JP2024017422A - Greenhouse gas emissions management method - Google Patents

Abstract

[Problem] The purpose is to provide a method for efficiently realizing management such as calculation of greenhouse gas emissions by business operators. SOLUTION: A method for managing greenhouse gas emissions according to an embodiment of the present invention, comprising: determining information regarding energy usage based on input information from a business terminal; Based on the above, greenhouse gas emissions are calculated, and when calculating the greenhouse gas emissions, emission source data is referred to, and the emission source data is data that the aforementioned business operator has the right to use. can only be referenced. [Selection diagram] Figure 12

Description

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

Regarding the greenhouse gas emissions of businesses associated with the use of fuel and electricity, etc., a reporting system targeting SCOPE 1 emissions (direct emissions from the company) and SCOPE 2 emissions (indirect emissions from the company) has become widespread, and SCOPE 1 and SCOPE 2 Efforts to calculate and reduce emissions are making progress.

Non-Patent Document 1 proposes calculating SCOPE 1, SCOPE 2, and SCOPE 3 emissions with the aim of further reducing greenhouse gases emitted by businesses.

“How to calculate supply chain emissions”, Ministry of the Environment, November 2017

However, although the technology disclosed in Non-Patent Document 1 discloses the method of calculating greenhouse gas emissions related to SCOPE 1, SCOPE 2, and SCOPE 3, each business operator, especially companies and local governments, has difficulty in calculating emissions. Therefore, collecting and inputting a huge amount of data, calculating emissions, and managing the calculation results requires a tremendous amount of effort and time. In particular, in the field of GHG emissions management, the number of SCOPEs for emissions subject to accounting is expanding, the data that serves as the basis for emissions calculation for each SCOPE is diverse, and data management for each business operator is particularly important. Improvements in operational efficiency through the introduction of cutting-edge technology have been delayed due to differences in methods and other factors.

Therefore, the present invention provides a method for efficiently realizing emissions management by reducing operational man-hours by utilizing cutting-edge technology in the field of GHG emissions management, such as calculating greenhouse gas emissions by business operators. The purpose is to provide.

A method for managing greenhouse gas emissions according to an embodiment of the present invention, the method comprising: determining information regarding energy usage based on information input by a business terminal; The greenhouse gas emissions are calculated based on the information regarding the amount, and when calculating the greenhouse gas emissions, emission source data is referred to, and the emission source data is used by the aforementioned business. Only authorized data can be referenced.

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

BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a diagram illustrating a greenhouse gas emissions management system according to a first embodiment of the present invention. FIG. 2 is a functional block diagram of a management terminal that constitutes a greenhouse gas emissions management system. FIG. 2 is a functional block diagram of a business terminal that constitutes a greenhouse gas emissions management system. FIG. 3 is a diagram illustrating details of company data according to the first embodiment of the present invention. FIG. 3 is a diagram illustrating details of bill information according to the first embodiment of the present invention. FIG. 3 is a diagram illustrating details of emission source data according to the first embodiment of the present invention. FIG. 3 is a diagram illustrating an example of transaction information according to the first embodiment of the present invention. FIG. 7 is a diagram illustrating another example of transaction information according to the first embodiment of the present invention. FIG. 3 is a flowchart diagram illustrating an example of a process for calculating greenhouse gas emissions according to the first embodiment of the present invention. FIG. 3 is a flowchart diagram illustrating an example of a process for predicting the cause of a change in greenhouse gas emissions according to the first embodiment of the present invention. FIG. 3 is a flowchart diagram illustrating an example of transaction processing for greenhouse gas emissions according to the first embodiment of the present invention. FIG. 2 is a flowchart illustrating an example of a process for referring to source data of greenhouse gas emissions according to the first embodiment of the present invention.

The contents of the embodiments of the present invention will be listed and explained. 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 managing greenhouse gas emissions, the method comprising:
Determine information regarding energy usage based on information input by the operator terminal,
Calculating greenhouse gas emissions based on the information on the usage amount,
The method is characterized by referring to emission source data when calculating the amount of greenhouse gas emissions,
The emission source data is a method in which only data that the business operator has authority to use can be referred to.
[Item 2]
The emission source data includes the emission source common data that can be referenced by multiple businesses and the emission source individual data that can be referenced by some of the multiple businesses, as described in item 1. Management method.
[Item 3]
The management method according to item 1, wherein the emission source data has different data that can be referenced depending on accounting month information of the business operator.

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

FIG. 1 is a diagram illustrating a greenhouse gas emissions management system according to a first embodiment of the present invention.

As shown in FIG. 1, in the emissions management system 1 according to the present embodiment, a manager terminal 100 and a plurality of business terminals 200A and 200B are interconnected via a communication network NW.

For example, the management terminal 100 receives basic information about the business operator and input information (for example, image data of bill information) for calculating greenhouse gas (for example, CO2) emissions from the business terminals 200A and 200B. do.

Furthermore, the management terminal 100 analyzes the received image data of the bill information using machine learning, extracts necessary items of the bill information included in the image data, and calculates the amount of greenhouse gas emissions. Furthermore, the management terminal 100 analyzes the calculated changes in greenhouse gas emissions over time (for example, increases and decreases) using machine learning, and predicts the cause of the changes.

Furthermore, the management terminal 100 has a wallet and connects to the public blockchain network NW. The management terminal 100 generates a single hash value using SHA256 or another hash function based on the information regarding greenhouse gas emissions for each predetermined period, and records it in the blockchain network as transaction information. On the blockchain network, a main 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 the blockchain. be done. Here, the hash generation and/or recording of transaction information to the blockchain can also be performed not through the management terminal 100 but through another terminal. In this case, the management terminal 100 transmits the greenhouse gas emissions calculated by the matching process to the other terminals. Furthermore, the management terminal 100 can record information regarding greenhouse gas emissions in the blockchain network as a smart contract. By using smart contracts, contracts regarding emissions trading with other businesses can be automatically generated, approved, and executed without going through a third party based on the above emissions information. . In addition, smart contracts allow each business operator to refer to transaction information without going through a management terminal, increasing service convenience and reducing operational costs.

Here, as mentioned above, in public blockchains, transactions are approved not by a specific administrator but by an unspecified number of nodes and miners, so compared to private blockchains, data has a higher degree of non-tampering. In this embodiment, it is preferable to use a public blockchain as a destination for recording power transactions because fault tolerance can be ensured and, therefore, transaction safety can be ensured. Typical public blockchains include Bitcoin, Ethereum, and the like. For example, Ethereum has higher tamperability and reliability than other public blockchains.

In addition, the management terminal 100 can associate information regarding greenhouse gas emissions with an identifier, etc., and record it in a blockchain network as a non-fungible token (hereinafter referred to as "NFT"). . NFT is, for example, a token issued by Etherium, a blockchain network platform, under the ERC721 standard, is a unit of data recorded on the blockchain network, and is non-fungible. . Since NFTs are recorded along with smart contracts on the blockchain and are traceable, it is possible to prove transaction information, including details and history, such as information on businesses that manage greenhouse gas emissions.

FIG. 2 is a functional block diagram of a management terminal that constitutes the emissions management system.

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).

The storage unit 120 stores programs, input data, etc. for executing various control processes and functions within the control unit 130, and is composed of RAM (Random Access Memory), ROM (Read Only Memory), etc. Ru. The storage unit 120 also includes a business data storage unit 121 that stores various data related to the business 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. , and an emission source data storage section 123 that stores information such as emission factor data necessary for calculating greenhouse gas emissions. Note that a database (not shown) storing various data may be constructed outside the storage unit 120 or the management terminal 100.

The control unit 130 controls the overall operation of the management terminal 100 by executing programs stored in the storage unit 120, and is composed of a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), etc. be done. The functions of the control unit 130 include an information reception unit 131 that receives information from external terminals such as the business terminal 200, analyzes image data such as bill information received from the business terminal, and refers to emission source data; The emissions calculation unit 132 calculates greenhouse gas emissions, and the causes of time-series changes in greenhouse gas emissions are calculated based on information included in bill information extracted by analyzing image data. a cause analysis unit 133 that analyzes the amount of greenhouse gas emissions; a transaction processing unit 134 that performs processing to compile information regarding greenhouse gas emissions for a predetermined period, generate a hash value, and record it as transaction information in the blockchain network; It has a report generation unit 135 that generates and transmits report data for outputting the results of cause analysis of greenhouse gas emissions and changes in emissions to business operators at predetermined intervals.

Although not shown, the control unit 130 includes an image generation unit and generates screen information displayed via a user interface of an external terminal such as the business terminal 200. For example, the information displayed on the user interface can be changed by arranging various images and texts in predetermined areas of the user interface based on predetermined layout rules using the image and text data stored in the storage unit 120 as materials. generate. Processing related to the image generation unit can also be executed by a GPU (Graphics Processing Unit).

Furthermore, the management terminal 100 further includes a wallet (not shown) necessary for recording transaction information on the blockchain network. Note that this wallet can also be kept outside the management terminal 100.

FIG. 3 is a functional block diagram of a business terminal that constitutes the emissions management system.

The business terminal 200 includes a communication section 210, a display operation section 220, a storage section 230, and a control section 240.

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.

The display operation unit 220 is a user interface used by the business operator to input instructions and display text, images, etc. in accordance with input data from the control unit 240. If the operator terminal 200 is a smartphone or a tablet terminal, the operator terminal 200 is comprised of a touch panel or the like. This display operation section 220 is activated by a control program stored in the storage section 230 and executed by the business terminal 200, which is a computer (electronic computer).

The storage unit 230 stores programs, input data, etc. for executing various control processes and functions within the control unit 240, and is composed of a RAM, a ROM, and the like. Furthermore, the storage unit 230 temporarily stores the contents of communication with the management terminal 100.

The control unit 240 controls the overall operation of the operator terminal 200 by executing a program stored in the storage unit 230, and is composed of a CPU, a GPU, and the like.

FIG. 4 is a diagram illustrating details of business data according to the first embodiment of the present invention.

The business data 1000 shown in FIG. 4 stores various data related to the business acquired from the business through the business terminal 200. In FIG. 4, for convenience of explanation, an example of one business operator (a business operator identified by business ID "10001") is shown, but information on a plurality of business operators can be stored. Various data related to business operators include, for example, basic information of business operators (e.g. business name, user name, address, industry type, contact information, email address, office name, affiliated company name, supply chain information) related business name, etc.), input information (e.g., image data of invoice information, etc.), analysis information (e.g., invoices extracted from image data, greenhouse gas emissions, changes in greenhouse gas emissions) information regarding cause prediction, etc.), customer information (e.g., customer ID, blockchain address, etc.), and offset report information (e.g., TXID, NFTID, etc.).

FIG. 6 is a diagram illustrating details of emission source data according to the first embodiment of the present invention. Here, when calculating greenhouse gas emissions, the amount of electricity used by the business, the amount of cargo transported, and the amount of waste processed are determined from input information such as bill information input by the business. , where the amount related to the scale of business activities (activity amount) such as various transaction amounts is multiplied by the emission intensity, the emission intensity data includes data regarding the emission intensity (emission intensity data). For example, if the amount of activity is the amount of electricity used, then the amount of emissions is CO2 emissions per 1 kWh of electricity, and if the amount of activity is the amount of cargo transported, then the amount of emissions is CO2 emissions per ton-kilometer of the amount of cargo transported. If the amount of CO2 emissions and activity amount is the amount of waste processed, examples include the amount of CO2 emissions per 1 ton of waste incinerated. Emission intensity data is basically based on emission factors (commonly available to multiple businesses) included in existing databases provided by the Ministry of the Environment, National Institute for Environmental Studies, IDEA, LCA Japan Forum, etc. For example, "1 Ministry of the Environment data 1", "5000 IDEA data 1", etc.), but in this embodiment, the emission coefficient based on the method of actually measuring emissions, or the emission coefficient determined by business operators including business partners. , emission factors such as emission factors that can be used individually by business companies (for example, "10001 XYZ data 1", etc.) can be included. Here, by managing the emission intensity data that can be commonly used by multiple businesses mentioned above and the emission intensity data that can be used by one or some of the businesses as one, it is possible to calculate emissions. The number of tables to be referenced can be reduced to one, and calculation processing speed can be improved. In addition, as shown in Figure 6, by storing a usable list in which each business operator manages the emissions intensity data that can be used (edited and/or viewed (referenced)), multiple businesses can It becomes possible to control the referenceable emission intensity and the referenceable emission intensity by one or some businesses. Furthermore, by managing data regarding the year and month of each company's press conference, it is also possible to control the emission intensity data available for each company's fiscal year and month. In addition, if the emission intensity data that can be used individually by the above business operators becomes recognized as data that can be used universally by multiple other businesses, the usable list will be updated. , the emissions intensity data that was previously available individually can now be used by other businesses, and in this way, it is now possible to flexibly manage emissions intensity data whose scope of use changes day by day. can. In addition, when the emission factor is updated as emission intensity data, the numerical value of the target emission intensity data can be updated, and one or more businesses can use the updated data. Become.

FIG. 9 is a flowchart illustrating an example of the greenhouse gas emissions calculation process according to the first embodiment of the present invention.

First, as processing in step S101, the information acquisition unit 131 of the control unit 130 of the management terminal 100 acquires image data including bill information collected by the business operator from the business terminal 200 via the network NW. do. The business operator sends invoices, receipts, slips, etc. (in this embodiment, these are collectively referred to as "invoices") via the business terminal 200 in a file format such as PDF, Excel, or JPG (this embodiment These are collectively referred to as "image data") and are uploaded to the management terminal 100. The image data acquired by the information acquisition unit 131 is stored as input information in the business data storage unit 121 of the storage unit 120.

Subsequently, as processing in step S102, the emission amount calculation unit 132 of the control unit 130 of the management terminal 100 analyzes the image data acquired in the previous step using machine learning. Here, when performing the image analysis, using a method called OCR, the emission amount calculation section 132 of the control section 130 of the management terminal 100 analyzes a plurality of various A learning model generated by learning image data of bill formats recognizes text from the image data and extracts items included in bill information as structured character string data. Here, for image analysis, it is also possible to use an image analysis engine (such as an OCR engine) provided by a business other than the management terminal 100 and linked through an API.

Image analysis is performed, for example, by recognizing and extracting text from image data that includes bill information, as shown in FIG. As shown in Figure 5, bill information includes various items included in the bill. Items include power consumption (kWh), total amount (yen), date (year/month), etc. In this example, the breakdown of the electricity bill is shown as an example, but in addition to electricity, it may also be a bill for other energy usage including gas and fuel. It may be a breakdown of receipts for expenses, receipts for commuting expenses for employers, invoices for transactions with cargo companies, and invoices for transactions with waste disposal companies. The emissions calculation unit 132 can extract amount information, activity amount information described below, etc. included in the bill information as text by analyzing the image data of the bill information. The extracted bill information is stored in the business data storage section 121 of the storage section 120 as analysis information. In this way, image analysis using machine learning allows businesses to acquire a huge amount of necessary information in the form of image data to calculate greenhouse gas emissions without having to input billing information manually. The information necessary for calculating greenhouse gas emissions can be accurately extracted through highly accurate image recognition, which makes calculating greenhouse gas emissions more efficient and highly accurate. I can do it.

Next, in step S103, the emissions calculation unit 132 of the control unit 130 calculates greenhouse gas emissions based on the bill information extracted from the image data. Here, greenhouse gas emissions are classified into SCOPE 1, SCOPE 2, and SCOPE 3. SCOPE 1 is the direct emission of greenhouse gases by the business itself (for example, emissions from fuel combustion and industrial processes), and SCOPE 2 is the emissions from other companies. In addition, SCOPE 3 is a standard for calculating emissions from the entire supply chain of an organization published by the GHG Protocol. This refers to emissions from the entire series of processes such as raw material procurement, manufacturing, distribution, sales, disposal, etc.). SCOPE 3 further includes 15 categories (1) purchased products/services, 2) capital goods, 3) fuel and energy-related activities not included in SCOPE 1 and SCOPE 2, 4) transportation and distribution (upstream), and 5) from operations. Waste generated, 6) Business trips, 7) Employee commuting, 8) Leased assets (upstream), 9) Transportation and delivery (downstream), 10) Processing of sold products, 11) Use of sold products, 12) 13) Leased assets (downstream), 14) Franchises, and 15) Investments). Here, greenhouse gases include carbon dioxide (CO2), methane (CH4), dinitrogen monoxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulfur hexafluoride (SF6), This includes nitrogen fluoride (NF3), but in this embodiment, CO2 will be explained as an example.

In addition, greenhouse gas emissions are calculated by defining the amount of electricity used by a business, the amount of cargo transported, the amount of waste processed, and the amount of various transactions as the amount of activity, and adding 1kWh of electricity to the amount of activity as the emission intensity. It is calculated by multiplying the CO2 emissions per use, the CO2 emissions per ton of freight transported, and the CO2 emissions per ton of waste incineration. Greenhouse gas emissions are calculated separately for SCOPE 1, SCOPE 2, and SCOPE 3 (15 categories for SCOPE 3), and the total emissions are calculated as supply chain emissions.

In this embodiment, the emissions calculation unit 132 extracts related bill information for each SCOPE 1, SCOPE 2, and SCOPE 3 (further by category for SCOPE 3), and extracts, for example, electricity consumption kWh from the bill information. Based on the above calculation method, emissions are calculated based on the above calculation method. The calculated emissions amount is stored in the business data storage section 121 of the storage section 120 as analysis information.

Subsequently, as processing in step S104, the report generation unit 135 of the control unit 130 generates a time-series record of emissions by SCOPE (further by category for SCOPE3) based on the information regarding the calculated emissions. Generate a visualized report showing the breakdown.

FIG. 10 is a flowchart illustrating an example of a process for predicting the cause of a change in greenhouse gas emissions according to the first embodiment of the present invention.

First, as processing in step S201, the cause analysis unit 133 of the control unit 130 of the management terminal 100 refers to information regarding the greenhouse gas emissions of the business operator calculated in step S103 of FIG. Here, regarding greenhouse gas emissions, refer to emissions by SCOPE (further by category for SCOPE 3). Furthermore, the cause analysis unit 133 can confirm changes (increases and decreases) in greenhouse gas emissions by referring to past emissions data of the same company. As described above, the amount of emissions is stored in the business data storage section 121 of the storage section 120 as analysis information.

Subsequently, as processing in step S202, the cause analysis unit 133 uses machine learning to analyze and predict the cause of the change in the amount of emissions based on the referenced information regarding the amount of emissions. Here, when performing the cause analysis, the cause analysis unit 133 of the control unit 130 of the management terminal 100 uses the above-referenced emission amount information, factors that affect changes (increases and decreases) in the emission amount, and the AI stored in the storage unit 120. A learning model stored in the model storage unit 122 that is generated by learning data related to factors that affect changes (increases and decreases) in emissions in advance is used to analyze data by SCOPE (further by category for SCOPE 3). Predict the causes of changes in emissions.

Here, factors that affect changes (increases and decreases) in output include, for example, weather, temperature, product demand and/or factory operation, store or factory business hours or operating hours, changes in equipment or equipment, software changes, etc. Factors include measures, power-saving actions, fuel conversion, changes in energy menu, changes in business trip or commuting volume, and amount of power generated by in-house power generation. Each of these factors is a factor that influences the output of any SCOPE. For example, the weather factor affects precipitation, wind volume, sunshine hours, and temperature; precipitation affects small water power generation, wind volume affects wind power generation, sunshine duration affects solar power generation, and temperature affects air conditioning. In addition, the amount of power generation affects the amount of in-house power generation, and the amount of in-house power generation affects the amount of CO2 emissions from electricity, thereby affecting the change in SCOPE2 emissions, while air conditioning This affects the amount of gas used, and the amount of gas used affects the amount of CO2 emissions from gas combustion, thereby affecting the change in the amount of emissions of SCOPE1. In addition, power-saving activities, factory operation due to product demand, and business hours affect power consumption and affect SCOPE 2. In addition, EMS, replacement of refrigeration equipment, introduction of energy-saving equipment, and amount of automobiles used also affect electricity consumption, which affects SCOPE2. This affects CO2 emissions from the fuel, thereby affecting SCOPE1.

In addition, the factor of product sales affects categories 1, 9, 10, 11, and 12 of SCOPE 3, capital investment is in category 2, renewable energy ratio and amount of energy procured is in category 3, number of transportation, transportation route, etc. Changes to categories 4 and 9, product loss rate to category 5, number of business trips and office workers to category 6, number of commuters and office employees to category 7, power consumption to category 8, processing reduction due to product improvement. is in category 10, improvements to energy-saving products are in category 11, increases in recycling rates are in category 12, tenant office electricity is in category 13, franchise emissions are in category 14, and investee emissions are in category 15. have an impact on each.

In this way, by using machine learning to learn which factors affect which SCOPEs or categories, and obtaining information on emissions and each factor from businesses, we can identify the causes of changes in emissions. predictions can be made. Here, by predicting the causes of emissions using machine learning, it is possible to efficiently and accurately predict factors that influence changes in greenhouse gas emissions for each business operator and each SCOPE.

Subsequently, as processing in step S203, the report generation unit 135 of the control unit 130 generates emissions by SCOPE (further by category for SCOPE 3) based on the information regarding the analyzed cause prediction of the change in emissions. Generate a visualized report on the causes of change.

FIG. 11 is a flowchart diagram illustrating an example of transaction processing for greenhouse gas emissions according to the first embodiment of the present invention.

First, as processing in step S301, the transaction processing unit 134 of the control unit 130 of the management terminal 100 refers to the company data stored in the company data storage unit 121 of the storage unit 120. Here, the business data to be referred to includes business analysis information (greenhouse gas emissions for each SCOPE) and the like.

Next, as a step S302, the transaction processing unit 134 generates a hash value based on the business entity data referenced in step S301. That is, the transaction processing unit 134 uses a hash function to generate one row of hash values for greenhouse gas emissions over a predetermined period, and records the hash values in the public blockchain as transaction information. On the blockchain network, a main 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 the blockchain. be done. Here, in this example, in order to reduce the cost related to blockchain recording, it is assumed that data is recorded on a layer 2 (for example, a side chain) that is different from the main blockchain (so-called layer 1).

Furthermore, the transaction processing unit 134 can assign and manage an NFTID in association with a blockchain record of greenhouse gas emissions of a business operator. More specifically, as shown in FIG. 4, the business data 1000 is given the customer ID of the business as customer information, stores the blockchain address to refer to, and stores the NFTID and NFT ID as the offset report information. A TXID can be assigned.

As shown in FIG. 7, blockchain addresses are associated with each NFTID on the blockchain network, and the NFTID and customer ID are managed on the management terminal 100. Information regarding greenhouse gas emissions related to business operators can be retrieved by referring to the blockchain address for each NFTID, such as NFTID "13" and "14", to read the details of the emissions information as shown in Figure 7. I can do it. Figure 7 shows information regarding the offset report that is associated with NFTID "14". A TXID is assigned in association with the offset report, and the offset report includes CO2 emissions by SCOPE, target year and month, and report publication date. It will be done. In addition to the CO2 emissions for the target year and month in this example, it is also possible to convert the most recent year's CO2 emissions, reduced CO2 emissions, and offset CO2 emissions into NFT.In this way, CO2 emissions can be managed using NFT. This allows businesses to trade NFT-based certificates while ensuring non-tampering and transaction reliability, and also makes it possible to prove emissions to third parties. can.

FIG. 2 is a flowchart illustrating an example of a process for referring to source data of greenhouse gas emissions according to the first embodiment of the present invention.

In the emissions calculation process in step S103, the emissions calculation unit 132 of the control unit 130 stores the information stored in the emissions basic unit data storage unit 123 of the storage unit 120 when referring to the emissions basic unit as the process in step S401. Refer to the emission source data 3000. Here, as the process of step S402, for example, when executing the emission amount calculation process for business operator A, the emission amount calculation unit 132 refers to the available list of emission source data 3000, and refers to the available list of emission source data 3000. Determine emission intensity data. As shown in Figure 6, business operator A can use "1 Ministry of the Environment Data 1", "2 Ministry of the Environment Data 2", etc. of the emission intensity data, and therefore can refer to each of the emission intensity data. Extract and perform emissions calculation processing. For example, when executing the emissions calculation process for business operator B, the emissions calculation unit 132 refers to the available list of emission source data 3000 and determines the emission intensity data that business operator B can use. do. As shown in Figure 6, business operator B can use "1 Ministry of the Environment data 1", "5000 IDEA data 1", "10001 XYZ data 1", etc. among the emission intensity data, so please refer to each Extract possible emissions intensity data and perform emissions calculation processing. In this way, according to this example, since it is possible to refer to emission intensity data that is centrally managed and available for each business operator, it is possible to refer to the emission intensity data table to be referenced. It is possible to improve calculation processing speed while reducing emissions intensity data, and to prevent other businesses from accessing emission intensity data that is only available to one or some businesses, thereby improving data security. Can be guaranteed.

The embodiments described above are merely illustrative to facilitate understanding of the present invention, and are not intended to be interpreted as limiting the present invention. It goes without saying that the present invention can be modified and improved without departing from its spirit, and that the present invention includes equivalents thereof.

100 Management terminal 200 Business terminal

Claims (3)

A method for managing greenhouse gas emissions, the method comprising:
Determine information regarding energy usage based on information input by the operator terminal,
Calculating greenhouse gas emissions based on the information on the usage amount,
The method is characterized by referring to emission source data when calculating the amount of greenhouse gas emissions,
The emission source data is a method in which only data that the business operator has authority to use can be referred to. 2. The emission source data includes emission source common data that can be referenced by a plurality of businesses and emission source individual data that can be referenced by some businesses among the plurality of businesses. management method. 2. The management method according to claim 1, wherein the emission source data has different referable data depending on accounting month information of the business operator.