Method for managing greenhouse gas emissions

Abstract

To provide a method for efficiently achieving evaluation of ESG data by a business operator.SOLUTION: According to an aspect of the present invention, there is provided a method of recommending a correspondent business based on an ESG evaluation of a business, the method being performed by a management terminal, wherein a controller of the management terminal determines the validity of the business as a correspondent with reference to report data for ESG evaluation received from the business.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 for GHG emissions management, such as the calculation of greenhouse gas emissions by businesses. Another aim of the present invention is to provide a method for efficiently evaluating ESG data by businesses. [Means for solving the problem]

[0007] According to one embodiment of the present invention, there is provided a method for recommending business partners based on ESG evaluations of business partners, which is executed by a management terminal, in which a control unit of the management terminal refers to report data for ESG evaluations received from the business partners and determines the suitability of the business partners as business partners. [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 recommending a business partner based on an ESG evaluation of a business according to a second embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0010] The contents of an embodiment of the present invention will be listed and explained below. A greenhouse gas emission management system (hereinafter simply referred to as "system") according to an embodiment of the present invention has the following configuration. [Item 1] A method for recommending business partners based on ESG evaluations of businesses, which is executed by a management terminal, The control unit of the management terminal Refer to the report data for ESG evaluation received from the business operator, A method for determining the appropriateness of the business operator as a business partner. [Item 2] Item 1. The method according to Item 1, wherein the control unit refers to an ESG score included in the report data. [Item 3] 2. The method according to item 1, wherein the control unit recommends an alternative business partner if the validity is not found. [Item 4] Item 10. The method according to item 1, wherein the business is a supplier. [Item 5] 4. The method according to item 3, wherein the control unit recommends the alternative business partner by referring to the ESG score.

[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 business operators 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 establishment information (e.g., address information for each business establishment), network name (e.g., SSID, IP address), image information (e.g., background image of the business establishment, image of a person, etc.), industry, contact information, email address, business establishment name, affiliated company name, business operator name related 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, 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 a method for recommending business partners based on ESG evaluations of business partners according to a second embodiment of the present invention. Conventionally, to evaluate ESG data, which evaluates a company's efforts in environmental, social, and governance areas, businesses (including supplier businesses) answer questions included in an ESG evaluation questionnaire sent by a (buyer) business. Based on the collected answers, the business is assigned a score associated with each question, thereby calculating the business's ESG score. The control unit 130 of the management terminal 100 stores the ESG score for each business partner in the business partner data storage unit 121 of the memory unit 120. Here, the business partner may be either a buyer business or a supplier business. Many buyer companies, such as manufacturers, are now requesting their suppliers to visualize and improve their ESG efforts, and there are an increasing number of cases in which certain standards (e.g., ESG score benchmarks) are established for conducting business. In such cases, if a supplier's ESG efforts do not improve or worsen, the buyer is likely to consider changing business partners. Conversely, from the supplier's perspective, a buyer whose ESG practices are not improving may be considered as a potential supplier. Therefore, this embodiment aims to provide a method for appropriately managing ESG practices by providing a method for presenting to buyers business partners for which a change should be considered based on ESG practices. A specific example will be described below. As preprocessing for the following steps, the information acquisition unit 131 of the control unit 130 of the management terminal 100 receives business operator data, including information about the business operator's industry, from the business operator terminal 200, and also receives questionnaire results for conducting ESG practices or ESG scores for each business operator (buyer and / or supplier business operator), and stores information about the business operator's industry and ESG report data (including ESG practices and / or scores) in the business operator data storage unit 121 of the memory unit 120.

[0054] First, in the process of step S401, the report generating unit 135 of the control unit 130 of the management terminal 100 refers to the received ESG report data of the business operator.

[0055] Next, in step S402, the report generation unit 135 determines whether the (supplier) business is an appropriate business partner for the (buyer) business. Specifically, the report generation unit 135 references the business's ESG score and determines that businesses with ESG scores above a reference ESG score are appropriate business partners. Conversely, businesses with ESG scores below the reference ESG score are determined to be inappropriate business partners. Here, the ESG score may be the sum of the "E," "S," and "G" scores. Depending on the business's industry, the report generation unit 135 may also determine the total score of any of the "E," "S," and "G" categories, or a score that emphasizes sub-categories within each category, using evaluation criteria. The report generation unit 135 may also reference the (annual or monthly) trend in ESG scores and determine that businesses that do not show improvement in their scores are inappropriate business partners. For example, a business whose ESG score is below a reference ESG score and has declined for two consecutive years may be determined to be inappropriate business partners.

[0056] Next, in step S403, if the above determination result indicates that the (supplier) business is not a suitable business partner for the (buyer) business, the report generation unit 135 extracts one or more businesses to recommend as business partners in place of the selected business and sends them to the business terminal 200 of the (buyer) business as recommended businesses. Here, as a criterion for determining a recommended business, the report generation unit 135 first refers to the industry information of the recommended business and determines whether the business is in the same industry as the original supplier business (and / or provides the same products), and then, as in step S402, refers to the business's ESG score and confirms its suitability as a business partner based on the benchmark ESG score, or determines its suitability as a business partner by checking the annual or monthly trends in the ESG score.

[0057] In this way, the ESG evaluation results can visualize whether the target company's ESG efforts are appropriate from the perspective of transaction appropriateness, thereby improving the efficiency of ESG evaluation management. Note that in this embodiment, an example is given in which a supplier business determines whether a buyer business is appropriate as a business partner, but it is also possible to similarly determine whether a buyer business is appropriate as a business partner of a supplier business based on the buyer business's ESG evaluation. This allows buyer businesses, like supplier businesses, to strengthen their ESG efforts and improve the appropriateness of ESG evaluation management throughout the entire supply chain.

[0058] 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]

[0059] 100 Management Terminal 200 Operator terminal

Claims

1. A method for recommending business partners based on ESG evaluations of businesses, which is executed by a management terminal, comprising: The control unit of the management terminal Refer to the report data for ESG evaluation received from the business operator, A method for determining the appropriateness of the business operator as a business partner.

2. The method according to claim 1 , wherein the control unit refers to an ESG score included in the report data.

3. The method of claim 1 , wherein the control unit recommends an alternative trading partner if the validity is not met.

4. The method of claim 1 , wherein the entity is a supplier.

5. The method according to claim 3 , wherein the control unit recommends the alternative business partner by referring to the ESG score.

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