Greenhouse gas emissions management method

Abstract

To provide a method of allowing a business operator to securely manage ESG data.SOLUTION: A method implemented by a management terminal to manage ESG-related data of a business operator involves causing a control unit of the management terminal to record ESG-related data of the business operator on a block chain network, and set an administrative privileges for the ESG-related data.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, i.e., 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 to calculate the emissions, calculate the emissions, and manage 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 management of ESG data by business operators.

Means for Solving the Problems

[0007] A method for managing data related to the ESG of a business operator, executed by a management terminal according to an embodiment of the present invention. The control unit of the management terminal records the data related to the ESG of the business operator in a blockchain network and sets the management authority for the data related to the ESG.

Effects of the Invention

[0008] According to the present invention, it is possible to provide a method for efficiently realizing input support for ESG evaluation by business operators.

Brief Description of the Drawings

[0009]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

Figure 9

Figure 10

Figure 11

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 managing data related to an operator's ESG, which is executed by a management terminal, The control unit of the management terminal, records data related to the operator's ESG in a blockchain network, and sets management authority for the data related to ESG. [Item 2] The method according to item 1, wherein the management authority is the right to view data related to the ESG. [Item 3] The method according to item 1, wherein the previous management authority is set for each file. [Item 4] The method according to item 1, wherein the management authority is set for each item of the ESG.

[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 a 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 emission amount of the greenhouse gas. Further, the management terminal 100 analyzes the change (for example, increase or decrease) in the time-series greenhouse gas emission amount 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 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, a block is generated based on the transaction information, the hash value recorded in the previous block, and the nonce value mined by the node, and is recorded following the previous block, forming a blockchain. Here, instead of the management terminal 100, the above hash generation and / or the recording of the transaction information in the blockchain can also be performed via other terminals. In this case, the management terminal 100 transmits the greenhouse gas emissions calculated by the matching process to other terminals. Furthermore, the management terminal 100 can record the information on 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, improving the service convenience and reducing the operation cost.

[0017] Here, as described above, in a public blockchain, the approval of transactions is performed not by a specific administrator but by an unspecified number of nodes or miners. Therefore, compared with a private blockchain, higher data immutability and fault tolerance can be ensured, and thus the security of transactions is ensured. Therefore, in this embodiment, it is preferable that the public blockchain is used as the destination for recording the power transaction. 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 identifiers and record it on a blockchain network as a non-fungible token (hereinafter referred to as "NFT"). An NFT is, for example, a token issued according to the "ERC721" standard of Ethereum, 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. Since an 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 the 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, 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 for storing various data related to the operator, and an AI model storage unit 122 for storing 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 program 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 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 result of the cause analysis of the change in emissions to the business operator every predetermined period.

[0023] Also, although not shown, 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 business 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 business operator terminal constituting 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 calculator).

[0029] The storage unit 230 stores programs for performing 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 operator terminal 200 by executing the programs stored in the storage unit 230, and is composed of a CPU, a GPU, etc.

[0031] Figure 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 acquired from the operator via the operator terminal 200. In FIG. 4, for the sake of 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 regarding 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 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 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, etc. (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. The image analysis unit 132 of the control unit 130 of the management terminal 100 uses a learning model generated by previously learning image data of a plurality of various formats of invoices stored in the AI model storage unit 122 of the storage unit 120 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 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 the image data including invoice information. As shown in FIG. 5, various items included in the invoice are listed 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) are listed. 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 amount of other energies including gas and fuel in addition to electricity, or other, for example, the receipt for transportation expenses of business trips, the receipt for commuting expenses of employees, the invoice accompanying the transaction with a freight forwarder, or the breakdown of the invoice accompanying the transaction with a waste disposal operator. 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 huge amount of necessary information for calculating the greenhouse gas emissions can be acquired as image data, and through highly accurate image recognition, the information necessary for calculating the greenhouse gas emissions can be accurately extracted. Therefore, the efficiency and high accuracy of calculating the 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. Further, 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, 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). In this embodiment, CO2 is taken as an example for explanation.

[0038] Also, the greenhouse gas emissions are defined with the operator's electricity usage, cargo transportation volume, waste treatment volume, and various transaction amounts as activity volumes. By multiplying the activity volumes by the emission factors, i.e., the CO2 emissions per 1 kWh of electricity usage, the CO2 emissions per 1 ton of cargo transportation, and the CO2 emissions per 1 ton of waste incineration, the greenhouse gas emissions are calculated. 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 further by category for SCOPE3), and among the claim information, calculates the emission amount based on the above calculation method, for example, based on the power consumption in kWh. 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 (and further by category for SCOPE3) 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 amount for each SCOPE (and further by category for SCOPE3) is 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 for 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, 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 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, which is stored in the AI model storage unit 122 of the storage unit 120 in advance, to predict the cause of the change in the emission amount for each SCOPE (for SCOPE3, further categorized by category).

[0044] Here, examples of the factors affecting the change (increase or decrease) in the emission amount include 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, changes in the energy menu, changes in business trip or commuting volume, and the amount of power generated by on-site power generation. Each of these factors is a factor that 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 amount of small hydropower generation, wind volume affects the amount of wind power generation, sunshine duration affects the amount of solar power generation, temperature affects air conditioning, and furthermore, the amount of power generation affects the amount of on-site power generation, and the amount of on-site power generation affects the CO2 emissions by electricity, thereby affecting the change in the emission amount of SCOPE2. On the other hand, air conditioning affects gas usage, and gas usage affects the CO2 emissions 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 power consumption and affect SCOPE2. Also, EMS, replacement of refrigeration equipment, introduction of energy-saving equipment, and automobile usage also affect power consumption and affect SCOPE2. In addition, automobile usage, fuel consumption, boiler usage, and boiler efficiency affect fuel usage and affect the CO2 emissions by fuel, thereby affecting SCOPE1.

[0045] In addition, the factor of the number of products sold 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 transports and transport route changes affect Categories 4 and 9, the product loss rate affects Category 5, the number of business trips and the number of employees going to the office affect Category 6, the number of commuters and the number of employees going to the office affect Category 7, the power consumption affects Category 8, the reduction of processing due to product improvement affects Category 10, the improvement to energy-saving products affects Category 11, the increase in the recycling rate affects Category 12, the office power of tenants 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 factors affect 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 using 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 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 (the 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 of one line for the greenhouse gas emissions during a predetermined period using a hash function, 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 made 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, in the operator data 1000, the operator's customer ID is assigned as customer information, and the blockchain address to be referred to is stored. As offset report information, an NFT ID and a 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, the NFT ID and the customer ID are managed. Therefore, for example, information on the greenhouse gas emissions of a business operator corresponding to the customer ID "2" can be obtained by referring to the blockchain address 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 on the offset report associated with the 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 in the target year and month of this example, the CO2 emissions in the most recent years, the reduced CO2 emissions, and the offset CO2 emissions can also be NFTized. In this way, by managing the CO2 emissions as NFTs, the business operator can conduct transactions of NFTized certificates in a state where non-tampering and transaction reliability are ensured, and can also prove the emissions to a third party.

[0053] FIG. 11 is a flowchart showing an example of a method for managing ESG data according to the second embodiment of the present invention. Conventionally, for ESG evaluations that assess a company's efforts in each of the environmental, social, and governance areas, data created by businesses (e.g., data related to greenhouse gas emissions, water intake, water recycling volume, wastewater volume, waste volume, chemical substance volume, pollutant volume, etc. in the "environment" area, data related to employee composition, management composition, recruitment, average length of service, age group composition, disabled person composition, salary, etc. in the "social" area, data related to the composition of the board of directors, board of auditors, top management, etc. in the "governance" area) (hereinafter, "ESG data") often contains important information, so not all data is disclosed. On the other hand, since it is also necessary to share the ESG efforts among the supply chains, authority management becomes important. Therefore, in this example, the ESG data of the business is managed on the blockchain, and the viewing range is set for each area and file, making it easier to share data among the supply chains and also making it easier to manage information.

[0054] First, as a pre - process for step S401, the information acquisition unit 131 of the control unit 130 of the management terminal 100 receives the above - mentioned ESG data from the business operator's business terminal 200 via the network NW and stores it as business office information in the business operator data storage unit 121 of the storage unit 120. Then, as the process of step S401, the transaction processing unit 134 of the control unit 130 of the management terminal 100 refers to the ESG data included in the business operator data stored in the business operator data storage unit 121 of the storage unit 120. Then, based on the referred ESG data, the transaction processing unit 134 generates a hash value 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 done in layer 2 (for example, a side chain) different from the main blockchain (so - called layer 1). Here, the transaction processing unit 134 can assign and manage an NFT ID in association with the blockchain recording of the business operator ESF data. Also, for each of the items of "environment", "society", and "governance" in the ESG data, a hash value can be generated and recorded in the blockchain, or for each of the multiple sub - items included in each item (for example, the item of "greenhouse gas emissions" in the "environment" item), a hash value can be generated and recorded in the blockchain.

[0055] Subsequently, as the process of step S402, the transaction processing unit 134 performs permission setting on the ESG data recorded in the blockchain. As an example of permission setting, an operator can set the public range (viewing range) of the ESG data. In particular, for the ESG data, for example, the public range can be set for each item of "environment", "society", and "governance", or for items related to "greenhouse gas emissions" among the items of "environment". In addition, the public range can be set for each file recorded in the blockchain. Also, by limiting the public range to the operators on the supply chain, information sharing between supply chains can be easily performed in a secure manner.

[0056] In this way, by recording the ESG data in the blockchain, while ensuring the non-tamperability and reliability of transactions, the ESG data can be disclosed, and by setting viewing permissions such as the public range, information sharing in a secure form can be performed in a simple form, and the EFG data can be proved to a third party.

[0057] The above-described embodiments are merely examples for facilitating the understanding of the present invention, and are not for limiting and interpreting 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

[0058] 100 Management Terminal 200 Operator Terminal

Claims

1. A method for managing data related to an operator's ESG, which is executed by a management terminal, comprising: a control unit of the management terminal records data related to the operator's ESG in a blockchain network, and sets management authority for the data related to the ESG.

2. The method according to claim 1, wherein the management authority is a viewing authority for data related to the ESG.

3. The method according to claim 1, wherein the previous management authority is set for each file.

4. The method according to claim 1, wherein the management authority is set for each item of ESG.

Images