Transformer product carbon footprint modeling method based on full life cycle evaluation

Abstract

The invention discloses a transformer product carbon footprint modeling method based on full life cycle evaluation. The transformer product carbon footprint modeling method comprises the steps of S1, system boundary confirmation; s2, integrating multi-source data; s3, constructing an accounting model; according to the method, through system boundary confirmation, multi-source data integration and accounting model construction, the full-life-cycle carbon emission of a transformer product is accurately quantified, and the method comprises the steps of raw material acquisition, raw material transportation, production and manufacturing, product transportation, product use and carbon footprints of all stages of retirement and recovery. All the processes are calculated according to the model, an effective tool is provided for environmental protection evaluation and green design, carbon emission of all links is assisted to be optimized, and theoretical support and practical guidance are provided for low-carbon transformation of the power industry.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon tracking of power equipment, and in particular to a carbon footprint modeling method for transformer products based on full life cycle assessment. Background Art

[0002] A carbon footprint refers to the total amount of CO2 emissions directly and indirectly caused by products and activities (individuals, groups, companies, etc.). It is a standard method for quantifying carbon emissions. The main methods for calculating carbon footprints in China and abroad include the IPCC method, the life cycle assessment method based on process analysis, and the input-output analysis method.

[0003] Driven by the global carbon neutrality strategy, carbon footprint accounting has become a key technology for the environmental management of power equipment. Major equipment suppliers are required to provide carbon footprint reports based on the entire life cycle. To establish and improve the carbon footprint management system for power industry products, local power companies are developing carbon footprint standards and conducting pilot carbon footprint assessments for power industry products. GB / T40092-2021 "Technical Specification for Eco-design Product Evaluation - Transformers" and DB37 / T3268-2018 "Green Product Evaluation Specification for Power Transformers" are both national and local standards that can serve as normative guidelines for green transformers.

[0004] There are four main technical deficiencies in the current transformer carbon footprint accounting:

[0005] First, existing evaluation systems often focus on the production or use phase of a product, failing to fully cover the entire lifecycle from raw material acquisition to raw material transportation, manufacturing, product transportation, product use, and decommissioning. The data collaboration mechanism for the entire lifecycle is imperfect. Transformers involve cross-industry data such as chemical, paper product production, and land transportation, but supplier carbon information disclosure rates are insufficient, making it difficult to trace the carbon footprint of key raw materials.

[0006] Second, carbon footprint modeling capabilities are insufficient. Current LCA methods mostly use static databases (such as Ecoinvent), which make it difficult to reflect changes in carbon emission intensity brought about by production process improvements. There are also discrepancies in the definition of the "cradle-to-grave" boundary in accounting, with some transportation links being included in the system boundary while others are not, resulting in a lack of horizontal comparability.

[0007] Third, there is a lack of characteristic parameters of power equipment and the system boundaries are vague;

[0008] Fourth, the standards are poorly applicable and fail to meet the needs of precise carbon management. While standards such as GB / T40092-2021 provide normative guidelines, they lack an operational modeling approach.

[0009] The current domestic life cycle assessment principle framework lacks characteristic parameters for power equipment. Specifically, there is still a lack of operational modeling methods for transformer products. As a result, the existing carbon footprint assessment has key problems such as blurred system boundaries, uneven data quality, and insufficient reflection of temporal and spatial differences, making it difficult to meet the precise carbon management needs under the "dual carbon" goals. Summary of the Invention

[0010] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, one purpose of the present invention is to propose a carbon footprint modeling method for transformer products based on full life cycle evaluation, which realizes accurate quantification of carbon emissions of transformer products throughout their entire life cycle through system boundary confirmation, multi-source data integration and accounting model construction, including the carbon footprint of each stage from raw material acquisition, raw material transportation, production and manufacturing, product transportation, product use to decommissioning and recycling. Calculations are carried out on each process according to the model to provide an effective tool for environmental assessment and green design, help optimize carbon emissions in each link, and provide theoretical support and practical guidance for the low-carbon transformation of the power industry.

[0011] In order to solve the above problems, the present invention provides a transformer product carbon footprint modeling method based on full life cycle assessment, comprising the following steps:

[0012] S1: System boundary confirmation: Define the six stages of the transformer's full life cycle carbon footprint, including raw material acquisition, raw material transportation, production and manufacturing, product transportation, product use and decommissioning and recycling;

[0013] S2: Multi-source data integration: Collect carbon emission data for each stage, including:

[0014] S2.1: Raw materials acquisition stage: data on the embodied carbon emissions from the mining, processing, and transportation of silicon steel, copper and aluminum conductors, insulating media, and structural materials;

[0015] S2.2: Raw materials transportation stage: dynamic emission factors based on supplier's geographic coordinates, transportation mode, vehicle type, load factor, transportation distance, and fuel type;

[0016] S2.3: Manufacturing stage: workshop-level process energy consumption, equipment operating parameters, waste disposal data, and electricity consumption data;

[0017] S2.4: Product transportation stage: product weight, transportation distance, transportation mode and vehicle energy efficiency parameters;

[0018] S2.5: Product use phase: operating environment parameters, load rate curve, regional power grid carbon emission factor, and maintenance energy consumption data;

[0019] S2.6: Decommissioning and recycling phase: dismantling energy consumption, transportation distance, waste disposal routes, and carbon offset data for recycled materials;

[0020] S3: Accounting model construction: Establish a hierarchical accounting model consisting of six core sub-modules:

[0021] Raw material acquisition stage: C raw =∑Q m ·EF m ;

[0022] Raw materials transportation stage: C trans_raw =∑(Q·EF trans d);

[0023] Manufacturing stage: C prod =∑E total ·EF energy ;

[0024] Product transportation stage: C trans_prod =∑(Q prod ·EF trans d);

[0025] Product use stage: C use =∑T·P avg ·EF grid ;

[0026] Decommissioning and recycling phase: C Eol =Q recycled ·EF recycling -Q refused β;

[0027] Among them, C raw is the carbon footprint of the raw material acquisition stage, Qm is the material usage, EF m is the material emission factor; C trans_raw is the carbon footprint of the raw material transportation stage, EF trans is the transport emission factor, d is the transport distance; C prod is the carbon footprint of the manufacturing stage, E total is the total energy consumption, EF energy is the unit energy emission factor; C trans_prod is the carbon footprint of the product transportation stage; C use is the carbon footprint of the product during use, T is the operating time, P avg is the average power, EF grid is the grid emission factor; C Eol Q is the carbon footprint of the decommissioning and recycling phase, recycled is the amount of recycled material, EF recycling is the recovery emission factor, Q refused is the amount of waste materials, β is the carbon emission coefficient of waste materials;

[0028] S4: Model validation and optimization: Verify the accuracy of the model through actual case data and optimize key parameters through sensitivity analysis.

[0029] Preferably, in the system boundary confirmation step, the transformer life cycle approach is used to define the scope, exclude irrelevant links, and ensure that the responsibility for carbon emissions is clearly assigned.

[0030] Preferably, in the multi-source data integration step, the Internet of Things is used to collect real-time operation data, and cross-industry data collaboration is achieved through the big data platform.

[0031] Preferably, in the accounting model construction step, the mass conservation method, the production capacity conversion method and the electric carbon analysis model are introduced for triple verification to ensure data consistency.

[0032] Preferably, in the model application step, a carbon footprint digital twin model is generated to support the carbon footprint assessment of power transmission and distribution equipment such as power capacitors and smart meters.

[0033] The advantages of the present invention compared with the prior art are:

[0034] (1) This method breaks through the limitation of traditional carbon accounting that only focuses on a single production link. By constructing a full life cycle evaluation system covering six major links: raw material acquisition, raw material transportation, production and manufacturing, product transportation, product use and decommissioning and recycling, it realizes the quantitative tracking and model construction of the carbon footprint of the entire transformer product chain.

[0035] (2) This method establishes a dynamic correlation model between production process parameters and carbon emissions, thereby achieving accurate carbon footprint traceability and process optimization guidance for key processes such as silicon steel sheet shearing, core stacking, and winding in the transformer manufacturing process.

[0036] (3) The carbon footprint digital twin model constructed by this method has replicable and popularizable application value. Its basic accounting architecture is applicable to power transmission and distribution equipment such as power capacitors and smart meters, and helps promote the implementation of the dual carbon goals in the electrical equipment manufacturing industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0038] Figure 1 This is a flow chart of carbon footprint calculation based on LCA in the present invention;

[0039] Figure 2 Schematic diagram of the transformer system boundary in the present invention;

[0040] Figure 3 This is the calculation logic diagram of the transformer carbon footprint calculation model in the present invention. DETAILED DESCRIPTION

[0041] The following describes in detail embodiments of the present application. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application and are not to be construed as limiting the present application.

[0042] In the description of this application, it should be noted that, unless otherwise expressly specified or limited, the terms "installed," "connected," and "connected" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; they can refer to internal communication between two components or the interaction between two components. Those skilled in the art will understand the specific meanings of the above terms in this application based on the specific circumstances.

[0043] The present invention will be described in further detail below with reference to the accompanying drawings.

[0044] Combine Figures 1 to 3 The present invention provides a transformer product carbon footprint modeling method based on full life cycle assessment, which divides the transformer product carbon footprint modeling method based on full life cycle assessment into a three-layer progressive framework of system boundary confirmation, multi-source data integration, and accounting model construction and application, to construct a full life cycle carbon footprint model covering the acquisition, transportation, manufacturing, use, and recycling of transformer raw materials.

[0045] (1) System boundary confirmation

[0046] When conducting a transformer lifecycle carbon footprint analysis, defining the system boundary is a key step in model construction. The system boundary determines the scope of processes, activities, and material flows included in the lifecycle assessment (LCA), ensuring the scientific and comprehensive nature of the model results. A transformer's lifecycle carbon footprint typically encompasses five key phases: raw material acquisition, raw material transportation, manufacturing, product transportation, product use, and decommissioning. During the raw material acquisition phase, the mining, smelting, and processing of steel, copper, aluminum, insulation materials, and insulating oil must be analyzed. The energy consumption and greenhouse gas emissions of these processes are key influencing factors. During the manufacturing phase, transformer assembly, welding, coating, and insulating oil injection consume resources such as electricity and fuel, requiring a detailed assessment of their carbon emissions. The transportation phase, encompassing the entire process from raw material transportation to finished transformer delivery, requires an assessment of the fuel consumption and corresponding emissions of various transportation vehicles (e.g., trucks, trains, and ships). The transformer's use phase focuses primarily on energy consumption and heat dissipation losses (including iron and copper losses) during operation. This phase is often the primary contributor to the lifecycle carbon footprint. The scrapping and recycling stage requires analysis of the recycling of materials after the transformer's life (such as the recycling efficiency of copper and iron) and the carbon emissions during the waste treatment process.

[0047] Reasonable system boundary analysis provides a scientific framework for the carbon footprint model of the transformer's entire life cycle, helps to comprehensively identify carbon emission sources at each stage, and provides data support and decision-making basis for achieving design optimization and emission reduction goals.

[0048] (2) Multi-source data integration

[0049] 1) Raw material acquisition stage

[0050] Carbon footprint assessments during the transformer raw material procurement phase require systematic quantification of the embodied carbon emissions of silicon steel, copper and aluminum conductors, insulation, and structural materials, encompassing the entire mining, processing, and transportation chain. Each material contributes significant energy consumption and greenhouse gas emissions during mining and processing, necessitating systematic data collection and analysis.

[0051] Silicon steel sheets, the core magnetic conductive material, undergo energy-intensive production processes, including iron ore mining, steelmaking, cold rolling, and annealing, resulting in a significant carbon footprint. The production of copper and aluminum conductive materials involves open-pit mining and electrolytic refining, particularly the aluminum oxide electrolysis process, which is energy-intensive and has a high carbon footprint. Insulating materials such as petroleum-based insulating oil can pose a methane leakage risk, and the energy consumption associated with its refining and processing, as well as the production of insulating paper, is also significant. Furthermore, structural materials such as carbon steel and stainless steel used in the core frame and casing also involve energy-intensive ore mining and smelting. Comprehensively collecting data on the production sources, processing techniques, and energy consumption of these materials is key to accurately assessing the carbon footprint of transformer materials.

[0052] 2) Raw material transportation stage

[0053] During the raw material transportation phase, multimodal logistics data must be integrated: including supplier geographic coordinates, transportation mode (road, rail, water, air), vehicle type (e.g., 30-ton diesel truck, 50,000-ton bulk carrier), actual load factor, and single-trip distance (the actual distance the raw materials are transported from the source to the manufacturing site). Emission factors must be dynamically matched based on the international transportation vehicle fuel type (diesel, LNG, heavy fuel oil) and CO2 equivalent per unit of turnover.

[0054] 3) Manufacturing stage

[0055] Data collection in the production and manufacturing stage needs to go deep into the workshop-level processes, covering energy consumption and exhaust emissions in links such as component assembly processes, equipment operation energy consumption, and waste treatment. For example, the cutting and coating processes of silicon steel sheets, and the stretching and insulation treatment of copper and aluminum wires. These processes often require high-temperature or high-voltage equipment, resulting in higher energy consumption and greenhouse gas emissions. Secondly, during the parts manufacturing process, it is necessary to record the production energy consumption and emissions of key components such as iron cores, coils, and fuel tanks in detail, especially in high-energy consumption processes such as welding, heat treatment, and painting, which usually involve the emission of pollutants such as carbon dioxide and volatile organic compounds (VOCs). In addition, the processing of insulating materials (such as cutting of insulating paper and processing of insulating oil) also requires quantification of energy consumption and carbon emissions.

[0056] During the assembly process, components such as the core, coils, and insulation are typically assembled using automated equipment. This process requires high power consumption during operation, which requires accurate recording. Transformers undergo multiple rounds of performance testing (such as withstand voltage and load testing), and the power consumption of test equipment and the indirect carbon emissions of test workers need to be included in the assessment.

[0057] 4) Product transportation stage

[0058] Measuring the carbon footprint during transportation requires integrating multiple parameters: product weight directly correlates to the base energy consumption of transportation, transportation distance determines the vehicle's operating schedule and total energy consumption, and different transportation modes (such as road, rail, and ship) correspond to different carbon emission factors, significantly affecting the accuracy of the results. Furthermore, attention should be paid to transportation vehicle energy efficiency parameters, including fuel type, specific energy consumption rate, and load utilization rate.

[0059] 5) Product use stage

[0060] Carbon footprint accounting during the product's use phase should be systematically analyzed with operational energy consumption as the core. First, the spatial characteristic parameters of the equipment's operating environment must be collected, including basic data such as the transformer's installation location (urban / rural), ambient temperature and humidity, and cooling system energy consumption. Dynamic operating characteristic parameters should also be acquired simultaneously, including time series data such as real-time load rate and hourly load curves.

[0061] To calculate carbon emission factors, it's necessary to collect carbon emission factors for the electricity supply in the region where the equipment is located, based on the geographic boundaries of the power grid. Maintenance frequency and energy consumption data should also be considered, including the energy required for equipment maintenance (such as the electricity consumed to operate the equipment) and the impact of component replacement (such as the replacement of insulating oil and its lifecycle carbon emissions).

[0062] 6) Decommissioning and recycling stage

[0063] Carbon footprint analysis during the transformer decommissioning and recycling phase requires precise data collection throughout the entire process, from disassembly and transportation to processing and recycling. The processing methods used during this phase directly determine the total carbon emissions and resource recovery efficiency over the entire lifecycle, making it a key node in building a circular economy.

[0064] The dismantling phase requires the collection of three key types of data: the energy consumption of equipment used in the dismantling of retired transformers, the electricity or fuel consumption at the dismantling site, and the types and quantities of waste generated. Secondly, in the transportation phase, it is necessary to record logistics and transportation data from the dismantling site to the recycling and processing site, including the type of transportation tool (such as trucks, ships, etc.), fuel consumption, transportation distance, etc. In the waste treatment and resource recycling phase, it is necessary to classify and trace the emission paths: the energy consumption of waste oil is calculated based on the regeneration / incineration method; the emission reduction of metal parts is calculated based on the recovery rate and smelting energy consumption; the carbon emissions of insulating materials are quantified based on the landfill / incineration / regeneration path.

[0065] (3) Accounting model construction and application

[0066] 1) Multi-stage hierarchical modeling architecture

[0067] Establish a hierarchical accounting model consisting of six core sub-modules:

[0068] Raw material acquisition stage: C raw =∑Q m ·EF m ;

[0069] Raw materials transportation stage: C trans_raw =∑(Q·EF trans d);

[0070] Manufacturing stage: C prod =∑E total ·EF energy ;

[0071] Product transportation stage: C trans_prod =∑(Q prod ·EF trans d);

[0072] Product use stage: C use =∑T·P avg ·EF grid ;

[0073] Decommissioning and recycling phase: C Eol =Q recycled ·EF recycling -Q refused β;

[0074] Among them, C raw is the carbon footprint of the raw material acquisition stage, Qm is the material usage, EF m is the material emission factor; C trans_raw is the carbon footprint of the raw material transportation stage, EF trans is the transport emission factor, d is the transport distance; C prod is the carbon footprint of the manufacturing stage, E total is the total energy consumption, EF energy is the unit energy emission factor; C trans_prod is the carbon footprint of the product transportation stage; C use is the carbon footprint of the product during use, T is the operating time, P avg is the average power, EF grid is the grid emission factor; C Eol Q is the carbon footprint of the decommissioning and recycling phase, recycled is the amount of recycled material, EF recycling is the recovery emission factor, Q refused is the amount of waste materials, β is the carbon emission coefficient of waste materials;

[0075] 2) Multi-source data processing engine

[0076] Based on the full life cycle information of transformer products, and leveraging technologies such as the Internet of Things and big data, we comprehensively collect carbon footprint-related data from all stages. We standardize all types of collected data, unifying the units, formats, and measurement methods to ensure data consistency and comparability. At the same time, we establish a data quality evaluation system to evaluate and screen data based on accuracy, completeness, timeliness, and representativeness. Data that does not meet quality requirements is eliminated to improve the reliability of the accounting model, ultimately forming a multi-level database.

[0077] 3) Model verification and optimization

[0078] Apply the constructed accounting model to actual transformer product cases and compare and verify it with the company's actual monitoring or existing carbon footprint data. If the model calculation results deviate significantly from the actual situation, the model parameters and calculation methods need to be checked and corrected.

[0079] By combining methods such as sensitivity analysis, we can identify the key factors and links that significantly impact the carbon footprint of transformer products, and then optimize and improve the model accordingly to enhance its accuracy and applicability. For example, if we find that energy consumption during the manufacturing phase significantly contributes to the carbon footprint, we can further refine the data collection and calculation methods for energy consumption during this phase.

[0080] This method, based on a full life cycle assessment (LCA) of transformer product carbon footprint modeling, draws on IPCC theory, LCA calculation methods, and relevant standards such as carbon footprint. It divides system boundaries according to the full life cycle stages, constructs a product carbon footprint accounting model, and clarifies the specific requirements for data collection and processing. Based on a unified model and algorithm, it completes carbon footprint accounting and analysis, helping power grid companies achieve green procurement and guiding upstream and downstream companies in achieving green development.

[0081] like Figure 1 As shown in the figure, this flowchart systematically constructs a methodological framework for calculating the carbon footprint of power transformers based on life cycle assessment (LCA). The process starts with the determination of objectives and scope, clarifying the research scope and product selection. On this basis, a comparability benchmark is established through the standardization of functional units, and the boundaries of the full life cycle system are defined based on the characteristics of the transformer industry. The activity level factor is the core input parameter for carbon emission calculations, and the system incorporates quantitative data from all stages of the transformer's life cycle. Emission factors are combined to achieve carbon equivalent conversion for each unit process. This framework combines methodological rigor with engineering applicability, providing power equipment manufacturers with a traceable and quantifiable carbon footprint assessment tool.

[0082] like Figure 2 As shown, the transformer carbon footprint accounting system is built based on the Life Cycle Assessment (LCA) methodology. Based on this, a hierarchical coupling algorithm is employed to implement differentiated carbon emissions accounting for six key processes: raw material acquisition, raw material transportation, manufacturing, product transportation, product use, and decommissioning and recycling. This is based on input material usage and activity intensity data. To ensure cross-batch data comparability, discrete data is normalized to standard functional units. A training dataset is constructed based on dictionary data, and a triple verification mechanism is implemented using the mass conservation method, production capacity conversion method, and electric carbon analysis model to ensure the physical consistency and accuracy of input and output data.

[0083] like Figure 3As shown, the system boundaries for the transformer's full lifecycle carbon footprint follow a "cradle-to-grave" approach, focusing on the core links directly related to product carbon emissions. These include raw material acquisition (including embodied carbon in mining and processing), transportation (calculating energy consumption based on the logistics chain), manufacturing (process energy consumption and waste emissions), product use (operating losses and maintenance impacts), and decommissioning and recycling (offsetting disassembly energy consumption with carbon dioxide emissions from recycled materials). The definition of system boundaries clarifies the scope of accounting and attribution of responsibilities, avoiding duplicate carbon emissions calculations or missing key data due to overlapping links, and ensuring the scientific nature, comparability, and traceability of the results.

[0084] Finally, all parts not fully described in the present invention adopt mature equipment and mature technical means in the prior art.

[0085] The present invention and its embodiments are described above. This description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if a person skilled in the art is inspired by this and, without departing from the purpose of the present invention, designs structures and embodiments similar to this technical solution without inventiveness, they shall fall within the scope of protection of the present invention.

Claims

1. A transformer product carbon footprint modeling method based on full life cycle assessment, characterized by: The following steps are involved: S1: System boundary confirmation: Define the six stages of the transformer's full life cycle carbon footprint, including raw material acquisition, raw material transportation, production and manufacturing, product transportation, product use and decommissioning and recycling; S2: Multi-source data integration: Collect carbon emission data for each stage, including: S2.1: Raw materials acquisition stage: data on the embodied carbon emissions from the mining, processing, and transportation of silicon steel, copper and aluminum conductors, insulating media, and structural materials; S2.2: Raw materials transportation stage: dynamic emission factors based on supplier's geographic coordinates, transportation mode, vehicle type, load factor, transportation distance, and fuel type; S2.3: Manufacturing stage: workshop-level process energy consumption, equipment operating parameters, waste disposal data, and electricity consumption data; S2.4: Product transportation stage: product weight, transportation distance, transportation mode and vehicle energy efficiency parameters; S2.5: Product use phase: operating environment parameters, load rate curve, regional power grid carbon emission factor, and maintenance energy consumption data; S2.6: Decommissioning and recycling phase: dismantling energy consumption, transportation distance, waste disposal routes, and carbon offset data for recycled materials; S3: Accounting model construction: Establish a hierarchical accounting model consisting of six core sub-modules: Raw material acquisition stage: C raw =∑Q m ·EF m ; Raw materials transportation stage: C trans_raw =∑(Q·EF trans d); Manufacturing stage: C prod =∑E total ·EF energy ; Product transportation stage: C trans_prod =∑(Q prod ·EF trans d); Product use stage: C use =∑T·P avg ·EF grid ; Decommissioning and recycling phase: C Eol =Q recycled ·EF recycling -Q refused β; Among them, C raw is the carbon footprint of the raw material acquisition stage, Qm is the material usage, EF m is the material emission factor; C trans_raw is the carbon footprint of the raw material transportation stage, EF trans is the transport emission factor, d is the transport distance; C prod is the carbon footprint of the manufacturing stage, E total is the total energy consumption, EF energy is the unit energy emission factor; C trans_prod is the carbon footprint of the product transportation stage; C use is the carbon footprint of the product during use, T is the operating time, P avg is the average power, EF grid is the grid emission factor; C Eol Q is the carbon footprint of the decommissioning and recycling phase, recycled is the amount of recycled material, EF recycling is the recovery emission factor, Q refused is the amount of waste materials, β is the carbon emission coefficient of waste materials; S4: Model validation and optimization: Verify the accuracy of the model through actual case data and optimize key parameters through sensitivity analysis.

2. The method for modeling the carbon footprint of a transformer product based on a full life cycle assessment according to claim 1, characterized in that: In the system boundary confirmation step, the transformer life cycle approach is used to define the scope, exclude irrelevant links, and ensure that the responsibility for carbon emissions is clearly allocated.

3. The method for modeling the carbon footprint of a transformer product based on a full life cycle assessment according to claim 1, characterized in that: In the multi-source data integration step, the Internet of Things is used to collect real-time operation data, and cross-industry data collaboration is achieved through the big data platform.

4. The method for modeling the carbon footprint of a transformer product based on a full life cycle assessment according to claim 1, characterized in that: In the accounting model construction step, the mass conservation method, production capacity conversion method and electric carbon analysis model are introduced for triple verification to ensure data consistency.

5. The method for modeling the carbon footprint of a transformer product based on a full life cycle assessment according to claim 1, characterized in that: In the model application step, a carbon footprint digital twin model is generated to support the carbon footprint assessment of power transmission and distribution equipment such as power capacitors and smart meters.

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