JP7841713B2 - Greenhouse gas emission management methods - Google Patents

Description

This invention relates to a method for managing greenhouse gas emissions.

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

Non-patent document 1 proposes calculating SCOPE3 emissions, which are emissions from the supply chain (the entire process including raw material procurement, manufacturing, logistics, sales, and disposal) of other related businesses, in order to further reduce greenhouse gas emissions emitted by businesses, in addition to SCOPE1 and SCOPE2 emissions.

"Approach to Calculating Supply Chain Emissions," Ministry of the Environment, November 2017.

However, while the technology disclosed in Non-Patent Document 1 provides information on methods for calculating greenhouse gas emissions related to SCOPE 3, businesses, particularly corporations and local governments, still expend considerable time and effort collecting and inputting vast amounts of data for emission calculations, calculating emissions, and managing the calculation results. In particular, within the GHG emission management domain, the increasing scope of SCOPEs subject to calculation, the diverse data underlying emission calculations for each SCOPE, and the differing data management methods among businesses all contribute to the slow progress in improving operational efficiency through the adoption of advanced technologies.

Therefore, the present invention aims to provide a method for efficiently achieving emissions management by reducing workload in the area of GHG emissions management, such as the calculation of greenhouse gas emissions by businesses, by utilizing advanced technology.

One embodiment of the present invention provides a method for managing greenhouse gas emissions, which, based on input from a business terminal, receives activity information including activity information and emission intensity information related to the business's products; refers to input setting value information related to the activity information that has been previously entered from the business terminal; and sends an alert to the business terminal when the entered activity information exceeds the input setting value information.

According to the present invention, it is possible to provide a method for efficiently enabling businesses to manage and calculate greenhouse gas emissions.

This diagram illustrates a greenhouse gas emission management system according to a first embodiment of the present invention. This is a functional block diagram of the management terminals that make up the greenhouse gas emissions management system. This is a functional block diagram of the business terminals that make up the greenhouse gas emissions management system. This figure illustrates the details of the business operator data according to the first embodiment of the present invention. This figure illustrates the details of invoice information according to the first embodiment of the present invention. This figure illustrates an example of transaction information according to the first embodiment of the present invention. This figure illustrates another example of transaction information according to the first embodiment of the present invention. This flowchart shows an example of the greenhouse gas emission calculation process according to the first embodiment of the present invention. This flowchart shows an example of a process for predicting the causes of changes in greenhouse gas emissions according to the first embodiment of the present invention. This flowchart shows an example of transaction processing for greenhouse gas emissions according to the first embodiment of the present invention. This is a flowchart illustrating an example of process control processing according to the first embodiment of the present invention. This flowchart shows another example of process control processing according to the first embodiment of the present invention. This flowchart shows an example of an approval process among the process management processes according to the first embodiment of the present invention.

The embodiments of the present invention will be described below. The greenhouse gas emission management system according to the embodiments of the present invention (hereinafter simply referred to as the "system") has the following configuration.
[Item 1]
A method for managing greenhouse gas emissions,
Based on input from the business terminal, the system receives activity information including activity information and emission intensity information related to the business's products.
Referencing the input setting value information related to the activity level information that has been previously entered from the aforementioned business operator terminal,
A method for sending an alert to the operator's terminal when the input activity level information exceeds the input setting value information.
[Item 2]
The management method described in item 1, wherein the input setting value information can be set for each emission intensity.
[Item 3]
The management method described in item 1, which determines whether the activity level information exceeds the input setting information based on past activity level information input.
[Item 4]
The management method described in item 1, which determines whether or not to apply the aforementioned input setting information for each person in charge of input.
[Item 5]
The management method described in item 1, wherein the input activity level information is generated as approval data, and when an approval request is made for the approval data, a rollback process is executed if the input activity level information exceeds the input setting value information.

<First Embodiment>
The following describes a system according to an embodiment of the present invention with reference to the drawings.

Figure 1 illustrates a greenhouse gas emission management system according to a first embodiment of the present invention.

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

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

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

Furthermore, the management terminal 100 has a wallet and connects to the public blockchain network NW. Based on the greenhouse gas emission information for each predetermined period, the management terminal 100 generates a single hash value using SHA256 or another hash function and records it on the blockchain network as transaction information. On the blockchain network, a 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, thus forming the blockchain. Here, the hash generation and/or recording of transaction information to the blockchain can also be performed via another terminal instead of the management terminal 100. In this case, the management terminal 100 transmits the greenhouse gas emission calculated in the matching process to the other terminal. Furthermore, the management terminal 100 can record the greenhouse gas emission information as a smart contract on the blockchain network. By using smart contracts, it is possible to automatically generate, approve, and execute contracts regarding emission trading with other businesses based on the emission information, without the need for a third party. Furthermore, smart contracts allow each service provider to access transaction information without needing a management terminal, improving service convenience and reducing operational costs.

Here, as mentioned above, in a public blockchain, transaction approval is performed not by a specific administrator, but by an unspecified number of nodes and miners. Therefore, compared to a private blockchain, it can guarantee higher data immutability and fault tolerance, thus ensuring transaction security. For this reason, in this embodiment, a public blockchain is preferable as the destination for recording electricity transactions. Representative public blockchains include Bitcoin and Ethereum, but Ethereum, for example, has higher immutability and reliability among public blockchains.

Furthermore, the management terminal 100 can associate information regarding greenhouse gas emissions with identifiers and record it on the blockchain network as a Non-Fungible Token (NFT). An NFT is, for example, a token issued under the "ERC721" standard of Etherium, a blockchain network platform. It is a unit of data recorded on the blockchain network and possesses non-fungibility. Because NFTs are recorded on the blockchain along with smart contracts and are traceable, they can certify transaction information, including details and history of business information managing greenhouse gas emissions.

Figure 2 is a functional block diagram of the management terminals that make 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 performed using a communication protocol such as TCP/IP (Transmission Control Protocol/Internet Protocol).

The memory unit 120 stores programs, input data, and other data for executing various control processes and functions within the control unit 130. It consists of RAM (Random Access Memory), ROM (Read Only Memory), etc. The memory unit 120 also includes a business data storage unit 121 for storing various data related to the business operator, and an AI model storage unit 122 for storing learning data and learning models acquired by AI (artificial intelligence). A database (not shown) containing various data may be constructed outside of 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), a GPU (Graphics Processing Unit), and the like. The control unit 130 includes the following functions: an information receiving unit 131 that receives information from external terminals such as the business operator's terminal 200; an image analysis unit 132 that analyzes image data such as invoice information received from the business operator's 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 the information contained in the extracted invoice information; a transaction processing unit 134 that aggregates information on greenhouse gas emissions for a predetermined period, generates a hash value, and records it as transaction information on the blockchain network; and a report generation unit 135 that generates and transmits report data to the business operator at predetermined intervals to output greenhouse gas emissions and the results of the cause analysis of changes in emissions.

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

Furthermore, the management terminal 100 also has a wallet (not shown) necessary for recording transaction information to the blockchain network. This wallet may also be located outside the management terminal 100.

Figure 3 is a functional block diagram of the business terminals that make up the emissions management system.

The carrier terminal 200 comprises a communication unit 210, a display and operation unit 220, a storage 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 performed 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 response to input data from the control unit 240. If the operator terminal 200 is a personal computer, it consists of a display and a keyboard or mouse; if the operator terminal 200 is a smartphone or tablet, it consists of a touch panel, etc. 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 calculator).

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

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

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

The business operator data 1000 shown in Figure 4 stores various data related to the business operator, obtained from the business operator via the business operator terminal 200. For the sake of explanation, Figure 4 shows an example of one business operator (identified by business operator ID "10001"), but it can store information for multiple business operators. Various data related to the business operator may include, for example, basic business operator information (e.g., company name, username, business location information (e.g., address information for each business location), network name (e.g., SSID, IP address), image information (e.g., background image of the business location, person image, etc.), industry, contact information, email address, business location name, affiliate company name, names of related businesses in the supply chain, etc.), input information (e.g., image data of invoice information, etc.), analysis information (e.g., information extracted from image data regarding invoices, greenhouse gas emissions, emission intensity, and predictions of the causes of changes in greenhouse gas emissions, etc.), customer information (e.g., customer ID, blockchain address, etc.), offset report information (e.g., TXID, NFTID), activity level information (e.g., activity information, item, emission intensity, input value setting information), and approval information (approver, approved activity level information, approval status), etc.).

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

First, as part of 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 via the network NW from the business operator terminal 200. 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, or JPG (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 storage 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. The image analysis unit 132 of the control unit 130 of the management terminal 100 uses a learning model, generated by previously learning from image data of various invoice 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 as structured string data. Here, for image analysis, it is also possible to use an image analysis engine (OCR engine, etc.) provided by a business other than the management terminal 100, which is linked via 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, various items included in an invoice can be listed as invoice information, such as the name of the breakdown of electricity charges, the amount for each breakdown (yen), contracted power (kW), electricity usage for each breakdown (kWh), total amount (yen), and date (year and month). In this example, an invoice breakdown for electricity charges is shown, but it could also be an invoice for charges related to the use of other energy, including gas and fuel, or an invoice breakdown for other items, such as a receipt for travel expenses for business trips, a receipt for employee commuting expenses, an invoice related to a transaction with a freight carrier, or an invoice related to a transaction with a waste disposal company. The image analysis unit 132 can extract monetary information, activity level information, etc., included in the invoice information as text by analyzing the image data of this invoice information. The extracted invoice information is stored as analysis information in the business data storage unit 121 of the storage unit 120. Thus, through machine learning-based image analysis, businesses can acquire a vast amount of necessary information for calculating greenhouse gas emissions as image data without having to manually input invoice information. Furthermore, highly accurate image recognition allows for precise extraction of the information required for greenhouse gas emission calculations, thereby improving the efficiency and accuracy of greenhouse gas emission calculations.

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 SCOPE1, SCOPE2, and SCOPE3. SCOPE1 is direct emissions of greenhouse gases by the business operator itself (e.g., emissions associated with fuel combustion and industrial processes), SCOPE2 is indirect emissions associated with the use of electricity, heat, gas, etc. supplied to the business operator by other companies, and SCOPE3 is the calculation standard for emissions across an organization's entire supply chain issued by the GHG Protocol, and refers to emissions from the business operator's supply chain (the entire flow including raw material procurement, manufacturing, logistics, sales, disposal, etc.). SCOPE 3 is further classified 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) Business Waste, (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. Here, greenhouse gases include carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulfur hexafluoride (SF6), and nitrogen trifluoride (NF3), but in this embodiment, CO2 will be used as an example.

Furthermore, greenhouse gas emissions are calculated by defining activity levels as the amount of electricity used by the business operator, the volume of goods transported, the amount of waste processed, and the value of various transactions. These activity levels are then multiplied by emission intensity factors: CO2 emissions per 1 kWh of electricity used, CO2 emissions per ton of goods transported, and CO2 emissions per ton of waste incineration. Greenhouse gas emissions are calculated separately for SCOPE1, SCOPE2, and SCOPE3 (SCOPE3 is further divided into 15 categories), and the total emissions are calculated as supply chain emissions.

In this embodiment, the image analysis unit 132 extracts relevant invoice information separately for SCOPE1, SCOPE2, and SCOPE3 (and further categorizes SCOPE3), and calculates emissions based on the above calculation method, for example, based on the electricity usage amount in kWh from the invoice information. The calculated emissions are stored as analysis information and activity level information in the business operator data storage unit 121 of the storage unit 120.

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

Figure 9 is a flowchart illustrating 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, as part of step S201, the cause analysis unit 133 of the control unit 130 of the management terminal 100 refers to the information on the operator's greenhouse gas emissions calculated in step S103 of Figure 8. Here, the greenhouse gas emissions are referred to by SCOPE (and further by category for SCOPE 3). Furthermore, the cause analysis unit 133 can confirm changes (increases or decreases) in emissions by referring to the same operator's past emission data. As described above, the emissions are stored as analysis information in the operator data storage unit 121 of the storage unit 120.

Next, in step S202, the cause analysis unit 133 analyzes and predicts the cause of the change in emissions using machine learning, based on the emission information referenced above. Here, during the cause analysis, the cause analysis unit 133 of the control unit 130 of the management terminal 100 uses the referenced emission information, factors influencing changes (increases and decreases) in emissions, and a learning model generated by learning data on factors influencing changes (increases and decreases) in advance, stored in the AI model storage unit 122 of the memory unit 120. This model then predicts the cause of the change in emissions for each SCOPE (and further for each category in the case of SCOPE 3).

Here, factors that influence changes (increases or decreases) in emissions include, for example, weather, temperature, product demand and/or factory operations, store or factory business hours or operating hours, changes in equipment or facilities, software measures, energy-saving activities, fuel switching, energy menu changes, changes in business or commuting volume, and the amount of electricity generated by private power generation. Each of these factors influences the emissions of one of the SCOPEs. For example, the weather factor influences precipitation, wind speed, sunshine hours, and temperature. Precipitation influences small-scale hydroelectric power generation, wind speed influences wind power generation, sunshine hours influence solar power generation, and temperature influences air conditioning. Furthermore, electricity generation influences private power generation, which in turn influences CO2 emissions from electricity, thereby influencing changes in SCOPE2 emissions. Meanwhile, air conditioning influences gas consumption, which in turn influences CO2 emissions from gas combustion, thereby influencing changes in SCOPE1 emissions. In addition, energy-saving activities, factory operations due to product demand, and business hours influence electricity consumption, which in turn influences SCOPE2. Furthermore, EMS, refrigeration equipment replacement, energy-saving equipment installation, and vehicle usage also affect electricity consumption, thus impacting SCOPE2. Additionally, vehicle usage, fuel efficiency, boiler usage, and boiler efficiency affect fuel consumption, which in turn affects CO2 emissions from fuel, and consequently impacts SCOPE1.

Furthermore, the factor of product sales influences categories 1, 9, 10, 11, and 12 of SCOPE 3. Capital investment influences category 2, renewable energy ratio and procured energy volume influence category 3, transportation frequency and route changes influence categories 4 and 9, product loss rate influences category 5, business travelers and office workers influence category 6, commuters and office employees influence category 7, power consumption influences category 8, processing reduction through product improvements influences category 10, improvements to energy-efficient products influence category 11, increased recycling rates influence category 12, tenant office electricity influences category 13, franchise emissions influence category 14, and investment destination emissions influence category 15.

In this way, by using machine learning to train the system to understand which factors affect which SCOPE or category, and by obtaining emission information and information on each factor from businesses, it is possible to predict the causes of changes in emissions. Here, by using machine learning to predict the causes of emissions, it is possible to efficiently and accurately predict the factors that influence changes in greenhouse gas emissions for each business and each SCOPE.

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

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

First, as part 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 referred to includes business operator analysis information (greenhouse gas emissions for each SCOPE), etc.

Next, in step S302, the transaction processing unit 134 generates a hash value based on the business operator data referenced in step S301. Specifically, the transaction processing unit 134 generates a single hash value for greenhouse gas emissions over a predetermined period using a hash function, and records the hash value 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 then recorded following the previous block, forming the blockchain. In this example, to reduce the cost associated with blockchain recording, the data is recorded on a different layer (e.g., a sidechain) from the main blockchain (so-called layer 1).

Furthermore, the transaction processing unit 134 can assign and manage NFT IDs by linking them to the blockchain records of the business operator's greenhouse gas emissions. More specifically, as shown in Figure 4, the business operator data 1000 can be assigned the business operator's customer ID as customer information, store the referenced blockchain address, and assign NFT IDs and TX IDs as offset report information.

As shown in Figure 6, on the blockchain network, each NFT ID is associated with a blockchain address, and the NFT ID and customer ID are managed on the management terminal 100. Therefore, for example, information regarding greenhouse gas emissions for a business corresponding to customer ID "2" can be accessed by referring to the blockchain address for each NFT ID, such as NFT IDs "13" and "14," allowing for the retrieval of detailed emission information as shown in Figure 7. Figure 7 shows information regarding an offset report associated with NFT ID "14." A TXID is assigned to the offset report, and the report includes CO2 emissions by SCOPE, the target month and year, and the report issuance date. In addition to the CO2 emissions for the target month and year in this example, recent CO2 emissions, reduced CO2 emissions, and offset CO2 emissions can also be converted into NFTs. By managing CO2 emissions as NFTs in this way, businesses can conduct transactions using NFT certificates while ensuring tamper-proofness and transaction reliability, and can also provide proof of emissions to third parties.

Figure 11 is a flowchart illustrating an example of process control processing according to the first embodiment of the present invention.

First, as part of the processing 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 on the scale of activities (so-called activity amount) related to the business operator's products (including activities of persons other than the business operator), and examples include category classification (any of the classifications of SCOPE1 to SCOPE3, and categories classified by emission cause of SCOPE3), item (for example, energy items such as gasoline, iron, ethylene, cement, etc.), usage amount (for example, energy (electricity, etc.) usage amount, cargo transport amount, waste processing amount, various transaction amounts), etc. Furthermore, the information acquisition unit 131 can receive activity information for each SCOPE classification of SCOPE1 to SCOPE3. For example, for business operator X, activity information for SCOPE1 (such as ethylene usage items), SCOPE2 (such as electricity usage), and SCOPE3 (such as the procurement weight of transported goods) can be received. Furthermore, the information acquisition unit 131 receives information on corresponding emission intensity for the received activity information from the business operator terminal 200 and calculates it as activity amount information. This process can also be replaced by receiving the information in the form of the invoice information and calculating the activity amount. The received and/or calculated activity amount information can then be stored as activity amount information in the business operator data 1000.

Next, as part of step S402, the report generation unit 135 of the control unit 130 of the management terminal 100 compares the activity amount information received from the business terminal 200 with the input setting value information. Here, the input setting value information refers to information regarding the input values that should be allowed for energy consumption related to the business's products, based on previously entered consumption data. The business can specify, for example, minimum, maximum, or multiplier (3x, 5x, 100x, etc.) values for values entered in the previous year, the previous month, the past three months, or their respective average values. The input setting value information can be set for the total value for each emission intensity, or it can be set for each individual input value, not limited to the same emission intensity. The management terminal 100 can receive the input setting value information in advance from the business terminal 200 and store it in the business data storage unit 121 of the storage unit 120. Furthermore, it is possible to set whether or not to apply the input setting value information to the entered consumption data for each person responsible for inputting the data.

The report generation unit 135, based on the energy usage included in the activity level information received in the previous step, determines that an energy usage value is an anomaly if, for example, the energy usage entered for the current month exceeds 100 times the energy usage entered for the previous month, using the referenced input setting value information (e.g., "100 times the previous month"). Here, when the input value exceeds the input setting value, such as when the input person enters usage for the past two months, the system can also refer to past input information and determine that the input value is valid. Furthermore, using machine learning, the system can determine whether the input value for the current month is valid regardless of whether it exceeds the input setting value, based on the input person's past input information (for example, by detecting that the input person enters usage every three months, or that they entered multiple invoice information when entering usage for the current month).

Next, in step S403, the report generation unit 135 sends an alert to the business terminal 200 if the input information exceeds the input setting value information. The alert can display information regarding the usage value entered by the data entry person and information regarding the pre-entered input setting value, indicating that the input information exceeds (or falls below) the input setting value information.

Figure 12 is a flowchart illustrating another example of process control processing according to the first embodiment of the present invention.

First, as part of step S501, the information acquisition unit 131 of the control unit 130 of the management terminal 100 receives activity information and activity amount information related to emission intensity from the business operator terminal 200 via the network NW. 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. This process can also be replaced by receiving the information in the form of the invoice information and calculating the activity amount. The received and/or calculated activity amount information can then be stored as activity amount information in the business operator data 1000.

Next, in step S502, an approval process is performed based on the request from the service provider terminal 200. A specific example of the approval process will be explained with reference to Figure 13.

First, as part of step S601, the report generation unit 135 of the control unit 130 of the management terminal 100 generates approval data based on the activity level information related to the approval target (for example, the registered activity level information mentioned above) and the approval data creation request based on the selection of approvers, which are received from the business operator terminal 200. The report generation unit 135 stores the generated approval data as approval information in the business operator data storage unit 121 of the storage unit 120. Here, the approval data may be generated based on one activity level information, or it may be generated based on multiple activity level information. Furthermore, the business operator can select multiple approvers, and approval data may be created without limit.

Next, as part of step S602, the report generation unit 135 sends the generated approval data, along with the activity volume information subject to approval, to the business terminal (not shown) of the approver, as an approval request.

Next, in step S603, the information acquisition unit 131 receives approval or rejection (or rejection) information from the approver's terminal. Here, the approver reviews the received approval data, confirming the activity level information subject to approval for each registered activity level record, and performs the approval process by operating selection buttons or similar controls to indicate approval or rejection. At this point, when deciding whether to approve or reject, a common rule can be established based on the input setting value information, stating that if the input value exceeds the input setting value, the activity level information for approval is rejected. Based on this rule, the process of automatically rejecting and rejecting the submitted activity level information can also be executed.

Next, in step S604, the report generation unit 135 updates the approval status of the approval information stored in the business operator data storage unit 121 of the storage unit 120 based on the received approval result. If the approval result is non-approval (returned), the process returns to step S502, and the report generation unit 135 transmits the approval data, based on the corrected activity level information, to the business operator terminal related to the approver.

After the approval process is completed, the next step, as part of step S503, is to record the approved activity data information along with the approval status on the blockchain network using transaction 134 of the control unit 130 of the management terminal 100. As part of the blockchain network recording process, the transaction processing unit 134 refers to the business data stored in the business data storage unit 121 of the memory unit 120 and generates a hash value based on the activity data information. For the activity data information, including greenhouse gas emissions for a predetermined period, a hash function is used to generate a single row of hash values, and these hash values are recorded on the public blockchain as transaction information. 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 then recorded following the previous block, thus forming the blockchain. In this example, to reduce transaction costs and thus reduce the costs associated with blockchain recording, it is also possible to record on a different layer 2 (e.g., a sidechain) from the main blockchain (so-called layer 1).

As described above, this example allows for the efficient execution of the approval flow by generating approval data based on activity level information registered by the business operator. Furthermore, by recording the approved activity level information on a blockchain network, the approved activity level information can be publicly recorded, ensuring the reliability and transparency of the information.

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

100 Management terminals 200 Operator terminals

Claims (4)

A method for managing greenhouse gas emissions,
The control unit of the management terminal is
Based on input from the business terminal, the system receives multiple activity data, including activity information and emission intensity information related to the business's products.
Multiple approval data corresponding to each of the aforementioned multiple activity level information are generated,
In the approval process for the multiple approval data, the input setting value information, which is set individually or for each emission intensity corresponding to each of the multiple activity amount information, is referenced, which has been entered in advance from the business operator terminal.
A method for executing the rejection process for the approval data when each of the multiple activity level information exceeds the input setting value information. The management method according to claim 1, wherein the input setting value information can be set for each emission intensity. The management method according to claim 1, wherein it determines whether the activity level information exceeds the input setting value information based on past activity level information input. The management method according to claim 1, wherein the decision of whether or not to apply the aforementioned input setting information is made for each person responsible for input.