Server and method for facilitating target setting for sustainable finance

Abstract

To provide a server and a method for facilitating the target setting of sustainable finance.SOLUTION: A financial definition system for facilitating sustainable financial targeting, comprising: a memory configured to store instructions; a communication interface to receive a sustainable financial goal type; and a processor to execute the stored instructions, wherein the processor is configured to: identify one or more applicable products associated with the goal type; and generate a solution space comprising a plurality of possible solutions associated with the goal type based on the identified applicable products; A predetermined model is used to predict one or more achievable performance indicator values based on the identified applicable commodities, and at least one solution is selected from a plurality of possible solutions in a solution space based on the predicted performance indicator values to set a sustainable financial objective.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] Various embodiments of the present disclosure generally relate to servers and methods that facilitate sustainable finance goal setting. [Background technology]

[0002] Green finance refers to financing projects that have the potential to have a positive impact on the environment, such as reducing greenhouse gas emissions or promoting renewable energy. Green finance is experiencing rapid growth, but gaps require attention. Effectively defining sustainable finance goals can strike a balance between being ambitious enough to prevent greenwashing and achievable enough for realistic implementation. For example, when setting targets for building energy consumption, if the target consumption is too high (e.g., it can be achieved by simply switching 10% of lighting to LEDs), it may be considered greenwashing. Conversely, if the target is set too low (e.g., it may be difficult to achieve, even if a large number of expensive green products are introduced, or the lead time is very long), it may be considered unrealistic. Summary of the Invention [Problem to be solved by the invention]

[0003] Conventional technologies can collect information about physical infrastructure, identify goals, determine achievable actions and the maximum possible value of the goals, and allow users to edit the achievable values ​​of the goals. However, conventional technologies may only consider possible values ​​of the goals. This means that conventional technologies may not be able to predict important factors that may affect implementation, such as cost and lead time. Therefore, conventional technologies may not be able to set practical goals for sustainable finance. These goals may be achievable given reasonable resources (e.g., time and cost).

[0004] There is therefore a need to provide improved solutions that can be used to effectively define sustainable financing targets. [Means for solving the problem]

[0005] According to various embodiments, a server for facilitating sustainable finance goal setting comprises: a memory configured to store instructions; a communications interface configured to receive a sustainable finance goal type; and a processor configured to execute the stored instructions, wherein the processor identifies one or more applicable products associated with the goal type; generates a solution space including a plurality of possible solutions associated with the goal type based on the identified applicable products; predicts, using a predetermined model, one or more achievable performance indicator values ​​based on the identified applicable products; and selects at least one solution from the plurality of possible solutions in the solution space based on the predicted performance indicator values ​​to set a sustainable finance goal.

[0006] The processor included in the server is further configured to perform the steps of collecting information about the one or more infrastructures and information about the one or more constraints; identifying applicable product types based on the target type, the collected information about the one or more infrastructures, the collected information about the one or more constraints, and predetermined product type application criteria; and identifying one or more applicable products for each applicable product type based on the collected information about the one or more infrastructures, the collected information about the one or more constraints, and predetermined product application criteria.

[0007] According to various embodiments, a computer-readable medium is provided that includes program instructions that, when executed by one or more processors, cause the one or more processors to perform the method of any one of the above embodiments. The computer-readable medium may include a non-transitory computer-readable medium. [Effects of the Invention]

[0008] Various embodiments may provide a server and method that facilitates sustainable finance goal setting.

[0009] The foregoing summary is illustrative and not intended to be limiting. In addition to the exemplary aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description.

[0010] The novel features and characteristics of the present disclosure are set forth in the appended claims. However, the disclosure itself, its preferred modes of use, further objects and advantages thereof will best be understood by reference to the following detailed description of illustrative embodiments taken in conjunction with the accompanying drawings. One or more embodiments will now be described, by way of example only, with reference to the accompanying drawings in which like reference numerals refer to like elements. [Brief explanation of the drawings]

[0011] [Figure 1] FIG. 1 is an example block diagram illustrating a server for facilitating sustainable finance goal setting according to various embodiments. [Figure 2] FIG. 2 is an example block diagram illustrating the system architecture of a sustainable finance definition system according to various embodiments. [Figure 3] FIG. 3 is an example flow diagram illustrating the overall process of the sustainable finance definition system according to various embodiments. [Figure 4] FIG. 4 is an example data flow diagram illustrating entity relationships in a sustainable finance ecosystem according to various embodiments. [Figure 5] FIG. 5 is an example data flow diagram illustrating entity relationships with multiple stakeholders by stakeholder type, according to various embodiments. [Figure 6] FIG. 6 is an example flow diagram illustrating a sustainable finance definition process during the project design phase according to various embodiments. [Figure 7] FIG. 7 is an example flow diagram illustrating a sustainable finance definition process during project execution according to various embodiments. [Figure 8] FIG. 8 is an example flow diagram illustrating a process for generating a solution space according to various embodiments. [Figure 9] FIG. 9 is an example data flow diagram illustrating the flow of information for generating a solution space according to various embodiments. [Figure 10] FIG. 10 is an example flow diagram illustrating a process for predicting and identifying achievable performance index values ​​according to various embodiments. [Figure 11] FIG. 11 is an example data flow diagram illustrating a process for identifying achievable performance index values ​​and respective solutions, according to various embodiments. [Figure 12] FIG. 12 is an example data flow diagram illustrating the creation and use of a performance prediction module according to various embodiments. [Figure 13] FIG. 13 is an example data flow diagram illustrating a performance index value prediction workflow according to various embodiments. [Figure 14] FIG. 14 is an example flow diagram illustrating a process for generating difficulty levels according to various embodiments. [Figure 15] FIG. 15 is an example data flow diagram illustrating a process for generating a difficulty level for a solution based on a performance index value, according to various embodiments. [Figure 16] FIG. 16 is an example flow diagram illustrating an interest rate recommendation process according to various embodiments. [Figure 17] FIG. 17 is an example flow diagram illustrating a method for facilitating sustainable finance goal setting according to various embodiments. DETAILED DESCRIPTION OF THE INVENTION

[0012] Those skilled in the art will appreciate that any block diagrams in this disclosure are conceptual views of illustrative systems embodying the principles of the present invention. Similarly, any flowcharts, flow diagrams, state transition diagrams, pseudocode, etc., are substantially depicted on a computer-readable medium and illustrate various processes that may be executed by a computer or processor, whether or not a computer or processor is explicitly depicted.

[0013] Embodiments described below in the context of a method are equally valid for a server, and vice versa. Furthermore, it will be understood that the described embodiments may be combined, e.g., parts of one embodiment may be combined with parts of another embodiment.

[0014] It will be understood that the characteristics described in this disclosure for a particular device may hold for any device described in this disclosure. Furthermore, it will be understood that for any device described in this disclosure, not all of the components described may necessarily be present in the device, and that only some, but not all, of the components may be present.

[0015] It should be understood that when used in the following description, the terms "top," "upper," "lower," "below," "side," "rear," "left," "right," "front," "side," "upper," "lower," etc. are used for convenience and to aid in relative location or understanding, and are not intended to limit the orientation of any device, structure, or any portion of any device or structure. Furthermore, singular terms include plural references unless the context clearly dictates otherwise. Similarly, the word "or" is intended to include "and" unless the context clearly dictates otherwise.

[0016] The term "coupled" (or "coupled") in this disclosure may be understood as being electrically coupled or mechanically coupled, e.g., attached, fixed, or in loose contact, and it will be understood that both direct and indirect couplings (in other words, couplings without direct contact) may be provided.

[0017] In order that the invention may be readily understood and put into practice, various embodiments are illustrated in the drawings by way of example, and not by way of limitation.

[0018] According to various embodiments, a server for facilitating sustainable finance goal setting comprises: a memory configured to store instructions; a communications interface configured to receive a sustainable finance goal type; and a processor configured to execute the stored instructions, wherein the processor identifies one or more applicable products associated with the goal type; generates a solution space including a plurality of possible solutions associated with the goal type based on the identified applicable products; predicts, using a predetermined model, one or more achievable performance indicator values ​​based on the identified applicable products; and selects at least one solution from the plurality of possible solutions in the solution space based on the predicted performance indicator values ​​to set a sustainable finance goal.

[0019] The processor included in the server is further configured to perform the steps of collecting information about the one or more infrastructures and information about the one or more constraints; identifying applicable product types based on the target type, the collected information about the one or more infrastructures, the collected information about the one or more constraints, and predetermined product type application criteria; and identifying one or more applicable products for each applicable product type based on the collected information about the one or more infrastructures, the collected information about the one or more constraints, and predetermined product application criteria.

[0020] In some embodiments, the processor is further configured to generate a solution space including multiple possible solutions by finding possible combinations of the identified application products for each application product type.

[0021] In some embodiments, the processor is further configured to update the selected solution based on the predicted performance index value using a predetermined algorithm.

[0022] In some embodiments, the processor is further configured to generate one or more difficulty levels for the updated solution based on the predicted performance index value.

[0023] The processor is further configured to determine a sustainable finance interest rate for the updated solution based on the generated difficulty level.

[0024] The processor is further configured to provide the predicted performance index value, the generated difficulty level, and the determined interest rate.

[0025] In some embodiments, the processor is further configured to set sustainable financial goals based on the updated solutions.

[0026] The processor further collects information regarding the implementation of the project, predicts one or more new achievable performance indicator values ​​based on the collected information regarding the implementation of the project, generates one or more new difficulty levels for the updated solution based on the predicted new performance indicator values, and determines a new interest rate for sustainable financing for the updated solution based on the generated new difficulty levels.

[0027] In some embodiments, the predetermined model comprises a performance prediction and the predetermined algorithm comprises a multi-objective optimization algorithm.

[0028] According to various embodiments, there is provided a method for facilitating sustainable finance goal setting, the method comprising the steps of: receiving a sustainable goal type, executed by a processor of a server; identifying one or more applicable products associated with the goal type; generating a solution space including a plurality of possible solutions associated with the goal type based on the identified applicable products; predicting, using a predetermined model, one or more achievable performance indicator values ​​based on the identified applicable products; and selecting at least one solution from the plurality of possible solutions in the solution space based on the predicted performance indicator values ​​to set a sustainable finance goal.

[0029] The method of this embodiment further includes the steps of: collecting information about one or more infrastructures and information about one or more constraints, executed by a processor of the server; identifying applicable product types based on the target type, the collected information about the one or more infrastructures, the collected information about the one or more constraints, and predetermined product type application criteria; and identifying one or more applicable products for each applicable product type based on the collected information about the one or more infrastructures, the collected information about the one or more constraints, and predetermined product application criteria.

[0030] The method of this embodiment further includes generating a solution space containing multiple possible solutions by finding possible combinations of the identified application products for each application product type.

[0031] The method of this embodiment further includes updating the selected solution based on the predicted performance index values ​​using a predetermined algorithm.

[0032] The method of this embodiment further includes generating one or more difficulty levels for the updated solutions based on the predicted performance index values.

[0033] The method of this embodiment further includes determining sustainable financing for the updated solution based on the generated difficulty level.

[0034] The method of this embodiment further includes providing the predicted performance index value, the generated difficulty level, and the determined interest rate.

[0035] The method of this embodiment further includes setting sustainable finance goals based on the updated solutions.

[0036] The method of this embodiment further includes, executed by a processor of the server, collecting information regarding the implementation of the project; predicting one or more new achievable performance indicator values ​​based on the collected information regarding the implementation of the project; generating one or more new difficulty levels for the updated solution based on the predicted new performance indicator values; and determining a new interest rate for sustainable financing for the updated solution based on the generated new difficulty levels.

[0037] In some embodiments, the predetermined model comprises a performance prediction and the predetermined algorithm comprises a multi-objective optimization algorithm.

[0038] According to various embodiments, a computer program product is provided that includes instructions to cause a server of any one of the above embodiments to perform the method steps of any one of the above embodiments.

[0039] According to various embodiments, a computer readable medium having stored thereon the above computer program product is provided.

[0040] According to various embodiments, there is provided a data processing apparatus configured to perform the method of any one of the above embodiments.

[0041] According to various embodiments, a computer program element is provided that includes program instructions that, when executed by one or more processors, cause the one or more processors to perform the method of any one of the above embodiments.

[0042] FIG. 1 is an example block diagram illustrating a server 10 for facilitating the setting of sustainable finance goals (also referred to as "sustainable finance goals") according to various embodiments.

[0043] In some embodiments, the server 10 may be implemented by, for example, a server computer and may include a communication interface 11 , a processor 12 , and a memory 13 .

[0044] In some embodiments, memory 13 (also referred to as a "database (DB)" or "storage") may temporarily or permanently store input data and / or output data. In some embodiments, memory 13 may store program code that enables server 10 to execute methods (such as those described with reference to FIG. 17). In some embodiments, the program may be incorporated into a software development kit (SDK). Memory 13 may include internal memory and / or external memory of server 10. External memory may include external storage media, such as, but not limited to, a memory card, a flash drive, or web storage.

[0045] Communications interface 11 may enable one or more external systems to communicate with processor 12 over a network. In some embodiments, communications interface 11 may send signals to and / or receive signals from external systems over the network.

[0046] In some embodiments, processor 12 may include, but is not limited to, a microprocessor, analog circuitry, digital circuitry, mixed-signal circuitry, logic circuitry, an integrated circuit, a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), or any combination thereof. Other types of implementations of the respective functions, described in more detail below, may also be understood as processor 12.

[0047] In some embodiments, the processor 12 may be connectable to the communication interface 11. In some embodiments, the processor 12 may be disposed in data or signal communication with the communication interface 11.

[0048] The communication interface 11 may receive a sustainable finance goal type. The communication interface 11 may receive the goal type from a computing device associated with a user. For example, the user may include, but is not limited to, an investor and / or a capital seeker. The goal type may refer to a type of sustainable finance goal that the capital seeker commits to achieving in sustainable finance. Examples of goal types include, but are not limited to, greenhouse gas (GHG) emission reduction, water and resource efficiency, human well-being, circular economy practices, and innovation for sustainability (e.g., supporting immature or emerging technologies).

[0049] Processor 12 may identify one or more applicable products in association with the target type. To identify the one or more applicable products, processor 12 may collect information about one or more infrastructures (hereinafter referred to as infrastructure) (also referred to as “information about one or more basic infrastructures,” “infrastructure information,” or “infrastructure data”) and information about one or more constraints (also referred to as “other constraints” or “constraint data”). Based on the target type, the collected information about the one or more infrastructures, the collected information about the one or more constraints, and predetermined product type applicability criteria, processor 12 identifies applicable product types, and based on the collected information about the one or more infrastructures, the collected information about the one or more constraints, and predetermined product applicability criteria, processor 12 identifies one or more applicable products for each applicable product type.

[0050] The processor 12 may collect information about one or more infrastructures and information about one or more constraints associated with the goal type. The processor 12 may collect information about the one or more infrastructures and information about the one or more constraints from the memory 13. In some embodiments, constraints may refer to conditions or restrictions that must be met during the optimization process. Constraints may be additional requirements imposed on the solution space to ensure that the generated solution is feasible and consistent with practical considerations for the problem. Examples of constraints include, but are not limited to, a user-selected product type, a maximum lead time, a maximum cost, etc.

[0051] In some embodiments, processor 12 may further collect product type application criteria (also referred to as "predetermined product type application criteria" or "predefined product type application criteria"). In some embodiments, processor 12 may collect the product type application criteria from memory 13. In some embodiments, the product type application criteria may reference a table indicating applicable product types in association with a target type and infrastructure type (extracted from information about one or more infrastructures).

[0052] Processor 12 may identify applicable product types based on the goal type, information about one or more infrastructures, information about one or more constraints, and predetermined product type applicability criteria. In some embodiments, the goal type, information about one or more infrastructures, and information about one or more constraints may be used to look up a product type applicability criteria table to identify applicable product types. For example, if the goal type includes "GHG emission reduction" and the information about one or more infrastructures indicates that the infrastructure type is a commercial building, by checking the product type criteria table, processor 12 may find that an air conditioning system is applicable as an applicable product type (see FIG. 9 ).

[0053] In some embodiments, for each applicable product type, there may be multiple sub-product types. In some embodiments, optionally, processor 12 may identify applicable sub-product types for an applicable product type based at least on predetermined product type applicability criteria.

[0054] In some embodiments, processor 12 may further collect sub-product type applicability criteria (also referred to as “predetermined sub-product type applicability criteria” or “predefined sub-product type applicability criteria”). In some embodiments, processor 12 may collect the sub-product type applicability criteria from memory 13. Processor 12 may identify applicable sub-product types based on information about one or more infrastructures, information about one or more constraints, and the predetermined sub-product type applicability criteria. In some embodiments, the sub-product type applicability criteria may reference a table that lists applicable sub-product types for each applicable product type. For example, if an air conditioning system is identified as an applicable product type, processor 12 may need to further identify which types (i.e., sub-product types) of air conditioning systems are applicable. Information about one or more infrastructures (e.g., available outdoor space for air conditioning) and information about one or more constraints may be used to search the sub-product type applicability criteria table to identify applicable sub-product types (e.g., unit air conditioning systems) (as described with reference to FIG. 9 ).

[0055] In some embodiments, there may be multiple products for each applicable product type (or for each applicable sub-product type). In some embodiments, processor 12 may identify one or more applicable products for each applicable product type (or for each applicable sub-product type).

[0056] In some embodiments, processor 12 may further collect product application criteria (also referred to as "predetermined product application criteria" or "predefined product application criteria"). In some embodiments, processor 12 may collect product application criteria from memory 13. Processor 12 may identify one or more applicable products for each applicable product type based on the predetermined product application criteria. As an example, processor 12 may identify one or more applicable products for each applicable product type based on information about one or more infrastructures, information about one or more constraints, and the predetermined product application criteria. In some other embodiments, processor 12 may identify one or more applicable products for each applicable sub-product type based on at least predetermined product application criteria. As an example, processor 12 may identify one or more applicable products for each applicable sub-product type based on information about one or more infrastructures, information about one or more constraints, and predetermined product application criteria. For example, if unit air conditioning is identified as an applicable sub-product type, processor 12 must further identify which unit air conditioning systems (i.e., products) are applicable. Infrastructure information 307 and other constraints 308 (see FIG. 9 ) may be used to search the product application criteria to identify one or more applicable products.

[0057] In some embodiments, processor 12 may further collect product parameters. Processor 12 may collect the product parameters from memory 13. Processor 12 may generate a solution space including multiple possible solutions (also referred to as "initial solutions") related to the target type based on the identified applicable products. Processor 12 may generate a solution space including multiple possible solutions related to the target type for the product parameters. Processor 12 may generate a solution space including multiple possible solutions for each applicable product type (or for each applicable sub-product type) by finding possible solutions for the identified applicable products. A solution may refer to a set of sustainable activities (e.g., upgrading to a more energy-efficient air conditioning system, switching to LED (light-emitting diode) lighting, etc.) aimed at achieving a good performance index value (e.g., reducing carbon emissions). In some embodiments, the solution space may represent a collection of all possible solutions.

[0058] In some embodiments, processor 12 can use a predetermined model to predict one or more achievable performance indicator values ​​(also referred to as "target performance indicator values") based on the identified applicable products. A performance indicator (also referred to as a "performance indicator type") may refer to a quantifiable metric used to measure the performance of an entity, e.g., infrastructure. Performance indicators include one or more measurable targets used as one or more sustainable finance targets, such as, but not limited to, carbon emission intensity, water consumption reduction, workplace safety record, product or material recycling rate, number of emerging technologies used, etc. Performance indicators may also include other quantifiable indicators that are not necessarily used as sustainable finance targets but may affect the implementation of a solution, such as, but not limited to, cost, lead time, total new / immature products used, total man-hours required for installation and operation, required expertise, number of available suppliers, number of permits required, etc. In some embodiments, the performance indicator value is a numerical value of the performance indicator (e.g., "20 tonnes / m2"). 2"). Processor 12 may use AI (artificial intelligence) and / or system modeling to predict achievable performance indicator values ​​of a plurality of possible solutions using a predetermined model. For example, the predetermined model may include a performance prediction model. The performance prediction model may refer to a model that predicts achievable performance indicator values ​​of sustainability impacts that may affect sustainable finance-related information (the definition of sustainable finance).

[0059] The processor 12 may select at least one solution from a plurality of possible solutions in the solution space based on the predicted performance index values ​​and set a sustainable finance goal. The processor 12 may update the selected solution based on the predicted performance index values ​​using a predetermined algorithm. For example, the predetermined algorithm may include a multi-objective optimization algorithm. The processor 12 may set a sustainable finance goal based on the updated solution.

[0060] Processor 12 may generate one or more difficulty levels (also referred to as "difficulty scores") for the updated solution based on the predicted performance index values. Processor 12 may determine a sustainable financing interest rate for the updated solution based on the generated difficulty level. Processor 12 may determine the interest rate for the updated solution based on the generated difficulty level. A machine learning algorithm or other algorithm may be used to determine the interest rate for the updated solution based on the generated difficulty level and interest rate data. In some embodiments, processor 12 may provide the predicted performance index values, the generated difficulty level, and the determined interest rate to a user.

[0061] The processor 12 may collect information about the implementation of the project at a later stage, for example, during the construction phase of a building. The processor 12 may predict one or more new achievable performance indicators based on the information about the project implementation. The processor 12 may generate one or more new difficulty levels for the updated solution based on the predicted new performance indicator values. The processor 12 may determine a new interest rate for sustainable financing of the updated solution based on the generated new difficulty levels. The processor 12 may provide the new performance indicator values, the new difficulty levels, and the new interest rates to a user.

[0062] FIG. 2 is an example block diagram illustrating the system architecture of a sustainable finance definition system (hereinafter, finance definition system) 1001 configured by server 10 according to various embodiments.

[0063] The financial definition system 1001 may include a user and data interface 1002. The user and data interface 1002 may facilitate data exchange between the financial definition system 1001 and one or more users, including capital seekers 1009, investors 1010, other users 1011, and external data sources 1012.

[0064] The financial definition system 1001 may include a main controller 1004. The main controller 1004 may exercise a series of control by invoking other modules in the financial definition system 1001.

[0065] The financial definition system 1001 may include a data engine 1013. In some embodiments, the data engine 1013 can read and write data in the database 1003 and collect data from and send data to the capital seekers 1009, investors 1010, other users 1011, and external data sources 1012 via the user and data interface 1002. The user and data interface 1002 collects data from and sends data to other modules in the financial definition system 1001.

[0066] The financial definition system 1001 may include a database 1003. The database 1003 may store at least one of infrastructure data, user data, goal type data, solution space data, product data (also referred to as “information about one or more products” or “product information”), product type eligibility criteria, sub-product type eligibility criteria, product eligibility criteria, constraint data, performance index type data, performance index value data, identified solution data, user-defined solution data, difficulty data, interest rate data (also referred to as “interest rate information”), and recommended goal data. In some embodiments, sources of data may include, but are not limited to, public information obtained from at least one of an API (application programming interface) or web scraping, information provided by stakeholders such as financial institutions, capital seekers, and / or green product providers.

[0067] The financial definition system 1001 may include a goal identification module 1014. The goal identification module 1014 may identify one or more applicable goal types and one or more performance indicator types based on the user-selected financial instrument type, information about capital seekers (also referred to as “capital seeker information” or “capital seeker data”), and information about one or more infrastructures.

[0068] The financial definition system 1001 may include a performance prediction module 1005. The performance prediction module 1005 may use one or more performance prediction models to predict one or more achievable performance index values ​​among multiple possible solutions for an identified performance index type based on infrastructure data and product data. In some embodiments, the performance prediction models may be created using AI (artificial intelligence) techniques (e.g., machine learning), systems modeling, and / or an integration of AI techniques and systems modeling. The performance prediction models may include, but are not limited to, environmental impact prediction models, social impact prediction models, cost prediction models, lead time prediction models, and other models that may predict achievable performance index values.

[0069] The financial definition system 1001 may include an optimization module 1006. The optimization module 1006 may generate a solution space based on applicable goal types, infrastructure data, product data, and constraint data, use multi-objective optimization techniques based on predicted performance index values ​​from the performance prediction module 1005 to find (select) at least one solution that may lead to achievable performance, and update the selected solution. The optimization module 1006 may further include functionality for using sensitivity analysis to identify key (e.g., most influential) variables or products on performance index values. Sensitivity analysis may be used to identify key (e.g., most influential) variables or products on performance index values. In some embodiments, sensitivity analysis may refer to quantitative values ​​used to evaluate how changes in model or system input variables affect outputs or outcomes. In some embodiments, the identified key variables or products may be used to define green finance, e.g., determine eligible uses of proceeds, determine key variables or products to monitor in green finance, determine the risk of failure to achieve targets by assessing the volatility or uncertainty associated with each key variable or product, or determine sustainable finance goals by generating achievable target values ​​based on the available ranges of key variables or products (and the generated targets may be provided along with associated risks). For example, "eligible uses of proceeds" may refer to "permissible allocations of sustainable finance loans or funds that outline where those funds can be appropriately spent."

[0070] The financial definition system 1001 may include a difficulty level calculation module 1007. The difficulty level calculation module 1007 may calculate one or more difficulty levels of solutions (including updated solutions and / or user-defined solutions) based on predicted performance index values ​​using a weighted sum method or other algorithm.

[0071] The financial definition system 1001 may include an interest rate recommendation module 1008. The interest rate recommendation module 1008 may use machine learning algorithms or other algorithms to generate recommended interest rates for each of the solutions based on the difficulty and interest rate data calculated by the difficulty calculation module 1007. For example, if the difficulty calculated by the difficulty calculation module 1007 is high, the interest rate recommendation module 1008 may apply a high interest rate.

[0072] It will be appreciated that the functionality described above and in more detail below may be implemented in different modular configurations. In some embodiments, different modules may be merged into a single module that can perform all of the functionality of the different merged modules. In other embodiments, a single module may be split into multiple modules that can perform all of the functionality of the split modules.

[0073] The functionality described above and in detail below may be implemented in a user-interactive manner, where the user can enter data while getting a real-time display of information and instructions from the financial definition system 1001. In some other embodiments, the functionality described above and in detail below may be implemented in a non-user-interactive manner, where the user submits infrastructure information and the financial definition system 1001 can return all generated information.

[0074] FIG. 3 is an example flow diagram illustrating the overall process of the financial definition system 1001 according to various embodiments.

[0075] The overall process of the financial definition system 1001 may include a project design phase (also referred to as the "design phase") and a project execution phase (also referred to as the "execution phase"). The project design phase may refer to the phase of designing a building, and the project execution phase may refer to the phase (e.g., later stages) of constructing a building. In some embodiments, the project design phase includes steps 901 through 905, and the project execution phase may include step 906.

[0076] In step 901, a financial definition system 1001 can receive a user request and data to generate a sustainable financial goal definition. In step 902, the financial definition system 1001 can collect relevant data, identify one or more goal types and one or more performance indicator types, and generate a solution space. In step 903, the financial definition system 1001 predicts one or more achievable performance index values ​​for the performance index types identified by the performance prediction module 1005 and identifies (selects and updates) at least one solution that may lead to the achievable performance index values ​​using a multi-objective optimization algorithm. In step 904, the financial definition system 1001 may generate one or more difficulty levels using a difficulty calculation module 1007 and recommend an interest rate for the identified solution using an interest rate recommendation module 1008. For example, the higher the difficulty level, the higher the interest rate may be assigned. In step 905, the financial definition system 1001 can display the goal type, the identified solution, the target performance index value, the difficulty level, and the recommended interest rate on a display means (not shown). The display means is a device with a normal display function, such as an LCD, provided as part of the server 10, and connected to the user and data interface 1002 of the financial definition system 1001. In step 906, the financial definition system 1001 can recalculate the achievable performance index value through the performance prediction module 1005, regenerate the difficulty level through the difficulty calculation module 1008, and recommend the interest rate through the interest rate recommendation module 1008 based on the project execution information in the project execution phase.

[0077] FIG. 4 is an example data flow diagram illustrating entity relationships in a sustainable finance ecosystem according to various embodiments.

[0078] Investors 1203 may send sustainable finance information, interest rate ranges, and available sustainable finance products to the sustainable finance platform 1201. Capital demanders 1204, who are demanders of funds, may send infrastructure information to the sustainable finance platform 1201. The finance platform 1201 may send sustainable finance information and infrastructure information to the financial (SF) definition system 1001. Product providers 1205 may send product information to the financial definition system 1001. The finance definition system 1001 may generate and send at least one of one or more goal types, one or more possible solutions, one or more performance index values, one or more difficulty levels, and recommended interest rates to the financial platform 1201. The financial platform 1201 may send the infrastructure information, capital demander information, goal types, possible solutions, performance index values, difficulty levels, and recommended interest rates to the investors 1203. The financial platform 1201 may send the goal types, possible solutions, performance index values, difficulty levels, and recommended interest rates to the capital demanders 1204.

[0079] The financial platform 1201 may include, but is not limited to, investors 1203, capital seekers 1204, and other user types 1101 (not shown in FIG. 4 ). The financial platform 1201 may provide a user interface for exchanging data with one or more users. For example, the other user types 1101 may include, but are not limited to, auditors and governance bodies. In some embodiments, the financial platform 1201 may include other functionality that supports the sustainable finance process, including, but not limited to, stakeholder engagement, risk assessment, and ESG (environmental, social, and governance) monitoring and reporting. The financial platform 1201 may also provide functionality that supports sustainable finance definition by exchanging information with the sustainable finance definition system 1001.

[0080] Financial platform 1201 and financial definition system 1001 may be implemented as two separate systems that may exchange information via methods such as an API, in which case financial platform 1201 is also comprised of another server having a configuration similar to server 10 shown in Figure 1. In some other embodiments, financial platform 1201 and financial definition system 1001 may be implemented as a single integrated system in which financial definition system 1001 may be implemented as a module of the integrated system.

[0081] FIG. 5 is an example data flow diagram illustrating entity relationships with multiple stakeholders by stakeholder type according to various embodiments.

[0082] Multiple investors 1203 a , 1203 b can receive information about one capital buyer 1204 .

[0083] Multiple capital buyers 1204 a, 1204 b can receive information about a single investor 1203 .

[0084] A single investor 1203 can receive information about multiple capital seekers 1204a, 1204b.

[0085] A single capital buyer 1204 can receive information about multiple investors 1203a, 1203b.

[0086] A number of product providers 1205 a , 1205 b can provide information to the sustainable finance definition system 1001 .

[0087] FIG. 6 is an example flow diagram illustrating a sustainable finance definition process during the project design phase performed by the finance definition system 1001 according to various embodiments.

[0088] The project design phase may include steps 101 through 108, as shown in FIG.

[0089] In step 101 , the data engine 1103 can collect information about one or more infrastructures and information about the capital demanders 1009 from the capital demanders 1009 and store the collected information in the database 1003 . In step 102, the goal identification module 1014 may identify applicable goal types and performance indicators based on the financial product type selected by the user and one or more of infrastructure information and information about the capital demander 1009. In step 103, the optimization module 1006 generates a solution space containing multiple possible solutions based on the applicable goal type, information about one or more infrastructures, information about one or more commodities, and information about one or more constraints. In step 104, the performance prediction module 1005 may use the performance prediction model to predict achievable performance index values ​​of multiple possible solutions for the identified performance index type based on information about the one or more infrastructures and information about the one or more products. In step 105, the optimization module 1006 may use multi-objective optimization techniques based on the predicted performance index values ​​generated by the performance prediction module 1005 to find (identify (select, update)) at least one that may lead to achievable performance. In step 106, the difficulty calculation module 1007 may calculate one or more difficulties of the solutions (which may include the solutions identified in step 105 and / or user-defined solutions) based on the predicted performance index values ​​generated by the performance prediction module 1005 using a weighted sum method or other algorithm. In step 107, the interest rate recommendation module 1008 may use machine learning or other algorithms to generate recommended interest rates for each of the solutions based on the difficulty levels and interest rate information generated by the difficulty calculation module 1007. In step 108, the main controller 1004 can send the goal type, solution, goal performance indicator value, difficulty level, and recommended interest rate to the user interface for display on the display means to inform the user.

[0090] FIG. 7 is an example flow diagram illustrating a sustainable finance definition process during project execution performed by the finance definition system 1001 according to various embodiments.

[0091] The project execution phase may include steps 109 through 113, as shown in FIG.

[0092] In step 109, the data engine 1013 can collect project implementation information from the capital demand party 1009 and store the collected information in the database 1003. For example, the project implementation information can include, but is not limited to, one or more actual solutions implemented, such as, but not limited to, green products installed, design parameters, etc. In step 110, the performance prediction module 1005 may recalculate one or more achievable performance indicator values ​​based on the project performance information. In step 111, the difficulty calculation model 1007 may regenerate one or more difficulty levels based on the recalculated performance index values ​​using a weighted sum method or other algorithm. In step 112, the interest rate recommendation module 1008 may regenerate the recommended interest rates using machine learning algorithms or other algorithms based on the recalculated difficulty level and interest rate information. In step 113, the main controller 1004 can transmit the recalculated performance index value, the regenerated difficulty level, and the regenerated recommended interest rate to the user interface and display them on the display means for the user.

[0093] FIG. 8 is an example flow diagram illustrating the process of generating a solution space performed by the financial definition system 1001 in various embodiments.

[0094] In step 201, the optimization module 1006 may identify applicable product types based on predetermined product type application criteria, a target type, information about one or more infrastructures, and information about one or more constraints. In step 202, the optimization module 1006 may identify applicable sub-product types based on predetermined sub-product type applicability criteria, information about one or more infrastructures, and information about one or more constraints. In step 203, the optimization module 1006 may identify applicable products based on predetermined product application criteria, information about one or more infrastructures, and information about one or more constraints. In step 204, the optimization module 1006 generates a solution space based on the identified applicable products and product parameters, such as product efficiency.

[0095] FIG. 9 is an example data flow diagram illustrating the flow of information for generating a solution space performed by the financial definition system 1001 in various embodiments.

[0096] The goal type 306, the infrastructure information 307, and the other constraints 308 may be used to consult a product type applicability criteria table 309 to identify applicable product types. For example, if the goal type 306 includes "GHG emission reduction" and the infrastructure information 307 indicates that the infrastructure type is a commercial building, then by consulting the product type applicability criteria table 309, the processor 12 of the server 10 (as shown in FIG. 1 ) may find that an air conditioning system is applicable as an applicable product type.

[0097] For each applicable product type, there may be multiple sub-product types. For example, if an air conditioning system is identified as an applicable product type, the processor 12 may need to further identify what type of air conditioning system (i.e., sub-product type) is applicable. Infrastructure information 307 (e.g., outdoor space available for an air conditioning system) and other constraints 308 can be used to look up a sub-product type applicability criteria table 310 to identify applicable sub-product types (e.g., unit-type air conditioning systems).

[0098] It will be appreciated that for a particular product type, there is no further classification of sub-product types. Instead, a particular product type may directly include multiple products. In this case, the sub-product type applicability criteria 310 and applicable sub-product types 302 may not exist, and applicable product types 301 may be used directly to generate applicable products 303.

[0099] For each applicable sub-product type, there may be multiple products. For example, if unit air conditioning systems are identified as an applicable sub-product type, processor 12 may need to further identify which unit air conditioning systems (i.e., products) are applicable. Infrastructure information 307 and other constraints 308 may be used to search product application criteria 311 to identify applicable products 303.

[0100] The solution space 304 can be created by finding all possible applicable products under each applicable product type. For example, for air conditioning systems, AAA and BBB air conditioning systems can be applicable, and for lighting systems, CCC and DDD lighting can be applicable. As an example, solution #1 can be AAA+CCC, solution #2 can be AAA+DDD, etc.

[0101] Product parameters 305, for example product efficiency, may be attached to each product in the solution space 304. The product parameters 305 can be used to predict performance index values ​​for the product using the performance prediction module 1005.

[0102] FIG. 10 is an example flow diagram illustrating a process performed by a financial definition system 1001 to predict and identify achievable performance indicator values ​​in accordance with various embodiments.

[0103] Figure 10 shows the process of predicting and identifying achievable performance indicators by employing a multi-objective optimization algorithm, such as NSGA-2. The goal of this process is to identify achievable performance indicator values ​​(carbon emissions, life cycle costs, lead times, etc.) and the corresponding solutions to achieve them.

[0104] In step 401, the optimization module 1006 may initialize a population (e.g., size N) by randomly selecting N individuals from the solution space 304 as a first generation. In some embodiments, a population may refer to a set of solutions. In some embodiments, a "solution space" includes all possible solutions, and a "population" may include only a subset of the possible solutions. Here, a "population" may refer to a set of candidate solutions or individuals that are iteratively evaluated and evolved as follows: Finding an optimal solution for selecting a solution. In some embodiments, the solution may include a combination of green products, such as an AAA air conditioning system and CCC lights. In step 402, the performance prediction module 1005 may use a performance prediction model to predict achievable performance index values ​​for the initial population based on infrastructure data and product data. In step 403, the optimization module 1006 ranks the initial population by assigning a rank value to each individual based on the predicted performance index value. In step 404, the optimization module 1006 selects a parent population from the current population based on the rank values ​​and performs crossover and mutation on the parent population to create a child population. Crossover may refer to combining genetic information from two parent individuals to generate one or more offspring. Mutation involves making small, random changes to an individual's genetic information and introducing new genetic material into the population. In step 405, the performance prediction module 1005 can predict performance index values ​​for the child population using a performance prediction model based on the infrastructure data and the product data. In step 406, the optimization module 1006 can evaluate the parents and their offspring by combining the parent and offspring populations, ranking them according to their predicted performance index values, and selecting the top N individuals as a new population to form the next generation. In step 407, the optimization module 1006 may check whether a stopping criterion is met. Stopping criteria may include a maximum number of iterations, satisfaction of a predetermined objective function value, identification of stagnation or little improvement, etc. If the stopping criterion is not met, steps 404 through 406 are repeated until the stopping criterion is met. If the stopping criteria are met, then in step 408, the optimization module 1006 may output the final (latest) population (and, in some embodiments, previous populations) and their performance index values ​​as the identified solutions and achievable performance index values, and stop.

[0105] It can be understood that other optimization algorithms, such as MOEA / D (Multi-Objective Evolutionary Algorithm Based on Decomposition), SPE2 (Strong Pareto Evolutionary Algorithm 2), PAES (Pareto Archive Evolutionary Strategy), etc., can be applied as an alternative to the presented algorithm in Figure 10.

[0106] The prediction and optimization results can also be used to conduct sensitivity analyses to identify variables or products that most affect performance indicator values. The identified variables or products may be used to define green finance, for example, to determine which green activities or products qualify for green finance, e.g., green loan revenue utilization. The identified variables or products can be used to determine key variables or products to monitor. The identified variables or products can be used to determine the risk of not achieving targets by assessing the variability or uncertainty associated with each key variable or product, such as fluctuations in resource availability, market conditions, or technological changes. The identified variables or products can be used to determine sustainable finance targets by generating achievable target values ​​based on the range of available key variables or products. The generated target values ​​can be provided along with associated risks.

[0107] FIG. 11 is an example data flow diagram illustrating the process performed by financial definition system 1001 (in step 105 of FIG. 6) to identify achievable performance index values ​​and respective solutions, according to various embodiments.

[0108] An initial population 701 of multiple solutions (individuals) may be ranked based on a predicted performance index value A (e.g., life cycle cost (LCC)) and a predicted performance index value B (e.g., carbon emissions) to form a ranked initial population 702. From the ranked initial population 702, a parent population may be selected based on the ranking and used to create a child population 703. The parent and child populations 703 may be combined and ranked to select a new generation population 704.

[0109] FIG. 12 is an example data flow diagram illustrating the creation and use of performance prediction module 1005 performed by financial definition system 1001 in accordance with various embodiments.

[0110] Existing labeled data, including existing infrastructure data (e.g., infrastructure characteristics and performance index values) and existing product data (e.g., product characteristics and performance index values) from database 1101, may be used to create a performance prediction model in step 1102. In step 1102, the performance prediction module 1005 may be generated using one or any combination of machine learning based models and system models (e.g., building performance simulation engines, CFD (computational fluid dynamics) engines, daylighting simulation engines, etc.).

[0111] The generated performance prediction module 1005 can receive input data 1103 including target infrastructure data (e.g., infrastructure characteristics) and product data (e.g., product characteristics) and output performance index values ​​1104. The performance index values ​​1104 can include, but are not limited to, environmental impacts (e.g., carbon emissions), social impacts (e.g., health and well-being, affordability), lead time, cost, etc.

[0112] FIG. 13 is an example data flow diagram illustrating a performance index value prediction workflow performed by the financial definition system 1001 according to various embodiments.

[0113] The target infrastructure data and target product data may be obtained from the user and data interface 1002 and the database 1003, respectively, by the data engine 1013. The data engine 1013 may process the data so that it can be received by the performance prediction module 1005.

[0114] The performance prediction module 1005 may include only the machine learning model 1301, may include only the system model 1302, or may include both the machine learning model 1301 and the system model 1302. When the performance prediction module 1005 includes both the machine learning model 1301 and the system model 1302, the machine learning model 1301 and the system model 1302 can exchange intermediate results (such as cooling loads, air conditioning system efficiency, etc.) and use them to generate a final output.

[0115] The machine learning model 1301 may be a black-box model that takes in input data and uses complex algorithms to generate predictions without providing explicit insight into its inner workings. The machine learning model 1301 may take inputs such as infrastructure characteristics (e.g., window-to-wall ratio) or product characteristics (e.g., chiller efficiency) and generate outputs such as energy consumption, hours of sunshine, and hours of natural ventilation.

[0116] The system model 1302 may be a white-box model, meaning that its internal mechanisms and components may be transparent. The system model 1302 may be designed with explicit and interpretable rules or equations that represent the relationships between different components or variables in the system. The system model 1302 may take, for example, infrastructure characteristics (e.g., total floor area) and product characteristics (e.g., LED efficiency) as inputs and generate outputs such as energy consumption, sunshine hours, natural ventilation hours, and costs.

[0117] The output generator 1303 can receive the output of the machine learning model 1301 and the system model 1302 and process the data so that it can be received by the optimization module 1006 as a performance index value 1104 .

[0118] It will be appreciated that when performance prediction module 1005 is used to estimate the cost of a solution, the calculation also incorporates factors such as carbon taxes and carbon credits, which may then be factored into either supplementing or offsetting the overall cost.

[0119] FIG. 14 is an example flow diagram illustrating a process performed by the financial definition system 1001 to generate difficulty levels according to various embodiments.

[0120] In step 501, the difficulty calculation module 1007 can assign a score (E1) to each solution based on the solution's impact on sustainability. Different methods, such as min-max normalization or ranking methods, can be used to assign the score E1. In min-max normalization, the sustainability impact values ​​can be normalized to a predefined range, e.g., [0, 1]. This can be done by subtracting the minimum value identified in the solution space and dividing it by the range, i.e., the difference between the maximum and minimum values ​​identified in the solution space. The ranking method may rank the identified sustainability impact values ​​in the solution space from highest to lowest (or vice versa). Points are assigned based on the ranking, with the highest rank receiving the highest score. Similarly, in steps 502, 503, and 504, scores C1, T1, and O1 may be assigned based on the solution's cost, solution's lead time, and other solution performance indicator values, respectively. Other performance indicators include, but are not limited to, the total new / immature products included, the total man-hours required for installation and operation, the expertise required, the number of suppliers available, and the number of permits required. In some embodiments, the order of steps 501 through 504 may be changed. In some embodiments, some of steps 501 through 504 may be omitted.

[0121] The "solutions" in steps 501 through 504 may include solutions identified in step 105 (as shown in FIG. 6) and user-defined solutions. In some embodiments, for user-defined solutions, performance prediction module 1005 may predict performance index values ​​based on solution information defined by the user, such as a product selected by the user. In some embodiments, for solutions identified in step 105 (as shown in FIG. 6), performance index values ​​may be extracted from the final population (or, in some embodiments, the previous population) in step 408 (as shown in FIG. 10).

[0122] In step 505, the difficulty calculation module 1007 may generate one or more difficulty levels for the solutions using a predefined method, such as assigning a weight to each indicator (i.e., the solution's sustainability impact, the solution's cost, the solution's lead time, and other performance indicator values) and using a weighted sum to calculate an aggregate difficulty level.

[0123] In step 506, the difficulty level calculation module 1007 can output the generated difficulty level.

[0124] It will be appreciated that the difficulty calculation module 1007 may also generate one or more difficulty levels for different target metric values, e.g., different values ​​of carbon emissions. In this embodiment, the difficulty level target metric value may be calculated using a predefined method, such as the average difficulty level of all identified solutions that have the potential to achieve the target metric value.

[0125] FIG. 15 is an example data flow diagram illustrating an example process performed by the financial definition system 1001 to generate a solution difficulty level based on a performance index value (in step 106 of FIG. 6) in accordance with various embodiments.

[0126] Table 801 may be an example of solutions (including solutions identified in step 105 (as shown in FIG. 6) and user-defined solutions) and their respective performance index values.

[0127] Tables 802A, 802B, and 802C show the assigned scores for each solution based on cost (Metric A), sustainability impact (Metric B), and lead time (Metric C), respectively.

[0128] The difficulty level in table 804 may be generated based on the assigned scores in tables 802A, 802B, and 802C and the predefined weights of each indicator 803 using a weighted sum method. For example, the difficulty level of solution #13 is calculated based on the following formula: 1 (value of indicator A) * 0.05 (weight of indicator A) + 10 (value of indicator B) * 0.8 (weight of indicator B) + 1 (value of indicator C) * 0.05 (weight of indicator C) + ... = 8.2 FIG. 16 is an example flow diagram illustrating the process of recommending interest rates (in step 107 of FIG. 6) performed by the financial definition system 1001 in various embodiments.

[0129] The interest rate recommendation module 1008 can obtain one or more of the solution difficulty (and in some embodiments, the difficulty of the target index value) and interest rate information in steps 601 and 602, respectively. Examples of interest rate information may be data on existing green finance definitions and their interest rates, and interest rate ranges defined by investors. In some embodiments, interest rates may be obtained from multiple sources, for example, an internal database 1003, inventor input via user and data interface 1002, external data sources 1012 using APIs or web scraping (as shown in FIG. 2).

[0130] In step 603, the interest rate recommendation module 1008 may generate a recommended interest rate using predefined interest rate recommendations based on the difficulty level and interest rate information. In some embodiments, one or more methods may be used for the interest rate recommendation. Example methods include: Example 1: Machine Learning Algorithms -Training machine learning models using historical data containing difficulty and corresponding interest rates. Example 2: Multi-factor model -Utilize a multi-factor model that takes into account factors such as difficulty, market environment, and risk profile. -Assign appropriate weights to each factor and calculate interest rates. Example 3: Market benchmarking -Analyze the interest rates offered in the market for similar goals and the difficulty of similar goals. - Setting benchmark interest rates based on market trends and competition. Example 4: Expected loss method Assessing the expected losses associated with implementing green solutions Consider the difficulty of green solutions. -Set an interest rate that will cover expected losses and provide a reasonable return on investment.

[0131] In step 604, the interest rate recommendation module 1008 outputs a recommended interest rate.

[0132] FIG. 17 is an example flow diagram illustrating a method for facilitating sustainable finance goal setting according to various embodiments.

[0133] The method may include receiving 911 a sustainable finance goal type.

[0134] The method may include identifying 912 one or more applicable products associated with the target type.

[0135] The method may include generating 913 solutions based on the identified relevant products, the solutions including multiple possible solutions related to the goal type.

[0136] The method may include using a predetermined model to predict 914 one or more achievable performance index values ​​based on the identified products of interest.

[0137] The method may include a step 915 of selecting at least one solution from a plurality of possible solutions in a solution space based on the predicted performance index values ​​and setting a sustainable finance goal.

[0138] As described above, various embodiments may propose a system and method that can be used for the effective definition of sustainable financial goals. Stakeholders, including but not limited to investors, capital seekers, and governance bodies, can benefit from this solution by using the system and method to easily and effectively define sustainable financial goals.

[0139] Various embodiments may be applied to a definition of sustainable finance that includes various types of systems, such as infrastructure (e.g., buildings, manufacturing facilities, industrial facilities, transportation systems) and products (e.g., automobiles, electronic devices).

[0140] Various embodiments can be used to set sustainable finance goals, including, but not limited to, goals related to any aspect of sustainability, such as environmental and social aspects. Examples of environmental aspects include energy consumption, water use, waste generation, greenhouse gas (GHG) emissions (e.g., operational carbon, upfront carbon, embedded carbon, whole-life carbon), pollution, biodiversity impact, etc. Examples of social aspects include human well-being, health, comfort, safety, etc.

[0141] While embodiments of the present invention have been particularly shown and specific embodiments described, it should be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. The scope of the present invention is therefore indicated by the appended claims, and all changes that come within the meaning and range of equivalents of the claims are therefore intended to be embraced. It will be understood that common numerals used in the associated drawings refer to components serving similar or identical purposes. As used in this disclosure, the singular, "a," "an," "the," and "the" are intended to include the plural unless the context clearly indicates otherwise. It will be further understood that, as used in this disclosure, the term "composition" specifies the presence of stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. Unless otherwise specified, the term "some" refers to one or more. Combinations such as "at least one of A, B, or C," "one or more of A, B, or C," "at least one of A, B, and C," "one or more of A, B, and C," "A, B, C, or any combination thereof" include any combination of A, B, and / or C, and may include multiples of A, multiples of B, or multiples of C. Specifically, combinations such as "at least one of A, B, or C," "one or more of A, B, or C," "at least one of A, B, and C," "one or more of A, B, and C," "A, B, C, or any combination thereof" may be A only, B only, C only, A and B, A and C, B and C, or A and B and C, and any such combination may include one or more members or components of A, B, or C. All structural and functional equivalents to the elements of the various embodiments described throughout this disclosure that are known, or that later become known, to those of ordinary skill in the art are expressly incorporated by reference into this disclosure and are intended to be encompassed by the claims.Moreover, nothing disclosed in this disclosure is intended to be generic, regardless of whether it is expressly recited in such a claim. The words "module," "mechanism," "element," and "apparatus" are not substitutes for the word "means." Thus, no element of a claim may be construed as a means-plus-function unless that element is expressly recited using the following language:

[0142] Finally, the language used in this disclosure has been chosen primarily for ease of reading and instruction, and may differ from that chosen to define or define the scope of the invention. Accordingly, the scope of the invention is not intended to be limited by this detailed description, but rather by any claims filed based hereon. Accordingly, this disclosure of embodiments of the invention is intended to illustrate, but not limit, the scope of the invention, which is set forth in the following claims.

[0143] While various aspects and embodiments have been described in this disclosure, other aspects and embodiments will be apparent to those skilled in the art. The various aspects and embodiments described in this disclosure are for illustrative purposes and are not intended to be limiting. The actual scope is indicated by the following claims. [Explanation of symbols]

[0144] 10 Financial Definition System 11 Communication Interface 12 Memory 1002 User and Data Interface 1003 database 1004 Main Controller 1005 Performance Prediction Module 1006 Optimization Module 1007 Difficulty Calculation Module 1008 Interest Rate Recommendation Module 1009 Capital demander 1010 Investor 1011 other users 1012 External Data Sources

Claims

1. 1. A server for facilitating sustainable finance goal setting, comprising: The server a memory configured to store instructions; a communications interface configured to receive a sustainable finance goal type; a processor configured to execute the stored instructions; The processor: Identifying one or more applicable products associated with the goal type; generating a solution space including a plurality of possible solutions related to the goal type based on the identified applicable products; using the predetermined model to predict one or more achievable performance index values ​​based on the identified applicable products; and selecting at least one solution from a plurality of possible solutions in the solution space based on the predicted performance index values ​​to set a sustainable finance goal. server.

2. 2. The server of claim 1, The processor further comprises: collecting information about one or more infrastructures and information about one or more constraints; Identifying applicable product types based on the target type, the collected information regarding the one or more infrastructures, the collected information regarding the one or more constraints, and predetermined product type applicability criteria; Identifying one or more applicable products for each applicable product type based on the collected information about the one or more infrastructures, the collected information about the one or more constraints, and predetermined product applicability criteria. server.

3. 3. The server according to claim 2, The processor: and generating a solution space including a plurality of possible solutions by determining possible combinations of the identified application products for each application product type. server.

4. 4. The server according to claim 3, The processor further comprises: and updating the selected solution based on the predicted performance index values ​​using a predetermined algorithm. server.

5. 5. The server according to claim 4, The processor: and further configured to generate one or more difficulty levels for the updated solution based on the predicted performance index value. server.

6. 6. The server according to claim 5, The processor further comprises: Determine the sustainable financing interest rate for the updated solution based on the generated difficulty. server.

7. 7. The server according to claim 6, The processor: Provides predicted performance index values, generated difficulty levels, and determined interest rates server.

8. 8. The server according to claim 7, The processor: Setting sustainable finance goals based on updated solutions server.

9. 9. The server according to claim 8, The processor further comprises: Collect information on project implementation, predicting one or more achievable new performance indicator values ​​based on information collected about project implementation; generating one or more new difficulty levels for the updated solutions based on the new predicted performance index values; Based on the new difficulty generated, determine the new interest rate for sustainable financing of the updated solution. server.

10. 10. The server according to claim 4, The predetermined model includes a performance prediction model, and the predetermined algorithm includes a multi-objective optimization algorithm. server.

11. 1. A method for facilitating sustainable finance goal setting executed by a server, comprising: The processor of the server Get the sustainable finance goal type, Identifying one or more applicable products associated with the goal type; generating a solution space containing a plurality of possible solutions related to the goal type based on the identified products; using the predetermined model to predict one or more achievable performance index values ​​based on the identified product; selecting at least one solution from a plurality of possible solutions based on the predicted performance index values; Setting sustainable finance goals method.

12. 12. The method of claim 11, The processor: collecting information about one or more infrastructures and information about one or more constraints; Identifying applicable product types based on the target type, the collected information regarding the one or more infrastructures, the collected information regarding the one or more constraints, and predetermined product type applicability criteria; Identifying one or more applicable products for each applicable product type based on the collected information regarding the one or more infrastructures, the collected information regarding the one or more constraints, and predetermined product applicability criteria. method.

13. 13. The method of claim 12, generating a solution space containing a plurality of possible solutions by determining possible combinations of the identified application products for each application product type. method.

14. 14. The method of claim 13, Update the selected solution based on the predicted performance index values ​​using a predetermined algorithm. method.

15. 15. The method of claim 14, generating one or more difficulty levels for the updated solution based on the predicted performance index values. method.

16. 16. The method of claim 15, and determining a sustainable finance interest rate for the updated solution based on the generated difficulty. method.

17. 17. The method of claim 16, and providing a predicted performance index value, a generated difficulty level, and a determined interest rate. method.

18. 18. The method of claim 17, Further including setting sustainable finance targets based on updated solutions. method.

19. 20. The method of claim 18, The processor: Collect information on project implementation, Predicting one or more achievable new performance indicator values ​​based on information collected about project implementation; generating one or more new difficulty levels for the updated solution based on the new predicted performance index values; Determine the new interest rate for sustainable financing for the updated solution based on the new difficulty level generated. method.

20. 20. The method of any one of claims 14 to 19, the predetermined model includes a performance prediction model, and the predetermined algorithm includes a multi-objective optimization algorithm; method.

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