Greenhouse gas emission reduction prediction system using animal waste

Abstract

This system provides a method for predicting greenhouse gas emission reductions using animal waste. [Solution] A system comprising at least one computer device, comprising a reduction prediction means for predicting the amount of greenhouse gas emissions to be reduced over a predetermined period by measures to reduce greenhouse gas emissions using animal excrement.

Description

Technical Field

[0001] The present invention relates to a system, a method, and a program.

Background Art

[0002] In recent years, initiatives aimed at reducing greenhouse gas emissions have been promoted worldwide. Facilities that raise animals, such as livestock business sites, fish farms, zoos, and aquariums, generate large amounts of animal excrement, so it is possible to implement measures to reduce greenhouse gas (GHG) emissions by utilizing the animal excrement.

Summary of Invention

Problems to be Solved by the Invention

[0003] At least one object of the present invention is to provide a system for predicting the reduction amount of greenhouse gas emissions achieved by utilizing animal excrement.

Means for Solving the Problems

[0004] The problems of the present invention are solved by: [1] A system comprising at least one computer device, the system comprising a reduction amount prediction unit that predicts the reduction amount of greenhouse gas emissions to be reduced in a predetermined period by a greenhouse gas emission reduction measure utilizing animal excrement; [2] The system according to [1], wherein the emission reduction measure includes performing methane fermentation utilizing excrement and / or using excrement as organic fertilizer; [3] The system according to [1] or [2], wherein the reduction amount prediction unit predicts the reduction amount based on the type of animal, the number of animals, the amount of excrement, and / or the type of feed given to the animals; [4] The system according to any one of [1] to [3], comprising a profit prediction unit that predicts profit from transferring carbon credits issued based on the predicted reduction amount; [5] A system according to any one of [1] to [4], comprising: a storage means for storing the cost or method for calculating the cost of implementing an emissions reduction measure in relation to the emissions reduction measure; and a cost forecasting means for predicting the cost incurred by implementing the emissions reduction measure by referring to the cost or method for calculating the cost stored in the storage means; [6] Profit forecasting means that predicts the profits obtained by implementing emissions reduction measures based on the revenues predicted by the revenue forecasting means and the costs predicted by the cost forecasting means. The system described in [5], comprising: [7] A method to be performed in a system comprising at least one computer device, comprising a reduction prediction step of predicting the amount of greenhouse gas emissions to be reduced over a predetermined period by measures to reduce greenhouse gas emissions using animal excrement; [8] A program that causes a computer device to function as a reduction prediction means for predicting the amount of greenhouse gas emissions to be reduced over a predetermined period by measures to reduce greenhouse gas emissions using animal waste; [9] A method performed on a computer device, comprising a reduction prediction step of predicting the amount of greenhouse gas emissions to be reduced over a predetermined period by measures to reduce greenhouse gas emissions using animal excrement; This can be achieved. [Effects of the Invention]

[0005] According to the present invention, a system can be provided for predicting the amount of greenhouse gas emission reduction using animal waste. [Brief explanation of the drawing]

[0006] [Figure 1] This is a block diagram showing the configuration of a system according to an embodiment of the present invention. [Figure 2] This is a block diagram showing the hardware configuration of a user terminal according to an embodiment of the present invention. [Figure 3]This is a block diagram showing the hardware configuration of a server device according to an embodiment of the present invention. [Figure 4] This figure shows a flowchart of the livestock information registration process according to an embodiment of the present invention. [Figure 5] This figure shows a flowchart of the reduction amount prediction process according to an embodiment of the present invention. [Modes for carrying out the invention]

[0007] Embodiments of the present invention will be described below with reference to the attached drawings. The following description of effects represents only one aspect of the effects of the embodiments of the present invention and is not limited to those described herein. The order of each process constituting the flowchart described below is not limited to any inconsistency or contradiction in the processing content. Furthermore, it is possible to omit some of the processes constituting the flowchart or to add new processes to each process constituting the flowchart, as long as no inconsistency or contradiction in the processing content occurs. In addition, the device that is the main entity executing each process constituting the flowchart can be changed to another device, as long as it does not contradict the spirit of the present invention. In this case, it is possible to change the processing content so as not to cause any inconsistency or contradiction in the processing content.

[0008] [System Configuration] Figure 1 is a block diagram showing the configuration of a system according to an embodiment of the present invention. System 1 shown in Figure 1 comprises a user terminal 2 and a server device 3. The user terminal 2 and the server device 3 are connected to each other via a communication network 4 so as to be able to communicate with each other. In System 1, either the user terminal 2 or the server device 3 can function as an information processing device. When either the user terminal 2 or the server device 3 functions as an information processing device, information is transmitted and received between the user terminal 2 and the server device 3 as needed.

[0009] The number of user terminals 2 is not particularly limited, as long as it is one or more. Furthermore, the server device 3 may function distributed across multiple computer devices. For example, instead of server device 3, distributed ledger technology such as blockchain may be used.

[0010] Furthermore, the processes performed in the following flowchart may be executed on a single computer device. In other words, System 1 only needs to have at least one computer device. For example, System 1 may consist of a user terminal 2.

[0011] User terminal 2 may be operated by a person in charge of an animal-keeping facility. Animal-keeping facilities may include livestock farms, fish farms, zoos, aquariums, etc. There may be one person or multiple people in charge. The person in charge may be in charge of a project related to reducing greenhouse gas emissions. Hereinafter, the person operating user terminal 2 will also be referred to as the user.

[0012] Server device 3 may be managed by the administrator of system 1. System 1 may also be equipped with an administrator terminal operated by the administrator.

[0013] Figure 2 is a block diagram showing the hardware configuration of a user terminal according to an embodiment of the present invention. The user terminal 2 comprises a control unit 21, RAM 22, storage unit 23, input unit 24, display unit 25, and communication interface 26, all of which are connected by a bus.

[0014] The control unit 21 consists of a CPU and ROM. The control unit 21 executes programs stored in the storage unit 23 and controls the user terminal 2. The RAM 22 is the work area of ​​the control unit 21. The storage unit 23 is a memory area for saving programs and data. In other words, the storage unit 23 functions as a recording medium that stores programs. The control unit 21 performs calculations based on the programs and data read from the RAM 22, as well as the data input at the input unit 24.

[0015] The display unit 25 has a display screen. The control unit 21 outputs a video signal for displaying an image on the display screen in accordance with the result of the arithmetic processing. Here, the display screen of the display unit 25 may be a touch panel provided with a touch sensor. In this case, the touch panel functions as the input unit 24.

[0016] The communication interface 26 can be connected to the communication network 4 by wireless or wired means, and can transmit and receive data to and from other computer devices via the communication network 4. The data received via the communication interface 26 is loaded into the RAM 22, and subjected to arithmetic processing by the control unit 21.

[0017] Figure 3 is a block diagram showing the hardware configuration of the server device according to an embodiment of the present invention. The server device 3 includes at least a control unit 31, a RAM 32, a storage unit 33, and a communication interface 34, which are connected to one another via an internal bus.

[0018] The control unit 31 is composed of a CPU and a ROM, executes a program stored in the storage unit 33, and controls the server device 3. Further, the control unit 31 includes an internal timer that measures time. The RAM 32 is a work area for the control unit 31. The storage unit 33 is a storage area for storing programs and data. That is, the storage unit 33 functions as a recording medium storing the programs. The control unit 31 reads the programs and data from the RAM 32, and executes program execution processing based on the information and the like received from each of the user terminals 2.

[0019] Further, the program may be stored in a recording medium such as a CD-ROM. In this case, the program stored in the recording medium may be installed in the user terminal 2 and / or the server device 3 to execute a predetermined function.

[0020] Alternatively, the program may be delivered from a computer device outside the system. In this case, the program delivered from the computer device outside the system may be installed on user terminal 2 and / or server device 3 to perform predetermined functions.

[0021] The following is an example of how a person in charge of a livestock farm (user) uses System 1 to decide on greenhouse gas emission reduction measures using animal waste to be adopted at the livestock farm. By registering information about the animals raised by the livestock farm (hereinafter also referred to as livestock information) in System 1, the user can simulate how much emissions can be reduced depending on what emission reduction measures are adopted at the livestock farm. For example, the user may log in to System 1 and register the livestock information as follows. When logging in to System 1, the user may, for example, enter information that can identify the livestock farm (hereinafter also referred to as the farm ID) and a password into User Terminal 2.

[0022] [Livestock Information Registration Process] Figure 4 is a flowchart showing the livestock information registration process according to an embodiment of the present invention. User terminal 2 receives input of livestock information from the user (step S101). User terminal 2 transmits the input livestock information to server device 3 (step S102). Server device 3 receives the livestock information transmitted from user terminal 2 (step S103). Server device 3 stores the received livestock information in storage unit 33 (step S104), and the livestock information registration process is completed.

[0023] The livestock information in step S101 is not particularly limited and can be designed as appropriate. Preferably, the livestock information in step S101 is information related to measures to reduce greenhouse gas emissions using animal waste.

[0024] The livestock information in step S101 may include, for example, the type of animal (cattle, pigs, chickens, etc.), the number of animals, the amount of excrement (feces, urine, etc.) (weight, volume, etc.), and the type of feed given to the animals (content of dietary fiber, starch, protein, fatty acids, additives, etc.). The type of feed given to the animals can be said to be information relevant to emission reduction measures, as it affects the amount of methane produced when animal excrement is methane-fermented and the nitrogen content contained in animal excrement.

[0025] In step S101, livestock information may be entered for all animals raised by the livestock business, or for some of the animals raised by the livestock business. For example, in step S101, livestock information may be entered for animals that the livestock business plans to use for emission reduction measures.

[0026] The livestock information in step S101 can include any other information besides that mentioned above. System 1 can refer to the livestock information as needed when making predictions such as greenhouse gas emission reductions and costs, as described later.

[0027] In step S104, livestock information may be stored in association with the business ID.

[0028] Once livestock information registration is complete, System 1 will be able to predict the amount of greenhouse gas emission reductions corresponding to emission reduction measures.

[0029] The types of greenhouse gases used to predict reductions are not particularly limited and can be designed as appropriate. For example, the types of greenhouse gases may include carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulfur hexafluoride (SF6), nitrogen trifluoride (NF3), etc. Furthermore, there may be two or more types of greenhouse gases. The types of greenhouse gases may or may not be convertible to carbon credits.

[0030] [Reduction amount prediction processing] Figure 5 is a flowchart showing the reduction amount prediction process according to an embodiment of the present invention. User terminal 2 accepts the user's selection of greenhouse gas (GHG) emission reduction measures (step S201).

[0031] Emission reduction measures in Step S201 are not particularly limited and can be designed as appropriate. Emission reduction measures may include methane fermentation using animal waste and / or the use of animal waste as organic fertilizer. By methane fermentation of waste (fermentation under anaerobic conditions) and recovery or combustion of the generated methane, greenhouse gas emissions can be considered reduced. Furthermore, by composting waste in a sealed composting facility or under aerobic conditions, the generation of methane and nitrous oxide can be suppressed, which can also be considered a reduction in greenhouse gas emissions. Additionally, by using waste as organic fertilizer, greenhouse gas emissions generated by the production of chemical fertilizers can be considered a reduction.

[0032] The server device 3 may pre-store at least one greenhouse gas emission reduction measure using animal waste in its storage unit 33. The user terminal 2 can display the emission reduction measures transmitted from the server device 3 on its display screen. In step S201, the user can select an emission reduction measure from the displayed measures for which they wish to perform a simulation. At this time, the user may select two or more emission reduction measures.

[0033] User terminal 2 transmits the selected emission reduction measures to server device 3 (step S202). Server device 3 receives the emission reduction measures transmitted from user terminal 2 (step S203). Server device 3 identifies the emission reduction measures for which the reduction amount will be predicted (step S204).

[0034] The method for identifying emission reduction measures in step S204 is not particularly limited and can be designed as appropriate. The server device 3 may identify emission reduction measures selected in step S201, or it may identify emission reduction measures different from those selected in step S201.

[0035] In step S204, along with the type of emission reduction measures, details of emission reduction measures that can be adopted at the livestock farm to which the user belongs may be specified. The server device 3 can refer to the livestock information registered in system 1 to specify details of emission reduction measures that can be adopted at the livestock farm to which the user belongs. Details of emission reduction measures may include, for example, the amount of excrement used for emission reduction measures, and the type and number of equipment to be introduced.

[0036] Server device 3 predicts the amount of greenhouse gas emission reduction that will be achieved over a predetermined period by the identified emission reduction measures (step S205).

[0037] The specified period in Step S205 is not particularly limited and can be designed as appropriate. The specified period may be equal to the period during which certification is possible under the carbon credit scheme. The period during which certification is possible varies depending on the type of carbon credit scheme and may be extended if certain conditions are met. The specified period may be, for example, 8 years or 16 years.

[0038] The types of carbon credit schemes are not particularly limited and can be designed as appropriate. Examples of carbon credit schemes include J-Credits, VCS (Verified Carbon Standard), CDM (Clean Development Mechanism), JCM (Joint Crediting Mechanism), and GS (Gold Standard). Users may also be able to choose the type of carbon credit scheme they utilize.

[0039] The method for predicting greenhouse gas emission reductions in step S205 is not particularly limited and can be designed as appropriate. In step S205, the prediction may be made by calculating the reduction amount according to the reduction calculation method stipulated by the carbon credit scheme used by the user.

[0040] For example, the amount of carbon dioxide (CO2) reduction when waste is fermented into methane and the resulting methane is recovered and burned may be calculated based on the following formula (1). The global warming potential (GWP) of methane may be the value determined by the carbon credit scheme used by the user. The combustion rate may be the percentage of methane that is burned. CO2 reduction amount = Amount of methane to be recovered (t) × GWP of methane × Combustion rate ... (1)

[0041] The "amount of methane to be recovered" in equation (1) above may be predicted based on, for example, the amount of excrement, the type of feed given to the animals, etc. For example, the server device 3 may store in advance the amount of methane that can be recovered by methane fermentation, corresponding to conditions such as the amount of excrement and the type of feed given to the animals.

[0042] Furthermore, the "amount of excrement" mentioned above may be predicted based on the type of animal, the number of animals, etc. For example, the server device 3 may have in advance stored the amount of excrement excreted by animals, corresponding to conditions such as the type of animal and the number of animals.

[0043] Furthermore, for example, the amount of carbon dioxide (CO2) reduction when excrement is used as organic fertilizer may be calculated based on the amount of excrement, or the amount of organic fertilizer produced from the excrement.

[0044] The reduction amount in step S205 may be converted into a reduction amount for a predetermined type of greenhouse gas. For example, the server device 3 may predict the reduction amount of methane by converting it into a reduction amount of carbon dioxide.

[0045] Server device 3 predicts the amount of carbon credits to be issued based on the predicted reduction in greenhouse gas emissions (step S206). For example, server device 3 may predict the amount of carbon credits to be issued by the carbon credit scheme used by the user if the entire predicted reduction in greenhouse gas emissions is certified.

[0046] Server device 3 predicts the revenue generated from transferring the predicted amount of carbon credits (step S207). The revenue may be the amount of carbon credits predicted in step S206 multiplied by the price per credit. The price per credit fluctuates, but server device 3 may predict the revenue based, for example, on the market price at the time step S207 is executed.

[0047] Server device 3 predicts the costs incurred by implementing the identified emission reduction measures (step S208).

[0048] The storage unit 33 of the server device 3 may store the costs associated with emission reduction measures, or the methods for calculating such costs. "Costs associated with emission reduction measures, or the methods for calculating such costs" may refer to "the costs incurred by implementing emission reduction measures, or the methods for calculating such costs." For example, the costs incurred by implementing emission reduction measures may include the costs of equipment introduced when implementing emission reduction measures (such as methane fermentation facilities and sealed composting facilities). The server device 3 can predict costs by calculating them based on the stored costs or cost calculation methods.

[0049] Server device 3 predicts the benefits obtained by implementing the identified emission reduction measures (step S209). The benefits may be the amount obtained by subtracting the costs predicted in step S208 from the revenue predicted in step S207.

[0050] The server device 3 transmits the prediction results to the user terminal 2 (step S210). In step S210, the types and / or details of emission reduction measures identified in step S204, the predicted amount of greenhouse gas emission reductions in step S205, the predicted amount of carbon credits in step S206, the predicted revenue in step S207, the predicted costs in step S208, and / or the predicted profits in step S209 may be transmitted as prediction results.

[0051] User terminal 2 receives the prediction results sent from server device 3 (step S211). User terminal 2 displays the received prediction results (step S212), and the reduction amount prediction process ends.

[0052] Users can review the displayed forecast results and consider whether to implement emission reduction measures at their livestock farms. The forecast results may be stored in the storage unit 33 of the server device 3, along with the farm ID, the time the processing was performed, and other relevant information.

[0053] Furthermore, if two or more emissions reduction measures are selected in step S201, details may be specified for each emissions reduction measure in step S204. In that case, in steps S205 to S209, the reduction amount, carbon credit amount, revenue, cost, and profit may be predicted for each emissions reduction measure. At the same time, the total reduction amount, total carbon credit amount, total revenue, total cost, and total profit from the two or more emissions reduction measures may also be predicted. In addition, in steps S210 to S212, the prediction results for each emissions reduction measure and the prediction results for the total of the two or more emissions reduction measures may be transmitted and displayed.

[0054] Furthermore, while the above example shows the user selecting emissions reduction measures in step S201, the user may also input or select conditions for the emissions reduction measures they wish to adopt, either in addition to or in conjunction with the user selection. The conditions for emissions reduction measures are not particularly limited and can be designed as appropriate. The conditions for emissions reduction measures may include, for example, targets for reduction amount, amount of carbon credits, revenue, cost, and / or profit. The conditions for emissions reduction measures may also include, for example, the performance of the equipment to be installed.

[0055] If the user inputs or selects the conditions for the emissions reduction measures they wish to adopt, it is preferable that the server device 3, in step S204, identifies the type and details of the emissions reduction measures to satisfy those conditions.

[0056] In the above example, we have given a forecast in the order of reduction amount, amount of carbon credits, revenue, cost, and profit, but the order in which each item is forecasted is not particularly limited and can be designed as appropriate. For example, server device 3 may be forecast in the order of cost, reduction amount, amount of carbon credits, revenue, and profit.

[0057] Furthermore, while the above example illustrates the use of System 1 to determine new emission reduction measures to be adopted at a livestock farm, System 1 may also be used by users belonging to livestock farms that are already implementing emission reduction measures. For example, a user may use System 1 to predict the amount of greenhouse gas emission reduction, carbon credits, revenue, costs, and / or profits resulting from emission reduction measures implemented at their livestock farm. In this case, the user may, for example, set the prediction to assume that the costs incurred by implementing emission reduction measures are zero.

[0058] Furthermore, while the above example shows a user belonging to a livestock business, the facility to which the user belongs can be any facility that raises animals. Animals may include mammals, birds, reptiles, amphibians, fish, insects, spiders, myriapods, crustaceans, mollusks, etc. For example, fish farms raise fish such as tuna and yellowtail, and zoos raise various animals such as elephants, rabbits, flamingos, and chameleons. In step S201, the user may also input animal information about the animals raised at the facility to which they belong.

[0059] Furthermore, while the above example describes a user registering livestock information and identifying emission reduction measures corresponding to that information, the user may also use System 1 to predict the general reduction in greenhouse gas emissions, the amount of carbon credits, revenue, costs, and / or benefits if specific measures were implemented, without registering livestock information.

[0060] Furthermore, the costs corresponding to the types and / or details of emission reduction measures, or the methods for calculating such costs, which are stored in the storage unit 33 of the server device 3, may be entered and edited by the administrator on the administrator terminal.

[0061] Furthermore, the storage unit 33 of the server device 3 may store a predictive model (learned model) that has been machine-trained using the type and / or details of emissions reduction measures as input data and cost as output data. Alternatively, the storage unit 33 of the server device 3 may store a predictive model (learned model) that has been machine-trained using the type and / or details of emissions reduction measures as input data and reduction amount, carbon credit amount, revenue, cost, and / or profit as output data. In this case, the server device 3 may use the predictive model (learned model) to perform the predictions in steps S205 to S209.

[0062] Thus, a system comprising at least one computer device can be provided with a reduction prediction means that predicts the amount of greenhouse gas emission reduction achieved over a predetermined period through measures to reduce greenhouse gas emissions using animal waste, thereby providing a system for predicting the amount of greenhouse gas emission reduction achieved through such measures.

[0063] Furthermore, by including methane fermentation using waste and / or the use of waste as organic fertilizer as emission reduction measures, it is possible to predict the amount of greenhouse gas emission reduction when methane fermentation using waste and / or the use of waste as organic fertilizer are adopted as emission reduction measures.

[0064] Furthermore, the reduction amount prediction means can predict the reduction amount based on the type of animal, the number of animals, the amount of excrement, and / or the type of feed given to the animals.

[0065] Furthermore, by incorporating a revenue forecasting mechanism that predicts the revenue generated from transferring carbon credits issued based on predicted reductions, the system can predict the revenue obtained from implementing greenhouse gas emission reduction measures.

[0066] Furthermore, by including a storage means that stores the costs or methods for calculating said emissions reduction measures in relation to said emissions reduction measures, and a cost prediction means that predicts the costs incurred by implementing emissions reduction measures by referring to the costs or cost calculation methods stored in the storage means, the costs incurred by implementing greenhouse gas emission reduction measures can be predicted.

[0067] Furthermore, by including a profit forecasting means that predicts the profits obtained by implementing emission reduction measures based on the revenues predicted by the revenue forecasting means and the costs predicted by the cost forecasting means, the system can predict the profits obtained by implementing greenhouse gas emission reduction measures. [Explanation of Symbols]

[0068] 1 System 2 User terminals 3 Server equipment 4. Communication Network 21 Control Unit 22 RAM 23 Storage Section 24 Input section 25 Display section 26 Communication Interfaces 31 Control Unit 32 RAM 33 Storage Section 34 Communication Interfaces

Claims

1. A system comprising at least one computer device, A reduction prediction method for predicting the amount of greenhouse gas emission reduction achieved over a predetermined period through measures to reduce greenhouse gas emissions using animal waste. A system equipped with these features.

2. The system according to claim 1, wherein the emission reduction measures include the implementation of methane fermentation using excrement and / or the use of excrement as organic fertilizer.

3. The system according to claim 1 or 2, wherein the reduction amount prediction means predicts the reduction amount based on the type of animal, the number of animals, the amount of excrement, and / or the type of feed given to the animals.

4. Revenue forecasting means for predicting revenue from transferring carbon credits issued based on predicted reductions. The system according to claim 1 or 2, comprising:

5. A storage means for storing the costs incurred by implementing emissions reduction measures or a method for calculating those costs, in relation to emissions reduction measures. A cost forecasting means predicts the costs incurred by implementing emission reduction measures by referring to costs or cost calculation methods stored in a memory means. The system according to claim 1 or 2, comprising:

6. Profit forecasting means predicts the profits obtained by implementing emissions reduction measures based on the revenues predicted by the revenue forecasting means and the costs predicted by the cost forecasting means. The system according to claim 5, comprising:

7. A method performed in a system comprising at least one computer device, A reduction prediction step that predicts the amount of greenhouse gas emissions reduced over a specified period by measures to reduce greenhouse gas emissions using animal waste. Having, method.

8. Computer equipment, A reduction prediction method for predicting the amount of greenhouse gas emission reduction achieved over a predetermined period through measures to reduce greenhouse gas emissions using animal waste. To make it function as program.

9. A method performed in a computer device, A reduction prediction step that predicts the amount of greenhouse gas emissions reduced over a specified period by measures to reduce greenhouse gas emissions using animal waste. Having, method.

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