JP2025104093A - Greenhouse gas emissions management method - Google Patents

Abstract

The present invention aims to provide a method for efficiently realizing management, such as calculation of greenhouse gas emissions by business operators.
[Solution] A method for managing greenhouse gas emissions according to one embodiment of the present invention, wherein a control unit of a management terminal refers to information regarding an enterprise's credit activity amount and information regarding greenhouse gas emissions, predicts a shortfall in the activity amount of the credits, sends an alert of the shortfall in the activity amount of the credits, and recommends the purchase of credits to offset the shortfall.
[Selection] Figure 11

Description

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

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

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

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

However, although the technology disclosed in Non-Patent Document 1 discloses methods for calculating greenhouse gas emissions related to SCOPE3, it takes a great deal of time and effort for each business operator, particularly companies and local governments, to collect and input huge amounts of data to calculate emissions, calculate emissions, and manage the calculation results. In particular, in the area of GHG emissions management, the number of SCOPEs for emissions to be calculated is expanding, the data on which emissions calculations are based varies widely for each SCOPE, and data management methods differ for each business operator, so improvements in business efficiency through the introduction of advanced technology are lagging behind.

The present invention aims to provide a method for reducing the amount of work required and efficiently managing emissions by utilizing advanced technology in the area of GHG emissions management, such as the calculation of greenhouse gas emissions by businesses.

A method for managing greenhouse gas emissions according to one embodiment of the present invention, comprising: receiving, from a business operator terminal, a request for allocation of credits to emissions, including information on the amount of activity of the credits; determining whether the request for allocation is an excessive request for allocation to the amount of emissions based on the received information on the amount of activity and information on the amount of greenhouse gas emissions stored in a memory unit; and, when the determination is that the request for allocation is excessive for the amount of emissions, transmitting an alert to the business operator terminal indicating that the request for allocation is excessive.

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

1 is a diagram illustrating a greenhouse gas emission management system according to a first embodiment of the present invention. FIG. 2 is a functional block diagram of a management terminal constituting the greenhouse gas emission management system. FIG. FIG. 2 is a functional block diagram of a business operator terminal constituting the greenhouse gas emission management system. FIG. 4 is a diagram illustrating details of business entity data according to the first embodiment of the present invention. FIG. 4 is a diagram illustrating details of invoice information according to the first embodiment of the present invention. FIG. 4 is a diagram illustrating an example of transaction information according to the first embodiment of the present invention. FIG. 11 is a diagram illustrating another example of transaction information according to the first embodiment of the present invention. FIG. 4 is a flowchart showing an example of a calculation process of greenhouse gas emissions according to the first embodiment of the present invention. FIG. 4 is a flowchart illustrating an example of a process for predicting the cause of a change in greenhouse gas emissions according to the first embodiment of the present invention. FIG. 4 is a flow chart illustrating an example of a transaction process of greenhouse gas emissions according to the first embodiment of the present invention. FIG. 4 is a flowchart illustrating an example of a credit recommendation process according to the first embodiment of the present invention.

The contents of an embodiment of the present invention will be listed and described below. A greenhouse gas emission management system (hereinafter simply referred to as the "system") according to an embodiment of the present invention has the following configuration.
[Item 1]
1. A method for managing greenhouse gas emissions, comprising:
The control unit of the management terminal is
Refer to information on the business entity's credit activity volume and information on greenhouse gas emissions, and forecast a shortfall in the credit activity volume;
Sending an alert of a lack of activity of said credits;
A method for recommending the purchase of credits to offset said shortfall.
[Item 2]
2. The management method according to claim 1, wherein the control unit transmits the alert to an operator terminal of the operator via a chat interface.
[Item 3]
2. The management method according to item 1, wherein the control unit transmits the alert on a screen that is displayed on the business operator terminal and that refers to the greenhouse gas emission record and the reduction target.
[Item 4]
2. The management method according to claim 1, wherein the control unit recommends information on credits that offset the shortage.
[Item 5]
2. The management method according to claim 1, wherein the control unit guides the user to a screen where the credit selected by the business operator for purchase can be purchased.

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

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

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

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

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

Furthermore, the management terminal 100 has a wallet and is connected to the public blockchain network NW. The management terminal 100 generates a single hash value using SHA256 or other hash function based on the information on the greenhouse gas emissions for each predetermined period, and records it as transaction information in the blockchain network. On the blockchain network, this block is generated based on the transaction information, the hash value recorded in the previous block, and the nonce value mined by the node, and is recorded following the previous block, forming a blockchain. Here, the hash generation and/or recording of the transaction information in the blockchain can be performed via another terminal, not via the management terminal 100. In this case, the management terminal 100 transmits the greenhouse gas emissions calculated by the matching process to the other terminal. Furthermore, the management terminal 100 can record the information on the greenhouse gas emissions in the blockchain network as a smart contract. By using the smart contract, a contract regarding an emission trading with another business operator can be automatically generated, approved, and executed without the intervention of a third party, based on the information on the emission. In addition, smart contracts allow each business operator to refer to transaction information without going through a management terminal, improving service convenience and reducing operational costs. Furthermore, the management terminal 100 is connected to a large-scale language model (LLM, for example, GPT) 300 that accepts a specific question from another terminal via an API (Application Programming Interface) via a network, analyzes the question, and generates an answer.

As described above, a public blockchain is one in which transactions are approved by an unspecified number of nodes and miners, rather than by a specific administrator, and therefore can ensure higher data tamper resistance and fault tolerance than a private blockchain. Therefore, the security of transactions is guaranteed, and therefore a public blockchain is preferable as the destination for recording energy transactions in this embodiment. Representative public blockchains include Bitcoin and Ethereum, and Ethereum, for example, is one of the public blockchains that has higher tamper resistance and reliability.

The management terminal 100 can also associate information about greenhouse gas emissions using an identifier or the like and record it on the blockchain network as a non-fungible token (hereinafter, "NFT"). NFTs are tokens issued, for example, according to the "ERC721" standard of Ethereum, a blockchain network platform, and are a unit of data recorded on the blockchain network and are non-fungible in nature. NFTs are recorded on the blockchain together with smart contracts and are traceable, making it possible to certify transaction information including details and history of business information that manages greenhouse gas emissions.

Figure 2 is a functional block diagram of the management terminal that constitutes 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 according to a communication protocol such as TCP/IP (Transmission Control Protocol/Internet Protocol).

The memory unit 120 stores programs for executing various control processes and functions in 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 an operator data storage unit 121 that stores various data related to operators, and an AI model storage unit 122 that stores learning data and learning models learned by AI (artificial intelligence) from the learning data. Note that a database (not shown) that stores various data may be constructed outside the memory unit 120 or the management terminal 100.

The control unit 130 controls the overall operation of the management terminal 100 by executing programs stored in the memory unit 120, and is composed of a CPU (Central Processing Unit), GPU (Graphics Processing Unit), etc. The functions of the control unit 130 include an information receiving unit 131 that receives information from an external terminal such as the business operator terminal 200, an image analysis unit 132 that analyzes image data such as invoice information received from the business operator terminal and calculates greenhouse gas emissions, a cause analysis unit 133 that analyzes the image data and analyzes the cause of time-series changes in greenhouse gas emissions calculated based on information contained in the extracted invoice information, a transaction processing unit 134 that performs processing to compile information on greenhouse gas emissions for a specified period, generate a hash value, and record it as transaction information in the blockchain network, and a report generation unit 135 that generates and transmits report data to output the results of an analysis of the causes of greenhouse gas emissions and changes in emissions to the business operator every specified period.

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

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

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

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

The communication unit 210 is a communication interface for communicating with the management terminal 100 via the network NW, and communication is performed according to 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. according to input data from the control unit 240, and is composed of a display, keyboard, and mouse if the operator terminal 200 is configured as a personal computer, and is composed of a touch panel, etc. if the operator terminal 200 is configured as a smartphone or tablet terminal. This display operation unit 220 is started up by a control program stored in the memory unit 230 and executed by the operator terminal 200, which is a computer (electronic calculator).

The memory unit 230 stores 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 communication with the management terminal 100.

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

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

The operator data 1000 shown in FIG. 4 stores various data related to the operator acquired from the operator via the operator terminal 200. For ease of explanation, an example of one operator (an operator identified by operator ID "10001") is shown in FIG. 4, but information on multiple operators can be stored. The various data related to the business may include, for example, basic information about the business (e.g., the business's corporate name, user name, business establishment information (e.g., address information for each business establishment, etc.), network name (e.g., SSID, IP address, etc.), image information (e.g., background image of the business establishment, image of a person, etc.), industry, contact information, email address, business establishment name, affiliated company name, business name related in the supply chain, etc.), input information (e.g., image data of invoice information, etc.), analysis information (e.g., invoice extracted from image data, greenhouse gas emissions, information on predicted causes of changes in greenhouse gas emissions, etc.), customer information (e.g., customer ID, blockchain address, etc.), and offset report information (e.g., TXID, NFTID, etc.), activity information (information on credits held, type of credits allocated to projects (products)/information on origin of raw materials and offset amount).

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

First, in the process 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 from the business operator terminal 200 via the network NW. The business operator uploads invoices, receipts, slips, etc. (collectively referred to as "invoices" in this embodiment) in file formats such as PDF, Excel, JPG, etc. (collectively referred to as "image data" in this embodiment) via the business operator terminal 200 to the management terminal 100. The image data acquired by the information acquisition unit 131 is stored as input information in the business operator data storage unit 121 of the memory unit 120.

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

Image analysis is performed, for example, by recognizing and extracting text from image data containing invoice information, as shown in FIG. 5. As shown in FIG. 5, invoice information includes various items included in the invoice, such as the name of the breakdown of the electricity charge, the amount (yen) for each breakdown, the contracted power (kW), the amount of electricity used for each breakdown (kWh), the total amount (yen), and the date (year and month). In this example, an invoice breakdown for an electricity charge is illustrated, but it may also be an invoice for charges related to the amount of other energy used, including gas and fuel, in addition to electricity, or may be, for example, a receipt for travel expenses, a receipt for an employee's commuting expenses, an invoice for transactions with a cargo company, or a breakdown of an invoice for transactions with a waste disposal company. The image analysis unit 132 can extract amount information, the activity amount information described below, and the like included in the invoice information as text by analyzing the image data of the invoice information. The extracted invoice information is stored as analysis information in the business data storage unit 121 of the memory unit 120. In this way, image analysis using machine learning allows businesses to obtain the vast amount of necessary information for calculating greenhouse gas emissions as image data without having to manually input invoice information, and highly accurate image recognition allows the information necessary for calculating greenhouse gas emissions to be accurately extracted, making it possible to calculate greenhouse gas emissions more efficient and with greater accuracy.

Next, in step S103, the image analysis unit 132 of the control unit 130 calculates the greenhouse gas emissions based on the invoice information extracted from the image data. Here, the greenhouse gas emissions are classified into SCOPE1, SCOPE2, and SCOPE3, where SCOPE1 is direct greenhouse gas emissions by the business 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 by other companies, and SCOPE3 is a calculation standard for the emissions of an organization's entire supply chain issued by the GHG Protocol, and refers to the emissions of the business's supply chain (the entire series of flows, including 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 and distribution (upstream), 5) waste from business, 6) business trips, 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 is used as an example.

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

In this embodiment, the image analysis unit 132 extracts related invoice information by SCOPE1, SCOPE2, and SCOPE3 (and further by category for SCOPE3), and calculates the emissions based on the invoice information, for example, the electricity usage (kWh), using the above calculation method. The calculated emissions are stored as analysis information in the business operator data storage unit 121 of the memory unit 120.

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

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

First, in the process of step S201, the cause analysis unit 133 of the control unit 130 of the management terminal 100 refers to the information on the greenhouse gas emissions of the business operator calculated in step S103 of FIG. 8. Here, for the greenhouse gas emissions, the amount of emissions by SCOPE (and further by category for SCOPE 3) is referred to. In addition, the cause analysis unit 133 can check the change (increase or decrease) in the amount of greenhouse gas emissions by referring to the past emission data of the same business operator. As described above, the emission amount is stored as analysis information in the business operator data storage unit 121 of the memory unit 120.

Next, in the process of step S202, the cause analysis unit 133 analyzes and predicts the cause of the change in emission amount by machine learning based on the emission amount information referenced above. Here, when analyzing the cause, the cause analysis unit 133 of the control unit 130 of the management terminal 100 predicts the cause of the change in emission amount by SCOPE (and further by category for SCOPE 3) by using the emission amount information referenced above, factors that affect the change (increase or decrease) in emission amount, and a learning model that has been generated by learning data on factors that affect the change (increase or decrease) in emission amount that is stored in advance in the AI model storage unit 122 of the memory unit 120.

Here, factors that affect the change (increase or decrease) in the output include, for example, weather, temperature, product demand and/or factory operation, store or factory business hours, changes in equipment or facilities, software measures, energy saving actions, fuel conversion, energy menu changes, changes in business trips or commuting, and the amount of power generated by private power generation. Each of these factors affects the emissions of any one of the SCOPEs. For example, the factor of weather affects precipitation, wind volume, hours of sunshine, and temperature, with precipitation affecting the amount of water power generation, wind volume affecting the amount of wind power generation, hours of sunshine affecting the amount of solar power generation, and temperature affecting air conditioning. Furthermore, the amount of power generation affects the amount of private power generation, which affects the amount of CO2 emissions from electricity, thereby affecting the change in the emissions of SCOPE2, while air conditioning affects the amount of gas usage, which affects the amount of CO2 emissions from gas combustion, thereby affecting the change in the emissions of SCOPE1. Furthermore, energy saving activities, factory operation due to product demand, and business hours affect electricity usage, which affects SCOPE2. In addition, EMS, replacement of refrigeration equipment, introduction of energy-saving equipment, and automobile usage also affect electricity usage and thus affect SCOPE2, while automobile usage, fuel efficiency, boiler usage, and boiler efficiency affect fuel usage and thus CO2 emissions from fuel, which in turn affect SCOPE1.

In addition, the factor of product sales volume affects categories 1, 9, 10, 11, and 12 in SCOPE3, capital investment affects category 2, renewable energy ratio and amount of energy procured affects category 3, number of transports and changes in transport route affects categories 4 and 9, product loss rate affects category 5, business trips and number of people going to the office affects category 6, number of people commuting and number of employees going to the office affects category 7, power consumption affects category 8, reduction in processing through product improvements affects category 10, improvements to energy-efficient products affects category 11, increase in recycling rate affects category 12, office electricity of tenants affects category 13, franchise emissions affects category 14, and emissions of investment targets affects category 15.

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

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

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

First, in 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 memory unit 120. Here, the business operator data to be referred to includes business operator analysis information (emissions of greenhouse gases for each SCOPE), etc.

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

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

As shown in FIG. 6, a blockchain address is associated with each NFTID on the blockchain network, and the management terminal 100 manages the NFTID and the customer ID. For example, information on greenhouse gas emissions related to a business corresponding to customer ID "2" can be read out by referencing the blockchain address for each NFTID, such as NFTID "13" and "14", as shown in FIG. 7. FIG. 7 shows information on an offset report associated with NFTID "14", in which a TXID is assigned in association with the offset report, and the offset report includes the CO2 emissions by SCOPE, the target year and month, and the issue date of the report. In addition to the CO2 emissions for the target year and month in this example, the most recent CO2 emissions, the reduced CO2 emissions, and the offset CO2 emissions can also be NFTized. In this way, by managing CO2 emissions with NFT, businesses can trade NFTized certificates while ensuring non-tampering and reliability of the transaction, and can also certify the emissions to third parties.

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

First, in the process of step S401, the report generating unit 135 of the control unit 130 of the management terminal 100 refers to information on the amount of activity of credits held by the business operator in the business operator data 1000 in the business operator data storage unit 121 of the storage unit 120 and information on greenhouse gas emissions, and predicts whether the amount of activity of credits is insufficient for the amount of greenhouse gas emissions based on the comparison result of these pieces of information. This credit shortage prediction process may be triggered by several events. For example, the credit shortage prediction process may be executed when a person in charge of the business operator sends a request to the management terminal 100 to refer to carbon offsets via the business operator terminal 200, or the credit shortage prediction process may be executed when a person in charge of the business operator sends a request to the management terminal 100 in the business operator terminal 200 to compare the greenhouse gas reduction target and the amount of greenhouse gas emissions. In addition, when a person in charge of the business operator asks a question or has a conversation about carbon offsets via the chat interface of the business operator terminal 200, a request for information about carbon offsets from the management terminal 100 may be used as a trigger to execute the carbon credit shortage prediction. For example, when a business manager is performing a simulation on the business terminal 200 based on greenhouse gas emission reduction targets and actual emissions, the degree of credit shortage risk can be determined in stages, such as "high/medium/low." Credit shortage prediction can also be performed for each business project (product). This process can also be performed by the management terminal 100 via the LLM 300, which calculates whether there will be a credit shortage and/or how much of a shortage there will be based on the business's energy usage status, and can also predict seasonal trends (monthly increases and decreases, such as during busy periods) by referring to sales performance data stored in the business data 2000.

Next, in step S402, when the report generation unit 135 of the control unit 130 of the management terminal 100 predicts that the business operator has a carbon credit shortage in the above-mentioned S401 process, it sends an alert (notification) to the business operator terminal 200 to notify the business operator of the shortage (and the amount of activity). Here, the report generation unit 135 can also send the alert to the business operator terminal 200 via a chat interface. At this time, as described above, when the LLM 300 predicts a credit shortage, the alert can also be sent via the LLM 300. Also, as described above, when the business operator makes a request to compare the greenhouse gas reduction target and the greenhouse gas emission amount, and refers to the screen comparing the reduction target and the emission amount, the alert can be displayed on the screen.

Subsequently, in the process of step S403, the report generating unit 135 of the control unit 130 of the management terminal 100 transmits information on credits to be purchased to offset greenhouse gases to the business operator terminal 200. Conventionally, it has been difficult for business operators to determine which credits to purchase as offsets due to different systems (for example, "non-fossil certificate, J-Credit" that can be used under the Energy Conservation Act, and J Blue Credit as another certification system), different usage periods and deadlines, etc. Therefore, in this example, for example, the LLM 300 can determine and recommend information on credits that business operators should purchase while referring to the business operator data 1000 and an external credit information database. In addition, as a simulation comparing the business operator's emission reduction target and actual emission, when a shortage (risk of not achieving the reduction target) is expected, a simulation or a stepwise judgment of the risk level, such as "high/medium/low," can be made to determine and recommend the purchase amount. For example, if the risk is "high," the purchase amount can be calculated from the maximum expected shortage based on the past years or the trends of comparable companies; if the risk is "low," the purchase amount can be calculated from the minimum expected shortage based on the past years or the trends of comparable companies; and if the risk is "medium," the purchase amount can be calculated as the intermediate or average between the above "high" and "low" cases. These risk levels can also be determined based on conversations between the business operator and the LLM 300 via a chat interface.

Next, in the process of step S404, the report generation unit 135 of the control unit 130 of the management terminal 100, in response to the presentation of information on the credits to be purchased, guides the business person to access a website for purchasing the credits via the business operator terminal 200 so that the business person can access the website when the business person selects information on the credits they wish to purchase via the business operator terminal 200 and makes a purchase request to the management terminal 100. Specifically, when the business person selects the credits they wish to purchase from the information on the various credits displayed on the business operator terminal 200, the business operator terminal 200 is redirected so that the business person can access a purchase screen for the credits, and the business person can proceed with the purchase process on the purchase screen.

As a result, in relation to greenhouse gas offsetting (offsetting emissions with carbon credits), business users have had difficulty determining which credits to purchase and how much of them to purchase due to the difficulty of predicting shortages and the large number of types of credits available. By applying this example, however, carbon offset management can be made easier and more efficient.

The above-described embodiment is merely an example to facilitate understanding of the present invention, and is not intended to limit the present invention. The present invention can be modified and improved without departing from the spirit of the invention, and it goes without saying that the present invention includes equivalents.

100 Management terminal 200 Business operator terminal

Claims (5)

1. A method for managing greenhouse gas emissions, comprising:
The control unit of the management terminal is
Refer to information on the business entity's credit activity volume and information on greenhouse gas emissions, and forecast a shortfall in the credit activity volume;
Sending an alert of a lack of activity of said credits;
A method for recommending the purchase of credits to offset said shortfall. The management method according to claim 1, wherein the control unit transmits the alert to the operator's operator terminal via a chat interface. The management method according to claim 1, wherein the control unit transmits the alert on a screen displayed on the business operator terminal that refers to the greenhouse gas emission record and reduction target. The management method according to claim 1, wherein the control unit recommends information on credits that offset the shortage. The management method according to claim 1 , wherein the control unit leads the user to a screen on which the credit selected by the business operator for purchase can be purchased.