Greenhouse gas emission reduction prediction system for livestock farms

Abstract

This system provides a method for predicting the reduction in greenhouse gas emissions at livestock farms. [Solution] A system comprising at least one computer device, comprising: identification means for identifying greenhouse gas emission reduction measures that can be adopted at a livestock farm; and reduction amount prediction means for predicting the amount of greenhouse gas emission reduction that will be achieved over a predetermined period by the identified emission reduction measures.

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 advanced globally. Livestock business establishments often have facilities such as barns and vast land, and are capable of adopting various measures to reduce greenhouse gas (GHG) emissions.

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 amount of reduction in greenhouse gas emissions at a livestock business establishment.

Means for Solving the Problems

[0004] The problem of the present invention is solved by: [1] A system comprising at least one computer device, the system comprising: an identification means for identifying greenhouse gas emission reduction measures that can be adopted at a livestock business establishment; and a reduction amount prediction means for predicting the amount of greenhouse gas emission reduction to be achieved in a predetermined period by the identified emission reduction measures; [2] The system according to [1], wherein the emission reduction measures include use of a photovoltaic power generation system, a wind power generation apparatus, a small hydroelectric power generation apparatus, a biomass power generation apparatus, geothermal heat, air heat, and / or groundwater at the livestock business establishment; [3] The system according to [1] or [2], comprising a revenue prediction means for predicting revenue from transferring carbon credits issued based on the predicted reduction amount; [4] A system according to any one of [1] to [3], comprising: a storage means for storing the cost of implementing said emissions reduction measures or a method for calculating said costs in relation to said emissions reduction measures; and a cost forecasting means for predicting the costs incurred by implementing the identified emissions reduction measures by referring to the costs or cost calculation methods stored in the storage means; [5] The system according to [4], comprising a profit forecasting means that predicts the profits obtained by implementing identified emissions reduction measures based on the revenues predicted by the revenue forecasting means and the costs predicted by the cost forecasting means; [6] A system according to any one of [1] to [5], comprising a combination identification means that identifies a combination of emissions reduction measures to achieve a target reduction, revenue, cost, and / or profit, based on the reduction amount predicted by the reduction amount prediction means, the revenue predicted by the revenue prediction means, the cost predicted by the cost prediction means, and / or the profit predicted by the profit prediction means; [7] The reduction prediction means predicts the total amount of reductions from multiple emission reduction measures corresponding to an identified combination, as described in [6]; [8] The system according to [6] or [7], wherein the revenue forecasting means forecasts the total amount of revenue from multiple emissions reduction measures corresponding to an identified combination; [9] A system according to any one of [6] to [8] in which the cost forecasting means predicts the total cost of multiple emissions reduction measures corresponding to an identified combination;

[10] A system according to any one of [6] to [9] in which the profit forecasting means predicts the total amount of profits from multiple emissions reduction measures corresponding to an identified combination;

[11] A method performed in a system comprising at least one computer device, comprising: an identification step of identifying greenhouse gas emission reduction measures that can be adopted by a livestock farm; and a reduction prediction step of predicting the amount of greenhouse gas emission reduction that will be reduced over a predetermined period by the identified emission reduction measures;

[12] A program that causes a computer device to function as an identification means for identifying greenhouse gas emission reduction measures that can be adopted by a livestock farm, and as a reduction prediction means for predicting the amount of greenhouse gas emission reductions that will be made over a predetermined period by the identified emission reduction measures;

[13] A method performed on a computer device, comprising: an identification step of identifying greenhouse gas emission reduction measures that can be adopted by a livestock farm; and a reduction prediction step of predicting the amount of greenhouse gas emission reduction that will be achieved over a predetermined period of time by the identified emission reduction measures; 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 at livestock farms. [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 business establishment 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. [Figure 6] This figure shows a flowchart of the combination identification 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 at a livestock business establishment. The person in charge may be a single individual or a plurality of individuals. The person in charge may be the person responsible for a project related to greenhouse gas emission reduction. Hereinafter, the person who operates the user terminal 2 is also referred to as a user.

[0012] The server device 3 may be managed by the administrator of the system 1. Further, the system 1 may include an administrator terminal operated by the administrator.

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

[0014] The control unit 21 is composed of a CPU and a ROM. The control unit 21 executes programs stored in the storage unit 23 to control the user terminal 2. The RAM 22 is a work area for the control unit 21. The storage unit 23 is a storage area for storing programs and data. That is, the storage unit 23 functions as a recording medium storing programs. The control unit 21 performs arithmetic processing based on the programs and data read from the RAM 22, as well as the data input via 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. Data received via the communication interface 26 is loaded into the RAM 22 and subjected to arithmetic processing by the control unit 21.

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

[0018] The control unit 31 is configured with a CPU and a ROM, executes programs stored in the storage unit 33, and controls the server apparatus 3. The control unit 31 also 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 processing based on information and the like received from each of the user terminals 2.

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

[0020] Alternatively, the programs may be distributed from a computer apparatus external to the system. In this case, the programs distributed from the computer apparatus external to the system may be installed in the user terminal 2 and / or the server apparatus 3 to execute 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 to be adopted at the livestock farm. By registering information about the livestock farm (hereinafter also referred to as farm information) in System 1, the user can simulate how much emissions can be reduced by adopting various emission reduction measures at the farm. For example, the user may log in to System 1 and register the farm 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 farm ID) and a password into user terminal 2.

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

[0023] The business information in step S101 is not particularly limited and can be designed as appropriate. Preferably, the business information in step S101 is information related to greenhouse gas emission reduction measures that can be adopted at the livestock business.

[0024] The business information in step S101 may include, for example, information about livestock barns owned by the livestock business. The livestock barn information may include the number of livestock barns owned by the livestock business, the location of each barn (latitude, longitude, altitude, etc.), the area of ​​the roof of each barn, the shape of the roof of each barn (roof slope angle, etc.), and the orientation (direction) of the roof of each barn. Solar power generation systems can be installed on the roofs of the livestock barns.

[0025] Furthermore, the business establishment information in step S101 may include, for example, livestock information concerning the livestock animals raised at the livestock business establishment. The livestock information may include the type of livestock animal (cattle, pigs, chickens, etc.), the number of livestock animals, and the amount of excrement (feces, urine, etc.) (weight, volume, etc.). The biomass power generation device may be capable of generating electricity by utilizing the excrement of livestock animals as biomass.

[0026] Furthermore, the business information in step S101 may include, for example, site information relating to the land owned by the livestock business. Site information may include the area of ​​land not used by the livestock business, the area of ​​the livestock business's land, etc. Solar power generation systems, wind power generation equipment, biomass power generation equipment, etc., can be installed on land not used by the livestock business. In addition, the ground beneath the site of a livestock business often maintains a constant temperature regardless of the season. Therefore, it is possible to utilize geothermal energy as heating or cooling by transporting heat using a heat pump.

[0027] Furthermore, the business information in step S101 may include information on hollow structures, such as abandoned tunnels, abandoned mines, and natural caves that can be used at a livestock farm. The information on hollow structures may include the type, size, number, and temperature of the internal space of the hollow structure. The inside of a hollow structure is often at a constant temperature regardless of the season. Therefore, it is possible to utilize the heat from the air as heating or cooling by transporting heat using a heat pump.

[0028] Furthermore, the business information in step S101 may include, for example, natural environment information regarding rivers, forests, and groundwater that can be used at the livestock business. The natural environment information may include the type, size (area), and number of natural environments. The natural environment information may also include the flow rate of water in rivers, the types of woody biomass that can be extracted from forests, their moisture content, and the temperature of groundwater. Small-scale hydroelectric power generation equipment can be installed in rivers. Woody biomass, which can be used as raw material for biomass power generation equipment (woody biomass power generation equipment), can be extracted from forests. Groundwater can be transported by pump and used as hot or cold water.

[0029] Furthermore, "groundwater" refers to water that springs from underground. The temperature and composition of groundwater are not particularly limited. Groundwater may even be hot spring water.

[0030] The business establishment information in step S101 may include any information other than that mentioned above. For example, the business establishment information may include the address (location) of the livestock business establishment, the number of employees, and the amount of electricity used. The business establishment information may also include the amount (weight, volume, etc.) of food waste, sewage sludge, food processing residues, plant waste, feed residues, combustible waste, waste oil, etc., generated at the livestock business establishment. System 1 can refer to the business establishment information as appropriate when making predictions such as the amount of greenhouse gas emission reduction and costs, as described later.

[0031] In step S104, the business information may be stored in association with the business ID.

[0032] Once the registration of business information is complete, System 1 will be able to predict the amount of greenhouse gas emission reductions corresponding to emission reduction measures.

[0033] 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.

[0034] [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).

[0035] The emission reduction measures in Step S201 are not particularly limited and can be designed as appropriate. Emission reduction measures may include the use of solar power systems, wind power generators, small-scale hydropower generators, biomass power generators, geothermal energy, air heat, and / or groundwater at livestock farms. Note that "use of solar power systems, wind power generators, small-scale hydropower generators, and / or biomass power generators" may mean "power generation by solar power systems, wind power generators, small-scale hydropower generators, and / or biomass power generators."

[0036] Furthermore, "small-scale hydropower generation" can refer to electricity generated using the water flow of naturally existing rivers and other bodies of water.

[0037] Furthermore, "biomass power generation" may refer to power generation that utilizes biomass, which is a biological resource derived from plants and animals. The method of biomass power generation may be a biochemical gasification method, a pyrolysis gasification method, or a direct combustion method. When biomass power generation is performed by a biochemical gasification method, the biomass power generation device may be capable of generating electricity using biomass such as livestock waste, food waste, sewage sludge, food processing residues, and plant waste. When biomass power generation is performed by a pyrolysis gasification method, the biomass power generation device may be capable of generating electricity using biomass such as woody materials and feed residues. When biomass power generation is performed by a direct combustion method, the biomass power generation device 1 may be capable of generating electricity using biomass such as woody materials, feed residues, combustible waste, and waste oil.

[0038] The server device 3 may pre-store at least one emission reduction measure that can be adopted by a livestock farm 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 the emission reduction measure they wish to simulate from the displayed measures. At this time, the user may select two or more emission reduction measures.

[0039] In step S201, the user may also select the type of emission reduction measure (e.g., "utilization of a solar power generation system") along with the details of the emission reduction measure. The details of the emission reduction measure may relate to the amount of electricity generated and energy consumed when the emission reduction measure is implemented. The details of the emission reduction measure may include, for example, information about the equipment to be installed when implementing the emission reduction measure. For example, if the type of emission reduction measure is "utilization of a solar power generation system," the details of the emission reduction measure may include the number of solar panels installed, the area, and the installation location. The installation location of the solar panels is a condition that affects the amount and duration of sunlight, and therefore can be said to be related to the amount of electricity generated. Also, if the type of emission reduction measure is "utilization of geothermal energy," the details of the emission reduction measure may include the number and size of heat pumps.

[0040] 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 greenhouse gas emission reduction measures that can be adopted at the livestock farm (step S204).

[0041] 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.

[0042] For example, if no rivers are registered as usable as business information, and "use of small-scale hydroelectric power generation equipment" is selected in step S201, then the emission reduction measure selected in step S201 is considered unsuitable for adoption at the livestock farm to which the user belongs. Therefore, in step S204, the server device 3 may identify other emission reduction measures that can be adopted at the livestock farm.

[0043] In step S204, along with the type of emission reduction measures, details of the 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 farm information registered in system 1 to specify details of the emission reduction measures that can be adopted at the livestock farm to which the user belongs.

[0044] If details of emission reduction measures are selected in step S201, the server device 3 may identify the details of the emission reduction measures selected in step S201, or it may identify emission reduction measures different from the details of the emission reduction measures selected in step S201. For example, if in step S201 the user selected a number of solar panels that cannot be installed at the livestock farm to which the user belongs, the server device 3 may, in step S204, identify the number of solar panels that can be installed at the livestock farm. In addition, the server device 3 may adopt the details of the emission reduction measures selected in step S201 and then identify further details that supplement the content.

[0045] 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).

[0046] 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.

[0047] 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.

[0048] 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.

[0049] For example, the amount of carbon dioxide (CO2) reduced by solar power generation systems, wind power generation equipment, small-scale hydropower generation equipment, and biomass power generation equipment may be calculated based on the following formula (1). Note that the emission factor may be determined by each electric power company. CO2 reduction (t-CO2) = Power generation (MWh) × Emission factor (t-CO2 / MWh) ...(1)

[0050] Furthermore, for example, the amount of carbon dioxide (CO2) reduction when using geothermal energy, air heat, and / or groundwater may be calculated based on the following formula (2). Note that the emission factor may be determined by each electric utility company. CO2 reduction (t-CO2) = (Energy consumption before implementation - Energy consumption after implementation) × Emission factor ... (2)

[0051] The amount of power generated in formula (1) above may be predicted based on emission reduction measures and / or business information of the livestock farm to which the user belongs. For example, the server device 3 may predict the amount of power generated based on the type and details of emission reduction measures identified in step S204.

[0052] For example, if the type of emission reduction measure is "utilization of a solar power generation system," the server device 3 may predict the amount of power generated based on the number of solar panels installed, the area, the installation location (roof tilt angle and direction), the power conversion efficiency (generation efficiency), the average amount of solar radiation and the amount of sunshine at the installation location. Alternatively, for example, the server device 3 may predict the amount of power generated by referring to the actual power generation data of other solar power generation systems in the vicinity of the livestock farm.

[0053] Alternatively, for example, if the type of emission reduction measure is "use of wind power generation equipment," the server device 3 may predict the amount of power generated based on the number of wind power generation equipment installed, the installation location (latitude, longitude, altitude, etc.), the power conversion efficiency (generation efficiency), etc., and the average wind speed at the installation location. Alternatively, for example, the server device 3 may predict the amount of power generated by referring to the actual power generation data of other wind power generation equipment in the vicinity of the livestock farm.

[0054] Alternatively, for example, if the type of emission reduction measure is "utilization of small-scale hydropower generation equipment," the server device 3 may predict the amount of power generated based on the number of small-scale hydropower generation equipment installed, their installation locations, power conversion efficiency (generation efficiency), and the water flow rate at the installation locations. Alternatively, for example, the server device 3 may predict the amount of power generated by referring to the actual power generation data of other small-scale hydropower generation equipment in the vicinity of the livestock farm.

[0055] Alternatively, for example, if the type of emission reduction measure is "utilization of biomass power generation equipment," the server device 3 may predict the amount of power generated based on the number of biomass power generation equipment installed, its scale (output), power conversion efficiency (power generation efficiency), the type of biomass used as raw material, its moisture content, etc. Alternatively, for example, the server device 3 may predict the amount of power generated by referring to the actual power generation data of other biomass power generation equipment using similar raw materials.

[0056] The storage unit 33 of the server device 3 may store the amount of power generated, or the method for calculating said power generation, corresponding to the type and / or details of emission reduction measures. Based on the stored amount of power generation or method for calculating said power generation, the server device 3 can predict the amount of power generation.

[0057] The energy consumption in formula (2) above may be predicted based on emission reduction measures and / or facility information of the livestock farm to which the user belongs. For example, the server device 3 may predict the amount of power generated based on the type and details of emission reduction measures identified in step S204.

[0058] For example, server device 3 may calculate the energy consumption before installation based on the electricity usage of the livestock farm, and predict the energy consumption after installation based on site information, cavity structure information, and / or natural environment information. The energy consumption after installation may be predicted based on the power consumption of heat pumps used to utilize geothermal and / or air heat, and / or the power consumption of pumps used to utilize groundwater.

[0059] The storage unit 33 of the server device 3 may store the energy consumption corresponding to the type and / or details of emission reduction measures, or the method for calculating said energy consumption. Based on the stored energy consumption or the method for calculating energy consumption, the server device 3 can predict energy consumption.

[0060] 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.

[0061] 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.

[0062] 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.

[0063] Server device 3 predicts the costs incurred by implementing the identified emission reduction measures (step S208).

[0064] The storage unit 33 of the server device 3 may store the costs corresponding to the types and / or details of emission reduction measures, or the methods for calculating said costs. "Costs corresponding to emission reduction measures, or the methods for calculating said costs" may refer to "costs incurred by implementing emission reduction measures, or the methods for calculating said costs." For example, the costs incurred by implementing emission reduction measures may include the costs of equipment (such as power generators, heat pumps, and pumps) installed when implementing emission reduction measures. The server device 3 can predict costs by calculating them based on the stored costs or cost calculation methods.

[0065] 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.

[0066] 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.

[0067] 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.

[0068] 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.

[0069] 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.

[0070] Furthermore, while the above example shows a 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. 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.

[0071] If the user inputs or selects the conditions for the emissions reduction measures they wish to adopt, it is preferable that in step S204, the server device 3 identifies the type of emissions reduction measures and the details of those measures in order to satisfy those conditions.

[0072] Furthermore, System 1 may be able to identify combinations of emission reduction measures that can be adopted at the livestock farm to which the user belongs, in accordance with the user's goals (desires).

[0073] [Combination Identification Process] Figure 6 is a flowchart showing the combination identification process according to an embodiment of the present invention. User terminal 2 receives input from the user regarding targets for reduction amount, amount of carbon credits, revenue, cost, and / or profit (step S301).

[0074] The method for inputting the target in step S301 is not particularly limited and can be designed as appropriate. For example, the target may be input by the user by entering a specific numerical value, or by selecting a predetermined range of numerical values.

[0075] User terminal 2 transmits the selected target to server device 3 (step S302). Server device 3 receives the target transmitted from user terminal 2 (step S303). Based on the predicted reduction amount, revenue, cost, and / or profit, server device 3 identifies a combination of emissions reduction measures to achieve the target reduction amount, revenue, cost, and / or profit (step S304).

[0076] In step S304, the method for identifying combinations of emission reduction measures is not particularly limited and can be designed as appropriate. For example, the server device 3 may identify combinations of emission reduction measures that can achieve the target entered in step S301 and can be adopted at the livestock farm to which the user belongs, based on the predicted reduction amount, revenue, cost, and / or profit, as predicted in the same manner as in steps S205 to S209 described above, and the registered business information.

[0077] In step S304, the server device 3 may identify two or more combinations.

[0078] Server device 3 predicts the total amount of reductions over a predetermined period from multiple emission reduction measures corresponding to the identified combination (step S305). Server device 3 may also predict the reduction amount for each emission reduction measure and sum the predicted reduction amounts. The method for predicting the reduction amount for each emission reduction measure can be adopted to the extent necessary, as described in step S205.

[0079] Server device 3 predicts the total amount of carbon credits to be issued based on the total amount of predicted greenhouse gas emission reductions (step S306). Server device 3 may also predict the amount of carbon credits for each emission reduction measure and sum the predicted amounts of carbon credits. The method for predicting the amount of carbon credits for each emission reduction measure can be adopted to the extent necessary, as described in step S206.

[0080] Server device 3 predicts the total amount of revenue from transferring the predicted amount of carbon credits (step S307). Server device 3 may also predict the revenue for each emissions reduction measure and sum the predicted revenues. The method for predicting the revenue for each emissions reduction measure can be adopted to the extent necessary, as described in step S207.

[0081] Server device 3 predicts the total cost incurred by implementing multiple emission reduction measures corresponding to the identified combination (step S308). Server device 3 may also predict the cost for each emission reduction measure and sum the predicted costs. The method for predicting the cost for each emission reduction measure can be adopted to the extent necessary, as described in step S208.

[0082] Server device 3 predicts the total amount of benefits obtained by implementing multiple emission reduction measures corresponding to the identified combination (step S309). Server device 3 may also predict the benefits for each emission reduction measure and sum the predicted benefits. The method for predicting the benefits for each emission reduction measure can be adopted to the extent necessary, as described in step S209.

[0083] The server device 3 transmits the prediction results to the user terminal 2 (step S310). In step S310, the types and / or details of the multiple emission reduction measures identified in step S304, the total amount of greenhouse gas emission reductions predicted in step S305, the total amount of carbon credits predicted in step S206, the total amount of revenues predicted in step S207, and / or the total amount of costs predicted in step S208, and the total amount of profits predicted in step S209 may be transmitted as prediction results.

[0084] User terminal 2 receives the prediction result sent from server device 3 (step S311). User terminal 2 displays the received prediction result (step S312), and the combination identification process ends.

[0085] Users can review the displayed prediction results and consider combinations of emission reduction measures to implement at their livestock farms. The prediction 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.

[0086] Furthermore, if two or more combinations are identified in step S304, then in steps S305 to S309, the reduction amount, carbon credit amount, revenue, cost, and profit for each emission reduction measure may be predicted for each combination. In addition, the total reduction amount, total carbon credit amount, total revenue, total cost, and total profit may also be predicted for each combination. Furthermore, in steps S310 to S312, the predicted results for each emission reduction measure and the total predicted results may be transmitted and displayed for each combination.

[0087] Furthermore, while the above examples show that the reduction amount prediction process and combination identification process predict the reduction amount, carbon credit amount, revenue, cost, and profit in that order, the order in which each item is predicted is not particularly limited and can be designed as appropriate. For example, in the reduction amount prediction process and combination identification process, the server device 3 may predict the cost, reduction amount, carbon credit amount, revenue, and profit in that order.

[0088] In the above example, System 1 was used to determine emissions reduction measures to be newly adopted at a livestock farm. However, System 1 may also be used by users belonging to livestock farms that are already implementing emissions reduction measures. For example, a user may use System 1 to predict the amount of greenhouse gas emissions reduction, carbon credits, revenue, costs, and / or profits resulting from emissions reduction measures implemented at a livestock farm. In this case, the user may, for example, set the prediction to assume that the costs incurred by implementing emissions reduction measures are zero.

[0089] Furthermore, while the above example shows a user registering business information and identifying emission reduction measures corresponding to that information, users 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 are implemented, without registering business information.

[0090] Furthermore, the amount of power generated corresponding to the type and / or details of emission reduction measures, or the method for calculating said power generation, and the cost corresponding to the type and / or details of emission reduction measures, or the method for calculating said cost, 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.

[0091] Furthermore, the storage unit 33 of the server device 3 may store a predictive model (learned model) that has been trained using machine learning, with the type and / or details of emissions reduction measures as input data and the amount of power generated and / or cost as output data. Alternatively, the storage unit 33 of the server device 3 may store a predictive model (learned model) that has been trained using machine learning, with the type and / or details of emissions reduction measures as input data and the amount of reduction, the amount of carbon credits, 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 and / or steps S305 to S309.

[0092] Thus, a system comprising at least one computer device can be provided for predicting greenhouse gas emission reductions at livestock farms by including identification means for identifying greenhouse gas emission reduction measures that can be adopted at livestock farms, and reduction prediction means for predicting the amount of greenhouse gas emission reductions that will be achieved over a predetermined period by the identified emission reduction measures.

[0093] Furthermore, by including the use of solar power generation systems, wind power generation equipment, small-scale hydropower generation equipment, biomass power generation equipment, geothermal energy, air heat, and / or groundwater in livestock farms as emission reduction measures, it is possible to predict the amount of greenhouse gas emission reduction when solar power generation systems, wind power generation equipment, small-scale hydropower generation equipment, biomass power generation equipment, geothermal energy, air heat, and / or groundwater are adopted as emission reduction measures.

[0094] 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.

[0095] Furthermore, by including a storage means that stores the cost or method for calculating the cost of implementing emission reduction measures in association with emission reduction measures, and a cost prediction means that predicts the cost incurred by implementing the identified emission reduction measures by referring to the cost or method for calculating the cost stored in the storage means, the cost incurred by implementing greenhouse gas emission reduction measures can be predicted.

[0096] Furthermore, by including a profit forecasting means that predicts the profits obtained by implementing identified 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.

[0097] Furthermore, by including a combination identification means that identifies a combination of emissions reduction measures to achieve a target reduction amount, revenue, cost, and / or profit, based on the reduction amount predicted by the reduction amount prediction means, the revenue predicted by the revenue prediction means, the cost predicted by the cost prediction means, and / or the profit predicted by the profit prediction means, the system can identify a combination of emissions reduction measures to achieve a target reduction amount, revenue, cost, and / or profit.

[0098] Furthermore, the reduction prediction means can predict the total amount of reductions from multiple emission reduction measures by predicting the sum of reductions from multiple emission reduction measures corresponding to a specified combination.

[0099] Furthermore, by predicting the total amount of revenue from multiple emissions reduction measures corresponding to a specified combination, the revenue forecasting means can predict the total amount of revenue obtained by implementing multiple emissions reduction measures.

[0100] Furthermore, by predicting the total cost of multiple emissions reduction measures corresponding to a specific combination, the cost prediction method can predict the total cost incurred by implementing multiple emissions reduction measures.

[0101] Furthermore, by predicting the total amount of profits from multiple emissions reduction measures corresponding to a specific combination, the profit forecasting method can predict the total amount of profits obtained by implementing multiple emissions reduction measures. [Explanation of Symbols]

[0102] 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, Identifying measures to reduce greenhouse gas emissions that can be adopted at livestock farms, A reduction prediction means for predicting the amount of greenhouse gas emissions reduced over a predetermined period by identified emission reduction measures, and A system equipped with these features.

2. The system according to claim 1, wherein emission reduction measures include the use of solar power generation systems, wind power generation devices, small hydropower generation devices, biomass power generation devices, geothermal energy, air heat, and / or groundwater at livestock farms.

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

4. 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 identified emission reduction measures by referring to costs or cost calculation methods stored in a memory means. The system according to claim 1, comprising:

5. Profit forecasting means predicts the profits obtained by implementing identified 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 4, comprising:

6. Combination identification means that identifies a combination of emissions reduction measures to achieve a target reduction amount, revenue, cost, and / or profit, based on the reduction amount predicted by the reduction amount prediction means, the revenue predicted by the revenue prediction means, the cost predicted by the cost prediction means, and / or the profit predicted by the profit prediction means. The system according to any one of claims 1 to 5, comprising:

7. The system according to claim 6, wherein the reduction amount prediction means predicts the total amount of reductions from multiple emission reduction measures corresponding to a specified combination.

8. The system according to claim 6, wherein the revenue forecasting means predicts the total amount of revenue from multiple emissions reduction measures corresponding to a specified combination.

9. The system according to claim 6, wherein the cost prediction means predicts the total cost of multiple emission reduction measures corresponding to a specified combination.

10. The system according to claim 6, wherein the profit forecasting means predicts the total amount of profits from multiple emissions reduction measures corresponding to a specified combination.

11. A method performed in a system comprising at least one computer device, A specific step to identify greenhouse gas emission reduction measures that can be adopted by livestock farms, A reduction prediction step that predicts the amount of greenhouse gas emissions reduced over a specified period by identified emission reduction measures, and Having, method.

12. Computer equipment, Identifying measures to identify greenhouse gas emission reduction measures that can be adopted by livestock farms, A reduction prediction means for predicting the amount of greenhouse gas emissions reduced over a predetermined period by identified emission reduction measures, and To make it function as program.

13. A method performed in a computer device, A specific step to identify greenhouse gas emission reduction measures that can be adopted by livestock farms, A reduction prediction step that predicts the amount of greenhouse gas emissions reduced over a specified period by identified emission reduction measures, and Having, method.

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