Greenhouse gas emissions management method

Abstract

To provide a method of enabling efficient greenhouse gas emissions management, including computation of greenhouse gas emissions by a business operator.SOLUTION: A greenhouse gas emissions management method involves causing a control unit of a management terminal to refer to information on a credit activity and greenhouse gas emissions of a business operator, predict a shortage in the credit activity, transmit an alert on the shortage of the credit activity, and recommend purchasing of a credit to offset the shortage.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 the calculation and reduction efforts of emissions in SCOPE1 and SCOPE2 have been progressing.

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

Prior Art Documents

Non-Patent Documents

[0004]

Non-Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

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

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

Means for Solving the Problems

[0007] A method for managing greenhouse gas emissions according to an embodiment of the present invention, which receives, from a business operator terminal, a request for allocating the credit to emissions, including information on the activity amount of the credit, and determines whether the received request for allocation is a request for excessive allocation to the emissions based on the information on the activity amount and the information on the greenhouse gas emissions stored in a storage unit, and when the determination is that the request for allocation is a request for excessive allocation to the emissions, transmits an alert to the business operator terminal indicating that the request for allocation is excessive.

Effects of the Invention

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

Brief Description of the Drawings

[0009]

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Embodiments for Carrying Out the Invention

[0010] The contents of the embodiments of the present invention will be listed and described. 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] A method for managing greenhouse gas emissions, The control unit of the management terminal refers to information on the activity volume of the operator's credit and information on greenhouse gas emissions, predicts a shortage in the activity volume of the credit, Sending an alert about the insufficient credit activity volume, A method of recommending the purchase of credits to offset the deficiency. [Item 2] The control unit transmits the alert to the operator terminal of the operator via a chat interface, according to the management method described in Item 1. [Item 3] The control unit transmits the alert on a screen that refers to the actual achievements and reduction targets of the greenhouse gas emissions displayed on the operator terminal, according to the management method described in Item 1. [Item 4] The control unit recommends information on credits to offset the deficiency, according to the management method described in Item 1. [Item 5] The control unit guides the operator to a screen where the credits selected by the operator for purchase are available for purchase, according to the management method described in 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 emissions management system according to the first embodiment of the present invention.

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

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

[0015] In addition, the management terminal 100 analyzes the received image data of the invoice information by machine learning, extracts the necessary items of the invoice information included in the image data, and calculates the greenhouse gas emissions. Further, the management terminal 100 analyzes the calculated changes (e.g., increases or decreases) in the greenhouse gas emissions over time by machine learning and predicts the causes of the changes.

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

[0017] Here, as described above, in a public blockchain, since the approval of transactions is carried out by an unspecified number of nodes or miners instead of a specific administrator, compared with a private blockchain, higher data immutability and fault tolerance can be ensured. Therefore, since the security of transactions is ensured, it is preferable to use a public blockchain as the destination for recording power transactions in this embodiment. Representative public blockchains include Bitcoin, Ethereum, etc. For example, Ethereum has higher immutability and reliability among public blockchains.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0032] The business operator data 1000 shown in FIG. 4 stores various data related to the business operator acquired from the business operator via the business operator terminal 200. In FIG. 4, for convenience of explanation, an example of one business operator (the business operator identified by the business operator ID "10001") is shown, but information of a plurality of business operators can be stored. As various data related to the business operator, for example, basic information of the business operator (for example, corporate name of the business operator, user name, business office information (for example, address information for each business office, etc.), network name (for example, SSID, IP address, etc.), image information (for example, background image of the business office, portrait image, etc.), business type, contact information, email address, business office name, related company name, business operator name related on the supply chain, etc.), input information (for example, image data of invoice information, etc.), analysis information (for example, information related to invoices extracted from image data, greenhouse gas emissions, prediction of causes of changes in greenhouse gas emissions, etc.), customer information (for example, customer ID, blockchain address, etc.), and offset report information (for example, TXID, NFTID, etc.), activity information (credit information held, information related to the type / origin of credit assigned to a project (product) / raw material and the offset amount) can be included.

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

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

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

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

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

[0038] Also, the greenhouse gas emissions are calculated by defining the usage amount of electricity, the transportation volume of goods, the treatment volume of waste, and various transaction amounts of the operator as activity amounts, and multiplying the activity amounts by the emission factors, namely, the CO2 emissions per 1 kWh of electricity used, the CO2 emissions per 1 ton of goods transported, and the CO2 emissions per 1 ton of waste incinerated. The greenhouse gas emissions are calculated separately for the above SCOPE1, SCOPE2, and SCOPE3 (for SCOPE3, by 15 categories), and the total emissions are calculated as the supply chain emissions.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0053] FIG. 11 is a flowchart showing an example of a process for recommending credit according to the first embodiment of the present invention.

[0054] First, as the process of step S401, the report generation unit 135 of the control unit 130 of the management terminal 100 refers to the information regarding the activity volume of credits held by the operator of the operator data 1000 in the operator data storage unit 121 of the storage unit 120 and the information regarding the greenhouse gas emissions volume, and based on the comparison result of these pieces of information, predicts whether the activity volume of credits is insufficient with respect to the greenhouse gas emissions volume. This prediction process for credit shortage may be performed triggered by several events. For example, when the person in charge of the operator sends a request to the management terminal 100 to refer to carbon offset via the operator terminal 200, the credit shortage prediction process may be executed, or when the person in charge of the operator sends a request to the management terminal 100 to compare the greenhouse gas reduction target and the greenhouse gas emissions volume at the operator terminal 200, the credit shortage prediction process may be executed. In addition, when the person in charge of the operator conducts a question or conversation regarding carbon offset via the chat interface of the operator terminal 200 and requests information regarding carbon offset to the management terminal 100, the carbon credit shortage prediction may be executed. For example, when the person in charge of the business is performing a simulation based on the greenhouse gas emissions reduction target and the actual emissions volume at the business terminal 200, the degree of credit shortage risk can also be judged step by step such as "high / medium / low". Also, the prediction of credit shortage can be executed for each project (product) of the operator. In addition, this process can also be executed by the management terminal 100 via the LLM300. The LLM300 calculates whether the credit is insufficient and / or how much it is insufficient based on the energy usage situation of the operator, and can also predict the seasonal trend (such as the increase or decrease standard per month during the peak season, etc.) while referring to the sales performance data and the like stored in the operator data 2000.

[0055] Subsequently, as the process of step S402, when the report generation unit 135 of the control unit 130 of the management terminal 100 predicts that the carbon credit of the operator is insufficient in the process of S401, it transmits to the operator terminal 200 a notice of the shortage (and its activity level) together with an alert (notification). Here, the report generation unit 135 can also transmit the alert to the operator terminal 200 via the chat interface. At this time, as described above, when the LLM 300 predicts a credit shortage, the alert can also be transmitted via the LLM 300. Also, as described above, when the person in charge of the business makes a request to compare the greenhouse gas reduction target with the greenhouse gas emissions amount and is referring to the screen for comparing the reduction target with the emissions amount, an alert may be displayed on that screen.

[0056] Subsequently, as the process of step S403, the report generation unit 135 of the control unit 130 of the management terminal 100 transmits information regarding the credits to be purchased to offset greenhouse gases to the business operator terminal 200. Conventionally, as information regarding credits, due to different systems (for example, "non-fossil certificates, J-credits" that can be used in the energy conservation law, and as other certification systems, J Blue credits), different usage periods and expiration dates, etc., it has been difficult for business operators to determine which credits should be purchased as offsets. Therefore, in this example, for example, the LLM 300 can refer to the business operator data 1000 and the database of external credit information to determine and recommend information regarding the credits that the business operator should purchase. Also, as a simulation comparing the business operator's emission reduction target and the actual emission performance, when a shortage (risk of not being able to achieve the reduction target) can be expected, the simulation and the degree of risk can be judged step by step like "high / medium / low" to determine and recommend the purchase amount. For example, when the risk is "high", the purchase amount can be calculated from the maximum expected shortage based on the trends of the business operator over the years or those of equivalent other companies. When the risk is "low", the purchase amount can be calculated from the minimum expected shortage based on the trends of the business operator over the years or those of equivalent other companies. When the risk is "medium", the purchase amount can be calculated as the intermediate value or the average value between the cases of "high" and "low" above. Also, these degrees of risk can also be judged based on the conversation between the business operator and the LLM 300 via the chat interface.

[0057] Subsequently, as the process of step S404, when the report generation unit 135 of the control unit 130 of the management terminal 100 presents information regarding the credit to be purchased, and the person in charge of the business selects information regarding the credit to be purchased via the business operator terminal 200 and makes a purchase request to the management terminal 100, the business operator terminal 200 is guided to be able to access the website for purchasing the credit. Specifically, when the person in charge of the business selects the credit to be purchased from among the information regarding various credits displayed on the business operator terminal 200, the business operator terminal 200 is redirected to be able to access the purchase screen of the credit, and the person in charge of the business can proceed with the purchase process on the said purchase screen.

[0058] As described above, in the case of business operator users, it was difficult to determine how much and which credits should be purchased due to reasons such as the difficulty of predicting shortages and the variety of credit types in relation to the offset of greenhouse gases (offsetting emissions by carbon credits). By applying this example, the management of carbon offsets can be facilitated and made more efficient.

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

Explanation of Reference Numerals

[0060] 100 Management terminal 200 Business operator terminal

Claims

1. A method for managing greenhouse gas emissions, wherein the control unit of the management terminal refers to information on the credit activity volume of the operator and information on greenhouse gas emissions, predicts a shortage in the credit activity volume, sends an alert regarding the shortage in the credit activity volume, and recommends purchasing credits to offset the shortage.

2. The management method according to Claim 1, wherein the control unit sends the alert to the operator terminal of the operator via a chat interface.

3. The management method according to Claim 1, wherein the control unit sends the alert on a screen that displays the actual greenhouse gas emissions and reduction targets and is shown on the operator terminal.

4. The management method according to Claim 1, wherein the control unit recommends information on credits to offset the shortage.

5. The management method according to Claim 1, wherein the control unit guides the operator to a screen where the credits selected by the operator for purchase are available for purchase.

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