Greenhouse gas emission management methods

Abstract

To provide a method of efficiently performing management of greenhouse gas emissions, including computation of the amount of emissions, by a business operator.SOLUTION: A greenhouse gas emissions management method according to an embodiment of the present invention comprises: receiving activity level information, including activity information and emission intensity information regarding products of a business operator, based on input from a business operator terminal; referring to input setting information related to the activity level information entered from the business operator terminal in advance; and transmitting an alert to the business operator terminal when the entered activity level information exceeds the input setting information.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] In Non-Patent Document 1, with the aim of further reducing greenhouse gas emissions by businesses, as emissions other than SCOPE1 and SCOPE2, suggestions have been made regarding calculating SCOPE3 emissions, that is, the emissions of the supply chain (the entire series of flows such as raw material procurement, manufacturing, logistics, sales, waste disposal, etc.) of other related businesses, etc.

Prior Art Documents

Non-Patent Documents

[0004]

Non-Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] However, while the technology disclosed in Non-Patent Document 1 provides information on methods for calculating greenhouse gas emissions related to SCOPE 3, businesses, especially corporations and local governments, spend a great deal of time and effort collecting and inputting vast amounts of data for emission calculations, calculating emissions, and managing the calculation results. In particular, in the field of GHG emission management, the scope of emissions subject to calculation is expanding, the data that forms the basis for emission calculations varies widely for each SCOPE, and data management methods differ from business to business, all of which have hindered the improvement of operational efficiency through the introduction of advanced technologies.

[0006] Therefore, the present invention aims to provide a method for efficiently managing greenhouse gas emissions by reducing the workload involved in the calculation of greenhouse gas emissions by businesses, in the field of GHG emission management. [Means for solving the problem]

[0007] A method for managing greenhouse gas emissions according to one embodiment of the present invention, comprising: receiving activity amount information including activity information and emission intensity information related to the business operator's products based on input from a business operator terminal; referring to input setting value information related to the activity amount information that has been previously entered from the business operator terminal; and sending an alert to the business operator terminal when the entered activity amount information exceeds the input setting value information. [Effects of the Invention]

[0008] According to the present invention, it is possible to provide a method for efficiently enabling businesses to manage and calculate greenhouse gas emissions. [Brief explanation of the drawing]

[0009] [Figure 1] This is a diagram illustrating a greenhouse gas emission management system according to a first embodiment of the present invention. [Figure 2] This is a functional block diagram of the management terminals that make up the greenhouse gas emissions management system. [Figure 3]This is a functional block diagram of the business terminals that make up the greenhouse gas emissions management system. [Figure 4] This figure illustrates the details of the business operator data according to the first embodiment of the present invention. [Figure 5] This figure illustrates the details of invoice information according to the first embodiment of the present invention. [Figure 6] This figure illustrates an example of transaction information according to the first embodiment of the present invention. [Figure 7] This figure illustrates another example of transaction information according to the first embodiment of the present invention. [Figure 8] This flowchart shows an example of the greenhouse gas emission calculation process according to the first embodiment of the present invention. [Figure 9] 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. [Figure 10] This flowchart shows an example of transaction processing for greenhouse gas emissions according to the first embodiment of the present invention. [Figure 11] This is a flowchart illustrating an example of process control processing according to the first embodiment of the present invention. [Figure 12] This flowchart shows another example of process control processing according to the first embodiment of the present invention. [Figure 13] This flowchart shows an example of an approval process among the process management processes according to the first embodiment of the present invention. [Modes for carrying out the invention]

[0010] 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 the input from the business operator terminal, receive activity amount information including activity information and emission unit information regarding the products of the business operator. Refer to the input setting value information related to the activity amount information that was previously input from the business operator terminal. A method of transmitting an alert to the business operator terminal when the input activity amount information exceeds the input setting value information. [Item 2] The management method according to item 1, wherein the input setting value information can be set for each emission unit. [Item 3] The management method according to item 1, wherein it is determined whether the activity amount information exceeds the input setting information based on the input of past activity amount information. [Item 4] The management method according to item 1, wherein it is determined by each input person whether to apply the input setting value information. [Item 5] Generate the input activity amount information as approval data, and when an approval request related to the approval data is made, perform a rollback process when the input activity amount information exceeds the input setting value information. The management method according to item 1.

[0011] <The 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 amount management system according to the first embodiment of the present invention.

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

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

[0015] 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., increase or decrease) using machine learning and predicts the cause of the changes.

[0016] 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, based on the emission information, contracts regarding emission trading with other businesses can be automatically generated, approved, and executed without the need for a third party. Furthermore, smart contracts enable each service provider to access transaction information without needing to use a management terminal, thereby improving service convenience and reducing operational costs.

[0017] 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 the security of transactions. 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.

[0018] 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 Ethereum, a blockchain network platform, and is a unit of data recorded on the blockchain network, possessing the characteristic of being non-fungible. Since NFTs are recorded on the blockchain along with smart contracts and are traceable, they can prove transaction information including details and history of business information that manages greenhouse gas emissions.

[0019] Figure 2 is a functional block diagram of the management terminals that make up the emissions management system.

[0020] The communication unit 110 is a communication interface for communicating with external terminals via a network NW, and communication is performed using a communication protocol such as TCP / IP (Transmission Control Protocol / Internet Protocol).

[0021] The memory unit 120 stores programs for executing various control processes and functions within the control unit 130, input data, etc., and is composed of RAM (Random Access Memory), ROM (Read Only Memory), etc. The memory unit 120 also has 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 a learning model that the AI ​​(artificial intelligence) has learned from the learning data. A database (not shown) containing various data may be built outside of the memory unit 120 or the management terminal 100.

[0022] The control unit 130 controls the overall operation of the management terminal 100 by executing a program stored in the memory unit 120, and is composed of a CPU (Central Processing Unit), a 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 terminal 200, an image analysis unit 132 that analyzes image data such as invoice information received from the business 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.

[0023] Although not shown in the figures, the control unit 130 also includes an image generation unit that generates screen information to be displayed via the user interface of an external terminal such as the operator terminal 200. For example, using image and text data stored in the storage unit 120 as source material, it generates information to be 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 performed by a GPU (Graphics Processing Unit).

[0024] 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.

[0025] Figure 3 is a functional block diagram of the business terminals that make up the emissions management system.

[0026] The carrier terminal 200 comprises a communication unit 210, a display and 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 using a communication protocol such as TCP / IP.

[0028] The display operation unit 220 is a user interface used by the operator to input instructions and display text, images, etc., in accordance with the 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, and 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).

[0029] 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 the contents of communications with the management terminal 100.

[0030] 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, and the like.

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

[0032] 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. In Figure 4, for the sake of explanation, an example of one business operator (a business operator identified by business operator ID "10001") is shown, but information on multiple business operators can be stored. Various data related to the business operator may include, for example, basic information of the business operator (e.g., the 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 on invoices extracted from image data, greenhouse gas emissions, emission intensity information, prediction of 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.

[0033] Figure 8 is a flowchart illustrating an example of the greenhouse gas emission calculation process according to the first embodiment of the present invention.

[0034] 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, and 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.

[0035] Next, as part 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 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 and extracts items included in the invoice information as structured string data, using a learning model that was generated in advance by learning from image data of various types of invoices stored in the AI ​​model storage unit 122 of the memory unit 120. 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.

[0036] 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 can include various items contained in an invoice, 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 used as an example, 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., contained 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. In this way, machine learning-based image analysis allows businesses to 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 enables the precise extraction of information necessary for calculating greenhouse gas emissions, thereby improving the efficiency and accuracy of greenhouse gas emission calculations.

[0037] 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.). SCOPE3 is further classified into 15 categories: (1) Products / Services Purchased, (2) Capital Goods, (3) Fuel and Energy-Related Activities Not Included in SCOPE1 and SCOPE2, (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 Products Sold, (11) Use of Products Sold, (12) Disposal of Products Sold, (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.

[0038] Furthermore, greenhouse gas emissions are calculated by defining activity levels as the amount of electricity used by a business operator, the amount of goods transported, the amount of waste processed, and the value of various transactions, and multiplying these activity levels by emission intensity, which is the amount of CO2 emissions per 1 kWh of electricity used, the amount of CO2 emissions per ton of goods transported, and the amount of CO2 emissions per ton of waste incinerated. 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.

[0039] In this embodiment, the image analysis unit 132 extracts relevant invoice information separately for SCOPE1, SCOPE2, and SCOPE3 (and further for SCOPE3 by category), and calculates emissions based on the above calculation method, for example, based on the amount of electricity used 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.

[0040] Next, as part of the processing in 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 by category for SCOPE 3), based on the calculated emissions information.

[0041] 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.

[0042] 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 refer to emissions by SCOPE (and further by category for SCOPE 3). The cause analysis unit 133 can also check changes (increases or decreases) in emissions by referring to past emission data of the same operator. As described above, the emissions are stored as analysis information in the operator data storage unit 121 of the storage unit 120.

[0043] Next, as part of step S202, the cause analysis unit 133 analyzes and predicts the causes of changes in emissions using machine learning based on the emission information referenced above. Here, in the cause analysis, the cause analysis unit 133 of the control unit 130 of the management terminal 100 predicts the causes of changes in emissions for each SCOPE (and further for each category in the case of SCOPE 3) using the emission information referenced above, factors that affect changes (increases and decreases) in emissions, and a learning model that was generated by learning data on factors that affect changes (increases and decreases) in emissions stored in the AI ​​model storage unit 122 of the memory unit 120 in advance.

[0044] 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 and thus influence SCOPE2. Furthermore, EMS, replacement of refrigeration equipment, introduction of energy-saving equipment, and vehicle usage also affect electricity usage and thus influence SCOPE2. In addition, vehicle usage, fuel efficiency, boiler usage, and boiler efficiency affect fuel usage, which in turn affect CO2 emissions from fuel and thus influence SCOPE1.

[0045] Furthermore, the number of products sold influences categories 1, 9, 10, 11, and 12 of SCOPE3, while capital investment influences category 2, renewable energy ratio and procured energy volume influence category 3, the number of deliveries and changes in delivery routes 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-saving 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.

[0046] In this way, by using machine learning to learn 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 performing emission cause prediction 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.

[0047] Next, as part of the processing in step S203, the report generation unit 135 of the control unit 130 generates a visualized report on the causes of changes in emissions, categorized by SCOPE (and further categorized for SCOPE 3), based on the information on the predicted causes of changes in emissions that has been analyzed above.

[0048] Figure 10 is a flowchart illustrating an example of transaction processing for greenhouse gas emissions according to the first embodiment of the present invention.

[0049] 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. The business operator data referred to here includes business operator analysis information (greenhouse gas emissions for each SCOPE), etc.

[0050] Next, as part of 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 hash value for one row using a hash function for greenhouse gas emissions over a predetermined period, 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, and is recorded following the previous block, thus forming the blockchain. In this example, in order to reduce the cost of blockchain recording, the data is recorded on a Layer 2 (e.g., a sidechain) that is different from the main blockchain (so-called Layer 1).

[0051] Furthermore, the transaction processing unit 134 can assign and manage NFTIDs in conjunction with the blockchain record 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 blockchain address to be referenced, and assign NFTIDs and TXIDs as offset report information.

[0052] As shown in Figure 6, on the blockchain network, each NFTID is associated with a blockchain address, and the NFTID and customer ID are managed on the management terminal 100. For example, information on greenhouse gas emissions for a business corresponding to customer ID "2" can be accessed by referring to the blockchain address for each NFTID, such as NFTID "13" and "14," and as shown in Figure 7, detailed emission information can be retrieved. Figure 7 shows information on an offset report associated with NFTID "14," where a TXID is assigned, and the offset report includes CO2 emissions by SCOPE, the target year and month, and the report issuance date. In addition to the CO2 emissions for the target year and month in this example, it is also possible to create NFTs for the most recent year's CO2 emissions, reduced CO2 emissions, and offset CO2 emissions. By managing CO2 emissions as NFTs in this way, businesses can trade NFT certificates while ensuring non-tampering and transaction reliability, and can also provide proof of emissions to third parties.

[0053] Figure 11 is a flowchart illustrating an example of process control processing according to the first embodiment of the present invention.

[0054] First, as part of 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 in SCOPE3), item (for example, energy items such as gasoline, iron, ethylene, cement, etc.), usage amount (for example, energy (electricity, etc.) usage amount, cargo transport volume, waste processing volume, 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, it can receive activity information for SCOPE1 (item usage amount of ethylene, etc.), activity information for SCOPE2 (electricity usage amount, etc.), and activity information for SCOPE3 (procurement weight of transported cargo, etc.). 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. This process can also be replaced by a process that receives the information in the form of the invoice information and calculates the activity amount. The received and / or calculated activity amount information can be stored as activity amount information in the business operator data 1000.

[0055] 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 operator's products, based on information about consumption amounts entered in the past. The business operator can specify, for example, a minimum value, a maximum value, or a multiplier (3 times, 5 times, 100 times, etc.) for values ​​entered in the previous year, values ​​entered in the previous month, values ​​entered in the past three months, or the average value of each. 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. In addition, it is possible to set whether or not to apply the input setting value information to the entered consumption amount for each person in charge of input.

[0056] The report generation unit 135, based on the energy consumption included in the activity level information received in the previous step, determines that an abnormal value exists if, for example, the energy consumption entered for the current month exceeds 100 times the energy consumption entered for the previous month, using the referenced input setting value information (e.g., "100 times the previous month"). Here, if the input value exceeds the input setting value, such as when the person entering the usage for the past two months, the system can also refer to past input information and determine that the input value is valid. Furthermore, machine learning can be used to determine whether the input value for the current month is valid, regardless of whether it exceeds the input setting value, based on the input information of the person entering the usage every three months (for example, by detecting whether the person entering the usage every three months or entering multiple invoice information when entering the usage for the current month).

[0057] Next, as part of step S403, the report generation unit 135 sends an alert to the business terminal 200 if the information entered in the above step exceeds the input setting value information. The alert can display information about the usage amount entered by the person in charge of input and information about the input setting value entered in advance, indicating that the input information exceeds (is above or below) the input setting value information.

[0058] Figure 12 is a flowchart illustrating another example of process control processing according to the first embodiment of the present invention.

[0059] 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 with a process that receives the information in the form of the invoice information and calculates the activity amount. The received and / or calculated activity amount information can be stored as activity amount information in the business operator data 1000.

[0060] Next, in step S502, an approval process is performed based on the request from the operator terminal 200. A specific example of the approval process will be explained with reference to Figure 13.

[0061] First, as part of the process in step S601, the report generation unit 135 of the control unit 130 of the management terminal 100 generates approval data based on the activity volume information related to the approval target (for example, the registered activity volume information mentioned above) and the approval data creation request based on the selection of approvers, which are received from the business terminal 200. The report generation unit 135 stores the generated approval data as approval information in the business data storage unit 121 of the storage unit 120. Here, the approval data may be generated based on one activity volume information, or it may be generated based on multiple activity volume information. Also, here, the business person in charge can select multiple approvers, and approval data may be created without limit.

[0062] 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.

[0063] Next, as part of step S603, the information acquisition unit 131 receives approval result information from the business terminal of the approver, indicating approval or rejection (or rejection). Here, the approver checks the received approval data for each registered activity level information (record) and performs the approval process by operating selection buttons, etc., to indicate approval or rejection. Here, when deciding whether to approve or reject, a common rule can be established based on the above input setting value information, stating that if the input value exceeds the input setting value, it will be rejected. Based on this rule, the process of automatically rejecting the activity level information for approval and sending it back can also be executed.

[0064] Next, as part of 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 not approved (returned), the process returns to step S502, and the report generation unit 135 sends the approval data, based on the corrected activity level information, to the business operator terminal related to the approver.

[0065] After the approval process is completed, the next step, as part of step S503, is for transaction 134 of the control unit 130 of the management terminal 100 to record the approved activity information along with the approval status on the blockchain network. 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 information. For the activity information, including greenhouse gas emissions for a predetermined period, a hash value for one row is generated using a hash function, and the hash value is 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, and is recorded following the previous block, thus forming the blockchain. In this example, in order to reduce transaction costs and 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).

[0066] 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 recorded in a public state, ensuring the reliability and transparency of the information.

[0067] The embodiments described above are merely illustrative to facilitate understanding of the present invention and are not intended to limit its interpretation. 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. [Explanation of Symbols]

[0068] 100 Management terminals 200 carrier terminals

Claims

1. 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.

2. The management method according to claim 1, wherein the input setting value information can be set for each emission intensity.

3. 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.

4. The management method according to claim 1, wherein each person responsible for input determines whether or not to apply the input setting information.

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