy consumption data needs to be compared and verified with the data from the energy supply department; assuming that the energy consumption of the enterprise is , the data for the energy supply sector is , the verification index is ,If the verification index is within the preset range, the data is considered reliable;

[0037] in, ;

[0038] For emission factor data, refer to international standards or authoritative data sources for verification; assume that the emission factor collected by yourself is , the emission factors from international standards or authoritative data sources are , the difference rate is , if the difference rate is within the preset range, the data collected by oneself is considered reliable.

[0039] Furthermore, in S4, based on historical data and literature review, the method for estimating parameters of the carbon emission flow model constructed in S2 includes:

[0040] For the carbon emission model of the upstream production stage, historical data on different raw material inputs, production equipment operating time and corresponding actual carbon emissions are collected; Corresponding carbon emission factor Related factors The relationship between (such as raw material quality) is:

[0041] ;

[0042] By collecting historical data, the regression coefficient of the carbon emission model in the upstream production stage is calculated. The calculation formula is:

[0043] ;

[0044] ;

[0045] In the formula, n is the number of data samples, and are the relevant factors of the carbon emission model and the mean of the carbon emission factors in the upstream production stage; is the factor representing the carbon emission factor corresponding to the input in the upstream production stage; The carbon emission model for the upstream production stage i factors related to the sample; The carbon emission model for the upstream production stage i The carbon emission factor of samples; among them, and is the regression coefficient of the carbon emission model in the upstream production stage, is the error term of the carbon emission model in the upstream production stage;

[0046] For the carbon emission model in the transportation stage, the regression model is:

[0047] ;

[0048] Calculate the regression coefficient of the carbon emission model in the transportation stage using the following formula:

[0049] ;

[0050] ;

[0051] ;

[0052] ;

[0053] in, represents the carbon emission factor of the transportation stage; is the number of data samples; is the transport volume; is the transport distance; for energy efficiency of transport; , , , is the regression coefficient of the carbon emission model in the transportation stage; is the error term of the carbon emission model in the transportation stage; is the mean of the transport volume; is the mean of the transport distance; is the mean value of energy efficiency of transportation vehicles; For the i The transport volume of samples; For the i The transportation distance of each sample; is the mean value of the carbon emission factor of the carbon emission model in the transportation stage; The first part of the carbon emission model for the transportation stage is i Carbon emission factors for each sample;

[0054] For the use phase carbon emissions model, the model is:

[0055] ;

[0056] Calculate the regression coefficient of the carbon emission model in the use phase using the following formula:

[0057] ;

[0058] ;

[0059] ;

[0060] in, is the carbon emission factor in the use phase; is the number of data samples; is the frequency of use; The duration of use; , , is the regression coefficient of the use stage; is the error term in the use phase; is the mean of the frequency of use; is the mean usage time; is the mean value of the carbon emission factor in the use phase; The carbon emission model for the use phase i The usage rate of samples; The carbon emission model for the use phase i The duration of use of each sample; The carbon emission model for the use phase i Carbon emission factors for each sample.

[0061] Furthermore, in S4, using a version control tool to manage changes in parameters and initial conditions includes:

[0062] S41. Use version control tools (such as Git) to record the change history of parameters and initial conditions, and record each modification in detail, including the time of modification, the person who modified it, the content of the modification, and the reason for the modification;

[0063] S42. Establish different branches to store different versions of parameters and initial conditions. Create a "main branch" to store stable versions and develop a "branch" to try new parameter estimation methods or make adjustments. At the same time, create other branches as needed, such as a "test branch" to test specific versions.

[0064] S43. Use labels to mark important version nodes to facilitate quick location and restoration of specific versions; label "v1.0" to indicate the initial version, "v2.0" to indicate a significantly improved version, etc. In addition, use more descriptive labels such as "optimized parameter version" or "new function version" according to the characteristics and purpose of the version;

[0065] S44. Regularly back up tags and data in version control tools to prevent data loss or corruption.

[0066] Further, the S5 includes:

[0067] S51. Determine the total carbon emissions. For the carbon emission flow model determined in step S2, the total carbon emissions are expressed as ,

[0068] S52. Parameter determination and total carbon emissions calculation, assuming that there is a n 1 Uncertain parameters, the carbon emission model in the transportation stage has n 2 There are uncertain parameters in the carbon emission model during the use phase. n 3 uncertain parameters; each parameter follows a normal distribution, and a set of parameter values ​​are obtained by random sampling, which are substituted into the corresponding carbon emission model to calculate carbon emissions;

[0069] S53. Analyze the simulation results, repeat the simulation multiple times, obtain a large number of simulation results, and analyze the distribution characteristics of these results, such as mean, standard deviation, confidence interval, etc.;

[0070] S54. Determine the degree of influence of each parameter on the carbon emission results, and conduct local sensitivity analysis and global sensitivity analysis to determine the degree of influence of each parameter on the carbon emission results.

[0071] In S54, the local sensitivity analysis is performed by changing the value of each parameter individually while keeping other parameters unchanged to calculate the change in the model output. , changing it from its initial value becomes , and then calculate the change in the corresponding model output in S2 ;

[0072] in, ;

[0073] Repeat this operation for each parameter to obtain the local sensitivity index of each parameter. If the change in the model output is large, it means that the parameter has a high sensitivity to the model output.

[0074] In S54, the global sensitivity analysis considers the situation where multiple parameters change simultaneously, analyzes the impact of the interaction between parameters on the model output, and uses the sensitivity analysis method of the Sobol index method to evaluate the importance and interaction of parameters;

[0075] Sensitivity analysis methods using the Sobol index method to assess the importance of parameters and interactions include:

[0076] The Sobol index of each parameter is calculated to evaluate the importance and interaction of parameters. The Sobol index is divided into first-order Sobol index and higher-order Sobol index.

[0077] Among them, the first-order Sobol index represents the contribution of a single parameter to the model output variance. , and its first-order Sobol index is calculated by the following formula:

[0078] ;

[0079] In the formula, represents the first-order Sobol exponent, represents variance; is the expectation of the model output with respect to all parameters; is the model output with respect to the parameters and Divide The mean of other parameters The larger the first-order Sobol index is, the more important the parameter is in affecting the model output.

[0080] The higher-order Sobol index is used to evaluate the contribution of the interaction between multiple parameters to the variance of the model output; the second-order Sobol index Representation parameters and The contribution of the interaction between and to the variance of the model output is calculated by the following formula:

[0081] ;

[0082] represents variance; is the expectation of the model output with respect to all parameters; where represents the mean of all parameters except and; Represents the model output with respect to the parameters and And except and The mean of other parameters expectations; Represents the model output with respect to the parameters and Divide The mean of other parameters The expectation of The expected output of the model when averaged with other parameters; Represents the model output with respect to the parameters and Divide The mean of other parameters expectations; Indicates the model output about and The expectation of other parameters besides and The expected output of the model.

[0083] Further, the S6 includes:

[0084] S61. Perform multiple random samplings based on the uncertain parameters determined in S5, and substitute the parameter values ​​obtained in each sampling into the model obtained in S2 to calculate the carbon emissions;

[0085] S62. The carbon emission samples obtained in each stage are summed up to obtain the carbon emissions in the upstream production stage, transportation stage and use stage; then the carbon emissions in the three stages are added together to obtain the total amount of the indirect carbon emission flow, i.e., the indirect carbon emission flow;

[0086] S63. Determine the weight of each stage according to its importance, calculate the weighted carbon emissions of each stage using the weight of each stage, and then add up the weighted carbon emissions of the three stages to obtain the total weighted carbon emissions; finally, calculate the carbon emission contribution ratio of different stages.

[0087] In summary, due to the adoption of the above technical solution, the beneficial technical effects of the invention are:

[0088] A dynamic accounting method for user indirect carbon emission flow considering source load uncertainty fully considers the key factor of source load uncertainty, which includes uncertainty in energy supply, uncertainty in user demand, and uncertainty in various parameters. The present invention adopts advanced data acquisition technology, which can more accurately obtain data related to carbon emissions, such as the input of raw materials, the operating time of production equipment, transportation distance, energy efficiency of transportation tools, frequency and duration of product use, etc. At the same time, by combining intelligent algorithms to analyze and process these data, the carbon emissions at each stage can be calculated more accurately, thereby achieving more accurate user indirect carbon emission accounting.

[0089] Accurate accounting provides a reliable basis for formulating effective emission reduction policies and measures, because only based on accurate carbon emission data can targeted and feasible emission reduction policies and measures be formulated, thus better achieving the goal of reducing carbon emissions.

[0090] In terms of parameter analysis, the present invention adopts a method that combines local sensitivity analysis and global sensitivity analysis. Local sensitivity analysis calculates the change in model output by changing the value of each parameter individually while keeping other parameters unchanged, thereby determining the sensitivity of each parameter to the carbon emission results. Global sensitivity analysis considers the situation where multiple parameters change at the same time, analyzes the impact of the interaction between parameters on the model output, and evaluates the importance and interaction of parameters by calculating the Sobol index. The comprehensive sensitivity analysis method can deeply understand the key factors of carbon emissions and provide stronger support for the formulation of emission reduction measures.

[0091] By calculating the carbon emission contribution ratio at different stages, it can provide an important basis for policy making and decision making. If the carbon emission contribution ratio at the upstream production stage is high, targeted measures can be taken to optimize the production process, improve energy efficiency or use cleaner energy to reduce carbon emissions at this stage. If the carbon emission contribution ratio at the transportation stage is large, the transportation route can be optimized, the energy efficiency of transportation tools can be improved or a more environmentally friendly mode of transportation can be adopted. This can more effectively improve emission reduction efficiency and achieve precise reduction of carbon emissions.

[0092] Finally, it is of great significance to use version control tools to record the change history of parameters and initial conditions. This makes the parameter change process traceable, which is convenient for managers to understand the evolution of parameters. At the same time, it ensures the stability of parameters and avoids problems such as inaccurate calculation results caused by arbitrary changes in parameters.

[0093] In summary, the invention has significant beneficial effects in terms of the accuracy of user indirect carbon emission accounting, the effectiveness of emission reduction policy formulation, the depth of parameter analysis, the pertinence of decision-making guidance, and the standardization of parameter management, and is of great significance for promoting carbon emission reduction and sustainable development. BRIEF DESCRIPTION OF THE DRAWINGS

[0094] Figure 1 It is a dynamic accounting method for user indirect carbon emission flow considering source load uncertainty;

[0095] Figure 2 Flowchart of the method for managing changes to parameters and initial conditions using version control tools;

[0096] Figure 3 Flowchart for uncertainty quantification analysis;

[0097] Figure 4 Flow chart of the indirect carbon emission flow accounting method. DETAILED DESCRIPTION

[0098] In order to make the purpose, technical solution and advantages of the invention more clear, the invention is further described in detail below in combination with the embodiments. It should be understood that the specific embodiments described here are only used to explain the invention and are not used to limit the invention.

[0099] like Figure 1 As shown, a dynamic accounting method for user indirect carbon emission flow considering source load uncertainty includes the following steps:

[0100] S1. Clarify the accounting objectives and scope. Through literature review and industry report analysis, combined with opinions collected from stakeholder meetings, the key areas and potential needs of accounting are clarified, the ultimate purpose of accounting is established, and a clear direction and foundation is provided for subsequent steps; the objectives and scope of the accounting framework are provided to ensure that all subsequent activities are carried out around these core objectives;

[0101] S2. Build an accounting framework, based on the goals and scopes specified in S1, construct a model of carbon emission flows, and design a database for storing data and parameters;

[0102] S3. Data collection and preprocessing: According to the requirements of the accounting framework, data is collected from statistical yearbooks, enterprise reports, monitoring data, questionnaires and other channels, and the data is cleaned and verified;

[0103] S4. Set parameters and initial conditions, estimate parameters of the carbon emission flow model constructed in S2, and use version control tools to manage changes in parameters and initial conditions;

[0104] S5. Uncertainty quantification analysis: simulation and sensitivity analysis based on the parameters and initial conditions set in S4;

[0105] S6. Indirect carbon emission flow accounting: Based on the results of S5 uncertainty analysis, a life cycle assessment is conducted, and the carbon emission data of each link is integrated to obtain the indirect carbon emission flow. The carbon emission contribution ratio of different stages is calculated based on the obtained indirect carbon emission flow.

[0106] In S1, literature review refers to extensively reviewing academic literature in related fields to understand the current research progress of user indirect carbon emissions accounting; for example, using existing accounting formulas and methods, such as the total amount of indirect carbon emissions ( ) can usually be expressed as the sum of carbon emissions in each link: ,in Indicates Carbon emissions at each stage.

[0107] Analyzing industry reports means understanding the carbon emission characteristics of different industries. For example, some high-energy-consuming industries may have a higher carbon emission factor in the production process, that is, a higher carbon emission per unit activity level.

[0108] Stakeholder meetings to collect opinions refer to organizing stakeholder meetings and inviting representatives from all parties to participate. For example, an enterprise may hope that the accounting results can help it optimize its production process to reduce carbon emission costs; government departments may pay attention to the role of accounting results in formulating macro-emission reduction policies.

[0109] Collate the opinions and identify key areas and potential needs; for example, if most stakeholders emphasize the importance of indirect carbon emissions in the transportation industry, then this industry can be identified as a key area;

[0110] Clarifying the ultimate purpose of accounting means clarifying the purpose of accounting based on the literature and opinions of stakeholders, for example, to assess the trend of indirect carbon emissions of users in a certain region in the next few years and formulate targeted emission reduction policies.

[0111] Providing goals and scope for the establishment of an accounting framework means building an accounting framework based on clear goals and scope; determining the time scope (such as the next five years) and spatial scope (specific regions) to ensure that subsequent activities are carried out around the accounting purpose.

[0112] In said S2, the method for constructing the carbon emission flow model includes: dividing the user's indirect carbon emissions into an upstream production stage, a transportation stage, and a use stage;

[0113] For the upstream production stage, the activity level is the input of different raw materials, the operating time of production equipment, etc. Assume that two raw materials are used in the production process and , and their input amounts are and , input amount The corresponding carbon emission factor is ; Input The corresponding carbon emission factor is ; At the same time, the production equipment running time is , the carbon emission factor of the equipment is , then the carbon emissions in the upstream production stage for:

[0114] ;

[0115] The activity level in the transportation stage takes into account factors such as transportation distance and energy efficiency of transportation tools in addition to transportation volume. , the energy efficiency of transportation is , Carbon emissions during transportation for:

[0116] ;

[0117] in Indicates the transport volume, represents the carbon emission factor of the transportation stage;

[0118] The activity level in the use phase can be determined based on the frequency and duration of use of the product; for:

[0119] ;

[0120] in Indicates the duration of use; Indicates the carbon emission factor during the use phase; Indicates frequency of use.

[0121] In S2, the data inventory includes carbon emission source data (such as the consumption of various energy sources and the corresponding carbon emission factors), carbon emission sink data (such as the absorption capacity of forest vegetation), carbon emission flow path data (such as transportation methods and transportation distances in the energy supply chain structure), and parameter data (such as conversion coefficients, etc.).

[0122] In the S3, the method for cleaning the data is as follows:

[0123] Use statistical methods to determine whether the data exceeds the reasonable range, set the data point , the mean is , the standard deviation is ,like ( kis a constant), it is considered as an outlier and corrected or deleted; for erroneous data, it is corrected by comparing with other reliable data sources. Suppose the data in the enterprise report is , the data from reliable data sources are , the difference rate is , if the difference rate If it exceeds the preset threshold, correction is performed;

[0124] in, ;

[0125] The difference rate threshold between the enterprise report data and the reliable data source data can be set to 10%. That is, when the difference rate exceeds 10%, correction is made; if the energy consumption in the enterprise report is 100 units, and the data from the reliable data source is 90 units, the difference rate is , exceeding the 10% threshold, requiring correction.

[0126] Duplicate data is removed through data deduplication to ensure data uniqueness. For example, for a data set , through the hash function ( MD5 ) Calculate the hash value of the data , if the hash values ​​of two data are the same, they are considered as duplicate data and removed;

[0127] The process of calculating the hash value using MD5 is expressed as:

[0128] Assume the data is , ,in is the calculated hash value;

[0129] For data d 1” and “ d 2", calculate their MD5 hash values ​​respectively:

[0130] ;

[0131] ;

[0132] if , then the two data are considered to be duplicated.

[0133] In S3, the method for verifying data is as follows:

[0134] Energy consumption data needs to be compared and verified with the data from the energy supply department; assuming that the energy consumption of the enterprise is , the data for the energy supply sector is , the verification index is ,If the verification index is within the preset range, the data is considered reliable;

[0135] in, ;

[0136] For emission factor data, refer to international standards or authoritative data sources for verification; assume that the emission factor collected by yourself is , the emission factors from international standards or authoritative data sources are , the difference rate is , if the difference rate is within the preset range, the data collected by oneself is considered reliable.

[0137] In S4, the method for estimating parameters of the carbon emission flow model constructed in S2 includes:

[0138] For the carbon emission model of the upstream production stage, historical data on different raw material inputs, production equipment operating time and corresponding actual carbon emissions are collected; Corresponding carbon emission factor Related factors The relationship between (such as raw material quality) is:

[0139] ;

[0140] By collecting historical data, the regression coefficient of the carbon emission model in the upstream production stage is calculated. The calculation formula is:

[0141] ;

[0142] ;

[0143] In the formula, n is the number of data samples, and are the relevant factors of the carbon emission model and the mean of the carbon emission factors in the upstream production stage; is the factor representing the carbon emission factor corresponding to the input in the upstream production stage; The carbon emission model for the upstream production stage i factors related to the sample; The carbon emission model for the upstream production stage i The carbon emission factor of samples; among them, and is the regression coefficient of the carbon emission model in the upstream production stage, is the error term of the carbon emission model in the upstream production stage;

[0144] For the carbon emission model in the transportation stage, the regression model is:

[0145] ;

[0146] Calculate the regression coefficient of the carbon emission model in the transportation stage using the following formula:

[0147] ;

[0148] ;

[0149] ;

[0150] ;

[0151] in, represents the carbon emission factor of the transportation stage; is the number of data samples; is the transport volume; is the transport distance; for energy efficiency of transport; , , , is the regression coefficient of the carbon emission model in the transportation stage; is the error term of the carbon emission model in the transportation stage; is the mean of the transport volume; is the mean of the transport distance; is the mean value of energy efficiency of transportation vehicles; For the i The transport volume of samples; For the i The transportation distance of each sample; is the mean value of the carbon emission factor of the carbon emission model in the transportation stage; The first part of the carbon emission model for the transportation stage is i Carbon emission factors for each sample;

[0152] For the carbon emission model in the use phase, the model is assumed to be:

[0153] ;

[0154] Calculate the regression coefficient of the carbon emission model in the use phase using the following formula:

[0155] ;

[0156] ;

[0157] ;

[0158] in, is the carbon emission factor in the use phase; is the number of data samples; is the frequency of use; The duration of use; , , is the regression coefficient of the use stage; is the error term in the use phase; is the mean of the frequency of use; is the mean usage time; is the mean value of the carbon emission factor in the use phase; The carbon emission model for the use phase i The usage rate of samples; The carbon emission model for the use phase i The duration of use of each sample; The carbon emission model for the use phase i Carbon emission factors for each sample.

[0159] In S4, using a version control tool to manage changes in parameters and initial conditions includes:

[0160] S41. Use version control tools (such as Git) to record the change history of parameters and initial conditions, and record each modification in detail, including the time of modification, the person who modified it, the content of the modification, and the reason for the modification;

[0161] S42. Establish different branches to store different versions of parameters and initial conditions; create a "master branch" to store stable versions and a "development branch" to try new parameter estimation methods or make adjustments; at the same time, create other branches as needed, such as a "test branch" to test specific versions;

[0162] S43. Use labels to mark important version nodes to facilitate quick location and restoration of specific versions; the label "v1.0" indicates the initial version, "v2.0" indicates a version with major improvements, etc. In addition, more descriptive labels can be used according to the characteristics and uses of the version, such as "optimized parameter version" and "new function version";

[0163] S44. Regularly back up tags and data in version control tools to prevent data loss or corruption.

[0164] The S5 includes:

[0165] S51. Determine the total carbon emissions. For the carbon emission flow model determined in step S2, the total carbon emissions are expressed as ,

[0166] S52. Parameter determination and total carbon emissions calculation, assuming that there is a n1 Uncertain parameters, the carbon emission model in the transportation stage has n 2 There are uncertain parameters in the carbon emission model during the use phase. n 3 uncertain parameters; each parameter follows a normal distribution, and a set of parameter values ​​are obtained by random sampling, which are substituted into the corresponding carbon emission model to calculate carbon emissions;

[0167] S53. Analyze the simulation results, repeat the simulation multiple times, obtain a large number of simulation results, and analyze the distribution characteristics of these results, such as mean, standard deviation, confidence interval, etc.;

[0168] S54. Determine the degree of influence of each parameter on the carbon emission results, and conduct local sensitivity analysis and global sensitivity analysis to determine the degree of influence of each parameter on the carbon emission results.

[0169] In S54, the local sensitivity analysis is performed by changing the value of each parameter individually while keeping other parameters unchanged to calculate the change in the model output. , changing it from its initial value becomes , and then calculate the change in the corresponding model output in S2 ;

[0170] in, ;

[0171] Repeat this operation for each parameter to obtain the local sensitivity index of each parameter. If the change in the model output is large, it means that the parameter has a high sensitivity to the model output.

[0172] In S54, the global sensitivity analysis considers the situation where multiple parameters change simultaneously, analyzes the impact of the interaction between parameters on the model output, and uses the Sobol index method to perform sensitivity analysis to evaluate the importance and interaction of parameters;

[0173] Sensitivity analysis was performed using the Sobol index method to assess the importance of parameters and interactions including:

[0174] The Sobol index of each parameter is calculated to evaluate the importance and interaction of parameters. The Sobol index is divided into first-order Sobol index and higher-order Sobol index.

[0175] Among them, the first-order Sobol index represents the contribution of a single parameter to the model output variance. , and its first-order Sobol index is calculated by the following formula:

[0176] ;

[0177] In the formula, represents the first-order Sobol exponent, represents variance; is the expectation of the model output with respect to all parameters; is the model output with respect to the parameters and Divide The mean of other parameters The larger the first-order Sobol index is, the more important the parameter is in affecting the model output.

[0178] The higher-order Sobol index is used to evaluate the contribution of the interaction between multiple parameters to the variance of the model output; the second-order Sobol index Representation parameters and The contribution of the interaction between and to the variance of the model output is calculated by the following formula:

[0179] ;

[0180] represents variance; is the expectation of the model output with respect to all parameters; where represents the mean of all parameters except and; Represents the model output with respect to the parameters and And except and The mean of other parameters expectations; Represents the model output with respect to the parameters and Divide The mean of other parameters The expectation of The expected output of the model when averaged with other parameters; Represents the model output with respect to the parameters and Divide The mean of other parameters expectations; Indicates the model output about and The expectation of other parameters besides and The expected output of the model.

[0181] The S6 includes:

[0182] S61. Perform multiple random samplings based on the uncertain parameters determined in S5, and substitute the parameter values ​​obtained in each sampling into the model obtained in S2 to calculate the carbon emissions;

[0183] S62. The carbon emission samples obtained in each stage are summed up to obtain the carbon emissions in the upstream production stage, transportation stage and use stage; then the carbon emissions in these three stages are added together to obtain the total amount of the indirect carbon emission flow, i.e., the indirect carbon emission flow;

[0184] S63. Determine the weight of each stage according to its importance, calculate the weighted carbon emissions of each stage using the weight of each stage, then add up the weighted carbon emissions of the three stages to obtain the total weighted carbon emissions; finally, calculate the carbon emissions contribution ratio of different stages;

[0185] In S63, the weight of the upstream production stage is , the weight of the transportation stage is , using the stage weights , then the weighted carbon emissions in the upstream production stage are , the weighted carbon emissions in the transportation stage are , the weighted carbon emissions in the use phase are , add up the weighted carbon emissions of the three stages, and get the total weighted carbon emissions: ;

[0186] Carbon emission contribution ratio at different stages:

[0187] The contribution ratio of carbon emissions in the upstream production stage is: ;

[0188] The contribution ratio of carbon emissions in the transportation stage is: ;

[0189] The contribution ratio of carbon emissions in the use phase is: ;

[0190] Calculating the carbon emission contribution ratio at different stages provides an important basis for making policies and decisions. For example, if the carbon emission contribution ratio at the upstream production stage is high, then we can consider taking measures to optimize the production process, improve energy efficiency or use cleaner energy to reduce carbon emissions at this stage. If the carbon emission contribution ratio at the transportation stage is large, then we can optimize the transportation route, improve the energy efficiency of transportation tools or adopt more environmentally friendly transportation methods.

[0191] The above description is a preferred embodiment of the invention and is not intended to limit the invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the invention should be included in the protection scope of the invention.

Claims

1. A dynamic accounting method for user indirect carbon emission flow considering source load uncertainty, characterized by: The following steps are involved: S1. Clarify the accounting objectives and scope. Through literature review and industry report analysis, combined with the opinions collected at the stakeholder meeting, the key areas and potential needs of the accounting are clarified, and the ultimate purpose of the accounting is established; S2. Build an accounting framework, based on the goals and scopes specified in S1, construct a model of carbon emission flows, and design a database for storing data and parameters; S3. Data collection and preprocessing: According to the requirements of the accounting framework, data is collected from statistical yearbooks, enterprise reports, monitoring data, and questionnaires, and the data is cleaned and verified; S4. Set parameters and initial conditions, estimate parameters of the carbon emission flow model constructed in S2, and use version control tools to manage changes in parameters and initial conditions; S5. Uncertainty quantification analysis: simulation and sensitivity analysis based on the parameters and initial conditions set in S4; S6. Indirect carbon emission flow accounting: Based on the results of S5 uncertainty analysis, life cycle assessment is carried out, and the carbon emission data of each link is integrated to obtain the indirect carbon emission flow. The carbon emission contribution ratio of different stages is calculated based on the obtained indirect carbon emission flow; In S2, the method for constructing a carbon emission flow model includes: The indirect carbon emissions of users are divided into the upstream production stage, transportation stage and use stage, and the carbon emission flow model of each stage is constructed. Method for constructing the carbon emission flow model in the upstream production stage: For the upstream production stage, the activity level is the input of different raw materials and the operating time of the production equipment. Assume that two raw materials are used in the production process. and , and their input amounts are and , input amount The corresponding carbon emission factor is ; Input The corresponding carbon emission factor is ; At the same time, the production equipment running time is , the carbon emission factor of the equipment is , then the carbon emissions in the upstream production stage for: ； The construction method of the carbon emission flow model in the transportation stage: In addition to the transportation volume, the activity level in the transportation stage also considers the transportation distance and the energy efficiency of the transportation tools; assuming that the transportation distance is , the energy efficiency of transportation is , Carbon emissions during transportation for: ； in Indicates the transport volume, represents the carbon emission factor of the transportation stage; Method for constructing a carbon emission flow model in the use phase: The activity level in the use phase is determined by the frequency and duration of use of the product; the carbon emissions in the use phase for: ； in Indicates the duration of use; Indicates the carbon emission factor during the use phase; Indicates frequency of use; In said S2, the data inventory includes carbon emission source data, carbon emission sink data, carbon emission flow path data and parameter data; In the S3, the method for cleaning the data is as follows: Use statistical methods to determine whether the data exceeds the reasonable range, set the data point , the mean is , the standard deviation is ,like ,in k If it is a constant, it is regarded as an abnormal value and corrected or deleted; for erroneous data, it is corrected by comparing with other reliable data sources. Suppose the data in the enterprise report is , the data from reliable data sources are , the difference rate is , if the difference rate If it exceeds the preset threshold, correction is performed; in, ; Duplicate data is removed through data deduplication to ensure data uniqueness. , calculate the hash value of the data through the hash function , if the hash values ​​of two data are the same, they are considered as duplicate data and removed; The process of calculating the hash value using MD5 is expressed as: Assume the data is , the hash value calculation formula is: ,in is the calculated hash value; For data" d 1” and " d 2", calculate their MD5 hash values ​​respectively: ； ； if , then the two data are considered to be duplicated; In S3, the method for verifying data is as follows: Energy consumption data needs to be compared and verified with the data from the energy supply department; assuming that the energy consumption of the enterprise is , the data for the energy supply sector is , the verification index is ,If the verification index is within the preset range, the data is considered reliable; in, ; For emission factor data, reference is made to international standards or authoritative data sources for verification; assuming that the collected emission factors are , the emission factors from international standards or authoritative data sources are , the difference rate is ,If the difference rate is within the preset range, the collected data is considered reliable; In S4, the method for estimating parameters of the carbon emission flow model constructed in S2 includes: For the carbon emission model of the upstream production stage, historical data on different raw material inputs, production equipment operating time and corresponding actual carbon emissions are collected; Corresponding carbon emission factor Related factors The relationship between them is: ； By collecting historical data, the regression coefficient of the carbon emission model in the upstream production stage is calculated. The calculation formula is: ； ； In the formula, n is the number of data samples, and are the relevant factors of the carbon emission model and the mean of the carbon emission factors in the upstream production stage; is the factor representing the carbon emission factor corresponding to the input in the upstream production stage; The carbon emission model for the upstream production stage factors related to the sample; The carbon emission model for the upstream production stage The carbon emission factor of samples; among them, and is the regression coefficient of the carbon emission model in the upstream production stage, is the error term of the carbon emission model in the upstream production stage; For the carbon emission model in the transportation stage, the regression model is: ； Calculate the regression coefficient of the carbon emission model in the transportation stage using the following formula: ； ； ； ； in, represents the carbon emission factor of the transportation stage; is the number of data samples; is the transport volume; is the transport distance; for energy efficiency of transport; , , , is the regression coefficient of the carbon emission model in the transportation stage; is the error term of the carbon emission model in the transportation stage; is the mean of the transport volume; is the mean of the transport distance; is the mean value of energy efficiency of transportation vehicles; For the The transport volume of samples; For the The transportation distance of each sample; is the mean value of the carbon emission factor of the carbon emission model in the transportation stage; The first part of the carbon emission model for the transportation stage is Carbon emission factors for each sample; For the use phase carbon emissions model, the model is: ； Calculate the regression coefficient of the carbon emission model in the use phase using the following formula: ； ； ； in, is the carbon emission factor in the use phase; is the number of data samples; is the frequency of use; The duration of use; , , is the regression coefficient of the use stage; is the error term in the use phase; is the mean of the frequency of use; is the mean usage time; is the mean value of the carbon emission factor in the use phase; The carbon emission model for the use phase The usage rate of samples; The carbon emission model for the use phase The duration of use of each sample; The carbon emission model for the use phase Carbon emission factors for each sample; In S4, using a version control tool to manage changes in parameters and initial conditions includes: S41. Use version control tools to record the change history of parameters and initial conditions, and keep detailed records of each modification, including the time of modification, the person who modified it, the content of the modification, and the reason; S42. Establish different branches to store different versions of parameters and initial conditions. Create a "main branch" to store stable versions and develop a "branch" to try new parameter estimation methods or make adjustments. At the same time, create other branches as needed, and other branches are used to test specific versions. S43. Use tags to mark important version nodes to facilitate quick location and restoration of specific versions; the tag "v1.0" indicates the initial version; "v2.0" indicates a version with significant improvements; S44. Regularly back up tags and data in the version control tool to prevent data loss or damage; The S5 includes: S51. Determine the total carbon emissions. For the carbon emission flow model determined in step S2, assume that the total carbon emissions It is expressed as: ； is the carbon emissions in the upstream production stage, Carbon emissions during transportation , is the carbon emissions during the use phase; S52. Parameter determination and total carbon emissions calculation. Assume that there is a n 1 Uncertain parameters, the carbon emission model in the transportation stage has n 2 There are uncertain parameters in the carbon emission model during the use phase. n 3 uncertain parameters; each parameter follows a normal distribution, and a set of parameter values ​​are obtained by random sampling, which are substituted into the corresponding carbon emission model to calculate carbon emissions; S53. Analyze the simulation results, repeat the simulation multiple times, obtain many simulation results, and analyze the distribution characteristics of these results; S54. Determine the degree of influence of each parameter on the carbon emission results, and conduct local sensitivity analysis and global sensitivity analysis to determine the degree of influence of each parameter on the carbon emission results; The S6 includes: S61. Perform multiple random samplings based on the uncertain parameters determined in S5, and substitute the parameter values ​​obtained in each sampling into the model obtained in S2 to calculate the carbon emissions; S62. The carbon emission samples obtained in each stage are summed up to obtain the carbon emissions in the upstream production stage, transportation stage and use stage; then the carbon emissions in the three stages are added together to obtain the total amount of the indirect carbon emission flow, i.e., the indirect carbon emission flow; S63. Determine the weight of each stage according to its importance, calculate the weighted carbon emissions of each stage using the weight of each stage, and then add up the weighted carbon emissions of the three stages to get the total weighted carbon emissions; finally, calculate the carbon emission contribution ratio of different stages.

2. According to claim 1, a dynamic accounting method for user indirect carbon emission flow considering source load uncertainty is characterized in that: In said S54: Local sensitivity analysis is to calculate the change in model output by changing the value of each parameter individually while keeping other parameters unchanged; , changing it from its initial value becomes , and then calculate the change in the corresponding model output in S2 ; in, ; Repeat this operation for each parameter to obtain the local sensitivity index of each parameter. If the change in the model output is large, it means that the parameter has a high sensitivity to the model output. Global sensitivity analysis considers the situation where multiple parameters change simultaneously, analyzes the impact of the interaction between parameters on the model output, and uses the Sobol index method to perform sensitivity analysis to evaluate the importance and interaction of parameters; Sensitivity analysis was performed using the Sobol index method to assess the importance of parameters and interactions including: The Sobol index of each parameter is calculated to evaluate the importance and interaction of parameters. The Sobol index is divided into first-order Sobol index and higher-order Sobol index. Among them, the first-order Sobol index represents the contribution of a single parameter to the model output variance. , and its first-order Sobol index is calculated by the following formula: ； In the formula, represents the first-order Sobol exponent, represents variance; is the expectation of the model output with respect to all parameters; is the model output with respect to the parameters and Divide The mean of other parameters The larger the first-order Sobol index is, the more important the parameter is in affecting the model output. The higher-order Sobol index is used to evaluate the contribution of the interaction between multiple parameters to the variance of the model output; the second-order Sobol index Representation parameters and The contribution of the interaction between and to the variance of the model output is calculated by the following formula: ； represents variance; is the expectation of the model output with respect to all parameters; where represents the mean of all parameters except and; Represents the model output with respect to the parameters and And except and The mean of other parameters expectations; Represents the model output with respect to the parameters and Divide The mean of other parameters The expectation of The expected output of the model when averaged with other parameters; Represents the model output with respect to the parameters and Divide The mean of other parameters expectations; Indicates the model output about and The expectation of other parameters besides and The expected output of the model.

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