Greenhouse gas emissions management method

Abstract

To provide a method of efficiently evaluating ESG data by a business operator.SOLUTION: A method for computing a score of a questionnaire for ESG evaluation of a business operator is implemented by a management terminal. The questionnaire comprises regardless of the industry of the business operator: commonly set question items; and individual items set based on the industry of the business operator. The method involves causing a control unit of the management terminal to: check if question items constituting the questionnaire are common items or not; and, if the question items are common items, apply a weight corresponding to the common items to scores allocated to the common items; and, if the question items are individual items, apply a weight corresponding to the individual items to scores allocated to the individual items.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 fuel, 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 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 regarding SCOPE3 and the like, for each business operator, especially in enterprises and local governments, etc., a huge amount of data collection and input is carried out for calculating emissions, calculating the emissions, and managing the calculation results, which consumes a great deal of labor and time. In particular, in the field of GHG emissions management, the SCOPE of emissions to be calculated is expanding, the data serving as the basis for calculating emissions 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 in the GHG emissions management field such as calculating greenhouse gas emissions by business operators by utilizing advanced technologies. 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 calculating scores of a questionnaire for ESG evaluation of a business operator, which is executed by a management terminal according to an embodiment of the present invention, wherein the questionnaire is composed of common question items set commonly regardless of the business type of the business operator and individual items set based on the business type of the business operator, and a control unit of the management terminal checks whether the question items constituting the questionnaire are common items. When the question items are common items, weighting corresponding to the common items is applied to the scores assigned to the common items. When the question items are individual items, weighting corresponding to the individual items is applied to the scores assigned to the individual items.

Effects of the Invention

[0008] According to the present invention, a method for efficiently realizing input support for ESG evaluation 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 are listed and explained. 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 calculating scores of a questionnaire for ESG evaluation of an operator, which is executed by a management terminal. The questionnaire is composed of common question items set commonly regardless of the business type of the operator and individual items set based on the business type of the operator. The control unit of the management terminal checks whether the question items constituting the questionnaire are common items, if the question item is a common item, a weighting corresponding to the common item is applied to the score assigned to the common item, if the question item is an individual item, a weighting corresponding to the individual item is applied to the score assigned to the individual item. [Item 2] The method according to Item 1, wherein the weighting corresponding to the individual item is set according to the business type of the operator. [Item 3] The method according to Item 1, wherein the control unit calculates a comprehensive score based on the score assigned to the common item to which the weighting corresponding to the common item is applied and the score assigned to the individual item to which the weighting corresponding to the individual item is applied.

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

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

[0013] As shown in FIG. 1, in the emission management system 1 in the present embodiment, 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 (for example, image data of invoice information) for calculating the greenhouse gas (for example, CO2) emission amount from the operator terminals 200A and 200B.

[0015] In addition, the management terminal 100 analyzes the received image data of the invoice information by machine learning, extracts the necessary items of the invoice information included in the image data, and calculates the greenhouse gas emissions. Further, the management terminal 100 analyzes the change (e.g., increase or decrease) in the greenhouse gas emissions 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 on the greenhouse gas emissions 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 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, the above 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 calculated by the matching process to the other terminal. Additionally, the management terminal 100 can record the information on the greenhouse gas emissions in the blockchain network as a smart contract. By using the smart contract, based on the information on the emissions, a contract regarding the emissions trading with other operators can be automatically generated, approved, and executed without the intervention of a third party. Also, with the smart contract, each operator can refer to the transaction information without going through the management terminal, enhancing the service convenience and reducing the operation cost.

[0017] Here, as described above, since public blockchain approves transactions not by specific administrators but by an unspecified number of nodes or miners, it can ensure higher data immutability and fault tolerance compared to private blockchain. Therefore, since the security of transactions is ensured, it is preferable to use a public blockchain as the destination for recording power transactions in this embodiment. Representative public blockchains include Bitcoin and Ethereum. 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 as a non-fungible token (Non-Fungible Token, hereinafter referred to as "NFT") in the blockchain network. 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 in the blockchain network and has a non-fungible nature. Since NFT is recorded on the blockchain together with a smart contract and is traceable, it can prove transaction information including details and history such as the information of the operator managing greenhouse gas emissions.

[0019] FIG. 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. For example, 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 a RAM (Random Access Memory), a ROM (Read Only Memory), etc. Further, the storage unit 120 has a business operator data storage unit 121 that stores various data related to business operators, and an AI model storage unit 122 that stores learning data and a learning model obtained by having AI (Artificial Intelligence) learn 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 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 the greenhouse gas emissions, a cause analysis unit 133 that analyzes the cause of the temporal change in 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 the emissions to the business operator every predetermined period.

[0023] Further, although not shown, the control unit 130 includes 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, using the images and text data stored in the storage unit 120 as materials, various images and texts are arranged in a predetermined area of the user interface based on a predetermined layout rule, thereby generating the information to be displayed on the user interface. The processing related to the image generation unit can also be executed by a GPU (Graphics Processing Unit).

[0024] Further, 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 external to 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 business 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 business operator to input instructions and display texts, images, etc. according to the input data from the control unit 240. When the business operator terminal 200 is configured by a personal computer, it is composed of a display, a keyboard, and a mouse. When the business operator terminal 200 is configured by a smartphone or a tablet terminal, it is composed of a touch panel or the like. This display operation unit 220 is activated by a control program stored in the storage unit 230 and executed by the business 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. Further, 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 business 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 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. In FIG. 4, for convenience of explanation, an example of one business operator (the business operator identified by the business operator ID "10001") is shown, but information of a plurality of business operators can be stored. As various data related to the business operator, for example, basic information of the business operator (for example, corporate name of the business operator, user name, business office information (for example, address information for each business office, etc.), network name (for example, SSID, IP address, etc.), image information (for example, background image of the business office, portrait, etc.), business type, contact information, email address, business office name, related company name, business operator name related 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 diagram showing an example of the 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, from the business operator terminal 200 via the network NW, image data including invoice information collected on the business operator side. The business operator uploads invoices, receipts, vouchers, etc. (collectively referred to as "invoices" in this embodiment) in file formats such as PDF, Excel, JPG, etc. (collectively referred to as "image data" in this embodiment) to the management terminal 100 via the business operator terminal 200. The image data acquired by the information acquisition unit 131 is stored as input information in the business operator data storage unit 121 of the storage unit 120.

[0035] 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 analyzing the image, a method called so-called OCR is used. The image analysis unit 132 of the control unit 130 of the management terminal 100 uses a learning model generated by previously learning the image data of invoices in a plurality of various formats stored in the AI model storage unit 122 of the storage unit 120 to recognize text from the image data and extract the items included in the invoice information as structured string data. Here, when analyzing the image, 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 and linked by an API.

[0036] Regarding image analysis, for example, as shown in FIG. 5, it is performed by recognizing and extracting text from image data containing claim information. As shown in FIG. 5, various items included in the claim are listed as claim 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), the date (year and month), etc. are listed. In this example, the breakdown of the electricity bill is illustrated, but it may also be a claim for the usage fee of other energies including gas and fuel in addition to electricity. Alternatively, for example, it may be the breakdown of the receipt for transportation expenses for business trips, the receipt for commuting expenses of employees, the claim accompanying the transaction with a freight forwarder, or the claim accompanying the transaction with a waste operator. The image analysis unit 132 can extract the amount information, the following activity amount information, etc. included in the claim information as text by analyzing the image data of these claim information. The extracted claim 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 claim information manually or otherwise, a vast and necessary amount of information for calculating greenhouse gas emissions can be obtained as image data, and through highly accurate image recognition, the information necessary for calculating greenhouse gas emissions can be accurately extracted. Therefore, it is possible to improve the efficiency and accuracy of calculating greenhouse gas emissions.

[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. Further, SCOPE3 is the calculation standard for the emissions of the entire supply chain of an organization issued by the GHG Protocol, and refers 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, the 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 is taken as an example for explanation.

[0038] Also, the greenhouse gas emissions are calculated by defining the usage amount of electricity, the transportation volume of goods, the processing volume of waste, and various transaction amounts of the operator as activity amounts, and multiplying the activity amounts by the emission factors per unit of activity, namely, the CO2 emissions per 1 kWh of electricity used, the CO2 emissions per 1 ton of goods transported, and the 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 (and for SCOPE3, further categorized by category), and among the claim information, calculates the emissions based on the above calculation method, for example, based on the power consumption in kWh. The calculated emissions are 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 emissions over time for each SCOPE (and for SCOPE3, further categorized by category) based on the information regarding the calculated emissions.

[0041] FIG. 9 is a flowchart showing an example of the cause prediction process for changes in greenhouse gas emissions 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 emissions of greenhouse gases of the operator calculated in step S103 of FIG. 8. Here, regarding the emissions of greenhouse gases, the emissions for each SCOPE (and for SCOPE3, further categorized by category) are referred to. Also, the cause analysis unit 133 can confirm the change (increase or decrease) in emissions by referring to the past emissions data of the same operator regarding the emissions of greenhouse gases. As described above, the emissions are stored as analysis information in the operator data storage unit 121 of the storage unit 120.

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

[0044] Here, examples of the factors that affect the change (increase or decrease) in the emission amount include weather, temperature, product demand and / or factory operation, business or operation hours of stores or factories, changes in devices or equipment, measures by software, energy-saving actions, fuel conversion, changes in the energy menu, changes in business trips or commuting volume, and the amount of power generation from self-generation. Each of these factors affects the emission amount of one of the SCOPEs. 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, and temperature affects air conditioning. 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, affecting SCOPE2. In addition, EMS, replacement of refrigeration devices, introduction of energy-saving equipment, and automobile usage also affect the power consumption, affecting SCOPE2. Moreover, automobile usage, fuel consumption, boiler usage amount, and boiler efficiency affect the fuel usage amount, affecting 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, reduction of processing due to product improvement affects Category 10, improvement to energy-saving products affects Category 11, 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 having machine learning 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 the change 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 emissions analyzed above.

[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 row using a hash function for the greenhouse gas emissions over 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. More specifically, as shown in FIG. 4, to the operator data 1000, a customer ID of the operator is assigned as customer information, and the blockchain address to be referred to is stored, and an NFT ID and a TX ID can be assigned as offset report information.

[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. 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 out as shown in FIG. 7. FIG. 7 shows information related to 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 issue date of the report 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 guaranteed, and can also prove the emissions to third parties.

[0053] FIG. 11 is a flowchart showing an example of a method for evaluating ESG data according to a second embodiment of the present invention. Conventionally, when making an input for predetermined questionnaire items for the evaluation of ESG data for evaluating 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 the ESG evaluation (any one of the items of environment, society, and governance, or further items included in each item) are different. For example, in an IT company, governance-related items are emphasized more than environment-related items, and in a fiber company, environment-related items are emphasized, so that the weight coefficients (weightings) associated with each item are different in the ESG evaluation. Therefore, in the present embodiment, in a questionnaire for evaluating ESG data composed of common questionnaire items regardless of the industry of the business operator and questionnaire items according to the industry, by setting the weighting for each item, a method for efficiently realizing the ESG evaluation by the business operator is provided. The method for evaluating ESG data is exemplified for the purpose of. As a preprocessing of the following steps, the information acquisition unit 131 of the control unit 130 of the management terminal 100 receives the answer results for the questionnaire for the evaluation of ESG data 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 calculates a score for evaluating the received response result. Here, as described above, the questionnaire is composed of common question items regardless of the business type of the business operator and question items corresponding to the business type of the business operator. In particular, depending on the business type, one of the items of "environment", "society", and "governance" is emphasized differently, and question items are set individually for any of the items. When calculating the score, the report generation unit 135 checks whether the target question item is a common question item regardless of the business type of the business operator (that is, whether it is a question item (individual item) corresponding to the business type of the business operator). At this time, the report generation unit 135 can refer to the questionnaire information stored in the business operator data storage unit 121 of the storage unit 120 to determine whether the target item is a common item, or based on a learning model generated by machine learning of the items included in the ESG data provided by business operators of multiple business types, determine whether it is a common item or an individual item.

[0055] As the confirmation result of the process of step S401 above, if the question item is a common question item regardless of the business type of the business operator, the process proceeds to step S402. As the process of step S402, the report generation unit 135 applies a weighting (for example, a weight coefficient of "0.5", etc.) corresponding to the common item to the score (for example, in the case of a five-level evaluation, any score from "1" to "5") assigned based on the response result of the common question item.

[0056] As the confirmation result of the process of step S401 above, if the question item is an individual question item set according to the business type of the business operator, as the process of step S403, the report generation unit 135 applies a weighting (for example, a weight coefficient of "0.6", etc.) corresponding to the individual item to the score (for example, in the case of a five-level evaluation, any score from "1" to "5") assigned based on the response result of the individual item.

[0057] The report generation unit 135 continues the processing from step S401 to step S402 or step S403 until the score calculation for all question items is completed, and calculates the total score by adding up the corrected scores calculated by applying weighting to the scores assigned to the answer results of each question item. Note that this process is

[0058] In this way, by applying appropriate weighting to the questionnaire results for the evaluation of ESG data created by the operator, an efficient ESG evaluation can be performed.

[0059] The above-described embodiments are merely examples for facilitating the understanding of the present invention, and are not intended to limit the interpretation of the present invention. It goes without saying that the present invention can be changed and improved without departing from its gist, and equivalents thereof are included in the present invention.

Explanation of Reference Numerals

[0060] 100 Management terminal 200 Operator terminal

Claims

Claim 1 A method for calculating scores of a questionnaire for an enterprise's ESG evaluation, which is executed by a management terminal, wherein the questionnaire is composed of common question items set commonly regardless of the industry type of the enterprise and individual items set based on the industry type of the enterprise, The control unit of the management terminal, checks whether the question items constituting the questionnaire are common items, when the question item is a common item, applies weighting corresponding to the common item to the score assigned to the common item, when the question item is an individual item, applies weighting corresponding to the individual item to the score assigned to the individual item. Claim 2 The method according to claim 1, wherein the weighting corresponding to the individual item is set according to the industry type of the enterprise. Claim 3 The method according to claim 1, wherein the control unit calculates an overall score based on the score assigned to the common item to which the weighting corresponding to the common item is applied and the score assigned to the individual item to which the weighting corresponding to the individual item is applied.

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