Greenhouse gas emission management methods

Abstract

The objective is to provide a method for businesses to efficiently manage and calculate their greenhouse gas emissions. [Solution] A method for managing greenhouse gas emissions according to one embodiment of the present invention, wherein the control unit of a management terminal receives submission information regarding credits for offsetting greenhouse gases from a business terminal, stores the submission information in the storage unit of the management terminal, receives transaction information regarding the credits, and updates the submission information.

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] In Non-Patent Document 1, in order to further reduce the greenhouse gas emissions emitted by businesses, as emissions other than SCOPE1 and SCOPE2, there are suggestions for calculating SCOPE3 emissions, that is, the emissions of the supply chain (the entire series of processes such as raw material procurement, manufacturing, logistics, sales, waste disposal, etc.) of other related businesses, etc.

Prior Art Documents

Non-Patent Documents

[0004]

Non-Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

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

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

[0007] A method for managing greenhouse gas emissions according to one embodiment of the present invention, wherein the control unit of a management terminal receives submission information regarding credits for offsetting greenhouse gases from a business terminal, stores the submission information in the storage unit of the management terminal, receives transaction information regarding the credits, and updates the submission information. [Effects of the Invention]

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

[0009] [Figure 1] This is a diagram illustrating a greenhouse gas emission management system according to a first embodiment of the present invention. [Figure 2] This is a functional block diagram of the management terminals that make up the greenhouse gas emissions management system. [Figure 3] This is a functional block diagram of the business terminals that make up the greenhouse gas emissions management system. [Figure 4] This figure illustrates the details of the business operator data according to the first embodiment of the present invention. [Figure 5] This figure illustrates the details of invoice information according to the first embodiment of the present invention. [Figure 6] This figure illustrates an example of transaction information according to the first embodiment of the present invention. [Figure 7] This figure illustrates another example of transaction information according to the first embodiment of the present invention. [Figure 8] This flowchart shows an example of the greenhouse gas emission calculation process according to the first embodiment of the present invention. [Figure 9] This flowchart shows an example of a process for predicting the causes of changes in greenhouse gas emissions according to the first embodiment of the present invention. [Figure 10] This flowchart shows an example of transaction processing for greenhouse gas emissions according to the first embodiment of the present invention. [Figure 11] This flowchart shows an example of a carbon credit greenwashing risk assessment process according to a second embodiment of the present invention. [Modes for carrying out the invention]

[0010] The embodiments of the present invention will be described below. The carbon credit greenwashing risk 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 determining the greenwashing risk of carbon credits, The control unit of the management terminal is The operator's terminal receives the developer name or project name that issues carbon credits. Search the public information related to the received developer name and project name, and based on that, A method for determining the greenwashing risk of carbon credits based on the aforementioned publicly available information and predetermined criteria. [Item 2] The method according to claim 1, wherein the public information is news. [Item 3] The method according to claim 1, wherein the public information is retrieved via an LLM. [Item 4] The method according to claim 1, wherein the determination is executed via an LLM. [Item 5] The method according to claim 1, wherein the criterion is the Core Carbon Principle (CCP).

[0011] <The First Embodiment> Hereinafter, a system according to an embodiment of the present invention will be described with reference to the drawings.

[0012] FIG. 1 is a diagram for explaining a greenhouse gas emission management system according to the first embodiment of the present invention.

[0013] As shown in FIG. 1, in the emission management system 1 in the present embodiment, an 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 (e.g., image data of invoice information) for calculating the greenhouse gas (e.g., CO2) emission amount 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 emission amount. Further, the management terminal 100 analyzes the change (e.g., increase or decrease) in the greenhouse gas emission amount over time calculated by machine learning and predicts the cause of the change.

[0016] Furthermore, the management terminal 100 has a wallet and connects to the public blockchain network NW. Based on the greenhouse gas emission information for each predetermined period, the management terminal 100 generates a single hash value using SHA256 or another hash function and records it on the blockchain network as transaction information. On the blockchain network, a block is generated based on the transaction information, the hash value recorded in the previous block, and the nonce value mined by the node, and is recorded following the previous block, thus forming the blockchain. Here, the hash generation and / or recording of transaction information to the blockchain can also be performed via another terminal instead of the management terminal 100. In this case, the management terminal 100 transmits the greenhouse gas emission calculated in the matching process to the other terminal. Furthermore, the management terminal 100 can record the greenhouse gas emission information as a smart contract on the blockchain network. By using smart contracts, based on the emission information, contracts regarding emission trading with other businesses can be automatically generated, approved, and executed without the need for a third party. Furthermore, smart contracts enable each service provider to access transaction information without needing to use a management terminal, thereby improving service convenience and reducing operational costs.

[0017] Here, as mentioned above, in a public blockchain, transaction approval is performed not by a specific administrator but by an unspecified number of nodes and miners. Therefore, compared to a private blockchain, it can guarantee higher data immutability and fault tolerance, thus ensuring the security of transactions. For this reason, in this embodiment, a public blockchain is preferable as the destination for recording electricity transactions. Representative public blockchains include Bitcoin and Ethereum, but Ethereum, for example, has higher immutability and reliability among public blockchains.

[0018] Furthermore, the management terminal 100 can associate information regarding greenhouse gas emissions with identifiers and record it on the blockchain network as a Non-Fungible Token (NFT). An NFT is, for example, a token issued under the "ERC721" standard of Ethereum, a blockchain network platform, and is a unit of data recorded on the blockchain network, possessing the characteristic of being non-fungible. Since NFTs are recorded on the blockchain along with smart contracts and are traceable, they can prove transaction information including details and history of business information that manages greenhouse gas emissions.

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

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

[0021] The memory unit 120 stores programs for executing various control processes and functions within the control unit 130, input data, etc., and is composed of RAM (Random Access Memory), ROM (Read Only Memory), etc. The memory unit 120 also has a business data storage unit 121 for storing various data related to the business operator, and an AI model storage unit 122 for storing learning data and a learning model that the AI ​​(artificial intelligence) has learned from the learning data. A database (not shown) containing various data may be built outside of the memory unit 120 or the management terminal 100.

[0022] The control unit 130 controls the overall operation of the management terminal 100 by executing a program stored in the memory unit 120, and is composed of a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), etc. The functions of the control unit 130 include an information receiving unit 131 that receives information from external terminals such as the business terminal 200, an image analysis unit 132 that analyzes image data such as invoice information received from the business terminal and calculates greenhouse gas emissions, a cause analysis unit 133 that analyzes the image data and analyzes the causes of time-series changes in greenhouse gas emissions calculated based on the information contained in the extracted invoice information, a transaction processing unit 134 that aggregates information on greenhouse gas emissions for a predetermined period, generates a hash value, and records it as transaction information on the blockchain network, and a report generation unit 135 that generates and transmits report data to the business operator at predetermined intervals to output greenhouse gas emissions and the results of the cause analysis of changes in emissions.

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

[0024] Furthermore, the management terminal 100 also has a wallet (not shown) necessary for recording transaction information to the blockchain network. This wallet may also be located outside the management terminal 100.

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

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

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

[0028] The display operation unit 220 is a user interface used by the operator to input instructions and display text, images, etc., in accordance with the input data from the control unit 240. If the operator terminal 200 is a personal computer, it consists of a display and a keyboard or mouse, and if the operator terminal 200 is a smartphone or tablet, it consists of a touch panel, etc. This display operation unit 220 is activated by a control program stored in the storage unit 230 and executed by the operator terminal 200, which is a computer (electronic calculator).

[0029] The memory unit 230 stores programs for executing various control processes and functions within the control unit 240, input data, etc., and is composed of RAM, ROM, etc. The memory unit 230 also temporarily stores the contents of communications with the management terminal 100.

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

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

[0032] The business data 1000 shown in Figure 4 stores various data related to a business obtained from the business via the business terminal 200. In Figure 4, for the sake of explanation, an example of one business (a business identified by business ID "10001") is shown, but information for multiple businesses can be stored. Various data related to a business can include, for example, basic business information (e.g., the business's corporate name, username, address, industry, contact information, email address, business name, affiliate company name, names of related businesses in the supply chain, etc.), input information (e.g., image data of invoice information, etc.), analysis information (e.g., information on invoices extracted from image data, greenhouse gas emissions, 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.).

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

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

[0035] Next, as part of step S102, the image analysis unit 132 of the control unit 130 of the management terminal 100 analyzes the image data acquired in the previous step using machine learning. Here, the image analysis uses a technique known as OCR, and the image analysis unit 132 of the control unit 130 of the management terminal 100 recognizes text from the image data and extracts items included in the invoice information as structured string data, using a learning model that was generated in advance by learning from image data of various types of invoices stored in the AI ​​model storage unit 122 of the memory unit 120. Here, for image analysis, it is also possible to use an image analysis engine (OCR engine, etc.) provided by a business other than the management terminal 100, which is linked via API.

[0036] Image analysis is performed, for example, by recognizing and extracting text from image data containing invoice information, as shown in Figure 5. As shown in Figure 5, invoice information can include various items contained in an invoice, such as the name of the breakdown of electricity charges, the amount for each breakdown (yen), contracted power (kW), electricity usage for each breakdown (kWh), total amount (yen), and date (year and month). In this example, an invoice breakdown for electricity charges is used as an example, but it could also be an invoice for charges related to the use of other energy, including gas and fuel, or an invoice breakdown for other items, such as a receipt for travel expenses for business trips, a receipt for employee commuting expenses, an invoice related to a transaction with a freight carrier, or an invoice related to a transaction with a waste disposal company. The image analysis unit 132 can extract monetary information, activity level information, etc., contained in the invoice information as text by analyzing the image data of this invoice information. The extracted invoice information is stored as analysis information in the business data storage unit 121 of the storage unit 120. In this way, machine learning-based image analysis allows businesses to acquire a vast amount of necessary information for calculating greenhouse gas emissions as image data without having to manually input invoice information. Furthermore, highly accurate image recognition enables the precise extraction of information necessary for calculating greenhouse gas emissions, thereby improving the efficiency and accuracy of greenhouse gas emission calculations.

[0037] Next, in step S103, the image analysis unit 132 of the control unit 130 calculates greenhouse gas emissions based on the invoice information extracted from the image data. Here, greenhouse gas emissions are classified into SCOPE1, SCOPE2, and SCOPE3. SCOPE1 is direct emissions of greenhouse gases by the business operator itself (e.g., emissions associated with fuel combustion and industrial processes), SCOPE2 is indirect emissions associated with the use of electricity, heat, gas, etc. supplied to the business operator by other companies, and SCOPE3 is the calculation standard for emissions across an organization's entire supply chain issued by the GHG Protocol, and refers to emissions from the business operator's supply chain (the entire flow including raw material procurement, manufacturing, logistics, sales, disposal, etc.). SCOPE3 is further classified into 15 categories: (1) Products / Services Purchased, (2) Capital Goods, (3) Fuel and Energy-Related Activities Not Included in SCOPE1 and SCOPE2, (4) Transportation and Distribution (Upstream), (5) Business Waste, (6) Business Travel, (7) Employee Commuting, (8) Leased Assets (Upstream), (9) Transportation and Distribution (Downstream), (10) Processing of Products Sold, (11) Use of Products Sold, (12) Disposal of Products Sold, (13) Leased Assets (Downstream), (14) Franchises, and (15) Investments. Here, greenhouse gases include carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulfur hexafluoride (SF6), and nitrogen trifluoride (NF3), but in this embodiment, CO2 will be used as an example.

[0038] Furthermore, greenhouse gas emissions are calculated by defining activity levels as the amount of electricity used by a business operator, the amount of goods transported, the amount of waste processed, and the value of various transactions, and multiplying these activity levels by emission intensity, which is the amount of CO2 emissions per 1 kWh of electricity used, the amount of CO2 emissions per ton of goods transported, and the amount of CO2 emissions per ton of waste incinerated. Greenhouse gas emissions are calculated separately for SCOPE1, SCOPE2, and SCOPE3 (SCOPE3 is further divided into 15 categories), and the total emissions are calculated as supply chain emissions.

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

[0040] Next, as part of the processing in step S104, the report generation unit 135 of the control unit 130 generates a visualized report showing the breakdown of emissions over time, categorized by SCOPE (and further by category for SCOPE 3), based on the calculated emissions information.

[0041] Figure 9 is a flowchart illustrating an example of a process for predicting the causes of changes in greenhouse gas emissions according to the first embodiment of the present invention.

[0042] First, as part of step S201, the cause analysis unit 133 of the control unit 130 of the management terminal 100 refers to the information on the operator's greenhouse gas emissions calculated in step S103 of Figure 8. Here, the greenhouse gas emissions refer to emissions by SCOPE (and further by category for SCOPE 3). The cause analysis unit 133 can also check changes (increases or decreases) in emissions by referring to past emission data of the same operator. As described above, the emissions are stored as analysis information in the operator data storage unit 121 of the storage unit 120.

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

[0044] Here, factors that influence changes (increases or decreases) in emissions include, for example, weather, temperature, product demand and / or factory operations, store or factory business hours or operating hours, changes in equipment or facilities, software measures, energy-saving activities, fuel switching, energy menu changes, changes in business or commuting volume, and the amount of electricity generated by private power generation. Each of these factors influences the emissions of one of the SCOPEs. For example, the weather factor influences precipitation, wind speed, sunshine hours, and temperature. Precipitation influences small-scale hydroelectric power generation, wind speed influences wind power generation, sunshine hours influence solar power generation, and temperature influences air conditioning. Furthermore, electricity generation influences private power generation, which in turn influences CO2 emissions from electricity, thereby influencing changes in SCOPE2 emissions. Meanwhile, air conditioning influences gas consumption, which in turn influences CO2 emissions from gas combustion, thereby influencing changes in SCOPE1 emissions. In addition, energy-saving activities, factory operations due to product demand, and business hours influence electricity consumption and thus influence SCOPE2. Furthermore, EMS, replacement of refrigeration equipment, introduction of energy-saving equipment, and automobile usage also affect electricity usage and thus influence SCOPE2. In addition, automobile usage, fuel efficiency, boiler usage, and boiler efficiency affect fuel usage, which in turn affect CO2 emissions from fuel and thus influence SCOPE1.

[0045] Furthermore, the number of products sold influences categories 1, 9, 10, 11, and 12 of SCOPE3, while capital investment influences category 2, renewable energy ratio and procured energy volume influence category 3, the number of deliveries and changes in delivery routes influence categories 4 and 9, product loss rate influences category 5, business travelers and office workers influence category 6, commuters and office employees influence category 7, power consumption influences category 8, processing reduction through product improvements influences category 10, improvements to energy-saving products influence category 11, increased recycling rates influence category 12, tenant office electricity influences category 13, franchise emissions influence category 14, and investment destination emissions influence category 15.

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

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

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

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

[0050] Next, as part of step S302, the transaction processing unit 134 generates a hash value based on the business data referenced in step S301. That is, the transaction processing unit 134 generates a hash value for one row using a hash function for greenhouse gas emissions over a predetermined period, and records the hash value as transaction information on the public blockchain. On the blockchain network, this block is generated based on the transaction information, the hash value recorded in the previous block, and the nonce value mined by the node, and is recorded following the previous block, thus forming the blockchain. In this example, in order to reduce the cost of blockchain recording, the data is recorded on a Layer 2 (e.g., a sidechain) that is different from the main blockchain (so-called Layer 1).

[0051] Furthermore, the transaction processing unit 134 can assign and manage NFTIDs in conjunction with the blockchain record of the business operator's greenhouse gas emissions. More specifically, as shown in Figure 4, the business operator data 1000 can be assigned the business operator's customer ID as customer information, store the blockchain address to be referenced, and assign NFTIDs and TXIDs as offset report information.

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

[0053] Figure 11 is a flowchart illustrating an example of a carbon credit greenwashing risk assessment process according to a second embodiment of the present invention.

[0054] Here, the operator of the management terminal 100 can manage so-called carbon credits as data, which are registered by credit creators or sales rights holders as greenhouse gas emission reduction amounts. In particular, the management terminal 100 stores the registered carbon offset credit data in the credit data storage unit 123 of the memory unit 120, and records the stored credit data on the blockchain network, associating it with an NFTID. For example, as information about a credit associated with a specific NFTID, a TXID is assigned to the credit, and (if the credit is traded) the customer ID, project ID, project type, credit (reduction amount), credit issuance date, (if the credit is traded) credit transaction date, etc., are included in the credit information. If a customer purchases a credit, the data can also be managed in conjunction with the customer's CO2 emission information. In this way, by managing credits as NFTs, while carbon offset credit transactions are currently managed by individual businesses and lack transparency, businesses can conduct transactions of NFT-based certificates regarding credits while ensuring non-tampering and reliability of transactions, and can also provide credit certification to third parties.

[0055] In recent years, concerns about greenwashing have become apparent in carbon credit trading. Greenwashing refers to "fake environmental measures" that appear to be implemented but lack substance. Amidst the growing demand for proactive environmental measures, the problem of greenwashing has also increased. Greenwashing causes significant distrust among users and also leads to delays in environmental measures, so users need to be able to discern genuine environmental measures. In particular, in the context of carbon credit trading, there is a risk of overcrediting, where something that generates only 10 tnCO2 is sold as 30 tnCO2 (through exaggerated descriptions). In reality, determining the greenwashing risk of carbon credits requires individual investigation and is time-consuming. Therefore, in this embodiment, we will describe a method for determining the greenwashing risk of carbon credits using AI.

[0056] First, as part of step S401, the management terminal 100 receives information about carbon credits, such as the name of the developer or project name that issues the registered carbon credits, from the business terminal 200 or from other credit marketplace terminals (not shown). Specifically, it receives information about the carbon credits to be traded (for example, project ID, project name, developer name, credit details (reduction amount), etc.). The management terminal 100 registers the received information about carbon credits by storing it as credit data 3000 in the credit data storage unit 123 of the storage unit 120.

[0057] Next, as part of step S402, the management terminal 100 sends an instruction to a large-scale language analysis model (LLM) (e.g., ChatGPT) based on information regarding carbon credits, instructing it to determine the greenwashing risk of developers and / or projects that issue carbon credits. Specifically, it refers to the name of the developer and / or project that issues carbon credits and sends an instruction (prompt) to the LLM to determine whether the developer and / or project is engaging in greenwashing by referring to publicly available information such as news articles. Here, when determining the greenwashing risk, the requirements for high-quality carbon credits (Core Carbon Principles (CCP)) can also be applied as a criterion for creating carbon credits. Furthermore, by including the country name in the project name, the greenwashing risk can also be determined by including the political risks of that country. In addition, when determining the greenwashing risk, if the information is reliable, it can refer not only to news articles but also to information posted on social media.

[0058] Next, as part of step S403, the management terminal 100 receives the greenwashing risk assessment result for the carbon credit developer and / or project from the LLM. Here, based on instructions from the management terminal 100, the LLM obtains publicly available information, such as news articles, about the target carbon credit developer and / or project by web crawling. The LLM can also periodically web crawl publicly available information such as news articles and store the web crawling results as a dataset. Here, in response to instructions from the management terminal 100, the LLM searches for publicly available information such as news articles about the target developer and / or project, and based on the detected publicly available information and predetermined judgment criteria (for example, requirements included in the Core Carbon Principles mentioned above), it can score (for example, an evaluation on a scale of A, B, C, etc.) whether the target developer and / or project is issuing carbon credits fraudulently, and make a greenwashing risk assessment. Alternatively, LLM may periodically assess the greenwashing risk of target developers and / or projects based on web crawling results of publicly available articles such as news articles and judgment criteria. If a particular developer and / or project is determined to be at risk, the LLM will add a note in red or the like to the row of the project in question in a list (system / spreadsheet) containing the names of developers and / or projects designated as risk check targets, to identify that there is a risk (at the same time, an email notification will be sent to the responsible terminal of the management terminal 100). The control unit 130 of the management terminal 100 stores the greenwashing risk assessment result for each carbon credit as credit data 3000 in the credit data storage unit 123 of the storage unit 120.

[0059] As described above, this embodiment makes it possible to automatically determine the greenwashing risk related to carbon credits.

[0060] The embodiments described above are merely illustrative to facilitate understanding of the present invention and are not intended to limit its interpretation. The present invention can be modified and improved without departing from its spirit, and it goes without saying that the present invention includes equivalents thereof. [Explanation of Symbols]

[0061] 100 Management terminals 200 carrier terminals

Claims

1. A method for determining the greenwashing risk of carbon credits, The control unit of the management terminal is The operator's terminal receives the developer name or project name that issues carbon credits. Search the public information related to the received developer name and project name, and based on that, A method for determining the greenwashing risk of carbon credits based on the aforementioned publicly available information and predetermined criteria.

2. The publicly available information is news, according to the method of claim 1.

3. The method according to claim 1, wherein the aforementioned publicly available information is searched via LLM.

4. The method according to claim 1, wherein the determination is performed via LLM.

5. The method according to claim 1, wherein the determination criterion is the core carbon criterion (CCP).

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