Product carbon footprint data quality evaluation method

Abstract

The invention discloses a product carbon footprint data quality evaluation method, which comprises the steps that a product carbon footprint data quality evaluation system comprising activity data and emission factors is constructed, activity data evaluation indexes comprise source reliability and statistical accuracy, and emission factor evaluation indexes comprise time representativeness, geographical representativeness and technical representativeness; comparing the evaluation factors in pairs by using a 1-9 scale method, constructing a discrimination matrix of each evaluation index, and obtaining the weight of each evaluation factor; checking and correspondingly adjusting the discrimination matrix based on the random consistency ratio; a carbon emission source of the product in each production stage is determined, and the uncertainty of each carbon emission source is determined according to an Einformation database and the weight; and calculating the carbon footprint overall uncertainty of the product life cycle based on an error transfer method. According to the method, the analytic hierarchy process and the data quality index are combined to carry out product carbon footprint data quality evaluation, and the influence of subjective factors on data quality evaluation is reduced.

Description

Technical Field

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

[0002] Global warming will have a huge negative impact on the natural environment and many aspects of human social and economic life, and 90% of the warming may come from the increase in greenhouse gas concentrations caused by human activities. Therefore, countries around the world are actively introducing policies and regulations to reduce greenhouse gas emissions. In June 2024, the Ministry of Ecology and Environment and other departments issued the "Implementation Plan for Establishing a Carbon Footprint Management System", encouraging key products such as electricity, coal, natural gas, fuel oil, steel, electrolytic aluminum, cement, fertilizer, hydrogen, lime, glass, ethylene synthetic ammonia, calcium carbide, methanol, lithium batteries, new energy vehicles, photovoltaics and electronic appliances to formulate and publish accounting rules and standards. In September 2024, the State Administration for Market Regulation approved the release of GB / T 24067-2024 "Quantitative Requirements and Guidelines for Carbon Footprint of Greenhouse Gas Products", which stipulates the research scope, principles and quantitative methods of product carbon footprint, and puts forward many qualitative requirements for data quality.

[0003] However, there is currently a lack of unified, standardized, and specific product carbon footprint data quality evaluation methods both at home and abroad. Commonly used empirical methods include interval analysis-based methods, probability analysis-based methods, and fuzzy set-based methods. The Chinese patent "A method and system for analyzing uncertainty in the carbon footprint of a transformer throughout its life cycle" (CN202311790161) uses a combination of interval analysis and probability analysis to obtain the uncertainty of the impact of various uncertain factors on the carbon emissions of the transformer throughout its life cycle. The Chinese patent "A method and management system for quantitative evaluation of carbon footprint" (CN 202410298497) uses the data quality index (DQI) method to evaluate the data quality of product carbon footprints. However, these methods are usually subject to strong subjective factors for data quality evaluation and cannot obtain satisfactory results. Summary of the invention

[0004] In view of the above problems, the present invention proposes a product carbon footprint data quality evaluation method. To solve the above technical problems, the technical solution adopted by the present invention is as follows:

[0005] A product carbon footprint data quality evaluation method comprises the following steps:

[0006] S1. Construct a product carbon footprint data quality evaluation system including activity data and emission factors based on the production process of the product. The activity data evaluation indicators include source reliability and statistical accuracy, and the emission factor evaluation indicators include time representativeness, geographical representativeness and technical representativeness;

[0007] S2, using the 1-9 scaling method to compare the evaluation factors in the evaluation index obtained in step S1 in pairs, respectively constructing a discriminant matrix for each evaluation index, and obtaining the weight of each evaluation factor;

[0008] S3, checking all the discriminant matrices obtained in step S2 based on the random consistency ratio, if satisfied, executing step S4, otherwise, adjusting the discriminant matrix and returning to step S2;

[0009] S4, determine the uncertainty of activity data evaluation indicators and emission factor evaluation indicators according to the Ecoinvent database, determine the carbon emission source of the product at each production stage, and confirm the uncertainty of each carbon emission source according to the obtained uncertainty and the weight obtained in step S2;

[0010] S5, calculating the overall uncertainty of the carbon footprint of the product life cycle based on the uncertainty obtained in step S4 using an error propagation method.

[0011] In step S2, the calculation formula of the weight of the corresponding evaluation factor in the activity data evaluation index is:

[0012]

[0013] Where W ADi represents the weight of the i-th evaluation factor in the activity data evaluation index, AD kj Denotes the kth evaluation factor AD k and evaluation factors AD j Compared with the importance scale obtained, AD ij Denotes the i-th evaluation factor AD i and the jth evaluation factor AD j Compared with the obtained importance scale, n represents the number of evaluation factors in the activity data evaluation index;

[0014] The calculation formula for the weight of the corresponding evaluation factor in the emission factor evaluation index is:

[0015]

[0016] Where W EFi′ Indicates the weight of the i′th element in the emission factor evaluation index, EF k′j′ Indicates the k′th evaluation factor EF k′ and the j′th evaluation factor EF j′ Compared with the importance scale obtained, EF i′j′ Indicates the i′th evaluation factor EF i′ and the j′th evaluation factor EF j′ Compared with the obtained importance scale, n′ represents the number of evaluation factors in the emission factor evaluation index.

[0017] In step S3, the formula to be tested is:

[0018]

[0019] In the formula, CR represents the random consistency ratio, CI represents the consistency index, and RI represents the average random consistency index;

[0020] The calculation formula of the consistency index CI is:

[0021]

[0022] In the formula, λ max represents the maximum eigenvalue of the discriminant matrix of the activity data evaluation index and the discriminant matrix of the emission factor evaluation index, Indicates the number of evaluation factors in the evaluation index.

[0023] In step S4, the calculation formula for the uncertainty of carbon emission sources is:

[0024]

[0025] Where U l represents the uncertainty of the lth carbon emission source, represents the uncertainty of the activity data of the lth carbon emission source, Represents the uncertainty of the emission factor of the lth carbon emission source.

[0026] In step S5, the calculation formula for the overall uncertainty of the carbon footprint of the product life cycle is:

[0027]

[0028] In the formula, CFP l represents the emission result of the lth carbon emission source, L represents the number of carbon emission sources in the product production process, and U l Represents the uncertainty of the lth carbon emission source.

[0029] Beneficial effects of the present invention:

[0030] 1. Combining the Analytic Hierarchy Process (AHP) with the Digital Quality Index (DQI) to evaluate the product carbon footprint data quality is a simple and convenient method that greatly reduces the impact of subjective factors on data quality evaluation.

[0031] 2. It can be widely applied to the carbon footprint life cycle assessment of various products, making the assessment results more reasonable and scientific. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0033] Figure 1 It is a schematic diagram of the process of the present invention;

[0034] Figure 2 This is a schematic diagram of the product carbon footprint data quality evaluation indicator architecture. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] Product Carbon Footprint: The sum of all greenhouse gas emissions and greenhouse gas removals of a product over its entire life cycle expressed in units of carbon dioxide equivalent.

[0037] Data quality evaluation: The collected product carbon footprint data is evaluated for quality through measurement comparison and other quality control methods, including data reliability, accuracy, completeness, consistency, etc.

[0038] A product carbon footprint data quality evaluation method, such as Figure 1 As shown, the following steps are included:

[0039] S1. Construct a product carbon footprint data quality evaluation system including activity data and emission factors based on the production process of the product. The activity data evaluation indicators include source reliability and statistical accuracy, and the emission factor evaluation indicators include time representativeness, geographical representativeness and technical representativeness;

[0040] Combined with the data collection and data quality requirements in GB / T 24067-2024 "Quantification Requirements and Guidelines for Carbon Footprint of Greenhouse Gas Products", the product carbon footprint data quality evaluation system is determined according to the Life Cycle Assessment (LCA) method, such as Figure 2As shown in the figure, there are two types of evaluation indicators: activity data and emission factors. The activity data evaluation indicators include two evaluation factors: source reliability and statistical accuracy, and the emission factor evaluation indicators include three evaluation factors: time representativeness, geographical representativeness, and technical representativeness. Therefore, a hierarchical evaluation indicator system is established.

[0041] The following is a brief introduction to the four evaluation indicators. For specific content, please refer to GB / T 24067-2024, which will not be described in detail in this embodiment. Source reliability: used to determine whether the source of activity data is primary data (data directly collected in the process owned, operated or controlled by the organization) or secondary data (data that cannot be directly collected, such as government statistical data, literature report data, expert estimation data, etc.). Statistical accuracy: used to determine whether the sample size of activity data is large enough, whether the measurement frequency is high enough, and whether the statistical time is appropriate. Time representativeness: used to determine the year of the emission factor or the time span of the collected data. Geographical representativeness: used to determine the geographical location of the data collected for the emission factor. Technical representativeness: used to determine the actual production technology reflected by the emission factor, that is, whether it reflects the actual process flow, technology and equipment type, raw material and energy consumption type, production scale and other factors.

[0042] S2, using the 1-9 scaling method to compare the evaluation factors in the evaluation index obtained in step S1 in pairs, respectively constructing a discriminant matrix for each evaluation index, and obtaining the weight of each evaluation factor;

[0043] The 1-9 scale is shown in Table 1:

[0044] Table 1 Meaning of importance scale

[0045]

[0046] Specifically, the 1-9 scaling method is used to compare the factors in each evaluation index in pairs, determine the relative importance scale between them, and construct a discriminant matrix based on the importance scale. This is a prior art and will not be described in detail in this embodiment. The discriminant matrix of the activity data evaluation index adopts U AD Indicates that the discriminant matrix of emission factor evaluation index adopts U EF express:

[0047]

[0048] In the formula, AD ij Denotes the i-th evaluation factor AD i and the jth evaluation factor AD j Compared with the importance scale obtained, AD ij >0, AD ji Indicates the evaluation factor AD jand evaluation factors AD i The importance scale obtained by comparison is i, j=1, 2, ..., n. In this embodiment, n=2.

[0049]

[0050] Where, EF i′j′ Indicates the i′th evaluation factor EF i′ and the j′th evaluation factor EF j′ Compared with the importance scale obtained, EF i′j′ >0, EF j′i′ Indicates evaluation factor EF j′ and evaluation factor EF i′ The obtained importance scale is i′, j′=1, 2, ..., n′. In this embodiment, n′=3.

[0051] The calculation formula for the weight of the corresponding evaluation factor in the activity data evaluation index is:

[0052]

[0053] Where W ADi represents the weight of the i-th evaluation factor in the activity data evaluation index, AD kj Denotes the kth evaluation factor AD k and evaluation factors AD j The importance scale obtained by comparison.

[0054] The calculation formula for the weight of the corresponding evaluation factor in the emission factor evaluation index is:

[0055]

[0056] Where W EFi′ Indicates the weight of the i′th element in the emission factor evaluation index, EF k′j′ Indicates the k′th evaluation factor EF k′ and the j′th evaluation factor EF j′ The importance scale obtained by comparison.

[0057] S3, checking all the discriminant matrices obtained in step S2 based on the random consistency ratio, if satisfied, executing step S4, otherwise, adjusting the discriminant matrix and returning to step S2;

[0058] Since the discriminant matrix obtained by pairwise comparison does not necessarily satisfy consistency, in order to ensure the accuracy and credibility of the discriminant matrix and avoid the influence of personal subjective factors on the judgment matrix, it is necessary to construct the discriminant matrix U AD and U EFPerform consistency test. The test formula is:

[0059]

[0060] In the formula, CR represents the random consistency ratio, CI represents the consistency index, and RI represents the average random consistency index. When CR < 0.1, the inconsistency is within the acceptable range, that is, the score is considered to meet the requirements, otherwise, the elements in the discriminant matrix need to be readjusted. The values ​​of the average random consistency index RI are shown in Table 2:

[0061] Table 2 R.I. Average random consistency index

[0062]

[0063] The calculation formula of consistency index CI is:

[0064]

[0065] In the formula, λ max Denotes the discriminant matrix U AD And the discriminant matrix U EF The largest characteristic root of Represents the number of evaluation factors in the evaluation index. When the discriminant matrix U AD When conducting the inspection, When the discriminant matrix U EF When conducting the inspection,

[0066] S4, determine the uncertainty of activity data evaluation indicators and emission factor evaluation indicators according to the Ecoinvent database, determine the carbon emission source of the product at each production stage, and confirm the uncertainty of each carbon emission source according to the obtained uncertainty and the weight obtained in step S2;

[0067] Referring to the uncertainty comparison table of product carbon footprint data quality indicators judged by experts in the Ecoinvent database (as shown in Table 3 below), the uncertainty of activity data evaluation indicators and emission factor evaluation indicators is established.

[0068] Table 3 Comparison table of uncertainty of product carbon footprint data indicators

[0069]

[0070] Uncertainty of activity data evaluation indicators Indicates that the corresponding calculation formula is:

[0071]

[0072] In the formula, u Lrepresents the uncertainty of the reliability of the activity data source, u T represents the uncertainty in the statistical accuracy of activity data, W AD1 Represents the weight of source reliability, W AD2 A weight that indicates statistical accuracy.

[0073] Uncertainty of emission factor evaluation indicators Indicates that the corresponding calculation formula is:

[0074]

[0075] In the formula, represents the uncertainty in the temporal representativeness of the emission factor, represents the uncertainty in the geographical representativeness of the emission factor, represents the uncertainty of the technical representativeness of the emission factor, W EF1 represents the weight of temporal representativeness, W EF2 represents the weight of geographical representation, W EF3 The weight indicating the representativeness of the technology.

[0076] For a certain carbon emission source, uncertainty analysis is performed based on error propagation to confirm the uncertainty of the carbon emission source. The corresponding calculation formula is:

[0077]

[0078] Where U l represents the uncertainty of the lth carbon emission source, represents the uncertainty of the activity data of the lth carbon emission source, Represents the uncertainty of the emission factor of the lth carbon emission source.

[0079] S5, calculating the overall uncertainty of the carbon footprint of the product life cycle based on the uncertainty obtained in step S4 using an error propagation method;

[0080] According to the life cycle assessment (LCA), a product carbon footprint inventory is established, including activity data and corresponding emission factors, to calculate the carbon footprint of each carbon emission source in the product life cycle. The result of the product carbon footprint is obtained by superimposing the carbon emissions of each emission source. The corresponding calculation formula is:

[0081]

[0082] In the formula, CFP l It represents the emission result of the lth carbon emission source, and L represents the number of carbon emission sources in the product production process.

[0083] The purpose of product carbon footprint data quality evaluation is to judge the data quality and credibility of the results in product carbon footprint evaluation. The following is the data quality evaluation of trimethyl phosphite product carbon footprint, specifically:

[0084] Step 1: Establish a hierarchical structure for product carbon footprint data quality evaluation;

[0085] Step 2: Construct a discriminant matrix and calculate the weights of each indicator;

[0086] Using the 1-9 scaling method, a pairwise comparison discriminant matrix was constructed according to the relative importance.

[0087] Table 4 Activity data discrimination matrix

[0088] Activity Data Quality UAD Source reliability Statistical accuracy Source reliability 1 5 Statistical accuracy 1 / 5 1

[0089] Correspondingly, the discriminant matrix for activity data quality evaluation is:

[0090]

[0091] Table 5 Emission factor discrimination matrix

[0092] Emission factor quality UEF Time representativeness Geographical representation Technical representative Time representativeness 1 3 1 / 5 Geographical representation 1 / 3 1 1 / 7 Technical representative 5 7 1

[0093] Correspondingly, the discriminant matrix for emission factor data quality evaluation is:

[0094]

[0095] According to formula (3) and formula (4), the weight of each evaluation factor in each evaluation index is calculated as follows:

[0096]

[0097] Similarly, W EF1 =0.193,W EF2 =0.083,W EF3 =0.724.

[0098] Since activity data and emission factors are equally important for carbon emission accounting, and their weights are both 0.5, the weight results of product carbon footprint data quality evaluation indicators determined by the hierarchical analysis method are shown in Table 6.

[0099] Table 6 Product carbon footprint data quality indicator weights

[0100] Evaluation indicators Weight Source reliability 0.417 Statistical accuracy 0.083 Time representativeness 0.097 Geographical representation 0.042 Technical representative 0.362

[0101] Step 3: Consistency check

[0102] For the constructed discriminant matrix U AD and U EFPerform consistency check and calculate the second-order matrix U AD Consistent, and CR EF =0.068<0.1, U EF There is also consistency.

[0103] Step 4: Establish product carbon footprint data quality indicator uncertainty

[0104] Taking the carbon footprint of trimethyl phosphite as an example, its product carbon footprint list from "cradle to gate" and indicator uncertainty (determined according to Table 3) are shown in Table 7.

[0105] Table 7 Uncertainty of product carbon footprint indicators

[0106]

[0107]

[0108] Step 5: Calculate the carbon footprint of the product throughout its life cycle and evaluate the quality of the product carbon footprint data

[0109] The uncertainty of each emission source is calculated according to formula (7), formula (8) and formula (9), and the uncertainty of the product carbon footprint is calculated according to formula (10).

[0110]

[0111] The present application also provides a product carbon footprint data quality evaluation system, including:

[0112] Evaluation system construction module: used to construct a product carbon footprint data quality evaluation system including activity data and emission factors based on the product production process. The activity data evaluation indicators include source reliability and statistical accuracy, and the emission factor evaluation indicators include time representativeness, geographical representativeness and technical representativeness;

[0113] Weight calculation module: used to compare the evaluation factors in the evaluation indicators obtained by the evaluation system construction module in pairs using the 1-9 scaling method, respectively construct the discriminant matrix of each evaluation indicator, and obtain the weight of each evaluation factor;

[0114] Data verification module: used to verify all discriminant matrices obtained by the weight calculation module based on the random consistency ratio. If satisfied, the uncertainty calculation module is started; otherwise, the discriminant matrix of the weight calculation module is adjusted;

[0115] Uncertainty calculation module: used to determine the uncertainty of activity data evaluation indicators and emission factor evaluation indicators based on the Ecoinvent database, determine the carbon emission sources of the product at each production stage, confirm the uncertainty of each carbon emission source based on the obtained uncertainty and the weight obtained by the weight calculation module, and calculate the overall uncertainty of the carbon footprint of the product life cycle based on the error transfer method.

[0116] The embodiment of the present application also provides a product carbon footprint data quality evaluation model, which is established by the product carbon footprint data quality evaluation method described above.

[0117] An embodiment of the present application further provides an electronic device, which includes a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, the steps of the above-mentioned product carbon footprint data quality evaluation method are executed.

[0118] The embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above-mentioned product carbon footprint data quality evaluation method are executed. Specifically, the storage medium can be a general storage medium, such as a mobile disk, a hard disk, etc. When the computer program on the storage medium is executed, the embodiment of the above-mentioned product carbon footprint data quality evaluation method can be executed.

[0119] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A product carbon footprint data quality evaluation method, characterized in that: The steps include: S1. Construct a product carbon footprint data quality evaluation system including activity data and emission factors based on the production process of the product. The activity data evaluation indicators include source reliability and statistical accuracy, and the emission factor evaluation indicators include time representativeness, geographical representativeness and technical representativeness; S2, using the 1-9 scaling method to compare the evaluation factors in the evaluation index obtained in step S1 in pairs, respectively constructing a discriminant matrix for each evaluation index, and obtaining the weight of each evaluation factor; S3, checking all the discriminant matrices obtained in step S2 based on the random consistency ratio, if satisfied, executing step S4, otherwise, adjusting the discriminant matrix and returning to step S2; S4, determine the uncertainty of activity data evaluation indicators and emission factor evaluation indicators according to the Ecoinvent database, determine the carbon emission source of the product at each production stage, and confirm the uncertainty of each carbon emission source according to the obtained uncertainty and the weight obtained in step S2; S5, calculating the overall uncertainty of the carbon footprint of the product life cycle based on the uncertainty obtained in step S4 using an error propagation method.

2. The product carbon footprint data quality evaluation method according to claim 1, characterized in that: In step S2, the calculation formula of the weight of the corresponding evaluation factor in the activity data evaluation index is: Where W ADi represents the weight of the i-th evaluation factor in the activity data evaluation index, AD kj Denotes the kth evaluation factor AD k and evaluation factors AD j Compared with the importance scale obtained, AD ij Denotes the i-th evaluation factor AD i and the jth evaluation factor AD j Compared with the obtained importance scale, n represents the number of evaluation factors in the activity data evaluation index; The calculation formula for the weight of the corresponding evaluation factor in the emission factor evaluation index is: Where W EFi′ Indicates the weight of the i′th element in the emission factor evaluation index, EF k′j′ Indicates the k′th evaluation factor EF k′ and the j′th evaluation factor EF j′ Compared with the importance scale obtained, EF i′j′ Indicates the i′th evaluation factor EF i′ and the j′th evaluation factor EF j′ Compared with the obtained importance scale, n′ represents the number of evaluation factors in the emission factor evaluation index.

3. The product carbon footprint data quality evaluation method according to claim 1, characterized in that: In step S3, the formula to be tested is: In the formula, CR represents the random consistency ratio, CI represents the consistency index, and RI represents the average random consistency index; The calculation formula of the consistency index CI is: In the formula, λ max represents the maximum eigenvalue of the discriminant matrix of the activity data evaluation index and the discriminant matrix of the emission factor evaluation index, Indicates the number of evaluation factors in the evaluation index.

4. The product carbon footprint data quality evaluation method according to claim 1, characterized in that: In step S4, the calculation formula for the uncertainty of carbon emission sources is: Where U l represents the uncertainty of the lth carbon emission source, represents the uncertainty of the activity data of the lth carbon emission source, Represents the uncertainty of the emission factor of the lth carbon emission source.

5. The product carbon footprint data quality evaluation method according to claim 1, characterized in that: In step S5, the calculation formula for the overall uncertainty of the carbon footprint of the product life cycle is: In the formula, CFP l represents the emission result of the lth carbon emission source, L represents the number of carbon emission sources in the product production process, and U l Represents the uncertainty of the lth carbon emission source.

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