Information processing system and information processing method

Abstract

To simply calculate compensation for greenhouse gas emission rights no matter what the level of greenhouse gas emissions is.SOLUTION: According to the present invention, an information processing system performs machine learning of an emission calculation model, performs machine learning of an emission right unit price calculation model, acquires a future emission estimation value by inputting a future value about activities of an undertaker in a machine learned emission calculation model, acquires future emission unit price by inputting a future input about a market environment in the machine learned emission right unit price model, calculates compensation for an emission limit margin value or an emission limit insufficient value in accordance with a magnitude relation between the emission limit value and an emission estimation value, and displays the calculated compensation.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to an information processing system and an information processing method for evaluating the profitability associated with a business's carbon offset activities. [Background technology]

[0002] Currently, there are concerns about the impact of the expansion of social activities on the global scale on climate change and ecosystems. Efforts to reduce the impact of social activities on the environment and make society sustainable, such as achieving carbon neutrality and sustainability, are essential requirements not only for national and local governments but also for individual business operators.

[0003] In order to show the impact of their business activities on the environment, businesses need to continually respond to the trend of ESG (Environment, Social, Governance) investment and the disclosure requirements of the Task Force on Climate-related Financial Disclosures. In addition, to achieve carbon neutrality, businesses need to understand the greenhouse gas portfolio associated with their business activities and develop scenarios for a long-term transition.

[0004] The path to carbon neutrality involves not only reducing greenhouse gas emissions, but also procuring energy through renewable or generated energy. It is also essential to utilize carbon offsetting activities, such as green certificates, afforestation, and the trading of carbon credits, which are the equivalent of greenhouse gas emission rights.

[0005] Patent Document 1 is CO-- 2 It is related to an emission rights selling price determination support device. The summary is that "the calculation unit calculates the CO of each power generation plant of each power generation company. 2 CO by collection company 2 CO after recovery 2 Calculate the emissions and CO 2 The total amount of emissions (after collection) is CO 2 CO held by collection company 2It is judged whether the total amount is smaller than the allowable emission amount determined by the emission rights. If the total amount is larger than the allowable emission amount, the CO 2 Accept the change in the recovery rate and 2 CO of each power plant to be recovered 2 CO after recovery 2 Recalculate the amount of emissions. On the other hand, if the total amount is less than the allowable amount of emissions, 2 Disposal costs and CO2 recovered 2 Profits from secondary use of CO 2 CO held by collection company 2 Emissions Credit CO 2 Emissions based on CO2 units 2 CO per Emission 2 The selling price of the emission rights will be determined. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] JP 2006-350401 A Summary of the Invention [Problem to be solved by the invention]

[0007] However, the device according to Patent Document 1 is intended to be implemented in a power plant. 2 Emissions and CO 2 The format of the various data that needs to be prepared in order to calculate the selling price of emission rights is fixed, and there is an area that needs improvement in order to make it easier to use for general businesses other than power generation companies.

[0008] In addition, the device according to Patent Document 1 detects CO when the total amount is greater than the allowable discharge amount. 2 Accept the change in the recovery rate and the CO after recovery 2 It just recalculates the emissions and 2 The focus is not on determining the price of purchasing the emission rights. The device is designed to determine the CO if the total amount is less than the allowable emissions.2 Determine the selling price of emission rights. 2 Disposal costs, CO recovered 2 Profits and COs obtained from secondary use of 2 CO held by collection company 2 Emissions Credit CO 2 If you don't know the amount of emissions, 2 A separate measure is needed to determine the selling price of emission rights. Therefore, an object of the present invention is to enable a general business entity to easily calculate the price of greenhouse gas emission rights, regardless of the level of greenhouse gas emissions. [Means for solving the problem]

[0009] The information processing system of the present invention includes an emission learning unit that machine-learns an emission calculation model, which takes values ​​related to an enterprise's activities as input and outputs the amount of greenhouse gas emissions emitted by the enterprise, using past values ​​related to the enterprise's activities and the past values ​​of the greenhouse gas emissions emitted by the enterprise as learning data; an emission credit unit price learning unit that machine-learns an emission credit unit price calculation model, which takes values ​​related to the environment of a market in which greenhouse gas emission credits are traded as input and outputs the unit price of greenhouse gas emission credits in the market, using past values ​​related to the market environment and the past values ​​of the emission credit unit price as learning data; a processing condition setting unit that receives future values ​​related to the enterprise's activities, future values ​​related to the market environment, and an emission limit value corresponding to the greenhouse gas emission credits held by the enterprise; and inputs the received future values ​​related to the enterprise's activities into the machine-learned emission calculation model. an emission credit unit price estimation unit that inputs a future value related to the accepted market environment into the machine-learned emission credit unit price calculation model and acquires an estimated future emission credit unit price from the machine-learned emission credit unit price calculation model; and a calculation unit that, when the emission limit value is greater than the estimated emission value, calculates a consideration for the emission limit margin value by multiplying the emission credit unit price estimated value by an emission limit margin value that is a result of subtracting the emission estimate value from the emission limit value, and, when the emission limit value is smaller than the estimated emission value, calculates a consideration for the emission limit deficit value by multiplying the emission credit unit price estimated value by an emission credit unit price estimated value and displays the calculated consideration. Other means will be described in the description of the embodiment of the invention. Effect of the Invention

[0010] According to the present invention, a general business entity can easily calculate the price of greenhouse gas emission rights, regardless of the level of greenhouse gas emissions. [Brief description of the drawings]

[0011] [Figure 1] FIG. 1 is a diagram illustrating a configuration of an information processing system. [Diagram 2] 13 is a flowchart of a discharge amount calculation model learning process. [Diagram 3] 13 is a flowchart of an emission credit unit price calculation model learning process procedure. [Figure 4] 13 is a flowchart of a fee calculation process. [Diagram 5] 13 is an example of a discharge amount estimation explanatory variable receiving screen. [Figure 6] 13 is an example of an explanatory variable acceptance screen for estimating an emission credit unit price. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] Hereinafter, an embodiment of the present invention (hereinafter referred to as "the present embodiment") will be described in detail with reference to the drawings. This embodiment is an example of a general business entity carrying out carbon offset activities. "General" means that it does not matter what type of business or size of company. A business entity can carry out carbon offset activities by 2 It would be convenient if companies could use information about their daily business activities to calculate (estimate) emissions. 2 It would be convenient if you could use the environmental information available in the market to calculate (estimate) the unit price of emission rights. 2 is an example of a greenhouse gas.

[0013] (Configuration of Information Processing System) 1 is a diagram showing the configuration of an information processing system 1. The information processing system 1 is a general computer. The information processing system 1 includes a central control device 11, an input device 12 such as a keyboard or a mouse, an output device 13 such as a display, a main memory device 14, an auxiliary memory device 15, and a communication device 16. The auxiliary memory device 15 stores an emission amount database 31, an emission credit unit price database 35, an emission amount calculation model 39, and an emission credit unit price calculation model 40. The auxiliary memory device 15 may be configured independent of the information processing system 1.

[0014] The emissions database 31 includes net emissions information 32, business activity emissions information 33, and renewable energy generation emission reduction information 34. The business activity emissions information 33 includes direct emissions information 33a, indirect emissions information 33b, and other parties' emissions information 33c (described in detail below). The emissions credit unit price database 35 includes emission credit unit price information 36, weather information 37, and business activity information 38 (described in detail below).

[0015] The processing condition setting unit 21, the emission amount learning unit 22, the emission amount estimation unit 23, the emission amount estimation unit 24, the emission amount estimation unit 25, and the calculation unit 26 written in the main memory device 14 are programs. In the following description, when the subject is described as "XX unit," this means that "the central control device 11 reads each program from the auxiliary memory device 15 to the main memory device 14, thereby realizing the function previously described in the program."

[0016] (Emissions Database 31) The business activity emissions information 33 stores business activity emissions in chronological order. The business activity emissions are greenhouse gas emissions resulting from the business activities of the company in the past. In this embodiment, the business activity emissions information 33 stores the business activity emissions of the entire supply chain in the business of the company after classifying them into three types.

[0017] Of these, the direct emission information 33a stores direct emission amounts in chronological order. Direct emission amounts are caused by greenhouse gas emissions by the business itself. For example, greenhouse gas emissions emitted by the business itself in the fuel combustion process or product manufacturing process are direct emission amounts.

[0018] Indirect emission information 33b stores indirect emissions in chronological order. Indirect emissions are caused by a business's consumption of energy supplied by another party. For example, greenhouse gas emissions caused by a business's consumption of energy (electricity, heat, steam, etc.) supplied by another party to the business are indirect emissions. In this case, it is the other party (the energy supplier) that actually emitted the greenhouse gases. The business that used the energy is assumed to have emitted greenhouse gases in the calculation of business activity emissions.

[0019] The third-party emission information 33c stores third-party emissions in chronological order. Third-party emissions result from greenhouse gas emissions by third parties (other than energy suppliers) in the supply chain to which the business operator belongs. For example, greenhouse gas emissions by parts manufacturers upstream in the supply chain and assembly subcontractors downstream are third-party emissions. In this case, it is naturally third parties that actually emitted the greenhouse gases. The business operator representing the supply chain is assumed to have emitted greenhouse gases in the calculation of business activity emissions.

[0020] The renewable energy generation emission reduction amount information 34 stores the renewable energy generation emission reduction amount in chronological order. The renewable energy generation emission reduction amount is the greenhouse gas emission amount reduced by a business operator in the past by implementing renewable energy and / or energy generation. For example, a business operator who generates renewable energy and / or generated energy using solar power generation equipment or wind power generation equipment is evaluated as having reduced the amount of greenhouse gas that would have been emitted if they had used thermal power generation equipment. This evaluated amount is the renewable energy generation emission reduction amount.

[0021] The net emission information 32 stores net emissions in chronological order. Net emissions are the net amount of greenhouse gas emissions resulting from the past activities of a business. Net emissions are basically the value obtained by subtracting the amount of emissions reduced by renewable energy generation from the amount of emissions from business activities, but this is often not so simple (see below for details).

[0022] (Emissions Credit Unit Price Database 35) Greenhouse gas emission rights are traded in the market for a fee. While the emission amount database 31 is a historical database relating to individual businesses, the emission right unit price database 35 is a historical database relating to such a market, and is more macroscopic.

[0023] The emission allowance unit price information 36 stores the emission allowance unit price in chronological order. The emission allowance unit price is the price (yen / kg) per unit of greenhouse gas emission allowance in the market in the past.

[0024] The weather information 37 stores meteorological observation values ​​in chronological order. The meteorological observation values ​​are past observation values ​​of the natural environment of the area to which the market belongs. The meteorological observation values ​​here include at least one of temperature, humidity, wind speed, wind direction, sunshine hours, and sunshine angle.

[0025] Business activity information 38 stores macroeconomic indicators in chronological order. Macroeconomic indicators are indicators that directly or indirectly indicate the past level of economic activity in the region to which the market belongs, such as national (prefectural and municipal) income, savings rate, working population, consumer population, population flow, industrial production, number of housing starts, and volume of transported goods. Macroeconomic indicators can also be said to be statistics on the economic environment of the region to which the market belongs.

[0026] (Emissions calculation model 39) The emission calculation model 39 is, for example, the following function F 1 It is. G t =F 1 (p t ,q t ,r t ,s t ) Here, G t is the net emission at time t. t is the direct emissions at time t. q t is the indirect emissions at time t. t is the amount of emissions from other parties at time t. t is the amount of emission reduction due to renewable energy generation at time t.

[0027] Function F 1 may be any function such as a linear function, a higher-order function, a logarithmic function, an exponential function, a trigonometric function, or a composite or combination function of these functions. 1 has multiple parameters (coefficients, etc.) that determine its position and shape in multi-dimensional space.

[0028] Furthermore, the function F 1 is the vector “(p t ,q t ,r t ,s t )” to get the scalar “G t In this case, a propagation parameter is defined for each node in the intermediate layer, which determines how much information is to be propagated to which node in the next intermediate layer.

[0029] The discharge learning unit 22 uses “p t ,q t ,r t ,s t " and "G t The above-mentioned parameters or the propagation parameters can be optimized by machine learning (including simpler multiple regression analysis, etc.) of the emission calculation model 39 using supervised learning data, which is a set of combinations of ".

[0030] (Necessity of emission calculation model39) The above-mentioned "p t ,q t ,r t ,s tIf the units of " and " are standardized as "kg", for example, the function F 1 becomes extremely simple, consisting only of addition and subtraction. t =p t +q t +r t -s t " holds true.

[0031] However, most businesses use units other than "kg" to measure weight. t ,q t ,r t ,s t ". For example, transportation companies often manage figures equivalent to direct emissions in terms of vehicle distance traveled (km), fuel consumption (kl), fuel purchase price (yen), and transported cargo weight (tons). Depending on the industry, production volume (units, tons) and factory operation rate (%) may be managed as figures equivalent to direct emissions. The same applies to indirect emissions, third-party emissions, and emission reductions from renewable energy generation.

[0032] Therefore, an emission calculation model 39 that can use units that are commonly used by businesses is required. t ,q t ,r t ,s t Although each of these is named "XX emissions" for ease of explanation, it is a broad concept that can ultimately be converted into the weight (kg) of greenhouse gases.

[0033] For example, if a business is a department store, any information such as the number of customers, the temperature in the store, the amount of merchandise sold, and the sales amount of merchandise can be stored as “p t ,q t ,r t The number of solar panels installed on the roof of a store, the operation rate, and the hours of sunlight on the roof of a store can be considered as “s t "It could become.

[0034] (Emissions rights unit price calculation model 40) The emission rights unit price calculation model 40 uses, for example, the following function F 2It is. W t =F 2 (x t ,y t ) Here, W t is the unit price of emission rights at time t. x t is the meteorological observation at time t. y t is the macroeconomic indicator at time t.

[0035] Function F 2 may be any function such as a linear function, a higher-order function, a logarithmic function, an exponential function, a trigonometric function, or a composite or combination function of these functions. 2 has multiple parameters (coefficients, etc.) that determine its position and shape in multi-dimensional space.

[0036] Furthermore, the function F 2 is the vector “(x t ,y t )” to get the scalar “W t In this case, a propagation parameter is defined for each node in the intermediate layer, which determines how much information is to be propagated to which node in the next intermediate layer.

[0037] The emission allowance unit price learning unit 24 uses “x t ,y t " and "W t The above-mentioned parameters or propagation parameters can be optimized by machine learning (including simpler multiple regression analysis, etc.) of the emission credit unit price calculation model 40 using supervised learning data, which is a set of combinations of "

[0038] In addition, the emission rights unit price calculation model40 is a system in which a business operator t ,y t For example, if the operator is a railway operator, the temperature along the railway line, the population along the railway line, and the railway transport population including competitors can be used as "x t ,y t"It could become.

[0039] (summary) As is clear from the above, the emission amount learning unit 22 inputs values ​​related to the activities of the business operator and machine-learns an emission amount calculation model 39 that outputs the amount of greenhouse gas emissions emitted by the business operator, using past values ​​related to the activities of the business operator and past values ​​of the amount of greenhouse gas emissions emitted by the business operator as learning data. Here, the "values ​​related to the activities of the business operator" correspond to direct emissions, indirect emissions, emissions from others, and emissions reductions due to renewable energy generation. The "amount of greenhouse gas emissions emitted by the business operator" corresponds to net emissions.

[0040] The emission credit unit price learning unit 24 inputs values ​​related to the environment of the market where greenhouse gas emission credits are traded, and machine-learns an emission credit unit price calculation model 40 that outputs the unit price of greenhouse gas emission credits in the market, using past values ​​related to the market environment and past values ​​of the emission credit unit price as learning data. The "market environment values" here correspond to meteorological observation values ​​and macroeconomic indicators.

[0041] (Processing Procedure) The processing procedures are explained below. There are three processing procedures: an emission amount calculation model learning processing procedure, an emission allowance unit price calculation model learning processing procedure, and a consideration calculation processing procedure. Before the consideration calculation processing procedure is executed, the emission amount calculation model learning processing procedure and the emission allowance unit price calculation model learning processing procedure are executed.

[0042] (Emissions calculation model learning process procedure) FIG. 2 is a flowchart of the emission calculation model learning process. In step S101, the emission amount learning unit 22 of the information processing system 1 acquires the net emission amount information 32. Specifically, the emission amount learning unit 22 acquires the net emission amount information 32 from the emission amount database 31.

[0043] In step S102, the emission amount learning unit 22 acquires the business activity emission amount information 33. Specifically, the emission amount learning unit 22 acquires from the emission amount database 31 the direct emission amount information 33a, the indirect emission amount information 33b, and the other party emission amount information 33c.

[0044] In step S103, the emission amount learning unit 22 acquires the renewable energy generation emission reduction amount information 34. Specifically, the emission amount learning unit 22 acquires the renewable energy generation emission reduction amount information 34 from the emission amount database 31.

[0045] In step S104, the emission amount learning unit 22 performs machine learning on the emission amount calculation model 39. Specifically, firstly, the emission amount learning unit 22 acquires from the auxiliary storage device 15 the emission amount calculation model 39 before machine learning. Secondly, the discharge learning unit 22 uses the information acquired in steps S101 to S103 to determine the discharge amount learning unit 22 as an explanatory variable “p t ,q t ,r t ,s t " and "G t We generate supervised training data that is a set of combinations of "

[0046] Thirdly, the emission amount learning unit 22 uses the supervised learning data generated in the “second” step S104 to machine-train the emission amount calculation model 39 acquired in the “first” step S104 (optimize parameters). Fourthly, the emission amount learning unit 22 stores the emission amount calculation model 39 that has undergone machine learning in the auxiliary storage device 15. Thereafter, the emission amount calculation model learning process procedure ends.

[0047] (Emissions credit unit price calculation model learning process procedure) FIG. 3 is a flowchart of the emission credit unit price calculation model learning process. In step S201, the emission permit unit price learning unit 24 of the information processing system 1 acquires the emission permit unit price information 36. Specifically, the emission permit unit price learning unit 24 acquires the emission permit unit price information 36 from the emission permit unit price database 35.

[0048] In step S202, the emission credit unit price learning unit 24 acquires the weather information 37. Specifically, the emission credit unit price learning unit 24 acquires the weather information 37 from the emission credit unit price database 35.

[0049] In step S203, the emission credit unit price learning unit 24 acquires the business activity information 38. Specifically, the emission credit unit price learning unit 24 acquires the business activity information 38 from the emission credit unit price database 35.

[0050] In step S204, the emission credit unit price learning unit 24 performs machine learning on the emission credit unit price calculation model 40. Specifically, first, the emission credit unit price learning unit 24 acquires from the auxiliary storage device 15 the emission credit unit price calculation model 40 before machine learning. Secondly, the emission rights unit price learning unit 24 uses the information acquired in steps S201 to S203 to calculate the explanatory variable “x t ,y t " and "W t We generate supervised training data that is a set of combinations of "

[0051] Third, the emission credit unit price learning unit 24 uses the supervised learning data generated in “second” step S204 to machine-train the emission credit unit price calculation model 40 acquired in “first” step S204 (optimize parameters). Fourthly, the emission credit unit price learning unit 24 stores the machine-learned emission credit unit price calculation model 40 in the auxiliary storage device 15. Thereafter, the emission credit unit price calculation model learning process procedure is terminated.

[0052] (Compensation calculation procedure) FIG. 4 is a flowchart of a fee calculation process. In step S301, the processing condition setting unit 21 of the information processing system 1 accepts the values ​​of explanatory variables at a target time point. Specifically, first, the processing condition setting unit 21 displays an explanatory variable acceptance screen 41 for estimating a discharge amount (FIG. 5) on the output device 13.

[0053] Secondly, the processing condition setting unit 21 accepts an input (selection) of an explanatory variable value at a future target time point by a user (such as a business operator) via the input device 12. Although we are in the middle of Fig. 4, the explanation will now move to Fig. 5.

[0054] (Explanatory variable acceptance screen for estimating emissions) FIG. 5 is an example of the explanatory variable reception screen 41 for estimating emission amount. The explanatory variable reception screen 41 for estimating emission amount displays direct emission amount, indirect emission amount, emission amount of others, and emission reduction amount of renewable energy generation energy in chronological order. These are acquired from the direct emission amount information 33a, the indirect emission amount information 33b, the emission amount of others information 33c, and the emission reduction amount of renewable energy generation energy information 34, respectively. "#" indicates different numerical values ​​in an abbreviated manner. For example, the user may select different past date values ​​for each explanatory variable (thick solid line), or may select all explanatory variables together with a common past date (thick dashed line). Note that the past dates are for explanatory purposes, and weekdays / holidays and market opening and closing are ignored (similar to FIG. 6).

[0055] Now, let us assume that the user selects the explanatory variable values ​​within the thick dashed lines. This indicates, for example, the following: - The user wants to know the emissions for the following year, for example, on January 7, 2024. -As a premise, we would like to input the explanatory variables as of January 7, 2021 into the trained emissions calculation model39. For explanation, refer back to FIG.

[0056] Thirdly, in step S301 to which the process has returned, the process condition setting unit 21 displays on the output device 13 the explanatory variable acceptance screen 42 for estimating the emission credit unit price (FIG. 6).

[0057] Fourth, the processing condition setting unit 21 accepts an input (selection) of explanatory variables at a future target time point by a user (such as a business operator) via the input device 12. Although we are in the middle of FIG. 4, the explanation will now shift to FIG.

[0058] (Explanatory variable acceptance screen for estimating emission credit unit price) 6 is an example of the explanatory variable reception screen 42 for estimating the unit price of emission credits. The explanatory variable reception screen 42 for estimating the unit price of emission credits displays meteorological observation values ​​and macroeconomic indicators in chronological order. These are obtained from the meteorological information 37 and the business activity information 38, respectively. "#" indicates a different numerical value in an abbreviated manner. For example, the user may select values ​​of different past dates for each explanatory variable (bold solid line), or may select all variables together with a common past date (bold dashed line).

[0059] Now, let us assume that the user selects the explanatory variable value within the thick solid line. This indicates, for example, the following: The user wants to know the unit price of emission rights on a certain day in early January 2024 of the following year, for example. As a prerequisite, we would like to input the weather observation values ​​for January 2, 2021 and the macroeconomic indicators for January 3, 2021 into the trained emission rights unit price calculation model 40.

[0060] The above is an example in which the values ​​of explanatory variables for a certain day in the future are selected from past actual values. However, the processing condition setting unit 21 may accept the user's selection of a future period spanning multiple days by using a thick dashed line. Furthermore, at this time, the processing condition setting unit 21 may accept the user's modification of the past actual values. In either case, the processing condition setting unit 21 will obtain the length of the target period in the future and the values ​​of each explanatory variable (average values ​​per day if the period spans multiple days). Refer back to FIG. 4 for explanation.

[0061] In step S302, the processing condition setting unit 21 accepts an emission limit value. Specifically, the processing condition setting unit 21 accepts an emission limit value input by a user via the input device 12. The emission limit value (kg) corresponds to the greenhouse gas emission credits held (already purchased) by the business operator. The business operator can emit greenhouse gases equal to or less than the emission limit value at no additional cost.

[0062] In step S303, the emission amount estimation unit 23 of the information processing system 1 calculates an emission amount estimation value. Specifically, firstly, the emission amount estimation unit 23 acquires the machine-learned emission amount calculation model 39 from the auxiliary storage device 15. Secondly, the emission amount estimation unit 23 inputs the values ​​of the explanatory variables input in “second” of step 301 into the machine-learned emission amount calculation model 39, and obtains the emission amount estimation value output by the machine-learned emission amount calculation model 39 as the objective variable.

[0063] In step S304, the emission credit unit price estimation unit 25 of the information processing system 1 calculates an estimated value of the emission credit unit price. Specifically, first, the emission credit unit price estimation unit 25 acquires the machine-learned emission credit unit price calculation model 40 from the auxiliary storage device 15. Secondly, the emission credit unit price estimation unit 25 inputs the values ​​of the explanatory variables input in “4” of step 301 into the machine-learned emission credit unit price calculation model 40, and obtains the emission credit unit price estimated value output as the objective variable by the machine-learned emission credit unit price calculation model 40.

[0064] In step S305, the calculation unit 26 of the information processing system 1 judges whether or not the emission limit value is greater than the emission estimated value. Specifically, if the emission limit value accepted in step S302 is greater than the emission estimated value calculated in step S303 (step S305 "Yes"), the calculation unit 26 proceeds to step S306. Otherwise (step S305 "No"), the calculation unit 26 proceeds to step S308. Note that if "emission limit value = emission estimated value", the calculation unit 26 displays on the output device 13 that "the compensation is zero" and then ends the compensation calculation processing procedure.

[0065] In step S306, the calculation unit 26 calculates an emission limit margin value. Specifically, the calculation unit 26 subtracts the emission estimated value calculated in step S303 from the emission limit value received in step S302, and sets the result as the emission limit margin value.

[0066] In step S307, the calculation unit 26 calculates the value of the emission limit margin value. Specifically, the calculation unit 26 multiplies the emission limit margin value calculated in step S306 by the estimated emission credit unit price calculated in step S304, and sets the result as the value of the emission limit margin value. The value of the emission limit margin value is the income (sales amount) for the business operator. Then, the process proceeds to step S310.

[0067] In step S308, the calculation unit 26 calculates the emission limit shortfall value. Specifically, the calculation unit 26 subtracts the emission limit value received in step S302 from the emission amount estimated value calculated in step S303, and sets the result as the emission limit shortfall value.

[0068] In step S309, the calculation unit 26 calculates the price of the emission limit shortfall. Specifically, the calculation unit 26 multiplies the emission limit shortfall calculated in step S308 by the estimated emission credit unit price calculated in step S304, and sets the result as the price. The price here is an expenditure (purchase price) for the business operator. Then, the process proceeds to step S310.

[0069] In step S310, the calculation unit 26 displays the consideration. Specifically, the calculation unit 26 displays the consideration calculated in step S307 or S309 on the output device 13 together with the selling price or the purchasing price. The calculation unit 26 may output the consideration to any other device via the communication device 16. Thereafter, the consideration calculation process procedure ends.

[0070] (Effects of this embodiment) (1) The information processing system enables a general business operator to calculate the compensation for the greenhouse gas emission limit surplus and the compensation for the emission limit shortfall. (2) The information processing system may display the consideration related to the purchase or sale of emission rights. (3) The information processing system can learn the emission calculation model using information that businesses manage on a daily basis. (4) The information processing system can learn the emission rights unit price calculation model using information that businesses manage on a daily basis.

[0071] The present invention is not limited to this embodiment, and includes various modified examples. For example, this embodiment has been described in detail to clearly explain the present invention, and is not necessarily limited to having all of the configurations described. In addition, it is possible to replace a part of the configuration of this embodiment with another configuration, and it is also possible to add another configuration to the configuration of this embodiment. In addition, it is possible to add, delete, or replace a part of the configuration of this embodiment with another configuration. [Explanation of symbols]

[0072] 1. Information Processing Systems 11 Central control unit 12 Input Devices 13 Output Devices 14 Main memory 15 Auxiliary storage 16 Communication Equipment 21 Processing condition setting section 22 Emissions Learning Section 23 Emission Estimation Department 24 Emissions Rights Unit Price Learning Section 25 Emissions Rights Unit Price Estimation Department 26 Calculation section 31 Emissions Database 32 Net Emissions Information 33 Business activity emissions information 33a Direct emissions information 33b Indirect emissions information 33c Others' emissions information 35 Emissions Credit Unit Price Database 36 Emissions credit unit price information 37 Weather Information 38 Business activity information 39 Emissions calculation model 40 Emissions rights unit price calculation model 41 Screen for accepting explanatory variables for estimating emissions 42 Screen for accepting explanatory variables for estimating emission rights unit price

Claims

1. an emission learning unit that machine-learns an emission calculation model in which values ​​related to the activities of an enterprise are input and the amount of greenhouse gas emissions emitted by the enterprise are output, using past values ​​related to the activities of the enterprise and past values ​​of the amount of greenhouse gas emissions emitted by the enterprise as learning data; an emission credit unit price learning unit that machine-learns an emission credit unit price calculation model, which receives an input of a value related to an environment of a market in which greenhouse gas emission credits are traded and outputs the unit price of greenhouse gas emission credits in the market, using past values ​​related to the environment of the market and past values ​​of the emission credit unit price as learning data; a processing condition setting unit that receives a future value related to the activity of the business operator, a future value related to the environment of the market, and an emission limit value corresponding to the greenhouse gas emission credits held by the business operator; an emission estimation unit that inputs future values ​​related to the activities of the accepted business entity into the emission calculation model that has been machine-learned, and acquires future emission estimation values ​​from the emission calculation model that has been machine-learned; an emission credit unit price estimation unit that inputs future values ​​related to the accepted market environment into the machine-learned emission credit unit price calculation model and acquires an estimated value of the future emission credit unit price from the machine-learned emission credit unit price calculation model; If the emission limit value is greater than the estimated emission value, calculating a value for the emission limit margin value by multiplying the emission limit margin value, which is a result of subtracting the estimated emission value from the emission limit value, by the estimated emission credit unit price; If the emission limit value is smaller than the estimated emission value, calculating a value for the emission limit deficit by multiplying the emission limit deficit value, which is a result of subtracting the emission limit value from the estimated emission value, by the estimated price of the emission rights; A calculation unit that displays the calculated price; An information processing system comprising:

2. The calculation unit is The consideration for the emission limit margin value is displayed as a selling price, Displaying the value of the emission limit shortfall as a purchase price; 2. The information processing system according to claim 1,

3. The value for the activity of the business operator is Direct emissions resulting from the greenhouse gas emissions of said business; Indirect emissions resulting from the business's consumption of energy supplied by others; Third-party emissions resulting from greenhouse gas emissions by other parties in the supply chain to which the business operator belongs; and The reduction includes at least one of the greenhouse gas emissions reduced by the business operator through the implementation of renewable energy and / or energy generation; 2. The information processing system according to claim 1,

4. The value of the market environment is Weather observations for the region in which the market is located; and Including at least one of macroeconomic indicators of the region to which the market belongs; 2. The information processing system according to claim 1,

5. The emission learning unit of the information processing system is an emission calculation model, which inputs values ​​related to the activities of a business and outputs the amount of greenhouse gas emissions emitted by the business, is machine-trained using past values ​​related to the activities of the business and past values ​​of the amount of greenhouse gas emissions emitted by the business as learning data; The emission rights unit price learning unit of the information processing system an emission rights unit price calculation model, which takes as input a value related to the environment of a market in which greenhouse gas emission rights are traded and outputs the unit price of greenhouse gas emission rights in said market, is machine-trained using past values ​​related to the environment of said market and past values ​​of said emission rights unit price as learning data; The processing condition setting unit of the information processing system includes: receiving a future value for the activity of the entity, a future value for the environment of the market, and an emission limit value corresponding to greenhouse gas emission credits held by the entity; The emission estimation unit of the information processing system Inputting future values ​​related to the activities of the accepted business into the machine-learned emission calculation model, and obtaining future emission estimates from the machine-learned emission calculation model; The emission rights unit price estimation unit of the information processing system inputting future values ​​relating to the accepted market environment into the machine-learned emission credit unit price calculation model, and obtaining an estimated future emission credit unit price from the machine-learned emission credit unit price calculation model; The calculation unit of the information processing system If the emission limit value is greater than the estimated emission value, calculating a value for the emission limit margin value by multiplying the emission limit margin value, which is a result of subtracting the estimated emission value from the emission limit value, by the estimated emission credit unit price; If the emission limit value is smaller than the estimated emission value, calculating a value for the emission limit deficit by multiplying the emission limit deficit value, which is a result of subtracting the emission limit value from the estimated emission value, by the estimated price of the emission rights; Displaying said calculated consideration; An information processing method comprising:

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