Method for accounting carbon footprint of product supply chain

Abstract

The invention relates to the technical field of supply chain management, and discloses a product supply chain carbon footprint accounting method, which comprises the following steps: (1) data collection and arrangement: collecting time sequence activity data of each link of a supply chain, including production data, transportation data and supplier data; acquiring corresponding emission factors including emission factors with specific areas and time; (2) constructing a dynamic life cycle evaluation model: establishing the dynamic life cycle evaluation model containing time dynamics, and taking activity data and emission factors as time-dependent input parameters; by introducing dynamic life cycle evaluation, the method can capture the time change of carbon emission of each link of the supply chain, including seasonal fluctuation, policy change, market demand change and other factors, and considers the influence of geographic positions, transportation routes and regional energy structures on carbon emission in detail in combination with the GIS technology, so that the method has the advantages of high efficiency and high reliability. The supply chain carbon footprint accounting precision is significantly improved, and the result is more accurate and reliable.

Description

Technical Field

[0001] The present invention relates to the technical field of supply chain management, and in particular to a method for calculating the carbon footprint of a product supply chain. Background Art

[0002] As global climate change becomes increasingly serious and the concept of sustainable development becomes more popular, companies are faced with the urgent need to reduce carbon emissions and fulfill environmental responsibilities in their business activities. As the entire process of a product from raw material acquisition to final delivery to consumers, the accounting and management of its carbon footprint is crucial to the company's sustainable development strategy. Accurately calculating the carbon footprint of a product supply chain not only helps companies identify high-carbon emission links and develop effective emission reduction strategies, but also enhances their market competitiveness and brand image.

[0003] Currently, many companies use life cycle assessment (LCA) methods to calculate the carbon footprint of their products. However, traditional LCA methods are usually static, based on average data and fixed system boundaries, and lack consideration of temporal dynamics and spatial geographic information. In addition, existing carbon footprint accounting methods often fail to make full use of geographic information system (GIS) technology and cannot accurately reflect the impact of the geographical location, transportation routes and regional energy structure of each link in the supply chain on carbon emissions. These methods meet the basic carbon footprint accounting needs to a certain extent, but they are limited in accuracy and practicality.

[0004] Existing technologies fail to comprehensively consider the temporal dynamic changes and spatial geographic characteristics of carbon emissions in the supply chain, resulting in low accuracy in accounting results and failure to accurately capture the temporal and spatial changes in carbon emissions at each link. This limits companies’ ability to accurately identify high-emission links in the supply chain and formulate effective emission reduction strategies, affecting supply chain optimization and the realization of sustainable development goals. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a method for calculating the carbon footprint of a product supply chain, which solves the problem that the prior art cannot comprehensively consider the temporal dynamic changes and spatial geographical characteristics of supply chain carbon emissions, resulting in low accuracy in carbon footprint calculation.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for calculating the carbon footprint of a product supply chain, comprising the following steps:

[0007] (1) Data collection and organization:

[0008] Collect time series activity data at all stages of the supply chain, including production data, transportation data, and supplier data;

[0009] Obtain corresponding emission factors, including region- and time-specific emission factors;

[0010] (2) Constructing a dynamic life cycle assessment model:

[0011] Develop dynamic life cycle assessment models that include time dynamics, using activity data and emission factors as time-dependent input parameters;

[0012] Define system boundaries and functional units, covering life cycle stages such as raw material acquisition, production, transportation, use and disposal;

[0013] (3) Integrate GIS data for spatial analysis:

[0014] Obtain geographic location data of supply chain-related locations and establish a spatial database;

[0015] Use geographic information systems to conduct spatial analysis, including transportation route optimization and regional emission factor overlay;

[0016] (4) Carbon emission calculation and result analysis:

[0017] Using dynamic dynamic life cycle assessment models and GIS spatial analysis results, calculate the carbon emissions of each link in the supply chain at different times and geographical locations;

[0018] Summarize the carbon emissions of each link to obtain the total carbon footprint of the product supply chain.

[0019] Preferably, the activity data includes:

[0020] Production data: Obtain energy consumption, raw material usage and waste generation in the production process from the enterprise resource planning system or energy consumption meter;

[0021] Transportation data: Tracking equipment and logistics provider data through geographic information systems to obtain transportation methods, vehicle types, transportation distances, loads and fuel consumption;

[0022] Supplier data: Obtain suppliers’ production energy consumption and emissions data through data collection questionnaires and data sharing agreements.

[0023] Preferably, obtaining emission factors includes:

[0024] Obtain standard emission factors from international and national authoritative sources, such as the IPCC and GHG Protocol;

[0025] Obtain carbon emission factors for power grids in each region, taking into account temporal variations and regional differences.

[0026] Preferably, the steps of constructing a dynamic life cycle assessment model include:

[0027] Establish modules for each unit process in the dynamic life cycle assessment model software, including raw material production, manufacturing, transportation, use and waste treatment;

[0028] Set key parameters as time-dependent variables to support the input and calculation of time series data;

[0029] Through formulas or scripts, related parameters are associated to achieve parametric modeling.

[0030] Preferably, the steps of integrating geographic information system data for spatial analysis include:

[0031] Use geocoding tools to convert addresses of supply chain-related locations into geographic coordinates;

[0032] Obtain spatial context data on road networks, administrative divisions, and topography from public data sources;

[0033] Leverage GIS network analysis capabilities to optimize transportation routes, taking into account distance, time, cost and carbon emissions.

[0034] Preferably, the spatial analysis further comprises:

[0035] Superimpose regional power grid carbon intensity data to calculate the carbon emission differences of electricity consumption in different regions;

[0036] Spatial interpolation methods were used to estimate emission factors for missing areas.

[0037] Preferably, the steps of calculating carbon emissions include:

[0038] For each unit process, apply the formula:

[0039] Carbon emissions = transportation distance x emission factor;

[0040] The calculation is repeated over the time step to obtain the carbon emission data at each time point and geographical location.

[0041] Preferably, the process further includes scenario analysis and optimization steps, specifically including:

[0042] Set different scenario parameters, specifically: technology upgrades, supplier changes, transportation mode adjustments, and policy changes;

[0043] Adjust relevant parameters in the dynamic life cycle assessment model, run the model, and obtain carbon emission results under various scenarios;

[0044] Compare and analyze the results of different scenarios and formulate supply chain optimization strategies and emission reduction measures.

[0045] Preferably, the process further includes steps of report preparation and visualization, specifically including:

[0046] Associate carbon emission results with geographic locations and visualize them on a geographic information system platform;

[0047] Use charts and maps to show the temporal and spatial distribution of carbon emissions and identify high-emission links and areas;

[0048] Prepare detailed accounting reports, including methods, data, result analysis and optimization suggestions.

[0049] Preferably, the system deployment and continuous improvement steps are also included, specifically including:

[0050] Develop software tools that integrate LCA models and GIS functions to support automatic data updating and calculation;

[0051] Integrate the model into the company's ERP and supply chain management systems to achieve continuous monitoring and management of carbon footprint;

[0052] Establish a feedback mechanism, regularly update models and data, and adjust optimization strategies based on actual results.

[0053] The present invention provides a method for calculating the carbon footprint of a product supply chain. It has the following beneficial effects:

[0054] 1. By introducing dynamic life cycle assessment, this method can capture the temporal changes in carbon emissions in each link of the supply chain, including seasonal fluctuations, policy changes, market demand changes and other factors. Combined with GIS technology, the impact of geographical location, transportation routes and regional energy structure on carbon emissions is considered in detail. In this way, the calculation accuracy of the carbon footprint of the supply chain is significantly improved, and the results are more accurate and reliable.

[0055] 2. The present invention can carefully identify the links and areas with high carbon emissions in the supply chain. Through scenario analysis and model simulation, enterprises can evaluate the effects of different emission reduction measures and supply chain adjustment plans. For example, compare the impact of different transportation methods, supplier selection or production process upgrades on carbon emissions. This provides a scientific basis for enterprises to formulate effective supply chain optimization and emission reduction strategies, which helps to reduce operating costs and improve supply chain efficiency.

[0056] 3. The accurate carbon footprint accounting and active emission reduction actions of the present invention demonstrate the company's commitment to sustainable development. By disclosing carbon emission information and emission reduction results, companies can enhance their green brand image and meet the environmental protection expectations of consumers and investors. This will not only help meet the market demand for low-carbon products, but also enhance the competitiveness of companies in domestic and foreign markets and obtain more business opportunities. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0058] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by ordinary technicians in the field without creative work based on the embodiments of the present invention shall fall within the scope of protection of the present invention.

[0059] Please refer to the attached Figure 1 The embodiment of the present invention provides a method for calculating the carbon footprint of a product supply chain, comprising the following steps:

[0060] Step 1: Determine the accounting scope and objectives

[0061] 1.1 Clarify the purpose of accounting

[0062] Before implementing this method, you first need to clarify the purpose and needs of the accounting. Through communication with the company's management and relevant departments, determine the main goals of calculating the carbon footprint of the product supply chain, such as: Compliance needs: meet the carbon emission reporting requirements of the government or industry. Internal management: provide data support for companies to formulate emission reduction strategies and optimize the supply chain. Market competition: enhance the company's green image and meet consumers' demand for low-carbon products.

[0063] 1.2 Defining system boundaries

[0064] The determination of system boundaries is the key to life cycle assessment and directly affects the completeness and accuracy of accounting results. System boundaries should cover all relevant links of products from raw material acquisition to waste treatment, including: Raw material acquisition: mining, processing and transportation of raw materials. Manufacturing: the production and assembly process of products. Transportation and logistics: transportation and warehousing of products in the supply chain.

[0065] Use phase: the process of using the product in the hands of consumers (if applicable). Disposal: the recycling, reuse or disposal of the product after its life cycle. At the same time, it is necessary to clarify the geographical scope, determine the countries and regions involved, and the time range, determine the time span (such as annual) and time resolution (such as monthly, quarterly) of the accounting.

[0066] 1.3 Determine the functional unit

[0067] Functional units are the benchmark for life cycle assessment and are used to quantify inputs and outputs. Appropriate functional units should be selected based on product characteristics, for example: Per product (e.g., a mobile phone, a piece of clothing). Per kilogram of product (applicable to bulk or bulk commodities). Per unit of service (e.g., per kilogram of transport service).

[0068] Step 2: Data collection and organization

[0069] 2.1 Data Requirements Analysis

[0070] According to the system boundary, list in detail the types of data that need to be collected, including: Activity data: reflects the actual activity volume of each link in the supply chain. Production data: energy consumption (electricity, gas, fuel oil), raw material usage, waste generation. Transportation data: transportation mode, distance, load, fuel type and consumption. Supplier data: supplier's production energy consumption and emission information. Emission factor: coefficient used to convert activity data into carbon emissions. Standard emission factor: universal emission factor from authoritative organizations. Regional and time-specific emission factors: taking into account the differences in energy structure in different regions and time.

[0071] 2.2 Activity data collection

[0072] Production data collection: Extract energy and raw material usage data in the production process from the company's ERP system. Install energy consumption meters on key equipment to monitor energy consumption in real time. Record waste generation and treatment methods.

[0073] Transport data collection: Work with the logistics department to obtain information on transport methods, routes and vehicles. Use GPS devices to track transport routes and accurately calculate transport distances. Record fuel types and consumption.

[0074] Supplier data collection: Send data collection questionnaires to suppliers to obtain their production energy consumption and emission data. Sign a data sharing agreement to ensure the accuracy and timeliness of the data.

[0075] 2.3 Emission factor acquisition

[0076] Standard emission factors: Obtain general emission factors from authoritative organizations such as IPCC and GHG Protocol, and refer to emission factor guidelines issued by countries or regions.

[0077] Regional and time-specific emission factors: Obtain carbon emission factors for power grids in each region to reflect the local energy structure. Consider seasonal or time-based changes in emission factors, such as differences in carbon intensity corresponding to peak and valley electricity prices.

[0078] 2.4 Data collation and verification

[0079] Data collation: Use Excel or database software to classify and collate the collected data. Ensure that the data format is unified and the units are consistent to facilitate subsequent calculations.

[0080] Data verification: Check the rationality of data by comparing with historical data or industry averages. Identify and handle outliers and missing values, and confirm with data providers when necessary.

[0081] Step 3: Build a dynamic life cycle assessment (LCA) model

[0082] 3.1 Selection of LCA software and tools

[0083] Choose the right LCA software to support dynamic analysis and data integration: OpenLCA: open source software that supports custom databases and models. SimaPro, GaBi: commercial software that provides rich functions and database support.

[0084] 3.2 Establishing the LCA model structure

[0085] Define unit processes: Establish corresponding unit process modules according to each link of the supply chain, such as raw material production, product manufacturing, transportation, use and waste disposal.

[0086] Set inputs and outputs: Define the input and output flows of materials and energy for each unit process, including raw materials, energy consumption, products, and waste.

[0087] Introduction of time dynamics: Activity data and emission factors are set as time series data to reflect the characteristics of changes over time. Time parameters are added to the model to support calculations based on time steps (such as monthly).

[0088] 3.3 Parametric Modeling

[0089] Set variable parameters: Set key parameters (such as energy consumption, production volume, and transportation distance) as adjustable variables to facilitate scenario analysis.

[0090] Parameter correlation and calculations: Use formulas or scripts to correlate related parameters, for example, an increase in production leads to an increase in energy consumption and raw material requirements.

[0091] Step 4: Integrate GIS data for spatial analysis

[0092] 4.1 Obtaining geographic location data

[0093] Supply chain node positioning: Use geocoding tools to convert the addresses of factories, warehouses, suppliers, and customers into longitude and latitude coordinates. Establish a geographic database of supply chain nodes.

[0094] Spatial background data collection: Obtain spatial data such as road networks, administrative divisions, and topography from the National Geographic Information Center or open source platforms. Obtain regional power grid distribution and energy infrastructure data.

[0095] 4.2 GIS platform construction

[0096] Choose GIS software: ArcGIS: Provides powerful spatial analysis capabilities and professional support.

[0097] QGIS: Open source software, flexible and easy to use, supports custom plug-ins.

[0098] Data import and management: Import supply chain nodes and spatial background data into GIS software. Establish spatial database to manage and store spatial data.

[0099] 4.3 Spatial Analysis

[0100] Transportation route optimization: Utilize the network analysis function of GIS to calculate the optimal transportation route between nodes in the supply chain, taking into account multiple factors such as shortest distance, least time, lowest cost and lowest carbon emissions.

[0101] Regional emission analysis: Superimpose regional power grid carbon intensity data to analyze differences in electricity carbon emissions in different regions. Use spatial interpolation methods to estimate emission factors for missing areas.

[0102] 4.4 Integration of GIS and LCA Model

[0103] Data exchange: Export GIS analysis results (such as transport distances, regional emission factors) into standard formats (such as CSV, Excel) and import them into the LCA model as input parameters.

[0104] Spatial visualization: Associate the carbon emission results calculated by LCA with the geographic location and perform spatial visualization on the GIS platform.

[0105] Step 5: Carbon emission calculation and result analysis

[0106] 5.1 Calculation of carbon emissions from unit processes

[0107] Apply the calculation formula:

[0108] Carbon emissions = transportation distance x emission factor;

[0109] Dynamic calculation: For each unit process, the calculation is repeated over the time step to obtain the carbon emission data at each time point.

[0110] 5.2 Summary of overall carbon emissions in the supply chain

[0111] Process accumulation: Accumulate the carbon emission data of all unit processes in time and space to obtain the carbon emissions of the entire supply chain.

[0112] Scope classification: According to the greenhouse gas accounting standards, emissions are classified into: Scope 1: Direct emissions (emissions from the company itself). Scope 2: Indirect energy emissions (emissions from purchased electricity, heat, etc.). Scope 3: Other indirect emissions (emissions from upstream and downstream of the supply chain).

[0113] 5.3 Result verification and calibration

[0114] Comparative analysis: Compare the calculated results with historical data, industry benchmarks or third-party accounting results to verify the rationality of the results.

[0115] Uncertainty Analysis: Monte Carlo simulation is used to randomly sample key parameters and evaluate the uncertainty range of the results.

[0116] Step 6: Scenario Analysis and Optimization

[0117] 6.1 Scenario Design

[0118] Set different scenario parameters: Technology upgrade scenario: Introduce new technology or equipment to improve energy efficiency and reduce emissions. Supplier change scenario: Select suppliers with low carbon emissions and optimize the supply chain structure. Transportation mode adjustment scenario: Change the transportation mode or route and choose a plan with lower carbon emissions. Policy change scenario: Simulate the impact of policies such as carbon taxes and emission limits.

[0119] 6.2 Model operation and result analysis

[0120] Parameter adjustment: According to the scenario settings, adjust the relevant parameters in the model, such as energy consumption rate, emission factor, transportation distance, etc.

[0121] Run the model: Run the adjusted model in the LCA software to calculate the carbon emission results under each scenario.

[0122] Comparison and interpretation of results: Compare and analyze the carbon emission results of different scenarios, identify the links with the greatest emission reduction potential, and evaluate the feasibility and economic efficiency of the optimization plan.

[0123] 6.3 Optimization Strategy Formulation

[0124] Supply chain optimization: Based on the results of scenario analysis, specific supply chain optimization measures are proposed, such as adjusting suppliers and improving logistics networks.

[0125] Implementation of emission reduction measures: Develop an implementation plan, clarify responsible departments, time nodes and resource requirements.

[0126] Step 7: Report preparation and visualization

[0127] 7.1 Preparation of Carbon Footprint Report

[0128] Report content: Abstract: Overview of the accounting purpose, methods and main results. Methods and data: Detailed description of the accounting methods, data sources and assumptions. Results presentation: Present carbon emission results in the form of charts and text. Scenario analysis: Describe the analysis results and impacts under different scenarios. Recommendations and action plans: Propose optimization suggestions and implementation plans.

[0129] Compliance with standards: Reports should follow relevant standards, such as ISO 14064 and GHG Protocol, to ensure the credibility and comparability of the results.

[0130] 7.2 Visualization

[0131] Map visualization: Display the spatial distribution of carbon emissions on the GIS platform, and intuitively identify high-emission areas and links.

[0132] Graphical display: Use bar charts, line charts, pie charts, etc. to show the temporal changes and composition of carbon emissions.

[0133] Interactive dashboards: Develop interactive visualization tools to facilitate management and relevant personnel to query and analyze data.

[0134] Step 8: System deployment and continuous improvement

[0135] 8.1 Software Tool Development and Integration

[0136] Develop customized tools: Develop a software platform that integrates LCA models and GIS functions to achieve automatic data updating and calculation.

[0137] System integration: Integrate the carbon footprint accounting system with the company's ERP and supply chain management systems to achieve information sharing and process automation.

[0138] 8.2 Data Update and Maintenance

[0139] Regular updates: Establish a data update mechanism to obtain the latest activity data and emission factors on a regular basis.

[0140] Data quality management: Establish data management specifications to ensure the accuracy, consistency and completeness of data.

[0141] 8.3 Continuous Improvement Mechanism

[0142] Performance monitoring: Regularly evaluate the effectiveness of emission reduction measures and monitor changing trends in carbon emissions.

[0143] Feedback and optimization: Collect feedback from various departments and adjust models and strategies in a timely manner.

[0144] Training and publicity: Carry out training to improve employees' low-carbon awareness and skills and promote participation of all employees.

[0145] Embodiment 1:

[0146] To better illustrate the method of the present invention, a company that produces electronic products (such as smart phones) is taken as an example to describe in detail how to apply the method to calculate the carbon footprint of its product supply chain.

[0147] The company is a multinational enterprise that produces and sells smartphones, with a supply chain covering many countries and regions around the world. The main components of the products include chips, screens, batteries, casings, etc., which are provided by different suppliers. Manufacturing is completed in China, and the products are sold to the global market. The company hopes to accurately calculate the carbon footprint of its smartphone product supply chain to meet regulatory requirements, enhance its green image, and provide a basis for supply chain optimization and emission reduction strategy formulation.

[0148] Step 1: Determine the accounting scope and objectives

[0149] 1.1 Clarify the purpose of accounting

[0150] Compliance requirements: Meet the carbon emission reporting requirements of various countries and comply with relevant environmental laws and regulations. Internal management: Identify high-emission links in the supply chain and formulate emission reduction strategies. Market competition: Show consumers the company's environmental commitment and enhance the brand image.

[0151] 1.2 Defining system boundaries

[0152] Life cycle stages: Raw material acquisition: production and transportation of components such as chips, screens, batteries, and casings. Manufacturing: assembly and testing process of smartphones. Transportation and logistics: transportation from production plants to distribution centers around the world. Use phase: consider the energy consumption of smartphones during use (optional). Disposal: recycling and treatment after the product life cycle ends (optional). Geographical scope: mainly involves China (manufacturing), South Korea (screen supplier), Japan (battery supplier), the United States (chip supplier) and global sales markets.

[0153] 1.3 Determine the functional unit

[0154] Functional unit: A smartphone.

[0155] 1.4 Determine time boundaries

[0156] Time range: Calculate the supply chain carbon footprint of the previous year (e.g. 2022). Time resolution: Collect and calculate data on a monthly basis.

[0157] Step 2: Data collection and organization

[0158] 2.1 Data Requirements Analysis

[0159] Activity data: Component production data: Suppliers’ energy consumption, raw material usage, and waste generation. Manufacturing data: Factory electricity and gas consumption, production equipment energy consumption, waste disposal, etc. Transportation data: Logistics data of component transportation to the factory, and logistics data of finished products transportation to various markets.

[0160] Emission factors: Carbon emission factors of power grids in various countries. Fuel emission factors of different modes of transportation (such as sea, air, and land transportation).

[0161] 2.2 Activity data collection

[0162] Component supplier data: Send data collection questionnaires to major component suppliers in South Korea, Japan, and the United States to obtain energy consumption and emission data in their production processes. Sign confidentiality and data sharing agreements to ensure the reliability of the data.

[0163] Manufacturing data: Obtain electricity and gas consumption data during the production process from the ERP system of the Chinese factory. Install energy consumption metering equipment to monitor the energy consumption of key production equipment in real time.

[0164] Transport data: Work with logistics departments and logistics providers to obtain detailed data on component and finished product transport, including transport mode, distance, fuel type and consumption. Use GPS devices to track transport routes and accurately record transport distances.

[0165] 2.3 Emission factor acquisition

[0166] Standard emission factors: Obtain standard fuel emission factors from IPCC and GHG Protocol.

[0167] Regional power grid emission factors: Obtain power grid carbon emission factors for countries such as China, South Korea, Japan, and the United States, taking into account differences in energy structures among countries.

[0168] 2.4 Data collation and verification

[0169] Data collation: Use Excel and database software to collate all collected data according to supply chain links and chronological order.

[0170] Data verification: Check the rationality of data by comparing with historical data and industry averages. Communicate with suppliers and internal departments to confirm abnormal data.

[0171] Step 3: Build a dynamic life cycle assessment (LCA) model

[0172] 3.1 Selection of LCA software and tools

[0173] Choose OpenLCA software, which supports custom models and databases and is suitable for data integration of multinational supply chains.

[0174] 3.2 Establishing the LCA model structure

[0175] Define unit processes: Component production process: Establish unit processes for each major component (chip, screen, battery, casing), and input energy consumption and emission data provided by suppliers. Manufacturing process: Establish the production process unit of the factory, and input energy consumption and material use data. Transportation process: Establish unit processes for component transportation and finished product transportation respectively, and input transportation mode, distance and fuel consumption data.

[0176] Set inputs and outputs: Define the inputs (raw materials, energy) and outputs (products, emissions, waste) of each unit process.

[0177] 3.3 Introduction of temporal dynamics

[0178] Set activity data and emission factors as monthly time series data to reflect changes in each link of the supply chain over time. Add time parameters to the model to support carbon emissions calculation on a monthly basis.

[0179] 3.4 Parametric Modeling

[0180] Set key parameters (such as production volume, energy consumption, and transportation distance) as variable parameters to facilitate subsequent scenario analysis. Use formulas to relate parameters, for example, an increase in production volume will lead to an increase in energy consumption and raw material use.

[0181] Step 4: Integrate GIS data for spatial analysis

[0182] 4.1 Obtaining geographic location data

[0183] Supply chain node positioning: Get the geographic coordinates of suppliers, factories, logistics centers and major markets. Spatial background data collection: Get spatial data such as global road networks, shipping routes, and air routes.

[0184] 4.2 GIS platform construction

[0185] Choose ArcGIS software, which supports global spatial analysis and professional map production. Import supply chain nodes and spatial background data to establish a spatial database.

[0186] 4.3 Spatial analysis

[0187] Transportation route optimization: Use ArcGIS network analysis tools to calculate the optimal transportation routes from suppliers to factories and from factories to markets for finished products, taking into account factors such as mode of transportation (sea, air, land), distance, time, cost, and carbon emissions.

[0188] Regional emission analysis: superimpose carbon emission factor data of power grids in various countries to analyze the differences in carbon emissions from electricity consumption in different regions.

[0189] 4.4 Integration of GIS and LCA Model

[0190] The transport distance, route and regional emission factor data obtained from GIS analysis were exported and imported into the OpenLCA model as input parameters for calculation. In ArcGIS, the carbon emission results calculated by the LCA model were associated with the geographical location to produce a carbon emission spatial distribution map.

[0191] Step 5: Carbon emission calculation and result analysis

[0192] 5.1 Calculation of carbon emissions from unit processes

[0193] Application formula:

[0194] Carbon emissions (kg CO2e) = activity data × emission factor

[0195] For each unit process and each month, calculate the carbon emissions. For example: Chip production: supplier's monthly energy consumption (kWh) × local grid emission factor (kg CO2e / kWh). Transportation process: transportation distance (km) × emission factor of transportation mode (kg CO2e / km).

[0196] 5.2 Summary of overall carbon emissions in the supply chain

[0197] The carbon emission data of each unit process is aggregated by time and supply chain stage to obtain the carbon emissions of each month and each link. It is classified into scope 1, scope 2 and scope 3 emissions to facilitate compliance with reporting standards.

[0198] 5.3 Result verification and calibration

[0199] Compare the calculated results with historical data and industry benchmarks to verify the rationality of the results. Perform uncertainty analysis to evaluate the impact of key parameters (such as emission factors, energy consumption data) on the results.

[0200] Step 6: Scenario Analysis and Optimization

[0201] 6.1 Scenario Design

[0202] Scenario 1: Introducing renewable energy: Assume that solar power generation facilities are installed in factories in China to reduce electricity purchases from the grid.

[0203] Scenario 2: Supplier change: Select a battery supplier with lower carbon emissions, located geographically closer, to reduce transportation emissions.

[0204] Scenario 3: Adjustment of transportation mode: Change part of air transportation to sea transportation to reduce carbon emissions during transportation.

[0205] 6.2 Model operation and result analysis

[0206] Parameter adjustment: Adjust the energy consumption, emission factors and transportation data in the model according to the scenario settings. Run the model: Run the adjusted model in OpenLCA to calculate the carbon emission results under each scenario.

[0207] Compare the total carbon emissions and changes in each link between the baseline scenario and each optimized scenario.

[0208] 6.3 Optimization Strategy Formulation

[0209] Based on the results of scenario analysis, determine the most feasible and emission reduction-effective optimization plan.

[0210] Develop implementation plans, such as installing solar energy facilities in factories to reduce carbon emissions from electricity. Establish cooperation with suppliers with low carbon emissions and optimize the supplier network. Optimize logistics strategies, increase the proportion of sea transportation, and reduce carbon emissions from transportation.

[0211] Step 7: Report preparation and visualization

[0212] 7.1 Preparation of Carbon Footprint Report

[0213] Report content: Summary: The purpose, methods and main findings of the accounting. Methods and data: A detailed description of the accounting process, data sources and assumptions. Results presentation: Use charts to show the carbon emissions of each link in the supply chain and the overall situation. Scenario analysis: Describe the emission reduction effect of each optimization scenario. Recommendations and action plans: Propose specific emission reduction measures and implementation steps. Standard compliance: The report complies with the requirements of ISO 14064 and GHG Protocol.

[0214] 7.2 Visualization

[0215] Map display: Create a supply chain carbon emissions map in ArcGIS to intuitively display the distribution of carbon emissions in various regions.

[0216] Graphical display: Use bar charts and line charts to display the temporal changes and composition of carbon emissions.

[0217] Interactive reports: Develop electronic interactive reports to facilitate management and relevant departments to query and analyze data.

[0218] Step 8: System deployment and continuous improvement

[0219] 8.1 Software Tool Development and Integration

[0220] Develop a customized software platform integrating OpenLCA and ArcGIS to achieve automatic data update and automatic model calculation. Connect the carbon footprint accounting system with the company's ERP and supply chain management system to achieve automatic data extraction and update.

[0221] 8.2 Data Update and Maintenance

[0222] Establish a regular data collection and update mechanism to obtain the latest activity data and emission factors every month. Develop a data quality management system to ensure the accuracy and consistency of data.

[0223] 8.3 Continuous Improvement Mechanism

[0224] Performance monitoring: Regularly monitor the implementation effect of emission reduction measures and track the changing trend of carbon emissions.

[0225] Feedback and optimization: Collect feedback from various departments and adjust optimization strategies and model parameters in a timely manner.

[0226] Training and publicity: Provide training to employees to enhance their awareness of carbon footprint accounting and emission reduction measures and promote full participation.

[0227] Embodiment 2:

[0228] To further illustrate the method of the present invention, a large food manufacturing company is taken as an example to describe in detail how to apply the method to calculate the carbon footprint of its product supply chain.

[0229] The food manufacturing company mainly produces and sells various frozen foods, such as dumplings, wontons, and buns. Its supply chain involves the procurement of agricultural raw materials, food processing and production, and cold chain logistics and distribution. The raw materials mainly include wheat flour, meat, vegetables, etc., which come from farmers and suppliers in different regions. The company hopes to accurately calculate the carbon footprint of its product supply chain, identify high-carbon emission links, formulate effective emission reduction strategies, and enhance the company's sustainable development capabilities.

[0230] Step 1: Determine the accounting scope and objectives

[0231] 1.1 Clarify the purpose of accounting

[0232] Compliance requirements: Meet the national regulatory requirements for carbon emissions in the food industry and respond to the government's call for energy conservation and emission reduction. Internal management: Optimize the supply chain structure and reduce energy consumption and operating costs. Market competition: Enhance the company's green brand image and meet consumers' demand for environmentally friendly products.

[0233] 1.2 Defining system boundaries

[0234] Life cycle stages: Raw material acquisition: wheat planting, meat and vegetable farming and planting, and raw material transportation. Manufacturing: energy consumption and emissions in food processing and packaging. Cold chain logistics: refrigerated transportation and storage of products. Use stage: energy consumption in consumer cooking and eating (optional). Waste disposal: recycling and treatment of packaging materials. Geographical scope: mainly involves the supply of raw materials in various provinces in China, the production base is located in a province, and the products are sold all over the country.

[0235] 1.3 Determine the functional unit

[0236] Functional unit: One ton of quick-frozen dumplings.

[0237] 1.4 Determine time boundaries

[0238] Time range: Calculate the supply chain carbon footprint of the previous year (e.g. 2022). Time resolution: Collect and calculate data on a quarterly basis.

[0239] Step 2: Data collection and organization

[0240] 2.1 Data Requirements Analysis

[0241] Activity data: Raw material production data: Energy consumption, fertilizer and pesticide usage in the planting and breeding of wheat, meat and vegetables. Manufacturing data: Power and gas consumption of processing equipment, energy consumption of refrigeration equipment, and packaging material usage. Cold chain logistics data: Type of refrigerated transport vehicles, transport distance, fuel consumption, and energy consumption of refrigeration equipment. Storage data: Power consumption and temperature control of cold storage.

[0242] Emission factors: Emission factors for different energy sources (electricity, diesel, natural gas). Emission factors for the production and use of fertilizers and pesticides. Emission factors for the production of packaging materials (plastics, cartons).

[0243] 2.2 Activity data collection

[0244] Raw material supplier data: Work with farmers and suppliers to obtain information on energy consumption, fertilizer and pesticide usage during planting and breeding. Obtain industry average data through relevant departments or associations to supplement missing information.

[0245] Manufacturing data: Obtain energy consumption data for production equipment and refrigeration equipment from the factory's energy management system. Record the purchase quantity and type of packaging materials.

[0246] Cold chain logistics data: Work with the logistics department to obtain the type, quantity, fuel type, single transportation distance and frequency of refrigerated transport vehicles. Monitor the energy consumption of refrigeration equipment and record the temperature control of cold storage.

[0247] 2.3 Emission factor acquisition

[0248] Energy emission factors: Obtain emission factors for electricity, diesel, and natural gas from the Energy Emission Factor Manual published by the state. Agricultural input emission factors: Refer to the IPCC guidelines to obtain emission factors for the production and use of fertilizers and pesticides. Packaging material emission factors: Obtain production emission factors for plastics and paper products from relevant industry databases.

[0249] 2.4 Data collation and verification

[0250] Data collation: Use Excel and database software to collate data by supply chain link and time sequence. Data verification: Compare with industry average data to verify the rationality of data. Communicate with suppliers and internal departments to confirm abnormal data.

[0251] Step 3: Build a dynamic life cycle assessment (LCA) model

[0252] 3.1 Selection of LCA software and tools

[0253] SimaPro software is selected for LCA modeling and multi-link data processing of complex supply chains.

[0254] 3.2 Establishing the LCA model structure

[0255] Define unit processes: Wheat planting process: including land cultivation, sowing, fertilization, irrigation, harvesting, etc. Meat and vegetable production process: including breeding feed, energy consumption, waste treatment, etc. Food processing process: flour processing, filling making, product molding, quick freezing.

[0256] Cold chain logistics process: refrigerated transportation and warehousing links.

[0257] Specify inputs and outputs: Define the material and energy inputs, product outputs, wastes, and emissions for each process.

[0258] 3.3 Introduction of temporal dynamics

[0259] The parameters of agricultural production affected by seasons (such as fertilizer application and irrigation amount) are set as time-dependent variables. Quarterly time parameters are added to the model to reflect the differences in energy consumption and emissions in different seasons.

[0260] 3.4 Parametric Modeling

[0261] Set key parameters (such as yield per unit area, energy consumption rate, and transportation distance) as variable parameters to facilitate scenario analysis. Use formulas to relate agricultural inputs and outputs, such as the relationship between fertilizer use and crop yields.

[0262] Step 4: Integrate GIS data for spatial analysis

[0263] 4.1 Obtaining geographic location data

[0264] Supply chain node positioning: Obtain the geographic coordinates of farmers, suppliers, factories, cold storage and sales markets.

[0265] Spatial background data collection: Obtain spatial information such as road network, land use, and meteorological data.

[0266] 4.2 GIS platform construction

[0267] Choose QGIS software, which supports open source plug-ins and custom development, suitable for agricultural and logistics spatial analysis. Import supply chain nodes and spatial data to establish a spatial database.

[0268] 4.3 Spatial analysis

[0269] Transport route optimization: Use QGIS network analysis plug-in to optimize the transportation routes of raw materials and products, reducing transportation distance and carbon emissions.

[0270] Regional characteristics analysis: Analyze the impact of soil and climate on agricultural production in different regions and evaluate their carbon emission differences.

[0271] Spatial interpolation: Spatial interpolation estimation of missing regional data (such as energy consumption data of some farmers).

[0272] 4.4 Integration of GIS and LCA Model

[0273] Export GIS analysis results (such as transportation distance, regional emission factors) and import them into the SimaPro model. In QGIS, visualize the carbon emission results calculated by the LCA model and create a supply chain carbon emission map.

[0274] Step 5: Carbon emission calculation and result analysis

[0275] 5.1 Calculation of carbon emissions from unit processes

[0276] Apply the calculation formula:

[0277] Carbon emissions (kg CO2e) = activity data × emission factor

[0278] For each unit process and quarter, calculate the carbon emissions. For example: Wheat cultivation: fertilizer use (tons) multiplied by the emission factor for fertilizer production and use. Refrigerated transportation: transportation distance (km) multiplied by the emission factor for refrigerated vehicles.

[0279] 5.2 Summary of overall carbon emissions in the supply chain

[0280] Summarize the carbon emissions of each unit process on a quarterly basis to obtain the total annual carbon emissions. Analyze the carbon emission proportion of each link and identify high-emission links.

[0281] 5.3 Result verification and calibration

[0282] Compare the calculation results with industry data and historical data to verify their rationality. Perform uncertainty analysis to evaluate the reliability of data and models.

[0283] Step 6: Scenario Analysis and Optimization

[0284] 6.1 Scenario Design

[0285] Scenario 1: Optimizing agricultural production methods: promoting organic farming and reducing the use of chemical fertilizers and pesticides.

[0286] Scenario 2: Improve energy efficiency: Upgrade processing equipment and refrigeration equipment to reduce energy consumption.

[0287] Scenario 3: Optimize the logistics network: Establish regional distribution centers to reduce transportation distance and frequency.

[0288] 6.2 Model operation and result analysis

[0289] Adjust model parameters to simulate carbon emissions under various scenarios. Compare and analyze the emission reduction effects and economic costs of each scenario.

[0290] 6.3 Optimization Strategy Formulation

[0291] According to the results of the scenario analysis, feasible emission reduction measures are formulated, such as: Cooperating with farmers to promote organic farming technology; Investing in upgrading energy-saving equipment and applying for government energy-saving subsidies; Optimizing the cold chain logistics network, reducing the empty load rate, and improving transportation efficiency.

[0292] Step 7: Report preparation and visualization

[0293] 7.1 Preparation of Carbon Footprint Report

[0294] Prepare detailed carbon footprint reports including methodology, data, results and recommendations. Ensure reports comply with national and international standards such as ISO 14067.

[0295] 7.2 Visualization

[0296] Create a supply chain carbon emissions map in QGIS to visually display the distribution of carbon emissions in different regions. Use charts to show the proportion and trend of carbon emissions in each link.

[0297] Step 8: System deployment and continuous improvement

[0298] 8.1 Software Tool Development and Integration

[0299] Develop customized tools that integrate SimaPro and QGIS to achieve automatic data updates and convenient model operation. Integrate with the company's management information system (MIS) to promote data sharing and process optimization.

[0300] 8.2 Data Update and Maintenance

[0301] Establish a data updating mechanism to regularly collect the latest activity data and emission factors.

[0302] 8.3 Continuous Improvement Mechanism

[0303] Regularly evaluate the effectiveness of emission reduction measures, adjust strategies, and continuously reduce carbon emissions.

[0304] Embodiment three:

[0305] To further illustrate the method of the present invention, an international clothing brand company is taken as an example to describe in detail how to apply the method to calculate the carbon footprint of its product supply chain.

[0306] The clothing brand company designs, produces and sells all kinds of clothing products. Its supply chain involves textile raw material production, fabric processing, clothing manufacturing, global logistics distribution and retail. Raw materials include cotton, polyester, wool, etc., and suppliers are spread all over the world. The company hopes to improve the transparency of the supply chain and meet the expectations of consumers and investors for sustainable development by calculating the carbon footprint of the supply chain.

[0307] Step 1: Determine the accounting scope and objectives

[0308] 1.1 Clarify the purpose of accounting

[0309] Compliance and disclosure: meet ESG reporting requirements and disclose carbon emission information to the public. Supply chain management: identify high-carbon emission suppliers and promote green transformation of the supply chain. Brand value: strengthen the brand image of sustainable development and attract consumers who pay attention to environmental protection.

[0310] 1.2 Defining system boundaries

[0311] Life cycle stages: Raw material production: cotton planting, chemical fiber production, wool collection, etc. Fabric processing: spinning, weaving, dyeing, finishing, etc. Garment manufacturing: cutting, sewing, packaging.

[0312] Logistics and distribution: Transportation and warehousing from production bases to global retail stores. Use and waste phase: Use, washing and final disposal of clothing (optional). Geographical scope: Involving multiple countries and regions in Asia, Africa, America and Europe.

[0313] 1.3 Determine the functional unit

[0314] Functional Unit: One standard T-shirt.

[0315] 1.4 Determine time boundaries

[0316] Time range: Calculate the supply chain carbon footprint of the previous year (e.g. 2022). Time resolution: Collect and calculate data on a half-year basis.

[0317] Step 2: Data collection and organization

[0318] 2.1 Data Requirements Analysis

[0319] Activity data: Raw material data: Energy consumption and chemical use in the production of cotton, polyester and wool. Processing data: Energy and water consumption in spinning, weaving and dyeing. Manufacturing data: Energy consumption and waste generation in garment factories. Logistics data: Transportation methods, distances and fuel consumption of raw materials, semi-finished products and finished products.

[0320] Emission factors: Emission factors for electricity and fuels in different countries. Emission factors for the production and use of dyes and chemicals.

[0321] 2.2 Activity data collection

[0322] Supplier data: Send data collection forms to major raw material suppliers, processing plants, and manufacturers to obtain energy consumption, material use, and emission data. Establish a supplier environmental performance database to continuously track and update data.

[0323] Logistics Data: Work with logistics departments and third-party logistics companies to obtain detailed shipping data.

[0324] Internal data: Obtain energy consumption and production data of own factories from the company's management system.

[0325] 2.3 Emission factor acquisition

[0326] Regional emission factors: Obtain emission factors of power grids in various countries to reflect the differences in energy structures in different regions.

[0327] Chemical emission factors: Refer to industry databases or literature to obtain emission factors for dyes and auxiliaries.

[0328] 2.4 Data collation and verification

[0329] Data collation: Use professional database management software to establish a supply chain carbon emissions database. Data verification: Audit and conduct on-site verification of the data provided by suppliers to ensure accuracy.

[0330] Data supplement: For missing data, industry averages or estimation methods are used to supplement them.

[0331] Step 3: Build a dynamic life cycle assessment (LCA) model

[0332] 3.1 Selection of LCA software and tools

[0333] GaBi software was selected for LCA modeling of complex, multi-regional supply chains.

[0334] 3.2 Establishing the LCA model structure

[0335] Define unit processes: Raw material production process: cotton planting (including irrigation, fertilization, picking), chemical fiber production (including raw material extraction, polymerization). Fabric processing process: spinning, weaving, dyeing, finishing, etc. Clothing manufacturing process: cutting, sewing, packaging.

[0336] Define inputs and outputs: energy and material inputs, and product and waste outputs for each process.

[0337] 3.3 Introduction of temporal dynamics

[0338] Incorporate seasonal factors (such as cotton harvest season, energy price fluctuations) into the model. Set a half-year time step to reflect the dynamic changes in the supply chain.

[0339] 3.4 Parametric Modeling

[0340] Set key parameters as variable parameters, such as supplier energy efficiency and transportation mode selection. Establish correlations between parameters to reflect the impact of upstream and downstream supply chains.

[0341] Step 4: Integrate GIS data for spatial analysis

[0342] 4.1 Obtaining geographic location data

[0343] Supply chain node positioning: Obtain the geographic coordinates of all suppliers, factories, logistics centers, and retail stores.

[0344] Spatial background data collection: obtaining global terrain, transportation networks, regional energy data, etc.

[0345] 4.2 GIS platform construction

[0346] Choose ArcGIS Online to facilitate global team collaboration and data sharing. Build a supply chain geographic information system to manage spatial data.

[0347] 4.3 Spatial analysis

[0348] Supplier geographical distribution analysis: Analyze the global distribution of suppliers and assess the impact of geographical location on carbon emissions.

[0349] Transport route optimization: Calculate the best global transport routes, taking into account the combination of sea, air and land transport.

[0350] Regional energy structure analysis: Overlay regional power grid data to assess differences in carbon emissions produced in different regions.

[0351] 4.4 Integration of GIS and LCA Model

[0352] Import the spatial analysis results into the GaBi model to adjust emission factors and transportation parameters. Visualize carbon emission data in ArcGIS and create a global supply chain carbon emission map.

[0353] Step 5: Carbon emission calculation and result analysis

[0354] 5.1 Calculation of carbon emissions from unit processes

[0355] Apply the calculation formula:

[0356] Carbon emissions (kg CO2e) = activity data × emission factor

[0357] Calculate the carbon emissions of each supplier, processing plant, and manufacturing plant. Calculate the carbon emissions of each transportation link.

[0358] 5.2 Summary of overall carbon emissions in the supply chain

[0359] Summarize the carbon emissions of all unit processes to get the carbon footprint of each T-shirt. Analyze the carbon emission contributions of different links and regions.

[0360] 5.3 Result verification and calibration

[0361] Compare with industry data and historical data to verify the rationality of the results. Conduct sensitivity analysis to identify the factors that have the greatest impact on carbon emissions.

[0362] Step 6: Scenario Analysis and Optimization

[0363] 6.1 Scenario Design

[0364] Scenario 1: Choose sustainable raw materials: Use organic cotton or recycled polyester to reduce carbon emissions in raw material production.

[0365] Scenario 2: Optimize supplier network: Give priority to suppliers with high energy efficiency and low carbon emissions.

[0366] Scenario 3: Improve transportation methods: increase the proportion of sea transportation, reduce air transportation, and reduce carbon emissions from long-distance transportation.

[0367] 6.2 Model operation and result analysis

[0368] Adjust model parameters to simulate carbon emissions under various scenarios. Compare carbon footprints of different scenarios to assess emission reduction potential and implementation costs.

[0369] 6.3 Optimization Strategy Formulation

[0370] Formulate supplier selection criteria and use carbon emissions as an important assessment indicator. Cooperate with suppliers to promote clean production and energy efficiency. Optimize global logistics strategies and reduce unnecessary transportation links.

[0371] Step 7: Report preparation and visualization

[0372] 7.1 Preparation of Carbon Footprint Report

[0373] Prepare carbon emission reports that meet the requirements of GRI and CDP for reference by the public and investors. The report content includes accounting methods, data sources, results analysis and emission reduction plans.

[0374] 7.2 Visualization

[0375] Publish supply chain carbon emissions maps on ArcGIS Online to demonstrate corporate transparency. Use interactive charts to display the carbon footprint of each product line for consumers to query.

[0376] Step 8: System deployment and continuous improvement

[0377] 8.1 Software Tool Development and Integration

[0378] Develop a cloud-based platform that integrates GaBi and ArcGIS Online to enable data collaboration and model sharing among global teams. Integrate with the company's supply chain management system (SCM) and customer relationship management system (CRM) to promote carbon emission management throughout the entire process.

[0379] 8.2 Data Update and Maintenance

[0380] Establish a regular data collection and update mechanism to continuously track suppliers’ carbon emissions performance. Apply blockchain technology to enhance the credibility and traceability of data.

[0381] 8.3 Continuous Improvement Mechanism

[0382] Hold supplier conferences regularly to share best practices and promote the sustainable development of the industry as a whole. Establish internal incentive mechanisms to encourage employees and teams to propose innovative emission reduction solutions.

[0383] Experimental steps:

[0384] Example 1: Carbon footprint calculation of electronic product supply chain

[0385] Experimental Procedure Step 1: Data Collection

[0386] Activity data collection: Component supplier data: Obtain energy consumption and emission data from suppliers of components such as chips, screens, batteries, and casings. Manufacturing data: Collect electricity and gas consumption data from Chinese factories. Transportation data: Obtain data on the mode, distance, fuel consumption, etc. of component transportation and finished product transportation.

[0387] Emission factor acquisition: Obtain standard emission factors from IPCC and GHG Protocol.

[0388] Obtain carbon emission factors for power grids in China, South Korea, Japan, the United States and other countries.

[0389] Step 2: Model construction

[0390] Build a dynamic LCA model: Use OpenLCA software to build a model that includes component production, product manufacturing, transportation, etc. Input activity data and emission factors into the model and set it as a monthly time series.

[0391] GIS integration: Use ArcGIS software to obtain the geographic location of supply chain nodes, and conduct transportation route optimization and regional emission analysis.

[0392] Step 3: Carbon emissions calculation and analysis

[0393] Unit process carbon emission calculation: Calculate the carbon emissions of each component production, manufacturing and transportation link.

[0394] Scenario analysis: Design three scenarios: introduction of renewable energy, supplier change, and transportation mode adjustment. Adjust model parameters and calculate carbon emissions under each scenario.

[0395] Step 4: Result verification and report preparation

[0396] Result verification: Compare the model calculation results with historical data and industry averages.

[0397] Report Preparation: Prepare a carbon footprint report including methodology, data, results and recommendations.

[0398] Example 2: Carbon footprint calculation for the food manufacturing enterprise supply chain

[0399] Experimental steps: Step 1: Data collection

[0400] Activity data collection: Raw material data: Collect data on the planting and breeding of wheat, meat, and vegetables. Manufacturing data: Obtain energy consumption data for processing equipment and refrigeration equipment. Cold chain logistics data: Collect energy consumption and transportation distance for refrigerated transportation and warehousing.

[0401] Emission factor acquisition: Obtain emission factors for energy, fertilizers, pesticides, and packaging materials.

[0402] Step 2: Model building

[0403] Build a dynamic LCA model: Use SimaPro software to build a model covering raw material production, food processing, and cold chain logistics. Input activity data into the model in quarterly time series.

[0404] GIS integration: Use QGIS software to obtain the geographic location of supply chain nodes, optimize transportation routes and analyze regional characteristics.

[0405] Step 3: Carbon emissions calculation and analysis

[0406] Unit process carbon emission calculation: Calculate the carbon emissions of wheat planting, meat and vegetable production, processing and cold chain logistics.

[0407] Design three scenarios: optimize agricultural production methods, improve energy efficiency, and optimize logistics networks. Adjust model parameters and calculate carbon emissions under each scenario.

[0408] Step 4: Result verification and report preparation

[0409] Result verification: Compare the model results with industry data and historical data. Report preparation: Prepare carbon footprint report to provide decision support.

[0410] Example 3: Carbon footprint calculation of clothing brand supply chain

[0411] Experimental procedures

[0412] Step 1: Data Collection

[0413] Activity data collection: Raw material data: Collect production data of cotton, polyester, and wool. Processing and manufacturing data: Obtain energy consumption data for spinning, weaving, dyeing, and garment manufacturing. Logistics data: Collect global transportation methods, distances, and fuel consumption.

[0414] Emission factor acquisition: Obtain emission factors of power grids and chemicals in various countries.

[0415] Step 2: Model building

[0416] Build a dynamic LCA model: Use GaBi software to build a model covering raw material production, fabric processing, clothing manufacturing, and logistics. Set the activity data as a time series with a half-year time step.

[0417] GIS Integration: Use ArcGIS Online to obtain the geographic locations of global supply chain nodes. Perform supplier geographic distribution analysis and transportation route optimization.

[0418] Step 3: Carbon emissions calculation and analysis

[0419] Unit process carbon emission calculation: Calculate the carbon emissions from raw material production, processing, manufacturing and transportation.

[0420] Design three scenarios: select sustainable raw materials, optimize supplier network, and improve transportation methods. Adjust model parameters and calculate carbon emissions under each scenario.

[0421] Step 4: Result verification and report preparation

[0422] Result verification: Compare the model results with industry data and historical data. Report preparation: Prepare carbon emission reports that meet the requirements of GRI and CDP.

[0423] Table 1 Comparison of carbon emission results of each example in the baseline scenario

[0424] Links / Implementation Examples Embodiment 1 Embodiment 2 Embodiment 3 Raw material production 25 500 8 Manufacturing 15 300 5 Transport and Logistics 10 200 12 Use phase 5 - 2 Disposal 2 50 1 Total carbon emissions 57 1050 28

[0425] Table 2 Comparison of carbon emission reduction effects of optimization scenarios in various embodiments

[0426]

[0427]

[0428] Table 3 Comparison of total carbon emissions before and after scenario analysis for each example

[0429]

[0430] Table 4 Comparison of the proportion of carbon emissions in each link to total emissions

[0431] Links / Implementation Examples Embodiment 1 Embodiment 2 Embodiment 3 Raw material production 43.9 47.6 28.6 Manufacturing 26.3 28.6 17.9 Transport and Logistics 17.5 19.0 42.7 Use and disposal 12.3 4.8 10.7 total 100 100 100

[0432] Data result analysis: Comparison of total carbon emissions: Example 2 (food manufacturing) has the highest total carbon emissions, mainly due to the high energy consumption of raw material production (agricultural activities) and cold chain logistics in the food industry.

[0433] Example 3 (clothing brand) has the lowest total carbon emissions, but the proportion of transportation and logistics is the highest, reflecting the large transportation carbon emissions caused by the global supply chain.

[0434] Comparison of emission reduction effects: The optimization scenarios of the three embodiments all achieved a carbon emission reduction of more than 14%, demonstrating the emission reduction potential of the method of the present invention in different industries.

[0435] Example 2 achieved the maximum absolute emission reduction (150 kg CO2e / ton) by optimizing agricultural production methods.

[0436] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for calculating the carbon footprint of a product supply chain, characterized in that: The following steps are involved: (1) Data collection and organization: Collect time series activity data at all stages of the supply chain, including production data, transportation data, and supplier data; Obtain the corresponding emission factors, including region- and time-specific emission factors; (2) Constructing a dynamic life cycle assessment model: Develop dynamic life cycle assessment models that include time dynamics, using activity data and emission factors as time-dependent input parameters; Define system boundaries and functional units, covering life cycle stages such as raw material acquisition, production, transportation, use and disposal; (3) Integrate GIS data for spatial analysis: Obtain geographic location data of supply chain-related locations and establish a spatial database; Use of geographic information systems for spatial analysis, including transport route optimization and overlay of regional emission factors; (4) Carbon emission calculation and result analysis: Using dynamic dynamic life cycle assessment models and GIS spatial analysis results, calculate the carbon emissions of each link in the supply chain at different times and geographical locations; Summarize the carbon emissions of each link to obtain the total carbon footprint of the product supply chain.

2. A method for calculating the carbon footprint of a product supply chain according to claim 1, characterized in that: Activity data includes: Production data: Obtain energy consumption, raw material usage, and waste generation during the production process from the enterprise resource planning system or energy consumption meters; Transportation data: Tracking equipment and logistics provider data through geographic information systems to obtain transportation methods, vehicle types, transportation distances, loads and fuel consumption; Supplier data: Obtain suppliers’ production energy consumption and emissions data through data collection questionnaires and data sharing agreements.

3. A method for calculating the carbon footprint of a product supply chain according to claim 1, characterized in that: The acquisition of emission factors includes: Obtain standard emission factors from international and national authoritative sources, such as the IPCC and GHG Protocol; Obtain carbon emission factors for power grids in each region, taking into account temporal variations and regional differences.

4. A method for calculating the carbon footprint of a product supply chain according to claim 1, characterized in that: The steps to construct a dynamic life cycle assessment model include: Establish modules for each unit process in the dynamic life cycle assessment model software, including raw material production, manufacturing, transportation, use and waste treatment; Set key parameters as time-dependent variables to support the input and calculation of time series data; Through formulas or scripts, related parameters are associated to achieve parametric modeling.

5. A method for calculating the carbon footprint of a product supply chain according to claim 1, characterized in that: The steps to integrate GIS data for spatial analysis include: Use geocoding tools to convert addresses of supply chain-related locations into geographic coordinates; Obtain spatial context data on road networks, administrative divisions, and topography from public data sources; Leverage GIS network analysis capabilities to optimize transportation routes, taking into account distance, time, cost and carbon emissions.

6. A method for calculating the carbon footprint of a product supply chain according to claim 5, characterized in that: Spatial analysis further includes: Superimpose regional power grid carbon intensity data to calculate the carbon emission differences of electricity consumption in different regions; Emission factors for missing areas were estimated using spatial interpolation methods.

7. A method for calculating the carbon footprint of a product supply chain according to claim 1, characterized in that: in, The steps for calculating carbon emissions include: For each unit process, apply the formula: Carbon emissions = transportation distance x emission factor; The calculation is repeated over the time step to obtain the carbon emission data at each time point and geographical location.

8. A method for calculating the carbon footprint of a product supply chain according to claim 1, characterized in that: It also includes the steps of scenario analysis and optimization, including: Set different scenario parameters, specifically: technology upgrades, supplier changes, transportation mode adjustments, and policy changes; Adjust relevant parameters in the dynamic life cycle assessment model, run the model, and obtain carbon emission results under various scenarios; Compare and analyze the results of different scenarios and formulate supply chain optimization strategies and emission reduction measures.

9. A method for calculating the carbon footprint of a product supply chain according to claim 1, characterized in that: It also includes steps for report preparation and visualization, including: Associate carbon emission results with geographic locations and visualize them on a geographic information system platform; Use charts and maps to show the temporal and spatial distribution of carbon emissions and identify high-emission links and areas; Prepare detailed accounting reports including methods, data, result analysis and optimization suggestions.

10. A method for calculating the carbon footprint of a product supply chain according to claim 1, characterized in that: It also includes steps for system deployment and continuous improvement, including: Develop software tools that integrate LCA models and GIS functions to support automatic data updating and calculation; Integrate the model into the company's ERP and supply chain management systems to achieve continuous monitoring and management of carbon footprint; Establish a feedback mechanism, regularly update models and data, and adjust optimization strategies based on actual results.

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