Greenhouse gas emissions management method

Abstract

To provide a method of efficiently assisting business operators in inputting data for emissions management and the like.SOLUTION: According to an embodiment of the present invention, a method implemented by a management terminal for assisting a business operator in greenhouse gas emissions management is provided, the method involving causing a control unit of the management terminal to receive questionnaire information regarding greenhouse gas emissions from a business operator terminal of the business operator, and tally the greenhouse gas emissions on the basis of the questionnaire information.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 the calculation and reduction efforts of emissions in SCOPE1 and SCOPE2 have been progressing.

[0003] In Non-Patent Document 1, with the aim of further reducing the greenhouse gas emissions discharged by businesses, as emissions other than SCOPE1 and SCOPE2, suggestions have been made regarding the calculation of 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.

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 rapidly, the data on which emissions calculations are 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 required in the GHG emissions management field, such as calculating greenhouse gas emissions by business operators, by utilizing advanced technologies.

Means for Solving the Problems

[0007] A method for assisting in the management of greenhouse gas emissions 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 receives questionnaire information regarding greenhouse gas emissions from the business operator terminal of the business operator, and aggregates the greenhouse gas emissions based on the questionnaire information.

Effects of the Invention

[0008] According to the present invention, it is possible to provide a method for efficiently realizing input assistance for emissions management by business operators.

Brief Description of the Drawings

[0009]

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Embodiments for Carrying Out the Invention

[0010] The content of the embodiment of the present invention will be listed and described. A greenhouse gas emissions 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 supporting the management of greenhouse gas emissions of an operator, which is executed by a management terminal, wherein a control unit of the management terminal receives questionnaire information regarding greenhouse gas emissions from the operator terminal of the operator, and totals the greenhouse gas emissions based on the questionnaire information. [Item 2] The method according to item 1, wherein the questionnaire information is information regarding a questionnaire collected from a supply chain operator of the operator. [Item 3] The method according to Item 1, wherein the control unit detects an abnormal value of the input information included in the questionnaire information. [Item 4] The method according to Item 3, wherein the control unit detects the abnormal value based on the actual value stored in the storage unit.

[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 greenhouse gas emission amount. Further, the management terminal 100 analyzes the change (for example, 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 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, the current block is generated, recorded following the immediately preceding block, and a blockchain is formed. 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 through the matching process to the other terminals. Furthermore, the management terminal 100 can record the information regarding the greenhouse gas emissions in the blockchain network as a smart contract. By using the smart contract, based on the above-mentioned emissions information, a contract regarding the emissions trading with other operators can be automatically generated, approved, and executed without involving 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, it is possible to ensure higher data immutability and fault tolerance, 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 an identifier or the like and record it on the 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 Etherium, which is a platform of the blockchain network, and is a unit of data recorded on the blockchain network, having a non-fungible nature. Since the 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 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 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 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 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] Furthermore, the management terminal 100 also 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] Figure 3 is a functional block diagram of the business 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 calculator).

[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 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 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 for 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 the image data of a plurality of claim forms in 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 claim 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 in cooperation through 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 claim information. As shown in FIG. 5, various items included in the claim form can be cited 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 power 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 is exemplified, but it may be a claim form for the usage fee of other energies including gas and fuel in addition to electricity. Alternatively, for example, it may be the breakdown of a receipt for transportation expenses for business trips, a receipt for commuting expenses of employees, a claim form associated with transactions with a freight forwarder, or a claim form associated with transactions with a waste disposal 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 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, it is possible to realize the efficiency improvement and high accuracy of the calculation of 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. 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 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 will be used 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 used, the CO2 emissions per 1 ton of cargo transported, and the CO2 emissions per 1 ton of waste incinerated, 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 (for SCOPE3, further categorized by category), and among the claim information, for example, based on the electricity consumption in kWh, calculates the emissions based on the above calculation method. 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 (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 greenhouse gas emissions of the operator calculated in step S103 of FIG. 8. Here, regarding the greenhouse gas emissions, the emissions 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 emissions by referring to the past emission data of the same operator for the greenhouse gas emissions. 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 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, energy menu changes, changes in business trips or commuting volume, and the amount of power generated by self-generation. 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 changes in transportation routes 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 commuting employees and the number of employees going to the office 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 office power of tenants affects Category 13, the emissions of franchises affect Category 14, and the emissions of investment destinations 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 causes of changes in emissions. Here, by predicting the causes of emissions using machine learning, it is possible to efficiently and accurately predict the factors that affect the 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 causes of changes in emissions for each SCOPE (and further for each category in the case of SCOPE3) based on the information on the predicted causes of changes 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 row using a hash function for the greenhouse gas emissions during a predetermined period, and records the hash value as transaction information in the public blockchain. 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, 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, 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, and the NFT ID and 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 an operator corresponding to customer ID "2" can be retrieved by referring to the blockchain addresses for each NFT ID, such as NFT IDs "13" and "14", to read the details of the emissions information 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 report issuance 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. By managing CO2 emissions as NFTs in this way, 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 the aggregation process of greenhouse gas emissions according to the first embodiment of the present invention. Conventionally, for the management of greenhouse gas emissions, when inputting for predetermined items, an operator, which is a buyer company, requests an operator, which is a supplier company, to input for predetermined items for emissions management including the calculation of emissions in the form of a questionnaire or the like. After collecting the response results, the response results need to be manually input and the emissions aggregated. Therefore, in this example, a method for automatically aggregating business location information is exemplified for the purpose of efficiently realizing the aggregation for emissions management by operators.

[0054] First, as the process of step S401, the information acquisition unit 131 of the control unit 130 of the management terminal 100 receives, via the network NW, the questionnaire response results (energy type, energy consumption, etc.) from the business operator terminal 200 of the business operator of the above-mentioned buyer company, and stores them in the business operator data storage unit 121 of the storage unit 120 as input data for calculating greenhouse gas emissions, thereby performing registration. Note that depending on the questionnaire, in addition to or instead of the above items, it may include emission factors and / or greenhouse gas emissions.

[0055] Subsequently, as the process of step S402, the report generation unit 135 of the control unit 130 of the management terminal 100 aggregates the received questionnaire response results. As the aggregation method, data input into the input items (energy type, energy consumption, etc.) constituting the questionnaire is extracted as structured data and stored in the business operator data storage unit 121 as basic information for calculating greenhouse gas emissions; a method of performing image analysis on the questionnaire information as image data by machine learning, extracting the text information extracted from the image, and then storing it in the business operator data storage unit 121 as structured data, etc. may be mentioned. Here, the report generation unit 135 checks whether the received questionnaire information contains outliers. As a method for detecting outliers, the management terminal 100, for example, stores the questionnaire response results implemented once a year for the same business operator. If the numerical value is extremely large or small compared to the past performance value, or if the input energy type is different from the past input value, the input data is detected as an outlier, and a notification is sent via the business operator terminal 200 to the supplier business operator to confirm the questionnaire response results and / or re-answer. If there are no outliers, the process proceeds to step S403.

[0056] Here, as the process of step S403, the report generation unit 135 aggregates the greenhouse gas emissions input as the questionnaire results or the data necessary for calculating the greenhouse gas emissions, and calculates the greenhouse gas emissions of each SCOPE as the supply chain emissions by summing them up.

[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 assisting in managing the greenhouse gas emissions of a business operator, which is executed by a management terminal, comprising: a control unit of the management terminal receives questionnaire information regarding greenhouse gas emissions from the business operator's terminal of the business operator, and aggregates the greenhouse gas emissions based on the questionnaire information.

2. The method according to claim 1, wherein the questionnaire information is information regarding a questionnaire collected from the business operator's supply chain operators.

3. The method according to claim 1, wherein the control unit detects an abnormal value of the input information included in the questionnaire information.

4. The method according to claim 3, wherein the control unit detects the abnormal value based on the performance value stored in the storage unit.

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