GHG emission estimation device

Abstract

To provide a GHG emission estimation device that enables a quicker understanding of GHG emission from a manufactured product.SOLUTION: A GHG emission estimation device 40, which estimates GHG emission of a manufactured product, includes: a storage unit 46 that stores a GHG estimation model 50 that is constructed on the basis of a GHG emission contribution past value 60 related to GHG emission of a manufactured product in the past and that calculates GHG emission using a GHG emission contribution current value 66 of a manufactured product to be estimated as a parameter; and a processing unit 44 that estimates the GHG emission of the manufactured product by applying the GHG emission contribution current value 66 to the GHG estimation model 50.SELECTED DRAWING: Figure 4

Description

Technical Field

[0001] This disclosure relates to a technique for estimating GHG emissions generated during the manufacture of products.

Background Art

[0002] Patent Document 1 includes a food consumption weight estimator that estimates the consumption weight of food using food sales data and food price data, and an environmental impact estimator that estimates the environmental impact of food using the consumption weight estimated by the food consumption weight estimator and the environmental impact coefficient of the food. It discloses an environmental impact estimation device.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] According to the technique disclosed in Patent Document 1, since the environmental impact of food is estimated using food sales data etc., the timing of grasping the environmental impact is delayed.

[0005] It is desired to be able to grasp the GHG emissions of products earlier.

[0006] Therefore, an object of the present disclosure is to enable earlier grasping of the GHG emissions of products.

Means for Solving the Problems

[0007] This GHG emission estimation device is a GHG emission estimation device for estimating the GHG emissions of a product. It is constructed based on past GHG emission contribution values involved in the past GHG emissions of the product, and stores a GHG estimation model that obtains the GHG emissions by using the current GHG emission contribution value of the product to be estimated as a parameter. The device includes a processing unit that applies the current GHG emission contribution value to the GHG estimation model to estimate the GHG emissions of the product.

Advantages of the Invention

[0008] According to this GHG emission estimation device, a GHG estimation model is constructed based on past GHG emission contribution values, and by applying the current GHG emission contribution value to the estimation model, the GHG emissions of the product can be grasped earlier.

Brief Description of the Drawings

[0009]

Figure 1

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Modes for Carrying Out the Invention

[0010] {Embodiment} Hereinafter, a GHG emission estimation device according to an embodiment will be described.

[0011] For the sake of convenience of explanation, an example of calculating the amount of GHG (Greenhouse Gas) emitted when manufacturing a product will be described.

[0012] FIG. 1 is an example showing the GHG emission locations when manufacturing a product. As shown in the figure, it is assumed that parts 22 are manufactured in the first manufacturing factory 10A, and a product 24 is manufactured using the parts 22 in the second manufacturing factory 10B. That is, the product 24 is manufactured through a first manufacturing process in the first manufacturing factory 10A and a second manufacturing process in the second manufacturing factory 10B.

[0013] Raw materials 20 are carried into the first manufacturing factory 10A. Electric power is supplied to the first manufacturing factory 10A from the first energy supplier 14. In the first manufacturing factory 10A, the raw materials 20 are processed using electric power to manufacture parts 22.

[0014] Therefore, it is assumed that the amount of GHG emitted by the manufacturing of parts 22 is calculated by integrating the GHG emission amount derived from the raw materials 20 and the GHG emission amount derived from processing generated by processing the raw materials 20 into parts 22.

[0015] The GHG emission amount derived from the raw materials 20 may be the GHG emission amount generated by the production or manufacturing of the raw materials 20. The GHG emission amount derived from the raw materials 20 may include both or either the GHG amount emitted by the transportation of the raw materials 20 and the GHG amount emitted by the storage of the raw materials 20.

[0016] The GHG emission amount derived from processing generated by processing the raw materials 20 into parts 22 may be the GHG amount emitted by the processing of the raw materials 20. The GHG emission amount derived from processing may include the GHG amount emitted by the storage of both or either the raw materials 20 and the parts 22 in the first manufacturing factory 10A.

[0017] The GHG emissions from processing may be understood as the GHG emissions resulting from the electricity supplied from the first energy supplier 14 to the first manufacturing plant 10A. The first energy supplier 14 may have a renewable energy power plant and a non-renewable energy power plant. A non-renewable energy power plant is a power plant that emits GHG by power generation, such as a thermal power plant. A renewable energy power plant is a power plant that generates electricity using renewable energy, such as a solar power plant or a wind power plant, and does not emit GHG. The GHG emissions from processing can depend not only on the amount of electricity consumed during processing but also on the amount or proportion of renewable energy used in the consumed electricity.

[0018] The raw materials in the first manufacturing plant 10A are an example of the starting materials for manufacturing the parts 22, and the GHG emissions from the raw materials are an example of the GHG emissions from the starting materials.

[0019] Note that some of the plurality of parts 22 manufactured in the first manufacturing plant 10A may be sent to the second manufacturing plant 10B, and the rest may be sent to other factories or the like.

[0020] Parts 22 are carried into the second manufacturing plant 10B. Electric power is supplied to the second manufacturing plant 10B from the second energy supplier 16. In the second manufacturing plant 10B, the parts 22 are processed using electric power to manufacture the product 24.

[0021] It is assumed that the GHG emitted during the manufacture of the product 24 is calculated by integrating the GHG emissions from the starting materials derived from the parts 22 and the GHG emissions from processing generated by processing the parts 22 to manufacture the product 24.

[0022] As described above, the GHG emissions from the starting materials can be understood by integrating the GHG emissions from the raw materials 20 and the GHG emissions from processing in the first manufacturing plant 10A.

[0023] The GHG emissions from processing in the second manufacturing plant 10B may be the amount of GHG emitted by processing the parts 22 for manufacturing the product 24. The GHG emissions from processing may include the amount of GHG emitted by storing either or both of the parts 22 and the product 24 in the second manufacturing plant 10B.

[0024] The GHG emissions from processing in the second manufacturing plant 10B may be understood as the GHG emissions resulting from the electricity supplied from the second energy supplier 16 to the second manufacturing plant 10B. Similar to the first energy supplier 14, the second energy supplier 16 may have a renewable energy power plant and a non-renewable energy power plant. Therefore, the GHG emissions from processing may depend not only on the amount of electricity consumed, but also on the amount or proportion of renewable energy used among the amount of electricity consumed.

[0025] The parts 22 received at the second manufacturing plant 10B are an example of starting materials for manufacturing the product 24, and the GHG emissions from the parts 22 are an example of the GHG emissions from starting materials in the manufacturing process of the second manufacturing plant 12.

[0026] FIG. 2 is a block diagram showing a process for calculating the GHG emissions emitted by manufacturing the product 24.

[0027] Focus on the first manufacturing process in the first manufacturing plant 10A.

[0028] When the regular-period purchase quantity of the raw materials 20 supplied to the first manufacturing process is grasped, the GHG emissions resulting from the purchased raw materials 20 are calculated by multiplying the regular-period purchase quantity by the raw material unit factor. The regular period is, for example, one day, one week, one month, a quarter, half a year, or one year. The raw material unit factor is the GHG emissions of the raw materials per predetermined unit. The predetermined unit of the raw materials may be a weight unit, a volume unit, or a quantity unit.

[0029] Also, during the regular period, by multiplying the amount of electricity that emits GHG among the amount of electricity consumed when processing the raw material 20 to manufacture the part 22 by the energy unit yuan, the GHG emissions from processing can be grasped. Note that the energy unit yuan is the amount of GHG emitted per unit power consumption, and by multiplying the amount of electricity by the energy unit yuan, the GHG emissions are calculated.

[0030] The amount of electricity that emits GHG among the amount of electricity consumed when processing the raw material 20 to manufacture the part 22 may be calculated by subtracting the amount of electricity generated by renewable energy during the regular period from the amount of electricity consumed in the first manufacturing process during the regular period. The amount of electricity that emits GHG may be calculated by multiplying the amount of electricity consumed in the first manufacturing process during the regular period by the renewable energy ratio α_p. The renewable energy ratio α_p can be calculated by dividing the amount of electricity generated by renewable energy during the regular period by the total amount of electricity generated.

[0031] Then, by integrating the GHG emissions due to the purchased raw material 20 and the GHG emissions from processing, the amount of GHG emitted by the part 22 manufactured in the first manufacturing process is obtained.

[0032] If a part of the part 22 manufactured in the first manufacturing process is sent to the second manufacturing process and the rest is not sent to the second manufacturing process, the obtained GHG amount is apportioned according to the amount of the part 22 sent to the second manufacturing process.

[0033] For example, divide the amount of GHG emitted by the part 22 manufactured in the first manufacturing process by the total manufacturing amount during the regular period, and multiply the divided value by the delivery amount during the regular period. The delivery amount is the amount of the part 22 sent from the first manufacturing process to the second manufacturing process. Thereby, the GHG emissions Gs_p of the part 22 sent from the first manufacturing process to the second manufacturing process during the regular period are calculated.

[0034] Focusing on the second manufacturing process in the second manufacturing plant 10B, consider calculating the total GHG emissions of the product 24. It is assumed that the manufacturing period of the product 24 for which the total GHG emissions are to be grasped is the same as the regular period or a period longer than the regular period.

[0035] As described above, the GHG emissions Gs_p of the component 22 sent from the first manufacturing process to the second manufacturing process are calculated for each regular period. The GHG emissions Gs_p for each regular period are integrated according to the manufacturing period of the product 24. Thereby, the GHG emissions of the delivered component 22 are grasped during the manufacturing period of the product 24 for which the total GHG emissions are to be grasped.

[0036] Also, during the regular period, by multiplying the amount of electricity that emits GHG among the amount of electricity consumed when manufacturing the product 24 by processing the component 22 by the energy unit price, the GHG emissions from processing in the second manufacturing process can be grasped.

[0037] Also, similar to the first manufacturing process, the amount of electricity that emits the above GHG may be calculated by subtracting the amount of electricity generated by renewable energy during the regular period from the amount of electricity consumed in the second manufacturing process during the regular period. Similarly to the above, the amount of electricity that emits GHG may be calculated by multiplying the amount of electricity consumed in the second manufacturing process during the regular period by the renewable energy ratio.

[0038] When the GHG emissions from component processing are calculated for each regular period, the GHG emissions from processing are integrated according to the manufacturing period of the product 24. Thereby, the GHG emissions from processing are grasped during the manufacturing period of the product 24 for which the total GHG emissions are to be grasped.

[0039] By integrating the GHG emissions of the delivered component 22 and the GHG emissions from processing in the second manufacturing process, the GHG emissions of the delivered component 22 during the manufacturing period of the product 24 for which the total GHG emissions are to be grasped are grasped.

[0040] Here, consider an operator in the second manufacturing process calculating the GHG emissions of the product 24. Regarding the GHG emissions of the product 24 manufactured in the second manufacturing process, if there are reported values of the GHG emissions Gs_p of the component 22 sent from the first manufacturing process and the reported value regarding the GHG emissions from the electricity supplied by the second power supplier, it can be calculated. Note that the reported value from the second power supplier may be the GHG emissions itself due to power consumption. If the energy unit value is a known value, the reported value from the second power supplier may be the power consumption amount and the power consumption amount by renewable energy, or may be the power consumption amount and the renewable energy ratio.

[0041] Here, the reported value of the GHG emissions Gs_p of the component 22 sent from the first manufacturing process and the reported value regarding the GHG emissions from the electricity supplied by the second power supplier may not be provided promptly at the time of manufacturing the product 24.

[0042] For example, there may be cases where reported values are provided from the first power supplier and the second power supplier every predetermined reporting period. In this case, depending on the length of the reporting period, each report may be delayed compared to the time of manufacturing the product 24. Also, depending on the difference in the operating entities of the first manufacturing process and the second manufacturing process, or the timing of the calculation process of the GHG emissions in the first manufacturing process, etc., the report of the GHG emissions Gs_p from the first manufacturing process may be delayed compared to the time of manufacturing the product 24.

[0043] The present disclosure relates to a technique for enabling earlier grasp of the GHG emissions of the product 24.

[0044] FIG. 3 is a functional block diagram schematically showing a GHG emissions estimation device 30 for estimating the GHG emissions of the product 24.

[0045] As shown in the figure, the GHG emissions estimation device 30 includes a GHG estimation model 50.

[0046] The GHG estimation model 50 is a model that is constructed based on past GHG emission participation values and calculates the GHG emissions using the current GHG emission participation values as parameters.

[0047] The past GHG emission participation values are values related to the GHG emissions of the past products 24. The past GHG emission participation values are values involved in calculating the GHG emissions of products 24 manufactured in the past compared to the products 24 for which GHG emissions are to be estimated. Therefore, the past GHG emission participation values may be values in a regular period prior to the regular period corresponding to the product 24 for which GHG emissions are to be estimated. The past GHG emission participation values may be values in the regular period immediately before the regular period corresponding to the product 24 for which GHG emissions are to be estimated, but this is not essential.

[0048] The past GHG emission participation values are values among the past manufacturing situations of the products 24 that affect the emissions of the GHG emissions. The past GHG emission participation values may be values accessible to the operator of the second manufacturing process among the values that affect the emissions of the GHG emissions.

[0049] The past GHG emission participation values related to the first manufacturing process may be, for example, the quantity of parts 22 delivered, the GHG emissions caused by the parts 22, and values related to the use of renewable energy. The GHG emission estimation device 30 related to the second manufacturing process may be, for example, the manufacturing quantity of the product 24, the amount of GHG emitted by the processing of the product 24, and values related to the use of renewable energy. The values related to the use of renewable energy may be the renewable energy ratio, the amount of electricity generated by renewable energy in the consumed electricity, or the amount of GHG that can be reduced by the use of renewable energy.

[0050] The current GHG emission participation values are values related to the GHG emissions of the product 24 for which GHG emissions are to be estimated and are values that can be grasped during the manufacturing of the product 24. The current GHG emission participation values are, for example, the quantity of parts 22 delivered and the manufacturing quantity of the product 24. The current GHG emission participation values may be some of the current values among the past GHG emission participation value group.

[0051] Based on the group of past GHG emission contribution values, the model generation unit 34 generates a GHG estimation model 50 that calculates the GHG emission amount from some of the current situation values of the group of past GHG emission contribution values. Then, by applying the current situation value of the GHG emission contribution, which is some of the group of past GHG emission contribution values, to the GHG estimation model 50, the GHG emission amount of the product 24 to be estimated is estimated.

[0052] As a result, even if all of the GHG emission contribution values for the period corresponding to the product 24 for which the GHG emission amount is to be estimated are not grasped at the time of manufacturing the product 24, the GHG emission amount can be estimated.

[0053] The GHG estimation model 50 may include, as an adjustment parameter, a renewable energy evaluation value corresponding to the usage situation of the renewable energy used in the manufacture of the product 24. The renewable energy evaluation value is a value indicating the usage situation of the renewable energy in the first manufacturing process or the second manufacturing process. The renewable energy evaluation value may be evaluated by the power generation amount by the renewable energy, the power generation ratio by the renewable energy, or the amount of GHG reduced by the renewable energy. The renewable energy evaluation value may be the ratio of renewable energy in the first energy supplier 14 or the second energy supplier 16, the power generation amount of the renewable energy, or the amount of GHG reduced by the use of the renewable energy.

[0054] The advantages of the GHG estimation model 50 including the renewable energy evaluation value as an adjustment parameter are as follows.

[0055] That is, it is considered that the GHG emissions derived from raw materials and the amount of electric power for processing the component 22 or the product 24 are unlikely to fluctuate rapidly. On the other hand, the amount of electric power generated by renewable energy is considered to be likely to fluctuate according to the power generation situation. For example, in the case of a solar power plant, the power generation amount fluctuates depending on the sunshine hours, in the case of a wind power plant, the power generation amount fluctuates depending on the wind speed and wind direction, and in the case of a wave power plant, the power generation amount may fluctuate depending on the wave height.

[0056] Therefore, if the GHG estimation model 50 includes a renewable energy evaluation value as an adjustment parameter, the renewable energy evaluation value can be adjusted to estimate the GHG emissions according to the utilization situation of renewable energy. The renewable energy evaluation value may be arbitrarily adjusted by the user in view of the location conditions and power generation situation of the power generation facilities of the first energy supplier 14 or the second energy supplier 16 that supply the electric power contributing to the processing of the product 24 to be estimated, or a value corresponding to a past similar situation may be set, or a model for calculating the renewable energy evaluation value from the weather situation may be generated based on past values, experiences, etc., and the renewable energy evaluation value may be calculated by the model.

[0057] FIG. 4 is a block diagram showing the electrical configuration of the GHG emission estimation device 40.

[0058] The GHG emission estimation device 40 includes a GHG emission estimation processing device 42. The GHG emission estimation processing device 42 is constituted by a computer including a processor 44 such as a CPU, a storage device 46, an interface circuit 47, etc.

[0059] The GHG emission estimation processing device 42 may be communicably connected to the first terminal 12A in the first manufacturing process and the second terminal 12B in the second manufacturing process via the interface circuit 47.

[0060] For the first terminal 12A, for example, the renewable energy ratio α_p is input from the first energy supplier 14. From the first terminal 12A to the GHG emission estimation processing device 42, for example, the quantity Ns_c of parts delivered during a regular period corresponding to the part 22 which is the product to be estimated, the renewable energy ratio α_p, the quantity Ns_p of delivered parts, and the GHG emissions Gs_p of the delivered parts during a regular period in the past compared to the said regular period are provided.

[0061] For the second terminal 12B, for example, the GHG emissions Geslf_p resulting from the use of energy during a regular period are input from the second energy supplier 16. From the second terminal 12B to the GHG emission estimation processing device 42, for example, the production quantity Nslf_c of the product 24 during a regular period corresponding to the product 24 to be estimated, the total production quantity Nslf_all_p of the product 24 during a regular period in the past compared to the said regular period, and the GHG emissions Geslf_p resulting from the use of energy are provided.

[0062] The GHG emission estimation processing device 42 may be connected to the input device 48 via the interface circuit 47. The input device 48 may be a man-machine interface that receives instructions from the user. The man-machine interface may be a keyboard including a plurality of switches, a pointing device such as a mouse that receives operation inputs to the screen, or a touch panel or the like. The GHG emission estimation processing device 42 can receive, via the input device 48, the estimated GHG emissions Gn_est, Gnslf_est reduced by the use of renewable energy during a regular period corresponding to the product 24 to be estimated, and the estimated GHG emissions Gn_est_p, Gnslf_est_p reduced by the use of renewable energy during a regular period in the past compared to the said regular period. Note that the estimated GHG emissions Gn_est, Gn_est_p are the estimated GHG emissions reduced with respect to the energy supplied by the first energy supplier 14, and the estimated GHG emissions Gnslf_est, Gnslf_est_p are the estimated GHG emissions reduced with respect to the energy supplied by the second energy supplier 16.

[0063] When the reduced estimated GHG emissions Gn_est, Gnslf_est, Gn_est_p, and Gnslf_est_p are obtained by a computer through the application of the model, the computer may be connected to the GHG emissions estimation processing device 42, or the GHG emissions estimation processing device 42 may execute the process of obtaining the reduced estimated GHG emissions.

[0064] The above-mentioned processor 44 includes a circuit. The processor 44 is an example of a processing unit for estimating the GHG emissions of a product. Also, the processor 44 is an example of a processing unit for generating the GHG estimation model 50.

[0065] The storage device 46 is composed of a non-volatile storage device such as an HDD (hard disk drive) or an SSD (Solid-state drive). For example, a program 46a and the GHG estimation model 50 are stored in the storage device 46. The storage device 46 is an example of a storage unit for storing the GHG estimation model.

[0066] The program 46a describes the processes for the processor 44 to realize its functions as a processing unit. Therefore, by the processor 44 executing the processes described in the program 46a stored in the storage device 46, for the GHG estimation model 50, the process as a processing unit for estimating the GHG emissions of a product by applying the GHG emission participation current value is executed. Similarly, by the processor 44 executing the processes described in the program 46a stored in the storage device 46, the process as a processing unit for generating the GHG estimation model 50 based on the past GHG emission participation values is executed.

[0067] Note that the processor 44 may be one or a plurality. A plurality of processors 44 may be incorporated in one computer. A plurality of processors 44 may be incorporated in a plurality of computers, and the plurality of computers may perform the processes as the above-mentioned processing unit in a distributed manner.

[0068] The GHG emission estimation device 40 may include a display device 49. The display device 49 may be a liquid crystal display device, an organic EL (Electro-luminescence) display device, or the like. The display device 49 may be a display device provided in a smartphone, a tablet terminal, or the like. The estimated GHG emissions of the product 24 may be displayed on the display device 49.

[0069] The storage device 46 stores a GHG estimation model 50. The GHG estimation model 50 includes a first GHG estimation model 51A in the first manufacturing process and a second GHG estimation model 51B in the second manufacturing process. The first GHG estimation model 51A estimates the GHG emissions of the component 22 manufactured in the first manufacturing process. The second GHG estimation model 51B estimates the GHG emissions derived from processing in the second manufacturing process.

[0070] The storage device 46 also stores a past GHG emission contribution value 60, a current GHG emission contribution value 66, and a renewable energy evaluation value 68. Each of the values of the past GHG emission contribution value 60, the current GHG emission contribution value 66, and the renewable energy evaluation value 68 is a value given from the first terminal 12A, the second terminal 12B, or the input device 48 through the interface circuit 47.

[0071] The past GHG emission contribution value 60 is a value for generating the GHG estimation model 50 and includes a past value 61A for the first estimation model and a past value 61B for the second estimation model.

[0072] The past value 61A for the first estimation model is a value for generating the first GHG estimation model 51A and is, for example, the quantity of parts received Ns_p, the GHG emissions Gs_p of the received parts, the renewable energy ratio α_p, and the estimated GHG emissions Gn_est_p reduced by the use of renewable energy during a past regular period.

[0073] Focusing on the component 22 which is a product in the first manufacturing process, the delivery quantity Ns_p is an example of the past processing quantity of the component 22 as a product, the GHG emission quantity Gs_p is an example of the past GHG emission quantity derived from the raw materials and processing of the component 22, and the renewable energy ratio α_p is an example of the past ratio of renewable energy used for the processing of the component 22.

[0074] The past value 61B for the second estimation model is a value for generating the second GHG estimation model 51B. For example, it is the production quantity Nslf_all_p of the product 24 in the past regular period, the GHG emission quantity Geslf_p caused by the use of energy, and the estimated GHG emission quantity Gnslf_est_p reduced by the use of renewable energy.

[0075] The current situation value 66 of GHG emission participation is a value substituted into the parameters when the GHG estimation model 50 is applied. The current situation value 66 of GHG emission participation includes the value of the delivery quantity Ns_c of the component as the first production quantity information and the value of the production quantity Nslf_c of the product 24 as the second production quantity information. The value of the delivery quantity Ns_c of the component and the value of the production quantity Nslf_c of the product 24 are examples of production quantity information corresponding to the quantity of the product to be estimated. The specific numerical value defined by the delivery quantity Ns_c of the component is substituted into the parameter Ns_c of the first GHG estimation model 51A, and the specific numerical value defined by the production quantity Nslf_c of the product 24 is substituted into the parameter Nslf_c of the second GHG estimation model 51B.

[0076] The evaluation value 68 of renewable energy is a value substituted into the adjustment parameter when the GHG estimation model 50 is applied. The evaluation value 68 of renewable energy includes the values of the estimated GHG emission quantities Gn_est and Gnslf_est reduced by the use of renewable energy. The value of the reduced estimated GHG emission quantity Gn_est is substituted into the adjustment parameter Gn_est of the first GHG estimation model 51A, and the value of the reduced estimated GHG emission quantity Gnslf_est is substituted into the adjustment parameter Gnslf_est of the second GHG estimation model 51B.

[0077] A more specific description will be given for the GHG estimation model 50.

[0078] First, in the first manufacturing process, the relationship between the GHG emissions and each value involved in the GHG emissions is examined.

[0079] The following values can be considered as values that can be grasped during the regular period. Note that at the time of model generation, it is preferable to use each value in the regular period closest to the manufacturing time of the product 24 to be estimated.

[0080] Quantity of parts 22 delivered during the regular period: Ns_p GHG emissions corresponding to the delivered parts 22 during the regular period: Gs_p Ratio of renewable energy in the energy consumed in the first manufacturing process during the regular period: α_p Each of the above values may be a value reported, for example, from the operator of the first manufacturing process via the first terminal 12A.

[0081] In addition, the following values can be considered as values that can be estimated from the meteorological conditions, etc. of the power plant of the first energy supplier 14.

[0082] Estimated GHG emissions that could be reduced by the use of renewable energy when manufacturing all parts 22 in the first manufacturing process at regular intervals: Gn_est_p Gn_est_p can be estimated based on the weather conditions of the power plant managed by the first energy supplier 14. For example, based on the weather conditions of the power plant, the power generation amount En_est by renewable energy is estimated. More specifically, if the power plant is a solar power plant, a model is generated in which the power generation amount En_est increases as the sunshine duration becomes longer, and the sunshine duration is applied to the model to estimate the power generation amount by renewable energy. Also, if the power plant is a wind power plant, a model is generated in which the power generation amount En_est increases as the wind speed increases, and the wind speed is applied to the model to estimate the power generation amount by renewable energy. Further, if the power plant is a wave power plant, the power generation amount by renewable energy may be estimated such that the power generation amount fluctuates as the wave height increases. Also, the power generation amount in the current weather conditions may be estimated from past data associating past weather conditions with the power generation amount by renewable energy.

[0083] Also, the following values can be considered as values estimable from the above respective values.

[0084] Estimated GHG emissions corresponding to the raw materials of the unit amount of the component 22 in a regular period: Gog_est Estimated GHG emissions when manufacturing the unit amount of the component 22 with non-renewable energy: Ge_est Estimated total production value Nall_est_p of the component 22 in the first manufacturing process in a regular period Note that the reason for setting the estimated total production value Nall_est_p of the component 22 in the first manufacturing process in a regular period as a value different from the delivery quantity Ns_p is that it is assumed that a part of the total production quantity of the components manufactured in the first manufacturing process is delivered to the second manufacturing process.

[0085] Using the above respective values, the following relational expressions hold.

[0086] First, the GHG emissions per unit quantity of component 22 are equal to the value obtained by dividing the total GHG emissions Gs_p of the delivered component 22 by the delivered quantity Ns_p. Also, the GHG emissions per unit quantity of component 22 are equal to the sum of the estimated GHG emissions Gog_est corresponding to the raw materials per unit quantity of component 22 and the GHG amount emitted by the processing of per unit quantity of component 22. Here, the GHG amount emitted by the processing of per unit quantity of component 22 is calculated by multiplying the estimated GHG emissions Ge_est when manufacturing per unit quantity of component 22 using non-renewable energy by the ratio (1 - α_p) of energy that is not renewable energy. Therefore, the following formula (1) holds.

[0087] Gs_p / Ns_p = Gog_est + (1 - α_p)Ge_est ···(1) Next, the total estimated production quantity Nall_est_p of the component is equal to the value obtained by dividing the estimated GHG emissions Gn_est_p that can be reduced by the use of renewable energy by the GHG amount that can be reduced by the use of renewable energy when processing per unit quantity of component 22. Here, the GHG amount that can be reduced by the use of renewable energy when processing per unit quantity of component 22 can be calculated by multiplying the estimated GHG emissions Ge_est when manufacturing per unit quantity of component 22 using non-renewable energy by the renewable energy ratio α_p. Therefore, the following formula (2) holds.

[0088] Nall_est_p = Gn_est_p / {α_p × Ge_est} ···(2) Next, considering estimating the estimated GHG emissions Gog_est corresponding to the raw materials per unit quantity of component 22 and the estimated GHG emissions Ge_est when manufacturing per unit quantity of component 22 using non-renewable energy from the above formula (1).

[0089] In each of a plurality of past regular periods \(i, i + 1,\cdots,i + N\), GHG emissions amounts \(Gs_p(i),Gs_p(i + 1),\cdots,Gs_p(i + N)\) corresponding to the delivered parts 22, delivery amounts \(Ns_p(i),Ns_p(i + 1),\cdots,Ns_p(i + N)\) of the parts 22, and renewable energy ratios \(\alpha_p(i),\alpha_p(i + 1),\cdots,\alpha_p(i + N)\) are acquired and stored in the storage device 46.

[0090] Also, in a plurality of past regular periods, the amount of energy for processing raw materials and parts hardly fluctuates suddenly. Therefore, the estimated GHG emissions amounts \(Gog\_est\) and \(Ge\_est\) may be regarded as maintaining constant values.

[0091] When each of the above plurality of regular periods \(i, i + 1,\cdots,i + N\) is expressed by a determinant, it becomes as follows in determinant 1 below.

[0092]

Equation

[0093] Then, by the least squares method, the estimated GHG emissions amounts \(Gog\_est\) and \(Ge\_est\) can be derived so as to minimize the square of the norm of the difference between the left side and the right side of determinant 1. That is, the GHG emissions amount \(Gog\_est\) derived from raw materials and the GHG emissions amount \(Ge\_est\) derived from processing can be obtained based on the delivery amount \(Ns_p\) of the parts 22, the GHG emissions amount \(Gs_p\) corresponding to the parts 22, and the renewable energy ratio \(\alpha_p\). In particular, by being based on a plurality of combinations of the delivery amount \(Ns_p\) of the parts 22, the GHG emissions amount \(Gs_p\) corresponding to the parts 22, and the renewable energy ratio \(\alpha_p\), it is easy to estimate appropriate values of the GHG emissions amount \(Gog\_est\) derived from raw materials and the GHG emissions amount \(Ge\_est\) derived from processing.

[0094] When applying the least squares method as described above, for each of a plurality of regular periods i, i+1, ···, i+N, a weighting coefficient corresponding to the weighting of the plurality of past regular periods i, i+1, ···, i+N may be multiplied. For example, for the plurality of past regular periods i, i+1, ···, i+N, a weighting coefficient that decreases in value as going back in the past may be set. Thereby, the estimated GHG emissions Gog_est and Ge_est in which values closer to the present are more strongly reflected are derived.

[0095] Then, for a predetermined regular period, by substituting the renewable energy ratio α_p, the estimated GHG emissions Gn_est_p that could be reduced, and the estimated GHG emissions Gog_est into the above formula (2), the total production volume Nall_est_p of parts in the predetermined regular period is estimated.

[0096] FIG. 5 is a block diagram showing the first GHG estimation model 51A. Using each value estimated as described above, a model for estimating the GHG emissions in the first manufacturing process can be generated as follows.

[0097] The period for estimating the GHG emissions is set as the sample period.

[0098] First, the quantity of parts 22 received Ns_c in the regular period is divided by the sample period, whereby the quantity of parts 22 received per sample period is calculated.

[0099] Also, when manufacturing the unit quantity of parts 22 without using renewable energy, the GHG emissions derived from both raw materials and processing are calculated as the sum of the above estimated GHG emissions Gog_est and the estimated GHG emissions Ge_est.

[0100] Furthermore, the estimated GHG emissions Gn_est that can be reduced by using renewable energy are divided by the estimated total production volume Nall_est_p of part 22 during the regular period, so that the estimated GHG emissions that can be reduced per unit amount of part 22 are calculated. Since the estimated GHG emissions Gn_est that can be reduced by using renewable energy can be estimated based on the same concept as the estimated GHG emissions Gn_est_p that can be reduced, it can be estimated even without the reported value from the first manufacturing process. The estimated GHG emissions Gn_est that can be reduced by using renewable energy are an example of a renewable energy evaluation value, and in particular, an example of a second renewable energy evaluation value for evaluating the utilization status of renewable energy in the first manufacturing process.

[0101] By subtracting the estimated GHG emissions that can be reduced by using renewable energy from the GHG emissions derived from both raw materials and processing, the GHG emissions per unit amount of part 22 considering the use of renewable energy are estimated. By multiplying the GHG emissions by the delivery volume of part 22 per the above sample period, the GHG emissions per sample period in the first manufacturing process are estimated.

[0102] The above process is an example of a process for estimating the GHG emissions after the first manufacturing process based on the maximum GHG emissions from processing, the GHG emission reduction amount by using renewable energy, and the GHG emissions from raw materials. Note that the value corresponding to the delivery volume of part 22 or the target period may be multiplied at any stage before and after subtracting the value corresponding to the estimated GHG emissions that can be reduced. In any case, it can be evaluated as the same process of estimating the maximum GHG emissions discharged when manufacturing the product to be estimated by non-renewable energy based on the delivery volume Ns_c and subtracting the value corresponding to the GHG emissions that can be reduced from the estimated maximum GHG emissions.

[0103] The above-estimated GHG emissions are calculated using the estimated GHG emissions Gn_est that could have been reduced by the use of renewable energy as an adjustment parameter. Therefore, by adjusting the estimated GHG emissions Gn_est according to weather conditions, etc., the GHG emissions can be easily estimated appropriately according to the utilization status of renewable energy.

[0104] This first GHG estimation model 51A can be generated as a model including the GHG emissions Gog_est derived from raw materials per unit quantity of the component 22 as a product, the GHG emissions Ge_est derived from processing per unit quantity of the component 22 assuming it is processed by non-renewable energy, and Gn_est as a renewable energy evaluation value according to the utilization status of the renewable energy used for processing the component 22. When focusing on the first manufacturing process, the first GHG estimation model 51A is an example of a model for estimating GHG emissions based on the estimated GHG emissions Gog_est from the starting materials of the component 22 as a product in the first manufacturing process.

[0105] Next, in the second manufacturing process, the relationship between the GHG emissions and each value involved in the emission of GHG is examined.

[0106] The following values can be considered as values that can be grasped during a regular period. Note that at the time of model generation, it is preferable to use each value during the regular period closest to the manufacturing time of the product 24 to be estimated.

[0107] Production volume of the product 24 at regular intervals: Nslf_all_p GHG emissions caused by the energy for manufacturing the product 24 during the regular period: Geslf_p Each of the above values can be, for example, a value that can be grasped by the operator of the second manufacturing process and may be a value reported via the second terminal 12B.

[0108] In addition, the following values can be considered as values that can be estimated from the meteorological conditions, etc. of the power plant of the second energy supplier 16.

[0109] Estimated GHG emissions that can be reduced by using renewable energy when manufacturing product 24 by processing part 22 in the second manufacturing process at regular intervals: Gnslf_est_p Gnslf_est_p can be estimated based on the weather conditions of the power plant managed by the second energy supplier 16 in the same way as Gn_est_p.

[0110] Also, the following values can be considered as values that can be estimated from the above values.

[0111] Estimated value of GHG emissions discharged by the energy used to process a unit quantity of product 24 without using renewable energy: Gogslf_est The following relational expressions hold when using the above values.

[0112] That is, the GHG emissions Gogslf_est when processing a unit quantity of product 24 without using renewable energy is equal to the value obtained by dividing the GHG emissions when processing product 24 without using renewable energy during the regular period by the production volume Nslf_all_p of product 24. Also, the GHG emissions when processing product 24 without using renewable energy during the regular period can be calculated by adding the estimated GHG emissions Gnslf_est_p that can be reduced by using renewable energy at regular intervals to the GHG emissions Geslf_p from the processing of product 24 during the regular period.

[0113] Therefore, the following formula (3) holds.

[0114] Gogslf_est = (Geslf_p + Gnslf_est_p) / Nslf_all_p ··· (3) Then, for a predetermined regular period, by substituting Geslf_p, Gnslf_est_p, and Nslf_all_p, which are known or estimated values, into the above formula (3), the GHG emissions Gogslf_est of the product 24 in the said predetermined regular period are estimated. The term of the estimated GHG emissions Gnslf_est_p to be reduced may be omitted, but by considering the estimated GHG emissions Gnslf_est_p to be reduced, a more appropriate GHG emissions Gogslf_est of the product 24 is estimated.

[0115] FIG. 6 is a block diagram showing the second GHG estimation model 51B. Using each value estimated as described above, a model for estimating the GHG emissions in the second manufacturing process can be generated as follows.

[0116] The period for estimating the GHG emissions is set as the sample period.

[0117] First, the estimated GHG emissions from processing when manufacturing a unit amount of the product 24 without using renewable energy are Gogslf_est.

[0118] Also, the production amount Nslf_c of the product 24 in the sample period is divided by the sample period to calculate the production amount of the product 24 per sample period.

[0119] By multiplying the above estimated GHG emissions Gogslf_est by the production amount of the product 24 per sample period, the GHG emissions from processing per sample period are estimated. This process is an example of a process for estimating the maximum GHG emissions discharged when processing the product 24 to be estimated by non-renewable energy based on the production amount Nslf_c, which is production amount information.

[0120] Also, during the sample period, the estimated GHG emissions Gnslf_est that could be reduced by using renewable energy can be estimated in the same way as the estimated GHG emissions Gn_est_p that could be reduced. For example, based on the weather conditions of the power plant, the power generation amount Enslf_est by renewable energy can be estimated in the same way as En_est, and Gnslf_est can be calculated by multiplying the Enslf_est by the energy unit factor. The estimated GHG emissions Gnslf_est that could be reduced by using renewable energy is an example of the renewable energy evaluation value, and in particular, it is an example of the second renewable energy evaluation value for evaluating the utilization status of renewable energy in the second manufacturing process.

[0121] Then, by subtracting the estimated GHG emissions Gnslf_est that could be reduced by using renewable energy during the sample period from the GHG emissions from processing per sample period when using non-renewable energy, the GHG emissions from processing of the product 24 per sample period are estimated. This process is an example of a process for estimating the GHG emission reduction amount when using renewable energy based on the estimated GHG emissions Gnslf_est that could be reduced, which is a renewable energy evaluation value, and estimating the GHG emissions of the product 24 based on the maximum GHG emissions and the GHG emission reduction amount. Note that the value corresponding to the production amount or period of the target product 24 may be multiplied at any stage.

[0122] The above-estimated GHG emissions are calculated using the estimated GHG emissions Gnslf_est that could be reduced by using renewable energy as an adjustment parameter. Therefore, by adjusting the estimated GHG emissions Gnslf_est according to the weather conditions, etc., the GHG emissions are likely to be appropriately estimated according to the utilization status of renewable energy.

[0123] This second GHG estimation model 51B can be generated as a model including the GHG emissions Gogslf_est from processing per unit quantity of the product 24 when processed with non-renewable energy, and the estimated GHG emissions Gnslf_est that can be reduced as a renewable energy evaluation value according to the utilization status of renewable energy used for processing the product 24. The second GHG estimation model 51B is an example of a model for calculating the GHG emissions from processing of the product 24 per sample period by applying the production quantity Nslf_c. When estimating GHG, by setting the estimated GHG emissions Gnslf_est according to weather conditions and the like, the GHG emissions according to the utilization status of renewable energy are estimated.

[0124] By integrating the GHG emissions per sample period of the component 22 delivered from the first manufacturing process and the GHG emissions per sample period of the product 24 manufactured in the second manufacturing process, the amount of GHG emitted for manufacturing the product 24 is estimated. Note that focusing on the second manufacturing process, the GHG emissions of the component 22 calculated for the first manufacturing process can be understood as the GHG emissions from the starting materials of the second manufacturing process.

[0125] FIG. 7 is a flowchart showing the process of estimating the total GHG emissions of the product 24 using the GHG estimation model 50 including the first GHG estimation model 51A and the second GHG estimation model 51B by the GHG emissions estimation processing device 42.

[0126] In step S1, the past values 61A for the first estimation model and the past values 61B for the second estimation model are received as the GHG emission participation past values 60.

[0127] In the next step S2, the processor 44 performs the above arithmetic processing to calculate each constant of the first GHG estimation model 51A and the past values 61B for the second estimation model. Thereby, the first GHG estimation model 51A is generated based on the past values 61A for the first estimation model, and the second GHG estimation model 51B is generated based on the past values 61B for the second estimation model.

[0128] In step S3, the production amount Ns_c as the current situation value of GHG emissions involved in the first manufacturing process and the adjustment parameter Gn_est according to the weather situation are received.

[0129] In step S4, the production amount Ns_c and the adjustment parameter Gn_est are applied to the first GHG estimation model 51A, and the GHG emission amount of the component 22 delivered from the first manufacturing process is estimated.

[0130] In step S5, the production amount Nslf_c as the current situation value of GHG emissions involved in the second manufacturing process and the adjustment parameter Gnslf_est according to the weather situation are received.

[0131] In step S6, the production amount Nslf_c and the adjustment parameter Gnslf_est are applied to the second GHG estimation model 51B, and the GHG emission amount of the component 22 delivered from the first manufacturing process is estimated.

[0132] In step S7, the GHG emission amount estimated in step S4 and the GHG emission amount estimated in step S6 are integrated, and the total GHG emission amount of the product 24 is estimated.

[0133] In step S8, it is preferable that the total GHG emission amount is displayed on the display device 49. For example, as shown in FIG. 8, it may be displayed as "The GHG emission amount per sample period is ○○○ kg-CO2". In this case, the breakdown of the GHG emission amount in the first manufacturing process and the GHG emission amount in the second manufacturing process may be displayed.

[0134] In step S9, the presence or absence of the current value of the GHG emission amount Gs_p of the component 22 and the current value of the GHG emission amount Geslf_p derived from the processing of the product 24 is determined. When it is determined that there is no report of each current value from the first terminal 12A and the second terminal 12B, the process returns to step S8 to continue displaying the estimated total GHG emission amount. When it is determined that there is a report of each current value, the process proceeds to step S10.

[0135] In step S10, the total GHG is calculated by integrating the value reported as the current value.

[0136] In step S11, the total GHG calculated in step S10 is displayed on the display device 49. Thereby, the process ends.

[0137] According to the GHG emission estimation device 30 configured as described above, the GHG estimation model 50 is constructed based on the past GHG emission participation value 60, and the current GHG emission participation value 66 of the product 24 is applied to the GHG estimation model 50. Therefore, the GHG emissions of the product 24 can be grasped earlier than before the GHG emissions of the product 24 are reported.

[0138] In addition, since the GHG estimation model 50 is a model including, for example, the renewable energy evaluation values Gn_est and Gnslf_est as the renewable energy evaluation values corresponding to the utilization status of the renewable energy used in the manufacture of the product as adjustment parameters, the GHG emissions can be estimated according to the utilization status of the renewable energy. For example, when a large amount of renewable energy is used, the GHG emissions of the product 24 can be estimated so as to reduce the GHG emissions corresponding to the amount of renewable energy used.

[0139] In addition, the current GHG emission participation value 66 includes the production amount information Ns_c corresponding to the amount of the product to be estimated, and the GHG estimation model 50 estimates the maximum GHG emissions based on the production amount information Ns_c, and estimates the GHG emission reduction amount when renewable energy is used based on the renewable energy evaluation values such as the renewable energy evaluation value Gn_est, and estimates the GHG emissions based on the maximum GHG emissions and the GHG emission reduction amount. Therefore, the GHG emissions can be estimated according to the utilization status of the renewable energy.

[0140] In addition, since the first GHG estimation model 51A included in the GHG estimation model 50 further estimates the GHG emissions based on the GHG emissions derived from the raw material 20 of the component 22 which is the product, the GHG emissions of the component 22 which is the product can be estimated in consideration of the GHG emissions derived from the starting materials such as the raw material 20.

[0141] Also, when the product 24 is manufactured through a first manufacturing process and a second manufacturing process, the GHG estimation model 50 includes a first GHG estimation model 51A in the first manufacturing process and a second GHG estimation model 51B in the second manufacturing process. And the first GHG estimation model 51A includes an adjustment parameter Gn_est as a first renewable energy evaluation value in the first manufacturing process. Also, the second GHG estimation model includes an adjustment parameter Gnslf_est as a second renewable energy evaluation value in the second manufacturing process.

[0142] Then, the first GHG estimation model 51A estimates the maximum GHG emission amount based on the first production amount information Ns_c, estimates the GHG emission reduction amount based on the adjustment parameter Gn_est, and estimates the GHG emission amount after the first manufacturing process based on the maximum GHG emission amount, the GHG emission reduction amount, and the GHG emission amount derived from the starting material.

[0143] Also, the second GHG estimation model 51B estimates the maximum GHG emission amount based on the second production amount information Nslf_c, estimates the GHG emission reduction amount based on the adjustment parameter Gnslf_est, and estimates the GHG emission amount attributed to the second manufacturing process based on the maximum GHG emission amount and the GHG emission reduction amount.

[0144] Furthermore, the GHG emission amount of the product 24 is estimated based on the GHG emission amount estimated by the first GHG estimation model 51A and the GHG emission amount estimated by the second GHG estimation model 51B. Therefore, when the product 24 is manufactured through multiple processes, it is easy to estimate the GHG emission amount.

[0145] Further, the first GHG estimation model 51A is a model including the GHG emission amount Gog_est derived from the starting materials per unit amount of the component 22 which is a product, the GHG emission amount Ge_est derived from the processing per unit amount of the component 22 assuming that it is processed by non-renewable energy, and the adjustment parameter Gn_est which is a renewable energy evaluation value according to the utilization situation of the renewable energy used for the processing of the component 22. The estimated GHG emission amount Gog_est derived from the starting materials and the estimated GHG emission amount Ge_est derived from the processing included in the first GHG estimation model 51A are obtained based on the past processing amount Ns_p, the past GHG emission amount Gs_p, and the past renewable energy ratio α_p. Therefore, based on the past processing amount Ns_p, the past GHG emission amount Gs_p, and the renewable energy evaluation value, which are easy to grasp or estimate in the second manufacturing process, the estimated GHG emission amount derived from the starting materials and the estimated GHG emission amount derived from the processing, which are difficult to grasp in the second manufacturing process, can be inferred.

[0146] In this case, by basing on a plurality of combinations of the past processing amount Ns_p, the past GHG emission amount Gs_p, and the renewable energy evaluation value, the estimated GHG emission amount derived from the starting materials and the estimated GHG emission amount derived from the processing can be inferred more appropriately.

[0147] Also, when the GHG emission amount Gogslf_est derived from the processing per unit amount of the product 24 assuming that it is processed by non-renewable energy cannot be directly known, as in the second GHG estimation model 51B, by using a model including the GHG emission amount Gogslf_est derived from the processing per unit amount of the product 24 assuming that it is processed by non-renewable energy and the adjustment parameter Gnslf_est as the renewable energy evaluation value according to the utilization situation of the renewable energy used for the processing of the product 24, it is easy to estimate the GHG emission amount according to the utilization situation of the renewable energy. In this case, the GHG emission amount Gogslf_est derived from the processing assuming that it is processed by non-renewable energy can be obtained based on the past processing amount Nslf_all_p of the product 24 and the past GHG emission amount Geslf_p caused by the processing of the product 24.

[0148] In addition, the memory device 46 stores past values of GHG emission participation, and the processor 44 executes the above arithmetic processing based on the past values of GHG emission participation, thereby calculating the respective constants of the first GHG estimation model 51A and the second GHG estimation model 51B, and the GHG estimation model 50 can be generated.

[0149] In addition, the estimated GHG emission amount of the product 24 is displayed on the display device 49, so that the user can visually recognize the estimated GHG emission amount.

[0150] The GHG emission amount estimation device 30 configured as described above can exhibit effectiveness, for example, in the following background.

[0151] That is, each country including Japan has declared to achieve carbon neutrality by 2050. For this reason, it is conceivable that each national government requests each company to formulate a plan for GHG emissions according to the scale of corporate activities and the like. Under this background, it is desirable for each company to take measures to reduce GHG emissions while maintaining corporate activities. In order to meet this demand, it is desirable to grasp the GHG emissions generated in the manufacture of a company's products in consideration of the entire supply chain.

[0152] In this regard, in International Publication No. 2022 / 244145, in order to estimate the environmental impact of food using sales data of food and the like, it is difficult to quickly estimate GHG emissions at the time of food production and the like. In addition, in order to estimate the environmental impact based on the environmental impact coefficient registered in advance for each food, when a large amount of renewable energy is used during food production, the GHG emissions will be overestimated.

[0153] Therefore, as described above, by applying the GHG emission participation status value 66 that can be easily grasped during the manufacture of the product 24 to the GHG estimation model 50, the GHG emissions of the product 24 can be grasped earlier. Also, by adjusting, for example, the adjustment parameters Gn_est and Gnslf_est as the renewable energy evaluation values, it is possible to estimate the GHG emissions in real time and taking into account renewable energy according to the utilization status of renewable energy. As a result, it becomes easy to adjust the manufacturing plan in accordance with the GHG emission limit.

[0154] In addition, when the above GHG is estimated, the GHG generated by the transportation of the component 22 may be added. For example, a transfer function for calculating the GHG generated by transportation using the transportation distance of the component 22 or the amount of the component 22 as a parameter is preset, and the transportation distance or amount of the component 22 is substituted into the parameter of the transfer function to calculate the GHG. The transfer function may be set as a function in which the GHG increases as the transportation distance of the component 22 or the amount of the component 22 increases. It may also be set based on the past actual calculated value of the GHG generated by the transportation of the component 22.

[0155] In the above embodiment, an example in which the product 24 is manufactured through the first manufacturing process and the second manufacturing process has been described. However, it may be configured as an apparatus for estimating the GHG emissions of the product manufactured through the first manufacturing process.

[0156] Further, when the product 24 is manufactured through a plurality of first manufacturing processes and a second manufacturing process, it may be configured as an apparatus for estimating the GHG emissions. In this case, the plurality of first manufacturing processes may have a serial relationship in which parts are sequentially processed, or may have a parallel relationship in which a plurality of parts are supplied to the second manufacturing process. In the former case, the first GHG estimation model 51A may be applied to the most upstream first manufacturing process, and the second GHG estimation model 51B may be applied to the first manufacturing process and the second manufacturing process downstream of the most upstream. Also, in the latter case, the first GHG estimation model 51A may be applied to each of the plurality of parallel first manufacturing processes. In any case, the GHG emissions of the final product can be estimated by integrating the GHG emissions estimated for each manufacturing process.

[0157] Also, the adjustment parameter as the renewable energy evaluation value may be the power generation amounts En_est and Enslf_est by non-renewable energy instead of the reducible GHG amounts Gn_est and Gnslf_est. If the energy unit is known, the reducible GHG amounts Gn_est and Gnslf_est can be calculated by multiplying the power generation amounts En_est and Enslf_est by non-renewable energy by the energy unit. Also, the adjustment parameter as the renewable energy evaluation value may be the renewable energy ratio.

[0158] The present disclosure discloses the following aspects.

[0159] A first aspect is a GHG emissions estimation apparatus for estimating the GHG emissions of a product, including a storage unit that stores a GHG estimation model constructed based on past GHG emission participation past values involved in the GHG emissions of the past product and obtains the GHG emissions using the current GHG emission participation status value of the product to be estimated as a parameter, and a processing unit that applies the current GHG emission participation status value to the GHG estimation model to estimate the GHG emissions of the product.

[0160] Accordingly, a GHG estimation model is constructed based on past GHG emission involvement values, and by applying the current GHG emission involvement status values to the estimation model, the GHG emissions of the product can be grasped earlier.

[0161] A second aspect is the GHG emissions estimation device according to the first aspect, wherein the GHG estimation model is a model including, as an adjustment parameter, a renewable energy evaluation value corresponding to the utilization status of renewable energy used in the production of the product.

[0162] Accordingly, the GHG emissions can be estimated according to the utilization status of renewable energy.

[0163] A third aspect is the GHG emissions estimation device according to the second aspect, wherein the current GHG emission involvement status values include production amount information corresponding to the amount of the product to be estimated, and the GHG estimation model estimates the maximum GHG emissions discharged when processing the product to be estimated by non-renewable energy based on the production amount information, estimates the GHG emission reduction amount when using renewable energy based on the renewable energy evaluation value, and is a model for estimating the GHG emissions based on the maximum GHG emissions and the GHG emission reduction amount.

[0164] Accordingly, the GHG emissions can be estimated according to the utilization status of renewable energy.

[0165] A fourth aspect is the GHG emissions estimation device according to the third aspect, wherein the GHG estimation model is further a model for estimating the GHG emissions based on the GHG emissions from the starting materials of the product.

[0166] Accordingly, the GHG emissions of the product can be estimated considering the GHG emissions from starting materials such as those from materials.

[0167] The fifth aspect is a GHG emission estimation device according to any one of the second to fourth aspects, wherein the product is manufactured through a first manufacturing process and a second manufacturing process, the GHG estimation model includes a first GHG estimation model in the first manufacturing process and a second GHG estimation model in the second manufacturing process, the first GHG estimation model includes, as the renewable energy evaluation value, a first renewable energy evaluation value in the first manufacturing process, the second GHG estimation model includes, as the renewable energy evaluation value, a second renewable energy evaluation value in the second manufacturing process, the GHG emission involvement status value includes first production quantity information corresponding to the quantity of the product to be estimated in the first manufacturing process and second production quantity information corresponding to the quantity of the product to be estimated in the second manufacturing process, the first GHG estimation model estimates the maximum GHG emissions discharged when processing the product to be estimated by non-renewable energy based on the first production quantity information, estimates the GHG emission reduction amount when using renewable energy based on the first renewable energy evaluation value, and is a model for estimating the GHG emissions after the first manufacturing process based on the maximum GHG emissions, the GHG emission reduction amount, and the GHG emissions from the starting materials of the product; the second GHG estimation model estimates the maximum GHG emissions discharged when processing the product to be estimated by non-renewable energy based on the second production quantity information, estimates the GHG emission reduction amount when using renewable energy based on the second renewable energy evaluation value, and is a model for estimating the GHG emissions caused by the second manufacturing process based on the maximum GHG emissions and the GHG emission reduction amount; and the GHG estimation model is a model for estimating the GHG emissions of the product based on the GHG emissions after the first manufacturing process estimated by the first GHG estimation model and the GHG emissions caused by the second manufacturing process estimated by the second GHG estimation model.

[0168] Thereby, when a product is manufactured through multiple processes, it is easy to estimate the GHG emissions.

[0169] A sixth aspect is the GHG emission estimation device according to the fourth or fifth aspect, wherein the past GHG emission participation value includes the past processing amount of the product, the past GHG emissions attributable to the starting material and processing of the product, and the past ratio of renewable energy used for processing the product, and the GHG estimation model includes the estimated GHG emissions per unit amount of the product attributable to the starting material, the estimated GHG emissions per unit amount of the product in the case of being processed by non-renewable energy, and the renewable energy evaluation value according to the utilization status of the renewable energy used for processing the product, and the estimated GHG emissions attributable to the starting material and the estimated GHG emissions attributable to the processing are values obtained based on the past processing amount, the past GHG emissions, and the past ratio of renewable energy.

[0170] Thereby, even if it is not possible to directly know the estimated GHG emissions attributable to the starting material and the estimated GHG emissions attributable to the processing, based on the past processing amount, the past GHG emissions, and the renewable energy evaluation value, the estimated GHG emissions attributable to the material and the estimated GHG emissions attributable to the processing can be inferred.

[0171] A seventh aspect is the GHG emission estimation device according to the sixth aspect, wherein the estimated GHG emissions attributable to the starting material and the estimated GHG emissions attributable to the processing are values obtained based on a combination of a plurality of past values of the past processing amount, the past GHG emissions, and the past ratio of renewable energy.

[0172] Thereby, based on a plurality of past values, the estimated GHG emissions attributable to the starting material and the estimated GHG emissions attributable to the processing can be inferred.

[0173] The eighth aspect is a GHG emission amount estimation device according to any one of the second to seventh aspects, wherein the past GHG emission participation value includes the past processing amount of the product and the past GHG emission amount resulting from the processing of the product, and the GHG estimation model includes an estimated GHG emission amount per unit amount of the product processed by non-renewable energy and a renewable energy evaluation value according to the utilization status of the renewable energy used for the processing of the product, and the estimated GHG emission amount from processing is a value obtained based on the past processing amount of the product and the past GHG emission amount resulting from the processing of the product.

[0174] Thereby, when it is not possible to directly know the estimated GHG emission amount per unit amount of the product processed by non-renewable energy, this value can be inferred based on the past processing amount of the product and the past GHG emission amount resulting from the processing of the product.

[0175] The ninth aspect is a GHG emission amount estimation device according to any one of the first to eighth aspects, wherein the past GHG emission participation value related to the past GHG emission amount of the product is stored in the storage unit, and the processing unit generates the GHG estimation model based on the past GHG emission participation value.

[0176] Thereby, when the past GHG emission participation value is given, the processing unit generates a GHG estimation model.

[0177] Further, the tenth aspect is a GHG emission amount estimation device according to any one of the first to ninth aspects, further comprising a display device for displaying the estimated GHG emission amount of the product.

[0178] Thereby, the estimated GHG emission amount is visually recognized through the display device.

[0179] In addition, each configuration described in the above embodiments and each modification can be appropriately combined as long as they do not conflict with each other.

[0180] The functions of the elements disclosed in this specification can be executed using a circuit or processing circuit including a general-purpose processor, a dedicated processor, an integrated circuit, an ASIC (Application Specific Integrated Circuits), a conventional circuit, and / or a combination thereof configured or programmed to execute the disclosed functions. Since a processor includes transistors and other circuits, it is regarded as a processing circuit or a circuit. In the present disclosure, a circuit, unit, or means is hardware that executes the recited functions or hardware programmed to execute the recited functions. The hardware may be the hardware disclosed in this specification or other known hardware programmed or configured to execute the recited functions. When the hardware is a processor considered to be a type of circuit, the circuit, means, or unit is a combination of hardware and software, and the software is used for configuring the hardware and / or the processor.

[0181] The above descriptions are illustrative in all aspects and the present invention is not limited thereto. Innumerable variations not illustrated can be assumed without departing from the scope of the present invention.

Description of Reference Numerals

[0182] 10A First manufacturing plant 10B Second manufacturing plant 14 First energy supplier 16 Second energy supplier 20 Raw materials 22 Parts 24 Products 30;40 GHG emission estimation device 34 Model generation unit 42 GHG emission estimation processing device 44 Processor 46 Storage device 46a Program 49 Display device 50 GHG Estimation Model 51A First GHG Estimation Model 51B Second GHG Estimation Model 60 Past Values of GHG Emission Involvement 61A Past Values for the First Estimation Model 61B Past Values for the Second Estimation Model 66 Current Values of GHG Emission Involvement 68 Renewable Energy Evaluation Value

Claims

1. A GHG emission estimation device for estimating the GHG emissions of a product, a storage unit that stores a GHG estimation model that is constructed based on past GHG emission participation past values related to the GHG emissions of the product and obtains the GHG emissions by using, as a parameter, the current GHG emission participation status value of the product to be estimated; a processing unit that applies the current GHG emission participation status value to the GHG estimation model to estimate the GHG emissions of the product; A GHG emission estimation device comprising the above.

2. The GHG emission estimation device according to Claim 1, wherein the GHG estimation model is a model that includes, as an adjustment parameter, a renewable energy evaluation value corresponding to the utilization status of renewable energy used in the manufacture of the product.

3. The GHG emission estimation device according to Claim 2, wherein the current GHG emission participation status value includes production quantity information corresponding to the quantity of the product to be estimated, and the GHG estimation model estimates the maximum GHG emissions that would be emitted when the product to be estimated is processed using non-renewable energy based on the production quantity information, estimates the GHG emission reduction amount when renewable energy is used based on the renewable energy evaluation value, and estimates the GHG emissions based on the maximum GHG emissions and the GHG emission reduction amount.

4. The GHG emission estimation device according to Claim 3, wherein the GHG estimation model further estimates the GHG emissions based on the GHG emissions from the starting materials of the product.

5. The GHG emission estimation device according to Claim 2, wherein the product is manufactured through a first manufacturing process and a second manufacturing process, the GHG estimation model includes a first GHG estimation model in the first manufacturing process and a second GHG estimation model in the second manufacturing process, the first GHG estimation model includes, as the renewable energy evaluation value, a first renewable energy evaluation value in the first manufacturing process, and the second GHG estimation model includes, as the renewable energy evaluation value, a second renewable energy evaluation value in the second manufacturing process. The GHG emission participation current value includes first production quantity information corresponding to the quantity of the product to be estimated in the first manufacturing process and second production quantity information corresponding to the quantity of the product to be estimated in the second manufacturing process. The first GHG estimation model estimates the maximum GHG emission amount emitted when processing the product to be estimated using non-renewable energy based on the first production quantity information, estimates the GHG emission reduction amount when using renewable energy based on the first renewable energy evaluation value, and is a model for estimating the GHG emission amount after the first manufacturing process based on the maximum GHG emission amount, the GHG emission reduction amount, and the GHG emission amount derived from the starting material of the product. The second GHG estimation model estimates the maximum GHG emission amount emitted when processing the product to be estimated using non-renewable energy based on the second production quantity information, estimates the GHG emission reduction amount when using renewable energy based on the second renewable energy evaluation value, and is a model for estimating the GHG emission amount attributable to the second manufacturing process based on the maximum GHG emission amount and the GHG emission reduction amount. The GHG estimation model is a GHG emission amount estimation device that estimates the GHG emission amount of the product based on the GHG emission amount after the first manufacturing process estimated by the first GHG estimation model and the GHG emission amount attributable to the second manufacturing process estimated by the second GHG estimation model.

6. The GHG emission amount estimation device according to claim 4 or claim 5, The GHG emission participation past value includes the past processing quantity of the product, the past GHG emission amount attributable to the starting material and processing of the product, and the past ratio of renewable energy used for processing the product. The GHG estimation model is a model including the estimated GHG emission amount per unit quantity of the product derived from the starting material, the estimated GHG emission amount per unit quantity of the product attributable to processing assuming processing with non-renewable energy, and the renewable energy evaluation value corresponding to the usage status of the renewable energy used for processing the product. The GHG emission amount estimation device, wherein the estimated GHG emission amount derived from the starting material and the estimated GHG emission amount attributable to processing are values obtained based on the past processing quantity, the past GHG emission amount, and the past ratio of renewable energy.

7. The GHG emission estimation device according to claim 6, wherein the estimated GHG emissions from the starting material and the estimated GHG emissions from the processing are values obtained based on a combination of a plurality of past values of the past processing amount, the past GHG emissions, and the past ratio of renewable energy. The GHG emission estimation device.

8. The GHG emission estimation device according to any one of claims 2 to 5, wherein the past GHG emission participation values include the past processing amount of the product and the past GHG emissions resulting from the processing of the product, the GHG estimation model is a model including the estimated GHG emissions from processing per unit amount of the product assuming processing by non-renewable energy and the renewable energy evaluation value according to the utilization status of the renewable energy used in the processing of the product, the estimated GHG emissions from the processing are values obtained based on the past processing amount of the product and the past GHG emissions resulting from the processing of the product. The GHG emission estimation device.

9. The GHG emission estimation device according to any one of claims 1 to 5, wherein the storage unit stores the past GHG emission participation values related to the past GHG emissions of the product, the processing unit generates the GHG estimation model based on the past GHG emission participation values. The GHG emission estimation device.

10. The GHG emission estimation device according to any one of claims 1 to 5, wherein the GHG emission estimation device further includes a display device that displays the estimated GHG emissions of the product.

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