Product carbon footprint basic database construction method

Abstract

The invention relates to a product carbon footprint basic database construction method. According to the method, government supervision data, enterprise live-action data and industry standard data are integrated, a carbon footprint model of a production stage and a full life cycle is constructed in combination with a life cycle evaluation method, data cleaning is performed on original data, carbon footprint factors are graded and dynamically updated based on an energy efficiency level, and a carbon footprint model is obtained. Accurate calculation of industrial chain carbon footprint factors, effective integration of multi-source heterogeneous data and dynamic iteration of a database are achieved, the problems that an existing database is insufficient in regional representativeness, poor in timeliness and low in technical transparency are solved, and finally a localized carbon footprint basic database which is comprehensive in coverage, traceable and compatible with international standards is established. And reliable data support is provided for carbon accounting and international trade barrier coping.

Description

Technical Field

[0001] The present invention belongs to the technical field of environmental management, and particularly relates to a method for constructing a basic database of product carbon footprint. Background Art

[0002] Currently, carbon footprint databases at home and abroad generally have problems such as insufficient regional representativeness of data, poor timeliness, low technical transparency, weak traceability, and limited coverage. In addition, existing databases lack an energy efficiency grading mechanism and cannot reflect the true energy efficiency differences of enterprises, resulting in a large deviation between the accounting results and actual emissions. With the implementation of international carbon barriers such as the EU's Carbon Border Adjustment Mechanism (CBAM), there is an urgent need to establish a localized, iterative, and high-precision carbon footprint database to support product carbon labeling certification and enhance international competitiveness. Summary of the Invention

[0003] Based on this, it is necessary to provide a method for constructing a basic database of product carbon footprint for the above technical problems.

[0004] In a first aspect, the present application provides a method for constructing a basic database of product carbon footprint, including:

[0005] S1: Based on the analysis of the upstream and downstream of the industrial chain, determine the research scope and system boundary, and generate a target definition data set;

[0006] S2: According to the target definition data set, collect government supervision data, enterprise actual situation data, and industry standard data, and generate an original data table;

[0007] S3: Clean and grade the energy efficiency level of the original data table to generate a standardized data set;

[0008] S4: Based on the standardized data set and system boundary, use the life cycle assessment method to construct a production stage model and a full life cycle model, and generate a carbon footprint factor data table according to the production stage model and the full life cycle model;

[0009] S5: Construct a database based on the carbon footprint factor data table;

[0010] S6: Verify the database through comparison with international databases and on-site monitoring to obtain verification results; construct dynamic iteration rules based on the verification results, and update the database according to the dynamic iteration rules.

[0011] In a second aspect, the present application also provides a system for constructing a basic database of product carbon footprint, including:

[0012] A target definition module, configured to determine the research scope and system boundary based on the analysis of the upstream and downstream of the industrial chain, and generate a target definition data set;

[0013] A data collection module, which is used to collect government supervision data, enterprise actual situation data and industry standard data according to a target-defined data set, and generate an original data table;

[0014] A data processing module, which is used to clean the original data table and classify the energy efficiency level, and generate a standardized data set;

[0015] A carbon footprint factor generation module, which is used to construct a production stage model and a full life cycle model by using the life cycle assessment method based on the standardized data set and the system boundary, and generate a carbon footprint factor data table according to the production stage model and the full life cycle model;

[0016] A database construction module, which is used to construct a database based on the carbon footprint factor data table;

[0017] A database dynamic iteration module, which is used to verify the database through international database comparison and on-site monitoring to obtain a verification result; construct a dynamic iteration rule based on the verification result, and update the database according to the dynamic iteration rule.

[0018] Thirdly, the present application also provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, it implements a method for constructing a product carbon footprint basic database as in the first aspect.

[0019] Fourthly, 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, it implements a method for constructing a product carbon footprint basic database as in the first aspect.

[0020] The above-mentioned method for constructing a product carbon footprint basic database integrates government supervision data, enterprise actual situation data and industry standard data, constructs carbon footprint models for the production stage and the full life cycle by combining the life cycle assessment method, cleans the original data, classifies the carbon footprint factors based on the energy efficiency level and updates them dynamically, realizes the accurate calculation of the carbon footprint factors in the industrial chain, the effective integration of multi-source heterogeneous data and the dynamic iteration of the database, solves the problems of insufficient regional representativeness, poor timeliness and low technical transparency of the existing database, and finally establishes a local carbon footprint basic database that covers comprehensively, is traceable and compatible with international standards, providing reliable data support for carbon accounting and coping with international trade barriers. Description of the Drawings

[0021] To more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the accompanying drawings required for the description of the embodiments or related technologies. Obviously, the accompanying drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0022] Figure 1 It is a schematic flow chart of a method for constructing a basic database of product carbon footprint provided by the present invention;

[0023] Figure 2 It is a schematic structural diagram of a system for constructing a basic database of product carbon footprint provided by the present invention. Detailed implementation manners

[0024] In order to make the objectives, technical solutions and advantages of the present application more clear and understandable, the present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0025] Refer to Figure 1 , which shows a schematic flow chart of a method for constructing a basic database of product carbon footprint provided by the present application. The method includes the following steps:

[0026] S1: Based on the analysis of the upstream and downstream of the industrial chain, determine the research scope and system boundary, and generate a target definition dataset.

[0027] Specifically, deeply study the textile industrial chain, covering key links from petrochemical products to textile printing and dyeing, such as the production of polyester fiber raw materials from PTA to PET, from PET to POY production, etc., and clarify the main participants and mutual relationships in each link. According to the Life Cycle Assessment (LCA) method, select the "cradle to gate" or "cradle to grave" method to define the research scope. The former is applicable to intermediate products, and the latter is applicable to end products to ensure the comprehensiveness and accuracy of the research. Clearly define the starting point and ending point of the research, for example, starting from the acquisition of raw and auxiliary materials, to the production, transportation, use of the product, until the final disposal or recycling, to provide a clear framework for subsequent data collection and model construction.

[0028] Organize the above analysis results into a dataset, that is, a target definition dataset. This dataset includes key information such as the research object, scope, and system boundary.

[0029] S2: According to the target definition dataset, collect government supervision data, enterprise actual situation data and industry standard data, and generate an original data table.

[0030] Specifically, collect the implementation reports of pollution discharge permits and carbon verification data of textile enterprises, etc., and obtain information such as enterprise names, geographical locations, industry categories, product types and outputs, raw and auxiliary material consumption, energy consumption, greenhouse gas emissions, etc.

[0031] Through on-site research, collection of enterprise energy management system operation and certification data, etc., master the actual production situation data such as production process routes, production equipment, material losses, solid waste generation, etc.

[0032] Collect the current standards of products, literature published by domestic and foreign scholars, carbon footprint assessment reports published by enterprises, etc., for data comparison and verification.

[0033] Organize the various types of data collected above into a table in a unified format and structure, that is, the original data table.

[0034] S3: Clean the original data table and classify the energy efficiency levels to generate a standardized data set.

[0035] Specifically, screen, proofread and normalize the original data, remove outliers, incorrect data and duplicate data to ensure the accuracy and reliability of the data.

[0036] According to the principles of the same production products and similar process flows, etc., classify and screen the sample data, calculate the weighted average value, and then classify the data according to different energy efficiency levels, such as Class I (advanced), Class II (average), Class III (poor), etc., to reflect the energy efficiency status of different enterprises.

[0037] Integrate the cleaned data and the energy efficiency classification results into a standardized data set, that is, the standardized data set.

[0038] S4: Based on the standardized data set and the system boundary, use the life cycle assessment method to construct a production stage model and a full life cycle model, and generate a carbon footprint factor data table according to the production stage model and the full life cycle model.

[0039] Specifically, use the LCA (Life Cycle Assessment) method to quantitatively evaluate the entire life cycle of the product, covering all stages from the acquisition of raw and auxiliary materials to product production, transportation, use until waste disposal or recycling and reuse, and comprehensively evaluate its environmental impact.

[0040] According to the standardized data set and the system boundary, establish a carbon footprint calculation model for the production stage, and calculate the carbon emissions of the product during the production process, including the carbon footprint in aspects such as raw and auxiliary material consumption, energy resource use, and waste emissions.

[0041] Based on the production stage model, it is further extended to the entire life cycle of the product, considering the carbon emissions in stages such as the acquisition, transportation, use, and disposal of raw and auxiliary materials, and a complete life cycle carbon footprint calculation model is constructed.

[0042] The carbon footprint factor data of the product is calculated through the above model and organized into a table to obtain the carbon footprint factor data table.

[0043] S5: Construct a database based on the carbon footprint factor data table.

[0044] Specifically, according to the structure and content of the carbon footprint factor data table, design the architecture of the database, including the creation of data tables, the definition of fields, the setting of indexes, etc., to ensure the efficient storage and rapid query of the database.

[0045] Import the data in the carbon footprint factor data table into the database according to the designed database architecture, complete the storage and organization of the data, and form a complete product carbon footprint basic database.

[0046] S6: Verify the database through international database comparison and on-site monitoring to obtain verification results; construct dynamic iteration rules based on the verification results and update the database according to the dynamic iteration rules.

[0047] Specifically, compare the constructed database with well-known international carbon footprint databases (such as Ecoi nvent, Gabi, etc.), analyze the differences and similarities of the data, and evaluate the rationality and scientificity of the database.

[0048] Carry out actual measurement work by distributing research forms to typical enterprises and participating in on-site research, etc., obtain real data and compare and verify it with the data in the database to further confirm the accuracy of the data.

[0049] Integrate the results of international database comparison and on-site monitoring to form a detailed verification report, pointing out the problems and deficiencies in the database.

[0050] According to the verification results, formulate dynamic iteration rules, clarify the frequency, method, and standard of data update, such as iteratively updating the data source every 5 years, and manufacturers of similar products can link their own calculation results to the database during self-evaluation, etc.

[0051] According to the dynamic iteration rules, regularly update and supplement the data in the database to ensure the timeliness and integrity of the database, so that it can better reflect the actual situation and meet the changing needs.

[0052] The above method for constructing a product carbon footprint basic database integrates government supervision data, enterprise actual situation data, and industry standard data, constructs carbon footprint models for the production stage and the entire life cycle by combining the life cycle assessment method, and cleans, classifies, and dynamically updates the data based on the energy efficiency grading standard, realizing the accurate calculation of carbon footprint factors in the industrial chain, the effective integration of multi-source heterogeneous data, and the dynamic iteration of the database, solving the problems of insufficient regional representativeness, poor timeliness, and low technical transparency of existing databases, and finally establishing a local carbon footprint basic database that covers comprehensively, is traceable, and is compatible with international standards, providing reliable data support for carbon accounting and coping with international trade barriers.

[0053] In an alternative embodiment, S2 includes the following steps:

[0054] S21: Based on the pollutant discharge permit execution report, collect the product output, raw and auxiliary material consumption, energy and resource consumption, and wastewater discharge of the enterprise, and generate a production activity data table.

[0055] Specifically, relevant data can be obtained from the pollutant discharge permit execution report submitted by the enterprise. This report can contain various environmental data generated by the enterprise during the production process and is a record of the enterprise's own pollution discharge situation in accordance with regulatory requirements.

[0056] The data content can specifically include:

[0057] 1) Product output: Record the output of various products of the enterprise during a specific period to understand the production scale.

[0058] 2) Raw and auxiliary material consumption: Collect the consumption of various raw materials and auxiliary materials used in the production process, such as textile fibers, chemical auxiliaries, etc., to clarify the resource input.

[0059] 3) Energy and resource consumption: Statistically collect various energy and resources consumed by the enterprise during the production process, including electricity, heat, coal, natural gas, water, etc., to comprehensively master the energy and resource usage situation.

[0060] 4) Wastewater discharge: Record the amount of wastewater generated by the enterprise during the production process to understand the potential impact of wastewater discharge on the environment.

[0061] Organize the collected data into a table, with each row representing a product or production batch, and each column corresponding to indicators such as product output, raw and auxiliary material consumption, energy and resource consumption, and wastewater discharge.

[0062] S22: Based on the carbon verification report, collect the fossil fuel combustion emissions, industrial production process emissions, and electricity and heat consumption data, and generate an emission data table.

[0063] Specifically, it can be based on a professional carbon verification report, which comprehensively and systematically reviews and accounts for the greenhouse gas emissions of an enterprise according to specific verification standards and procedures.

[0064] The data content can specifically include:

[0065] 1) Fossil fuel combustion emissions: Statistically calculate the emissions of greenhouse gases such as carbon dioxide generated by an enterprise during the production process due to the combustion of fossil fuels such as coal, oil, and natural gas. This is an important part of carbon footprint calculation.

[0066] 2) Industrial production process emissions: Collect the emissions of greenhouse gases generated by non-combustion activities such as chemical reactions and material handling during the production process, such as carbonate decomposition in cement production and reaction emissions in chemical synthesis. This is crucial for accurately assessing the carbon footprint.

[0067] 3) Electricity and heat consumption data: Record the amount of electricity and heat obtained by the enterprise from the external power grid and heat supply system. Since carbon emissions also occur during the production and transmission of energy, it can be accounted for according to the corresponding emission factors.

[0068] Organize the above-mentioned collected emission-related data into a table, with each row corresponding to a specific production process or time period, and each column respectively recording fossil fuel combustion emissions, industrial production process emissions, and electricity and heat consumption data, etc.

[0069] S23: Through on-site research, collect the power, operation time, and material loss data of the enterprise's production line equipment to generate an on-site research data table.

[0070] Specifically, the method of on-site research can be adopted, directly going deep into the enterprise's production site, communicating with relevant enterprise personnel, and observing the actual operation of the production line.

[0071] The data content can specifically include:

[0072] 1) Production line equipment power: Record the rated power and actual operating power of each main equipment on the production line to understand the energy consumption characteristics of the equipment, which is of great significance for accurately accounting for energy consumption and carbon emissions during the production process.

[0073] 2) Operation time: Statistically calculate the actual operation time of the equipment within a specific period. Combining with the equipment power, the total energy consumption of the equipment can be calculated, and it is also helpful for analyzing production efficiency and energy utilization efficiency.

[0074] 3) Material loss data: Collect the loss amount and loss rate of raw and auxiliary materials during the processing and transfer links in the production process, such as the breakage and flying of textile fibers during spinning and weaving. This has reference value for comprehensively evaluating resource utilization efficiency and cost control.

[0075] S24: Integrate and process the production activity data table, emission data table, and on-site investigation data table to obtain the original data table.

[0076] Specifically, integrate the data tables from different channels that reflect production information in different aspects to form a comprehensive and systematic data set, providing a unified basis for subsequent data cleaning, analysis, and model construction.

[0077] The integration method can specifically include:

[0078] 1) Data matching and association: Match and associate the relevant records in the production activity data table, emission data table, and on-site investigation data table through common fields such as enterprise name, product type, and production batch to ensure that the data of the same production process or product in different data tables can correspond.

[0079] 2) Data format unification: Standardize the data formats in different data tables, such as unifying the date format, unit representation method, etc., to avoid data processing errors caused by format differences.

[0080] 3) Data merging and supplementation: Horizontally or vertically merge the associated data tables, integrate each data index into the same row or the same column to form a complete data record. For missing data items, supplement or estimate according to the actual situation to ensure data integrity.

[0081] 5) Data consistency check: Check the consistency of the integrated data to ensure that the data of the same index in different data tables is consistent in value. For example, the energy consumption in the production activity data table should match the electricity and heat consumption data in the emission data table. If there are differences, further verification and correction are required.

[0082] After the above integration and processing steps, an original data table containing data on production activities, emissions, and equipment operation is finally obtained. This data table provides comprehensive and accurate basic data support for subsequent data processing and analysis.

[0083] In an alternative embodiment, S3 includes the following steps:

[0084] S31: Clean the original data table to obtain a preliminary cleaned data set; wherein, the cleaning process includes:

[0085] Conduct a material balance test on the original data table. When the input-output ratio is less than or equal to the preset threshold, determine that the data is incomplete and delete the corresponding records.

[0086] Eliminate the data records in the original data table whose difference between the dispersion degree and the industry average is not within the preset range.

[0087] Specifically, organize and summarize the material input and output data in the original data table. Calculate the input-output ratio for each record, with the formula: Input-output ratio = (Output material quantity + Lost material quantity) / Input material quantity. Set a preset threshold, which can be slightly less than 1, for example, 0.95. When the input-output ratio is less than or equal to this threshold, the data is considered incomplete and may have problems, such as inaccurate estimation of material loss or incorrect data recording. Delete the records with unqualified input-output ratios to ensure the integrity and reliability of the remaining data.

[0088] Collect relevant data in the industry, calculate the industry average and standard deviation, and determine the normal distribution range of the data. For each record in the original data table, calculate the difference between it and the industry average, and compare this difference with the preset range (which can be the industry average ± 2 times the standard deviation). Eliminate the data records with a dispersion exceeding the preset range to reduce the impact of abnormal data on subsequent analysis.

[0089] After the above cleaning steps, remove the incomplete and abnormal data records, retain the relatively complete and accurate data, and form a preliminary cleaned data set, providing a more reliable basis for subsequent data grading processing.

[0090] S32: Based on the energy efficiency grading standard, perform energy consumption grading processing on the preliminary cleaned data set to generate a standardized data set containing energy efficiency labels.

[0091] Specifically, according to the current energy efficiency grading standards, such as "Limit and Calculation Method for Unit Comprehensive Energy Consumption of Polyester (Long and Short) Fibers DB33 / 683-2019", etc., determine the energy consumption range per unit product corresponding to different energy efficiency levels.

[0092] The energy consumption grading processing per unit product can specifically include:

[0093] 1) Data preparation: Extract the energy consumption data per unit product from the preliminary cleaned data set, including various energy consumption amounts (such as electricity, heat, coal, natural gas, etc.).

[0094] 2) Energy consumption calculation: According to the corresponding calculation methods and formulas, convert the energy consumption amounts of different types of energy into a unified energy consumption index, such as standard coal consumption or carbon dioxide emissions, etc.

[0095] 3) Grading judgment: Compare the calculated energy consumption value per unit product with the energy efficiency grading standard to determine the energy efficiency level it belongs to, such as Grade I (advanced), Grade II (average), Grade III (poor), etc.

[0096] 4) Energy efficiency label addition: Add an energy efficiency label to each record in the data set to clearly identify its corresponding energy efficiency level, facilitating subsequent classification analysis and management.

[0097] After energy efficiency grading, a standardized data set containing energy efficiency labels is obtained. This data set not only meets the standardized requirements in terms of data format and content, but also further refines the data through energy efficiency grading, providing more accurate and discriminatory data support for subsequent carbon footprint factor calculation and database construction. It helps to analyze the carbon footprint situation at different energy efficiency levels more deeply and provides more targeted data basis for enterprises' energy conservation, emission reduction and low-carbon development.

[0098] In an optional embodiment, S4 includes the following steps:

[0099] S41: Define the production stage model boundary according to the system boundary. Based on the production stage model boundary, use the life cycle assessment method to construct the production stage model; based on the standardized data table and the production stage model, model the input and output data of a single production link to generate a production stage footprint factor data table; the production stage carbon footprint factor is calculated by the following formula:

[0100]

[0101] where, E 生产 is the production stage carbon footprint factor, AD 生产,i is the production stage activity level data linked to the declared unit, EF 生产,i is the production stage emission factor; i represents the i-th activity within the production stage; n represents the total number of activities within the production stage.

[0102] Specifically, clarify the start and end links of the production stage according to the system boundary, such as from the raw and auxiliary materials entering the production plant to the product leaving the factory.

[0103] Adopt the life cycle assessment method to establish a carbon footprint calculation model for the production stage, which can cover all relevant material inputs, energy consumption and emission outputs.

[0104] Based on the standardized data table and the production stage model, model the input and output data of a single production link. Specifically, it can include:

[0105] 1) Activity level data extraction: Extract the activity level data AD 生产,i linked to the declared unit for each activity in the production stage from the standardized data table, such as the usage amount of raw and auxiliary materials, energy resource consumption, etc.

[0106] 2) Emission factor acquisition: Determine the emission factor EF 生产,i corresponding to each activity, such as the carbon dioxide emission factor per unit of energy consumption.

[0107] 3) Carbon footprint factor calculation: Use the formula Calculate the carbon footprint factor E in the production stage 生产 , which is the sum of carbon emissions from each activity.

[0108] Organize the calculated carbon footprint factor data in the production stage into a table, recording the carbon footprint factors of different products and different production links.

[0109] S42: Define the boundary of the life cycle stage model according to the system boundary. Based on the boundary of the life cycle stage model, construct the life cycle stage model using the life cycle assessment method; based on the standardized data table and the life cycle stage model, conduct a series connection process on the upstream raw and auxiliary material and energy production data and local production data to generate a life cycle carbon footprint factor data table; the life cycle carbon footprint factor is calculated by the following formula:

[0110]

[0111] Among them, E 全生命周期 is the life cycle carbon footprint factor, AD 全生命周期,i is the life cycle activity level data linked to the declared unit, EF 全生命周期,i is the life cycle emission factor; j represents the jth activity within the life cycle stage; m represents the total number of activities within the life cycle stage.

[0112] Specifically, clarify the starting and ending links of the life cycle stage according to the system boundary, such as from obtaining raw and auxiliary materials to the final disposal or recycling of the product.

[0113] Adopt the life cycle assessment method to establish a carbon footprint calculation model for the entire life cycle, which can cover all stages such as obtaining raw and auxiliary materials, production, transportation, use, and disposal.

[0114] Based on the standardized data table and the life cycle stage model, conduct a series connection process on the upstream raw and auxiliary material and energy production data and local production data. Specifically, it can include:

[0115] 1) Activity level data integration: Integrate the activity level data AD 全生命周期,i linked to the declared unit in each stage of the life cycle, such as energy and resource consumption in the production process of raw and auxiliary materials, fuel use in the transportation process, etc.

[0116] 2) Emission factor application: Apply the corresponding emission factor EF 全生命周期,i , such as the emission factor in the production process of raw and auxiliary materials, the emission factor in the transportation process, etc.

[0117] 3) Carbon footprint factor calculation: Use the formula to calculate the life cycle carbon footprint factor E 全生命周期, which is the sum of carbon emissions in all stages.

[0118] The calculated data on the carbon footprint factor of the entire life cycle are organized into a table to record the carbon footprint factor of the product at each stage of its life cycle.

[0119] S43: Perform association mapping processing on the production phase carbon footprint factor data table and the life cycle carbon footprint factor data table to generate a unified carbon footprint factor data table.

[0120] Specifically, the carbon footprint factor data table of the production stage is associated with the carbon footprint factor data table of the entire life cycle, and the corresponding records in the two tables are associated through common fields such as product name, production link, and time.

[0121] Integrate the relevant data in the two tables to ensure that the carbon footprint factor data of each product at different stages can correspond to each other and generate a unified carbon footprint factor data table.

[0122] Check whether there are any data inconsistencies in the associated data table, such as whether the carbon footprint factor of the production stage of the same product matches the factor of the corresponding stage in the entire life cycle. If there is any inconsistency, it can be corrected.

[0123] After the above processing, a unified data table containing the carbon footprint factors of the product in the production stage and each stage of the entire life cycle is obtained, that is, the unified carbon footprint factor data table.

[0124] S44: Based on the grade threshold of the energy efficiency label, the carbon footprint factor values in the unified carbon footprint factor data table are matched and divided to generate a graded carbon footprint factor data table as the carbon footprint factor data table.

[0125] Specifically, the energy efficiency label added in the previous step is used to obtain the grade threshold defined therein, which is used to distinguish different energy efficiency grades, such as grade I (advanced), grade II (average), and grade III (poor).

[0126] Match the carbon footprint factor value in the unified carbon footprint factor data table with the grade threshold of the energy efficiency label. Specifically, it can include:

[0127] 1) Threshold setting: According to the grade threshold defined by the energy efficiency label, determine the numerical range of the carbon footprint factor corresponding to different energy efficiency grades.

[0128] 2) Numerical matching: Match the carbon footprint factor value of each product with the corresponding threshold range to determine its energy efficiency level.

[0129] 3) Data division: According to the matching results, the data in the unified carbon footprint factor data table is divided into different energy efficiency level groups to generate a graded carbon footprint factor data table.

[0130] In the classified data table, statistical indicators such as the average value and range of the carbon footprint of each energy efficiency level can be further calculated to provide more targeted data support for subsequent analysis and decision-making.

[0131] In an optional embodiment, S5 includes the following steps:

[0132] S51: Design the database table structure; the database table structure includes a primary key field, foreign key association fields, numerical fields, and descriptive fields. The primary key field is the UU ID. The foreign key association fields include a product type code, a process code, a measurement area code, and an applicable area code. The numerical fields include: a functional unit field, an input inventory field, an output inventory field, and an emission factor field. The descriptive fields include: product description, process description, system boundary, and data contributor.

[0133] Specifically, the primary key field can use a Universally Unique Identifier (UU ID) as the primary key field to uniquely identify each carbon footprint factor record, ensuring the uniqueness of each record globally, facilitating data integration and sharing, and avoiding errors caused by data duplication or conflicts.

[0134] The foreign key association fields can include:

[0135] 1) Product type code: By associating with the product type code, carbon footprint data for specific product types can be queried and analyzed, such as corresponding product categories, first-level classifications, second-level classifications, and third-level classifications, which helps to understand the differences in carbon footprints among different products.

[0136] 2) Process code: Used to associate relevant information about the process, corresponding to process types such as dyeing, setting, and packaging. By analyzing the carbon footprint data of different processes, key links for energy conservation and emission reduction can be identified.

[0137] 3) Measurement area code: Identifies the area data based on which the carbon footprint factor is measured, such as the energy structure and emission factors in different regions within Zhejiang Province, reflecting the impact of regional differences on the carbon footprint and helping to formulate targeted regional emission reduction strategies.

[0138] 4) Applicable area code: Identifies the applicable area range of the carbon footprint factor, such as the whole country, Zhejiang Province, etc., ensuring the accurate application of carbon footprint data and avoiding data misuse caused by regional differences.

[0139] The numerical fields can include:

[0140] 1) Functional unit field: Defines the functional unit corresponding to the carbon footprint factor, such as per kilogram of product, per square meter of fabric, etc., ensuring the comparability and consistency of carbon footprint data and facilitating horizontal comparison between different products or processes.

[0141] 2) Input inventory fields: Record the quantities of various materials and energy inputs during the production process, such as the consumption of raw and auxiliary materials (e.g., PTA, polyester grey fabric, non-ionic surfactant, compressed air, heat energy, natural gas), transportation data (corresponding road freight consumption), energy and resource consumption (e.g., Zhejiang Power Grid electricity, industrial water, steam), etc., which provide basic data for carbon footprint calculation and are important bases for evaluating the environmental impact of production activities.

[0142] 3) Output inventory fields: Record various outputs generated during the production process, such as wastewater discharge (e.g., textile printing and dyeing wastewater volume, municipal sludge volume), waste data (e.g., waste plastic mixture), etc., which help to comprehensively evaluate the environmental impact of production activities and understand the resource utilization efficiency and waste treatment situation during the production process.

[0143] 4) Emission factor fields: Store the emission factors corresponding to various activities, such as the carbon dioxide emission factor per unit of energy consumption, the emission factor per unit of raw material use, etc., which are used to calculate specific carbon emissions and are one of the core data for carbon footprint accounting.

[0144] Descriptive fields may include:

[0145] 1) Product description: A detailed description of the product, including product name, specifications, uses, etc., which facilitates users to understand and identify different products and ensures that the required information can be accurately obtained during data query and analysis.

[0146] 2) Process description: A detailed description of the process, including process name, process flow, technical parameters, etc. For example, the dipping process includes pretreatment, dyeing, cleaning, dehydration and width opening, setting, and pre-shrinking. This description helps to understand the characteristics of different processes and their carbon emissions and provides data support for process optimization and energy conservation and emission reduction.

[0147] 3) System boundary: Define the system boundary for carbon footprint calculation, such as "cradle-to-gate: from raw and auxiliary material acquisition to the end of factory shipment". Ensure the integrity and consistency of carbon footprint data and avoid data omission or duplication caused by unclear system boundaries.

[0148] 4) Data contributor: Record the source and contributor information of the data, such as enterprise name, institutional code, data collection personnel, etc., which is convenient for data traceability and verification and ensures the credibility and reliability of the data.

[0149] S52: Based on the primary key field and the foreign key association field, associate the carbon footprint factor data table with the standardized data set and establish an indexing rule.

[0150] Specifically, associate the carbon footprint factor data with the original data that has been cleaned and standardized. Use the primary key field (UU ID) and foreign key association fields (product type code, process code, measurement area code, applicable area code) as the association keys to ensure accurate data matching.

[0151] The association method may include:

[0152] 1) Data matching: Match the carbon footprint factor data table with the corresponding records in the standardized dataset through the association keys to ensure that each piece of carbon footprint factor data can find the corresponding original data support.

[0153] 2) Data integration: Integrate the matched data to form a comprehensive data table that includes carbon footprint factor data and original data.

[0154] 3) Data consistency check: Check whether there are inconsistent situations in the associated data, such as whether the information such as product type and process in the carbon footprint factor data matches the original data. If there are inconsistencies, correct them.

[0155] 4) Establish index rules: Establish indexes for the association keys in the database to improve the efficiency of data query and association. The index rules include the type of index (such as B-tree index, hash index, etc.) and the order of index fields, etc., which can be optimized according to the actual data volume and query requirements.

[0156] S53: Generate a database based on the database table structure and index rules.

[0157] Specifically, the generation of the database may include the following steps:

[0158] 1) Database initialization: In the selected database management system, create a new database instance, set basic parameters such as the encoding and character set of the database to provide a container for data storage.

[0159] 2) Data table creation: According to the database table structure designed in S51, create the corresponding data tables in the database, define the fields, data types, constraint conditions, etc. of each table to ensure that the structure of the data tables is consistent with the design.

[0160] 3) Data import: Import the associated data into the corresponding data tables according to the structure of the database tables to ensure the accuracy and integrity of the data.

[0161] 4) Index creation: According to the index rules established in S52, create indexes for relevant fields in the database to optimize the performance of data query and association.

[0162] 5) Database optimization and maintenance: Optimize the generated database, such as adjusting database parameters, optimizing query statements, etc., to improve the operating efficiency of the database; at the same time, establish a database backup and recovery mechanism to ensure data security and reliability.

[0163] The above method for constructing a product carbon footprint basic database integrates government supervision data, enterprise field research data, and industry standard data to build a multi-source heterogeneous data collection system. Based on material conservation inspection, energy efficiency grading, and life cycle assessment methods, the data is cleaned, modeled, and verified. Combining dynamic iterative rules and an international database comparison mechanism, it realizes the accurate calculation of carbon footprint factors in the textile industry chain, the efficient integration of local data, and the continuous optimization of the database, effectively solving the problems of insufficient regional representativeness, poor timeliness, and low technical transparency of existing databases. Finally, it forms a carbon footprint basic database covering the entire life cycle, traceable and compatible with international standards, providing data support for carbon label certification, international trade barrier response, and enterprise low-carbon transformation.

[0164] It should be understood that although the steps in the flowcharts involved in the above embodiments are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0165] Based on the same inventive concept, the embodiments of the present application also provide a system for implementing the above-mentioned method for constructing a product carbon footprint basic database. The solution provided by this system to solve problems is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the product carbon footprint basic database construction system provided below can refer to the limitations on the method for constructing a product carbon footprint basic database in the above text, and will not be repeated here.

[0166] In an exemplary embodiment, as Figure 2 shown, a product carbon footprint basic database construction system 20 is provided, including:

[0167] A target definition module 21, configured to determine the research scope and system boundary based on the analysis of the upstream and downstream of the industrial chain, and generate a target definition data set.

[0168] The data acquisition module 22 is used to collect government supervision data, enterprise actual scene data, and industry standard data according to the target defined data set, and generate an original data table.

[0169] The data processing module 23 is used to clean the original data table and classify the energy efficiency level, and generate a standardized data set.

[0170] The carbon footprint factor generation module 24 is used to construct a production stage model and a full life cycle model based on the standardized data set and the system boundary by using the life cycle assessment method, and generate a carbon footprint factor data table according to the production stage model and the full life cycle model.

[0171] The database construction module 25 is used to construct a database based on the carbon footprint factor data table.

[0172] The database dynamic iteration module 26 is used to verify the database through international database comparison and on-site monitoring to obtain a verification result; construct a dynamic iteration rule based on the verification result, and update the database according to the dynamic iteration rule.

[0173] Optionally, the data acquisition module 22 includes:

[0174] The production activity data acquisition unit 221 is used to collect the product output, raw material and auxiliary material consumption, energy and resource consumption, and wastewater discharge of the enterprise based on the pollutant discharge permit execution report, and generate a production activity data table.

[0175] The emission data acquisition unit 222 is used to collect the fossil fuel combustion emissions, industrial production process emissions, and power and heat consumption data based on the carbon verification report, and generate an emission data table.

[0176] The equipment operation data acquisition unit 223 is used to collect the production line equipment power, operation time, and material loss data of the enterprise through on-site research, and generate an on-site research data table.

[0177] The data integration unit 224 is used to integrally process the production activity data table, the emission data table, and the on-site research data table to obtain the original data table.

[0178] Optionally, the data processing module 23 includes:

[0179] The data cleaning unit 231 is used to clean the original data table to obtain a preliminary cleaned data set; among them, the data cleaning unit 231 includes:

[0180] The material conservation inspection sub-unit 2311 is used to perform a material conservation inspection on the original data table. When the input-output ratio is less than or equal to the preset threshold, it is determined that the data is incomplete and the corresponding record is deleted.

[0181] The dispersion test subunit 2312 is used to eliminate the data records in the original data table whose difference between the dispersion and the industry average is not within the preset range.

[0182] The energy efficiency grading unit 232 is used to perform energy consumption grading for per unit product on the preliminary cleaned data set based on the energy efficiency grading standard, and generate a standardized data set including energy efficiency labels.

[0183] Optionally, the carbon footprint factor generation module 24 includes:

[0184] The production stage model construction unit 241 is used to define the production stage model boundary according to the system boundary, construct the production stage model based on the production stage model boundary by using the life cycle assessment method; model the input and output data of a single production link based on the standardized data table and the production stage model, and generate a production stage footprint factor data table; the production stage carbon footprint factor is calculated by the following formula:

[0185]

[0186] where, E 生产 is the production stage carbon footprint factor, AD 生产,i is the production stage activity level data linked to the declared unit, EF 生产,i is the production stage emission factor; i represents the i-th activity in the production stage; n represents the total number of activities in the production stage.

[0187] The full life cycle stage model construction unit 242 is used to define the full life cycle stage model boundary according to the system boundary, construct the full life cycle stage model based on the full life cycle stage model boundary by using the life cycle assessment method; perform a series connection process on the upstream raw and auxiliary materials and energy production data and the local production data based on the standardized data table and the full life cycle stage model, and generate a full life cycle carbon footprint factor data table; the full life cycle carbon footprint factor is calculated by the following formula:

[0188]

[0189] where, E 全生命周期 is the full life cycle carbon footprint factor, AD 全生命周期,i is the full life cycle activity level data linked to the declared unit, EF 全生命周期,i is the full life cycle emission factor; j represents the j-th activity in the full life cycle stage; m represents the total number of activities in the full life cycle stage.

[0190] The carbon footprint factor association and mapping unit 243 is used to perform association and mapping processing on the production stage carbon footprint factor data table and the full life cycle carbon footprint factor data table, and generate a unified carbon footprint factor data table.

[0191] A carbon footprint factor grading unit 244 is used to perform matching and partitioning processing on the carbon footprint factor values in the unified carbon footprint factor data table based on the grade thresholds of the energy efficiency label, and generate a graded carbon footprint factor data table as the carbon footprint factor data table.

[0192] Optionally, the database construction module 25 includes:

[0193] A database table structure design unit 251 is used to design the database table structure; the database table structure includes a primary key field, a foreign key association field, a numerical field, and a descriptive field. The primary key field is a UUID, and the foreign key association fields include a product type code, a process code, a measurement area code, and an applicable area code. The numerical fields include: a functional unit field, an input inventory field, an output inventory field, and an emission factor field. The descriptive fields include: a product description, a process description, a system boundary, and a data contributor.

[0194] A data association and indexing unit 252 is used to associate the carbon footprint factor data table with the standardized data set based on the primary key field and the foreign key association field, and establish an indexing rule.

[0195] A database generation unit 253 is used to generate a database based on the database table structure and the indexing rule.

[0196] An embodiment of the present application also provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps in the foregoing method embodiments are implemented.

[0197] An embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps in the foregoing method embodiments are implemented.

[0198] For the device embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The device embodiments described above are only illustrative. The components described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the present disclosure solution. Those of ordinary skill in the art can understand and implement it without creative work.

[0199] The above-described embodiments merely represent several implementation manners of the embodiments of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation to the scope of the patent of the embodiments of the application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the embodiments of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the embodiments of the present application.

Claims

1. A method for constructing a basic database of product carbon footprint, characterized in that, The method includes: S1: Based on the analysis of the upstream and downstream of the industrial chain, determine the research scope and system boundary, and generate a target definition data set; S2: According to the target definition data set, collect government supervision data, enterprise actual situation data and industry standard data, and generate an original data table; S3: Clean and classify the energy efficiency level of the original data table to generate a standardized data set; S4: Based on the standardized data set and the system boundary, use the life cycle assessment method to construct a production stage model and a full life cycle model, and generate a carbon footprint factor data table according to the production stage model and the full life cycle model; S5: Construct a database based on the carbon footprint factor data table; S6: Verify the database through international database comparison and on-site monitoring to obtain a verification result; construct a dynamic iteration rule based on the verification result, and update the database according to the dynamic iteration rule.

2. The method according to claim 1, wherein The S2 includes: S21: Based on the pollutant discharge permit execution report, collect the product output, raw and auxiliary material consumption, energy and resource consumption, and wastewater discharge of the enterprise to generate a production activity data table; S22: Based on the carbon inventory report, collect the fossil fuel combustion emissions, industrial production process emissions, and electricity and heat consumption data to generate an emission data table; S23: Through on-site research, collect the production line equipment power, operation time, and material loss data of the enterprise to generate an on-site research data table; S24: Integrate and process the production activity data table, the emission data table, and the on-site research data table to obtain the original data table.

3. The method according to claim 1, wherein The S3 includes: S31: Clean the original data table to obtain a preliminary cleaned data set; among them, the cleaning process includes: Conduct a material balance test on the original data table. When the input-output ratio is less than or equal to a preset threshold, determine that the data is incomplete and delete the corresponding record; Delete the data records in the original data table whose difference between the dispersion degree and the industry average value is not within the preset range; S32: Based on the energy efficiency grading standard, conduct unit product energy consumption grading on the preliminary cleaned data set to generate the standardized data set containing energy efficiency labels.

4. The method according to claim 3, wherein The S4 includes: S41: Define the production stage model boundary according to the system boundary. Based on the production stage model boundary, use the life cycle assessment method to construct the production stage model; based on the standardized data table and the production stage model, conduct modeling on the input and output data of a single production link to generate a production stage footprint factor data table; the production stage carbon footprint factor is calculated by the following formula: Among them, E 生产 is the carbon footprint factor of the production stage, AD 生产,i is the activity level data of the production stage linked to the declarant, EF 生产,i is the emission factor of the production stage; i represents the i-th activity within the production stage; n represents the total number of activity items within the production stage; S42: Define the full life cycle stage model boundary according to the system boundary. Based on the full life cycle stage model boundary, use the life cycle assessment method to construct the full life cycle stage model; based on the standardized data table and the full life cycle stage model, conduct series processing on the upstream raw and auxiliary material and energy production data and local production data to generate a full life cycle carbon footprint factor data table; the full life cycle carbon footprint factor is calculated by the following formula: Among them, E 全生命周期 is the full - life - cycle carbon footprint factor, AD 全生命周期,i is the full - life - cycle activity level data linked to the declaring entity, EF 全生命周期,i is the full - life - cycle emission factor; j represents the j - th activity within the full - life - cycle stage; m represents the total number of activity items within the full - life - cycle stage; S43: Perform an association mapping process on the carbon footprint factor data table of the production stage and the carbon footprint factor data table of the whole life cycle to generate a unified carbon footprint factor data table; S44: Based on the grade threshold of the energy efficiency label, perform a matching and partitioning process on the carbon footprint factor values in the unified carbon footprint factor data table to generate a hierarchical carbon footprint factor data table as the carbon footprint factor data table.

5. The method according to claim 3, wherein The said S5 includes: S51: Design the database table structure; the database table structure includes a primary key field, a foreign key association field, a numerical field, and a descriptive field. The primary key field is UUID, the foreign key association fields include product type code, process code, measurement area code, and applicable area code. The numerical fields include: functional unit field, input inventory field, output inventory field, and emission factor field. The descriptive fields include: product description, process description, system boundary, and data contributor; S52: Based on the primary key field and the foreign key association field, perform an association process on the carbon footprint factor data table and the standardized data set to establish an indexing rule; S53: Generate the database based on the database table structure and the indexing rule.

6. A system for constructing a basic database of product carbon footprint, characterized in that, The said system includes: A target definition module, which is used to determine the research scope and system boundary based on the analysis of the upstream and downstream of the industrial chain, and generate a target definition data set; A data collection module, which is used to collect government supervision data, enterprise actual situation data, and industry standard data according to the target definition data set, and generate an original data table; A data processing module, which is used to clean the original data table and classify the energy efficiency level to generate a standardized data set; A carbon footprint factor generation module, which is used to construct a production stage model and a whole life cycle model by using the life cycle assessment method based on the standardized data set and the system boundary, and generate a carbon footprint factor data table according to the production stage model and the whole life cycle model; A database construction module, which is used to construct a database based on the carbon footprint factor data table; A database dynamic iteration module, which is used to verify the database through international database comparison and on-site monitoring to obtain a verification result; construct a dynamic iteration rule based on the verification result, and update the database according to the dynamic iteration rule.

7. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method according to any one of claims 1 to 5.

Images