JP7841715B2 - Greenhouse gas emission management methods - Google Patents

Description

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

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

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

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

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

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

One embodiment of the present invention provides a method for managing greenhouse gas emissions, comprising: receiving an allocation request for said credits, including information on the activity level of the credits, from a business terminal; determining whether the allocation request is an excessive allocation request for said emissions based on the received activity level information and information on greenhouse gas emissions stored in a storage unit; and, if the determination is that the allocation request is an excessive allocation request, sending an alert to the business terminal indicating that the allocation request is excessive.

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

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

The embodiments of the present invention will be described below. The greenhouse gas emission management system according to the embodiments of the present invention (hereinafter simply referred to as the "system") has the following configuration.
[Item 1]
A method for managing greenhouse gas emissions,
The control unit of the management terminal is
Referencing information on the activity level of credits and greenhouse gas emissions of the operators, we predict a shortage in the said activity level of credits.
Send an alert if the activity level of the aforementioned credits is insufficient.
A method of recommending the purchase of credits to offset the aforementioned shortfall.
[Item 2]
The management method described in item 1, wherein the control unit transmits the alert to the operator's terminal via a chat interface.
[Item 3]
The management method according to item 1, wherein the control unit transmits the alert on a screen displayed on the operator's terminal that references the actual greenhouse gas emissions and reduction targets.
[Item 4]
The control unit recommends credit information to offset the deficit, as described in item 1.
[Item 5]
The control unit guides the business operator to a screen where the selected credits can be purchased, as described in item 1.

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

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

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

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

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

Furthermore, the management terminal 100 has a wallet and connects to the public blockchain network NW. Based on the greenhouse gas emission information for each predetermined period, the management terminal 100 generates a single hash value using SHA256 or another hash function and records it on the blockchain network as transaction information. On the blockchain network, a block is generated based on the transaction information, the hash value recorded in the previous block, and the nonce value mined by the node, and is recorded following the previous block, thus forming the blockchain. Here, the hash generation and/or recording of transaction information to the blockchain can also be performed via another terminal instead of the management terminal 100. In this case, the management terminal 100 transmits the greenhouse gas emission calculated in the matching process to the other terminal. Furthermore, the management terminal 100 can record the greenhouse gas emission information as a smart contract on the blockchain network. By using smart contracts, it is possible to automatically generate, approve, and execute contracts regarding emission trading with other businesses based on the emission information, without the need for a third party. Furthermore, smart contracts enable each service provider to access transaction information without going through the management terminal, improving service convenience and reducing operational costs. In addition, the management terminal 100 connects to a large-scale language model (LLM, e.g., GPT) 300 via the network, receiving predetermined question texts from other terminals via an API (Application Programming Interface), analyzing the question texts, and generating answer texts.

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

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

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

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

The memory unit 120 stores programs, input data, and other data for executing various control processes and functions within the control unit 130. It consists of RAM (Random Access Memory), ROM (Read Only Memory), etc. The memory unit 120 also includes a business data storage unit 121 for storing various data related to the business operator, and an AI model storage unit 122 for storing learning data and learning models acquired by AI (artificial intelligence). A database (not shown) containing various data may be constructed outside of the memory unit 120 or the management terminal 100.

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

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

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

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

The carrier terminal 200 comprises a communication unit 210, a display and operation unit 220, a storage unit 230, and a control unit 240.

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

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

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

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

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

The business operator data 1000 shown in Figure 4 stores various data related to the business operator, obtained from the business operator via the business operator terminal 200. For the sake of explanation, Figure 4 shows an example of one business operator (identified by business operator ID "10001"), but it can store information for multiple business operators. Various data related to the business operator may include, for example, basic business operator information (e.g., company name, username, business location information (e.g., address information for each business location), network name (e.g., SSID, IP address), image information (e.g., background image of the business location, images of people, 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.), analytical information (e.g., information extracted from image data regarding invoices, greenhouse gas emissions, and predictions of the 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.), and activity information (information on held credits, type of credits/raw material origin and offset amount assigned to projects (products)).

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

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

Next, in step S102, the image analysis unit 132 of the control unit 130 of the management terminal 100 analyzes the image data acquired in the previous step using machine learning. Here, the image analysis uses a technique known as OCR. The image analysis unit 132 of the control unit 130 of the management terminal 100 uses a learning model, generated by previously learning from image data of various invoice formats stored in the AI model storage unit 122 of the storage unit 120, to recognize text from the image data and extract items included in the invoice information as structured string data. Here, for image analysis, it is also possible to use an image analysis engine (OCR engine, etc.) provided by a business other than the management terminal 100, which is linked via API.

Image analysis is performed, for example, by recognizing and extracting text from image data containing invoice information, as shown in Figure 5. As shown in Figure 5, various items included in an invoice can be listed as invoice information, such as the name of the breakdown of electricity charges, the amount for each breakdown (yen), contracted power (kW), electricity usage for each breakdown (kWh), total amount (yen), and date (year and month). In this example, an invoice breakdown for electricity charges is shown, but it could also be an invoice for charges related to the use of other energy, including gas and fuel, or an invoice breakdown for other items, such as a receipt for travel expenses for business trips, a receipt for employee commuting expenses, an invoice related to a transaction with a freight carrier, or an invoice related to a transaction with a waste disposal company. The image analysis unit 132 can extract monetary information, activity level information, etc., included in the invoice information as text by analyzing the image data of this invoice information. The extracted invoice information is stored as analysis information in the business data storage unit 121 of the storage unit 120. Thus, through machine learning-based image analysis, businesses can acquire a vast amount of necessary information for calculating greenhouse gas emissions as image data without having to manually input invoice information. Furthermore, highly accurate image recognition allows for precise extraction of the information required for greenhouse gas emission calculations, thereby improving the efficiency and accuracy of greenhouse gas emission calculations.

Next, in step S103, the image analysis unit 132 of the control unit 130 calculates greenhouse gas emissions based on the invoice information extracted from the image data. Here, greenhouse gas emissions are classified into SCOPE1, SCOPE2, and SCOPE3. SCOPE1 is direct emissions of greenhouse gases by the business operator itself (e.g., emissions associated with fuel combustion and industrial processes), SCOPE2 is indirect emissions associated with the use of electricity, heat, gas, etc. supplied to the business operator by other companies, and SCOPE3 is the calculation standard for emissions across an organization's entire supply chain issued by the GHG Protocol, and refers to emissions from the business operator's supply chain (the entire flow including raw material procurement, manufacturing, logistics, sales, disposal, etc.). SCOPE 3 is further classified into 15 categories: (1) Purchased Products/Services, (2) Capital Goods, (3) Fuel and Energy-Related Activities Not Included in SCOPE 1 and SCOPE 2, (4) Transportation and Distribution (Upstream), (5) Business Waste, (6) Business Travel, (7) Employee Commuting, (8) Leased Assets (Upstream), (9) Transportation and Distribution (Downstream), (10) Processing of Sold Products, (11) Use of Sold Products, (12) Disposal of Sold Products, (13) Leased Assets (Downstream), (14) Franchises, and (15) Investments. Here, greenhouse gases include carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulfur hexafluoride (SF6), and nitrogen trifluoride (NF3), but in this embodiment, CO2 will be used as an example.

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

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

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

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

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

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

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

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

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

Next, 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 analyzed information regarding the predicted causes of changes in emissions.

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

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

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

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

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

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

First, as part of step S401, the report generation unit 135 of the control unit 130 of the management terminal 100 refers to the information regarding the amount of credit activity held by the business and the information regarding greenhouse gas emissions in the business data 1000 of the business data storage unit 121 of the storage unit 120, and predicts whether the amount of credit activity will be insufficient relative to greenhouse gas emissions based on the comparison results of this information. This credit shortage prediction process may be triggered by several events. For example, the credit shortage prediction process may be executed when a business representative sends a request to the management terminal 100 via the business terminal 200 to refer to carbon offsets, or when a business representative sends a request to the management terminal 100 via the business terminal 200 to compare greenhouse gas reduction targets with greenhouse gas emissions. In addition, the carbon credit shortage prediction may be executed when a business representative asks a question or has a conversation about carbon offsets via the chat interface of the business terminal 200, or when a request for information about carbon offsets is sent to the management terminal 100. For example, when a business manager is performing simulations on the business terminal 200 based on greenhouse gas emission reduction targets and actual emission results, they can also make a graded judgment on the degree of credit shortage risk, such as "high/medium/low." Furthermore, credit shortage forecasts can be performed for each business's project (product). This process can also be performed by the management terminal 100 via the LLM 300. The LLM 300 calculates whether and/or by how much a credit shortage will occur based on the business's energy usage, and can also predict seasonal trends (monthly increases and decreases, such as during peak seasons) by referring to sales performance data and other information stored in the business data 2000.

Next, as part of step S402, the report generation unit 135 of the control unit 130 of the management terminal 100, when it predicts a shortage of carbon credits for the business operator in the process of S401, sends an alert (notification) to the business operator terminal 200 indicating the shortage (and the amount of activity). The report generation unit 135 can also send the alert to the business operator terminal 200 via a chat interface. In this case, as described above, if the LLM 300 predicts a credit shortage, it can also send the alert via the LLM 300. Furthermore, as described above, when a business operator requests a comparison between greenhouse gas reduction targets and greenhouse gas emissions, and is viewing the screen comparing reduction targets and emissions, an alert may be displayed on that screen.

Next, as part of step S403, the report generation unit 135 of the control unit 130 of the management terminal 100 transmits information about credits that should be purchased to offset greenhouse gas emissions to the business terminal 200. Previously, it was difficult for businesses to determine which credits to purchase for offsetting due to differences in systems (for example, "non-fossil fuel certificates, J-credits" usable under the Energy Conservation Act, and other certification systems such as J-Blue Credits), usage periods, and expiration dates. Therefore, in this example, for example, the LLM 300 can determine and recommend information about credits that businesses should purchase by referring to business data 1000 and an external credit information database. Furthermore, if a shortfall (risk of not achieving the reduction target) is anticipated based on a simulation comparing the business's emission reduction target with actual emissions, the system can perform simulations and make graded judgments on the degree of risk (e.g., "high/medium/low") to determine and recommend the purchase amount. For example, if the risk is "high," the purchase quantity can be calculated based on the maximum expected shortage, derived from past trends or those of comparable companies. If the risk is "low," the purchase quantity can be calculated based on the minimum expected shortage, derived from past trends or those of comparable companies. If the risk is "medium," the purchase quantity can be calculated as an intermediate or average value between the "high" and "low" risk levels. Furthermore, these risk levels can also be determined based on conversations between the business and LLM300 via a chat interface.

Next, as part of step S404, the report generation unit 135 of the control unit 130 of the management terminal 100, upon receiving the information regarding the credits to be purchased, guides the business representative to access the website for purchasing those credits when the business representative selects the information regarding the credits they wish to purchase via the business terminal 200 and submits a purchase request to the management terminal 100. Specifically, when the business representative selects the credits they wish to purchase from the various credit information displayed on the business terminal 200, the business terminal 200 is redirected to access the purchase screen for those credits, and the business representative can proceed with the purchase process on that screen.

Therefore, for business users, determining which credits to purchase and in what quantities has been difficult due to the difficulty of predicting shortages and the wide variety of credit types, in relation to greenhouse gas offsetting (offsetting emissions with carbon credits), this example can be applied to simplify and streamline the management of carbon offsets.

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

100 Management terminals 200 Operator terminals

Claims (5)

A method for managing greenhouse gas emissions,
The control unit of the management terminal is
Referencing information on the operator's credit activity and greenhouse gas emissions, compare the operator's greenhouse gas emission reduction targets with its actual greenhouse gas emissions , and predict a shortage in credit activity.
Send an alert if the activity level of the aforementioned credits is insufficient.
The degree of risk of insufficient activity in the aforementioned credits is determined,
A method of recommending the purchase of credits to offset the deficit, depending on the degree of risk. The management method according to claim 1, wherein the control unit transmits the alert to the operator's 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 operator's terminal that references the actual greenhouse gas emissions and reduction targets. The control unit recommends credit information to offset the deficit, as described in claim 1. The management method according to claim 1, wherein the control unit guides the business operator to a screen where the credits selected for purchase can be purchased.