Greenhouse gas emissions management method

Abstract

To provide a method of enabling efficient ESG data management by a business operator.SOLUTION: According to an embodiment of the present invention, a method implemented by a management terminal for managing data on ESG evaluation of a business operator is disclosed, the method comprising receiving location information from a business operator terminal of the business operator, determining a country in which a business establishment of the business operator is based on the location information, and determining laws and regulations of the target country based on the country in which the business establishment is.SELECTED DRAWING: Figure 11

Description

[Technical Field]

[0001] The present invention relates to a method for managing greenhouse gas emissions. [Background technology]

[0002] With regard to greenhouse gas emissions from businesses resulting from the use of fuel, electricity, etc., reporting systems targeting SCOPE 1 emissions (direct emissions by the company) and SCOPE 2 emissions (indirect emissions by the company) have become widespread, and efforts to calculate and reduce emissions in SCOPE 1 and SCOPE 2 have progressed.

[0003] Non-Patent Document 1 proposes that, with the aim of further reducing greenhouse gas emissions by businesses, SCOPE 3 emissions should be calculated as emissions other than SCOPE 1 and SCOPE 2, i.e., emissions from the supply chain of other related businesses (the entire series of processes including raw material procurement, manufacturing, logistics, sales, and disposal). [Prior art documents] [Non-patent literature]

[0004] [Non-Patent Document 1] "Concepts for Calculating Supply Chain Emissions," Ministry of the Environment, November 2017 Summary of the Invention [Problem to be solved by the invention]

[0005] However, although the technology disclosed in Non-Patent Document 1 discloses methods for calculating greenhouse gas emissions related to SCOPE 3, it takes a great deal of time and effort for each business, particularly companies and local governments, to collect and input a huge amount of data to calculate emissions, calculate emissions, and manage the calculation results.In particular, in the area of ​​GHG emissions management, the scope of emissions to be calculated is expanding, the data that forms the basis for emissions calculations varies widely for each scope, and data management methods differ for each business, so improvements in business efficiency through the introduction of advanced technology are lagging behind.

[0006] Therefore, the present invention aims to provide a method for efficiently managing GHG emissions by utilizing cutting-edge technology to reduce the number of work hours required in the area of ​​GHG emissions management, such as the calculation of greenhouse gas emissions by businesses. Another object of the present invention is to provide a method for efficiently managing ESG data by businesses. [Means for solving the problem]

[0007] According to one embodiment of the present invention, a method for managing data related to an ESG evaluation of an operator is executed by a management terminal, which method receives location information from the operator terminal of the operator, determines the country in which the operator's business establishment is located based on the location information, and determines the laws and regulations of the target country based on the country in which the business establishment is located. [Effects of the Invention]

[0008] According to the present invention, it is possible to provide a method for efficiently supporting input for ESG evaluation by business operators. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a diagram illustrating a greenhouse gas emission management system according to a first embodiment of the present invention. [Figure 2] FIG. 2 is a functional block diagram of a management terminal constituting the greenhouse gas emission management system. [Figure 3] FIG. 2 is a functional block diagram of a business operator terminal that constitutes the greenhouse gas emission management system. [Figure 4] FIG. 3 is a diagram illustrating details of business operator data according to the first embodiment of the present invention. [Figure 5] FIG. 3 is a diagram illustrating details of invoice information according to the first embodiment of the present invention. [Figure 6] FIG. 3 is a diagram illustrating an example of transaction information according to the first embodiment of the present invention. [Figure 7] FIG. 10 is a diagram illustrating another example of transaction information according to the first embodiment of the present invention. [Figure 8] FIG. 3 is a flowchart showing an example of a process for calculating greenhouse gas emissions according to the first embodiment of the present invention. [Figure 9] FIG. 3 is a flowchart illustrating an example of a process for predicting causes of changes in greenhouse gas emissions according to the first embodiment of the present invention. [Figure 10] FIG. 3 is a flowchart showing an example of a transaction process of greenhouse gas emissions according to the first embodiment of the present invention. [Figure 11] FIG. 10 is a flowchart illustrating an example of a method for managing ESG evaluation data according to a second embodiment of the present invention. [Figure 12] FIG. 10 is a flowchart showing another example of the method for managing ESG evaluation data according to the second embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0010] The contents of the embodiments of the present invention will be listed and explained below. An ESG management system (hereinafter simply referred to as "system") according to an embodiment of the present invention has the following configuration. [Item 1] A method for managing data related to ESG evaluations of business operators, which is executed by a management terminal, comprising: receiving location information from the operator terminal of the operator; determining the country in which the business is located based on the location information; determining the laws and regulations of the target country based on the country where the business establishment is located. [Item 2] The method according to claim 1, further comprising receiving network information from the operator terminal, and determining the business establishment of the operator based on the network information. [Item 3] The method according to Item 2, wherein the network information is either a network name or an IP address. [Item 4] The method according to Item 1, wherein the management terminal further receives questions for the ESG evaluation and generates points for the business operator for answering the questions based on the legal regulations. [Item 5] The method described in Item 1, wherein the management terminal performs machine learning based on the question and the provisions of the legal regulations related to the question, and generates a learning model for outputting points for answering the question.

[0011] 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 illustrating a greenhouse gas emission management system according to a first embodiment of the present invention.

[0013] As shown in FIG. 1, in an emission amount management system 1 according to this embodiment, an administrator terminal 100 and a plurality of business operator terminals 200A and 200B are connected to each other via a communication network NW.

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

[0015] The management terminal 100 also analyzes the received image data of the invoice information using machine learning, extracts 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.

[0016] Furthermore, the management terminal 100 has a wallet and is connected to the public blockchain network NW. Based on the information on greenhouse gas emissions for each predetermined period, the management terminal 100 generates a single hash value using SHA256 or another hash function and records it as transaction information on the blockchain network. On the blockchain network, a block is generated based on the transaction information, the hash value recorded in the previous block, and a nonce value mined by the node. This block is recorded after the previous block, forming a blockchain. The hash generation and / or recording of transaction information on the blockchain can also be performed via another terminal rather than the management terminal 100. In this case, the management terminal 100 transmits the greenhouse gas emissions calculated in the matching process to the other terminal. Furthermore, the management terminal 100 can record information on greenhouse gas emissions as a smart contract on the blockchain network. Using a smart contract, contracts for emissions trading with other businesses can be automatically generated, approved, and executed based on the emissions information without the involvement of a third party. In addition, smart contracts allow each business operator to access transaction information without going through a management terminal, increasing service convenience and reducing operational costs.

[0017] As described above, a public blockchain allows transactions to be 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, thereby ensuring the security of transactions. Therefore, a public blockchain is preferable as the destination for recording energy transactions in this embodiment. Typical public blockchains include Bitcoin and Ethereum, and Ethereum, for example, has higher tamper resistance and reliability than other public blockchains.

[0018] Furthermore, the management terminal 100 can associate information regarding 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, in the "ERC721" standard of Etherium, 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 prove transaction information, including details and history of business operators managing greenhouse gas emissions.

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

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

[0021] The storage unit 120 stores input data, programs for executing various control processes and functions in the control unit 130, and is composed of RAM (Random Access Memory), ROM (Read Only Memory), and the like. The storage 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 obtained by AI (artificial intelligence) learning the learning data. A database (not shown) that stores various data may be constructed outside the storage unit 120 or the management terminal 100.

[0022] The control unit 130 controls the overall operation of the management terminal 100 by executing programs stored in the storage 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 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 the greenhouse gas emissions calculated based on information included in the extracted invoice information, a transaction processing unit 134 that compiles information on greenhouse gas emissions for a predetermined period, generates a hash value, and records the hash value as transaction information on the blockchain network, and a report generation unit 135 that generates and transmits report data to the business operator every predetermined period to output the results of an analysis of greenhouse gas emissions and the cause of changes in emissions.

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

[0024] The management terminal 100 also has a wallet (not shown) necessary for recording transaction information in the blockchain network. Note that this wallet may also be located outside the management terminal 100.

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

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

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

[0028] The display operation unit 220 is a user interface used by the business operator to input instructions and display text, images, etc. in accordance with input data from the control unit 240, and is configured with a display, keyboard, and mouse when the business operator terminal 200 is configured as a personal computer, and is configured with a touch panel, etc. when the business 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 storage unit 230 and executed by the business operator terminal 200, which is a computer (electronic calculator).

[0029] The storage unit 230 stores input data, programs for executing various control processes and functions within the control unit 240, and is composed of RAM, ROM, etc. The storage 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 the programs stored in the storage unit 230, and is configured from a CPU, a GPU, and the like.

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

[0032] The business operator data 1000 shown in FIG. 4 stores various data related to the business operator acquired from the business operator via the business operator terminal 200. For ease of explanation, FIG. 4 shows an example of one business operator (a business operator identified by the business operator ID "10001"), but information on multiple businesses can be stored. The various data related to the business operator can include, for example, basic information about the business operator (e.g., the business operator's corporate name, user name, business operator information (e.g., document data such as investor relations information and personnel information), industry, contact information, email address, business establishment name, affiliated company name, names of businesses related in the supply chain, etc.), input information (e.g., image data of invoice information, etc.), analysis information (e.g., information regarding invoices extracted from image data, greenhouse gas emissions, 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.).

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

[0034] 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. (in this embodiment, these are collectively referred to as "invoices") to the management terminal 100 via the business operator terminal 200 in file formats such as PDF, Excel, JPG, etc. (in this embodiment, these are collectively referred to as "image data"). 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.

[0035] 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, 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 that has been generated in advance by learning from image data of multiple invoices in various formats and that is stored in the AI ​​model storage unit 122 of the memory unit 120, and extracts items contained in the invoice information as structured character string data. Here, the image analysis can also use an image analysis engine (such as an OCR engine) provided by a business operator other than the management terminal 100, which is linked via an API.

[0036] Image analysis is performed 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, such as the electricity bill breakdown, the amount (yen) for each breakdown, the contracted power (kW), the amount of electricity used (kWh) for each breakdown, the total amount (yen), and the date (year and month). While this example illustrates an electricity bill breakdown, it may also be an invoice for other energy usage, including electricity, gas, and fuel. It may also be a receipt for travel expenses, a receipt for employee commuting expenses, an invoice for a transaction with a freight company, or a breakdown of an invoice for a transaction with a waste disposal company. The image analysis unit 132 analyzes the image data of this invoice information to extract, as text, the amount information, activity level information, and other information contained in the invoice information. The extracted invoice information is stored as analysis information in the business operator 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 information necessary to calculate greenhouse gas emissions as image data without having to manually enter invoice information, and highly accurate image recognition makes it possible to accurately extract the information necessary to calculate greenhouse gas emissions, thereby making it possible to calculate greenhouse gas emissions more efficient and with greater accuracy.

[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 SCOPE 1, SCOPE 2, and SCOPE 3, with SCOPE 1 being direct greenhouse gas emissions by the business itself (e.g., emissions associated with fuel combustion and industrial processes), SCOPE 2 being indirect emissions associated with the use of electricity, heat, gas, etc. supplied to the business by other companies, and SCOPE 3 being the calculation standard for emissions throughout an organization's supply chain issued by the GHG Protocol, and referring to emissions throughout a business's supply chain (the entire series of processes, including raw material procurement, manufacturing, logistics, sales, and disposal). 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) waste generated from operations, 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 this embodiment will be described using CO2 as an example.

[0038] Greenhouse gas emissions are calculated by defining a business's electricity consumption, cargo transport volume, waste disposal volume, and various transaction amounts as activity volume, and multiplying this activity volume by the CO2 emissions per kWh of electricity used, CO2 emissions per ton of cargo transported, and CO2 emissions per ton of waste incinerated as emissions intensity. 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.

[0039] In this embodiment, the image analysis unit 132 extracts related invoice information by SCOPE 1, SCOPE 2, and SCOPE 3 (and further by category for SCOPE 3), and calculates emissions based on the invoice information, for example, the amount of electricity used (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.

[0040] Next, in the processing of 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 regarding the calculated emissions.

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

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

[0043] Next, in the processing of step S202, the cause analysis unit 133 analyzes and predicts the cause of changes in emission amounts 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 changes in emission amounts by SCOPE (and further by category for SCOPE 3) by using the emission amount information referenced above, factors that affect changes (increases or decreases) in emission amounts, and a learning model that has been generated in advance by learning data related to factors that affect changes (increases or decreases) in emission amounts that is stored in the AI ​​model storage unit 122 of the memory unit 120.

[0044] Factors that affect changes (increases or decreases) in output include, for example, weather, temperature, product demand and / or factory operation, store or factory business hours, equipment or facility changes, software measures, energy-saving activities, fuel conversions, energy menu changes, changes in business trips or commuting, and the amount of electricity generated by privately-owned power plants. Each of these factors affects emissions in one of the following scopes. For example, weather affects precipitation, wind volume, hours of sunshine, and temperature. Precipitation affects the amount of hydroelectric power generation, wind volume affects the amount of wind power generation, hours of sunshine affect the amount of solar power generation, and temperature affects air conditioning. Furthermore, the amount of electricity generated affects the amount of privately-owned power generation, which affects CO2 emissions from electricity, thereby affecting emissions in SCOPE 2. Meanwhile, air conditioning affects gas usage, which affects CO2 emissions from gas combustion, thereby affecting emissions in SCOPE 1. Energy-saving activities, factory operation due to product demand, and business hours affect electricity usage, which in turn affects SCOPE 2. In addition, EMS, replacement of refrigeration equipment, introduction of energy-saving equipment, and automobile usage also affect electricity usage and thus affect SCOPE 2. In addition, automobile usage, fuel efficiency, boiler usage, and boiler efficiency affect fuel usage, which affects CO2 emissions from fuel, thereby affecting SCOPE 1.

[0045] In addition, the factor of product sales volume affects categories 1, 9, 10, 11, and 12 in SCOPE 3, capital investment affects category 2, renewable energy ratio and amount of energy procured affects category 3, number of transports and changes in transport routes affects categories 4 and 9, product loss rate affects category 5, business trips and number of people commuting to the office affects category 6, number of people commuting to the office and number of employees commuting to the office affects category 7, electricity consumption affects category 8, reduction in processing through product improvements affects category 10, improvements to energy-efficient products affects category 11, increased recycling rate affects category 12, electricity used in tenant offices affects category 13, franchise emissions affects category 14, and emissions from investment targets affects category 15.

[0046] In this way, by using machine learning to learn which factors affect which scope or category, and then obtaining information on emissions and information on 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 for each scope.

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

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

[0049] 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.

[0050] 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 a row of hash values ​​for greenhouse gas emissions over a predetermined 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 of blockchain recording, it is assumed that the data is recorded in layer 2 (e.g., a side chain) different from the main blockchain (so-called layer 1).

[0051] Furthermore, the transaction processing unit 134 can assign and manage NFTIDs by linking them to the blockchain records of the greenhouse gas emissions of the business. More specifically, as shown in Fig. 4, the business's customer ID can be assigned as customer information to the business data 1000, the blockchain address to be referenced can be stored, and the NFTID and TXID can be assigned as offset report information.

[0052] As shown in Figure 6, each NFT ID is associated with a blockchain address on the blockchain network, and the management terminal 100 manages NFT IDs and customer IDs. For example, information about greenhouse gas emissions for a business associated with customer ID "2" can be retrieved by referencing the blockchain address for each NFT ID, such as NFT IDs "13" and "14," as shown in Figure 7. Figure 7 shows information about an offset report associated with NFT ID "14." A TXID is assigned to the offset report, and the offset report includes information about CO2 emissions by scope, the target year and month, and the report's publication date. In addition to the CO2 emissions for the target year and month in this example, recent CO2 emissions, reduced CO2 emissions, and offset CO2 emissions can also be converted into NFTs. By managing CO2 emissions in this way, businesses can trade NFT-converted certificates while ensuring tamper-resistance and transaction reliability, and can also verify emissions to third parties.

[0053] FIG. 11 is a flowchart illustrating an example of an ESG evaluation data management method according to a second embodiment of the present invention. Conventionally, to evaluate a company's efforts in environmental, social, and governance areas, a supplier responds to questions corresponding to each of the above items contained in a questionnaire provided by a business partner directly or via the management terminal 100. The control unit 130 of the management terminal 100 then performs an ESG evaluation based on the responses to each of the above items in accordance with predetermined evaluation criteria, generating data related to the ESG evaluation. For example, if a supplier is located in a different country from the buyer (e.g., China), the answers to questions posed to the supplier must comply with local laws and regulations. To address this need, if a supplier understands the key points to note in light of local laws and regulations when answering questions, the supplier can respond smoothly and the ESG evaluation can be made more efficient. A method for improving the efficiency of such an ESG evaluation is described below.

[0054] First, in the process of step S401, the information acquisition unit 131 of the control unit 130 of the management terminal 100 receives information about the address of the business establishment as business establishment information from the business establishment terminal 200 via the network NW, and stores the information as business establishment information in the business establishment data storage unit 121 of the memory unit 120, thereby performing registration. Here, the business establishment address refers to the address of the business establishment's domestic and overseas bases, such as its head office and branches, and a business can register multiple business establishment addresses. For example, the address of the Tokyo head office can be registered as "Marunouchi..., Chiyoda-ku, Tokyo," and the address of the Beijing branch office can be registered as "Beijing..., People's Republic of China."

[0055] Next, at a predetermined timing, such as when an operator logs in to use an application provided by the management terminal 100 from the operator terminal 200, or when performing some input processing for calculating greenhouse gas emissions, the information acquisition unit 131 of the control unit 130 of the management terminal 100 receives location information of the operator terminal 200 from the operator terminal 200 via the network NW, as processing in step S402. Here, the location information can be received, for example, via a GPS built into the operator terminal 200.

[0056] Next, in step S403, the information acquisition unit 131 of the control unit 130 of the management terminal 100 references the business address registered as the business establishment information based on the location information received from the business operator terminal 200, confirms which business establishment address the location information matches, and determines the business address as the business information. For example, if the location information received from the business operator terminal 200 indicates "Beijing," the management terminal 100 references the business operator's business establishment information stored in the business operator data storage unit 121, determines that it matches "Beijing, People's Republic of China," which indicates the address of the Beijing branch office, and determines that the location from which the business operator is currently logged in via the business operator terminal 200 is the "Beijing branch office." This allows the base address of the business from which the business operator is logged in to be correctly determined.

[0057] FIG. 12 is a flowchart showing another example of the method for managing ESG evaluation data according to the second embodiment of the present invention.

[0058] First, as a preprocessing step in step S501, the business (in this example, a supplier business) accesses a questionnaire screen for answering the ESG evaluation questionnaire provided by the management terminal 100 via the business terminal 200. The questionnaire screen displays information about major ESG categories, such as "Environment," "Society," "Governance," "Quality," and "Human Rights," as well as subcategories associated with each category, such as "Environment" (e.g., "Whether or Not an Environmental Policy Is Established," "Whether or Not Environmental Training Is Conducted," "SBT Certification," "TCFD Disclosure," "Scope 1," "Scope 2," and "Scope 3." Multiple questions are displayed for each category. For example, a question related to the categories "Environment" and "Whether or Not an Environmental Policy Is Established" may include, "Do you have an environmental policy or guidelines?" Some questions, such as "Do you have work rules?", require attention to the differences in laws and regulations depending on the country where the business responding is located. For example, some countries require the adoption of work rules when employing a certain number of employees, while others do not.

[0059] First, in the process of step S501, the report generation unit 135 of the control unit 130 of the management terminal 100 determines the legal (regulatory) information required to assist in answering the question based on information about the country in which the business is located. The report generation unit 135 determines the applicable law (e.g., China) based on the address / country in which the business is located determined in step S403 above, and references the legal information of the target country that is pre-stored in the storage unit 120. The legal information includes laws such as the Company Act and Labor Act, as well as the provisions of each law.

[0060] Next, in step S502, the report generation unit 135 determines, based on the referenced legal information, points to note when the business operator answers each of the multiple questions included in the questionnaire (e.g., legal provisions related to the question, advice on recommended answers in light of the provisions, example answers, etc.). For example, in China, the advice for the question, "Do you have work rules?" is, "If you employ a certain number of workers or more, you are required to establish work rules." Here, the control unit 130 of the management terminal 100 performs machine learning based on the questionnaire questions and the legal provisions related to the questions to generate a learning model for outputting key points for answering the questions. The control unit 130 stores the model in advance in the AI ​​model storage unit 122 of the memory unit 120. Using the questionnaire questions and the business operator's location as input information, the control unit 130 can analyze and output the key points for answering based on the learning model.

[0061] Next, in the process of step S503, the report generation unit 135 displays the determined answer points on the business operator terminal 200 of the business operator. The business operator can check the determined answer points (for example, advice for answering) displayed (in a display format such as a pop-up or highlight) near the answer input field for each question on the questionnaire answer screen displayed on the business operator 200.

[0062] In this way, by advising businesses on responses that comply with the laws and regulations of the country in which they are located, we can improve the efficiency with which businesses respond to questionnaires and ensure the consistency of the responses.

[0063] The above-described embodiment is merely an example for facilitating 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 thereof, and it goes without saying that the present invention includes equivalents thereof. [Explanation of symbols]

[0064] 100 Management Terminal 200 Operator terminal

Claims

1. A method for managing data related to ESG evaluations of businesses, executed by a management terminal, comprising: receiving location information from the operator terminal of the operator; determining the country in which the business is located based on the location information; determining the laws and regulations of the target country based on the country where the business establishment is located.

2. The method of claim 1 , further comprising receiving network information from the operator terminal and determining an establishment of the operator based on the network information.

3. The method of claim 2 , wherein the network information is one of a network name and an IP address.

4. The method according to claim 1 , wherein the management terminal further receives questions for the ESG evaluation and generates points for the business entity for answering the questions based on the legal regulations.

5. The method according to claim 1 , wherein the management terminal performs machine learning based on the question and the provisions of the legal regulations related to the question, and generates a learning model for outputting points for answering the question.

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