Greenhouse gas emissions management method

Abstract

To provide a method of enabling efficient greenhouse gas emissions management, including computation of greenhouse gas emissions by a business operator.SOLUTION: A greenhouse gas emissions management method according to an embodiment of the present invention comprises receiving image data of invoice information from a business operator terminal, extracting information on emission intensity based on a learning model using the image data as input data, and recommending the information on the emission intensity.SELECTED DRAWING: Figure 11

Description

Technical Field

[0001] The present invention relates to a method for managing greenhouse gas emissions.

Background Art

[0002] Regarding the greenhouse gas emissions of businesses associated with the use of fuels, electricity, etc., a reporting system targeting SCOPE1 emissions (direct emissions of the company itself) and SCOPE2 emissions (indirect emissions of the company itself) has become widespread, and progress has been made in calculating and reducing emissions in SCOPE1 and SCOPE2.

[0003] Non-Patent Document 1 proposes calculating SCOPE3 emissions, that is, the emissions of the supply chain (the entire series of processes such as raw material procurement, manufacturing, logistics, sales, and disposal) of other related businesses, etc., as emissions other than SCOPE1 and SCOPE2 in order to further reduce the greenhouse gas emissions discharged by businesses.

Prior Art Documents

Non-Patent Documents

[0004]

Non-Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] However, although the technology disclosed in Non-Patent Document 1 discloses a method for calculating greenhouse gas emissions related to SCOPE3, etc., for each business operator, especially in enterprises and local governments, etc., a huge amount of data collection and input is required for calculating emissions, calculating the emissions, and managing the calculation results, which is extremely time-consuming and laborious. In particular, in the field of GHG emissions management, the SCOPE of emissions to be calculated is expanding, the data on which emissions calculations are based vary widely for each SCOPE, and the data management methods also differ for each business operator. Therefore, the introduction of advanced technologies for improving business efficiency has been delayed.

[0006] Therefore, an object of the present invention is to provide a method for efficiently realizing emissions management by reducing the man-hours required in the GHG emissions management field, such as calculating greenhouse gas emissions by business operators, by utilizing advanced technologies.

Means for Solving the Problems

[0007] A method for managing greenhouse gas emissions according to an embodiment of the present invention, which receives image data of invoice information from a business operator terminal, extracts information related to emission unit rates based on a learning model using the image data as input data, and recommends the information related to the emission unit rates.

Effects of the Invention

[0008] According to the present invention, it is possible to provide a method for efficiently realizing management such as calculating greenhouse gas emissions by business operators.

Brief Description of the Drawings

[0009]

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Embodiments for Carrying Out the Invention

[0010] The contents of the embodiments of the present invention are listed and described. A greenhouse gas emissions management system (hereinafter simply referred to as "system") according to an embodiment of the present invention has the following configuration. [Item 1] A method for managing greenhouse gas emissions, receiving image data of invoice information from an operator terminal, using the image data as input data, extracting information regarding emission factors based on a learning model, and a method for recommending the information regarding the emission factors. [Item 2] The learning model is generated by inputting information regarding emission factors associated with past input data similar to the input data, and is the management method described in Item 1. [Item 3] The management method according to item 2, which recommends information on emission coefficients associated with past input data similar to the input data based on the learning model. [Item 4] The management method according to item 1, wherein the input data and the past input data each include text information recognized based on image data.

[0011] <First Embodiment> Hereinafter, a system according to an embodiment of the present invention will be described with reference to the drawings.

[0012] FIG. 1 is a diagram for explaining a greenhouse gas emission management system according to a first embodiment of the present invention.

[0013] As shown in FIG. 1, in the emission management system 1 in the present embodiment, the administrator terminal 100 and a plurality of operator terminals 200A and 200B are mutually connected via a communication network NW.

[0014] For example, the management terminal 100 receives basic information on the operator and input information (for example, image data of invoice information) for calculating the greenhouse gas (for example, CO2) emission amount from the operator terminals 200A and 200B.

[0015] In addition, the management terminal 100 analyzes the received image data of the invoice information by machine learning, extracts the necessary items of the invoice information included in the image data, and calculates the greenhouse gas emission amount. Further, the management terminal 100 analyzes the calculated change (for example, increase or decrease) in the greenhouse gas emission amount over time by machine learning and predicts the cause of the change.

[0016] Furthermore, the management terminal 100 has a wallet and is connected to the public blockchain network NW. Based on the information regarding the greenhouse gas emissions for each of the above-mentioned predetermined periods, the management terminal 100 generates a single hash value using SHA256 or another hash function and records it in the blockchain network as transaction information. On the blockchain network, based on the transaction information, the hash value recorded in the previous block, and the nonce value mined by the node, this block is generated, recorded following the previous block, and a blockchain is formed. Here, the above hash generation and / or the recording of the transaction information in the blockchain can also be performed not by the management terminal 100 but via another terminal. In this case, the management terminal 100 transmits the greenhouse gas emissions calculated by the matching process to the other terminal. Furthermore, the management terminal 100 can record the information regarding the greenhouse gas emissions in the blockchain network as a smart contract. By using the smart contract, based on the information regarding the emissions, a contract regarding the emissions trading with other operators can be automatically generated, approved, and executed without the intervention of a third party. Also, with the smart contract, each operator can refer to the transaction information without going through the management terminal, improving service convenience and reducing operation costs. Furthermore, the management terminal 100 connects to a large language model (LLM, for example, GPT) 300 that receives a predetermined question from another terminal via the network through an API (Applicaton Progamming Interface), analyzes the question, and generates an answer.

[0017] Here, as described above, in a public blockchain, the approval of transactions is carried out not by a specific administrator but by an unspecified number of nodes or miners. Therefore, compared with a private blockchain, higher data immutability and fault tolerance can be ensured. Thus, since the security of transactions is ensured, it is preferable that the public blockchain is used as the destination for recording power transactions in this embodiment. Representative public blockchains include Bitcoin and Ethereum. For example, Ethereum has higher immutability and reliability among public blockchains.

[0018] In addition, the management terminal 100 can associate information related to greenhouse gas emissions with an identifier or the like and record it as a non-fungible token (Non-Fungible Token (hereinafter, referred to as "NFT")) in the blockchain network. NFT is, for example, a token issued according to the "ERC721" standard of Etherium, which is a platform of the blockchain network. It is a unit of data recorded in the blockchain network and has a non-fungible nature. Since NFT is recorded on the blockchain together with a smart contract and is traceable, it can prove transaction information including details and history such as the information of the operator managing greenhouse gas emissions.

[0019] FIG. 2 is a functional block diagram of the management terminal constituting the emission management system.

[0020] The communication unit 110 is a communication interface for communicating with an external terminal via the network NW. For example, communication is carried out according to a communication protocol such as TCP / IP (Transmission Control Protocol / Internet Protocol).

[0021] The storage unit 120 stores programs for executing various control processes and each function in the control unit 130, input data, etc., and is composed of a RAM (Random Access Memory), a ROM (Read Only Memory), etc. Further, the storage unit 120 has a business operator data storage unit 121 that stores various data related to business operators, and an AI model storage unit 122 that stores learning data and a learning model obtained by having AI (Artificial Intelligence) learn the learning data. Note that a database (not shown) storing various data may be constructed outside the storage unit 120 or the management terminal 100.

[0022] The control unit 130 controls the overall operation of the management terminal 100 by executing the programs stored in the storage unit 120, and is composed of a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), etc. As functions of the control unit 130, there are an information reception unit 131 that receives information from external terminals such as the business operator terminal 200, an image analysis unit 132 that analyzes image data such as invoice information received from the business operator terminal and calculates the greenhouse gas emissions, a cause analysis unit 133 that analyzes the cause of the time-series change in the greenhouse gas emissions calculated based on the information included in the extracted invoice information by analyzing the image data, a transaction processing unit 134 that generates a hash value by summarizing information on greenhouse gas emissions for a predetermined period and performs a process of recording it as transaction information in the blockchain network, and a report generation unit 135 that generates and transmits report data for outputting the greenhouse gas emissions and the results of the cause analysis of the change in the emissions to the business operator every predetermined period.

[0023] Also, although not shown, the control unit 130 has an image generation unit and generates screen information to be displayed via a user interface of an external terminal such as the operator terminal 200. For example, using the images and text data stored in the storage unit 120 as materials, various images and texts are arranged in a predetermined area of the user interface based on a predetermined layout rule, thereby generating information to be displayed on the user interface. The processing related to the image generation unit can also be executed by a GPU (Graphics Processing Unit).

[0024] In addition, the management terminal 100 further has a (not shown) wallet necessary for recording transaction information in the blockchain network. Note that this wallet can also be provided outside the management terminal 100.

[0025] FIG. 3 is a functional block diagram of an operator terminal constituting the emission management system.

[0026] The operator terminal 200 includes a communication unit 210, a display operation unit 220, a storage unit 230, and a control unit 240.

[0027] The communication unit 210 is a communication interface for communicating with the management terminal 100 via the network NW, and communication is performed according to a communication protocol such as TCP / IP.

[0028] The display operation unit 220 is a user interface used for an operator to input instructions and display texts, images, etc. according to input data from the control unit 240. When the operator terminal 200 is configured by a personal computer, it is composed of a display, a keyboard, and a mouse. When the operator terminal 200 is configured by a smartphone or a tablet terminal, it is composed of a touch panel or the like. This display operation unit 220 is activated by a control program stored in the storage unit 230 and executed by the operator terminal 200 which is a computer (electronic computing device).

[0029] The storage unit 230 stores various control processes, programs for executing each function in the control unit 240, input data, etc., and is composed of a RAM, a ROM, etc. Further, the storage unit 230 temporarily stores the communication content with the management terminal 100.

[0030] The control unit 240 controls the overall operation of the business operator terminal 200 by executing the programs stored in the storage unit 230, and is composed of a CPU, a GPU, etc.

[0031] FIG. 4 is a diagram for explaining the details of the business operator data according to the first embodiment of the present invention.

[0032] The business operator data 1000 shown in FIG. 4 stores various data related to the business operator acquired from the business operator via the business operator terminal 200. In FIG. 4, for convenience of explanation, an example of one business operator (the business operator identified by the business operator ID "10001") is shown, but information of a plurality of business operators can be stored. The various data related to the business operator can include, for example, basic information of the business operator (for example, corporate name of the business operator, user name, address, business type, contact information, email address, business office name, related company name, names of business operators related in the supply chain, etc.), input information (for example, image data of invoice information, etc.), analysis information (for example, information related to invoices extracted from image data, greenhouse gas emissions, prediction of the cause of changes in greenhouse gas emissions, etc.), customer information (for example, customer ID, blockchain address, etc.), and offset report information (for example, TXID, NFTID, etc.).

[0033] FIG. 8 is a flowchart diagram showing an example of the calculation process of the greenhouse gas emissions according to the first embodiment of the present invention.

[0034] First, as the process of step S101, the information acquisition unit 131 of the control unit 130 of the management terminal 100 acquires, from the business operator terminal 200 via the network NW, image data including bill information collected on the business operator side. The business operator uploads bills, receipts, vouchers, etc. (collectively referred to as "bills" in this embodiment) in file formats such as PDF, Excel, and JPG (collectively referred to as "image data" in this embodiment) to the management terminal 100 via the business operator terminal 200. The image data acquired by the information acquisition unit 131 is stored as input information in the business operator data storage unit 121 of the storage unit 120.

[0035] Subsequently, as the process of step S102, the image analysis unit 132 of the control unit 130 of the management terminal 100 analyzes the image data acquired in the previous step by machine learning. Here, when performing image analysis, a method called so-called OCR is used. The image analysis unit 132 of the control unit 130 of the management terminal 100 uses a learning model generated by previously learning image data of bills in a plurality of various formats stored in the AI model storage unit 122 of the storage unit 120 to recognize text from the image data and extract items included in the bill information as data of structured character strings. Here, when performing image analysis, it is also possible to use an image analysis engine (such as an OCR engine) provided by a business operator other than the management terminal 100 and linked via an API.

[0036] Regarding image analysis, for example, as shown in FIG. 5, it is performed by recognizing and extracting text from image data containing claim information. As shown in FIG. 5, examples of claim information include various items included in the claim, such as the breakdown name of the electricity bill, the amount (yen) for each breakdown, the contract power (kW), the electricity consumption (kWh) for each breakdown, the total amount (yen), the date (year and month), etc. In this example, the breakdown of the electricity bill is illustrated, but it may also be a claim for the usage fee of other energies including gas and fuel in addition to electricity, or other examples such as a receipt for transportation expenses for business trips, a receipt for commuting expenses of employees, a claim associated with transactions with freight forwarders, or a breakdown of a claim associated with transactions with waste operators. The image analysis unit 132 can extract the amount information, the following activity amount information, etc. included in the claim information as text by analyzing the image data of these claim information. The extracted claim information is stored as analysis information in the operator data storage unit 121 of the storage unit 120. In this way, through image analysis by machine learning, without the operator inputting claim information manually or otherwise, a vast and necessary amount of information for calculating greenhouse gas emissions can be obtained as image data, and through highly accurate image recognition, the information necessary for calculating greenhouse gas emissions can be accurately extracted. Therefore, it is possible to achieve the efficiency improvement and high accuracy of the calculation of greenhouse gas emissions.

[0037] Next, in step S103, the image analysis unit 132 of the control unit 130 calculates the greenhouse gas emissions based on the claim information extracted from the image data or based on the information regarding the energy consumption obtained from the LLM300. Here, the greenhouse gas emissions are classified into SCOPE1, SCOPE2, and SCOPE3. SCOPE1 refers to the direct emissions of greenhouse gases by the operator itself (e.g., emissions associated with fuel combustion and industrial processes), SCOPE2 refers to the indirect emissions associated with the use of electricity, heat, gas, etc. supplied from other companies to the operator, and further, SCOPE3 is the calculation standard for the emissions of the entire supply chain of an organization issued by the GHG Protocol, referring to the emissions of the operator's supply chain (the entire series of processes such as raw material procurement, manufacturing, logistics, sales, and disposal). SCOPE3 is further classified into 15 categories: (1) purchased products / services, (2) capital goods, (3) fuel and energy-related activities not included in SCOPE1 and SCOPE2, (4) transportation, distribution (upstream), (5) waste from the business, (6) business trips, (7) commuting of employees, (8) leased assets (upstream), (9) transportation, distribution (downstream), (10) processing of sold products, (11) use of sold products, (12) disposal of sold products, (13) leased assets (downstream), (14) franchises, (15) investments). Here, the greenhouse gases include carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulfur hexafluoride (SF6), and nitrogen trifluoride (NF3), and in this embodiment, CO2 will be used as an example for explanation.

[0038] Also, the amount of greenhouse gas emissions is defined by using the operator's electricity consumption, volume of cargo transportation, volume of waste treatment, and various transaction amounts as activity volumes, and multiplying the activity volumes by the emission factors per unit of activity, namely, the CO2 emissions per 1 kWh of electricity used, the CO2 emissions per 1 ton of cargo transportation, and the CO2 emissions per 1 ton of waste incineration. The amount of greenhouse gas emissions is calculated separately for the above SCOPE1, SCOPE2, and SCOPE3 (for SCOPE3, by category of the 15 categories), and the total emissions are calculated as the supply chain emissions.

[0039] In this embodiment, the image analysis unit 132 extracts relevant claim information for each of SCOPE1, SCOPE2, and SCOPE3 (and for SCOPE3, further categorized by category). Among the claim information, for example, based on the electricity consumption in kWh, the emission amount is calculated based on the above calculation method. The calculated emission amount is stored as analysis information in the operator data storage unit 121 of the storage unit 120.

[0040] Subsequently, as the process of step S104, the report generation unit 135 of the control unit 130 generates a visualized report showing the breakdown of the emission amount over time for each SCOPE (and for SCOPE3, further categorized by category) based on the information regarding the calculated emission amount.

[0041] FIG. 9 is a flowchart showing an example of the cause prediction process for the change in greenhouse gas emissions according to the first embodiment of the present invention.

[0042] First, as the process of step S201, the cause analysis unit 133 of the control unit 130 of the management terminal 100 refers to the information regarding the greenhouse gas emissions of the operator calculated in step S103 of FIG. 8. Here, regarding the greenhouse gas emissions, the emissions for each SCOPE (and for SCOPE3, further categorized by category) are referred to. Also, the cause analysis unit 133 can confirm the change (increase or decrease) in the emission amount by referring to the past emission amount data of the same operator for the greenhouse gas emissions. As described above, the emission amount is stored as analysis information in the operator data storage unit 121 of the storage unit 120.

[0043] Subsequently, as the process of step S202, based on the information regarding the emissions amount referred to above, the cause analysis unit 133 analyzes and predicts the cause of the change in the emissions amount by machine learning. Here, in the cause analysis, the cause analysis unit 133 of the control unit 130 of the management terminal 100 uses the information on the emissions amount referred to above, the factors that affect the change (increase or decrease) in the emissions amount, and the learning model generated by learning the data on the factors that affect the change (increase or decrease) in the emissions amount, which is stored in the AI model storage unit 122 of the storage unit 120 in advance, to predict the cause of the change in the emissions amount for each SCOPE (for SCOPE3, further categorized by category).

[0044] Here, as factors that affect the change (increase or decrease) in the emissions amount, for example, factors such as weather, temperature, product demand and / or factory operation, store or factory business or operation hours, changes in equipment or facilities, measures by software, energy-saving actions, fuel conversion, energy menu changes, changes in business trip or commuting volume, and the power generation amount of self-generation can be mentioned. Each of these factors is a factor that affects the emissions amount of any SCOPE. For example, the factor of weather affects precipitation, wind volume, sunshine duration, and temperature. Precipitation affects the small hydropower generation amount, wind volume affects the wind power generation amount, sunshine duration affects the solar power generation amount, temperature affects air conditioning, and further, the power generation amount affects the self-generation amount, and the self-generation amount affects the CO2 emissions amount by electricity, thereby affecting the change in the emissions amount of SCOPE2. On the other hand, air conditioning affects the gas usage amount, and the gas usage amount affects the CO2 emissions amount by gas combustion, thereby affecting the change in the emissions amount of SCOPE1. Also, energy-saving activities, factory operation due to product demand, and business hours affect the electricity usage amount and affect SCOPE2. Also, EMS, replacement of refrigeration equipment, introduction of energy-saving equipment, and automobile usage amount also affect the electricity usage amount and affect SCOPE2. In addition, automobile usage amount, fuel consumption, boiler usage amount, and boiler efficiency affect the fuel usage amount and affect the CO2 emissions amount by fuel, thereby affecting SCOPE1.

[0045] In addition, the factor of the number of products sold affects Categories 1, 9, 10, 11, and 12 of SCOPE3, equipment investment affects Category 2, the renewable energy ratio and the amount of procured energy affect Category 3, the number of transports and transport route changes affect Categories 4 and 9, the product loss rate affects Category 5, the number of business trips and the number of office visits affect Category 6, the number of commuters and the number of office employees affect Category 7, power consumption affects Category 8, reduction of processing due to product improvement affects Category 10, improvement to energy-saving products affects Category 11, increase in the recycling rate affects Category 12, the office power of tenants affects Category 13, the emissions of franchises affect Category 14, and the emissions of investment destinations affect Category 15, respectively.

[0046] In this way, by using machine learning to learn which factor affects which SCOPE or category, and by obtaining information on emissions and information on each factor from the business operator, it is possible to predict the cause of changes in emissions. Here, by predicting the cause of emissions using machine learning, it is possible to efficiently and accurately predict the factors that affect changes in greenhouse gas emissions for each business operator and for each SCOPE.

[0047] Subsequently, as the process of step S203, the report generation unit 135 of the control unit 130 generates a visualized report on the cause of the change in emissions for each SCOPE (and for each category in the case of SCOPE3) based on the information on the predicted cause of the change in the analyzed emissions.

[0048] FIG. 10 is a flowchart showing an example of the transaction process for greenhouse gas emissions according to the first embodiment of the present invention.

[0049] First, as the process of step S301, the transaction processing unit 134 of the control unit 130 of the management terminal 100 refers to the business operator data stored in the business operator data storage unit 121 of the storage unit 120. Here, the business operator data to be referred to includes the analysis information of the business operator (greenhouse gas emissions for each SCOPE), etc.

[0050] Next, as the processing in step S302, the transaction processing unit 134 generates a hash value based on the operator data referred to in step S301. That is, the transaction processing unit 134 generates a hash value for one row using a hash function for the greenhouse gas emissions amount in a predetermined period, and records the hash value as transaction information in the public blockchain. On the blockchain network, a block is generated based on the transaction information, the hash value recorded in the immediately preceding block, and the nonce value mined by the node, and is recorded following the immediately preceding block to form a blockchain. Here, in this example, in order to reduce the cost related to blockchain recording, it is assumed that the recording is performed in layer 2 (for example, a side chain) different from the main blockchain (so-called layer 1).

[0051] In addition, the transaction processing unit 134 can assign and manage an NFT ID in association with the blockchain record of the operator's greenhouse gas emissions amount. More specifically, as shown in FIG. 4, in the operator data 1000, the operator's customer ID is assigned as customer information and the blockchain address to be referred to is stored, and the NFT ID and TX ID can be assigned as offset report information.

[0052] As shown in FIG. 6, on the blockchain network, blockchain addresses are associated with each NFT ID. In the management terminal 100, NFT IDs and customer IDs are managed. Therefore, for example, information regarding the greenhouse gas emissions of a business operator corresponding to customer ID "2" can be obtained by referring to the blockchain addresses for each NFT ID, such as NFT IDs "13" and "14", and as shown in FIG. 7, the details of the emissions information can be read out. FIG. 7 shows information regarding the offset report associated with NFT ID "14". A TXID is assigned in association with the offset report, and the CO2 emissions by SCOPE, the target year and month, and the report issuance date are included in the offset report. In addition to the CO2 emissions for the target year and month in this example, the CO2 emissions for the most recent years, the reduced CO2 emissions, and the offset CO2 emissions can also be NFTized. In this way, by managing CO2 emissions as NFTs, business operators can conduct transactions of NFTized certificates while ensuring non-falsification and transaction reliability, and can also prove the emissions to third parties.

[0053] FIG. 11 is a flowchart showing an example of the process for automatically assigning emission factors for calculating greenhouse gas emissions according to the first embodiment of the present invention.

[0054] First, as a preprocessing of this process, image data of a plurality of invoices and / or receipts (with energy consumption described) are used as input data, and natural language analysis is performed through image analysis based on the input data. By inputting information regarding emission factors as correct data corresponding to the recognized image data and / or text data, a machine learning model is generated. The learning model can be stored in the AI model storage unit 122 of the storage unit 120 of the management terminal 100 and can also be used for machine learning processing in the management terminal 100. Alternatively, a learning model can be generated in the LLM 300, and the management terminal 100 and the LLM 300 can cooperate to execute machine learning processing.

[0055] As the process of step S401, the information acquisition unit 131 of the control unit 130 of the management terminal 100 acquires, from the business operator terminal 200 via the network NW, image data including invoice information and / or receipt documents collected on the business operator side. The business operator uploads invoices, receipts, vouchers, etc. (collectively referred to as "invoices" in this embodiment) to the management terminal 100 in file formats such as PDF, Excel, JPG, etc. (collectively referred to as "image data" in this example) via the business operator terminal 200. The image data acquired by the information acquisition unit 131 is stored as input information in the business operator data storage unit 121 of the storage unit 120.

[0056] Subsequently, as the process of step S402, the image analysis unit 132 of the control unit 130 of the management terminal 100 analyzes the image data acquired in the previous step by machine learning. Here, when performing image analysis, a method called so-called OCR is used. The image analysis unit 132 of the control unit 130 of the management terminal 100 uses a learning model generated by previously learning image data of invoices in a plurality of various formats stored in the AI model storage unit 122 of the storage unit 120 to recognize text from the image data and extract items included in the invoice information and / or receipt information as structured string data. Here, when performing image analysis, it is also possible to use an image analysis engine (such as an OCR engine) provided by a business operator other than the management terminal 100 and linked via an API. Further, in this step, the image analysis unit 132 refers to a learning model stored in the AI model storage unit 122 of the storage unit 120 or generated for the above-mentioned generation of emission unit information via the LLM300, and performs a process of searching for and extracting information on one or more emission units corresponding to past input data that is similar to the recognized and structured text data.

[0057] Subsequently, as the process of step S403, the report generation unit 135 of the control unit 130 of the management terminal 100 recommends the information on the emission unit extracted above as a candidate for the emission unit for the input data. The person in charge of the business operator checks, selects, and / or determines the information on one or more emission units corresponding to the input image data via the application screen or chat interface screen displayed on the business operator terminal 200, thereby determining the information on the emission unit for the input data. Note that the information on the emission unit thus determined is input into the learning model as correct data, and the learning model is updated.

[0058] Thus, although the original invoice and receipt did not contain information on the emission unit, according to this example, it becomes possible to infer the emission unit for the input invoice and / or receipt image data, and the efficiency of the greenhouse gas emission amount calculation process can be improved.

[0059] The above-described embodiments are merely examples for facilitating the understanding of the present invention, and are not for limiting and interpreting the present invention. It goes without saying that the present invention can be changed and improved without departing from its gist, and equivalents thereof are included in the present invention.

Description of Reference Numerals

[0060] 100 Management terminal 200 Business operator terminal

Claims

**Claim 1** A method for managing greenhouse gas emissions, comprising: receiving image data of invoice information from an operator terminal; using the image data as input data, extracting information on emission intensity based on a learning model; and recommending the information on emission intensity. **Claim 2** The management method according to claim 1, wherein the learning model is generated by inputting information on emission intensity associated with past input data similar to the input data. **Claim 3** The management method according to claim 2, further comprising recommending, based on the learning model, information on emission intensity associated with past input data similar to the input data. **Claim 4** The management method according to claim 1, wherein the input data and the past input data each include text information recognized based on image data.

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