Carbon footprint data quality evaluation method

Abstract

The invention relates to the technical field of carbon footprint analysis, in particular to a carbon footprint data quality evaluation method, which comprises the following steps of listing all processes of each life cycle stage in a target product system boundary; judging the type of each process according to the decision tree, and matching a data quality evaluation strategy according to the process type; implementing a data quality evaluation process, and outputting all index scores; calculating the weighted average of the scores of the same index of the target product; calculating a data quality evaluation comprehensive score of the carbon footprint research of the target product according to the weighted average; and evaluating the data quality grade of the target product carbon footprint research according to the data quality evaluation comprehensive score. According to the method, the quality evaluation strategies of various types of data in the carbon footprint of the target product are determined, the decision tree is adopted to guide selection of the evaluation strategies, the data quality evaluation process is completed, the data quality grade is determined, and the comparability, quality and credibility of the research result of the carbon footprint of the target product can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon footprint analysis, and in particular to a method for evaluating the data quality of a carbon footprint. Background Art

[0002] With social progress and economic development, human civilization is also facing increasingly severe difficulties and challenges in terms of environment, resources and population, among which: environmental impacts include climate change, acidification, particulate matter, resource utilization, photochemical ozone formation, ozone layer depletion, human toxicity, ionizing radiation, land use, water consumption, ecotoxicity, eutrophication, etc. Climate change, as a category of impact for measuring carbon footprint, has received great attention from countries, enterprises and research institutions in recent years. At present, a lot of carbon footprint research has been carried out in China, and various carbon footprint certificates have appeared on the market. Related research is basically based on the international standard ISO 14067-2018. The standard lists the quality items that the data must meet, and there is no set of methods, qualitative or quantitative evaluation requirements to constrain it. The quality of data is the biggest constraint on the application of the Life Cycle Assessment (LCA) method, and it is also an important basis for the comparison, credibility and effectiveness of carbon footprint results. Therefore, scientific, comprehensive and efficient evaluation of data quality is a necessary condition for improving the reliability of carbon footprint results.

[0003] The carbon footprint research currently being conducted basically does not have any qualitative or quantitative evaluation of data quality. Relevant research only uses uncertainty representation to represent the overall data quality. None of the above methods take into account the specific characteristics of the entire data quality of the carbon footprint research. Although the latter can express the uncertainty of a single data, when the data used is not suitable for the research purpose, it will lead to poor data quality in the carbon footprint research results and will not be able to provide comparable expression results. Summary of the invention

[0004] The technical problem to be solved by the present invention is: in order to solve the technical problem of the single quality evaluation index of existing carbon footprint data, the present invention provides a carbon footprint data quality evaluation method, which clarifies the quality evaluation of various types of data in the carbon footprint research of the target product, and can improve the comparability, quality and credibility of the carbon footprint research results of the target product.

[0005] The technical solution adopted by the present invention to solve the technical problem is: a carbon footprint data quality evaluation method, comprising the following steps:

[0006] S1. List all processes in each life cycle stage within the target product system boundary;

[0007] S2. Determine the type of each process in step S1 according to the decision tree, and match the data quality evaluation strategy according to the process type;

[0008] S3. Implement the data quality evaluation process and output all indicator scores;

[0009] S4, summarizing all the index scores in step S3, and calculating the weighted average of the same index score of the target product;

[0010] S5. Calculate the comprehensive score of data quality evaluation of the carbon footprint research of the target product according to the weighted average in step S4;

[0011] S6. Evaluate the data quality level of the carbon footprint research of the target product according to the comprehensive score of the data quality evaluation in step S5.

[0012] Therefore, by clarifying the quality evaluation strategies for various types of data in the carbon footprint of the target product, using the decision tree to guide the selection of the evaluation strategy, completing the data quality evaluation process and determining the data quality level, the comparability, quality and credibility of the target product carbon footprint research results can be improved, which is an important basis for proving the effectiveness of the target product carbon footprint research.

[0013] Furthermore, the step S1 includes the following steps:

[0014] S1-1. Select the system boundary of the target product carbon footprint research;

[0015] S1-2, according to the system boundary in step S1, determine the life cycle stages or sub-stages included;

[0016] S1-3. According to the life cycle phase or sub-phase in step S2, list all the processes involved.

[0017] Furthermore, the step S2 comprises the following steps:

[0018] S2-1. The party that declares the carbon footprint of the target product;

[0019] S2-2, if the target product process occurs in a manufacturing enterprise, then company-level data is used and the first evaluation strategy is adopted;

[0020] S2-3. If the target product process does not occur in the manufacturing enterprise, but complete supplier-level data is available, the first evaluation strategy is adopted;

[0021] S2-4. If the target product process does not occur in the manufacturing enterprise and only partial supplier-level data can be obtained, the second evaluation strategy is adopted;

[0022] S2-5. If the target product process does not occur in the manufacturing enterprise and the supplier-level data cannot be obtained, but the process is an important process, the third evaluation strategy is adopted;

[0023] S2-6. If the target product process does not occur in the manufacturing enterprise and supplier-level data cannot be obtained, but the process is not an important process, the fourth evaluation strategy is adopted.

[0024] Furthermore, the evaluation objects of the first evaluation strategy include:

[0025] Activity data, secondary data sets, and basic flows in the target product process;

[0026] The evaluation indicators of the first evaluation strategy include:

[0027] Completeness, methodological consistency, temporal representativeness, geographical representativeness, technical integrity, and accuracy;

[0028] The first evaluation strategy includes the following steps:

[0029] M1-1. Evaluate the index scores of activity data and basic flows in the target product process according to Table 1;

[0030] M1-2. Re-evaluate the data quality of the secondary data set in the target product process from step S2-2.

[0031] Furthermore, the evaluation object of the second strategy is:

[0032] Supplier-level data on the target product process;

[0033] The evaluation indicators of the second evaluation strategy include:

[0034] Completeness, methodological consistency, temporal representativeness, geographical representativeness, technical integrity, and accuracy;

[0035] The second evaluation strategy includes the following steps:

[0036] M2-1. List the original scores of each evaluation index of the process of the target product X old ;

[0037] M2-2. List the known supplier-level data of the target product and its original carbon footprint contribution rate CB in the process y,old ;

[0038] M2-3. Evaluate the activity data indicator score X of the target product supplier-level data according to Table 1 AD,1 and the basic flow index score X EF,1 ;

[0039] M2-4. Evaluate the secondary data set indicator score X of the target product supplier-level data according to Table 2 SD,1 ;

[0040] M2-5. Calculate the new scores X of each evaluation index of the process of the target product new ,

[0041] Evaluation index new score X new The calculation formula is:

[0042]

[0043] Where: y represents active data, secondary data sets, and basic streams, X y,1,i represents the evaluation index score of the i-th supplier-level data y, CB y,old,i Represents the raw contribution rate of carbon footprint of the ith supplier-level data y.

[0044] Furthermore, the evaluation object of the third evaluation strategy is:

[0045] Secondary data sets in the target product process;

[0046] The evaluation indicators of the third evaluation strategy include:

[0047] Completeness, methodological consistency, temporal representativeness, geographical representativeness, technical integrity, and accuracy;

[0048] The third evaluation strategy includes the following steps:

[0049] M3-1. Calculate the carbon footprint contribution rate CB of all processes of the target product j,process ,

[0050] Carbon footprint contribution rate CB j,process The calculation formula is:

[0051]

[0052] M3-2. Arrange the carbon footprint contribution rates of all processes of the target product in step M3-1 in order. If the total contribution rate is ≥ 90%, the process is an important process.

[0053] M3-3. Evaluate the secondary data set indicator scores of the target product in accordance with Table 2 to ensure compliance with the requirements;

[0054] Among them: CB j,process Represents the carbon footprint contribution rate of process j, in %, CF j,process represents the carbon footprint of process j in kgCO2e, CF total Represents the total carbon footprint of the target product in kgCO2e.

[0055] Furthermore, the evaluation object of the fourth evaluation strategy is:

[0056] Secondary data sets in the target product process;

[0057] The evaluation indicators of the fourth evaluation strategy include:

[0058] Completeness, methodological consistency, temporal representativeness, geographical representativeness, technical integrity, and accuracy;

[0059] The fourth evaluation strategy includes the following steps:

[0060] M4-1. List all processes of the target product except the first evaluation strategy, the second evaluation strategy and the third evaluation strategy.

[0061] M4-2. The raw index score of the secondary data set of the process using the target product.

[0062] Furthermore, step S3 includes the following steps:

[0063] S3-1, carry out the evaluation process according to the data quality evaluation strategy matched in step S2;

[0064] S3-2. Calculate the average score of the activity data indicators and the average score of the secondary data set indicators that have a correlation relationship;

[0065] S3-3. Output all indicator scores.

[0066] Furthermore, the step S4 comprises the following steps:

[0067] S4-1, summarizing all indicator scores in step S3;

[0068] S4-2. Calculate the weight WE of the target product process j,process ,

[0069] Weight WE j,process The calculation formula is:

[0070]

[0071] S4-3. Calculate the weighted average of the scores of the same indicator of the target product

[0072] Weighted average of scores for the same indicator The calculation formula is:

[0073]

[0074] Among them: WE j,process Express the carbon footprint weight of process j, in %, CBtotal It represents the sum of carbon footprint contribution rates of target products involved in the data quality evaluation process, in %, X j,process represents the evaluation index score of process j indicator X, where X represents completeness, method consistency, temporal representativeness, geographical location representativeness, technical completeness, and accuracy.

[0075] Further, in step S5, the calculation formula of the target product carbon footprint research data quality evaluation comprehensive score DQA is:

[0076]

[0077] Further, in step S6, if 0<DQA≤1.5, the data quality level is level 1; if 1.5<DQA≤2.0, the data quality level is level 2; if 2.0<DQA≤3.0, the data quality level is level 3; if 3.0<DQA≤4.0, the data quality level is level 4; if DQA>4, the data quality level is level 5.

[0078] Compared with the prior art, the present invention has the following beneficial effects:

[0079] By clarifying the quality evaluation strategies for various types of data in the target product carbon footprint, using a decision tree to guide the selection of evaluation strategies, completing the data quality evaluation process and determining the data quality level, the comparability, quality and credibility of the target product carbon footprint research results can be improved, which is an important basis for proving the effectiveness of the target product carbon footprint research. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] The present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0081] Figure 1 A flow chart of the carbon footprint data quality evaluation method of the present invention;

[0082] Figure 2 This is a flow chart of step S2 of the present invention. DETAILED DESCRIPTION

[0083] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0084] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In addition, features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, "multiple" means two or more.

[0085] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0086] like Figure 1 to Figure 2 FIG. 1 is the best embodiment of the present invention. The carbon footprint data quality evaluation method of this embodiment includes the following steps:

[0087] S1. List all processes in each life cycle stage within the target product system boundary;

[0088] S2. Determine the type of each process in step S1 according to the decision tree, and match the data quality evaluation strategy according to the process type;

[0089] S3. Implement the data quality evaluation process and output all indicator scores;

[0090] S4, summarizing all the index scores in step S3, and calculating the weighted average of the same index score of the target product;

[0091] S5. Calculate the comprehensive score of data quality evaluation of the carbon footprint research of the target product according to the weighted average in step S4;

[0092] S6. Evaluate the data quality level of the target product carbon footprint study according to the comprehensive score of the data quality evaluation in step S5. Therefore, by clarifying the quality evaluation strategy of various types of data in the target product carbon footprint, using the decision tree to guide the selection of the evaluation strategy, completing the data quality evaluation process and determining the data quality level, the comparability, quality and credibility of the target product carbon footprint study results can be improved, which is an important basis for proving the effectiveness of the target product carbon footprint study.

[0093] In this embodiment, step S1 includes the following steps:

[0094] S1-1. Select the system boundary of the target product carbon footprint research;

[0095] S1-2, according to the system boundary in step S1, determine the life cycle stages or sub-stages included;

[0096] S1-3. According to the life cycle phase or sub-phase in step S2, list all the processes involved.

[0097] Specifically, the system boundary of the target product consists of "start to middle stage" and "start to end".

[0098] For example, the life cycle stages included in "start to intermediate stage" are "raw materials stage" and "production stage"; the life cycle sub-stages included in "raw materials stage" are "raw materials acquisition and preprocessing sub-stage" and "raw materials transportation sub-stage"; "raw materials acquisition and preprocessing sub-stage" includes "natural resource mining process", "transportation or intermediate transportation process of natural resources from mining point to processing point and any location", "processing process of natural resources to raw materials" and "raw materials packaging process".

[0099] In this embodiment, step S2 includes the following steps:

[0100] S2-1. The party that declares the carbon footprint of the target product;

[0101] S2-2, if the target product process occurs in a manufacturing enterprise, then company-level data is used and the first evaluation strategy is adopted;

[0102] S2-3. If the target product process does not occur in the manufacturing enterprise, but complete supplier-level data is available, the first evaluation strategy is adopted;

[0103] S2-4. If the target product process does not occur in the manufacturing enterprise and only partial supplier-level data can be obtained, the second evaluation strategy is adopted;

[0104] S2-5. If the target product process does not occur in the manufacturing enterprise and the supplier-level data cannot be obtained, but the process is an important process, the third evaluation strategy is adopted;

[0105] S2-6. If the target product process does not occur in the manufacturing enterprise and the supplier-level data cannot be obtained, but the process is not an important process, the fourth evaluation strategy is adopted;

[0106] The evaluation objects of the first evaluation strategy include: activity data, secondary data sets and basic flows in the target product process; the evaluation indicators of the first evaluation strategy include: completeness (C), method consistency (M), time representativeness (T i R), geographic location representativeness (G e R), technical integrity (T e R) and accuracy (P);

[0107] The first evaluation strategy includes the following steps:

[0108] M1-1. Evaluate the index scores of activity data and basic flows in the target product process according to Table 1;

[0109] M1-2, re-evaluate the data quality of the secondary data set in the target product process from step S2-2;

[0110] The evaluation object of the second strategy is: supplier-level data in the target product process; the evaluation indicators of the second evaluation strategy include: completeness (C), method consistency (M), time representativeness (T i R), geographic location representativeness (G e R), technical integrity (T e R) and accuracy (P);

[0111] The second evaluation strategy includes the following steps:

[0112] M2-1. List the original scores of each evaluation index of the process of the target product X old ;

[0113] M2-2. List the known supplier-level data of the target product and its original carbon footprint contribution rate CB in the process y,old ;

[0114] M2-3. Evaluate the activity data indicator score X of the target product supplier-level data according to Table 1 AD,1 and the basic flow index score X EF,1 ;

[0115] M2-4. Evaluate the secondary data set indicator score X of the target product supplier-level data according to Table 2 SD,1 ;

[0116] M2-5. Calculate the new scores X of each evaluation index of the process of the target product new ,

[0117] Evaluation index new score X new The calculation formula is:

[0118]

[0119] The evaluation object of the third evaluation strategy is: the secondary data set in the target product process; the evaluation indicators of the third evaluation strategy include: completeness (C), method consistency (M), time representativeness (T i R), geographic location representativeness (G e R), technical integrity (T e R) and accuracy (P);

[0120] The third evaluation strategy includes the following steps:

[0121] M3-1. Calculate the carbon footprint contribution rate CB of all processes of the target product j,process ,

[0122] Carbon footprint contribution rate CB j,process The calculation formula is:

[0123]

[0124] M3-2. Arrange the carbon footprint contribution rates of all processes of the target product in step M3-1 in order. If the total contribution rate is ≥ 90%, the process is an important process.

[0125] M3-3. Evaluate the secondary data set indicator scores of the target product in accordance with Table 2 to ensure compliance with the requirements;

[0126] The evaluation object of the fourth evaluation strategy is: the secondary data set in the target product process; the evaluation indicators of the fourth evaluation strategy include: completeness (C), method consistency (M), time representativeness (T i R), geographic location representativeness (G e R), technical integrity (T e R) and accuracy (P);

[0127] The fourth evaluation strategy includes the following steps:

[0128] M4-1. List all processes of the target product except the first evaluation strategy, the second evaluation strategy and the third evaluation strategy.

[0129] M4-2. The original index score of the secondary data set of the process using the target product;

[0130] Where: y represents active data, secondary data sets, and basic streams, X y,1,i represents the evaluation index score of the i-th supplier-level data y, CB y,old,i represents the carbon footprint raw contribution rate of the ith supplier-level data y, CB j,process Represents the carbon footprint contribution rate of process j, in %, CF j,process represents the carbon footprint of process j in kgCO2e, CF total Indicates the total carbon footprint of the target product, in kgCO2e. Specifically, in step M1-2 of the first evaluation strategy, the secondary data set in the target product process also needs to be judged by the decision tree to determine its location and importance, so as to determine the evaluation strategy of the data quality of the secondary data set.

[0131] Specifically, in M3-2 of the third evaluation strategy, the carbon footprint contribution rates of all processes of the target product are arranged in order as CB1, CB2, ..., CB n And CB1>CB2>…>CB n , if CB1+CB2+…+CB k ≥90%, then CB1, CB2, …, CB k It is an important process, CB k+1 CB k+2 , …, CB n Not an important process.

[0132] Specifically, X old The scores derived from the supplier-level data are X y,1,i From Table 1 and Table 2, CB y,old,i Carbon footprint results from supplier-level data or corresponding process carbon footprint results, CF j,process CF total All of them are derived from the quantitative results of the life cycle inventory of carbon footprint research.

[0133] For example: the declaring parties include "raw material supply enterprises", "product manufacturing enterprises", "product using enterprises" and "product disposal enterprises".

[0134] Table 1 Evaluation index scores of activity data and basic streams

[0135]

[0136] Table 2 Evaluation index scores of secondary data sets

[0137]

[0138]

[0139] In this embodiment, step S3 includes the following steps:

[0140] S3-1, carry out the evaluation process according to the data quality evaluation strategy matched in step S2;

[0141] S3-2. Calculate the average score of the activity data indicators and the average score of the secondary data set indicators that have a correlation relationship;

[0142] S3-3. Output all indicator scores.

[0143] In this embodiment, step S4 includes the following steps:

[0144] S4-1, summarizing all indicator scores in step S3;

[0145] S4-2. Calculate the weight WE of the target product process j,process ,

[0146] Weight WE j,process The calculation formula is:

[0147]

[0148] S4-3. Calculate the weighted average of the scores of the same indicator of the target product

[0149] Weighted average of scores for the same indicator The calculation formula is:

[0150]

[0151] Among them: WE j,process Express the carbon footprint weight of process j, in %, CB total It represents the sum of carbon footprint contribution rates of target products involved in the data quality evaluation process, in %, X j,process represents the evaluation index score of process j indicator X, where X represents completeness, method consistency, temporal representativeness, geographical location representativeness, technical completeness, and accuracy. Specifically, CB total As a result of the selection of the data evaluation range, X j,process It is a calculated value, assigned value, or the score of the original data, which is determined by selecting different evaluation strategies.

[0152] In this embodiment, in step S5, the calculation formula of the target product carbon footprint research data quality evaluation comprehensive score DQA is:

[0153]

[0154] In this embodiment, in step S6, if 0<DQA≤1.5, the data quality level is level one, which is very good and no subsequent improvement is necessary; if 1.5<DQA≤2.0, the data quality level is level two, which is good and almost no subsequent improvement is necessary; if 2.0<DQA≤3.0, the data quality level is level three, which is qualified and further improvement is needed; if 3.0<DQA≤4.0, the data quality level is level four, which is poor and a lot of improvement is needed; if DQA>4, the data quality level is level five, which is very poor and data needs to be reacquired later.

[0155] The following is an example of carbon footprint data quality evaluation using decorative coatings:

[0156] Step S1: All relevant contents of step S1-1, step S1-2 and step S1-3 are summarized in Table 3; Step S2: Step S2-1, the declaring party is the manufacturing enterprise; all relevant contents of step S2-2, step S2-3, step S2-4, step S2-5 and step S2-6 are summarized in Table 4, Table 5, Table 6 and Table 7; Step S3: Step S3-1 adopts the index score of the first evaluation strategy data as shown in Table 8, step S3-1 adopts the index score calculation of the second evaluation strategy data as shown in Table 9, Table 10 and Table 11, step S3-1 adopts the index score calculation of the third evaluation strategy data as shown in Table 12, step S3-1 adopts the index score calculation of the fourth evaluation strategy data as shown in Table 13, and all index scores of step S3-2 and step S3-3 are summarized in Table 14; Step S5:

[0157]

[0158] Step S6: The data quality level of the carbon footprint of decorative paints is level 1, the data quality is very good, and no subsequent improvement is necessary.

[0159] Table 3 Life cycle phases and processes of decorative coatings from the beginning to the middle stage

[0160]

[0161]

[0162] Table 4 Activity data and basic flow of the processes involved in the first evaluation strategy for decorative paint matching

[0163]

[0164]

[0165] Table 5 Secondary data sets for the processes involved in the second evaluation strategy for decorative paint matching

[0166] process Some supplier-level data Secondary Datasets Calcined clay Electricity, transportation Second evaluation strategy

[0167] Table 6 Secondary data sets for the processes involved in the third evaluation strategy for decorative paint matching

[0168]

[0169] Table 7 Secondary data sets for the processes involved in the fourth evaluation strategy for decorative coating matching

[0170]

[0171]

[0172] Table 8 Index scores of decorative coatings using the first evaluation strategy data

[0173]

[0174]

[0175] Table 9 Raw scores of indicators of decorative coatings using the second evaluation strategy data

[0176]

[0177] Table 10 Index scores and raw contribution rates for supplier-level data on calcined clay

[0178]

[0179] Table 11 New scores of indicators for decorative coatings using the second evaluation strategy data

[0180]

[0181] Table 12 Index scores of decorative coatings using the third evaluation strategy data

[0182]

[0183] Table 13 Raw scores of indicators of decorative coatings using the fourth evaluation strategy data

[0184]

[0185]

[0186] Table 14 Summary of all index scores in decorative coatings

[0187]

[0188]

[0189] In summary, the present invention clarifies the quality evaluation strategies for various types of data in the carbon footprint of the target product, adopts a decision tree to guide the selection of the evaluation strategy, completes the data quality evaluation process and determines the data quality level, thereby improving the comparability, quality and credibility of the research results of the carbon footprint of the target product, and is an important basis for proving the effectiveness of the carbon footprint research of the target product.

[0190] The above description is based on the ideal embodiment of the present invention. Through the above description, relevant personnel can make various changes and modifications without departing from the technical concept of the present invention. The technical scope of the present invention is not limited to the content in the specification, and its technical scope must be determined according to the scope of the claims.

Claims

1. A method for evaluating the data quality of carbon footprint, characterized in that: The following steps are involved: S1. List all processes in each life cycle stage within the target product system boundary; S2. Determine the type of each process in step S1 according to the decision tree, and match the data quality evaluation strategy according to the process type; S3. Implement the data quality evaluation process and output all indicator scores; S4, summarizing all the index scores in step S3, and calculating the weighted average of the same index score of the target product; S5. Calculate the comprehensive score of data quality evaluation of the carbon footprint research of the target product according to the weighted average in step S4; S6. Evaluate the data quality level of the carbon footprint research of the target product according to the comprehensive score of the data quality evaluation in step S5.

2. The carbon footprint data quality evaluation method according to claim 1, characterized in that: The step S1 comprises the following steps: S1-1. Select the system boundary of the target product carbon footprint research; S1-2, according to the system boundary in step S1, determine the life cycle stages or sub-stages included; S1-3. According to the life cycle phase or sub-phase in step S2, list all the processes involved.

3. The carbon footprint data quality evaluation method according to claim 1, characterized in that: The step S2 comprises the following steps: S2-1. The party that declares the carbon footprint of the target product; S2-2, if the target product process occurs in a manufacturing enterprise, then company-level data is used and the first evaluation strategy is adopted; S2-3. If the target product process does not occur in the manufacturing enterprise, but complete supplier-level data is available, the first evaluation strategy is adopted; S2-4. If the target product process does not occur in the manufacturing enterprise and only partial supplier-level data can be obtained, the second evaluation strategy is adopted; S2-5. If the target product process does not occur in the manufacturing enterprise and the supplier-level data cannot be obtained, but the process is an important process, the third evaluation strategy is adopted; S2-6. If the target product process does not occur in the manufacturing enterprise and supplier-level data cannot be obtained, but the process is not an important process, the fourth evaluation strategy is adopted.

4. The carbon footprint data quality evaluation method according to claim 3, characterized in that: The evaluation objects of the first evaluation strategy include: Activity data, secondary data sets, and basic flows in the target product process; The evaluation indicators of the first evaluation strategy include: Completeness, methodological consistency, temporal representativeness, geographical representativeness, technical integrity, and accuracy; The first evaluation strategy includes the following steps: M1-1. Evaluate the index scores of activity data and basic flows in the target product process according to Table 1; M1-2. Re-evaluate the data quality of the secondary data set in the target product process from step S2-2.

5. The carbon footprint data quality evaluation method according to claim 3, characterized in that: The evaluation objects of the second strategy are: Supplier-level data on target product processes; The evaluation indicators of the second evaluation strategy include: Completeness, methodological consistency, temporal representativeness, geographical representativeness, technical integrity, and accuracy; The second evaluation strategy includes the following steps: M2-1. List the original scores of each evaluation index of the process of the target product X old ; M2-2. List the known supplier-level data of the target product and its original carbon footprint contribution rate CB in the process y,old ; M2-3. Evaluate the activity data indicator score X of the target product supplier-level data according to Table 1 AD,1 and the basic flow index score X EF,1 ; M2-4. Evaluate the secondary data set indicator score X of the target product supplier-level data according to Table 2 SD,1 ; M2-5. Calculate the new scores X of each evaluation index of the process of the target product new , Evaluation index new score X new The calculation formula is: Where: y represents active data, secondary data sets, and basic streams, X y,1,i represents the evaluation index score of the i-th supplier-level data y, CB y,old,i Represents the raw contribution rate of carbon footprint of the ith supplier-level data y.

6. The carbon footprint data quality evaluation method according to claim 3, characterized in that: The evaluation objects of the third evaluation strategy are: Secondary data sets in the target product process; The evaluation indicators of the third evaluation strategy include: Completeness, methodological consistency, temporal representativeness, geographical representativeness, technical integrity, and accuracy; The third evaluation strategy includes the following steps: M3-1. Calculate the carbon footprint contribution rate CB of all processes of the target product j,process , Carbon footprint contribution rate CB j,process The calculation formula is: M3-2. Arrange the carbon footprint contribution rates of all processes of the target product in step M3-1 in order. If the total contribution rate is ≥ 90%, the process is an important process. M3-3. Evaluate the secondary data set indicator scores of the target product in accordance with Table 2 to ensure compliance with the requirements; Among them: CB j,process Represents the carbon footprint contribution rate of process j, in %, CF j,process represents the carbon footprint of process j in kgCO2e, CF total Represents the total carbon footprint of the target product in kgCO2e.

7. The carbon footprint data quality evaluation method according to claim 3, characterized in that: The evaluation objects of the fourth evaluation strategy are: Secondary data sets in the target product process; The evaluation indicators of the fourth evaluation strategy include: Completeness, methodological consistency, temporal representativeness, geographical representativeness, technical integrity, and accuracy; The fourth evaluation strategy includes the following steps: M4-1. List all processes of the target product except the first evaluation strategy, the second evaluation strategy and the third evaluation strategy; M4-2. The raw index score of the secondary data set of the process using the target product.

8. The carbon footprint data quality evaluation method according to claim 1, characterized in that: The step S3 comprises the following steps: S3-1, carry out the evaluation process according to the data quality evaluation strategy matched in step S2; S3-2. Calculate the average score of the activity data indicators and the average score of the secondary data set indicators that have a correlation relationship; S3-3. Output all indicator scores.

9. The carbon footprint data quality evaluation method according to claim 1, characterized in that: The step S4 comprises the following steps: S4-1, summarizing all indicator scores in step S3; S4-2. Calculate the weight WE of the target product process j,process , Weight WE j,process The calculation formula is: S4-3. Calculate the weighted average of the scores of the same indicator of the target product Weighted average of scores for the same indicator The calculation formula is: Among them: WE j,process Express the carbon footprint weight of process j, in %, CB total It represents the sum of carbon footprint contribution rates of target products involved in the data quality evaluation process, in %, X j,process represents the evaluation index score of process j indicator X, where X represents completeness, method consistency, temporal representativeness, geographical location representativeness, technical completeness, and accuracy.

10. The carbon footprint data quality evaluation method according to claim 1, characterized in that: In step S5, the calculation formula of the target product carbon footprint research data quality evaluation comprehensive score DQA is:

11. The carbon footprint data quality evaluation method according to claim 10, characterized in that: In step S6, if 0<DQA≤1.5, the data quality level is level one; If 1.5<DQA≤2.0, the data quality level is level 2; If 2.0<DQA≤3.0, the data quality level is level 3; If 3.0<DQA≤4.0, the data quality level is level 4; If DQA>4, the data quality level is level five.

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