Information processing device

Abstract

An information processing device 100 according to the present disclosure comprises: a reception unit 121 that receives input of event information indicating the content of an event; and a prediction unit 122 that, on the basis of the event information, predicts a change, which is caused by the occurrence of the event, in an index that is set for each of the field of carbon neutral, the field of circular economy, and the field of nature positive.

Description

Information processing device

[0001] The present disclosure relates to an information processing device.

[0002] Decarbonization is being planned around the world, and "carbon credits" are being issued and circulated for removing greenhouse gases from the atmosphere or preventing their emission into the atmosphere. For example, Patent Document 1 discloses a system for trading carbon credits.

[0003] Japanese Patent Application Laid-Open No. 2023-26906

[0004] However, when considering environmental protection around the world, there is a problem in that simply focusing on the above-mentioned decarbonization is insufficient and further environmental protection cannot be achieved.

[0005] Therefore, an object of the present disclosure is to provide an information processing device that can further support environmental protection, which is the above-mentioned problem.

[0006] An information processing device according to one embodiment of the present disclosure includes: a receiving unit that receives input of event information representing the content of an event; and a prediction unit that predicts, based on the event information, changes in indicators set in the carbon neutral field, the circular economy field, and the nature positive field due to the occurrence of the event. An information processing method according to one embodiment of the present disclosure includes: an information processing device that receives input of event information representing the content of the event; and predicts, based on the event information, changes in indicators set in the carbon neutral field, the circular economy field, and the nature positive field due to the occurrence of the event. A program according to one embodiment of the present disclosure includes: causing an information processing device to execute processing to receive input of event information representing the content of the event; and predict, based on the event information, changes in indicators set in the carbon neutral field, the circular economy field, and the nature positive field due to the occurrence of the event.

[0007] With the above-described configuration, the present disclosure can provide an information processing device that can further support environmental protection.

[0008] FIG. 1 is a block diagram showing a configuration of a prediction device according to the present disclosure. FIG. 2 is a diagram showing a processing state by a prediction device according to the present disclosure. FIG. 3 is a diagram showing a processing state by a prediction device according to the present disclosure. FIG. 4 is a flowchart showing a processing operation of a prediction device according to the present disclosure. FIG. 5 is a block diagram showing a hardware configuration of an information processing device according to the present disclosure. FIG. 6 is a block diagram showing a configuration of an information processing device according to the present disclosure.

[0009] First Embodiment A first embodiment of the present disclosure will be described with reference to the drawings. Note that the drawings may be relevant to any embodiment.

[0010] [Configuration] The prediction device 10 of the present disclosure predicts changes in preset indicators in three fields related to environmental protection, namely, carbon neutrality, circular economy, and nature positive, due to the occurrence of a specific event. Here, carbon neutrality refers to efforts to achieve zero greenhouse gas emissions overall and is treated as an indicator for predicting information related to emissions, such as greenhouse gas emissions themselves. Furthermore, circular economy refers to efforts to promote the cyclical use of resources and is treated as an indicator for predicting information related to the amount of resource (material) waste itself and the amount and recycling rate of the resource that accompanies resource waste. Furthermore, nature positive refers to efforts to stop and restore the loss of biodiversity, i.e., the state in which all living things on Earth support each other and maintain balance, and is treated as an indicator for predicting information related to the amount of natural object loss itself. Natural objects include living organisms such as plants, water, and land, and thus the amount of biological loss and the amount of water and land loss can be used as indicators. In this embodiment, in addition to the indicators in the three fields described above, a change in cost, which is an indicator in the monetary field, due to the occurrence of a predetermined event is also predicted. However, the indicators in the three fields related to environmental protection and the monetary field predicted in this embodiment are only examples, and changes in indicators set in other fields may also be predicted.

[0011] In this embodiment, the predetermined event that may occur is an effort to improve indicators in any of the three environmental protection-related fields or monetary fields described above. For example, the event in this example is "installing solar panels for solar power generation." That is, the event is an effort to reduce fossil fuel-based power generation by using solar power generation and reduce greenhouse gas emissions, which are indicators in the field of carbon neutrality. Such an event is carried out, for example, by a power generation business operator. However, the events that may occur in this embodiment are not limited to the above-described content and may be any event carried out by any business operator, such as the introduction of electric vehicles (EVs) or material, chemical, or thermal recycling. Other examples include switching to LED lighting, replacing with high-efficiency equipment, modal shift, expanding the use of renewable energy such as wind power, introducing CCUS (Carbon dioxide Capture, Utilization and Storage), selecting materials with high recycling rates, selecting waste disposal methods, deciding whether to landfill, reducing the amount of water used, water purification, utilizing bioenergy, exhaust gas treatment, and afforestation.

[0012] Specifically, the prediction device 10 is configured with one or more information processing devices each including a calculation device and a storage device. As shown in FIG. 1 , an information processing terminal 20 operated by a business operator who implements the above-described events or a business operator who provides the above-described index prediction service is connected to the prediction device 10 via a network. As shown in FIG. 1 , the prediction device 10 also includes a reception unit 11 and a prediction unit 12. The functions of the reception unit 11 and the prediction unit 12 can be realized by the calculation device executing a program for realizing each function stored in the storage device. The prediction device 10 also includes a prediction data storage unit 13 configured with a storage device. Each component will be described in detail below.

[0013] As described below, the prediction data storage unit 13 stores prediction data used to predict changes in each of the above-mentioned indicators from event information that indicates the content of the event. The prediction data includes, for example, data that indicates the relationship between an event and an indicator. As an example, the prediction data may include the relationship between the above-mentioned event "installing solar panels" and the "amount of greenhouse gas emission reduction," the relationship between the event "installing solar panels" and the "amount of reduction in the forest area where the panels are installed," and the relationship between the event "installing solar panels" and the "land cost and installation cost of the forest where the panels are installed." Specifically, the prediction data may include data that indicates the relationship between the installation area of ​​solar panels and the amount of greenhouse gas emission reduction, the relationship between the installation area of ​​solar panels and the amount of reduction in the forest area, and the relationship between the installation area of ​​solar panels and the land cost and installation cost.

[0014] Furthermore, the prediction data includes, for example, data representing the relationship between an event and a situation that may arise as a result of the occurrence of the event, as well as data representing the relationship between the situation that may arise and an index. As an example, the prediction data may include data representing the relationship between the "amount of power generation" (situation) and the event "installing solar panels" described above, as well as data representing the relationship between the "amount of power generation" (situation) and the "amount of greenhouse gas emissions reduction" (index). Specifically, in addition to the relationship between the installation area of ​​solar panels and the amount of power generation, data may include data representing the relationship between the amount of power generation and the amount of greenhouse gas emissions reduction.

[0015] The prediction data also includes, for example, data representing the relationship between an event and an index after a predetermined period of time has elapsed since the occurrence of the event. Examples of the prediction data include the relationship between the event "installing solar panels" and the "amount of solar panels to be discarded in 20 years," and the relationship between the event "installing solar panels" and the "increase (recovery) in the area of ​​the forest where the panels will be installed in 40 years." Specifically, examples include data representing the relationship between the installation area of ​​solar panels and the amount of disposal due to their expected lifespan in 20 years, or the relationship between the installation area of ​​solar panels and the amount of forest area to be recovered in 40 years. The prediction data may also include general future market forecasts (such as solar panel cost forecasts, performance degradation forecasts, and resource supply forecasts).

[0016] Furthermore, the prediction data includes, for example, data that represents the relationship between a predetermined index and another index. As an example, the prediction data may include data that represents the relationship between the "amount of decrease in forest area" and the "amount of increase in greenhouse gas emissions" or the relationship between the "amount of discarded solar panels" and the "amount of increase in greenhouse gas emissions." Specifically, the prediction data may include data that represents the relationship between the area of ​​forest and the amount of greenhouse gas absorption, or the relationship between the amount of discarded solar panels and the amount of increase in greenhouse gas emissions.

[0017] Note that the prediction data is not necessarily limited to the data described above, and may be any data that can be used to predict changes in each indicator from event information that represents the content of the event, as will be described later.

[0018] The reception unit 11 receives input of event information indicating the content of the event from the information processing terminal 20. In this embodiment, as shown in FIG. 2, the event is "installing solar panels in a forest," and the event information indicating the specific content of the event is "cost: 150 million yen, installation area of ​​solar panels: 200 m." ２ " input is accepted.

[0019] The prediction unit 12 predicts changes in indicators for each field using the prediction data described above based on the received event information and outputs the prediction results. An example of the prediction results output by the prediction unit 12 is shown in FIG. 2, and an example of predicted changes in indicators is shown in FIG. 3. In FIGS. 2 and 3, "CN" represents the carbon neutral field, "CE" represents the circular economy field, "NP" represents the nature positive field, and "CO" represents the monetary field.

[0020] As an example, the prediction unit 12 first predicts the "amount of power generated by solar panels [kWh]" that represents a change in the situation in the field of carbon neutrality from the "installation area of ​​solar panels" that is event information, using the relationship between the installation area of ​​solar panels and the amount of power generated, which is prediction data. Then, the prediction unit 12 predicts the "amount of power generated by solar panels [kWh]" that represents the predicted change in the situation in the field of carbon neutrality, using the relationship between the amount of power generated, which is prediction data, and the amount of reduction in greenhouse gas emissions, to calculate the "amount of power generated by solar panels [kWh]" that represents the change in the situation in the field of carbon neutrality, using the relationship between the amount of power generated, which is prediction data, and the amount of reduction in greenhouse gas emissions. ２ ) reduction in emissions [t-CO ２ ]. Furthermore, the prediction unit 12 predicts "amount of cost reduction [yen]," which is a change in an indicator in the monetary field, from the "amount of power generation by solar panels [kWh]," which represents a change in the situation in the carbon neutral field predicted as described above, using the relationship between the amount of power generation and the electricity rate, which is the prediction data. In this way, the prediction unit 12 predicts a change in the situation in a predetermined field based on event information, and can also predict a change in an indicator in a certain field, including other fields, from the predicted change in the situation.

[0021] Also, as an example, the prediction unit 12 predicts the "amount of discarded solar panels [t]" representing a change in an index in the field of circular economy 20 years from now, from the "installation area of ​​solar panels" which is event information, using the relationship between the installation area of ​​solar panels which is prediction data and the amount of discarded solar panels due to the expected lifespan in 20 years. Furthermore, the prediction unit 12 predicts the "amount of discarded solar panels [t]" representing the predicted change in an index in the field of circular economy 20 years from now, from the "installation area of ​​solar panels" which is event information, using the relationship between the amount of discarded solar panels which is prediction data and the amount of greenhouse gas emissions. ２ ) Increase in emissions [t-CO ２ In this way, the prediction unit 12 can predict a change in an index in a predetermined field after a predetermined period of time has elapsed based on the event information, and can further predict changes in indexes in other fields from the predicted change in index in the predetermined field.

[0022] As another example, the prediction unit 12 predicts the "amount of recycled solar panels [t]" representing a change in an index in the field of circular economy 20 years from now, using the relationship between the installation area of ​​solar panels, which is prediction data, and the recycling rate associated with the disposal of panels expected 20 years from now, from the "installation area of ​​solar panels" which is event information. Furthermore, the prediction unit 12 predicts the "amount of recycled solar panels [t]" representing a change in an index in the field of circular economy 20 years from now, using the relationship between the amount of recycled solar panels, which is prediction data, and greenhouse gas emissions, from the "amount of recycled solar panels [t]" representing the predicted change in an index in the field of circular economy 20 years from now, ２ ) reduction in emissions [t-CO ２ In this way, the prediction unit 12 can predict a change in an index in a predetermined field after a predetermined period of time has elapsed based on the event information, and can further predict changes in indexes in other fields from the predicted change in index in the predetermined field.

[0023] Furthermore, as an example, the prediction unit 12 calculates the "amount of reduction in forest area [m ]" representing a change in an index in the field of nature positive, from the "solar panel installation area" which is the event information, using the relationship between the solar panel installation area which is the prediction data and the amount of reduction in forest area. ２ Furthermore, the prediction unit 12 predicts the "decrease in forest area [m ２ ], we used the relationship between forest area and greenhouse gas emissions, which is the forecast data, to calculate the change in the indicator in the field of carbon neutrality, "Greenhouse gas (CO ２ ) Increase in emissions [t-CO ２ In addition, the prediction unit 12 predicts the "decrease in forest area [m ２ ], the relationship between the area of ​​the forest, which is the prediction data, and the costs of land and installation is used to predict the change in an indicator in the monetary field, "Land and installation costs [yen]." In this way, the prediction unit 12 can predict changes in indicators in a specified field based on event information, and further, can predict changes in indicators in other fields from the predicted changes in indicators in the specified field.

[0024] In this way, as shown in FIG. 3 , assuming that an event such as the installation of solar panels occurs in 2025, the prediction unit 12 predicts each indicator for each field in 2025, as well as the indicators for each field in 2040, 2050, and 2070. In other words, it is possible to predict indicators in the carbon neutral, circular economy, nature positive, and monetary fields at the time of each event, such as the installation of solar panels in 2025, maintenance in 2040, disposal in 2050, and forest restoration in 2040. Note that in FIG. 3 , greenhouse gas emissions in the carbon neutral field (CN) are quantified as zero immediately before the installation of the solar panels, with emissions being negative and absorption (reduction) being positive. Furthermore, the amount of waste material in the circular economy field (CE) is quantified as a negative value when materials are discarded and a positive value when materials are recycled. In addition, the area of ​​forest in the nature positive (NP) category is quantified as 0 immediately before the solar panels are installed, and if the forest decreases, it is quantified as a minus, and if the forest increases, it is quantified as a plus. In addition, expenses in the monetary (CO) category are quantified as a plus if costs are incurred.

[0025] The prediction unit 12 then outputs the results of the prediction as described above to the information processing terminal 20 or the like. At this time, the prediction unit 12 may output each indicator set for each field at the time (time point) of each event in a table format, for example, as shown in FIG. 3. The prediction unit 12 may also output each indicator set for each field at the time (time point) of each event in a graph format, as shown in FIG. 4. At this time, when outputting in a graph format as shown in FIG. 4, the prediction unit 12 sets a radar chart with axes representing the carbon neutral field (CN), the circular economy field (CE), the nature positive field (NP), and the monetary field (CO) for each year of each event, and plots predicted values ​​on each axis and displays them connected by lines.

[0026] [Operation] Next, a description will be given of the operation of the above-described prediction device 10. First, it is assumed that various types of prediction data as described above are stored in advance in the prediction device 10. Note that the prediction device 10 may acquire the prediction data from an external device as necessary.

[0027] Then, the prediction device 10 receives input of event information from the information processing terminal 20 (step S1 in FIG. 5). For example, as shown in FIG. 2, the prediction device 10 receives event information such as "cost: 150 million yen, installation area of ​​solar panels: 200 m" ２ " input is accepted.

[0028] The prediction device 10 then predicts changes in each indicator in each field using prediction data based on the received event information. Specifically, the prediction device 10 predicts changes in preset indicators in each of the carbon neutral (CN), circular economy (CE), nature positive (NP), and monetary (CO) fields. For example, the prediction device 10 predicts changes in indicators such as greenhouse gas emissions, the amount of solar panel (material) waste, the amount of forest (living) loss, and monetary cost. In this case, the prediction device 10 predicts changes in indicators in a specific field from the event information, and further predicts changes in indicators in other fields from the predicted changes in indicators, or predicts changes in situations that may occur in each field from the event information and predicts changes in each indicator in each field from such changes in situations. Furthermore, the prediction device 10 predicts each indicator in each field at the time an event occurs, and also predicts each indicator in each field in the future after a predetermined period has passed.

[0029] The prediction device 10 then outputs the predicted indices for each field as described above to the information processing terminal 20 or to an external device. For example, the prediction device 10 outputs the indices for each field for each year in a table format as shown in Fig. 3, or outputs the indices for each field for each year in a graph format as shown in Fig. 4.

[0030] As a result, a user who receives the prediction results output by the prediction device 10 can simultaneously recognize changes in indicators in multiple fields, such as the carbon neutral field (CN), circular economy field (CE), nature positive field (NP), and monetary field (CO), when an event occurs. The user can also recognize changes in indicators in multiple fields over time. As a result, the user can decide on environmental protection efforts by comprehensively considering changes in indicators in multiple fields due to the occurrence of an event, and also taking into account changes over time. As a result, the system can support users in making decisions to achieve appropriate environmental protection, thereby further supporting environmental protection.

[0031] Second Embodiment Next, a second embodiment of the present disclosure will be described with reference to the drawings. This embodiment shows an outline of the configuration of the prediction device described in the above embodiment. Note that Figures 6 and 7 are diagrams for explaining the configuration, and these drawings may be relevant to any of the embodiments.

[0032] First, the hardware configuration of the information processing device 100 will be described with reference to Fig. 6. The information processing device 100 is configured as a general information processing device, and is equipped with the following hardware configuration, for example: CPU (Central Processing Unit) 101 (arithmetic unit); ROM (Read Only Memory) 102 (storage device); RAM (Random Access Memory) 103 (storage device); programs 104 loaded into RAM 103; storage device 105 storing programs 104; drive device 106 for reading and writing data from and to a storage medium 110 external to the information processing device; communication interface 107 for connecting to a communication network 111 external to the information processing device; input / output interface 108 for inputting and outputting data; and bus 109 for connecting the various components.

[0033] 6 shows an example of the hardware configuration of the information processing device 100, and the hardware configuration of the information processing device is not limited to the above-described case. For example, the information processing device may be configured with only a part of the above-described configuration, such as excluding the drive device 106. Furthermore, the information processing device may use a GPU (Graphics Processing Unit), a DSP (Digital Signal Processor), an MPU (Micro Processing Unit), an FPU (Floating Point Number Processing Unit), a PPU (Physics Processing Unit), a TPU (Tensor Processing Unit), a quantum processor, a microcontroller, or a combination thereof, instead of the above-described CPU.

[0034] The information processing device 100 can be equipped with the reception unit 121 and prediction unit 122 shown in FIG. 7 by having the CPU 101 acquire and execute the program group 104. The program group 104 is stored in advance in the storage device 105 or the ROM 102, for example, and is loaded into the RAM 103 and executed by the CPU 101 as needed. The program group 104 may be supplied to the CPU 101 via the communication network 111, or may be stored in advance in the storage medium 110, and the drive device 106 may read the program and supply it to the CPU 101. However, the reception unit 121 and prediction unit 122 described above may be constructed using dedicated electronic circuits for realizing such means.

[0035] The receiving unit 121 receives input of event information indicating the content of an event. The prediction unit 122 predicts changes in the indicators set in the carbon neutral field, the circular economy field, and the nature positive field due to the occurrence of the event, based on the event information.

[0036] As configured as described above, the present disclosure can predict changes in indicators in multiple fields, such as the carbon neutral field, the circular economy field, and the nature positive field, when an event occurs. As a result, users who use such prediction results can decide on environmental protection efforts by comprehensively considering changes in indicators in multiple fields due to the occurrence of an event. As a result, the disclosure can support users in making decisions to achieve appropriate environmental protection, thereby further supporting environmental protection.

[0037] In addition, at least one of the functions of the above-mentioned reception unit 121 and prediction unit 122 may be executed by an information processing device installed and connected anywhere on the network, that is, they may be executed by so-called cloud computing.

[0038] The above-described program can be stored and supplied to a computer using various types of non-transitory computer-readable media. Non-transitory computer-readable media include various types of tangible storage media. Examples of non-transitory computer-readable media include magnetic recording media (e.g., flexible disks, magnetic tapes, hard disk drives), magneto-optical recording media (e.g., magneto-optical disks), CD-ROMs (Read Only Memory), CD-Rs, CD-RWs, and semiconductor memories (e.g., mask ROMs, PROMs (Programmable ROMs), EPROMs (Erasable PROMs), flash ROMs, and RAMs (Random Access Memory)). The program can also be supplied to a computer by various types of transitory computer-readable media. Examples of transitory computer-readable media include electrical signals, optical signals, and electromagnetic waves. The transitory computer-readable media can be supplied to a computer via wired communication paths such as electric wires and optical fibers, or via wireless communication paths.

[0039] Although the present disclosure has been described above with reference to the above-described embodiments, the present disclosure is not limited to the above-described embodiments. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present disclosure within the scope of the present disclosure. Furthermore, each of the above-described embodiments can be combined with other embodiments as appropriate.

[0040] <Supplementary Notes> Some or all of the above embodiments can also be described as in the following supplementary notes. Below, an outline of the configurations of an information processing device, an information processing method, and a program according to the present disclosure will be described. However, the present disclosure is not limited to the following configurations. (Supplementary Note 1) An information processing device comprising: a reception unit that receives input of event information indicating the content of an event; and a prediction unit that predicts changes in indicators set in the field of carbon neutrality, the field of circular economy, and the field of nature positive due to the occurrence of the event based on the event information. (Supplementary Note 2) The information processing device according to Supplementary Note 1, wherein the prediction unit predicts changes in the indicators set in each of the fields after a predetermined period has elapsed since the occurrence of the event. (Supplementary Note 3) The information processing device according to Supplementary Note 1, wherein the prediction unit predicts changes in the indicators set in each of the fields at each of a plurality of predetermined periods after the occurrence of the event. (Supplementary Note 4) The information processing device according to Supplementary Note 1, wherein the prediction unit predicts changes in the indicators set for other of the fields based on the predicted changes in the indicators set for the specified field. (Supplementary Note 5) The information processing device according to Supplementary Note 1, wherein the prediction unit predicts changes in predetermined situations related to each of the fields that may be caused by the occurrence of the event based on the event information, and predicts changes in the indicators set for each of the fields based on the changes in the situations. (Supplementary Note 6) The information processing device according to Supplementary Note 5, wherein the prediction unit predicts changes in the indicators set for other of the fields based on the predicted changes in the situation related to the specified field. (Supplementary Note 7) The information processing device according to Supplementary Note 1, wherein the prediction unit predicts changes in indicators in the monetary field that may be caused by the occurrence of the event based on the event information.(Supplementary Note 8) The information processing device according to Supplementary Note 1, wherein the indicator set in the carbon neutral field is information on greenhouse gas emissions, the indicator set in the circular economy field is information on a predetermined amount of waste, and the indicator set in the nature positive field is information on a predetermined amount of natural material loss. (Supplementary Note 9) An information processing method, wherein the information processing device receives input of event information that indicates the content of an event, and predicts, based on the event information, changes in the indicators that are respectively set in the carbon neutral field, the circular economy field, and the nature positive field due to the occurrence of the event. (Supplementary Note 10) A program that causes an information processing device to execute a process of receiving input of event information that indicates the content of an event, and predicting, based on the event information, changes in the indicators that are respectively set in the carbon neutral field, the circular economy field, and the nature positive field due to the occurrence of the event.

[0041] This disclosure claims the benefit of priority based on patent application No. 2024-026670 filed in Japan on February 26, 2024, and all of the contents disclosed in the patent application are incorporated herein.

[0042] REFERENCE SIGNS LIST 10 Prediction device 11 Reception unit 12 Prediction unit 13 Prediction data storage unit 20 Information processing terminal 100 Information processing device 101 CPU 102 ROM 103 RAM 104 Program group 105 Storage device 106 Drive device 107 Communication interface 108 Input / output interface 109 Bus 110 Storage medium 111 Communication network 121 Reception unit 122 Prediction unit

Claims

1. An information processing device comprising: a reception unit that receives input of event information that indicates the content of an event; and a prediction unit that predicts, based on the event information, changes in indicators set in the fields of carbon neutrality, circular economy, and nature positive due to the occurrence of the event.

2. An information processing device according to claim 1, wherein the prediction unit predicts a change in the index set for each of the fields after a predetermined period has elapsed since the occurrence of the event.

3. An information processing device according to claim 1, wherein the prediction unit predicts changes in the indicators set for each of the fields at each of a plurality of predetermined time periods after the occurrence of the event.

4. An information processing device according to claim 1, wherein the prediction unit predicts changes in the indices set for other fields based on predicted changes in the indices set for a specific field.

5. An information processing device according to claim 1, wherein the prediction unit predicts, based on the event information, changes in pre-set situations related to each of the fields that may occur due to the occurrence of the event, and predicts changes in the indicators set for each of the fields based on the changes in the situations.

6. An information processing device according to claim 5, wherein the prediction unit predicts changes in the indicators set for other fields based on changes in the situation related to a specific field that has been predicted.

7. An information processing device according to claim 1, wherein the prediction unit predicts, based on the event information, a change in an index in the monetary field that may be caused by the occurrence of the event.

8. An information processing device as described in claim 1, wherein the indicators set in the field of carbon neutrality are information regarding greenhouse gas emissions, the indicators set in the field of circular economy are information regarding the amount of specified waste, and the indicators set in the field of nature positive are information regarding the amount of specified natural material lost.

9. An information processing method in which an information processing device accepts input of event information that describes the content of an event, and predicts, based on the event information, changes in indicators set in the fields of carbon neutrality, circular economy, and nature positive due to the occurrence of the event.

10. A computer-readable storage medium storing a program that causes an information processing device to execute a process of accepting input of event information that indicates the content of an event, and predicting, based on the event information, changes in indicators that have been set in the fields of carbon neutrality, circular economy, and nature positive due to the occurrence of the event.

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