Program, information processing device, method, and system

Abstract

Accurately determine eligibility for carbon credit creation in a given area. [Solution] A program to be executed by a computer having a processor and a memory. The program causes the processor to execute the following steps: accepting input of information about a candidate area for the creation of carbon credits from a user; acquiring multispectral satellite images of the candidate area for a predetermined period of time; analyzing the satellite images using a trained AI model and outputting a determination result as to whether the candidate area satisfies predetermined conditions, including a first condition indicating the state of land cover and a second condition indicating time-series changes; evaluating the reliability of a baseline set as an evaluation standard for the amount of carbon credits to be created in the candidate area and outputting the evaluation result; and presenting the determination result and evaluation result to the user.

Description

[Technical Field]

[0001] The present disclosure relates to a program, an information processing device, a method, and a system. [Background technology]

[0002] In recent years, research and development of technologies to support carbon credit projects has become active. For example, Patent Document 1 discloses a technology for determining whether an inspection area is a forest area using a captured image including the inspection area, and for an inspection area determined to be a forest area, determining whether the inspection area is also a forest area using a partial image of the inspection area taken some time ago (patrol processing). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2025-077213 Summary of the Invention [Problem to be solved by the invention]

[0004] The technology disclosed in Patent Document 1 outputs a judgment result indicating that the area is no longer a forest area as a result of the inspection process. However, the accuracy of the inspection judgment result is not necessarily sufficient.

[0005] An object of the present disclosure is to accurately determine the eligibility of a given area as a target for the creation of carbon credits. [Means for solving the problem]

[0006] To solve the above-mentioned problems, one embodiment of the present disclosure provides a program for execution by a computer having a processor and a memory. The program causes the processor to execute the following steps: accepting input of information about a candidate area for carbon credit creation from a user; acquiring satellite images of the candidate area for a predetermined period of time based on the input information; analyzing the satellite images for the predetermined period of time using a trained AI model to output a determination result as to whether the candidate area satisfies predetermined conditions indicating eligibility for carbon credit creation, the conditions including a first condition indicating the state of land cover at a specific time point and a second condition indicating time-series changes in land cover over the predetermined period; evaluating the reliability of a baseline set as an evaluation standard for the amount of carbon credits to be created in the candidate area, outputting the evaluation result; and presenting the determination result and the evaluation result to a user. [Effects of the Invention]

[0007] According to the present disclosure, it is possible to accurately determine the eligibility of a given area as a target for carbon credit creation. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a block diagram showing an example of the overall configuration of a system 1. FIG. [Figure 2] 1 is a block diagram showing an example of the configuration of a terminal device 10. FIG. [Figure 3] FIG. 2 is a block diagram showing an example of the configuration of a server 20. [Figure 4] FIG. 2 is a diagram showing the data structure of an eligibility condition table 2021. [Figure 5] 10 is a flowchart showing an example of the operation of the server 20. [Figure 6] FIG. 10 is a schematic diagram illustrating an example of a screen according to an embodiment of the present disclosure. [Figure 7] FIG. 2 is a block diagram showing the basic hardware configuration of a computer 90. DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. In all drawings describing the embodiments, common components are designated by the same reference numerals, and repeated explanations will be omitted. Note that the following embodiments do not unduly limit the content of the present disclosure described in the claims. Furthermore, not all components shown in the embodiments are necessarily essential components of the present disclosure. Furthermore, each drawing is a schematic diagram and is not necessarily a precise illustration.

[0010] [1. Overview] The system according to this embodiment accepts input of initial information regarding a candidate area for carbon credit creation. The initial information may be, for example, text data indicating the address of the candidate area, map data of the candidate area, etc. The system according to this embodiment acquires satellite images of the candidate area for a predetermined period of time based on the initial information. The system according to this embodiment uses a trained AI model to analyze satellite images from the past period of time and output a first determination result for each satellite image from the past period, which indicates whether the candidate area satisfies a first condition indicating eligibility for carbon credit creation. The system according to this embodiment calculates time-series changes in the first determination results from the past period of time and outputs a second determination result indicating whether the candidate area satisfies a second condition indicating eligibility for carbon credit creation. The first and second conditions vary depending on, for example, the type of carbon credit project, the greenhouse gas emission reduction / absorption method, etc. The system according to this embodiment evaluates the reliability of a baseline set as an evaluation standard for the amount of carbon credits to be created in the candidate area, and outputs the evaluation result. The method for evaluating the reliability of the baseline also varies depending on, for example, the type of carbon credit project, etc. The system according to this embodiment presents the first judgment result, the second judgment result, and the evaluation result to the user in the form of, for example, a report.

[0011] [2 System Configuration] <2-1 Overall Structure> FIG. 1 is a block diagram showing an example of the overall configuration of system 1. System 1 is a system for providing, for example, a service for evaluating the eligibility of a candidate area (hereinafter referred to as an eligibility evaluation service). A candidate area is, for example, a candidate land for implementing a carbon credit project, and can be a target for the creation of carbon credits. Eligibility is a concept that indicates, for example, the degree to which a candidate area is eligible as land for the creation of carbon credits.

[0012] There are no particular limitations on the types of carbon credit projects and methodologies (hereinafter referred to as "target projects") that are eligible for the qualification assessment service. Methodologies, for example, stipulate how a carbon credit project calculates reductions or removals and how carbon credits can be certified and issued. Specifically, the scope of application, calculation method for reductions / removals, monitoring method, etc. are determined for each type of project (e.g., renewable energy, forest protection, energy conservation, etc.). Examples of methodologies that are eligible for the qualification assessment service include the following: ARR (Afforestation, Reforestation and Revegetation): Afforestation, reforestation and revegetation REDD+ (Reducing Emissions from Deforestation and Forest Degradation, and the Role of Conservation, Sustainable Management of Forests, and Enhancement of Forest Carbon Stocks in Developing Countries): Reducing emissions from deforestation and forest degradation in developing countries, and conserving and sustainable forest management and enhancing forest carbon stocks. AWD (Alternate Wetting and Drying): Intermittent irrigation. WRC (Wetlands Restoration and Conservation): Wetlands restoration and conservation ·ALM(Agricultural Land Management): Farmland management

[0013] In the above example, for example, the type of carbon credit project to which ARR belongs is an absorption / removal project (nature-based). Also, for example, the type of carbon credit project to which REDD+ belongs is an emission reduction / avoidance project (forest conservation). Also, for example, the type of carbon credit project to which AWD belongs is an emission reduction / avoidance project (agriculture). Furthermore, the type of carbon credits (e.g., forest credits, etc.) planned to be generated from the candidate area will differ depending on, for example, the target project, etc.

[0014] The eligibility of a candidate area is determined, for example, based on the extent to which the candidate area satisfies predetermined conditions that indicate eligibility as land for the creation of carbon credits. There are no particular limitations on the content of the predetermined conditions, and they can be set flexibly depending on the content of the target project, etc. Below, we will provide examples of the predetermined conditions along with specific examples of the methodology.

[0015] (1) ARR The conditions set out in this methodology are mainly related to the historical condition of the land and the nature of the project activities. For example, according to Verra's methodology VM0047, the following conditions are set out: Land history requirements: The proposed project area must not have been a "managed forest" at any time during the past 10 years prior to the project start date. The proposed area must not be a mangrove forest or a tidal wetland such as a salt marsh. · Existing Biomass Condition: Removal of existing woody biomass prior to project activities must not involve commercial timber harvesting or degradation of native ecosystems. Conditions on planting density and monitoring methods: for example, planting density must exceed 50 planting units per hectare. This is intended to exclude census-based monitoring methods that may be applied when planting densities are low. · Conditions for elimination of overlap: The candidate area must not overlap with the project area of ​​any other VCS (Verified Carbon Standard) AFOLU (Agriculture, Forestry and Other Land Use) project.

[0016] (2) REDD+ The specified conditions in this methodology are mainly related to conservation activities of existing forests. Land classification criteria: The proposed area must be an area that has been classified as "forest" for the past 10 years. Conditions on the nature of the activity: Project activities may cover activities classified as Improved Forest Management (IFM) or REDD+ per se, and may be outside the scope of other specific methodologies, for example if the baseline scenario includes commercial forestry. Land history conditions: It must be confirmed through image data, etc., before the project is planned that there has been no removal of woody biomass that would lead to deforestation or forest degradation in the past 10 years. ·Conditions regarding elimination of overlap: It is required that the project does not overlap with the implementation area of ​​other carbon credit projects.

[0017] (3) AWD The conditions specified in this methodology include a wide range of technical requirements regarding land history, water management, cultivation activities, and additionality. For example, according to individual methodologies such as Verra, Gold Standard (GS), or JCM (Joint Crediting Mechanism), the following conditions may be included: Land history and classification criteria: Candidate areas must have been non-forested for the past 10 years and have a woody biomass coverage of less than 10%. Alternatively, candidate areas must be classified as "continuously used agricultural land," "residential land," or "other land" under the IPCC land use categories. In particular, rain-fed rice paddies, deep-water paddies, and non-irrigated lowland rice paddy systems such as farmland are excluded. Conditions for mandatory and optional activities: Mandatory activities include the introduction of improved irrigation management to reduce methane production, such as single-period drainage, intermittent irrigation (AWD), reduced flooding period, or direct seeding (DSR). Optional activities include the use of methanotrophs, avoidance of rice straw burning, improved nitrogen management (e.g., slow-release fertilizer), and biochar application. Water Management and Infrastructure Technical Requirements: Project rice fields must be equipped with controllable irrigation and drainage systems that can be designed to maintain flooded and non-flooded conditions. "Drainage" here is technically defined as "complete" when the water level reaches 15 cm below the soil surface, or when the water level remains between 0 cm and -15 cm below the soil surface for a total of 10 days, including at least three consecutive days. Re-irrigation may be required within two days of drainage completion to prevent yield loss. Conditions regarding additionality and regulations: The drainage implemented in the project must not be required by local laws, etc. Also, it must be proven that AWD is not already widespread in the local government (e.g., state level) that includes the project area (e.g., agricultural census, expert certification, remote sensing data, etc., confirming that the implementation rate is less than 20%). · Conditions on support to farmers: Project activities must include training and technical assistance to provide farmers with appropriate knowledge on field preparation, irrigation, drainage, and fertilization. Exclusion criteria: Activities that significantly reduce soil organic carbon (SOC) (e.g., increasing the amount of rice straw removed and introducing varieties with significantly less root mass) are not permitted. In addition, projects that change management practices outside of the rice-growing season (off-season) are not eligible, with the exception of avoiding post-harvest burning of crop residues.

[0018] (4) WRC This methodology is particularly applicable to restoration projects of tidal wetlands such as mangrove forests. For example, according to the Verra methodology VM0033 or GS methodology, the following conditions are set out: Land history and classification criteria: The candidate area must be a natural mangrove forest more than 10 years ago, but is now a non-mangrove area. It must also be a coastal area with an elevation of 0 to 50 meters, as confirmed using satellite data (e.g., SRTM DEM). Examples of eligible activities: Activities include "restoring hydrological conditions by removing tidal barriers," "modifying sediment supply by utilizing dredged material," "reintroducing native plant communities by sowing seeds or planting seedlings," and "removing invasive alien species." Land use conditions (leakage prevention): It must be proven that the land use prior to the start of the project has been abandoned for more than two years, or that commercial use is no longer profitable due to salt intrusion, etc., and that emissions from conversion of activities will not occur outside the area. - Ineligible conditions: Project activities such as "if they cause a decline in the groundwater level," "if nitrogen fertilizer is applied within the project area," or "if the baseline includes commercial forestry" are not covered by this methodology.

[0019] The predetermined conditions may consist of, for example, a first condition that indicates the state or condition of the candidate area at a given time, and a second condition whose fulfillment can only be determined once the changes in the state or condition of the candidate area over time are known. In the example above, the first condition would be "not overlapping with the implementation area of ​​other carbon credit projects," "having controllable irrigation and drainage facilities," "the candidate area's elevation must be between 0 and 50 meters," and "no nitrogen fertilizer application as part of the project activities." Meanwhile, the second condition would be "not being a 'managed forest' for the past 10 years" in the case of ARR, "not having multiple drainage events in the past two years prior to the project start" in the case of AWD, or "having been a natural mangrove forest for more than 10 years" in the case of WRC.

[0020] The candidate areas covered by the qualification assessment service are not limited to land in Japan, but may also be land in other countries. In this case, the target projects will comply with the carbon credit types and certification standards established by, for example, Verra, Gold Standard, and JCM (Joint Crediting Mechanism).

[0021] 1 includes, for example, a terminal device 10, a server 20, a satellite 30, and an AI system 40. The terminal device 10, the server 20, and the AI ​​system 40 are communicatively connected, for example, via a network 80. The satellite 30 transmits, for example, various types of satellite data to a ground station (not shown). The ground station is communicatively connected to the network 80, and, for example, receives a transmission request from the server 20 and transmits the satellite data to the server 20 via the network 80.

[0022] 1 shows an example in which the system 1 includes one terminal device 10, but for example, the system 1 may include two or more terminal devices 10. In FIG. 1, an example in which the system 1 includes one server 20 is shown, but for example, a collection of multiple devices may be one server 20. The way in which the multiple functions required to realize the server 20 are allocated to one or multiple pieces of hardware can be determined appropriately depending on the processing capacity of each piece of hardware and / or the specifications required for the server 20, etc.

[0023] While FIG. 1 shows an example in which system 1 includes one AI system 40, system 1 may include two or more AI systems 40. Also, while FIG. 1 shows an example in which AI system 40 is independent from server 20, server 20 may include the functions of AI system 40. In other words, server 20 may store an AI model (details of which will be described later) included in AI system 40.

[0024] The terminal device 10 is, for example, an information processing device operated by a user who uses the qualification assessment service. The user is, for example, a person in charge of a carbon credit project. The person in charge of a carbon credit project is responsible for, for example, managing and administering the carbon credit project, as well as managing the farmers participating in the project. The person in charge of a carbon credit project can have a variety of backgrounds, such as managers of agricultural-related companies, engineers or researchers with specialized agricultural knowledge, farmers, and experts in water management or environmental protection.

[0025] The terminal device 10 is realized by, for example, a mobile terminal such as a smartphone or a tablet. In this embodiment, the terminal device 10 is assumed to be a smartphone. The terminal device 10 may also be realized by, for example, a desktop personal computer (PC), a laptop PC, or the like.

[0026] The terminal device 10 includes a communication IF (Interface) 12, an input device 13, an output device 14, a memory 15, a storage 16, and a processor 19. The input device 13 is a device for receiving input operations from a user (for example, a touch panel, a touch pad, a pointing device such as a mouse, a keyboard, etc.). The output device 14 is a device for presenting information to a user (a display, a speaker, etc.). In this embodiment, the terminal device 10 is assumed to include a touch panel in which the input device 13 and the output device 14 are integrated.

[0027] The server 20 is, for example, an information processing device for managing and operating a qualification assessment service, and is an information processing device realized by a computer connected to a network 80. As shown in Fig. 1, the server 20 includes a communication IF 22, an input / output IF 23, a memory 25, a storage 26, and a processor 29. The input / output IF 23 functions as an input device for receiving input operations from a manager / operator of the qualification assessment service, and as an interface for an output device for outputting information to the manager / operator.

[0028] The artificial satellite 30 acquires, for example, a satellite image (hereinafter abbreviated as satellite image) of the candidate area as satellite data and transmits it to a ground station. While Fig. 1 shows an example in which the system 1 includes one artificial satellite 30, for example, the system 1 may include two or more artificial satellites 30. When two or more artificial satellites 30 are included, the types of the artificial satellites 30 may be the same or different from each other.

[0029] In this embodiment, the satellite 30 acquires optical satellite images (e.g., visible light images, near-infrared images, etc.) as satellite images and transmits them to the server 20 via a ground station. Note that the satellite images transmitted by the satellite 30 to the server 20 via a ground station may be, for example, SAR (Synthetic Aperture Radar) images, spectral satellite images, or topographical satellite images (DEM: Digital Elevation Model), etc.

[0030] The AI ​​system 40 is, for example, a system including a trained AI model (hereinafter abbreviated as "AI model"). The AI ​​model is, for example, a decision tree algorithm or a neural network such as a convolutional neural network (CNN) or a recurrent neural network (RNN). Also, for example, the AI ​​model is a generative AI model such as a multimodal generative AI model.

[0031] In this embodiment, the satellite images input to the AI ​​model (e.g., semantic segmentation model) are 10m resolution multispectral images obtained from the Sentinel-2 satellite. Specifically, to analyze the state of vegetation, the near-infrared (NIR) band is used in addition to the visible red, green, and blue bands. This image data is preprocessed using atmospheric correction and cloud masking to remove clouds and shadows, and then used as input data for the AI ​​model.

[0032] Furthermore, to improve the analytical accuracy of the AI ​​model, the Normalized Difference Vegetation Index (NDVI) or Improved Difference Vegetation Index (EVI) calculated from the above bands is extracted as a feature and added to the input data. NDVI is calculated as (NIR-Red) / (NIR+Red), with higher values ​​indicating higher vegetation activity. This enables more accurate land cover classification, such as whether land is "forest" or "grassland."

[0033] The AI ​​model used in this embodiment employs a U-Net architecture suitable for classifying each pixel in an image. The training data used for training is past satellite images that have been manually annotated (labeled) on a pixel-by-pixel basis by agricultural or forestry experts using GIS (geographic information system) tools to identify areas that meet specific conditions (first conditions), such as "areas that have not been forested in the past 10 years" or "wetlands." By training the model using this training dataset, an AI model is constructed that, when given an unknown satellite image, probabilistically outputs whether each pixel satisfies the specified conditions.

[0034] The AI ​​system 40 uses, for example, an AI model to analyze satellite images from a predetermined period of time in the past that are received from the server 20. Specifically, for example, the AI ​​system 40 inputs satellite images from a predetermined period of time in the past into the AI ​​model, and causes the AI ​​model to output analysis results corresponding to each of the satellite images from the predetermined period of time in the past.

[0035] The "predetermined past period" is a period that can be set, for example, depending on the content of the specified conditions, and the point in time to be used as the reference for going back in time can be determined arbitrarily. For example, if the target project is ARR or REDD+, it may be the past 10 years, or if it is AWD, it may be the past 5 years. However, from the perspective of ensuring the accuracy of the qualification assessment, it is preferable to use the most recent point in time before the application for the qualification assessment service as the reference for going back in time. In addition, the number of satellite images acquired during the specified past period, the timing of acquisition, etc. can also be set arbitrarily.

[0036] The analysis result is, for example, information regarding a determination as to whether or not the candidate area satisfies a first condition. In this embodiment, the analysis result is a determination result as to whether or not the candidate area satisfies the first condition (hereinafter, referred to as a first determination result). The first determination result is an example of a determination result according to one aspect of the present disclosure.

[0037] The content of the first determination result is not particularly limited as long as it indicates whether the candidate area satisfies the first condition. The first determination result may be, for example, the area value of an area of ​​the candidate area that is determined to satisfy the first condition (hereinafter, the first condition satisfying area). Also, for example, the first determination result may be map data (polygon data, etc.) of the first condition satisfying area.

[0038] For example, the AI ​​system 40 may include one or more AI models. Furthermore, for example, if the AI ​​system 40 includes multiple AI models, the AI ​​models may be of the same type, or may be of different types, such as one being a neural network and the other being a multimodal generative AI model.

[0039] Each information processing device, such as the terminal device 10, the server 20, and the AI ​​system 40, is configured by a computer 90 (see FIG. 7) equipped with an arithmetic unit and a storage device. The basic hardware configuration of the computer 90 and the basic functional configuration of the computer 90 realized by the basic hardware configuration will be described later. Note that for each of the terminal device 10, the server 20, and the AI ​​system 40, descriptions that overlap with the basic hardware configuration and basic functional configuration of the computer 90 will be omitted.

[0040] <2-2 Terminal Device Configuration> Fig. 2 is a block diagram showing an example configuration of the terminal device 10 shown in Fig. 1. As shown in Fig. 2, the terminal device 10 includes a communication unit 120, an input device 13, an output device 14, an audio processing unit 170, a microphone 171, a speaker 172, a camera 160, a position information sensor 150, an acceleration sensor 155, a storage unit 180, and a control unit 190. The blocks included in the terminal device 10 are electrically connected by, for example, a bus or the like.

[0041] The communication unit 120 performs processing such as modulation and demodulation for communication between the terminal device 10 and an external device (for example, the server 20). The communication unit 120 performs transmission processing on the signal generated by the control unit 190 and transmits it to the external device. The communication unit 120 performs reception processing on the signal received from the external device and outputs it to the control unit 190.

[0042] The input device 13 is a device for a user to input instructions or information. The input device 13 is realized, for example, by a touch-sensitive device 131 that inputs instructions by touching an operation surface. When the terminal device 10 is a PC or the like, the input device 13 may be realized by a reader, keyboard, mouse, or the like. The input device 13 converts instructions input by the user into electrical signals and outputs them to the control unit 190. The input device 13 may include, for example, a receiving port that receives electrical signals input from an external input device.

[0043] The output device 14 is a device for presenting information to a user. The output device 14 is realized, for example, by a display 141. The display 141 displays various types of information according to the control of the control unit 190. The display 141 is realized, for example, by an LCD (Liquid Crystal Display) or an organic EL (Electro-Luminescence) display.

[0044] The audio processing unit 170 performs, for example, digital-to-analog conversion processing of an audio signal. The audio processing unit 170 converts a signal provided from the microphone 171 into a digital signal and provides the converted signal to the control unit 190. The audio processing unit 170 also provides the audio signal to the speaker 172. The audio processing unit 170 is realized, for example, by a processor for audio processing. The microphone 171 receives audio input and provides an audio signal corresponding to the audio input to the audio processing unit 170. The speaker 172 converts the audio signal provided from the audio processing unit 170 into audio and outputs the audio to the outside of the terminal device 10.

[0045] Camera 160 is an imaging device that captures images using visible light. In other words, camera 160 is a device that receives visible light using a light-receiving element and outputs image data as an image capture signal. Camera 160 captures an image of a subject in a certain direction and within a certain image capture range relative to terminal device 10, and outputs image data as the image capture result. If camera 160 has a function that allows the image capture range, or more precisely, the angle of view, to be adjustable, camera 160 also outputs information regarding this angle of view. This function is known as a zoom function.

[0046] The position information sensor 150 is a sensor that detects the position of the terminal device 10 and is generally a GNSS device, such as a GPS module. The GPS module is a receiving device used in a satellite positioning system. In a satellite positioning system, signals are received from at least three or four satellites, and the current position of the terminal device 10 equipped with the GPS module is detected as coordinate values ​​based on the received signals. The position information sensor 150 may detect the current position of the terminal device 10 from the position of a wireless base station to which the terminal device 10 connects via the communication unit 120.

[0047] The acceleration sensor 155 is a sensor that detects acceleration applied to the terminal device 10. Preferably, the acceleration sensor 155 has a function of detecting tilt around each axis (X-axis, Y-axis, Z-axis) of a three-dimensional coordinate system with the position of the terminal device 10 as the origin. The acceleration sensor 155 having such a function can detect the attitude of the terminal device 10, that is, the direction with respect to the X-axis, Y-axis, and Z-axis, by detecting the gravitational acceleration of the gravitational force with respect to the earth.

[0048] 1, and stores data and programs used by the terminal device 10. The programs include application programs such as a web browser application.

[0049] The control unit 190 is realized, for example, by the processor 19 reading a program stored in the storage unit 180 and executing instructions included in the program. The control unit 190 controls the operation of the terminal device 10. The control unit 190 performs the functions of an operation reception unit 191, a transmission / reception unit 192, and a presentation control unit 193 by operating in accordance with the read program.

[0050] The operation reception unit 191 performs processing for receiving instructions or information input from the input device 13. Specifically, the operation reception unit 191 receives instructions or information input from the touch-sensitive device 131. The transmission / reception unit 192 performs processing for the terminal device 10 to transmit and receive data to and from an external device in accordance with a communication protocol. Specifically, the transmission / reception unit 192 transmits instructions or information input by the user to the server 20. The transmission / reception unit 192 receives information transmitted from the server 20. The presentation control unit 193 controls the output device 14 to present various information to the user.

[0051] In this embodiment, the operation receiving unit 191 receives input of initial information by the user. The initial information is, for example, information about the candidate area, and includes position information (e.g., latitude and longitude, address) of the candidate area, polygon data of the candidate area, etc. The initial information may be stored in advance in the storage unit 180. The transmission / reception unit 192 transmits the received initial information to the server 20 via the communication unit 120.

[0052] <2-3 Server configuration> Fig. 3 is a block diagram showing an example of the configuration of the server 20 shown in Fig. 1. As shown in Fig. 3, the server 20 performs the functions of a communication unit 201, a storage unit 202, and a control unit 203.

[0053] The communication unit 201 performs processing for the server 20 to communicate with an external device (for example, the terminal device 10). The storage unit 202 is realized by the memory 25 and the storage 26, and stores data and programs used by the server 20. The programs include application programs such as a web browser application.

[0054] The storage unit 202 stores, for example, an eligibility condition table 2021 and an application 2022. The eligibility condition table 2021 is a table that stores, for example, predetermined conditions (first and second conditions) for each target project or the like.

[0055] The application 2022 is an application for managing the user's use of the qualification assessment service. The application 2022 runs, for example, in the background of other applications installed on the server 20, and monitors the processes executed by the user. The user can access the application 2022 by using, for example, a web browser application installed on the terminal device 10.

[0056] The server 20 may, for example, perform a predetermined analysis process by grasping and managing the usage status of the application 2022. Furthermore, for example, the application 2022 may be installed in the terminal device 10 and stored in the storage unit 180.

[0057] The control unit 203 is realized by the processor 29 reading a program stored in the storage unit 202 and executing instructions included in the program. The control unit 203 controls the operation of the server 20. The control unit 203 operates in accordance with the read program to fulfill the functions of a reception control module 2031, a transmission control module 2032, a presentation control module 2033, and a qualification evaluation module 2034.

[0058] The reception control module 2031 controls the process by which the server 20 receives signals from external devices in accordance with a communication protocol. The transmission control module 2032 controls the process by which the server 20 transmits signals to external devices in accordance with a communication protocol. The presentation control module 2033 controls the process of presenting various types of information to the user.

[0059] The qualification evaluation module 2034, for example, acquires satellite images for a predetermined period of time from the satellite 30 via a ground station based on initial information acquired from the terminal device 10. The qualification evaluation module 2034, for example, causes the AI ​​system 40 to analyze the satellite images for the predetermined period of time and output the analysis result as a first judgment result.

[0060] The eligibility assessment module 2034, for example, determines a time-series change in the first judgment results for a predetermined period in the past, and outputs a judgment result (hereinafter, "second judgment result") as to whether the candidate area satisfies the second condition. The second judgment result is an example of a judgment result according to one embodiment of the present disclosure.

[0061] There are no particular limitations on the content of the second determination result, as long as it indicates whether the candidate area satisfies the second condition. The second determination result may be, for example, data showing time-series changes in the area value of the area satisfying the first condition. Also, for example, the second determination result may be data showing time-series changes in the shape and size of map data (e.g., polygon data, etc.) of the area satisfying the first condition.

[0062] The qualification evaluation module 2034 outputs a judgment result indicating whether the candidate area satisfies the predetermined conditions, i.e., a final judgment result, by, for example, comprehensively considering the contents of the first judgment result and the contents of the second judgment result. For example, for a candidate area in which all first judgment results over a predetermined period of time in the past have indicated that the first condition is satisfied and the second judgment result has indicated that the second condition is satisfied, the qualification evaluation module 2034 outputs a judgment result indicating that the predetermined conditions are satisfied. However, if, for example, the proportion of first judgment results over a predetermined period of time in the past that have indicated that the first condition is not satisfied, or the degree of non-satisfaction in cases in which the second judgment result has indicated that the second condition is not satisfied, is below a predetermined threshold (which can be set arbitrarily), the qualification evaluation module 2034 also outputs a judgment result indicating that the predetermined conditions are satisfied for that area.

[0063] There are no particular limitations on the content of the determination result output by the eligibility assessment module 2034. The determination result may be, for example, data indicating time-series changes in the area value of an area among the candidate areas that has been determined to satisfy a predetermined condition (hereinafter, a condition-satisfying area). Furthermore, for example, the determination result may be data indicating time-series changes in the shape and size of map data (e.g., polygon data, etc.) of the condition-satisfying area.

[0064] The eligibility assessment module 2034 assesses the reliability of the baseline set as the assessment standard for the amount of carbon credits (hereinafter abbreviated as credit amount) to be created in the candidate area, for example, and outputs the assessment result.

[0065] The baseline is, for example, the trend in greenhouse gas reductions / removals that serves as the evaluation standard for the amount of credits, and is an important factor for accurately evaluating the additionality and reductions / removals of a carbon credit project. Additionality, for example, means that the revenue from carbon credits obtained through greenhouse gas emission reduction / removal activities makes it possible to carry out those reductions / removals. The amount of credits is the amount of greenhouse gas reductions / removals certified as carbon credits. The carbon credit certification body that is the target of the qualification assessment service may be, for example, a public institution such as a national or local government, or it may be a private certification body.

[0066] Specifically, for example, the eligibility assessment module 2034 selects and executes a baseline setting method according to the target project, etc. For example, if the target project, etc. is ARR, the eligibility assessment module 2034 sets the baseline based on an approach based on land use history, trends in time-series changes in LULC (Land Use and Land Cover) data, etc. LULC data will be described in detail in the first modified example below. For example, if the target project, etc. is REDD+, the eligibility assessment module 2034 sets the baseline based on time-series analysis of LULC data and land cover conversion maps, a reference area approach, a business-as-usual scenario, etc. Furthermore, if the target project, etc. is AWD, the eligibility assessment module 2034 sets the baseline based on past non-flooded periods reported by farmers cultivating rice in paddy fields in the candidate area, an approach based on existing practices, application of emission factors, use of IPCC guidelines, etc.

[0067] Furthermore, for example, the qualification evaluation module 2034 evaluates the reliability of the baseline using an evaluation method suited to the target project or the like.

[0068] For example, if the target project, etc. is an ARR, the qualification assessment module 2034 sets, as the first control area, an area around the candidate area that has similar environmental conditions and vegetation conditions to the candidate area and where no artificial afforestation activities are carried out. The qualification assessment module 2034, for example, sets, as the baseline, the results of monitoring vegetation changes over a certain period in the first control area. The monitoring of vegetation changes is performed, for example, by acquiring and analyzing satellite images for a certain period. The qualification assessment module 2034, for example, sets, as the synthetic project area, an area around the first control area where no artificial afforestation activities are carried out. The qualification assessment module 2034, for example, sets, as the second control area, an area around the synthetic project area that has similar environmental conditions and vegetation conditions to the synthetic project area and where no artificial afforestation activities are carried out. The qualification assessment module 2034 evaluates the reliability of the baseline, for example, depending on whether the difference between the monitoring results of vegetation changes over a certain period in the synthetic project area and the monitoring results of vegetation changes over a certain period in the second control area falls within a predetermined confidence band (confidence band: can be set arbitrarily). If the difference value falls within the predetermined confidence band, the reliability is evaluated as high, and the greater the deviation from the range, the lower the reliability is evaluated.

[0069] The selection of this control area is carried out automatically using the following algorithm. First, the elevation, slope, soil type, and average and variance of NDVI over the past five years of the candidate area are defined as feature vectors. Next, areas within a 50km radius of the candidate area are searched for, and the Euclidean distance between each area and the feature vector is calculated. Areas where this distance is below a specified threshold are extracted as similar control area candidates.

[0070] The "confidence band" into which the difference (Δ) in vegetation change between the synthetic project area and the second control area should fall is calculated statistically. Specifically, the standard error (SE) of the difference (Δ) calculated from past data is found, and the 95% confidence interval is set as "mean value of Δ ± 1.96 × SE." The baseline (vegetation change in the first control area) set from the candidate area is then evaluated for reliability on a scale of "high," "medium," or "low" depending on whether it falls within this confidence band. If the difference falls within the confidence band, the reliability of the baseline is evaluated as "high."

[0071] The synthetic project area is, in other words, an area that simulates anthropogenic impacts. "Anthropogenic impacts" refers to the land cover changes caused by human activities that are expected to occur in the area if the carbon credit project is not implemented. This corresponds to the "business as usual" scenario.

[0072] Specifically, for example, if the target project is ARR, the human impacts include the following: Unplanned deforestation for small-scale agricultural use -Degradation of forests due to excessive harvesting of firewood and charcoal -Destruction of vegetation due to livestock grazing

[0073] "Simulating anthropogenic influences" means that changes in vegetation that may result from these influences are modeled and predicted based on past data. In this embodiment, for example, trends in land use change over the past 10 years in the area surrounding the synthetic project area are analyzed. Then, using socioeconomic parameters such as distance from roads, proximity to existing agricultural land, and population density, the rate of future deforestation and forest degradation that may occur in the area is calculated using a prediction model (e.g., a spatial regression model). The future decreasing trend in the vegetation index (e.g., NDVI) output by this prediction model corresponds to "time-series changes in the vegetation index in the synthetic project area that mimics anthropogenic influences."

[0074] In other words, the server 20 sets a control area with environmental conditions similar to those of the candidate area, calculates the difference between the time series change of the vegetation index in the control area and the time series change of the vegetation index in a synthetic project area that simulates human influence, and evaluates the reliability of the baseline based on whether the calculated difference falls within a statistically set confidence band.

[0075] For example, if the target project is REDD+, the eligibility assessment module 2034 acquires actual measured values ​​of deforestation over a certain period of time in the past (learning period) and constructs a prediction model for future deforestation. The actual measured values ​​of deforestation over a certain period of time in the past are, for example, a land cover conversion map created based on LULC data from a certain period of time in the past. The constructed prediction model is, for example, a probabilistic model, spatial statistical analysis, or machine learning model, and serves as a reference level, which is the baseline for REDD+. For example, the eligibility assessment module 2034 compares the future deforestation predicted by the reference level with the actual measured values ​​of deforestation over a certain period of time in the past (verification period), measures the error, and evaluates the reference level (baseline) according to the error value. The smaller the error value, the higher the reliability rating.

[0076] Furthermore, if the target project, etc., is an AWD, the eligibility assessment module 2034 acquires, from the terminal device 10 or the memory unit 202, information indicating past non-flooded periods (hereinafter, "declaration periods") declared by farmers cultivating rice in paddy fields in the candidate area, and sets the number of days in the declaration period as the baseline. The eligibility assessment module 2034, for example, acquires and analyzes satellite images for the declaration period to estimate the non-flooded periods in the paddy fields in the candidate area. The eligibility assessment module 2034, for example, calculates the difference between the estimated non-flooded period and the declaration period, and compares the difference value with a predetermined threshold (which can be set arbitrarily) to evaluate the reliability of the baseline. The greater the degree to which the difference value is below the predetermined threshold, the higher the reliability assessment is, and the greater the degree to which the difference value exceeds the threshold, the lower the reliability assessment is.

[0077] [3 Data Structure] 4 is a diagram showing the data structure of tables stored in the server 20. Note that FIG. 4 is merely an example and does not exclude data that is not listed. Furthermore, even data listed in the same table may be stored in separate storage areas in the storage unit 202.

[0078] 4 is a diagram showing the data structure of the eligibility condition table 2021. The eligibility condition table 2021 shown in FIG. 4 is a table that stores, for example, predetermined conditions (first condition and second condition) for determining the eligibility of a candidate area according to the target project, etc. The various information stored in this table is referenced, for example, when the server 20 performs an eligibility assessment for the candidate area. The eligibility condition table 2021 has, for example, columns for the target project, etc., the first condition, the second condition, condition details, baseline setting method, and reliability assessment method, with the project ID as a key.

[0079] The item "project ID" is a column that stores, for example, an identifier for uniquely identifying a carbon credit project.

[0080] The item "Target Projects, etc." is a column that stores information indicating, for example, the type of carbon credit project and the greenhouse gas emission reduction / absorption method. For example, ARR, REDD+, AWD, etc. are stored in the item "Target Projects, etc."

[0081] The "First Condition" item is a column that stores the first condition, which indicates the status or state of a candidate area at a certain point in time, among the predetermined conditions that indicate eligibility for carbon credit creation. For example, in the case of ARR, conditions such as "it must be implemented on land that has not been classified as 'forest' in the past 10 years" and in the case of AWD, conditions such as "it must be within a specified distance from the location of irrigation facilities" and "it must be implemented in an area that is a paddy field" are stored in the "First Condition" item.

[0082] The "Second Condition" item is an item that stores, for example, a second condition, one of the predetermined conditions that indicates eligibility for the creation of carbon credits, that can only be determined to be met once the time-series changes in the situation and state of the candidate area are identified. For example, in the case of AWD, a condition such as "the project must be carried out in an area that has been a rice paddy field for the past five years" is stored in the "Second Condition" item.

[0083] The item "Condition details" is a column that stores detailed information indicating, for example, the specific contents of the first and second conditions, the reference values, the threshold values, etc. For example, the item "Condition details" stores the specific numerical value of the woody biomass coverage rate, the specific numerical value of the distance from the irrigation facility, the method for determining the "specified distance," the details of the methodology, etc.

[0084] The item "Baseline setting method" is a column that stores information indicating the baseline setting method according to the target project, etc. For example, in the case of ARR, "approach based on land use history" or "trend of time-series changes in LULC data" are stored in the item "Baseline setting method," while in the case of REDD+, "time-series analysis of LULC data and land cover conversion maps" is stored.

[0085] The item "Reliability assessment method" is a column that stores information indicating the reliability assessment method of the baseline depending on the target project, etc. For example, in the case of ARR, the specific content of "monitoring results of vegetation changes over a certain period in the first control area and differential assessment between the synthetic project area and the second control area" is stored in the item "Reliability assessment method," and in the case of REDD+, the specific content of "measurement of the error between a future deforestation prediction model and past actual measurements" is stored in the item "Reliability assessment method."

[0086] [4 actions] An example of the operation of the server 20 according to this embodiment will be described below.

[0087] 5, the server 20 accepts input of initial information by a user. Specifically, for example, the operation accepting unit 191 accepts input of initial information including position information of the candidate area, polygon data of the candidate area, etc. The transmitting / receiving unit 192, for example, transmits the accepted initial information to the server 20. The reception control module 2031, for example, receives the initial information transmitted from the terminal device 10. As a result, the server 20 accepts input of the initial information.

[0088] In step S12, the server 20 acquires satellite images for a predetermined period of time in the past based on the initial information. Specifically, for example, the qualification module 2034 acquires satellite images for a predetermined period of time from the satellite 30 via a ground station based on the initial information acquired from the terminal device 10. The type of satellite images used is optical satellite images (visible light images, near-infrared images, etc.). The predetermined period of time in the past is a period that can be set depending on the target project, etc., and examples include the past 10 years for ARR and REDD+, and the past 5 years for AWD.

[0089] In step S13, the server 20 uses the AI ​​model to analyze satellite images from the past for a predetermined period of time, and outputs a determination result as to whether the candidate area satisfies a predetermined condition. Specifically, for example, the qualification assessment module 2034 transmits the acquired satellite images from the past for a predetermined period of time to the AI ​​system 40. The AI ​​system 40, for example, inputs the satellite images from the past for a predetermined period of time received from the server 20 into the AI ​​model, and causes the AI ​​model to output analysis results corresponding to each of the satellite images from the past for a predetermined period of time as first determination results. The output first determination results may be, for example, area values ​​of the first condition-satisfying areas, map data of the first condition-satisfying areas, etc. The AI ​​system 40, for example, transmits the first determination results from the AI ​​model for a predetermined period of time to the server 20.

[0090] The eligibility assessment module 2034, for example, determines changes in the first judgment results over a predetermined period of time in the past based on the first judgment results over a predetermined period of time received from the AI ​​system 40. The eligibility assessment module 2034, for example, reads out second conditions corresponding to the target project, etc. from the eligibility condition table 2021, determines whether the candidate area satisfies the second condition by referring to the changes over time, and outputs the second judgment result. The output second judgment result may be, for example, data indicating changes over time in the area value of the area satisfying the first condition, data indicating changes over time in the shape and size of the map data of the area satisfying the first condition, etc.

[0091] The qualification evaluation module 2034 outputs a final judgment result by, for example, comprehensively considering the first judgment result and the second judgment result for a predetermined period of time in the past. The final judgment result may be, for example, data showing time-series changes in the area value of the condition-satisfying region, data showing time-series changes in the shape and size of map data (e.g., polygon data, etc.) of the condition-satisfying region, etc.

[0092] The eligibility evaluation module 2034 may, for example, associate and store at least one of the first judgment result, the second judgment result, and the final judgment result in the eligibility condition table 2021 as needed.

[0093] In step S14, the server 20 evaluates the reliability of the baseline and outputs the evaluation result. Specifically, for example, the eligibility assessment module 2034 selects a baseline setting method appropriate for the target project, etc. from the eligibility condition table 2021 and executes it. The eligibility assessment module 2034 selects an assessment method appropriate for the target project, etc. from the eligibility condition table 2021 and evaluates the reliability of the baseline. For example, in the case of ARR, the eligibility assessment module 2034 sets a first control area that has similar environmental and vegetation conditions to the candidate area and where no artificial afforestation activities are carried out, and sets the vegetation change monitoring results therein as the baseline. Then, the reliability is evaluated based on whether the difference between the monitoring results of vegetation changes in the synthetic project area and the second control area falls within a predetermined confidence band. Also, for example, in the case of REDD+, the eligibility assessment module 2034 constructs a future deforestation prediction model (reference level) from actual measurements of past deforestation and evaluates the reliability based on the error between the reference level and the actual measurements during the verification period. The eligibility evaluation module 2034 may, for example, associate and store the evaluation results in the eligibility condition table 2021.

[0094] In step S15, the server 20 presents the final judgment result and evaluation result to the user. Specifically, for example, the presentation control module 2033 transmits information for displaying each of the final judgment result and evaluation result to the terminal device 10. The transmission / reception unit 192, for example, transmits information for displaying these results received from the server 20 to the display 141. The presentation control unit 193, for example, accepts a display request from the user and causes the display 141 to display the final judgment result and evaluation result.

[0095] For example, the final judgment results and evaluation results displayed on the display 141 together represent the evaluation of the suitability of the candidate area. Furthermore, there are no particular limitations on the manner in which the final judgment results and evaluation results are presented to the user, and for example, the final judgment results and evaluation results may be presented to the user in the form of a report summarizing them.

[0096] [5 Summary] As described above, in this embodiment, the terminal device 10 accepts input of initial information and transmits it to the server 20. The qualification assessment module 2034 acquires satellite images for a predetermined period of time in the past based on this initial information. The qualification assessment module 2034 analyzes satellite images for a predetermined period of time in the past using an AI model, outputs a first judgment result and a second judgment result, and then outputs a final judgment result. This makes it possible to judge the qualification of a candidate area based on both the first condition indicating the situation and state of the candidate area at a certain point in time and the second condition indicating changes over time.

[0097] Furthermore, in this embodiment, the eligibility assessment module 2034 assesses the reliability of the baseline based on information read from the eligibility condition table 2021, and outputs the assessment results. In this way, by selecting an appropriate baseline setting method and reliability assessment technique stored in the eligibility condition table 2021 depending on the type of target project, it becomes possible to accurately assess the additionality and reduction / removal amount of the carbon credit project.

[0098] Therefore, the server 20 can make highly accurate judgments on the eligibility of candidate areas by taking into account various information sources and changes over time. In particular, by taking into account not only information at a single point in time but also past time-series changes, judgments can be made that are more in line with reality, and the reliability of carbon credits can be improved. In addition, by evaluating the reliability of the baseline, it becomes possible to provide users with highly transparent information on the accuracy of the credit amount and the feasibility of the project.

[0099] [6 Screen Examples] An example screen according to one embodiment of the present disclosure will be described below with reference to Fig. 6. Fig. 6 is a schematic diagram showing an example screen of display 141 presented to the user.

[0100] 6, an evaluation report 1411 is displayed on the display 141. The evaluation report 1411 is displayed on the display 141 under the control of the presentation control module 2033 via the presentation control unit 193. The evaluation report 1411 includes three areas, roughly divided into an area 1412 for "condition satisfaction judgment," an area 1413 for "reliability evaluation," and an area 1414 for "qualification evaluation."

[0101] Area 1412 is an area that displays information about the result of the determination (final determination result) of whether or not the candidate area satisfies a predetermined condition. In the example of FIG. 6, it displays information that the target period is "the last 10 years" and the area of ​​the candidate area is "155 km²". 2 " and the change in the area value of the area satisfying the first condition is shown in chronological order. This change in area value shows how the area of ​​the area satisfying the first condition has changed over the last 10 years. In the example of Figure 6, the final decision result is "Of the candidate areas, the area is 139 km2. 2 This corresponds to the process in which the presentation control unit 193 presents the final judgment result output by the eligibility evaluation module 2034 to the user.

[0102] Area 1413 is an area that presents information related to the baseline reliability evaluation. In the example of FIG. 6, the evaluation method states that "the difference between the monitoring results of vegetation changes over a certain period in the synthetic project area and the monitoring results of vegetation changes over the same period in the second control area was calculated, and it was confirmed whether the difference fell within the confidence band." The evaluation result indicates that "the difference fell within the confidence band, and the baseline reliability is high." This corresponds to the process in which the presentation control unit 193 presents to the user the evaluation results of the baseline reliability output by the qualification evaluation module 2034.

[0103] Area 1414 is an area that presents information about the evaluation of the eligibility of the candidate area, which is a combination of the contents of the final judgment result displayed in area 1412 and the contents of the evaluation result displayed in area 1413. In the example of Fig. 6, the area of ​​the candidate area, the area of ​​the condition-satisfying area, the baseline reliability evaluation (here, "high"), and the eligibility evaluation result that "the condition-satisfying area is eligible (eligible area)" are displayed.

[0104] 6 is merely an example, and various variations are envisioned for the display content and display mode of the evaluation report 1411. For example, information may be presented in various formats according to the convenience of the user, such as a graph display of each numerical value, visualization of the condition-satisfying area on a map, or addition of detailed explanatory text.

[0105] [7 Variations] <7-1 First Modification> In this embodiment, an example has been described in which the AI ​​model outputs a first determination result as the analysis result. However, the AI ​​model may output, for example, a provisional determination result indicating whether the candidate area satisfies the first condition as the analysis result. In this case, the server 20 or the AI ​​system 40 may output the first determination result by, for example, taking into account the content of the LULC data of the candidate area in the analysis result.

[0106] LULC data includes, for example, land use data and land cover data. Land use data is information that indicates how humans use or manage land, focusing on socioeconomic aspects and human activities. Examples of land use data include agriculture (rice cultivation, field crops, pasture), forestry (afforestation, logging), residential areas, industrial land, conservation areas, and fallow land. Land cover data is information that indicates the type and distribution of physical elements covering the earth's surface (e.g., vegetation, water bodies, bare land, buildings, etc.). This information can be directly identified from remote sensing data such as satellite images and aerial photographs. Examples of land cover data include forests, grasslands, agricultural land, wetlands, water surfaces, urban areas, bare land, snow and ice, etc.

[0107] The LULC data is composed of, for example, a combination of image data extracted and classified from satellite or aerial images and attribute data (text, numerical values). For example, the LULC data is managed in a database of a geographic information system (GIS). The server 20 or the AI ​​system 40, for example, searches the database by referring to the initial information received from the terminal device 10, and reads and acquires the LULC data of the candidate area for a predetermined period of time in the past.

[0108] In this way, according to the first modification, the first assessment result is output by adding the content of the LULC data of the candidate area to the analysis result, which improves the accuracy of the first assessment result compared to when the analysis result is immediately converted into the first assessment result, for example.

[0109] In the first modification, the analysis result output from the AI ​​model is processed by taking into account the LULC data. However, this is not limiting. For example, when inputting satellite images from a predetermined period of the past into the AI ​​model, the AI ​​system 40 may also input LULC data from the predetermined period of the past acquired from the database. Then, the AI ​​system 40 may output the analysis result from the AI ​​model, in which the LULC data has been taken into account, as the first judgment result.

[0110] <7-2 Second Modification> In this embodiment, an example has been described in which the server 20 outputs the determination result of whether the candidate area satisfies the predetermined conditions and the evaluation result of the baseline reliability. However, the server 20 may, for example, use the determination result to perform further processing. That is, the server 20 may, for example, further perform processing to estimate the amount of carbon credits to be created in an area of ​​the candidate area that has been determined to be eligible for carbon credit creation (hereinafter referred to as an eligible area) based on the determination result (which comprehensively takes into account the first determination result and the second determination result).

[0111] In the second modification, the qualified area is a candidate area that is determined to satisfy a predetermined condition and has an evaluation result that is high, at or above a certain level (which can be set arbitrarily). However, the qualified area may also be, for example, a candidate area that is determined to satisfy the predetermined condition even though its evaluation result is below the certain level.

[0112] Specifically, for example, the eligibility assessment module 2034 identifies an eligible area from among the candidate areas by verifying whether each point or each section within the candidate area is determined to satisfy predetermined conditions and whether the assessment result is a high evaluation of a certain level or higher. The eligibility assessment module 2034, for example, estimates the amount of carbon credits to be created in the identified eligible area.

[0113] The estimation of the carbon credit amount is performed by applying, for example, various known calculation methods depending on the target project, etc. That is, the eligibility assessment module 2034 performs the estimation by utilizing the following information while referring to the assessment method and methodology depending on the target project, etc., stored in the eligibility condition table 2021.

[0114] (1) LULC data The LULC data described in the first variant provides important information that serves as the basis for calculating carbon credits, as it identifies land types and their changes. For example, in ARR, identifying the area of ​​land that was previously non-forest and has now become forest provides the basis for calculating removals. For example, in AWD, identifying paddy field areas using LULC data is essential, as changes in the area and management methods of paddy fields affect greenhouse gas emissions.

[0115] (2) Amount of biomass and carbon stocks In the case of forest credits, the amount of credit is directly linked to the amount of aboveground biomass and carbon stocks in the soil. These data are obtained or estimated using known techniques such as: Remote sensing: Information such as vegetation indices (e.g., NDVI, EVI, etc.), backscattering coefficients, tree height, and canopy area is extracted from satellite images (optical, SAR), aerial photographs, LiDAR data, etc., and the amount of biomass on the ground is estimated by combining this information with empirical relationships and machine learning models to correlate the amount of biomass. LiDAR data is particularly effective for estimating tree volume from highly accurate 3D information and converting it into biomass volume. Ground measurements: Tree diameter, height, species, etc. are measured directly on-site before the project is implemented, and the biomass of individual trees and the entire forest stand is estimated by applying this to a known calculation formula called an allometry formula. It is common to improve the accuracy of estimation by combining data obtained by remote sensing with data obtained by ground measurements (e.g., by performing supervised learning on a machine learning model using ground measurement data and then performing wide-area estimation using remote sensing data). Emission factors and absorption factors: These are specified in the IPCC Guidelines and the methodologies established by carbon credit certification organizations such as Verra, Gold Standard, JCM, etc. The eligibility assessment module 2034 applies these emission factors or absorption factors to, for example, the area of ​​the identified eligible area, changes in LULC data, changes in estimated biomass amount / carbon stock amount, and related activity data (e.g., rice cultivation period, water management method, etc.), to calculate the amount of greenhouse gases reduced / absorbed in the eligible area as CO2e (carbon dioxide equivalent), and estimate this as the amount of credits. Comparison with baseline: The estimated credit amount is compared with the baseline. The eligibility assessment module 2034, for example, calculates the difference between the actual greenhouse gas reduction / removal amount in the eligible area and the baseline, taking into account the evaluation results of the reliability of the baseline, and estimates this as the final credit amount.

[0116] In this way, according to the second modification, after the eligibility determination of the candidate area is completed, it is possible to apply various publicly known techniques to estimate in detail and with high accuracy the actual amount of carbon credits that will be generated in the identified eligible area. This allows the user to grasp the specific outcome forecast of the carbon credit project in advance, enabling more accurate business planning and investment decisions.

[0117] In the second modification, if the carbon credits are forest credits, the server 20 may estimate the amount of forest credits by taking into account the results of an analysis of the amount of above-ground biomass in the eligible area. Above-ground biomass refers to the total amount of plant matter, such as trees and undergrowth, that make up a forest, and these are the main part that absorb carbon dioxide from the atmosphere through photosynthesis and store it as carbon.

[0118] Various known techniques can be applied to obtain analytical results regarding the amount of terrestrial biomass. For example, the following techniques can be used:

[0119] (1) Use of high-resolution remote sensing data LiDAR data: LiDAR data can accurately grasp the three-dimensional structure of a forest by emitting a laser from an aircraft or drone and measuring the reflection time from the ground and the tree canopy. This makes it possible to measure the height of each tree, the diameter of the tree canopy, the density of the canopy, and so on in detail. For example, the qualification assessment module 2034 estimates the volume of trees based on this information obtained from the LiDAR data and multiplies it by a known density coefficient for each tree species to calculate the amount of aboveground biomass. The amount of aboveground biomass obtained in this way directly reflects the amount of carbon stocks in the forest, providing a highly reliable estimate. High-resolution optical satellite imagery / aerial imagery: High-resolution optical satellite imagery from sources such as Sentinel-2 and Planet Labs, and aerial imagery from drones, are useful for understanding forest canopy coverage, tree species classification, health status, and the like. The qualification assessment module 2034, for example, calculates a vegetation index from this image data, identifies the range of individual trees and forest stands using a machine learning model, and evaluates their health and growth. The qualification assessment module 2034, for example, takes into account the evaluation results of health and growth as auxiliary information for estimating the amount of biomass on the ground.

[0120] (2) Use of estimation models Allometric Equations: A statistical model for estimating with high accuracy the amount of aboveground biomass, which is difficult to measure directly, from easily measurable tree traits (e.g., diameter at breast height (DBH), tree height, etc.). The qualification assessment module 2034 inputs tree trait information obtained from, for example, remote sensing data into the allometric equations to estimate the amount of aboveground biomass. Machine learning model: A machine learning model is constructed that is trained using a large amount of ground measurement data and corresponding remote sensing data as training data. The qualification assessment module 2034, for example, estimates the amount of biomass on the ground by inputting the latest remote sensing data of the qualified area into the constructed machine learning model. In particular, RNN is effective when dealing with time-series satellite images, while CNN is effective when capturing spatial features. Ecosystem model: The eligibility assessment module 2034 can use an ecosystem model (e.g., process-based models, empirical models) that takes into account forest growth, carbon cycle, climate, soil conditions, etc. to simulate the long-term increase in aboveground biomass, and can also take the simulation results into account when estimating the amount of credits.

[0121] The eligibility assessment module 2034, for example, combines the analysis results regarding the amount of above-ground biomass as described above with the area of ​​the identified eligible area, and further applies an emission factor or absorption factor to calculate the amount of carbon dioxide absorbed in the eligible area and estimate the amount of forest credits. In this case, the eligibility assessment module 2034 estimates the amount of forest credits based on the amount of increase in above-ground biomass if the target project is ARR, or based on the amount of above-ground biomass preserved as a result of reducing deforestation if the target project is REDD+.

[0122] According to this, when carbon credits are forest credits, incorporating specific analytical results on the amount of aboveground biomass into the estimation process will enable a more scientific and rigorous estimation of the amount of forest credits. This is extremely effective in increasing the reliability of the results of carbon credit projects and improving compliance with international certification standards.

[0123] [8 Basic Computer Hardware Configuration] 7 is a block diagram showing the basic hardware configuration of a computer 90. The computer 90 includes at least a processor 901, a main memory device 902, an auxiliary memory device 903, and a communication IF 991 (interface), which are electrically connected to one another by a communication bus.

[0124] The processor 901 is hardware for executing an instruction set written in a program, and is composed of an arithmetic unit, registers, peripheral circuits, and the like.

[0125] The main memory device 902 is used to temporarily store programs, data to be processed by the programs, etc. For example, it is a volatile memory such as a DRAM (Dynamic Random Access Memory).

[0126] The auxiliary storage device 903 is a storage device for saving data and programs, such as a flash memory, a hard disk drive (HDD), a magneto-optical disk, a CD-ROM, a DVD-ROM, or a semiconductor memory.

[0127] The communication IF 991 is an interface for inputting and outputting signals for communicating with other computers via a network using wired or wireless communication standards.

[0128] The network is composed of the Internet, a LAN, various mobile communication systems constructed by wireless base stations, etc. For example, the network includes 3G, 4G, and 5G mobile communication systems, LTE (Long Term Evolution), and wireless networks (e.g., Wi-Fi (registered trademark)) that can connect to the Internet via a predetermined access point. In the case of a wireless connection, communication protocols include, for example, Z-Wave (registered trademark), ZigBee (registered trademark), and Bluetooth (registered trademark). In the case of a wired connection, the network also includes a direct connection using a USB (Universal Serial Bus) cable, etc.

[0129] It should be noted that the computer 90 can be virtually realized by distributing all or part of each hardware configuration across multiple computers 90 and interconnecting them via a network. In this way, the computer 90 is a concept that includes not only a computer 90 housed in a single housing or case, but also a virtualized computer system.

[0130] [9 Basic Functional Configuration of a Computer] The following describes the functional configuration of a computer realized by the basic hardware configuration (FIG. 7) of the computer 90. The computer includes at least the functional units of a control unit, a storage unit, and a communication unit.

[0131] The functional units of the computer 90 can also be realized by distributing all or part of the functional units among multiple computers 90 interconnected via a network. The computer 90 is a concept that includes not only a single computer 90 but also a virtualized computer system.

[0132] The control unit is realized by the processor 901 reading out various programs stored in the auxiliary storage device 903, expanding them in the main storage device 902, and executing processing in accordance with the programs. The control unit can realize functional units that perform various types of information processing depending on the type of program. In this way, the computer is realized as an information processing device that performs information processing.

[0133] The storage unit is realized by a main storage device 902 and an auxiliary storage device 903. The storage unit stores data, various programs, and various databases. Furthermore, the processor 901 can allocate a storage area corresponding to the storage unit in the main storage device 902 or the auxiliary storage device 903 in accordance with the programs. Furthermore, the control unit can cause the processor 901 to execute processes for adding, updating, and deleting data stored in the storage unit in accordance with the various programs.

[0134] A database refers to a relational database, which manages data sets called masters and tables in a tabular format structurally defined by rows and columns, by relating them to each other. In a database, a table is called a table, a master, a column in a table is called a column, and a row in a table is called a record. In a relational database, relationships between tables and masters can be set and associated.

[0135] Typically, each table and each master has a column set as a primary key to uniquely identify a record, but setting a primary key to a column is not essential. The control unit can cause the processor 901 to add, delete, or update records in specific tables and masters stored in the storage unit according to various programs.

[0136] Furthermore, by storing data, various programs, and various databases in the storage unit, it can be considered that the information processing device and information processing system according to the present disclosure have been manufactured.

[0137] Note that the databases and masters in this disclosure may include any data structure in which information is structurally defined (such as a list, dictionary, associative array, or object). The data structure also includes data that can be considered as a data structure by combining data with functions, classes, methods, etc. written in any programming language.

[0138] The communication unit is realized by the communication IF 991. The communication unit realizes a function of communicating with other computers 90 via a network. The communication unit can receive information transmitted from other computers 90 and input the information to the control unit. The control unit can cause the processor 901 to execute information processing on the received information in accordance with various programs. In addition, the communication unit can transmit information output from the control unit to other computers 90.

[0139] Furthermore, the above-described configurations, functions, processing units, processing means, etc. may be partially or entirely implemented in hardware, for example, by designing them as integrated circuits. The present invention can also be realized by software program code that implements the functions of the embodiments. In this case, a storage medium on which the program code is recorded is provided to a computer, and a processor included in the computer reads the program code stored in the storage medium. In this case, the program code itself read from the storage medium implements the functions of the above-described embodiments, and the program code itself and the storage medium on which it is stored constitute the present invention. Examples of storage media for providing such program code include flexible disks, CD-ROMs, DVD-ROMs, hard disks, SSDs, optical disks, magneto-optical disks, CD-Rs, magnetic tapes, non-volatile memory cards, and ROMs.

[0140] Furthermore, the program code that realizes the functions described in this embodiment can be implemented in a wide range of program or script languages, such as assembler, C / C++, perl, Shell, PHP, Java (registered trademark), JavaScript, and TypeScript.

[0141] Furthermore, the program code of the software that realizes the functions of the embodiments may be distributed via a network and stored in a storage means such as a computer's hard disk or memory, or in a storage medium such as a CD-RW or CD-R, and the processor of the computer may read and execute the program code stored in the storage means or the storage medium.

[0142] The functions performed by the components described herein may be implemented in circuitry or processing circuitry, including general-purpose processors, application-specific processors, integrated circuits, ASICs (Application Specific Integrated Circuits), CPUs (Central Processing Units), conventional circuits, and / or combinations thereof, programmed to perform the described functions. A processor includes transistors and other circuits and is considered to be circuitry or processing circuitry. A processor may also be a programmed processor that executes programs stored in memory.

[0143] In this specification, a circuitry, unit, or means is hardware that is programmed to realize or performs the described functions, which may be any hardware disclosed herein or any hardware known to be programmed to realize or perform the described functions.

[0144] If the hardware is a processor considered to be a type of circuitry, the circuitry, means, or unit is a combination of the hardware and the software used to configure the hardware and / or processor.

[0145] Although several embodiments of the present disclosure have been described above, these embodiments can be embodied in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and modifications are intended to be included in the scope of the inventions and their equivalents as defined in the claims, as well as in the scope and spirit of the inventions.

[0146] [10 Appendix] The matters described in the above embodiments will be supplemented below.

[0147] <Appendix 1> A program to be executed by a computer having a processor and a memory, the program causing the processor to execute the following steps: accepting input of information from a user regarding a candidate area for the creation of carbon credits; acquiring satellite images of the candidate area for a predetermined period of time based on the information; analyzing satellite images for a predetermined period of time using a trained AI model to output a determination result as to whether the candidate area satisfies predetermined conditions indicating eligibility as a candidate area for the creation of carbon credits, the conditions including a first condition indicating the state of land cover at a specific point in time and a second condition indicating time-series changes in land cover over a predetermined period; evaluating the reliability of a baseline set as an evaluation standard for the amount of carbon credits to be created in the candidate area, outputting the evaluation result; and presenting the determination result and evaluation result to the user.

[0148] <Appendix 2> The candidate area is foreign land (Appendix 1) in the program.

[0149] <Appendix 3> A program described in (Appendix 1) or (Appendix 2), which further causes the processor to execute a step of estimating the amount of carbon credits to be created in areas of the candidate areas that are determined to be eligible for carbon credit creation based on the judgment results.

[0150] <Appendix 4> The carbon credits are forest credits, and in the estimation step, the amount of forest credits is estimated taking into account the analytical results regarding the amount of above-ground biomass in the area determined to be eligible (Appendix 3).

[0151] <Appendix 5> An information processing device comprising a control unit and a storage unit, wherein the control unit executes all steps in the program according to any one of (Supplementary Note 1) to (Supplementary Note 4).

[0152] <Appendix 6> A method executed by a computer having a processor and a memory, wherein the processor executes all steps in the program described in any one of (Appendix 1) to (Appendix 4).

[0153] <Appendix 7> A system comprising one or more processors that execute all steps in the program described in any one of (Appendix 1) to (Appendix 4). [Explanation of symbols]

[0154] 1. System 10...Terminal device 120…Communications Department 13...Input device 14...Output device 15...Memory 16…Storage 19...Processor 20...Server 22...Communication IF 23...Input / output IF 25…Memory 26…Storage 29...Processor 30…Artificial satellite 40...AI system

Claims

1. A program to be executed by a computer having a processor and a memory, The program causes the processor to: receiving input from a user of information regarding a candidate area for generating carbon credits; acquiring satellite images of the candidate area for a predetermined period of time in the past based on the information; a step of analyzing the satellite images from a predetermined period of the past using a trained AI model, and outputting a determination result as to whether the candidate area satisfies predetermined conditions, including a first condition indicating the state of land cover at a specific point in time and a second condition indicating time-series changes in land cover over a predetermined period, which indicate that the candidate area is eligible for the creation of the carbon credits; A step of evaluating the reliability of a baseline set as an evaluation standard for the amount of carbon credits to be created in the candidate area and outputting the evaluation result; presenting the judgment result and the evaluation result to a user; A program that executes the following.

2. The program of claim 1 , wherein the candidate area is a foreign land.

3. The program of claim 1 further causes the processor to execute a step of estimating the amount of carbon credits to be created in areas of the candidate areas that are judged to be eligible for the creation of carbon credits based on the judgment result.

4. the carbon credits are forest credits; The program according to claim 3, wherein the estimation step estimates the amount of forest credits by taking into account analysis results regarding the amount of aboveground biomass in the area determined to be eligible.

5. 5. An information processing apparatus comprising: a control unit; and a storage unit, wherein the control unit executes all steps of the program according to claim 1.

6. A method executed by a computer having a processor and a memory, wherein the processor executes all the steps of the program of any one of claims 1 to 4.

7. A system comprising one or more processors that execute all steps in the program according to any one of claims 1 to 4.

Images