JP7770035B2 - Greenhouse gas emissions management methods - Google Patents

Description

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

Regarding greenhouse gas emissions from businesses associated with the use of fuel, electricity, etc., reporting systems covering SCOPE 1 emissions (a company's direct emissions) and SCOPE 2 emissions (a company's indirect emissions) have become widespread, and progress has been made in calculating and reducing emissions in SCOPE 1 and SCOPE 2.

Non-Patent Document 1 proposes that, in order to further reduce greenhouse gas emissions by businesses, SCOPE 3 emissions, i.e., emissions from the supply chain (the entire series of processes including raw material procurement, manufacturing, logistics, sales, and disposal) of other related businesses, be calculated as emissions other than SCOPE 1 and SCOPE 2.

"Concepts for Calculating Supply Chain Emissions," Ministry of the Environment, November 2017

However, while the technology disclosed in Non-Patent Document 1 discloses methods for calculating greenhouse gas emissions related to SCOPE 3, it takes a great deal of time and effort for each business, particularly corporations and local governments, to collect and input vast amounts of data to calculate emissions, calculate emissions, and manage the calculation results. In particular, in the area of GHG emissions management, the number of SCOPEs for emissions subject to calculation is expanding, the data used to calculate emissions varies widely for each SCOPE, and data management methods differ for each business, which has led to delays in improving business efficiency through the introduction of cutting-edge technology.

The present invention aims to provide a method for efficiently managing GHG emissions by utilizing cutting-edge technology to reduce work hours in the area of GHG emissions management, such as the calculation of greenhouse gas emissions by businesses.

A greenhouse gas emissions management method according to one embodiment of the present invention receives activity mass information including activity information and emission intensity information related to the business's products based on input from a business terminal, transmits the received activity mass information to an approver terminal as approval data, receives indication information related to the approval data from the approver terminal, records the indication information, generates indication information based on the recorded indication information, and transmits the generated indication information to the business terminal when generating new approval data that differs from the approval data.

The present invention provides a method for businesses to efficiently manage greenhouse gas emissions, including calculations.

1 is a diagram illustrating a greenhouse gas emission management system according to a first embodiment of the present invention. FIG. FIG. 2 is a functional block diagram of a management terminal constituting the greenhouse gas emission management system. FIG. 2 is a functional block diagram of a business operator terminal that constitutes the greenhouse gas emission management system. FIG. 4 is a diagram illustrating details of business entity data according to the first embodiment of the present invention. FIG. 4 is a diagram illustrating details of invoice information according to the first embodiment of the present invention. FIG. 3 is a diagram illustrating an example of transaction information according to the first embodiment of the present invention. FIG. 10 is a diagram illustrating another example of transaction information according to the first embodiment of the present invention. FIG. 3 is a flowchart illustrating an example of a process for calculating greenhouse gas emissions according to the first embodiment of the present invention. FIG. 4 is a flowchart illustrating an example of a process for predicting causes of changes in greenhouse gas emissions according to the first embodiment of the present invention. FIG. 4 is a flowchart illustrating an example of a transaction process for greenhouse gas emissions according to the first embodiment of the present invention. FIG. 4 is a flowchart illustrating an example of a process management process according to the first embodiment of the present invention. FIG. 10 is a flowchart illustrating an example of approval processing in the process management processing according to the first embodiment of the present invention. FIG. 10 is a flowchart illustrating an example of an indication information generation process in the process management process according to the first embodiment of the present invention.

The contents of an embodiment of the present invention will be described below. A greenhouse gas emission management system (hereinafter simply referred to as "system") according to an embodiment of the present invention has the following configuration.
[Item 1]
1. A method for managing greenhouse gas emissions, comprising:
receiving activity information including activity information and emission intensity information related to the business's products based on input from the business's terminal;
transmitting the received activity amount information as approval data to an approver terminal;
receiving, from the approver terminal, indication information regarding the approval data;
Record the indicated information;
A method for generating indication information based on the recorded indication information, and transmitting the generated indication information to the business operator terminal when generating new approval data that differs from the approval data.
[Item 2]
2. The management method according to item 1, wherein the activity information includes any one of a SCOPE classification, an energy item, and an amount of usage.
[Item 3]
2. The management method according to item 1, wherein the activity information is received for each of SCOPE 1 to SCOPE 3.
[Item 4]
2. The management method according to claim 1, further comprising: generating a learning model based on the recorded indication information; and generating the indication information based on the generated learning model.
[Item 5]
5. The management method according to item 4, wherein weighting information is assigned to each of the recorded pieces of indicated information, and the weighting information is set higher for the most recently recorded piece of indicated information.

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

Figure 1 is a diagram illustrating a greenhouse gas emissions management system according to a first embodiment of the present invention.

As shown in FIG. 1, in the emissions management system 1 of this embodiment, an administrator terminal 100 and multiple business operator terminals 200A and 200B are interconnected via a communications network NW.

For example, the management terminal 100 receives basic information about the business and input information (e.g., image data of invoice information) for calculating greenhouse gas (e.g., CO2) emissions from the business terminals 200A and 200B.

The management terminal 100 also uses machine learning to analyze the received image data of the invoice information, extracts the necessary items of invoice information contained in the image data, and calculates greenhouse gas emissions. The management terminal 100 also uses machine learning to analyze the calculated changes in greenhouse gas emissions over time (e.g., increases or decreases) and predicts the causes of the changes.

Furthermore, the management terminal 100 has a wallet and is connected to the public blockchain network NW. Based on the information on greenhouse gas emissions for each specified period, the management terminal 100 generates a single hash value using SHA256 or another hash function and records it as transaction information on the blockchain network. On the blockchain network, a current block is generated based on the transaction information, the hash value recorded in the previous block, and the nonce value mined by the node, and is recorded following the previous block, forming a blockchain. Here, the hash generation and/or recording of the transaction information on the blockchain can be performed via another terminal rather than the management terminal 100. In this case, the management terminal 100 transmits the greenhouse gas emissions calculated in the matching process to the other terminal. Furthermore, the management terminal 100 can record information on greenhouse gas emissions on the blockchain network as a smart contract. By using a smart contract, contracts for emissions trading with other businesses can be automatically generated, approved, and executed based on the information on emissions without the involvement of a third party. Additionally, smart contracts allow each business operator to access transaction information without using a management terminal, increasing service convenience and reducing operational costs.

Here, as mentioned above, a public blockchain allows transactions to be approved not by a specific administrator but by an unspecified number of nodes and miners, and therefore can ensure greater data tamper resistance and fault tolerance than a private blockchain. Therefore, the security of transactions is guaranteed, and therefore a public blockchain is preferable as the destination for recording energy transactions in this embodiment. Typical public blockchains include Bitcoin and Ethereum, but Ethereum, for example, has higher tamper resistance and reliability than other public blockchains.

Furthermore, the management terminal 100 can associate information regarding greenhouse gas emissions using an identifier or the like and record it on the blockchain network as a non-fungible token (hereinafter, "NFT"). NFTs are tokens issued, for example, in the "ERC721" standard of Ethereum, a blockchain network platform. They are a unit of data recorded on the blockchain network and are non-fungible. NFTs are recorded on the blockchain along with smart contracts and are traceable, making it possible to prove transaction information, including details and history of business operators managing greenhouse gas emissions.

Figure 2 is a functional block diagram of the management terminal that makes up the emissions management system.

The communication unit 110 is a communication interface for communicating with external terminals via the network NW, and communication is carried out using a communication protocol such as TCP/IP (Transmission Control Protocol/Internet Protocol).

The memory unit 120 stores input data, programs for executing various control processes and functions within the control unit 130, and is composed of RAM (Random Access Memory), ROM (Read Only Memory), etc. The memory unit 120 also has an operator data storage unit 121 that stores various data related to operators, and an AI model storage unit 122 that stores learning data and learning models created by AI (artificial intelligence) learning the learning data. Note that a database (not shown) storing various data may be constructed outside the memory unit 120 or the management terminal 100.

The control unit 130 controls the overall operation of the management terminal 100 by executing programs stored in the memory unit 120, and is composed of a CPU (Central Processing Unit), GPU (Graphics Processing Unit), etc. The functions of the control unit 130 include an information receiving 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 greenhouse gas emissions; a cause analysis unit 133 that analyzes the image data and analyzes the causes of time-series changes in greenhouse gas emissions calculated based on information contained in the extracted invoice information; a transaction processing unit 134 that compiles information related to greenhouse gas emissions for a predetermined period, generates a hash value, and records this as transaction information on the blockchain network; and a report generation unit 135 that generates and transmits report data to output the results of an analysis of greenhouse gas emissions and changes in emissions to the business operator every predetermined period.

In addition, although not shown, the control unit 130 has an image generation unit that generates screen information to be displayed via the user interface of an external terminal such as the business operator terminal 200. For example, using image and text data stored in the storage unit 120 as material, the control unit 130 generates information to be displayed in the user interface by arranging various images and text in predetermined areas of the user interface based on predetermined layout rules. Processing related to the image generation unit can also be performed by a GPU (Graphics Processing Unit).

The management terminal 100 also has a wallet (not shown) required for recording transaction information on the blockchain network. Note that this wallet can also be located outside the management terminal 100.

Figure 3 is a functional block diagram of the business operator terminal that makes up the emissions management system.

The operator terminal 200 includes a communication unit 210, a display operation unit 220, a memory unit 230, and a control unit 240.

The communication unit 210 is a communication interface for communicating with the management terminal 100 via the network NW, and communication is carried out using a communication protocol such as TCP/IP.

The display operation unit 220 is a user interface used by the operator to input instructions and display text, images, etc. in accordance with input data from the control unit 240. If the operator terminal 200 is configured as a personal computer, it is composed of a display, keyboard, and mouse, and if the operator terminal 200 is configured as a smartphone or tablet terminal, it is composed of a touch panel, etc. This display operation unit 220 is activated by a control program stored in the memory unit 230 and executed by the operator terminal 200, which is a computer (electronic calculator).

The memory unit 230 stores input data, programs for executing various control processes and functions within the control unit 240, and is composed of RAM, ROM, etc. The memory unit 230 also temporarily stores the contents of communications with the management terminal 100.

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

Figure 4 is a diagram explaining details of business operator data according to the first embodiment of the present invention.

The operator data 1000 shown in Figure 4 stores various data related to operators acquired from the operators via the operator terminal 200. For ease of explanation, Figure 4 shows an example of one operator (the operator identified by operator ID "10001"), but information on multiple operators can be stored. The various data related to businesses may include, for example, basic information about the business (e.g., the business's corporate name, user name, business establishment information (e.g., address information for each business establishment), network name (e.g., SSID, IP address), image information (e.g., background image of the business establishment, image of a person), industry, contact information, email address, business establishment name, name of affiliated company, name of business associated in the supply chain), input information (e.g., image data of invoice information), analysis information (e.g., invoice extracted from image data, greenhouse gas emissions, emissions intensity information, information related to predicted causes of changes in greenhouse gas emissions, etc.), customer information (e.g., customer ID, blockchain address), offset report information (e.g., TXID, NFTID), activity amount information (e.g., activity information, item, emissions intensity), and approval information (approver, activity amount information to be approved, approval status, information on comments made by the approver regarding items to be approved), etc.

Figure 8 is a flowchart showing an example of the greenhouse gas emission calculation process according to the first embodiment of the present invention.

First, in step S101, the information acquisition unit 131 of the control unit 130 of the management terminal 100 acquires image data including invoice information collected by the business operator from the business operator terminal 200 via the network NW. The business operator uploads invoices, receipts, slips, etc. (collectively referred to as "invoices" in this embodiment) to the management terminal 100 via the business operator terminal 200 in file formats such as PDF, Excel, JPG, etc. (collectively referred to as "image data" in this embodiment). 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 memory unit 120.

Next, in 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 using machine learning. Here, the image analysis uses a technique known as OCR, and the image analysis unit 132 of the control unit 130 of the management terminal 100 recognizes text from the image data using a learning model that was generated in advance by learning from image data of multiple invoices in various formats and that is stored in the AI model storage unit 122 of the memory unit 120, and extracts items contained in the invoice information as structured character string data. Here, the image analysis can also use an image analysis engine (such as an OCR engine) provided by a business operator other than the management terminal 100, which is linked via an API.

Image analysis is performed, for example, by recognizing and extracting text from image data containing invoice information, as shown in Figure 5. As shown in Figure 5, invoice information includes various items included on an invoice, such as the electricity bill breakdown, the amount (yen) for each breakdown, the contracted power (kW), the amount of electricity used (kWh) for each breakdown, the total amount (yen), and the date (year and month). While this example illustrates an electricity bill breakdown, it may also be an invoice for other energy usage, including electricity, gas, and fuel. It may also be a receipt for travel expenses, a receipt for employee commuting expenses, an invoice for transactions with a freight company, or a breakdown of an invoice for transactions with a waste disposal company. By analyzing the image data of this invoice information, the image analysis unit 132 can extract, as text, the amount information and activity level information (described below) included in the invoice information. The extracted invoice information is stored as analysis information in the business data storage unit 121 of the memory unit 120. In this way, image analysis using machine learning allows businesses to obtain the vast amount of information necessary to calculate greenhouse gas emissions as image data without having to manually input invoice information, and highly accurate image recognition makes it possible to accurately extract the information necessary to calculate greenhouse gas emissions, thereby achieving more efficient and accurate calculations of greenhouse gas emissions.

Next, in step S103, the image analysis unit 132 of the control unit 130 calculates greenhouse gas emissions based on the invoice information extracted from the image data. Here, greenhouse gas emissions are classified into SCOPE 1, SCOPE 2, and SCOPE 3. SCOPE 1 represents direct greenhouse gas emissions by the business itself (e.g., emissions associated with fuel combustion and industrial processes). SCOPE 2 represents indirect emissions associated with the use of electricity, heat, gas, etc. supplied to the business by other companies. Furthermore, SCOPE 3 is a calculation standard for emissions throughout an organization's supply chain issued by the GHG Protocol, and refers to emissions throughout a business's supply chain (the entire process, including raw material procurement, manufacturing, logistics, sales, and disposal). SCOPE 3 is further divided into 15 categories: (1) purchased products/services, 2) capital goods, 3) fuel and energy-related activities not included in SCOPE 1 and SCOPE 2, 4) transportation and distribution (upstream), 5) waste generated by operations, 6) business travel, 7) employee commuting, 8) leased assets (upstream), 9) transportation and distribution (downstream), 10) processing of sold products, 11) use of sold products, 12) disposal of sold products, 13) leased assets (downstream), 14) franchises, and 15) investments. Greenhouse gases include carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulfur hexafluoride (SF6), and nitrogen trifluoride (NF3). This embodiment will use CO2 as an example.

Greenhouse gas emissions are calculated by defining a business's electricity consumption, cargo transport volume, waste disposal volume, and various transaction amounts as activity volume, and multiplying this activity volume by the CO2 emissions per kWh of electricity used, CO2 emissions per ton of cargo transported, and CO2 emissions per ton of waste incinerated as emissions units. Greenhouse gas emissions are calculated for each of the above SCOPE 1, SCOPE 2, and SCOPE 3 (SCOPE 3 is divided into 15 categories), and the total emissions are calculated as supply chain emissions.

In this embodiment, the image analysis unit 132 extracts related invoice information for each of SCOPE 1, SCOPE 2, and SCOPE 3 (and further for SCOPE 3 by category), and calculates emissions using the above calculation method based on the amount of electricity used (kWh) in the invoice information. The calculated emissions are stored as analysis information and activity amount information in the business operator data storage unit 121 of the memory unit 120.

Next, in step S104, the report generation unit 135 of the control unit 130 generates a visualized report showing a breakdown of emissions over time by SCOPE (and further by category for SCOPE 3) based on the information related to the calculated emissions.

Figure 9 is a flowchart showing an example of a process for predicting the causes of changes in greenhouse gas emissions according to the first embodiment of the present invention.

First, in the processing of step S201, the cause analysis unit 133 of the control unit 130 of the management terminal 100 references the information related to the business operator's greenhouse gas emissions calculated in step S103 of FIG. 8. Here, greenhouse gas emissions are referenced by SCOPE (and further by category for SCOPE 3). In addition, the cause analysis unit 133 can check changes (increases or decreases) in greenhouse gas emissions by referencing the same business operator's past emission data. As described above, the emissions are stored as analysis information in the business operator data storage unit 121 of the memory unit 120.

Next, in step S202, the cause analysis unit 133 analyzes and predicts the cause of changes in emissions using machine learning based on the emission information referenced above. During cause analysis, the cause analysis unit 133 of the control unit 130 of the management terminal 100 predicts the cause of changes in emissions by SCOPE (and further by category for SCOPE 3) using a learning model generated by learning the emission information referenced above, factors that affect changes (increases or decreases) in emissions, and data related to factors that affect changes (increases or decreases) in emissions that is stored in advance in the AI model storage unit 122 of the memory unit 120.

Here, factors that affect changes (increases or decreases) in emissions include, for example, weather, temperature, product demand and/or factory operation, store or factory business or operating hours, equipment or facility changes, software measures, energy-saving actions, fuel conversions, energy menu changes, changes in business trips or commuting, and the amount of electricity generated by self-generated power. Each of these factors affects the emissions of one SCOPE. For example, weather affects precipitation, wind volume, hours of sunshine, and temperature; precipitation affects the amount of hydroelectric power generation, wind volume affects the amount of wind power generation, hours of sunshine affect the amount of solar power generation, and temperature affects air conditioning. Furthermore, the amount of electricity generated affects the amount of self-generated power, which affects the CO2 emissions from electricity, thereby affecting the emissions of SCOPE2. Meanwhile, air conditioning affects gas usage, which affects the CO2 emissions from gas combustion, thereby affecting the emissions of SCOPE1. Furthermore, energy-saving activities, factory operation due to product demand, and business hours affect electricity usage, which in turn affects SCOPE2. In addition, EMS, replacement of refrigeration equipment, introduction of energy-saving equipment, and vehicle usage also affect electricity usage and thus SCOPE2; vehicle usage, fuel efficiency, boiler usage, and boiler efficiency also affect fuel usage, which in turn affects CO2 emissions from fuel, thereby affecting SCOPE1.

In addition, the factor of product sales volume affects categories 1, 9, 10, 11, and 12 in SCOPE 3, capital investment affects category 2, renewable energy ratio and amount of energy procured affects category 3, number of transports and changes in transport routes affects categories 4 and 9, product loss rate affects category 5, business trips and number of people commuting to the office affects category 6, number of people commuting to the office and number of employees commuting to the office affects category 7, electricity consumption affects category 8, reduced processing through product improvements affects category 10, improvements to energy-efficient products affects category 11, increased recycling rates affects category 12, tenant office electricity affects category 13, franchise emissions affects category 14, and investment emissions affects category 15.

In this way, machine learning is used to learn which factors affect which SCOPE or category, and by obtaining information about emissions and information about each factor from businesses, it is possible to predict the causes of changes in emissions. Here, by predicting the causes 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 and each SCOPE.

Next, in step S203, the report generation unit 135 of the control unit 130 generates a visualized report on the causes of changes in emissions by SCOPE (and further by category for SCOPE 3) based on the information regarding the predicted causes of changes in emissions analyzed above.

Figure 10 is a flowchart showing an example of greenhouse gas emission transaction processing according to the first embodiment of the present invention.

First, in step S301, the transaction processing unit 134 of the control unit 130 of the management terminal 100 references the business operator data stored in the business operator data storage unit 121 of the memory unit 120. The business operator data referenced here includes business operator analysis information (e.g., greenhouse gas emissions for each SCOPE), etc.

Next, in step S302, the transaction processing unit 134 generates a hash value based on the business data referenced in step S301. That is, the transaction processing unit 134 generates a row of hash values for greenhouse gas emissions over a specified period using a hash function, and records the hash value as transaction information in the public blockchain. On the blockchain network, this block is generated based on the transaction information, the hash value recorded in the previous block, and the nonce value mined by the node, and is recorded following the previous block, forming a blockchain. Here, in this example, in order to reduce the costs associated with blockchain recording, it is assumed that the data is recorded in layer 2 (e.g., a side chain) different from the main blockchain (so-called layer 1).

The transaction processing unit 134 can also assign and manage NFTIDs by linking them to the blockchain records of the business's greenhouse gas emissions. More specifically, as shown in FIG. 4, the business's customer ID can be assigned as customer information to the business data 1000, the referenced blockchain address can be stored, and the NFTID and TXID can be assigned as offset report information.

As shown in FIG. 6, each NFT ID is associated with a blockchain address on the blockchain network, and the management terminal 100 manages the NFT ID and customer ID. For example, information regarding greenhouse gas emissions for a business associated with customer ID "2" can be retrieved by referencing the blockchain address for each NFT ID, such as NFT IDs "13" and "14," as shown in FIG. 7. FIG. 7 shows information related to an offset report associated with NFT ID "14." A TXID is assigned in association with the offset report, and the offset report includes the CO2 emissions by SCOPE, the target year and month, and the report's publication date. In addition to the CO2 emissions for the target year and month in this example, the most recent CO2 emissions, reduced CO2 emissions, and offset CO2 emissions can also be converted into NFTs. By managing CO2 emissions in this way as NFTs, businesses can trade NFT-converted certificates while ensuring the integrity and reliability of transactions, and can also verify emissions to third parties.

Figure 11 is a flowchart showing an example of processing related to greenhouse gas reduction target management according to the first embodiment of the present invention.

First, in step S401, the information acquisition unit 131 of the control unit 130 of the management terminal 100 receives input of activity information and activity amount information related to emission intensity from the business operator terminal 200 via the network NW. Here, activity information refers to information related to the amount (so-called activity amount) of the scale of activities related to the business operator's products (including activities of persons other than the business operator), such as category classification (any of the classifications SCOPE1 to SCOPE3 and categories classified by emission cause in SCOPE3), item (e.g., energy items such as gasoline, iron, ethylene, cement), usage amount (e.g., energy (electricity, etc.) usage amount, cargo transportation amount, waste disposal amount, various transaction amounts), etc. Furthermore, the information acquisition unit 131 can receive activity information for each SCOPE classification SCOPE1 to SCOPE3. For example, for business operator X, activity information for SCOPE1 (such as items of ethylene usage), activity information for SCOPE2 (such as electricity usage), and activity information for SCOPE3 (such as the procurement weight of transported cargo) can be received. Furthermore, the information acquisition unit 131 receives information on the corresponding emission intensity for the received activity information from the business operator terminal 200 and calculates it as activity amount information. Note that this process can also be replaced by a process in which the information is received in the form of the invoice information described above and the activity amount is calculated. In this way, the received and/or calculated activity amount information can be stored as activity amount information in the business operator data 1000.

Next, in step S402, approval processing is performed based on a request from the business operator terminal 200. A specific example of approval processing will be described with reference to Figure 12.

First, in step S501, the report generation unit 135 of the control unit 130 of the management terminal 100 generates approval data based on the activity amount information related to the approval target (e.g., the registered activity amount information) received from the business operator terminal 200 and a request for approval data creation based on the selection of an approver. The report generation unit 135 stores the generated approval data as approval information in the business operator data storage unit 121 of the memory unit 120. Here, the approval data may be generated based on one piece of activity amount information, or may be generated based on multiple pieces of activity amount information. Furthermore, here, the business operator can select multiple approvers, and an unlimited amount of approval data may be created.

Next, in step S502, the report generation unit 135 sends the generated approval data to the business operator terminal (not shown) associated with the approver, along with the activity amount information that is the subject of the approval request.

Next, in step S503, the information acquisition unit 131 receives approval result information, indicating approval or denial (return), from the business operator terminal associated with the approver. Here, the approver performs the approval process by operating a selection button, etc., to indicate approval or denial, while checking the activity amount information to be approved for each registered activity amount information (record) for the received approval data.

Here, at the time of approval, the approver can make comments about the activity amount information that is the subject of the approval request via a specified input screen on the approver's business terminal. Examples of comments include "the units are wrong" or "approver B should be added," and the comments entered by the approver are recorded as comment information in the business data 1000 in the storage unit 120 as approval information. The recorded comment information can be stored and managed as common rules for the same activity amount input person, approver, or business. Furthermore, machine learning can be performed based on the accumulated input person, approver, approval data, and comment information, and the generated learning model can be stored in the AI model storage unit 122 of the storage unit 120.

The following describes the process for generating the indicated information shown in Figure 13, which is executed following the recording of the indicated information.

This process is intended to be executed when the business manager (the person responsible for entering activity amount information) begins the approval process and can be performed as preprocessing for step S501. First, in step S601, the report generation unit 135 of the control unit 130 of the management terminal 100 references the indication management information when generating approval data based on the activity amount information related to the approval target (e.g., the registered activity amount information) received from the business operator terminal 200 and a request for approval data creation based on the approver's selection. For example, the report generation unit 135 references the approval information stored in the business data storage unit 121 of the memory unit 120 or a learning model generated based on the indication information stored in the AI model storage unit 122 of the memory unit 120. Based on information related to each item included in the activity amount information for which approval is requested, information related to the business manager, and/or information related to the selected approver, the report generation unit 135 generates new indication information based on the previous or previously registered indication information. For example, the report generation unit 135 can generate indication information such as "the units are different" for the emission intensity unit included in the item for which approval is currently requested, based on previously registered indication information. Here, if multiple pieces of indicated information are recorded for the same item, new indicated information can be generated based on the indicated information recorded on the most recent date and time, or a higher weighting (coefficient) can be assigned to the most recently recorded indicated information among the indicated information stored as the learning model, and indicated information can be generated as output information. Alternatively, a higher weighting can be assigned to information that has been indicated more than a certain number of times.

Next, in step S602, the report generation unit 135 sends the generated indication information to the business operator terminal 200 associated with the business operator who will begin the approval process. The indication information can also be presented as an alert, such as, "This indication was made in the past. Has it been addressed?" In response to the alert displayed on the approval flow application start screen of the business operator terminal 200, the business operator can select either an option to cancel the application by indicating "I understand and will correct it," or an option to start the application by indicating "I understand and will proceed without correcting it." An approval flow that utilizes such indication information can reduce approval returns and improve the efficiency of the approval flow for emissions applications, even in situations where there are few personnel in charge of GHG/CO2 emissions management operations.

Next, returning to FIG. 12 , in the process of step S504, the report generation unit 135 updates the approval status of the approval information stored in the business operator data storage unit 121 of the memory unit 120 based on the received approval result. Here, if the approval result is disapproval (returned), the process returns to step S502 again, and the report generation unit 135 transmits the approval data based on the corrected activity amount information to the business operator terminal associated with the approver.

After the approval process is completed, in step S403, the transaction processor 134 of the control unit 130 of the management terminal 100 records the approved activity amount information along with the approval status on the blockchain network. As part of the blockchain network recording process, the transaction processor 134 references the business entity data stored in the business entity data storage unit 121 of the memory unit 120 and generates a hash value based on the activity amount information. For the activity amount information, including greenhouse gas emissions over a specified period, a hash function is used to generate a row of hash values, and the hash value is recorded as transaction information on the public blockchain. On the blockchain network, this block is generated based on the transaction information, the hash value recorded in the previous block, and the nonce value mined by the node. This block is recorded following the previous block, forming a blockchain. In this example, to reduce transaction costs and the costs associated with blockchain recording, the transaction can also be recorded on a layer 2 (e.g., a side chain) separate from the main blockchain (so-called layer 1).

As described above, according to this example, approval data can be generated based on activity amount information registered by a business operator, and the approval flow can be executed efficiently. Furthermore, by recording the approved activity amount information on the blockchain network, the approved activity amount information can be recorded in a public state, ensuring the reliability and transparency of the information.

The above-described embodiments are merely examples intended to facilitate understanding of the present invention and are not intended to limit the scope of the present invention. The present invention can be modified and improved without departing from its spirit, and it goes without saying that the present invention includes equivalents thereof.

100 Management terminal 200 Business operator terminal

Claims (5)

1. A computer-implemented method for managing greenhouse gas emissions, comprising:
The computer receives activity amount information including activity information and emission intensity information related to the business's products based on input from the business terminal,
transmitting the received activity amount information as approval data to an approver terminal;
receiving, from the approver terminal, indication information regarding the approval data;
Record the indicated information;
A method for generating new indication information for the new activity amount information based on the indication information recorded in the past , and transmitting the generated new indication information to the business operator terminal before transmitting the new activity amount information as the approval data to the approver terminal. The management method described in claim 1, wherein the activity information includes any of SCOPE classification, energy item, and usage amount. The management method described in claim 1, wherein the activity information is received for each of SCOPE 1 to SCOPE 3. The management method described in claim 1, further comprising generating a learning model based on the recorded indication information, and generating the indication information based on the generated learning model. 5. The management method according to claim 4, wherein weighting information is assigned to each of the recorded pieces of indicated information, and the weighting information is set higher for the most recently recorded piece of indicated information.