JP7761282B2 - Greenhouse gas emissions management methods - Google Patents

Description

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

Regarding greenhouse gas emissions from businesses associated with the use of fuel, electricity, etc., reporting systems covering SCOPE 1 emissions (a company's direct emissions) and SCOPE 2 emissions (a company's indirect emissions) have become widespread, and progress has been made in calculating and reducing emissions in SCOPE 1 and SCOPE 2.

Non-Patent Document 1 proposes that, in order to further reduce greenhouse gas emissions by businesses, SCOPE 3 emissions, i.e., emissions from the supply chain (the entire series of processes including raw material procurement, manufacturing, logistics, sales, and disposal) of other related businesses, be calculated as emissions other than SCOPE 1 and SCOPE 2.

"Concepts for Calculating Supply Chain Emissions," Ministry of the Environment, November 2017

However, while the technology disclosed in Non-Patent Document 1 discloses methods for calculating greenhouse gas emissions related to SCOPE 3, it takes a great deal of time and effort for each business, particularly corporations and local governments, to collect and input vast amounts of data to calculate emissions, calculate emissions, and manage the calculation results. In particular, in the area of GHG emissions management, the number of SCOPEs for emissions subject to calculation is expanding, the data used to calculate emissions varies widely for each SCOPE, and data management methods differ for each business, which has led to delays in improving business efficiency through the introduction of cutting-edge technology.

The present invention aims to provide a method for efficiently managing GHG emissions by utilizing cutting-edge technology to reduce work hours in the area of GHG emissions management, such as the calculation of greenhouse gas emissions by businesses.

A method for managing greenhouse gas emissions according to one embodiment of the present invention receives location information related to product waste from a business operator terminal, estimates disposal activities related to the product waste based on the location information, and calculates greenhouse gas emissions based on information related to the disposal activities.

The present invention provides a method for businesses to efficiently manage greenhouse gas emissions, including calculations.

1 is a diagram illustrating a greenhouse gas emission management system according to a first embodiment of the present invention. FIG. FIG. 2 is a functional block diagram of a management terminal constituting the greenhouse gas emission management system. FIG. 2 is a functional block diagram of a business operator terminal that constitutes the greenhouse gas emission management system. FIG. 4 is a diagram illustrating details of business entity data according to the first embodiment of the present invention. FIG. 4 is a diagram illustrating details of invoice information 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. 10 is a diagram illustrating another example of transaction information according to the first embodiment of the present invention. FIG. 3 is a flowchart illustrating an example of a process for calculating greenhouse gas emissions according to the first embodiment of the present invention. FIG. 4 is a flowchart illustrating an example of a process for predicting causes of changes in greenhouse gas emissions according to the first embodiment of the present invention. FIG. 4 is a flowchart illustrating an example of a transaction process for greenhouse gas emissions according to the first embodiment of the present invention. FIG. 4 is a flowchart showing an example of a disposal process estimation process in the greenhouse gas emission calculation process according to the first embodiment of the present invention.

The contents of an embodiment of the present invention will be described below. A greenhouse gas emission management system (hereinafter simply referred to as "system") according to an embodiment of the present invention has the following configuration.
[Item 1]
1. A method for managing greenhouse gas emissions, comprising:
Location information related to product disposal is received from the business terminal,
Estimating waste activities related to waste of the product based on the location information;
calculating greenhouse gas emissions based on information about the disposal activity.
[Item 2]
The method according to item 1, further receiving report information on a disposal method for the product from the business operator terminal.
[Item 3]
Item 2. The method according to item 1, wherein time information is acquired from the operator terminal together with the location information.
[Item 4]
4. The method according to claim 3, wherein the estimation includes estimating a method and time for disposing of the waste product based on the location information.

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

Figure 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 of this embodiment, an administrator terminal 100 and multiple business operator terminals 200A and 200B are interconnected via a communications network NW.

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

The management terminal 100 also uses machine learning to analyze the received image data of the invoice information, extracts the necessary items of invoice information contained in the image data, and calculates greenhouse gas emissions. The management terminal 100 also uses machine learning to analyze the calculated changes in greenhouse gas emissions over time (e.g., increases or decreases) and predicts the causes of the changes.

Furthermore, the management terminal 100 has a wallet and is connected to the public blockchain network NW. Based on the information on greenhouse gas emissions for each specified period, the management terminal 100 generates a single hash value using SHA256 or another hash function and records it as transaction information on the blockchain network. On the blockchain network, a current 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, the hash generation and/or recording of the transaction information on the blockchain can be performed via another terminal rather than the management terminal 100. In this case, the management terminal 100 transmits the greenhouse gas emissions calculated in the matching process to the other terminal. Furthermore, the management terminal 100 can record information on greenhouse gas emissions on the blockchain network as a smart contract. By using a smart contract, contracts for emissions trading with other businesses can be automatically generated, approved, and executed based on the information on emissions without the involvement of a third party. Smart contracts also allow each business operator to access transaction information without using a management terminal, increasing service convenience and reducing operational costs.

Here, as mentioned above, a public blockchain allows transactions to be approved not by a specific administrator but by an unspecified number of nodes and miners, and therefore can ensure greater data tamper resistance and fault tolerance than a private blockchain. Therefore, the security of transactions is guaranteed, and therefore a public blockchain is preferable as the destination for recording energy transactions in this embodiment. Typical public blockchains include Bitcoin and Ethereum, but Ethereum, for example, has higher tamper resistance and reliability than other public blockchains.

Furthermore, the management terminal 100 can associate information regarding greenhouse gas emissions using an identifier or the like and record it on the blockchain network as a non-fungible token (hereinafter, "NFT"). NFTs are tokens issued, for example, in the "ERC721" standard of Ethereum, a blockchain network platform. They are a unit of data recorded on the blockchain network and are non-fungible. NFTs are recorded on the blockchain along with smart contracts and are traceable, making it possible to prove transaction information, including details and history of business operators managing greenhouse gas emissions.

Figure 2 is a functional block diagram of the management terminal that makes up the emissions management system.

The communication unit 110 is a communication interface for communicating with external terminals via the network NW, and communication is carried out using a communication protocol such as TCP/IP (Transmission Control Protocol/Internet Protocol).

The memory unit 120 stores input data, programs 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. The memory unit 120 also has an operator data storage unit 121 that stores various data related to operators, and an AI model storage unit 122 that stores learning data and learning models created by AI (artificial intelligence) learning the learning data. Note that a database (not shown) storing various data may be constructed outside the memory 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 memory unit 120, and is composed of a CPU (Central Processing Unit), GPU (Graphics Processing Unit), etc. The functions of the control unit 130 include an information receiving 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 greenhouse gas emissions; a cause analysis unit 133 that analyzes the image data and analyzes the causes of time-series changes in greenhouse gas emissions calculated based on information contained in the extracted invoice information; a transaction processing unit 134 that compiles information related to greenhouse gas emissions for a predetermined period, generates a hash value, and records this as transaction information on the blockchain network; and a report generation unit 135 that generates and transmits report data to output the results of an analysis of greenhouse gas emissions and changes in emissions to the business operator every predetermined period.

In addition, although not shown, the control unit 130 has an image generation unit that generates screen information to be displayed via the user interface of an external terminal such as the business operator terminal 200. For example, using image and text data stored in the storage unit 120 as material, the control unit 130 generates information to be displayed in the user interface by arranging various images and text in predetermined areas of the user interface based on predetermined layout rules. Processing related to the image generation unit can also be performed by a GPU (Graphics Processing Unit).

The management terminal 100 also has a wallet (not shown) required for recording transaction information on the blockchain network. Note that this wallet can also be located outside the management terminal 100.

Figure 3 is a functional block diagram of the business operator terminal that makes up the emissions management system.

The operator terminal 200 includes a communication unit 210, a display operation unit 220, a memory unit 230, and a control unit 240.

The communication unit 210 is a communication interface for communicating with the management terminal 100 via the network NW, and communication is carried out using a communication protocol such as TCP/IP.

The display operation unit 220 is a user interface used by the 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 configured as a personal computer, it is composed of a display, keyboard, and mouse, and if the operator terminal 200 is configured as a smartphone or tablet terminal, it is composed of a touch panel, etc. This display operation unit 220 is activated by a control program stored in the memory unit 230 and executed by the operator terminal 200, which is a computer (electronic calculator).

The memory unit 230 stores input data, programs for executing various control processes and functions within the control unit 240, and is composed of RAM, ROM, etc. The memory unit 230 also temporarily stores the contents of communications with the management terminal 100.

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

Figure 4 is a diagram explaining details of business operator data according to the first embodiment of the present invention.

The business operator data 1000 shown in FIG. 4 stores various data related to the business operator acquired from the business operator via the business operator terminal 200. For ease of explanation, FIG. 4 shows an example of one business operator (a business operator identified by business operator ID "10001"), but information on multiple businesses can be stored. The various data related to the business operator may include, for example, basic information about the business operator (e.g., the business operator's corporate name, user name, business establishment information (e.g., address information for each business establishment), network name (e.g., SSID, IP address), image information (e.g., background image of the business establishment, image of a person, etc.), industry, contact information, email address, business establishment name, names of affiliated companies, names of businesses related in the supply chain, etc.), input information (e.g., image data of invoice information, etc.), analysis information (e.g., invoices extracted from image data, greenhouse gas emissions, information related to predicted causes of changes in greenhouse gas emissions, etc.), customer information (e.g., customer ID, blockchain address, etc.), and offset report information (e.g., TXID, NFTID, etc.).

Figure 8 is a flowchart showing an example of the greenhouse gas emission calculation process according to the first embodiment of the present invention.

First, in step S101, the information acquisition unit 131 of the control unit 130 of the management terminal 100 acquires image data including invoice information collected by the business operator from the business operator terminal 200 via the network NW. The business operator uploads invoices, receipts, slips, etc. (collectively referred to as "invoices" in this embodiment) to the management terminal 100 via the business operator terminal 200 in file formats such as PDF, Excel, JPG, etc. (collectively referred to as "image data" in this embodiment). The image data acquired by the information acquisition unit 131 is stored as input information in the business operator data storage unit 121 of the memory unit 120.

Next, in 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 using machine learning. Here, the image analysis uses a technique known as OCR, and the image analysis unit 132 of the control unit 130 of the management terminal 100 recognizes text from the image data using a learning model that was generated in advance by learning from image data of multiple invoices in various formats and that is stored in the AI model storage unit 122 of the memory unit 120, and extracts items contained in the invoice information as structured character string data. Here, the image analysis can also use an image analysis engine (such as an OCR engine) provided by a business operator other than the management terminal 100, which is linked via an API.

Image analysis is performed, for example, by recognizing and extracting text from image data containing invoice information, as shown in Figure 5. As shown in Figure 5, invoice information includes various items included on an invoice, such as the electricity bill breakdown, the amount (yen) for each breakdown, the contracted power (kW), the amount of electricity used (kWh) for each breakdown, the total amount (yen), and the date (year and month). While this example illustrates an electricity bill breakdown, it may also be an invoice for other energy usage, including electricity, gas, and fuel. It may also be a receipt for travel expenses, a receipt for employee commuting expenses, an invoice for transactions with a freight company, or a breakdown of an invoice for transactions with a waste disposal company. By analyzing the image data of this invoice information, the image analysis unit 132 can extract, as text, the amount information and activity level information (described below) included in the invoice information. The extracted invoice information is stored as analysis information in the business data storage unit 121 of the memory unit 120. In this way, image analysis using machine learning allows businesses to obtain the vast amount of information necessary to calculate greenhouse gas emissions as image data without having to manually input invoice information, and highly accurate image recognition makes it possible to accurately extract the information necessary to calculate greenhouse gas emissions, thereby achieving more efficient and accurate calculations of greenhouse gas emissions.

Next, in step S103, the image analysis unit 132 of the control unit 130 calculates greenhouse gas emissions based on the invoice information extracted from the image data. Here, greenhouse gas emissions are classified into SCOPE 1, SCOPE 2, and SCOPE 3. SCOPE 1 represents direct greenhouse gas emissions by the business itself (e.g., emissions associated with fuel combustion and industrial processes). SCOPE 2 represents indirect emissions associated with the use of electricity, heat, gas, etc. supplied to the business by other companies. Furthermore, SCOPE 3 is a calculation standard for emissions throughout an organization's supply chain issued by the GHG Protocol, and refers to emissions throughout a business's supply chain (the entire process, including raw material procurement, manufacturing, logistics, sales, and disposal). SCOPE 3 is further divided into 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), 5) waste generated by operations, 6) business travel, 7) employee commuting, 8) leased assets (upstream), 9) transportation and 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. Greenhouse gases include carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulfur hexafluoride (SF6), and nitrogen trifluoride (NF3). This embodiment will use CO2 as an example.

Greenhouse gas emissions are calculated by defining a business's electricity consumption, cargo transport volume, waste disposal volume, and various transaction amounts as activity volume, and multiplying this activity volume by the CO2 emissions per kWh of electricity used, CO2 emissions per ton of cargo transported, and CO2 emissions per ton of waste incinerated as emissions units. Greenhouse gas emissions are calculated for each of the above SCOPE 1, SCOPE 2, and SCOPE 3 (SCOPE 3 is divided into 15 categories), and the total emissions are calculated as supply chain emissions.

In this embodiment, the image analysis unit 132 extracts related invoice information for each of SCOPE 1, SCOPE 2, and SCOPE 3 (and further for SCOPE 3 by category), and calculates emissions using the above calculation method based on the electricity usage (kWh) in the invoice information, for example. The calculated emissions are stored as analysis information in the business operator data storage unit 121 of the memory unit 120.

Next, in step S104, the report generation unit 135 of the control unit 130 generates a visualized report showing a breakdown of emissions over time by SCOPE (and further by category for SCOPE 3) based on the information related to the calculated emissions.

Figure 9 is a flowchart showing an example of a process for predicting the causes of changes in greenhouse gas emissions according to the first embodiment of the present invention.

First, in the processing of step S201, the cause analysis unit 133 of the control unit 130 of the management terminal 100 references the information related to the business operator's greenhouse gas emissions calculated in step S103 of FIG. 8. Here, greenhouse gas emissions are referenced by SCOPE (and further by category for SCOPE 3). In addition, the cause analysis unit 133 can check changes (increases or decreases) in greenhouse gas emissions by referencing the same business operator's past emission data. As described above, the emissions are stored as analysis information in the business operator data storage unit 121 of the memory unit 120.

Next, in step S202, the cause analysis unit 133 analyzes and predicts the cause of changes in emissions using machine learning based on the emission information referenced above. During cause analysis, the cause analysis unit 133 of the control unit 130 of the management terminal 100 predicts the cause of changes in emissions by SCOPE (and further by category for SCOPE 3) using a learning model generated by learning the emission information referenced above, factors that affect changes (increases or decreases) in emissions, and data related to factors that affect changes (increases or decreases) in emissions that is stored in advance in the AI model storage unit 122 of the memory unit 120.

Here, factors that affect changes (increases or decreases) in output include, for example, weather, temperature, product demand and/or factory operation, store or factory business hours, equipment or facility changes, software measures, power-saving actions, fuel conversions, energy menu changes, changes in business trips or commuting, and the amount of power generated by self-generated electricity. Each of these factors affects the emissions of one SCOPE. For example, weather affects precipitation, wind volume, hours of sunshine, and temperature; precipitation affects the amount of hydroelectric power generation, wind volume affects the amount of wind power generation, hours of sunshine affect the amount of solar power generation, and temperature affects air conditioning. Furthermore, the amount of power generation affects the amount of self-generated electricity, which affects CO2 emissions from electricity, thereby affecting changes in the emissions of SCOPE2. Meanwhile, air conditioning affects gas usage, which affects CO2 emissions from gas combustion, thereby affecting changes in the emissions of SCOPE1. Furthermore, power-saving actions, factory operation due to product demand, and business hours affect electricity usage, which in turn affects SCOPE2. In addition, EMS, replacement of refrigeration equipment, introduction of energy-saving equipment, and vehicle usage also affect electricity usage and thus SCOPE2; vehicle usage, fuel efficiency, boiler usage, and boiler efficiency also affect fuel usage, which in turn affects CO2 emissions from fuel, thereby affecting SCOPE1.

In addition, the factor of product sales volume affects categories 1, 9, 10, 11, and 12 in SCOPE 3, capital investment affects category 2, renewable energy ratio and amount of energy procured affects category 3, number of transports and changes in transport routes affects categories 4 and 9, product loss rate affects category 5, business trips and number of people commuting to the office affects category 6, number of people commuting to the office and number of employees commuting to the office affects category 7, electricity consumption affects category 8, reduced processing through product improvements affects category 10, improvements to energy-efficient products affects category 11, increased recycling rates affects category 12, tenant office electricity affects category 13, franchise emissions affects category 14, and investment emissions affects category 15.

In this way, machine learning is used to learn which factors affect which SCOPE or category, and by obtaining information about emissions and information about each factor from businesses, 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 changes in greenhouse gas emissions for each business and each SCOPE.

Next, in step S203, the report generation unit 135 of the control unit 130 generates a visualized report on the causes of changes in emissions by SCOPE (and further by category for SCOPE 3) based on the information regarding the predicted causes of changes in emissions analyzed above.

Figure 10 is a flowchart showing an example of greenhouse gas emission transaction processing according to the first embodiment of the present invention.

First, in step S301, the transaction processing unit 134 of the control unit 130 of the management terminal 100 references the business operator data stored in the business operator data storage unit 121 of the memory unit 120. The business operator data referenced here includes business operator analysis information (e.g., greenhouse gas emissions for each SCOPE), etc.

Next, in step S302, the transaction processing unit 134 generates a hash value based on the business data referenced in step S301. That is, the transaction processing unit 134 generates a row of hash values for greenhouse gas emissions over a specified period using a hash function, and records the hash value as transaction information in the public blockchain. On the blockchain network, this 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 costs associated with blockchain recording, it is assumed that the data is recorded in layer 2 (e.g., a side chain) different from the main blockchain (so-called layer 1).

The transaction processing unit 134 can also assign and manage NFTIDs by linking them to the blockchain records of the business's greenhouse gas emissions. More specifically, as shown in FIG. 4, the business's customer ID can be assigned as customer information to the business data 1000, the referenced blockchain address can be stored, and the NFTID and TXID can be assigned as offset report information.

As shown in FIG. 6, each NFT ID is associated with a blockchain address on the blockchain network, and the management terminal 100 manages the NFT ID and customer ID. For example, information regarding greenhouse gas emissions for a business associated with customer ID "2" can be retrieved by referencing the blockchain address for each NFT ID, such as NFT IDs "13" and "14," as shown in FIG. 7. FIG. 7 shows information related to an offset report associated with NFT ID "14." A TXID is assigned in association with the offset report, and the offset report includes the CO2 emissions by SCOPE, the target year and month, and the report's publication date. In addition to the CO2 emissions for the target year and month in this example, the most recent CO2 emissions, reduced CO2 emissions, and offset CO2 emissions can also be converted into NFTs. By managing CO2 emissions in this way as NFTs, businesses can trade NFT-converted certificates while ensuring the integrity and reliability of transactions, and can also verify emissions to third parties.

Figure 11 is a flowchart showing an example of the waste activity estimation process in the greenhouse gas emission calculation process according to the first embodiment of the present invention. When calculating greenhouse gas emissions in this example, the waste activity is based on reports on the disposal of products by businesses, and a coefficient related to the emission intensity of the waste activity is determined based on location information at waste facilities of businesses involved in the waste activity, such as waste transport businesses, thereby improving the accuracy of the calculation of emissions.

First, in step S401, the information acquisition unit 131 of the control unit 130 of the management terminal 100 acquires location information such as GPS from the business operator terminal 200 (which in this example includes terminals related to businesses involved in the waste disposal activities) via the network NW. For example, if a waste transport business stays at incineration facility X for three hours and then at landfill Y for five hours, the GPS information installed in the transport vehicle is acquired as location information along with time information. Alternatively, the time when the location information was acquired can be stored. The location information acquired by the information acquisition unit 131 is stored as input information in the business operator data storage unit 121 of the memory unit 120.

Next, in step S402, the information acquisition unit 131 of the control unit 130 of the management terminal 100 acquires report information related to the disposal activities of product waste from the business operator terminal 200 via the network NW. The report information includes information about the business operators involved in the disposal activities and information about the waste disposal location, such as, for example, that transportation company A contracted by the business operator incinerates the waste in Chichibu, Saitama, or that transportation company B disposes of the waste in Choshi, Chiba. The report information acquired by the information acquisition unit 131 is stored as input information in the business operator data storage unit 121 of the memory unit 120. Note that this step may be omitted if it is possible to calculate the amount of emissions related to disposal activities based on location information alone.

Next, in step S403, the report generation unit 135 of the control unit 130 estimates product disposal activities based on the manifest information. The report generation unit 135 determines the waste incineration coefficient and/or landfill coefficient when calculating greenhouse gas emissions based on the acquired location information and/or report information. For example, as described above, if a waste transporter stays at incineration facility X for three hours and then at landfill Y for five hours, the disposal method and the time required for disposal can be estimated, and the coefficient can be determined based on the estimated disposal method and time. Here, if the transport vehicle enters a recycling facility or stays at a location for a certain period of time without returning directly to the base, the report generation unit 135 can send a notification to the business operator terminal 200, such as "Please enter the recycling rate," to prompt the operator to correct the information.

Next, in step S404, the report generation unit 135 of the control unit 130 calculates the greenhouse gas emissions based on the estimated disposal information. According to the above example, the image analysis unit 132 estimates the waste disposal method and time based on the location information and/or report information, and can therefore determine an incineration coefficient and/or landfill coefficient based on these, and calculate the greenhouse gas emissions by multiplying the activity amount by the coefficient.

In this way, previously, businesses calculated greenhouse gas emissions related to waste disposal activities based on disposal rules established when they signed a contract with a waste disposal company. However, the calculation process can now be performed based on location information and/or report information from waste transport vehicles collected from actual waste disposal companies, improving the accuracy of calculations of emissions related to waste activities and streamlining business input and management for the calculation process.

The above-described embodiments are merely examples intended to facilitate understanding of the present invention and are not intended to limit the scope of the present invention. The present invention can be modified and improved without departing from its spirit, and it goes without saying that the present invention includes equivalents thereof.

100 Management terminal 200 Business operator terminal

Claims (2)

A method for managing greenhouse gas emissions of a business operator , comprising:
receiving location information and time information relating to product waste from the business terminal of the business ;
Estimating waste activities related to waste of the product based on the location information;
calculating greenhouse gas emissions based on information about the disposal activities;
The method , wherein the estimation includes estimating a disposal method and a time required for disposal of the waste product based on the location information and the time information . The method according to claim 1 , further comprising receiving report information on a method for disposing of the product from the business operator's terminal.