GHG emission derivation device, GHG emission derivation method, and program

Abstract

This invention provides a GHG emission calculation device, a GHG emission calculation method, and a program for calculating greenhouse gas (GHG) emissions based on the content and amount of activities. [Solution] The GHG emission extraction device 100 includes: an acquisition unit that acquires first activity amount data indicating the activity content and the amount of activity for each activity content that is the target of greenhouse gas (GHG) emission extraction; a selection unit that selects a first emission intensity format corresponding to the type of first activity amount data from among a plurality of emission intensity formats that indicate the emission intensity for each activity content predetermined for each type of activity amount data; a determination unit that determines at least one emission intensity for each activity content shown in the first activity amount data based on the first emission intensity format; and an extraction unit that extracts GHG emissions for each activity amount for each activity content shown in the first activity amount data based on the at least one emission intensity.

Description

Technical Field

[0001] The present invention relates to a GHG emission amount derivation device, a GHG emission amount derivation method, and a program.

Background Art

[0002] Patent Document 1 discloses a carbon dioxide emission amount calculation system that calculates the carbon dioxide emission amount for each electrical device. [Prior Art Document] [Patent Document] [Patent Document 1] Japanese Unexamined Patent Application Publication No. 2013-25487

Summary of the Invention

[0003] A GHG emission amount derivation device according to an aspect of the present invention may include an acquisition unit that acquires first activity amount data indicating the activity content that is the target of GHG emission amount derivation and the activity amount for each activity content. The GHG emission amount derivation device may include a selection unit that selects a first emission factor format corresponding to the type of the first activity amount data from a plurality of emission factor formats indicating the emission factors for each activity content determined in advance for each type of activity amount data. The GHG emission amount derivation device may include a determination unit that determines at least one emission factor for each activity content indicated in the first activity amount data based on the first emission factor format. The GHG emission amount derivation device may include a derivation unit that derives the GHG emission amount for each activity amount of each activity content indicated in the first activity amount data based on the at least one emission factor.

[0004] The type of the activity amount data may correspond to the type of the application used to create the activity amount data.

[0005] The type of the activity amount data may further correspond to the business type of the activity subject.

[0006] The activity content may include trader information regarding the trader involved in the activity.

[0007] The aforementioned activities may include location information related to the places where the activities take place.

[0008] The GHG emission derivation device may include a presentation unit that presents activity content from the first activity data for which the determination unit cannot determine at least one emission intensity based on the first emission intensity format. The GHG emission derivation device may also include a reception unit that receives emission intensity for the presented activity content from the user.

[0009] The reception unit may receive from the user the designation of an emission intensity database to be referenced in order to determine the emission intensity for the presented activity, from among a plurality of emission intensity databases that show emission intensity for each activity, and may receive from the user the emission intensity for the presented activity from among the emission intensity shown in the received emission intensity database.

[0010] The aforementioned GHG emissions may include at least other indirect emissions, which represent GHG emissions from the operator's activities that are not included in the indirect emissions, which represent GHG emissions indirectly generated by the operator's purchase of energy. The emission intensity format may indicate at least one emission intensity corresponding to at least one category of the other indirect emissions, depending on the nature of the activity, if the activity falls under the scope of the other indirect emissions.

[0011] The GHG emission derivation device may include an extraction unit that extracts at least one set of at least one category and at least one emission intensity for other indirect emissions, corresponding to activity content shown in the first activity data for which the determination unit cannot determine at least one emission intensity based on the first emission intensity format, from other existing emission intensity formats corresponding to the type of first activity data. The GHG emission derivation device may further include a receiving unit that receives from the user a set of activity content for which determination is not possible from among the at least one set.

[0012] The aforementioned GHG emissions may include at least other indirect emissions, which are not included in indirect emissions, which represent GHG emissions from the operator's activities, but are not included in indirect emissions, which represent GHG emissions indirectly emitted by the operator through the operator's purchase of energy. The GHG emissions derivation device may include an extraction unit that extracts at least one set of at least one category and at least one emission intensity for other indirect emissions corresponding to each activity shown in the new activity data, from an existing emission intensity format corresponding to a new type of activity data. The GHG emissions derivation device may further include a generation unit that generates a new emission intensity format for the new activity data by identifying one set for each activity shown in the new activity data from the at least one set, in accordance with the user's instructions.

[0013] The extraction unit may extract a predetermined number of sets, sorted by confidence level, as the "at least one set" according to a machine learning model that uses combinations of activity type and activity content identified from existing emission intensity formats, and at least one category and at least one emission intensity related to other indirect emissions, as training data.

[0014] A method for deriving GHG emissions according to one aspect of the present invention may include a step in which an acquisition unit acquires first activity amount data from a storage unit that shows the activity content and the amount of activity for each activity content that is the target of greenhouse gas (GHG) emission derivation. The GHG emission derivation method may include a step in which a selection unit selects a first emission intensity format corresponding to the type of first activity amount data from among a plurality of emission intensity formats stored in the storage unit that show the emission intensity for each activity content predetermined for each type of activity amount data. The GHG emission derivation method may include a step in which a determination unit determines at least one emission intensity for each activity content shown in the first activity amount data based on the first emission intensity format. The GHG emission derivation method may include a step in which a derivation unit derives GHG emissions for each of the activity amounts for each activity content shown in the first activity amount data based on the at least one emission intensity.

[0015] A program according to one aspect of the present invention may use a computer as a GHG emission derivation device.

[0016] The above summary of the invention does not enumerate all of its features. Furthermore, subcombinations of these features may also constitute an invention. [Brief explanation of the drawing]

[0017] [Figure 1] This figure shows an example of a CSV file, which is an example of activity level data. [Figure 2] This figure shows an example of a user interface that displays the input screen for the emission intensity format. [Figure 3] This figure shows the results of calculating GHG emissions after assigning activity data to the emission intensity format. [Figure 4] This figure shows an example of a user interface for the emission intensity format. [Figure 5] This figure shows a continuation of the user interface for the emission intensity format shown in Figure 4. [Figure 6]It is a diagram showing an example of a user interface after activity data is read into a GHG emission derivation device. [Figure 7] It is a diagram showing an example of a user interface in the emission factor format when the activity content is electricity. [Figure 8] It is a diagram showing an example of a user interface after activity data is read into a GHG emission derivation device when the activity content is electricity. [Figure 9] It is a diagram showing an example of a GHG emission derivation device according to this embodiment. [Figure 10] It is a diagram showing an example of each part constituting a GHG emission derivation device. [Figure 11] It is a flowchart showing an example of a procedure for deriving GHG emissions by a GHG emission derivation device. [Figure 12] It is a flowchart showing an example of a procedure for creating a new emission factor format by a GHG emission derivation device. [Figure 13] It is a diagram showing an example of a hardware configuration.

Embodiments for Carrying Out the Invention

[0018] Hereinafter, the present invention will be described through embodiments of the invention. However, the following embodiments do not limit the invention according to the claims. Also, not all combinations of features described in the embodiments are essential for the solution means of the invention.

[0019] In recent years, attempts have been made to grasp and manage the entire corporate activities by deriving not only the greenhouse gas emissions (GHG emissions) of the operator itself but also the supply chain emissions showing the GHG emissions of all supply chains related to business activities. The supply chain emissions indicate the GHG emissions (thousand t-CO2) generated due to organizational activities in the entire series of processes such as raw material procurement, manufacturing, logistics, sales, and disposal of the operator. The supply chain emissions are composed of Scope 1, Scope 2, and Scope 3.

[0020] Scope 1 represents direct emissions, which are greenhouse gas emissions directly emitted by the business itself. Scope 2 represents indirect emissions (energy-related indirect emissions), which are greenhouse gas emissions indirectly generated when a business purchases energy such as electricity, heat, and steam from other companies, such as power companies, and uses that purchased energy. Scope 3 represents other indirect emissions, which are greenhouse gas emissions from activities in the business's supply chain that are not included in Scope 1 or Scope 2. Scope 3 is further classified into 15 categories depending on the type of activity.

[0021] Category 1 represents "Purchased Products and Services." Category 2 represents "Capital Goods." Category 3 represents "Fuel and Energy Activities Not Covered by Scope 1 and Scope 2." Category 4 represents "Transportation and Delivery (Upstream)." Category 5 represents "Waste from Business Activities." Category 6 represents "Business Travel." Category 7 represents "Employee Commuting." Category 8 represents "Leased Assets (Upstream)." Category 9 represents "Transportation and Delivery (Downstream)." Category 10 represents "Processing of Products Sold." Category 11 represents "Use of Products Sold." Category 12 represents "Disposal of Products Sold." Category 13 represents "Leased Assets (Downstream)." Category 14 represents "Franchise." Category 15 represents "Investments." Scope 3 further includes "Other," which represents indirect GHG emissions not covered by the 15 categories. GHG emissions falling under "Other" include, for example, GHG emissions related to the daily lives of employees or consumers.

[0022] Supply chain emissions are the sum of Scope 1 emissions, Scope 2 emissions, and Scope 3 emissions. Across Scope 1 through Scope 3, the basic formula is Activity Volume × Emission Intensity. For Scope 3 emissions, the basic formula is derived for each of the 15 categories and then summed up. Activity Volume in the basic formula refers to the scale of the business's activities. Examples include electricity consumption, cargo transport volume, waste processing volume, and various transaction values. Activity Volume is collected from various internal data, literature data, industry average data, and product design values. Emission Intensity is the amount of CO2 emissions per unit of activity. Examples include CO2 emissions per 1 kWh of electricity used, CO2 emissions per ton-kilometer of cargo transport, and CO2 emissions per ton of waste incineration. Emission Intensity is generally selected from existing databases, but methods such as directly measuring emissions or obtaining emission derivation results from business partners are also available.

[0023] By the way, deriving supply chain emissions is not easy. This is because it involves a series of steps: extracting individual activity details from a vast amount of data on various businesses, classifying each activity into three types of scopes, further classifying Scope 3 activities into categories, and then selecting appropriate emission intensity units. In particular, emission intensity units are diverse, and manually selecting the appropriate emission intensity unit for each one is extremely laborious.

[0024] Therefore, this embodiment provides a GHG emission derivation device, a GHG emission derivation method, and a program that can easily calculate supply chain emissions (hereinafter sometimes referred to as GHG emissions).

[0025] First, we will outline the flow of GHG emission calculation in this embodiment. The user first prepares a CSV file 10, which is a file from an accounting system, business system, human resources system, ERP system, etc., that contains various data including the business operator's activities and activity levels, as shown in Figure 1. CSV file 10 is an example of activity level data. The activity level data may be in a predetermined format other than a CSV file, as long as the content of each item, such as the business operator's activities and activity levels, is shown in a predetermined format.

[0026] The GHG emissions calculation device reads a CSV file specified by the user and displays an input screen for generating an emission intensity format corresponding to the CSV file. Figure 2 shows an example of a user interface 20 that displays an input screen for generating an emission intensity format. The user interface 20 includes at least the following items: activity details 21, activity amount 22, scope 23, category 24 (only if applicable to scope 3), and emission intensity 25.

[0027] The GHG emissions calculation device accepts input from the user for the necessary information for each item based on the CSV file 10. When the OK button 26 at the bottom of the screen is pressed, the GHG emissions calculation device determines that the input is complete. This establishes the mapping between the activity details 21, scope 23, category 24, and emission intensity 25. This emission intensity format 20 can be reused when a new CSV file is loaded into the GHG emissions calculation device. In this embodiment, a CSV file is loaded into the GHG emissions calculation device, but this embodiment is not limited to this. For example, data may be entered individually by a person into the GHG emissions calculation device. Alternatively, data may be directly acquired by sensors or directly acquired from other systems via API linkage.

[0028] Figure 3 shows an example of a user interface 30 that displays the results screen after calculating GHG emissions by applying the emission intensity format to activity data. When the OK button 26 on the user interface 20 of the input screen shown in Figure 2 is pressed, the GHG emission derivation device calculates the GHG emissions for each activity by multiplying the activity amount of each activity by the emission intensity of each activity, and outputs the user interface 30.

[0029] Figure 4 shows an example of a user interface 40 that displays a creation screen for creating an emission intensity format in an application that embodies a GHG emission derivation device. The [Item Name 1] and [Item Name 2] shown in area 41 of the creation screen 40 correspond to data representing the activity content. Here, column number "5" in the CSV file is specified for [Item Name 1], and column number "4" in the CSV file is specified for [Item Name 2]. The [Activity Amount] shown in area 42 of the user interface 40 corresponds to data representing the activity amount. Here, column number "6" in the CSV file is specified for [Activity Amount]. In the user interface 20 shown in Figure 2, for clarity, one column representing the activity content is included in each record. However, the number of columns representing the activity content is not limited to one; there may be two or more. For example, there may be three columns representing the activity content.

[0030] Figure 5 shows an example of the user interface 50 for the emission intensity format creation screen, which is displayed following the user interface 40 of the emission intensity format creation screen in Figure 4. In area 51 of user interface 50, "Petroleum products (tCO2 / kl)" is specified as [Corresponding Emission Intensity 1]. Area 54 of user interface 50 shows [Corresponding Emission Intensity 2]. In this way, multiple emission intensities can be specified for a single activity.

[0031] For example, in this embodiment, it is possible to specify three emission intensity units. The correspondence between activity content and emission intensity units can be stored using the [Correspondence Table between Item Name and Emission Intensity Unit] shown in area 43 of the creation screen 40. For example, for one activity content, the correspondence between item names 1 to 3 and emission intensity units 1 to 3 can be specified and stored. Furthermore, the user interface 20 of the input screen for the emission intensity unit format in Figure 2 shows an example where one activity content is associated with one scope, category, and one emission intensity unit.

[0032] However, multiple scope categories and multiple emission intensity units may be associated with a single activity. For example, for a single activity such as "gasoline sales," Scope 3 categories 10 (processing of sold products), 11 (use of sold products), and 12 (disposal of sold products) may be associated, and each may be associated with three different emission intensity units. This is because Scope 3 can involve calculating GHG emissions across multiple categories from the same activity. In area 52 of the user interface 50, "Scope 3 and Category 1" is specified for [Target Scope / Category]. In area 53 of the user interface 50, a database showing emission intensity units corresponding to the activity is shown for [Emission Intensity Database]. More specifically, area 53 shows the emission intensity database by the Ministry of the Environment and the emission intensity database by IDEA. Area 53 also shows a custom emission intensity database that users can create and manage themselves. The user interfaces 40 and 50 in Figures 4 and 5, which are screens for creating emission intensity formats, are merely illustrative examples, and in practice, the items shown in Figures 4 and 5 may be omitted or added.

[0033] Figure 6 shows an example of the user interface 60 after the CSV file has been assigned to the emission intensity format in Figures 4 and 5 and loaded into the GHG emission calculation device. The user interface 60 includes two data representing the activity content as [Item Name 1] and [Item Name 2], so it is displayed as [Item Name 1] Gasoline + [Item Name 2] Supplier A, [Item Name 1] Gasoline + [Item Name 2] Supplier B, etc. In this way, the GHG emission calculation device can distinguish the activity content for each supplier, even if it is the same gasoline, and calculate the GHG emissions for each. By including multiple columns representing the activity content, the data representing the activity content can be set more precisely and GHG emissions can be calculated. Area 61 of the user interface 60 displays the display name when the CSV file was imported into the GHG emission calculation device. In area 61 of the user interface 60, if multiple items are specified as the activity content, the item to be displayed as the display name is specified from among those items. Here, the same column number "5" as the column number specified in Item Name 1 is specified. Once the user confirms that the CSV file has been loaded correctly, they press the [Approve] button 62. The GHG emission calculation device then calculates the GHG emissions by multiplying the activity level by the emission intensity.

[0034] Figure 7 shows an example of the user interface 70 for creating the emission intensity format, particularly when the activity involves electricity. The fields [Supply Point Name 1] and [Supply Point Name 2] shown in area 72 of the user interface 70 correspond to the data representing the activity. Here, column number "2" in the CSV file is specified for [Supply Point Name 1].

[0035] In the case of electricity, one supply point identification number (a 22-digit number) can include, for example, three supply point names. This is similar to how, as mentioned earlier with respect to Figure 4, the number of columns representing the activity is not limited to one. For example, even within the same Kanto area, GHG emissions can be calculated separately for store A and store B, such as [Supply Point Name 1] Store A + [Supply Point Name 2] Kanto Area and [Supply Point Name 1] Store B + [Supply Point Name 2] Kanto Area.

[0036] In the case of electricity, multiple scopes, multiple categories, and multiple emission intensity units may be associated with a single activity. The [Usage] shown in area 74 of the user interface 70 corresponds to the data representing the activity amount. Here, column number 4 in the CSV file is specified for [Usage]. Figure 8 shows an example of the user interface 80 after the CSV file has been assigned to the emission intensity format via the user interface 70 shown in Figure 7 and loaded into the GHG emission derivation device. The [Add Corresponding Point] in area 81 of the user interface 80 is for adding supply point information.

[0037] The above is an overview of the process for creating the emission intensity format and calculating GHG emissions in this embodiment. Once the emission intensity format is created, subsequent users, even if they are different users, will be presented with the scope, category, and emission intensity from the previously created emission intensity format, provided their industry and application type are the same as those of the user who previously created the format. This eliminates the need for users to manually assign the scope, category, and emission intensity to each activity.

[0038] The user's industry can be entered as user information when registering with the GHG emissions calculation device. The application type can be specified by having the GHG emissions calculation device identify the source application for creating the CSV file.

[0039] When presenting scopes, categories, and emission factors from an existing emission factor format, for example, multiple emission factor formats from multiple users may be aggregated, and sets of scope, category, and emission factor mappings with the same industry, same application type, and same activity content may be sorted and counted. The top 5 sets, for example, may then be presented in descending order of frequency. In this way, users can reuse existing emission factor formats. This allows for the quick loading of CSV files into the GHG emissions derivation device.

[0040] Furthermore, the GHG emissions derivation device may identify the scopes, categories, and emission intensity to be presented according to a learning model trained using training data in which the user's industry and activities are input and scopes, categories, and emission intensity are output. In this case, the learning model is a classifier. When the industry and activities are input to the trained learning model, the GHG emissions derivation device may derive the confidence level of multiple sets of scopes, categories, and emission intensity mappings and present, for example, the top 5 sets in order of highest confidence. In this way, the user does not need to create an emission intensity format; they only need to select the appropriate set from the presented sets of scopes, categories, and emission intensity.

[0041] The following describes the specific details of this embodiment. Figure 9 shows an example of a GHG emission derivation device 100 according to this embodiment. The GHG emission derivation device 100 communicates with the emission intensity database 200 via the network 150. The GHG emission derivation device 100 calculates GHG emissions by multiplying the emission intensity registered in the emission intensity database 200 by the activity level. Examples of emission intensity databases include the emission intensity database by the Ministry of the Environment, the emission intensity database by IDEA, the Environmental Load Intensity Data Book (3EID) from input-output tables, and the LCA database by the LCA Japan Forum.

[0042] Figure 10 shows an example of the components that make up the GHG emission derivation device 100. The GHG emission derivation device 100 has a generation unit 102, an acquisition unit 104, a selection unit 106, a determination unit 108, a derivation unit 110, a storage unit 112, a presentation unit 114, a reception unit 116, and an extraction unit 118. The GHG emission derivation device 100 according to this embodiment may be a computer. The generation unit 102, acquisition unit 104, selection unit 106, determination unit 108, derivation unit 110, presentation unit 114, reception unit 116, and extraction unit 118 may be implemented in a central processing unit. The storage unit 112 may be implemented in memory.

[0043] The acquisition unit 104 acquires first activity amount data that shows the activity content and the amount of activity for each activity content that are subject to the derivation of GHG emissions. The first activity amount data includes the activity content and the amount of activity for each activity content. The activity amount data may be a file (e.g., a CSV file) obtained by converting a file from, for example, an accounting system into a format that can be read by the GHG emission derivation device 100. The activity content indicates the activities of a business operator that are directly or indirectly involved in GHG emissions. The activity content may be, for example, "business trips by employees". For example, the derivation method is as follows: If we derive GHG emissions from business trips from the number of employees, the activity level would be, for example, "5 It is acceptable to write "00 people (number of employees)".

[0044] The selection unit 106 selects a first emission intensity format corresponding to the type of first activity data from among multiple emission intensity formats stored in the storage unit 112, each format being predetermined for each type of activity data and indicating the emission intensity for each activity. The type of activity data corresponds to the type of application used to create the activity data. The type of activity data also corresponds to the industry of the activity entity. An activity entity is an entity that directly or indirectly emits GHGs, such as a business that wishes to derive the GHG emissions associated with its own activities.

[0045] For example, the type of activity data is identified by the user's industry and the type of application from which the activity data was created. The application may be a program for running an accounting system, business system, human resources system, or ERP system on a computer. The type of application from which the data was created may be indicated in the activity data. The user's industry may also be indicated in the activity data. Alternatively, the storage unit 112 may store user identification information that identifies the user and industry information that indicates the user's industry in association. The activity data may include user identification information. The selection unit 106 may then refer to the activity data to identify the type of application from which the data was created, and refer to the storage unit 112 to identify the user's industry corresponding to the user identification information. The storage unit 112 may also store the type of application used by the user in association with the user identification information. The selection unit 106 selects, as the first emission intensity format, an emission intensity format from among multiple emission intensity formats created in advance by multiple users, in which the user's industry and the type of application from which the activity data was created are the same.

[0046] The determination unit 108 determines at least one emission intensity for each activity shown in the first activity data, based on the first emission intensity format. The first emission intensity format shows the emission intensity in relation to the activity. If the activity is "business trip by an employee," the first emission intensity format shows "0.103 t-CO2 / person·year" as the emission intensity per employee for "business trip by an employee."

[0047] The derivation unit 110 derives GHG emissions for each activity level of the activity content shown in the first activity data, based on at least one emission intensity. For example, the derivation unit 110 derives the GHG emissions for the activity content "Business trips by employees" as follows: Number of employees × Emission intensity per employee = 500 people × 0.130 t-CO2 / person·year = 65 t-CO2.

[0048] The activity description includes vendor information about the companies involved in the activity. Vendor information includes, for example, information about business partners and electricity suppliers. The activity description also includes location information about the places where the activity takes place. Location information includes, for example, store name, building name, and geographical area.

[0049] GHG emissions include at least other indirect emissions, which represent GHG emissions from the business's activities that are not included in indirect emissions, which represent GHG emissions indirectly emitted by the business as a result of purchasing energy. As mentioned above, supply chain emissions include Scope 1 emissions, Scope 2 emissions, and Scope 3 emissions. Indirect emissions, which represent GHG emissions indirectly emitted by the business as a result of purchasing energy, are Scope 2 emissions. Other indirect emissions, which represent GHG emissions from the business's activities, are Scope 3 emissions.

[0050] The emission intensity format indicates at least one emission intensity corresponding to at least one category of the activity that falls under the scope of other indirect emissions, if the activity falls under the scope of other indirect emissions. In other words, if the activity falls under Scope 3, the emission intensity format indicates at least one emission intensity corresponding to at least one category of the activity that falls under Scope 3. In particular, in the case of Scope 3, multiple categories and multiple emission intensity units may be associated with a single activity.

[0051] The presentation unit 114 presents activity content shown in the first activity volume data for which the determination unit 108 cannot determine at least one emission intensity based on the first emission intensity format. It is possible that the determination unit 108 cannot determine an emission intensity from the selected first emission intensity format for the activity content shown in the first activity volume data. This occurs when the user's industry and application type are the same, and the selected first emission intensity format... This can occur if the activity specified in step T does not match the activity described in the first activity data.

[0052] For example, if the activity specified in the selected first emission intensity format is "sale of gasoline" instead of "business trip by employee," the determination unit 108 cannot determine the emission intensity. Alternatively, even if the user's industry and application type are the same, and the activity specified in the selected first emission intensity format is the same as that in the first activity amount data, if the units of activity amount do not match, the determination unit 108 cannot determine the emission intensity. If the first emission intensity format specifies that GHG emissions from business trips are derived from the number of employees, the activity amount is the number of employees.

[0053] In contrast, if the first activity data shows the distance traveled as the activity level of an employee's business trip, the first emission intensity format needs to associate the emission intensity with the amount of business trip expenses for the activity of the employee's business trip. However, the first emission intensity format does not associate the emission intensity with the amount of business trip expenses. In this case, the determination unit 108 cannot determine the emission intensity for business trips by employees.

[0054] The reception unit 116 receives the emission intensity from the user for the presented activity. The user specifies, for example, the emission intensity per travel expense amount for passenger railways, namely employee business trips, which is "0.00137 kg-CO2 / yen".

[0055] Optionally, when the presentation unit 114 presents activity details that cannot be determined based on the first emission intensity format, it may search for other emission intensity formats that are identical in only one of the application type and the industry of the activity entity, and that specify the same activity details. If other emission intensity formats are found, the presentation unit 114 may present the emission intensity entered in the other emission intensity format to the user along with the activity details that cannot be determined. The user may select the emission intensity entered in the other emission intensity format if it is appropriate.

[0056] Furthermore, the reception unit 116 may accept from the user the designation of an emission intensity database to be referenced to determine the emission intensity for the presented activity, from among multiple emission intensity databases that show emission intensity for each activity. For example, the user may select from the displayed emission intensity database by the Ministry of the Environment, the emission intensity database by IDEA, the Environmental Load Intensity Data Book (3EID) from input-output tables, or the LCA database by the LCA Japan Forum. Alternatively, the user may specify their own emission intensity database. In addition, the reception unit 116 may accept from the user the emission intensity for the presented activity from among the emission intensity shown in the accepted emission intensity database.

[0057] The extraction unit 118 extracts at least one set of at least one category and at least one emission intensity related to other indirect emissions, corresponding to the activity content shown in the first activity data that the determination unit 108 cannot determine based on the first emission intensity format, from other existing emission intensity formats corresponding to the type of first activity data. If the determination unit 108 cannot determine the emission intensity based on the selected first emission intensity format, the extraction unit 118 extracts at least one set of at least one category and at least one emission intensity from other existing emission intensity formats different from the first emission intensity format, for which the user's industry, application type, and activity content are the same, and the presentation unit 114 presents the extracted at least one set to the user. The reception unit 116 receives from the user the set for the activity content that cannot be determined from among the at least one set. The user selects an appropriate set from the presented at least one set of at least one category and at least one emission intensity and designates it for the activity content that cannot be determined.

[0058] The extraction unit 118 sorts and counts sets of category-to-emission intensity mappings from among multiple existing emission intensity formats having the same industry, same application type, and same activity content, extracts the top 5 sets in descending order of the number of entries, and the presentation unit 114 may display the extracted top 5 sets. For example, the presentation unit 114 may present the top 5 sets. Alternatively, the extraction unit 118 sorts and counts sets of category-to-emission intensity mappings from among multiple existing emission intensity formats having the same industry and same activity content, or the same application type and same activity content, extracts the top 5 sets in descending order of the number of entries, and the presentation unit 114 may present the top 5 sets. Alternatively, the extraction unit 118 sorts and counts sets of category-to-emission intensity mappings from among multiple existing emission intensity formats having the same activity content, extracts the top 5 sets in descending order of the number of entries, and the presentation unit 114 may present the top 5 sets.

[0059] Up to this point, we have explained how the GHG emissions calculation device determines emission intensity from a pre-created emission intensity format and derives GHG emissions. In contrast, the following describes the case where a user loads new activity data into the GHG emissions calculation device and generates a new emission intensity format. It is assumed that the activity content in the activity data corresponds to any category in Scope 3.

[0060] The acquisition unit 104 acquires new activity data that shows the activity content and the activity amount for each activity content that are the subject of GHG emission derivation. The acquisition unit 104 may acquire activity data corresponding to a file name specified by the user from the storage unit 112. The acquisition unit 104 may acquire new activity data sent from other devices as an attachment to an email, etc., via the network 150. The extraction unit 118 may identify the type of new activity data by referring to the new activity data to identify the type of application that created it, and by referring to the storage unit 112 to identify the industry of the user corresponding to the user identification information. The extraction unit 118 extracts at least one set of at least one category and at least one emission intensity related to other indirect emissions corresponding to each activity content shown in the new activity data from the existing emission intensity format corresponding to the type of new activity data. Next, the generation unit 102 generates a new emission intensity format for the new activity data by identifying one set for each activity content shown in the new activity data from at least one set, according to the user's instructions.

[0061] The extraction unit 118 extracts at least one set of at least one category and at least one emission intensity with identical activity content from among multiple emission intensity formats created in advance by multiple users, from emission intensity formats for which the user's industry and application type are the same, and the presentation unit 114 presents the extracted at least one set to the user.

[0062] The extraction unit 118 sorts and counts the sets of category and emission intensity mappings for each identical set, extracts the sets up to a specific top in descending order of the number of entries, and the presentation unit 114 may present the extracted sets up to a specific top. The presentation unit 114 may, for example, present the top 5 sets. If it is not possible to extract at least one set of at least one category and at least one emission intensity with identical activity content from existing emission intensity formats for which the user's industry, application type, and activity content are the same, the extraction unit 118 may extract at least one set of at least one category and at least one emission intensity with identical activity content from multiple existing emission intensity formats for the same industry but with different application types.

[0063] Alternatively, the extraction unit 118 may extract at least one set of at least one category and at least one emission intensity that have the same activity content from among multiple existing emission intensity formats that are different in the user's industry but the same in application type. If it is still not possible to extract at least one set, the extraction unit 118 may extract at least one set of at least one category and at least one emission intensity that have the same activity content from among existing emission intensity formats that are different in the user's industry and application type. The user then selects the set appropriate for the activity content from among the at least one set presented.

[0064] If the extraction unit 118 cannot extract at least one category and at least one set of emission intensity units, the reception unit 116 may accept a specification from the user for an appropriate set of category and emission intensity units for the activity. In this case, the presentation unit 114 may further present the user with an emission intensity database to be referenced in order to determine the emission intensity units for the activity. The generation unit 102 generates a new emission intensity format for the new activity data by identifying the set for the activity according to the user's specification.

[0065] The above describes an example in which the extraction unit 118 extracts at least one set of at least one category and at least one emission intensity that have the highest number of identical activity content from among multiple existing emission intensity formats. In contrast, the following describes an embodiment in which the extraction unit 118 extracts at least one set of at least one category and at least one emission intensity according to the activity content shown in the activity volume data, using a trained learning model.

[0066] The extraction unit 118 may extract at least one set of a predetermined number of sets in descending order of confidence, based on a machine learning model that uses combinations of activity type and activity content identified from an existing emission intensity format, and at least one category and at least one emission intensity related to other indirect emissions, as training data. The machine learning model may be a deep learning model.

[0067] The extraction unit 118 may derive the confidence level using the output of a Softmax function or the like. The generation unit 102 may, for example, use training data in which the user's industry and activities are input and scope, category, and emission intensity are output to train a learning model that shows the relationship between the type of activity data and the activity content and at least one category and at least one emission intensity related to other indirect emissions, according to a supervised learning algorithm, generate a trained learning model, and store it in the storage unit 112. The algorithm may be any type of algorithm such as a neural network, support vector machine, multiple regression analysis, or decision tree. Here, data input to the learning model may be individual input by a person or direct acquisition of data by a sensor or the like. When the industry and activities are input, the extraction unit 118 may derive the confidence level of the correspondence set of scope, category, and emission intensity according to the trained learning model, and extract sets from the sets with high confidence levels up to a specific top set. The extraction unit 118 may, for example, extract up to the top 5 sets.

[0068] Figure 11 is a flowchart showing an example of the procedure for deriving GHG emissions using the GHG emission derivation device according to this embodiment.

[0069] In S100, the acquisition unit 104 acquires the application type, the user's industry, and activity level data. The acquisition unit 104 may refer to the activity level data to acquire the type of the application that created it, and refer to the storage unit 112 to acquire the user's industry corresponding to the user identification information.

[0070] In S102, the selection unit 106 selects an emission intensity format from a plurality of existing emission intensity formats that have been created in advance, according to the application type and the user's industry. The selection unit 106 selects an emission intensity format from a plurality of existing emission intensity formats that is the same for the user's industry and application type.

[0071] In S104, the determination unit 108 determines at least one emission intensity for each activity shown in the activity data, according to the selected emission intensity format. If the activity falls under Scope 3, the determination unit 108 may determine one emission intensity for each of the multiple categories corresponding to the activity.

[0072] In S106, the determination unit 108 determines whether there is an activity for which at least one emission intensity has not been determined.

[0073] If available, proceed to S108. In S108, the reception unit 116 receives at least one emission factor from the user for the undetermined activity. Here, the extraction unit 118 sorts and counts sets of category and emission factor mappings from among the existing multiple emission factor formats for which the user's industry and application type are the same, extracts sets up to a specific top in descending order of the number of items, and the presentation unit 114 may present the extracted sets up to a specific top. The reception unit 116 then receives at least one emission factor by accepting a set for the undetermined activity from among the presented sets.

[0074] If none is found, proceed to S110. In S110, the derivation unit 110 derives the GHG emissions for each activity according to the respective emission intensity for each activity in the activity data.

[0075] By following the above procedure, the optimal emission intensity format can be selected from existing emission intensity formats for new activity data, taking into account the user's industry and the type of application from which the activity data is generated. Therefore, the effort of manually selecting the appropriate emission intensity for each new activity data and creating a new emission intensity format can be reduced. In addition, the processing load on the GHG emission derivation device 100 when creating a new emission intensity format can be reduced.

[0076] Figure 12 is a flowchart showing an example of the procedure for creating a new emission intensity format using the GHG emission derivation device 100 according to this embodiment.

[0077] In S200, the acquisition unit 104 acquires the application type, the user's industry, and activity level data. The acquisition unit 104 may refer to the activity level data to acquire the type of the application that created it, and refer to the storage unit 112 to acquire the user's industry corresponding to the user identification information.

[0078] In S202, the extraction unit 118 extracts existing emission intensity formats corresponding to the application type and the user's industry. The extraction unit 118 may extract existing emission intensity formats from among multiple existing emission intensity formats that are the same for the user's industry and application type.

[0079] In S204, the extraction unit 118 extracts at least one set of at least one Scope 3 category and at least one emission intensity corresponding to each activity content shown in the activity data from the extracted existing emission intensity format. The extraction unit 118 may sort and count the sets of Scope 3 category and emission intensity correspondences from the existing emission intensity format, and extract the sets up to a specific top in descending order of the number of occurrences. The extraction unit 118 may extract a predetermined number of sets in descending order of confidence, according to a trained learning model that shows the relationship between the type and content of the activity data and at least one Scope 3 category and at least one emission intensity, as at least one set.

[0080] In S206, the generation unit 102 identifies one set from at least one set for each activity in response to the user's instructions. The presentation unit 114 may present at least one set of at least one category and at least one emission intensity of Scope 3 extracted by the extraction unit 118 for each activity. The reception unit 116 may accept one set from the at least one set presented for each activity as the user's instruction for each activity.

[0081] In S208, the generation unit 102 generates a new emission intensity format based on each set identified for each activity.

[0082] As described above, when creating a new emission intensity format, at least one set of at least one Scope 3 category and at least one emission intensity will be presented for each activity, following the existing emission intensity format. This reduces the effort required to manually select the appropriate emission intensity for each new activity data and create a new emission intensity format.

[0083] Figure 13 shows an example of a computer 1300 in which multiple aspects of the present invention may be embodied in whole or in part. A program installed on the computer 1300 can cause the computer 1300 to function as an operation associated with an apparatus according to an embodiment of the present invention, or as one or more "parts" of said apparatus. Alternatively, the program can cause the computer 1300 to execute said operation or said one or more "parts". The program can cause the computer 1300 to execute a process or a stage of said process according to an embodiment of the present invention. Such a program may be executed by the CPU 1312 to cause the computer 1300 to execute a particular operation associated with some or all of the blocks in the flowcharts and block diagrams described herein.

[0084] The computer 1300 according to this embodiment includes a CPU 1312 and RAM 1314, which are interconnected by a host controller 1310. The computer 1300 also includes a communication interface 1322 and input / output units, which are connected to the host controller 1310 via an input / output controller 1320. The computer 1300 also includes a ROM 1330. The CPU 1312 operates according to programs stored in the ROM 1330 and RAM 1314, thereby controlling each unit.

[0085] The communication interface 1322 communicates with other electronic devices via a network. A hard disk drive may store programs and data used by the CPU 1312 in the computer 1300. The ROM 1330 stores boot programs and / or programs that depend on the computer 1300's hardware, such as a boot program executed by the computer 1300 upon activation. Programs are provided via a computer-readable recording medium such as a CD-ROM, USB memory, or IC card, or via a network. Programs are installed in RAM 1314, which is also an example of a computer-readable recording medium, or in ROM 1330, and executed by the CPU 1312. The information processing described within these programs is read by the computer 1300, resulting in coordination between the programs and the various types of hardware resources described above. An apparatus or method may be configured to implement the operation or processing of information in accordance with the use of the computer 1300.

[0086] For example, when communication is performed between a computer 1300 and an external device, the CPU 1312 may execute a communication program loaded into RAM 1314 and, based on the processing described in the communication program, instruct the communication interface 1322 to perform communication processing. Under the control of the CPU 1312, the communication interface 1322 reads the transmission data stored in the transmission buffer area provided in RAM 1314 or a recording medium such as a USB memory, sends the read transmission data to the network, or writes the received data received from the network to a receive buffer area or the like provided on the recording medium. Furthermore, the CPU 1312 may read all or necessary parts of a file or database stored on an external storage medium such as a USB memory stick into the RAM 1314, and perform various types of processing on the data in the RAM 1314. The CPU 1312 may then write the processed data back to the external storage medium.

[0087] Various types of information, such as various types of programs, data, tables, and databases, may be stored on the recording medium and subjected to information processing. The CPU 1312 may perform various types of processing on the data read from the RAM 1314, including various types of operations, information processing, conditional judgments, conditional branching, unconditional branching, information retrieval / replacement, etc., as described throughout this disclosure and specified by the program instruction sequence, and write the results back to the RAM 1314. The CPU 1312 may also retrieve information in files, databases, etc., within the recording medium. For example, if multiple entries are stored in the recording medium, each having an attribute value of a first attribute associated with an attribute value of a second attribute, the CPU 1312 may search among the multiple entries for an entry that matches the condition for which the attribute value of the first attribute is specified, read the attribute value of the second attribute stored in that entry, and thereby obtain the attribute value of the second attribute associated with the first attribute that satisfies a predetermined condition.

[0088] The program or software module described above may be stored on or near computer 1300 on a computer-readable storage medium. Alternatively, a recording medium such as a hard disk or RAM provided within a server system connected to a dedicated communication network or the Internet can be used as a computer-readable storage medium, thereby providing the program to computer 1300 via the network.

[0089] Computer-readable media may include any tangible device capable of storing instructions that can be executed by a suitable device. As a result, computer-readable media having instructions stored therein will comprise a product containing instructions that can be executed to create means for performing operations specified in a flowchart or block diagram. Examples of computer-readable media may include electronic storage media, magnetic storage media, optical storage media, electromagnetic storage media, semiconductor storage media, etc. More specific examples of computer-readable media may include floppy disks (registered trademark), diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), electrically erasable programmable read-only memory (EEPROM (registered trademark)), static random access memory (SRAM), compact disk read-only memory (CD-ROM), digital versatile disk (DVD), Blu-ray (RTM) disk, memory stick, integrated circuit card, etc.

[0090] Computer-readable instructions may include either source code or object code written in any combination of one or more programming languages. Source code or object code may include conventional procedural programming languages. These conventional procedural programming languages ​​may include assembler instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or object-oriented programming languages ​​such as Smalltalk®, Java®, C++, etc., and the "C" programming language or similar programming languages. Computer-readable instructions may be provided locally or via a wide area network (WAN) such as a local area network (LAN) or the internet to the processor or programmable circuit of a general-purpose computer, a special-purpose computer, or other programmable data processing device. The processor or programmable circuit may execute computer-readable instructions to create means for performing operations specified in a flowchart or block diagram. Examples of processors include computer processors, processing units, microprocessors, digital signal processors, controllers, microcontrollers, etc.

[0091] Although the present invention has been described above using embodiments, the technical scope of the present invention is not limited to the scope described in the above embodiments. It will be apparent to those skilled in the art that various modifications or improvements can be made to the above embodiments. It will be clear from the claims that such modified or improved forms may also be included in the technical scope of the present invention.

[0092] It should be noted that the execution order of operations, procedures, steps, and stages in the apparatus, systems, programs, and methods shown in the claims, specifications, and drawings is not explicitly stated as "before," "prior to," etc., and that these can be implemented in any order unless the output of a previous process is used in a later process. Even if the operation flow in the claims, specifications, and drawings is described using phrases such as "first," "next," etc. for convenience, it does not mean that it is essential to perform the operations in that order. [Explanation of symbols]

[0093] 100 GHG emissions derivation device 200 Emissions Intensity Database 102 Generation part 104 Acquisition Department 106 Selection Section 108 Decision Section 110 Derivation part 112 Storage section 114 Presentation section 116 Reception Department 118 Extraction part 1300 Computers 1310 Host Controller 1312 CPU 1314 RAM 1320 Input / Output Controller 1322 Communication Interface 1330 ROM

Claims

1. An acquisition unit acquires first activity amount data that shows the activities that are the target of greenhouse gas (GHG) emission calculations and the amount of activity for each activity, A selection unit that selects a first emission intensity format corresponding to the type of first activity data from among a plurality of emission intensity formats that show predetermined emission intensity for each activity content for each type of activity data, A determination unit that determines at least one emission intensity for each activity shown in the first activity data based on the first emission intensity format, A GHG emission derivation device comprising a derivation unit that derives GHG emissions for each activity amount for each activity content shown in the first activity data, based on the aforementioned at least one emission intensity.

2. The GHG emission derivation device according to claim 1, wherein the type of activity data corresponds to the type of application used to create the activity data.

3. The type of activity data further corresponds to the industry of the activity provider, as described in claim 2. G emissions derivation device.

4. The aforementioned activities include vendor information relating to the vendors involved in the activities, as specified in any of claims 1 to 3. One of the GHG emission extraction devices described.

5. The aforementioned activity includes location information relating to the place where the activity takes place, as per any one of claims 1 to 4. One of the GHG emission extraction devices described.

6. A presentation unit presents activity content from the first activity data for which the determination unit cannot determine at least one emission intensity based on the first emission intensity format, A GHG emission derivation device according to any one of claims 1 to 5, comprising a receiving unit for receiving emission intensity from a user for the presented activity content.

7. The aforementioned reception unit is The GHG emission derivation device according to claim 6, wherein the user specifies an emission intensity database to be referenced from among multiple emission intensity databases showing emission intensity for each activity, in order to determine the emission intensity for the presented activity, and the user specifies the emission intensity for the presented activity from among the emission intensity shown in the accepted emission intensity database.

8. The aforementioned GHG emissions include at least other indirect emissions, which are not included in indirect emissions, which are GHG emissions generated by the business's activities, and which are not included in indirect emissions, which are GHG emissions generated indirectly by the business's purchase of energy. The GHG emission derivation device according to any one of claims 1 to 5, wherein the emission intensity format indicates at least one emission intensity corresponding to at least one category according to the activity of the other indirect emissions when the activity falls under the scope of the other indirect emissions.

9. An extraction unit extracts, from other existing emission intensity formats corresponding to the type of first activity data, at least one set of at least one category and at least one emission intensity relating to other indirect emissions, corresponding to the activity content shown in the first activity data for which the determination unit cannot determine at least one emission intensity based on the first emission intensity format, The GHG emission derivation device according to claim 8, further comprising a reception unit that receives from the user a set of activity content that cannot be determined from among the at least one set.

10. The aforementioned GHG emissions include at least other indirect emissions, which are not included in indirect emissions, which are GHG emissions generated by the business operator's activities, and the aforementioned GHG emissions derivation device is An extraction unit extracts at least one set of at least one category and at least one emission intensity related to other indirect emissions, corresponding to each activity shown in the new activity data, from an existing emission intensity format corresponding to the new activity data type. A GHG emission derivation device according to any one of claims 1 to 5, further comprising a generation unit that generates a new emission intensity format for new activity data by identifying one set for each activity content shown in the new activity data from among the at least one set according to the user's instructions.

11. The extraction unit is The GHG emission derivation device according to claim 10, wherein a predetermined number of sets are extracted as the at least one set, in descending order of confidence, according to a machine learning model that uses combinations of activity type and activity content identified from an existing emission intensity format, and at least one category and at least one emission intensity related to other indirect emissions, as training data.

12. The acquisition unit acquires from the storage unit first activity amount data that shows the activity content and the amount of activity for each activity content that are subject to the derivation of greenhouse gas (GHG) emissions, The selection unit selects a first emission intensity format corresponding to the type of first activity data from among a plurality of emission intensity formats stored in the storage unit that indicate the emission intensity for each activity content predetermined for each type of activity data, The determination unit determines at least one emission intensity for each activity shown in the first activity data based on the first emission intensity format, A method for deriving GHG emissions, comprising the step of a derivation unit deriving GHG emissions for each activity amount for each activity content shown in the first activity amount data, based on the at least one emission intensity.

13. A program for causing a computer to function as a GHG emission derivation device according to any one of claims 1 to 11.

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