Greenhouse Gas Emission Management Method

Abstract

To provide a method of efficiently performing management of greenhouse gas emissions, including computation of the amount of emissions, by a business operator.SOLUTION: A greenhouse gas emissions management method according to an embodiment of the present invention comprises: receiving activity level information, including activity information and emission intensity information regarding products of a business operator, based on input from a business operator terminal; and predicting the input timing of activity level information based on the received activity level information and the timing the activity level information was input.SELECTED DRAWING: Figure 11

Description

Technical Field

[0001] The present invention relates to a method for managing greenhouse gas emissions.

Background Art

[0002] Regarding the greenhouse gas emissions of businesses associated with the use of fuels, electricity, etc., a reporting system targeting SCOPE1 emissions (direct emissions of the company itself) and SCOPE2 emissions (indirect emissions of the company itself) has become widespread, and progress has been made in calculating and reducing emissions in SCOPE1 and SCOPE2.

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

Prior Art Documents

Non-Patent Documents

[0004]

Non-Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

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

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

Means for Solving the Problems

[0007] A method for managing greenhouse gas emissions according to an embodiment of the present invention, which receives activity amount information including activity information and emission factor information related to a product of a business operator based on an input from a business operator terminal, and predicts the input time of the activity amount information based on the received activity amount information and the input time of the activity amount information.

Effects of the Invention

[0008] According to the present invention, it is possible to provide a method for efficiently realizing management such as the calculation of greenhouse gas emissions by business operators.

Brief Description of the Drawings

[0009]

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Mode for Carrying Out the Invention

[0010] The content of the embodiment of the present invention will be listed and described. A greenhouse gas emission management system (hereinafter simply referred to as "system") according to the embodiment of the present invention has the following configuration. [Item 1] A method for managing greenhouse gas emissions, receiving activity amount information including activity information and emission factor information regarding the products of an operator based on an input by the operator terminal, A method for predicting the input time of the activity amount information based on the received activity amount information and the input time of the activity amount information. [Item 2] The activity information includes any one of SCOPE classification, energy item, and usage amount, and is the management method described in Item 1. [Item 3] The management method according to item 1, which receives the activity information for each of SCOPE1 to SCOPE3. [Item 4] The management method according to item 1, which transmits an alert prompting the input of activity amount information to the operator terminal based on the input prediction.

[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 amount management system according to a first embodiment of the present invention.

[0013] As shown in FIG. 1, in the emission amount management system 1 in the present embodiment, the administrator terminal 100 and a plurality of operator terminals 200A and 200B are interconnected via a communication network NW.

[0014] For example, the management terminal 100 receives basic information regarding 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-specified periods, the management terminal 100 generates a single hash value using SHA256 or other hash functions and records it in the blockchain network as transaction information. On the blockchain network, based on the transaction information, the hash value recorded in the immediately preceding block, and the nonce value mined by the node, this block is generated, recorded following the immediately preceding block, and a blockchain is formed. Here, the 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. 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 information regarding the emissions, a contract regarding the emissions trading with other operators can be automatically generated, approved, and executed without going through 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, 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 be used as the destination for recording the power transaction. Representative public blockchains include Bitcoin, Ethereum, etc. For example, Ethereum has higher immutability and reliability among public blockchains.

[0018] In addition, the management terminal 100 can associate information regarding greenhouse gas emissions with an identifier or the like and record it as a non-fungible token (hereinafter referred to as "NFT") on a blockchain network. The 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. The NFT is recorded on the blockchain together with a smart contract and is traceable, so it can prove transaction information including details and history such as operator information for managing greenhouse gas emissions.

[0019] FIG. 2 is a functional block diagram of a management terminal constituting an emissions 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 a RAM (Random Access Memory), a ROM (Read Only Memory), etc. In addition, the storage unit 120 has a business operator data storage unit 121 for storing various data related to the business 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 operator terminal 200, an image analysis unit 132 that analyzes image data such as invoice information received from the operator terminal and calculates the greenhouse gas emissions, a cause analysis unit 133 that analyzes the cause of the time-series change of the greenhouse gas emissions calculated based on the information included in the extracted invoice information by analyzing the image data, a transaction processing unit 134 that performs a process of generating a hash value by summarizing information on greenhouse gas emissions for a predetermined period and 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 operator every predetermined period.

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

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

[0025] Figure 3 is a functional block diagram of the operator terminal 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 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 operator terminal 200 by executing the programs stored in the storage unit 230, and is composed of a CPU, a GPU, etc.

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

[0032] The operator data 1000 shown in FIG. 4 stores various data related to the operator 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, emission factor information, prediction of the cause of changes in greenhouse gas emissions, etc.), customer information (for example, customer ID, blockchain address, etc.), offset report information (for example, TXID, NFTID), and activity volume information (for example, activity information, item, emission factor, input time (date)), 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) to the management terminal 100 in file formats such as PDF, Excel, JPG (collectively referred to as "image data" in this embodiment) 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 claim forms 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 items included in the claim form 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 claim form information. As shown in FIG. 5, various items included in the claim form can be cited as claim form information. For example, items such as the breakdown name of the electricity bill, the amount (yen) for each breakdown, the contract power (kW), the electricity consumption (kWh) for each breakdown, the total amount (yen), and the date (year and month) can be cited. In this example, the breakdown of the electricity bill is exemplified, but it may be a claim form for the usage fee of other energies including gas and fuel in addition to electricity, or other examples may include a receipt for transportation expenses for business trips, a receipt for commuter expenses of employees, a claim form associated with transactions with freight forwarders, and a breakdown of a claim form associated with transactions with waste disposal operators. The image analysis unit 132 can extract the amount information, the following activity amount information, etc. included in the claim form information as text by analyzing the image data of these claim form information. The extracted claim form 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 form 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 the 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 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 will be used as an example for explanation.

[0038] Also, the greenhouse gas emissions are defined with the operator's electricity consumption, cargo transportation volume, waste treatment volume, and various transaction amounts as activity volumes. By multiplying the activity volumes by the emission factors, namely 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 this 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 emissions based on the above calculation method, for example, based on the power consumption in kWh. The calculated emissions are stored in the operator data storage unit 121 of the storage unit 120 as analysis information and activity information.

[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 further by category for SCOPE3) 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 further by category for SCOPE3) 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, based on the information regarding the emissions amount referred to above, the cause analysis unit 133 analyzes and predicts the cause of the change in the emissions amount by machine learning. Here, in the cause analysis, the cause analysis unit 133 of the control unit 130 of the management terminal 100 uses the information on the emissions amount referred to above, factors that affect the change (increase or decrease) in the emissions amount, and a learning model generated by learning data regarding factors that affect the change (increase or decrease) in the emissions amount, which was previously stored in the AI model storage unit 122 of the storage unit 120, to predict the cause of the change in the emissions amount for each SCOPE (for SCOPE3, further categorized by category).

[0044] Here, examples of factors that affect the change (increase or decrease) in the emissions amount include weather, temperature, product demand and / or factory operation, business or operation hours of stores or factories, changes in equipment or facilities, measures by software, energy-saving actions, fuel conversion, changes in energy menus, changes in business trips or commuting volume, and the amount of power generated by self-generation. Each of these factors is a factor that affects the emissions 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 emissions amount by electricity, thereby affecting the change in the emissions amount of SCOPE2. On the other hand, air conditioning affects the gas usage amount, and the gas usage amount affects the CO2 emissions amount by gas combustion, thereby affecting the change in the emissions amount of SCOPE1. Also, energy-saving activities, factory operation due to product demand, and business hours affect the electricity usage amount and affect SCOPE2. Also, EMS, replacement of refrigeration equipment, introduction of energy-saving equipment, and automobile usage amount also affect the electricity usage 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 emissions 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 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 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 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 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 during a predetermined period, and records the hash value as transaction information in the public blockchain. On the blockchain network, based on the transaction information, the hash value recorded in the previous block, and the nonce value mined by the node, this block is generated, recorded following the previous block, and a blockchain is formed. Here, in this example, in order to reduce the cost related to blockchain recording, it is assumed that the recording is 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, as customer information, the customer ID of the operator is assigned and the blockchain address to be referred to is stored, and as offset report information, the NFT ID and TX ID can be assigned.

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

[0053] FIG. 11 is a flowchart showing an example of a process related to the management of greenhouse gas reduction targets according to the first embodiment of the present invention.

[0054] First, as the process of step S401, the information acquisition unit 131 of the control unit 130 of the management terminal 100 receives, from the business operator terminal 200 via the network NW, the input of activity information and activity amount information related to the emission unit. Here, the activity information refers to information regarding the amount (so-called activity amount) related to the scale of activities (including the activities of persons other than the business operator) related to the products of the business operator. For example, it includes category classification (any classification of SCOPE1 to SCOPE3, and categories classified for each emission cause of SCOPE3), item (for example, items of energy such as gasoline, iron, ethylene, cement, etc.), usage amount (for example, the usage amount of energy (such as electricity), the transportation volume of goods, the treatment volume of waste, various transaction amounts), and the like. Further, the information acquisition unit 131 can receive the activity information for each SCOPE classification of SCOPE1 to SCOPE3. For example, for business operator X, it can receive the activity information of SCOPE1 (items such as the usage amount of ethylene), the activity information of SCOPE2 (usage amount of electricity, etc.), and the activity information of SCOPE3 (procurement weight of transported goods, etc.). Furthermore, the information acquisition unit 131 receives, from the business operator terminal 200, information related to the corresponding emission unit for the received activity information, and calculates it as the activity amount information. Note that this process can also be replaced with the process of receiving in the form of the above claim information and calculating the activity amount. In this way, the received and / or calculated activity amount information can be stored in the business operator data 1000 as activity amount information together with the input date.

[0055] Here, it is normal for the business operator to input the activity volume information at the timing of receiving the invoice (or at least within a certain period after receipt). However, there may be cases where the activity volume information is neglected due to some factors. Therefore, as the process of step S402, the report generation unit 135 of the control unit 130 of the management terminal 100 refers to the activity volume information of the business operator data 1000 and checks the content of the activity volume information and the input time (or the time of receiving or storing the activity volume information). Here, as the input time of the activity volume information by the business operator, in addition to within a certain period from the time of receiving the invoice as described above, it may also be input at regular intervals such as annually, quarterly, semi-annually, etc. Therefore, the report generation unit 135 predicts the time when the next activity volume should be input based on the content and input time of the referred activity volume information. For example, if the input times of the same type of activity volume in the previous and the time before last were April 2022 and April 2021 respectively, it is predicted that the next input time is April 2023 based on the cycle of the input times.

[0056] Next, as the process of step S403, the report generation unit 135 of the control unit 130 of the management terminal 100 sends an alert (notification) for urging input to the business operator terminal 200 through a predetermined means at the timing when the predicted time for the next business operator to input arrives based on the referred activity volume information. Specifically, when the business operator logs in to the application for inputting the activity volume, an alert can be displayed on the screen after logging in to the business operator terminal 200, or an alert can be displayed when transitioning to the activity volume input (registration) screen, or an alert can be sent via the email address registered by the business operator. Examples of the content of the alert include, but are not limited to, "Have you forgotten to input the data registered last time?" and "A period has passed since the time of registration one year ago, have you forgotten?" Here, the content of the activity volume information and the input time input last time can also be presented to the business operator together with the alert content. Also, an alert can be sent again if there is no information input by the business operator for a certain period after the alert is sent.

[0057] As described above, according to this example, based on the activity amount information pre-entered by the business operator, it is possible to predict the input timing of the activity amount after the next time by the business operator and appropriately send an alert to the business operator. Thereby, it is possible to prevent input omissions in advance, or even if there is an input omission, it is possible to follow up on the input.

[0058] 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 of the present invention are included therein.

Explanation of Reference Numerals

[0059] 100 Management Terminal 200 Business Operator Terminal

Claims

1. A method for managing greenhouse gas emissions, executed by a computer, comprising: the computer receives activity amount information including activity information and emission factor information regarding the products of an operator based on an input by an operator terminal; predicts the next input time of the activity amount information based on the input time of the activity amount information; sends an alert prompting the input of the activity amount information to the operator terminal based on the prediction of the next input time; a method of presenting to the operator terminal the content and input time of the activity amount information last input by the operator.

2. The management method according to claim 1, wherein the activity information includes any one of SCOPE classification, energy item, and usage amount.

3. The management method according to claim 1, wherein the activity information is received for each of SCOPE1 to SCOPE3.

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