Information processing apparatus, information processing method, and information processing program

Abstract

To easily predict GHG emissions related to specific merchandise scheduled to be purchased from a specific company for a future prediction target year.SOLUTION: An information processing apparatus includes: an input unit that receives the total purchase amount of specific merchandise scheduled to be purchased from a specific company for a prediction target year; an acquisition unit that acquires the GHG emissions in a reference year of the specific company, a reduction target for the specific company to reduce the GHG emissions in the prediction target year relative to the GHG emissions in the reference year, and a total sales amount plan for the specific company in the prediction target year; and an emissions prediction unit predicts the GHG emissions related to the specific merchandise scheduled to be purchased from the specific company in the prediction target year, on the basis of the total purchase amount of the specific merchandise scheduled to be purchased from the specific company in the prediction target year, the GHG emissions in the reference year of the specific company, the reduction target for the specific company to reduce the GHG emissions in the prediction target year relative to the emissions in the reference year, and the total sales amount plan for the specific company in the prediction target year.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The disclosed technology relates to an information processing device, an information processing method, and an information processing program. [Background technology]

[0002] A method for producing steel products in an electric furnace using electricity defined as non-fossil or zero-emission value is known (for example, Patent Document 1).

[0003] Also, an information processing method is known that includes the steps of: acquiring order information for steel-related products by a control unit; determining the amount of electricity required to manufacture the steel-related products in an electric furnace from the order information; reading out purchased certification information from a memory unit to certify that the electricity used in the manufacturing is electricity defined as non-fossil value or zero-emission value; and determining whether the amount of electricity required is within the range of the amount of electricity certified by the certification information (for example, Patent Document 2).

[0004] Furthermore, Patent Document 3 proposes a future prediction device that calculates the GHG emissions of a specified company in a base year and then estimates future GHG emissions by taking into account change scenarios in an economic model to those emissions. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Patent No. 7075530 [Patent Document 2] Patent No. 7123279 [Patent Document 3] WO2022 / 264283 Summary of the Invention [Problem to be solved by the invention]

[0006] The above Patent Documents 1 and 2 do not disclose a method for predicting the amount of GHG (Green House Gas) emissions associated with a specific product to be purchased from a specific company for a future target year of prediction.

[0007] Furthermore, in the above-mentioned Patent Document 3, the economic model used to predict future GHG emissions is limited to subjects that are widely known to the public, such as a prediction of hydrogen power generation in 2050 based on news information, and there is no clear consensus, making it difficult to make predictions regarding raw materials, components, processing, etc. that are the target of general material manufacturers.

[0008] The disclosed technology has been developed in consideration of the above points, and aims to easily predict GHG emissions associated with a specific product that is planned to be purchased from a specific company for a future target year of prediction. [Means for solving the problem]

[0009] A first aspect of the present disclosure is an information processing device that predicts GHG emissions associated with specific commodities to be purchased from a specific company for a future target year of prediction, and includes: an input unit that accepts a total purchase amount of the specific commodities to be purchased from the specific company for the target year of prediction; an acquisition unit that acquires the specific company's GHG emissions in a base year, a reduction target by which the specific company will reduce its GHG emissions for the target year of prediction compared to its GHG emissions in the base year, and a total sales plan of the specific company for the target year of prediction; and an emissions prediction unit that predicts GHG emissions associated with the specific commodities to be purchased from the specific company for the target year of prediction based on the total purchase amount of the specific commodities to be purchased from the specific company for the target year of prediction, the GHG emissions of the specific company for the base year, the reduction target by which the specific company will reduce its GHG emissions for the target year of prediction compared to its emissions in the base year, and the specific company's total sales plan for the target year of prediction. GHGs mentioned here are greenhouse gases, which are gases in the atmosphere that cause the greenhouse effect by absorbing some of the infrared rays emitted from the earth's surface. These include water vapor, carbon dioxide, methane, nitrous oxide, chlorofluorocarbons, etc., and can also be a mixture of one or more of these.

[0010] A second aspect of the present disclosure is an information processing method for predicting GHG emissions associated with specific commodities to be purchased from a specific company for a future target year of prediction, wherein a computer receives a total purchase amount of the specific commodities to be purchased from the specific company for the target year of prediction, acquires the GHG emissions of the specific company for a base year, a reduction target by which the specific company will reduce its GHG emissions for the target year of prediction compared to its GHG emissions in the base year, and a total sales plan of the specific company for the target year of prediction, and predicts GHG emissions associated with the specific commodities to be purchased from the specific company for the target year of prediction based on the total purchase amount of the specific commodities to be purchased from the specific company for the target year of prediction, the GHG emissions of the specific company for the base year, the reduction target by which the specific company will reduce its GHG emissions for the target year of prediction compared to its emissions in the base year, and the total sales plan of the specific company for the target year of prediction.

[0011] A third aspect of the present disclosure is an information processing program for predicting GHG emissions associated with specific commodities to be purchased from a specific company for a future target year of prediction, the program causing a computer to receive a total purchase amount of the specific commodities to be purchased from the specific company for the target year of prediction, obtain the specific company's GHG emissions for a base year, a reduction target by which the specific company will reduce its GHG emissions for the target year of prediction compared to its GHG emissions for the base year, and the specific company's total sales plan for the target year of prediction, and predict GHG emissions associated with the specific commodities to be purchased from the specific company for the target year of prediction based on the total purchase amount of the specific commodities to be purchased from the specific company for the target year of prediction, the specific company's GHG emissions for the base year, the reduction target by which the specific company will reduce its GHG emissions for the target year of prediction compared to its emissions in the base year, and the specific company's total sales plan for the target year of prediction. [Effects of the Invention]

[0012] According to the disclosed technology, it is possible to easily predict GHG emissions associated with a specific product that is planned to be purchased from a specific company for a future prediction year. [Brief explanation of the drawings]

[0013] [Figure 1] FIG. 1 is a schematic block diagram of an example of a computer that functions as an information processing apparatus according to an embodiment of the present invention. [Figure 2] 1 is a block diagram showing a configuration of an information processing apparatus according to an embodiment of the present invention; [Figure 3] 10 is a flowchart showing the flow of a prediction process of the information processing device of the present embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0014] An example of an embodiment of the disclosed technology will be described below with reference to the drawings. Note that the same or equivalent components and parts in each drawing are given the same reference numerals. Also, the dimensional proportions in the drawings are exaggerated for the convenience of explanation and may differ from the actual proportions.

[0015] <Configuration of the information processing device according to this embodiment> FIG. 1 is a block diagram showing the hardware configuration of an information processing device 10 according to this embodiment.

[0016] 1, an information processing device 10 includes a CPU (Central Processing Unit) 11, a ROM (Read Only Memory) 12, a RAM (Random Access Memory) 13, a storage 14, an input unit 15, a display unit 16, and a communication interface (I / F) 17. Each component is connected to each other via a bus 19 so as to be able to communicate with each other.

[0017] The CPU 11 is a central processing unit that executes various programs and controls each component. That is, the CPU 11 reads programs from the ROM 12 or the storage 14 and executes the programs using the RAM 13 as a work area. The CPU 11 controls the above components and performs various arithmetic processing in accordance with the programs stored in the ROM 12 or the storage 14. In this embodiment, an information processing program is stored in the ROM 12 or the storage 14. The information processing program may be a single program, or may be a group of programs consisting of multiple programs or modules.

[0018] The ROM 12 stores various programs and various data. The RAM 13 temporarily stores programs or data as a working area. The storage 14 is configured with an HDD (Hard Disk Drive) or SSD (Solid State Drive) and stores various programs including the operating system and various data.

[0019] The input unit 15 includes a pointing device such as a mouse and a keyboard, and is used to input various information including the total purchase amount of specific products to be purchased from a specific company for the forecast year.

[0020] The display unit 16 is, for example, a liquid crystal display, and displays various information including the results of forecasting GHG emissions from specific products to be purchased from specific companies for the future forecast year. The display unit 16 may be a touch panel type and function as the input unit 15.

[0021] The communication interface 17 is an interface for communicating with other devices, and uses standards such as Ethernet (registered trademark), FDDI, and Wi-Fi (registered trademark).

[0022] Next, a description will be given of the functional configuration of the information processing device 10. Fig. 2 is a block diagram showing an example of the functional configuration of the information processing device 10.

[0023] As shown in FIG. 2, the information processing device 10 functionally includes an acquisition unit 101 and a discharge amount prediction unit 102.

[0024] The acquisition unit 101 acquires the GHG emissions [kg-GHG] of a specific company in the base year, a reduction target for the specific company to reduce its GHG emissions for the forecast year relative to the GHG emissions for the base year, and the total sales plan [yen] of the specific company for the forecast year. Specifically, the acquisition unit 101 acquires the GHG emissions of the specific company in the base year, the reduction target for the specific company to reduce its GHG emissions for the forecast year relative to the GHG emissions for the base year, and the total sales plan of the specific company for the forecast year from a database that stores company information including the GHG emissions, reduction target, and total sales plan of companies.

[0025] Here, if the specific product is purchased from multiple specific companies, the acquisition unit 101 acquires, for each of the multiple specific companies, the GHG emissions of the specific company in the base year, the reduction target for the specific company to reduce its GHG emissions for the forecasted year relative to its GHG emissions for the base year, and the specific company's total sales plan for the forecasted year.

[0026] In addition, the acquisition unit 101 further acquires the GHG emissions [kg-GHG] for the specific product for the year including the present time, and the GHG emission coefficient [kg-GHG / kg] for the specific product for the year including the present time.

[0027] The emissions prediction unit 102 predicts the GHG emissions associated with the specific products to be purchased from a specific company for a future target year of prediction, based on the total purchase amount of the specific products to be purchased from the specific company for the target year of prediction, the GHG emissions of the specific company for the base year, the reduction target for the specific company to reduce its GHG emissions for the target year of prediction compared to the emissions in the base year, and the total sales plan of the specific company for the target year of prediction.

[0028] Specifically, the emission amount prediction unit 102 predicts the GHG emission amount Z [kg-GHG] for a specific product to be purchased from a specific company for a future prediction year according to the following formula. Z=α×P×Y / 100X

[0029] However, the reduction target is to aim for GHG emissions in the forecast year to be α parts by weight per 100 parts by weight of GHG emissions in the base year, P is the GHG emissions of the specific company in the base year, Y is the total purchase amount of specific products to be purchased from the specific company for the forecast year, and X is the total sales plan of the specific company for the forecast year. Note that GHG emissions may also be, for example, emissions in carbon dioxide equivalent by mass (kg-CO2eq).

[0030] The emission amount prediction unit 102 further predicts the GHG emission coefficient K [kg-GHG / kg] for the specific product to be purchased from the specific company for the prediction target year according to the following formula. K=Z / E

[0031] where E is the purchase amount [kg] of a specific product to be purchased from a specific company for the forecast year.

[0032] If the specific goods are purchased from multiple specific companies, the emissions prediction unit 102 predicts, for each of the multiple specific companies, the GHG emissions associated with the specific goods to be purchased from the specific company for the future target year of prediction, based on the total purchase amount of the specific goods to be purchased from the specific company for the target year of prediction, the GHG emissions of the specific company for the base year, the reduction target for the specific company to reduce its GHG emissions for the target year of prediction from the emissions in the base year, and the specific company's total sales plan for the target year of prediction. The emissions prediction unit 102 further predicts the total sum of GHG emissions associated with the specific goods to be purchased from the specific company for the target year of prediction as the GHG emissions associated with the specific goods for the target year of prediction.

[0033] The emission prediction unit 102 further predicts the reduction target amount S [kg-GHG] of GHG emissions related to the specific product for the prediction year according to the following formula. S=RZ

[0034] where R is the GHG emissions for a particular product for one year, including the present time.

[0035] The emission prediction unit 102 further predicts the reduction target amount ΔK [kg-GHG / kg] of the GHG emission coefficient for the prediction target year with respect to the GHG emission coefficient for the specific product for one year including the present time, according to the following formula. ΔK=R / DK

[0036] where D is the purchase amount [kg] of a specific product for the year including the present.

[0037] <Operation of the information processing device according to this embodiment> Next, the operation of the information processing device 10 will be described.

[0038] 3 is a flowchart showing the flow of prediction processing by the information processing device 10. The CPU 11 reads out an information processing program from the ROM 12 or the storage 14, expands it in the RAM 13, and executes it to perform the prediction processing. Note that the prediction processing is an example of an information processing method.

[0039] First, in step S100, the CPU 11 receives the total purchase amount of a specific product to be purchased from a specific company for the target year of prediction, which is input via the input unit 15.

[0040] In step S102, the CPU 11, as the acquisition unit 101, acquires the GHG emissions of a specific company in the base year, the reduction target for the specific company to reduce its GHG emissions in the forecasted year compared to its GHG emissions in the base year, and the specific company's total sales plan for the forecasted year.

[0041] Here, if a specific product is purchased from multiple specific companies, the CPU 11, as the acquisition unit 101, acquires, for each of the multiple specific companies, the GHG emissions of the specific company in the base year, the reduction target for the specific company to reduce its GHG emissions for the forecasted year compared to its GHG emissions in the base year, and the specific company's total sales plan for the forecasted year.

[0042] Furthermore, the CPU 11, as the acquisition unit 101, further acquires the GHG emission amount for the specific product for the year including the current time, and the GHG emission coefficient for the specific product for the year including the current time.

[0043] In step S104, the CPU 11, as the emissions prediction unit 102, predicts the GHG emissions Z for the specific products to be purchased from the specific company for the future target year of prediction, based on the total purchase amount of the specific products to be purchased from the specific company for the target year of prediction, the GHG emissions of the specific company in the base year, the reduction target for the specific company to reduce its GHG emissions for the target year of prediction compared to the emissions in the base year, and the total sales plan of the specific company for the target year of prediction, according to the following formula: Z=α×P×Y / 100X

[0044] If the specific goods are purchased from multiple specific companies, the CPU 11, as the emissions prediction unit 102, predicts, for each of the multiple specific companies, the GHG emissions associated with the specific goods to be purchased from the specific company for the future target year based on the total purchase amount of the specific goods to be purchased from the specific company for the target year of prediction, the GHG emissions of the specific company for the base year, the reduction target for the specific company to reduce its GHG emissions for the target year of prediction compared to its emissions in the base year, and the specific company's total sales plan for the target year of prediction, and further predicts the total GHG emissions associated with the specific goods to be purchased from the specific company for the future target year of prediction as the GHG emissions associated with the specific goods for the future target year of prediction.

[0045] In step S106, the CPU 11, functioning as the emission amount prediction unit 102, further predicts the GHG emission coefficient K for the specific product to be purchased from the specific company for the prediction target year according to the following formula. K=Z / E

[0046] In step S108, the CPU 11 functions as the emission prediction unit 102 to predict the reduction target amount S of GHG emissions related to the specific product for the prediction target year according to the following formula: S=RZ

[0047] In step S110, the CPU 11, functioning as the emission prediction unit 102, predicts a reduction target ΔK of the GHG emission coefficient for the prediction target year with respect to the GHG emission coefficient of the specific product for one year including the present time, according to the following formula. ΔK=R / DK

[0048] In step S112, the CPU 11 outputs the various prediction results to the display unit 16, and ends the prediction process.

[0049] As described above, the information processing device according to this embodiment predicts GHG emissions associated with specific products to be purchased from a specific company for a future target year of prediction, based on the total purchase amount of the specific products to be purchased from the specific company for the target year of prediction, the GHG emissions of the specific company for the base year, the reduction target for the specific company to reduce its GHG emissions for the target year of prediction compared to the emissions in the base year, and the specific company's total sales plan for the target year of prediction. This makes it possible to easily predict GHG emissions associated with specific products to be purchased from a specific company for a future target year of prediction.

[0050] <Example> An example of the method for predicting GHG emissions associated with a specific product to be purchased from a specific company, as described in the above embodiment, will be described.

[0051] Company A plans to purchase 600 tons of specific product b (product b) for 300 million yen in fiscal year 2030 from Company B, which has announced that its GHG emissions reduction target for fiscal year 2030 will be a 30% reduction compared to fiscal year 2013. In its medium-term plan, Company B has announced that its sales of specific product b for fiscal year 2030 will be 300 billion yen, and its GHG emissions in fiscal year 2013 were 1 million ton-GHG.

[0052] From the above, α = 70 [parts by weight] (= 100 - 30), P = 1 million [ton-GHG], Y = 300 million [yen], X = 300 billion [yen], so Z = 700 [ton-GHG], and the GHG emissions from product b that Company A plans to purchase in fiscal year 2030 are calculated to be 700 ton-GHG.

[0053] Furthermore, the total weight of specific product b that Company A plans to purchase for 300 million yen in fiscal year 2030 is 600 tons, so E = 600 [tons], Z = 700 [ton-GHG], and therefore K = 1.17 [ton-GHG / ton], and the GHG emission coefficient for specific product b in fiscal year 2030 was calculated.

[0054] Furthermore, Company B's current GHG emissions are 850,000 ton-GHG, its sales for the fiscal year are 250 billion yen, and Company A's purchases of specific product B amount to 250 million yen, with a total weight of 500 tons. Therefore, the GHG emissions of specific product B based on the current purchase amount are 850 ton-GHG, with R = 850 [ton-GHG] and D = 500 [ton]. Therefore, S = 150,000 [ton-GHG] and ΔK = 0.53 [ton-GHG / ton]. The GHG emission reduction amount for specific product B from now until fiscal year 2030 is calculated as 150,000 ton-GHG, and the target reduction amount for the emission factor is calculated as 0.53 ton-GHG / ton. In this way, the GHG emission reduction amount and the target reduction amount for the emission factor for specific product B by fiscal year 2030 have been calculated.

[0055] That is, the information processing method and information processing program make it possible to easily calculate future GHG emissions associated with the purchase of a specific product, future GHG emission coefficients associated with purchased materials, and target reduction amounts for the emission coefficients.

[0056] <Modification> The present invention is not limited to the above-described embodiment, and various modifications and applications are possible without departing from the spirit and scope of the present invention.

[0057] For example, various processes executed by the CPU after reading software (programs) in the above embodiments may be executed by various processors other than the CPU. Examples of processors in this case include dedicated electrical circuits, such as programmable logic devices (PLDs) (such as field-programmable gate arrays (FPGAs)) whose circuit configuration can be changed after manufacture, and application-specific integrated circuits (ASICs) that are processors with circuit configurations specifically designed to execute specific processes. Furthermore, the prediction process may be executed by one of these various processors, or by a combination of two or more processors of the same or different types (e.g., multiple FPGAs, or a combination of a CPU and an FPGA). Furthermore, the hardware structure of these various processors is, more specifically, an electrical circuit that combines circuit elements such as semiconductor elements.

[0058] In addition, in each of the above embodiments, the information processing program is described as being pre-stored (installed) in the storage 14, but the present invention is not limited to this. The program may be provided in a form stored in a non-transitory storage medium such as a CD-ROM (Compact Disk Read Only Memory), a DVD-ROM (Digital Versatile Disk Read Only Memory), or a USB (Universal Serial Bus) memory. The program may also be downloaded from an external device via a network.

[0059] The following additional notes are provided regarding the above-described embodiments.

[0060] (Additional note 1) An information processing device that predicts GHG emissions from a specific product to be purchased from a specific company for a future prediction year, Memory and at least one processor coupled to said memory; Including, The processor: receiving a total purchase amount of the specific product to be purchased from the specific company for the forecast year; Obtain the GHG emissions of the specific company for a base year, a reduction target for the specific company to reduce its GHG emissions for the forecast year relative to its GHG emissions for the base year, and a total sales plan for the specific company for the forecast year; Forecast the GHG emissions of the specific products to be purchased from the specific company for the forecast year based on the total purchase amount of the specific products to be purchased from the specific company for the forecast year, the GHG emissions of the specific company for the base year, the reduction target for the specific company to reduce the GHG emissions of the specific company for the forecast year compared to the emissions of the base year, and the total sales plan of the specific company for the forecast year. An information processing device configured as follows.

[0061] (Additional note 2) A non-transitory storage medium storing a program executable by a computer to execute a prediction process for predicting GHG emissions from a specific product to be purchased from a specific company for a future prediction year, The prediction process includes: receiving a total purchase amount of the specific product to be purchased from the specific company for the forecast year; Obtain the GHG emissions of the specific company for a base year, a reduction target for the specific company to reduce its GHG emissions for the forecast year relative to its GHG emissions for the base year, and a total sales plan for the specific company for the forecast year; Forecast the GHG emissions of the specific products to be purchased from the specific company for the forecast year based on the total purchase amount of the specific products to be purchased from the specific company for the forecast year, the GHG emissions of the specific company for the base year, the reduction target for the specific company to reduce the GHG emissions of the specific company for the forecast year compared to the emissions of the base year, and the total sales plan of the specific company for the forecast year. Non-transitory storage medium. [Explanation of symbols]

[0062] 10. Information processing equipment 11 CPU 14. Storage 15 Input section 16 Display 101 Acquisition Department 102 Emissions Forecasting Department

Claims

1. An information processing device that predicts GHG (Green House Gas) emissions from specific products to be purchased from a specific company for a future prediction year, an input unit that receives a total purchase amount of the specific product to be purchased from the specific company for the forecast year; an acquisition unit that acquires the GHG emissions of the specific company in a base year, a reduction target for the specific company to reduce its GHG emissions for the forecast target year relative to its GHG emissions for the base year, and a total sales plan of the specific company for the forecast target year; an emissions prediction unit that predicts GHG emissions from the specific products to be purchased from the specific company for the target year of prediction based on a total purchase amount of the specific products to be purchased from the specific company for the target year of prediction, GHG emissions of the specific company in a base year, a reduction target for the specific company to reduce GHG emissions for the target year of prediction compared to emissions in the base year, and a total sales plan of the specific company for the target year of prediction; An information processing device comprising:

2. The information processing apparatus according to claim 1 , wherein the emission amount prediction unit predicts the GHG emission amount Z for the specific product to be purchased from the specific company for the prediction target year according to the following formula: Z=α×P×Y / 100X where the reduction target is to reduce GHG emissions in the forecast year to α parts by weight per 100 parts by weight of GHG emissions in the base year, P is the GHG emissions of the specific company in the base year, Y is the total purchase amount of the specific product to be purchased from the specific company for the forecast year, and X is the total sales plan of the specific company for the forecast year.

3. The information processing device according to claim 2 , wherein the emission amount prediction unit further predicts a GHG emission coefficient K for the specific product to be purchased from the specific company for the prediction target year according to the following formula: K = Z / E Here, E is the purchase amount of the specific product to be purchased from the specific company for the forecast target year.

4. the input unit receives, for each of a plurality of specific companies that are purchasers of the specific products, a total purchase amount of the specific products to be purchased from the specific companies for the forecast year; the acquisition unit acquires, for each of the plurality of specific companies, the GHG emissions of the specific company in a base year, a reduction target for the specific company to reduce its GHG emissions for the forecast target year relative to its GHG emissions for the base year, and a total sales plan for the specific company for the forecast target year; the emission prediction unit predicts, for each of the plurality of specific companies, GHG emissions associated with the specific products to be purchased from the specific companies for a future target year of prediction, based on a total purchase amount of the specific products to be purchased from the specific companies for the target year of prediction, GHG emissions of the specific companies for a base year, a reduction target for the specific companies to reduce their GHG emissions for the target year of prediction relative to the emissions in the base year, and a total sales plan of the specific companies for the target year of prediction; The information processing device according to claim 1 , further comprising: predicting a total sum of GHG emissions from the specific products to be purchased from the specific company for the target year of prediction as the GHG emissions from the specific products for the target year of prediction.

5. The acquisition unit further acquires the GHG emissions for the specific product for one year including the current time, The information processing device according to claim 2 , wherein the emission prediction unit further predicts a target reduction amount S of GHG emissions for the specific product for the prediction target year according to the following formula: S = R - Z Here, R is the GHG emission amount for the specific product for one year including the present time.

6. The acquisition unit further acquires a GHG emission coefficient for the specific product for one year including the current time, 6. The information processing device according to claim 5, wherein the emission amount prediction unit further predicts a reduction target amount ΔK of the GHG emission coefficient for the prediction target year relative to the GHG emission coefficient for the specific commodity for one year including the present time, according to the following formula: ΔK = R / D−K Here, D is the purchase amount of the specific product for the year including the present time.

7. 7. The information processing device according to claim 1, wherein the GHG emissions are emissions equivalent to carbon dioxide mass conversion.

8. 1. An information processing method for predicting GHG emissions associated with a specific product to be purchased from a specific company for a future prediction year, comprising: receiving a total purchase amount of the specific product to be purchased from the specific company for the forecast year; Obtaining the GHG emissions of the specific company for a base year, a reduction target for the specific company to reduce its GHG emissions for the forecast target year relative to its GHG emissions for the base year, and a total sales plan for the specific company for the forecast target year; Based on the total purchase amount of the specific product to be purchased from the specific company for the prediction target year, the GHG emissions of the specific company in a base year, a reduction target for the specific company to reduce its GHG emissions for the prediction target year compared to the emissions in the base year, and the specific company's total sales plan for the prediction target year, the GHG emissions of the specific product to be purchased from the specific company for the prediction target year are predicted for the prediction target year. An information processing method performed by a computer.

9. An information processing program for predicting GHG emissions from a specific product to be purchased from a specific company for a future prediction year, receiving a total purchase amount of the specific product to be purchased from the specific company for the forecast year; Obtaining the GHG emissions of the specific company for a base year, a reduction target for the specific company to reduce its GHG emissions for the forecast target year relative to its GHG emissions for the base year, and a total sales plan for the specific company for the forecast target year; Based on the total purchase amount of the specific product to be purchased from the specific company for the prediction target year, the GHG emissions of the specific company in a base year, a reduction target for the specific company to reduce its GHG emissions for the prediction target year compared to the emissions in the base year, and the specific company's total sales plan for the prediction target year, the GHG emissions of the specific product to be purchased from the specific company for the prediction target year are predicted for the prediction target year. An information processing program that causes a computer to execute certain tasks.

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