Greenhouse gas emissions management method

Abstract

To provide a method for efficiently evaluating ESG data by a business operator.SOLUTION: A method for generating a report plan for evaluating ESG of a business operator is implemented by a management terminal. The method involves causing a control unit of the management terminal to refer to information on industry type of the business operator received from a business operator terminal of the business operator, refer to a learning model generated based on report data for ESG evaluation of a plurality of other business operators in the same industry type as the business operator, and generate a road map for ESG evaluation of the business operator.SELECTED DRAWING: Figure 11

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 progress has been made in calculating and reducing emissions in SCOPE1 and SCOPE2.

[0003] Non-Patent Document 1 proposes calculating SCOPE3 emissions, that is, the emissions of the supply chain (the entire series of processes such as raw material procurement, manufacturing, logistics, sales, and waste disposal) of other related businesses, etc., as emissions other than SCOPE1 and SCOPE2 in order to further reduce the greenhouse gas emissions discharged by businesses.

Prior Art Documents

Non-Patent Documents

[0004]

Non-Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] However, although the technology disclosed in Non-Patent Document 1 discloses a method for calculating greenhouse gas emissions related to SCOPE3, etc., for each business operator, especially in enterprises and local governments, etc., a huge amount of data collection and input is required for calculating emissions, calculating the emissions, and managing the calculation results, which is extremely time-consuming and laborious. In particular, in the field of GHG emissions management, the SCOPE of emissions to be calculated is expanding, the data on which the emissions calculation is based varies widely for each SCOPE, and the data management methods also differ for each business operator. Therefore, the introduction of advanced technologies for improving business efficiency has been delayed.

[0006] Therefore, an object of the present invention is to provide a method for efficiently realizing emissions management by reducing the man-hours of work by utilizing advanced technologies in the field of GHG emissions management such as the calculation of greenhouse gas emissions by business operators. Another object of the present invention is to provide a method for efficiently realizing the evaluation of ESG data by business operators.

Means for Solving the Problems

[0007] A method for creating a report plan for ESG evaluation of a business operator, which is executed by a management terminal according to an embodiment of the present invention. The control unit of the management terminal refers to the information on the business type of the business operator received from the business operator terminal of the business operator, and refers to a learning model generated based on report data for ESG evaluation of a plurality of business operators in the same industry as the business operator, which is different from the business operator, and generates a roadmap for ESG evaluation of the business operator.

Effects of the Invention

[0008] According to the present invention, a method for efficiently realizing the evaluation of ESG data by business operators can be provided.

Brief Description of the Drawings

[0009]

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Embodiments for Carrying Out the Invention

[0010] The contents of the embodiments of the present invention will be listed and described. 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 creating a report plan for ESG evaluation of an operator, which is executed by a management terminal, wherein a control unit of the management terminal refers to information regarding the business type of the operator received from the operator terminal of the operator, Referring to a learning model generated based on report data for ESG evaluations of a plurality of operators in the same industry as the operator, but different from the operator, A method for generating a roadmap for ESG evaluation of the operator. [Item 2] The roadmap includes an improvement plan for the indicators included in the ESG evaluation, as described in Item 1. [Item 3] The roadmap is generated based on report data of operators with high evaluations among the plurality of operators, as described in Item 1. [Item 4] The roadmap includes information on efforts regarding issues identified as a result of the ESG evaluation of the operator, as described in Item 1.

[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 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, the administrator terminal 100 and a plurality of 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 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 emission amount of greenhouse gases. 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 is connected to the public blockchain network NW. Based on the information regarding the greenhouse gas emissions amount for each of the above-mentioned predetermined periods, the management terminal 100 generates a single hash value using SHA256 or other hash functions, and records it as transaction information in the blockchain network. On the blockchain network, based on the transaction information, the hash value recorded in the immediately preceding block, and the nonce value mined by the node, this block is generated, recorded following the immediately preceding block, and a blockchain is formed. Here, the hash generation and / or the recording of the transaction information in the blockchain can also be performed not by the management terminal 100 but via another terminal. In this case, the management terminal 100 transmits the greenhouse gas emissions amount calculated by the matching process to the other terminal. Furthermore, the management terminal 100 can record the information regarding the greenhouse gas emissions amount in the blockchain network as a smart contract. By using the smart contract, based on the information regarding the above emissions amount, a contract regarding the emissions trading with other operators can be automatically generated, approved, and executed without the intervention of a third party. Also, due to the smart contract, each operator can refer to the transaction information without going through the management terminal, improving the service convenience and reducing the operation cost.

[0017] Here, as described above, in the public blockchain, since the approval of transactions is performed not by a specific administrator but by an unspecified number of nodes or miners, compared with the private blockchain, higher data immutability and fault tolerance can be ensured. Therefore, since the security of transactions is ensured, it is preferable that the public blockchain be used as the destination for recording the power transaction in the present embodiment. Representative public blockchains include Bitcoin, Ethereum, etc. For example, Ethereum has higher immutability and reliability among public blockchains.

[0018] In addition, the management terminal 100 can associate information related to greenhouse gas emissions with an identifier or the like and record it on the blockchain network as a non-fungible token (hereinafter referred to as "NFT"). The NFT is, for example, a token issued according to the "ERC721" standard of Etherium, which is a platform of the blockchain network. It is a unit of data recorded on the blockchain network and has a non-fungible nature. The NFT is recorded on the blockchain together with a smart contract and is traceable, so it can prove transaction information including details and history such as the information of the operator managing the greenhouse gas emissions.

[0019] Figure 2 is a functional block diagram of the management terminal constituting the emission management system.

[0020] The communication unit 110 is a communication interface for communicating with an external terminal via the 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 programs for executing various control processes and each function in the control unit 130, input data, etc., and is composed of RAM (Random Access Memory), ROM (Read Only Memory), etc. In addition, the storage unit 120 has an operator data storage unit 121 that stores various data related to the operator, and an AI model storage unit 122 that stores learning data and a learning model learned by AI (artificial intelligence) from the learning data. Note that a database (not shown) storing 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 the programs stored in the storage unit 120, and is composed of a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), etc. As functions of the control unit 130, there are an information reception unit 131 that receives information from external terminals such as the operator terminal 200, an image analysis unit 132 that analyzes image data such as invoice information received from the operator terminal and calculates the greenhouse gas emissions, a cause analysis unit 133 that analyzes the cause of the time-series change of the greenhouse gas emissions calculated based on the information included in the extracted invoice information by analyzing the image data, a transaction processing unit 134 that generates a hash value by summarizing information on greenhouse gas emissions for a predetermined period and performs a process of recording it as transaction information in the blockchain network, and a report generation unit 135 that generates and transmits report data for outputting the greenhouse gas emissions and the results of the cause analysis of the change in emissions to the operator every predetermined period.

[0023] Also, although not shown in the figure, the control unit 130 has an image generation unit and generates screen information displayed via the user interface of an external terminal such as the operator terminal 200. For example, by using the images and text data stored in the storage unit 120 as materials and arranging various images and texts in a predetermined area of the user interface based on a predetermined layout rule, the information displayed on the user interface is generated. The processing related to the image generation unit can also be executed by a GPU (Graphics Processing Unit).

[0024] Also, the management terminal 100 further has a (not shown) wallet necessary for recording transaction information in the blockchain network. Note that this wallet can also be provided outside the management terminal 100.

[0025] Figure 3 is a functional block diagram of the operator terminal that constitutes the emissions 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 performed according to a communication protocol such as TCP / IP.

[0028] The display operation unit 220 is a user interface used for the operator to input instructions and display text, images, etc. according to the input data from the control unit 240. When the operator terminal 200 is composed of a personal computer, it is composed of a display, a keyboard, and a mouse. When the operator terminal 200 is composed of a smartphone or a tablet terminal, it is composed 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 computer).

[0029] The storage unit 230 stores programs for executing various control processes and each function in the control unit 240, input data, etc., and is composed of a RAM, a ROM, etc. Also, the storage unit 230 temporarily stores the communication content 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 composed of a CPU, a GPU, etc.

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

[0032] The operator data 1000 shown in FIG. 4 stores various data related to the operator obtained from the operator via the operator terminal 200. In FIG. 4, for convenience of explanation, an example of one operator (the operator identified by the operator ID "10001") is shown, but information of a plurality of operators can be stored. As various data related to the operator, for example, basic information of the operator (for example, corporate name of the operator, user name, office information (for example, address information for each office, etc.), network name (for example, SSID, IP address, etc.), image information (for example, background image of the office, portrait, etc.), business type, contact information, email address, office name, related company name, names of related operators on the supply chain, etc.), input information (for example, image data of invoice information, etc.), analysis information (for example, information related to invoices extracted from image data, greenhouse gas emissions, prediction of the cause of changes in greenhouse gas emissions, etc.), customer information (for example, customer ID, blockchain address, etc.), and offset report information (for example, TXID, NFTID, etc.) can be included.

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

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

[0035] Subsequently, as the process of step S102, the image analysis unit 132 of the control unit 130 of the management terminal 100 analyzes the image data acquired in the previous step by machine learning. Here, when performing image analysis, a method called so-called OCR is used, and the image analysis unit 132 of the control unit 130 of the management terminal 100 uses a learning model generated by learning the image data of a plurality of various formats of invoices stored in the AI model storage unit 122 of the storage unit 120 in advance to recognize text from the image data and extract items included in the invoice information as structured string data. Here, when performing image analysis, it is also possible to use an image analysis engine (such as an OCR engine) provided by a business operator other than the management terminal 100 that is linked by an API.

[0036] Regarding image analysis, for example, as shown in FIG. 5, it is performed by recognizing and extracting text from the image data including invoice information. As shown in FIG. 5, various items included in the invoice can be cited as invoice information. For example, items such as the breakdown name of the electricity bill, the amount (yen) for each breakdown, the contract power (kW), the electricity consumption (kWh) for each breakdown, the total amount (yen), and the date (year and month) can be cited. In this example, the breakdown of the electricity bill invoice is exemplified, but it may be an invoice for the charges related to the usage of other energies including gas and fuel in addition to electricity, or, for example, a receipt for transportation expenses for business trips, a receipt for commuting expenses of employees, an invoice associated with transactions with freight forwarders, or a breakdown of an invoice associated with transactions with waste disposal operators. The image analysis unit 132 can extract the amount information, the following activity amount information, etc. included in the invoice information as text by analyzing the image data of these invoice information. The extracted invoice information is stored as analysis information in the business operator data storage unit 121 of the storage unit 120. In this way, through image analysis by machine learning, without the business operator inputting invoice information manually or otherwise, a vast amount of necessary information for calculating greenhouse gas emissions can be acquired as image data, and through highly accurate image recognition, the information necessary for calculating greenhouse gas emissions can be accurately extracted, so that the efficiency and high accuracy of calculating greenhouse gas emissions can be realized.

[0037] Next, in step S103, the image analysis unit 132 of the control unit 130 calculates the greenhouse gas emissions based on the claim information extracted from the image data. Here, the greenhouse gas emissions are classified into SCOPE1, SCOPE2, and SCOPE3. SCOPE1 refers to the direct emissions of greenhouse gases by the operator itself (for example, emissions associated with fuel combustion and industrial processes). SCOPE2 refers to the indirect emissions associated with the use of electricity, heat, gas, etc. supplied from other companies to the operator. Furthermore, SCOPE3 is the calculation standard for the emissions of the entire supply chain of an organization issued by the GHG Protocol, referring to the emissions of the operator's supply chain (the entire series of processes such as raw material procurement, manufacturing, logistics, sales, and waste disposal). SCOPE3 is further classified into 15 categories: (1) purchased products / services, (2) capital goods, (3) fuel and energy-related activities not included in SCOPE1 and SCOPE2, (4) transportation, distribution (upstream), (5) waste from the business, (6) business trips, (7) commuting of employees, (8) leased assets (upstream), (9) transportation, distribution (downstream), (10) processing of sold products, (11) use of sold products, (12) disposal of sold products, (13) leased assets (downstream), (14) franchises, (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). In this embodiment, CO2 will be used as an example for explanation.

[0038] Also, the greenhouse gas emissions are defined by taking the electricity consumption, volume of goods transported, volume of waste processed, and various transaction amounts of the operator as the activity volumes. The emissions are calculated by multiplying the activity volumes by the emission factors, which are the CO2 emissions per 1 kWh of electricity used, CO2 emissions per 1 ton of goods transported, and CO2 emissions per 1 ton of waste incinerated. The greenhouse gas emissions are calculated separately for the above SCOPE1, SCOPE2, and SCOPE3 (for SCOPE3, by category of the 15 categories), and the total emissions are calculated as the supply chain emissions.

[0039] In the present embodiment, the image analysis unit 132 extracts relevant claim information for each of SCOPE1, SCOPE2, and SCOPE3 (for SCOPE3, further categorized by category), and among the claim information, for example, based on the electricity consumption kWh, the emission amount is calculated based on the above calculation method. The calculated emission amount is stored as analysis information in the operator data storage unit 121 of the storage unit 120.

[0040] Subsequently, as the process of step S104, the report generation unit 135 of the control unit 130 generates a visualized report showing the breakdown of the emission amount over time for each SCOPE (for SCOPE3, further categorized by category) based on the information regarding the calculated emission amount.

[0041] FIG. 9 is a flowchart showing an example of the cause prediction process for the change in the greenhouse gas emission amount according to the first embodiment of the present invention.

[0042] First, as the process of step S201, the cause analysis unit 133 of the control unit 130 of the management terminal 100 refers to the information regarding the greenhouse gas emission amount of the operator calculated in step S103 of FIG. 8. Here, regarding the greenhouse gas emission amount, the emission amounts for each SCOPE (for SCOPE3, further categorized by category) are referred to. Also, the cause analysis unit 133 can confirm the change (increase or decrease) in the emission amount by referring to the past emission amount data of the same operator regarding the greenhouse gas emission amount. As described above, the emission amount is stored as analysis information in the operator data storage unit 121 of the storage unit 120.

[0043] Subsequently, as the process of step S202, based on the information regarding the emission amount referred to above, the cause analysis unit 133 analyzes and predicts the cause of the change in the emission amount by machine learning. Here, in the cause analysis, the cause analysis unit 133 of the control unit 130 of the management terminal 100 uses the information on the emission amount referred to above, the factors affecting the change (increase or decrease) in the emission amount, and the learning model generated by learning the data on the factors affecting the change (increase or decrease) in the emission amount stored in the AI model storage unit 122 of the storage unit 120 to predict the cause of the change in the emission amount for each SCOPE (for SCOPE3, further categorized by category).

[0044] Here, as factors affecting the change (increase or decrease) in the emission amount, for example, factors such as weather, temperature, product demand and / or factory operation, store or factory business or operation hours, changes in equipment or facilities, measures by software, energy-saving actions, fuel conversion, energy menu changes, changes in business trip or commuting volume, and the power generation amount of self-generation can be mentioned. Each of these factors is a factor affecting the emission amount of any SCOPE. For example, the factor of weather affects precipitation, wind volume, sunshine duration, and temperature. Precipitation affects the small hydropower generation amount, wind volume affects the wind power generation amount, sunshine duration affects the solar power generation amount, temperature affects air conditioning, and furthermore, the power generation amount affects the self-generation amount, and the self-generation amount affects the CO2 emission amount by electricity, thereby affecting the change in the emission amount of SCOPE2. On the other hand, air conditioning affects the gas usage amount, and the gas usage amount affects the CO2 emission amount by gas combustion, thereby affecting the change in the emission amount of SCOPE1. Also, energy-saving activities, factory operation due to product demand, and business hours affect the power consumption amount and affect SCOPE2. Also, EMS, replacement of refrigeration equipment, introduction of energy-saving equipment, and automobile usage amount also affect the power consumption amount and affect SCOPE2. In addition, automobile usage amount, fuel consumption, boiler usage amount, and boiler efficiency affect the fuel usage amount and affect the CO2 emission amount by fuel, thereby affecting SCOPE1.

[0045] In addition, the factor of product sales volume affects Categories 1, 9, 10, 11, and 12 of SCOPE3, equipment investment affects Category 2, the renewable energy ratio and the amount of procured energy affect Category 3, the number of transportation trips and transportation route changes affect Categories 4 and 9, the product loss rate affects Category 5, the number of business trips and the number of office visits affect Category 6, the number of commuters and the number of office employees affect Category 7, power consumption affects Category 8, processing reduction due to product improvement affects Category 10, improvement to energy-saving products affects Category 11, an increase in the recycling rate affects Category 12, the tenant's office power affects Category 13, the emissions of the franchise affect Category 14, and the emissions of the investment destination affect Category 15 respectively.

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

[0047] Subsequently, as the process of step S203, the report generation unit 135 of the control unit 130 generates a visualized report on the cause of the change in emissions for each SCOPE (and further for each category in the case of SCOPE3) based on the information on the predicted cause of the change in the analyzed emissions.

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

[0049] First, as 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 storage unit 120. Here, the business operator data to be referred to includes the analysis information of the business operator (greenhouse gas emissions for each SCOPE), etc.

[0050] Next, as the process of step S302, the transaction processing unit 134 generates a hash value based on the operator data referred to in step S301. That is, the transaction processing unit 134 generates a hash value for one line using a hash function for the greenhouse gas emissions amount during a predetermined period, and records the hash value as transaction information in the public blockchain. On the blockchain network, based on the transaction information, the hash value recorded in the previous block, and the nonce value mined by the node, this block is generated, recorded following the previous block, and a blockchain is formed. Here, in this example, in order to reduce the cost related to blockchain recording, it is assumed that the recording is performed in layer 2 (for example, a side chain) different from the main blockchain (so-called layer 1).

[0051] In addition, the transaction processing unit 134 can assign and manage an NFT ID in association with the blockchain record of the operator's greenhouse gas emissions amount. More specifically, as shown in FIG. 4, in the operator data 1000, as customer information, the customer ID of the operator is assigned and the blockchain address to be referred to is stored, and as offset report information, the NFT ID and TX ID can be assigned.

[0052] As shown in FIG. 6, on the blockchain network, blockchain addresses are associated with each NFT ID. In the management terminal 100, NFT IDs and customer IDs are managed. Therefore, for example, information on the greenhouse gas emissions of a business operator corresponding to customer ID "2" can be obtained by referring to the blockchain addresses for each NFT ID, such as NFT IDs "13" and "14", and the details of the emissions information can be read as shown in FIG. 7. FIG. 7 shows information on the offset report associated with NFT ID "14". A TXID is assigned in association with the offset report, and the CO2 emissions by SCOPE, the target year and month, and the report issue date are included in the offset report. In addition to the CO2 emissions for the target year and month in this example, the CO2 emissions for the most recent years, the reduced CO2 emissions, and the offset CO2 emissions can also be NFTized. In this way, by managing CO2 emissions as NFTs, business operators can conduct transactions of NFTized certificates in a state where non-falsification and transaction reliability are ensured, and can also prove the emissions to third parties.

[0053] FIG. 11 is a flowchart showing an example of a method for creating a roadmap for ESG evaluation of a business operator according to the second embodiment of the present invention. Conventionally, when inputting answers to predetermined questionnaire items for the evaluation of ESG data that evaluates a company's efforts in each item of environment, society, and governance, the questionnaire items for ESG evaluation may differ depending on the industry of the business operator (including supplier business operators). For example, depending on whether the supplier company is a raw material manufacturer or a fiber manufacturer, the items to be emphasized in ESG evaluation (any of the items of environment, society, and governance, or further items included in each item) are different. For example, in an IT company, items related to governance are more emphasized than items related to the environment, and in a fiber company, items related to the environment are more emphasized. Thus, in ESG evaluation, the weight coefficients (weightings) associated with each item are different. In this way, since the efforts of business operators towards ESG differ in terms of difficulty level, goal setting, etc. for each industry, the questionnaire items and evaluation methods also differ. From the perspective of a business operator, it is difficult to grasp what kind of ESG efforts other companies are making, so it is difficult to proceed with the company's own improvement based on the evaluation of the questionnaire. Therefore, a roadmap is provided that visualizes the points that the company should improve compared to companies that have obtained high evaluations in the same industry, etc., and how much the evaluation will improve by improving those points. Thereby, guidelines for a company's ESG evaluation efforts can be determined, leading to improvements in ESG efforts. Hereinafter, in this embodiment, a method for creating an optimal roadmap for ESG evaluation according to the industry of a business operator will be exemplified. 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 on the industry of the business operator from the business operator terminal 200.

[0054] First, as the process of step S401, the report generation unit 135 of the control unit 130 of the management terminal 100 refers to the received information on the industry of the business operator. The information on the industry of the business operator may be stored in advance as business operator data in the business operator data storage unit 121 of the storage unit 120.

[0055] Subsequently, as the process of step S402, the report generation unit 135 refers to the learning model stored in the AI model storage unit 122, which is generated by machine learning using, as input data, reports for ESG evaluations provided by a plurality of business operators in the same industry as the business operator but different from the business operator, in order to create a roadmap for optimal ESG evaluation for the business operator. For example, using the IR reports and sustainability reports of listed companies as teacher data, a learning model can be generated and stored, with the ESG-related issues for each industry, the efforts made to address those issues, and the target values and actual values of the indicators serving as the criteria for evaluation in each ESG evaluation item as the output data. Here, as the content included in the report for ESG evaluation, for each of the items of "environment", "society", and "governance", more detailed evaluation items such as greenhouse gas emissions, employee composition, and board of directors composition can be provided, and the information collected through questionnaires etc. (including the above indicators) for the evaluation items can be quantified for evaluation, and the evaluation results can include visualized data. Also, information regarding the issues identified based on the ESG evaluation and the company's efforts to address those issues can be included.

[0056] Subsequently, as the process of step S403, the report generation unit 135 uses data related to the business type of the operator and the ESG evaluation of the operator (including the target values and actual performance values of each evaluation item as indicators) as input data, and generates a roadmap for ESG evaluation based on the above learning model. As the roadmap, it may include evaluation items included in each of the "environment", "society", and "governance" items included in the reports of companies in the same industry, comparison data of the target values and actual performance values related to the evaluation items, issues identified from the comparison / evaluation results, and specific contents regarding the efforts for the issues. As the content of the roadmap, it is particularly preferable that it is common to companies in the same industry. Furthermore, as the report referred to for creating the roadmap, it is preferable that it is a report of a company that has received high social evaluation. Here, as the criteria for high evaluation, examples include companies ranked at the top in the ESG evaluation, high-profit companies, companies with high stock prices, etc., and an external database can also be referred to.

[0057] In this way, based on the results of the ESG questionnaire, it is possible to visualize what kind of industry the target company is in and what issues it has in ESG efforts. Accordingly, it becomes possible to automatically propose a learned ESG effort plan and generate a roadmap, thereby improving the efficiency of ESG evaluation.

[0058] The above-described embodiments are merely examples for facilitating the understanding of the present invention and are not for limiting the interpretation of the present invention. The present invention can be changed and improved without departing from its gist, and it goes without saying that equivalents of the present invention are included therein.

Explanation of Reference Numerals

[0059] 100 Management terminal 200 Operator terminal

Claims

1. A method for creating a report plan for an ESG evaluation of an operator, which is executed by a management terminal, comprising: The control unit of the management terminal: Referring to information regarding the business type of the operator received from the operator terminal of the operator; Referring to a learning model generated based on report data for ESG evaluations of a plurality of operators in the same business type as the operator but different from the operator; Generating a roadmap for the ESG evaluation of the operator.

2. The method according to claim 1, wherein the roadmap includes an improvement plan for indicators included in the ESG evaluation.

3. The method according to claim 1, wherein the roadmap is generated based on report data of operators who have received high evaluations among the plurality of operators.

4. The method according to claim 1, wherein the roadmap includes information regarding efforts for issues identified as a result of the ESG evaluation of the operator.

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