Systems and methods for evaluating the performance of green facilities

Abstract

A method for evaluating the performance of a green facility, comprising: generating a green function dataset using operational information collected from at least one green facility, green function information collected from at least one technology provider, and green facility information collected from at least one capital raiser; and predicting performance parameters of the proposed green facility using a performance prediction model based on input of the green facility information of the proposed green facility and the generated green function dataset to evaluate the performance of the proposed green facility.

Description

Technical Field

[0001] The present disclosure relates to a method and system for benchmarking / evaluating the performance of green facilities. The evaluation results can be used for benchmarking.

Background Art

[0002] The following discussion of the background is intended only to facilitate understanding of the present disclosure. It should be understood that this discussion does not constitute an admission that any of the referenced materials was published in any jurisdiction, was publicly known, or formed part of the common general knowledge of a person skilled in the art as of the priority date of the present application.

[0003] Following the Paris Agreement and the adoption of the Sustainable Development Goals (SDGs), green / sustainable finance is recognized as indispensable for financing climate change mitigation measures.

Summary of Invention

Problem to be Solved by the Invention

[0004] There are several barriers that hinder the growth of green finance. For capital seekers (capital raisers), it is difficult to define, select and verify eligible investments due to the lack of reliable data and methods. For investors, it is difficult to evaluate the greenness based on the customized definitions and selection criteria set by capital raisers.

[0005] Furthermore, depending on the green finance product, different consultants and external evaluators may be involved in assessing the impact on sustainability. In some cases, green finance products may involve different consultants and external evaluators engaged at different stages of green finance, such as the definition, selection and verification stages. This can result in inconsistencies and subjectivity.

[0006] Existing energy efficiency rating programs / software used by companies seeking capital to demonstrate sustainable performance include LEED (Leadership in Energy and Environmental Design), Singapore's Green Mark, and IFC's EDGE (Excellence in Design for Greater Efficiencies). Each of these rating systems has a different evaluation scheme, making it difficult for investors to compare projects across different systems. Because these rating schemes do not integrate features or characteristics to support green finance, none of them can attract all or the majority of capital seekers. One significant technical challenge for integrating these existing programs with green finance is the need for more transparent and up-to-date information on green features and existing facilities to produce reliable forecasts and benchmark results. For example, EDGE assumes a baseline and determines the selection and eligibility of green facilities based on market research and data collection conducted by agencies in multiple countries, as well as national and local building performance standards. For EDGE to remain relevant and effective, its assumptions and green performance information need to be updated in response to market changes. However, data collection and processing are time-consuming, and the data is hierarchical, resulting in low transparency and a low likelihood of being updated with the latest information as green technologies and green facilities rapidly evolve.

[0007] Therefore, it is necessary to develop a technical evaluation system to standardize the impact of green facilities and benchmark their performance.

[0008] This disclosure aims to provide a method and system for generating and utilizing reliable and up-to-date datasets for green facility forecasting and benchmarking by collecting and processing green facility information from various stakeholders, including but not limited to green facilities, capital providers (or their agents, e.g., consultants), and green technology providers.

[0009] Technical solutions are provided in the form of systems and methods for evaluating the performance of green facilities. [Means for solving the problem]

[0010] A method for evaluating the performance of a green facility includes the steps of: generating a green function dataset using operational information collected from at least one green facility, green function information collected from at least one technology provider, and green facility information collected from at least one capital provider; and inputting the proposed green facility information and the generated green function dataset into a performance prediction model and using the performance prediction model to predict the performance parameters of the proposed green facility, wherein the performance parameters are output from the performance prediction model to evaluate the performance of the proposed green facility.

[0011] According to various embodiments, the method may further include the steps of: generating a benchmark dataset using collected operational information, collected green function information, green facility information, and a generated green function dataset; calculating at least one benchmark for the proposed green facility using a benchmark calculation model based on the proposed green facility information and the generated benchmark dataset; and obtaining benchmark results for evaluating the performance of the proposed green facility by comparing the predicted performance parameters of the proposed green facility with the calculated benchmark.

[0012] Depending on the various embodiments, this may further include recommending green finance products to capital raisers and / or recommending green facilities to investors based on the obtained forecast and benchmark results.

[0013] Depending on the various embodiments, the process may further include evaluating and selecting green projects to use and / or implement based on the obtained forecast and benchmark results, as well as monitoring and verifying the use of the revenue.

[0014] Depending on the various embodiments, the verification of the achieved performance of the proposed green facility may further include: calculating at least one updated benchmark for the proposed green facility based on updated information and a generated benchmark dataset for the proposed green facility; and comparing the calculated updated benchmark with the achieved performance of the proposed green facility.

[0015] This method may further include: determining the type of benchmark based on the Key Point Indicators, Sustainability Performance Targets, and / or project eligibility criteria determined in the green finance definition; if the benchmark type is self-referential, calculating at least one benchmark using a performance prediction model based on historical performance data of existing facilities, information on existing facilities, and information on the proposed green facility; if the benchmark type is not self-referential, determining at least one benchmark using a benchmark calculation model based on the generated benchmark dataset and / or information on the proposed green facility; predicting the performance parameters of the proposed green facility using a performance prediction model based on information on the proposed green facility, performance data of existing facilities, and a green functionality dataset; and obtaining benchmark results by comparing the predicted performance parameters of the proposed green facility with at least one benchmark.

[0016] According to various embodiments, the performance prediction model can be a system model (such as a simplified engineering calculation or a complex computer simulation model) and / or a machine learning-based model.

[0017] In another embodiment, a system for evaluating the performance of a green facility comprises a green facility interface configured to collect operational information from at least one green facility, a user interface configured to connect with one or more users, and a processor connected to the green facility interface and the user interface, the processor generating a green function dataset using operational information collected from at least one green facility, green function information collected from at least one technology provider, and green facility information collected from at least one capital provider, and predicting the performance parameters of a proposed green facility using a performance prediction model based on the proposed green facility information and the generated green function dataset as inputs to evaluate the performance of the proposed green facility.

[0018] According to various embodiments, the processor may further be configured to generate a benchmark dataset using collected operational information, collected green function information, green facility information, and a generated green function dataset; to calculate at least one benchmark for the proposed green facility using a benchmark calculation model based on the proposed green facility information and the generated benchmark dataset; and to obtain benchmark results for evaluating the performance of the proposed green facility by comparing the predicted performance parameters of the proposed green facility with the calculated benchmark.

[0019] According to various embodiments, the system may further include a data storage device configured to store data, the data including operational information, green function information, green facility information, generated green function datasets, generated benchmark datasets, and prediction and benchmark results.

[0020] Depending on the specific implementation, data storage can be centralized or distributed.

[0021] Depending on the specific implementation, data storage may also be done using blockchain technology.

[0022] According to various embodiments, the processor is further configured to recommend green finance products to capital raisers and / or green facilities to investors based on the obtained prediction and benchmark results.

[0023] According to various embodiments, the processor further evaluates and selects green projects to use and / or implement based on the obtained predictions and benchmark results, and monitors and verifies the use of the revenue.

[0024] According to various embodiments, the processor may further be configured to verify the achieved performance of the proposed green facility, and in verifying the achieved performance, the processor is configured to calculate at least one updated benchmark of the proposed green facility based on updated information of the proposed green facility and a generated benchmark dataset, and to compare the calculated updated benchmark with the achieved performance of the proposed green facility.

[0025] According to various embodiments, the processor determines a type of benchmark based on key performance indicators, sustainability performance targets, and / or project eligibility criteria determined in green finance definitions, and if the type of benchmark is for against-self, calculates at least one benchmark using a performance prediction model based on historical performance data of an existing facility, information of the existing facility, and information of a proposed green facility. If the type of benchmark is not for against-self, the processor determines at least one benchmark using a benchmark calculation model based on a generated benchmark dataset and / or information of the proposed green facility, predicts performance parameters of the proposed green facility using a performance prediction model based on the information of the proposed green facility, performance data of the existing facility and a green function dataset, and compares the predicted performance parameters of the proposed green facility with the at least one benchmark to obtain a benchmark result. Effect of the Invention

[0026] According to various embodiments, the performance prediction model may be a system model (such as a simplified engineering calculation or a complex computer simulation model) and / or a machine learning-based model.

[0027] The present disclosure provides a system that can ensure consistent, reliable and effective prediction and benchmarking methods are used across different stages of different projects implemented by different sustainability engineering experts.

[0028] A common / standardized technical evaluation system for quantifying impacts and benchmarking the performance of green facilities helps ensure the credibility of sustainable financial products. It also helps green investors identify available investment opportunities and helps capital raisers access discounted green finance.

[0029] The above overview is illustrative and not intended to be limiting. Further embodiments, features, and characteristics beyond those described above will become apparent from the drawings and the detailed description below. Novel features and characteristics of the present disclosure are described in the attached claims. However, the present disclosure itself, more preferred uses, further purposes and its advantages will be best understood by referring to the following detailed description of exemplary embodiments together with the attached drawings. Hereinafter, one or more embodiments will be described purely illustratively with reference to the attached drawings. The same reference numerals indicate the same elements. [Brief explanation of the drawing]

[0030] [Figure 1] Figure 1 shows an example of a system for benchmarking / evaluating the performance of green facilities, according to some embodiments of the present disclosure. [Figure 2A] Figure 2A shows an example of a system for benchmarking / evaluating the performance of green facilities, according to some embodiments of the present disclosure. [Figure 2B] Figure 2B shows an example of a system for benchmarking / evaluating the performance of green facilities, according to some embodiments of the present disclosure. [Figure 3A] Figure 3A shows an example of a blockchain implementation of a system for benchmarking / evaluating the performance of green facilities. [Figure 3B] Figure 3B shows an example of a smart contract with the contract name "Performance Prediction". [Figure 4] Figure 4 shows an example of an integrated system combining a green facility performance benchmark and evaluation system with a stock exchange system. [Figure 5A] Figure 5A shows an example of a method for benchmarking / evaluating the performance of a green facility according to some embodiments of the present disclosure. [Figure 5B]Figure 5B shows an example of a method for benchmarking / evaluating the performance of a green facility, according to some embodiments of the present disclosure. [Figure 6] Figure 6 shows an example of an overview of the prediction and benchmarking performed by the benchmark and prediction unit. [Figure 7A] Figure 7A shows an example of detailed predictions and benchmarks performed by the benchmark and prediction unit. [Figure 7B] Figure 7B shows an example of a performance prediction model. [Figure 8] Figure 8 shows an example of a method for benchmarking / evaluating the performance of green facilities, using green bonds as an example. [Figure 9A] Figure 9A shows an example of the green functionality dataset generation process by the Lean Product Evaluation Department. [Figure 9B] Figure 9B shows an example of a feature dataset, indicated in green. [Figure 10A] Figure 10A shows an example of the benchmark dataset generation process. [Figure 10B] Figure 10B shows an example of a benchmark dataset. [Figure 10C] Figure 10C shows an example of a benchmark calculation model. [Figure 11] Figure 11 shows an example of recommendation processing by the financial product recommendation unit, green finance definition recommendation unit, green feature recommendation unit, interest recommendation unit, and asset value forecasting unit. [Figure 12] Figure 12 shows an example of the project selection (verification) process for each verification unit. [Figure 13] Figure 13 shows an example of the performance verification process performed by the performance verification unit. [Figure 14] Figure 14 shows an example of a benchmark process calibrated by the benchmark and prediction units. [Figure 15] Figure 15 shows an example of the sales monitoring and verification process performed by the sales monitoring and verification unit. [Figure 16]Figure 16 shows an example of the process for preparing a green financial product report for each reporting unit. [Figure 17] Figure 17 shows an example of the process for creating investor carbon impact reports using reporting units and asset impact calculation units. [Figure 18] Figure 18 shows an example of project selection (verification) for projects that have implemented blockchain technology. [Modes for carrying out the invention]

[0031] The following detailed description refers to the accompanying drawings illustrating specific details and embodiments in which this disclosure may be implemented. These embodiments are described in sufficient detail to enable those skilled in the art to implement this disclosure. Other embodiments may be used and modified structurally and logically without departing from the scope of this disclosure. Since some embodiments can be combined with one or more other embodiments to form new embodiments, the various embodiments are not necessarily mutually exclusive.

[0032] Embodiments described in the context of one system or method are equally valid for other systems or methods.

[0033] Features described in the context of one embodiment may correspond to the same or similar features in other embodiments. Features described in the context of one embodiment may be applicable to other embodiments even if they are not explicitly described in those other embodiments. Furthermore, additions and / or combinations and / or substitutions such as those described for features in the context of one embodiment may be applicable to the same or similar features in other embodiments.

[0034] As used herein, the term "and / or" includes any combination of one or more of the items listed in relation to it.

[0035] As used herein, the term “data” may be understood to include any suitable analog or digital information provided, for example, as a file, part of a file, a set of files, a signal or stream, part of a signal or stream, a set of signals or streams, etc. However, the term “data” may not be limited to the examples given herein and may represent any information in various forms as understood in the art.

[0036] As used herein, the term “processor” means, forms part of, or includes, some or all of the above, such as an ASIC (Application Specific Integrated Circuit), electronic circuit, combinational logic circuit; an FPGA (Field Programmable Gate Array) that executes code, other suitable hardware components that provide the functions described, or a system on a chip. A processor may include non-transient computer-readable media such as memory (shared, dedicated, or grouped) that stores the code executed by the processor.

[0037] Figures 1, 2A, and 2B are schematic diagrams of a system 100 for benchmarking / evaluating the performance of green facilities according to some embodiments of the present disclosure. The system 100 comprises a green facility interface 106, a user interface 107, and a processor 120 connected to the green facility interface 106 and the user interface 107. The green facility interface 106 is configured to connect to at least one green facility 105 and to collect operational data from at least one green facility 105. The user interface 107 is configured to connect to users including at least one capital raiser 102, at least one investor 103, and at least one technology provider 104.

[0038] The green facility interface 106 can automatically receive data (e.g., green facility operation information) from a green facility data collection and processing system (e.g., a smart energy management system) via a cloud network (e.g., via a PUSH / PULL API). In some embodiments, the green facility interface 106 may implement a Representation State Transfer Application Programming Interface (REST API).

[0039] System 100 can transmit and collect data from users 102, 103, and 104 via the user interface 107. System 100 collects green facility information 102a from a capital source (e.g., a green facility developer) 102 and transmits information generated by System 100 to the capital source 102. Green facility information 102a may include information about green projects, applicants, green facility design and operation, and revenue use. Information received by the capital source 102 from System 100 may include information generated to facilitate the capital source 102's decision-making and activities, such as recommendations for green finance definitions, recommendations for green finance products, evaluation and verification results of green projects, and reports. System 100 collects green finance product information 103a from an investor 103 and transmits information generated by System 100 to the investor 103. Green finance product information 103a may include the types of green finance products offered to the investor and the conditions of the green finance products they intend to invest in. The information that investor 103 receives from system 100 includes information generated to facilitate investor 103's decision-making and activities, such as recommendations for green facilities, recommendations for green projects, asset value forecasts, information on how proceeds are used, and reports. System 100 collects green functionality information 104a from technology provider 104 and transmits the information generated by system 100 to technology provider 104. Green functionality information 104a may include information on green products (e.g., product efficiency and materials), green services (e.g., energy audit service information), and verification of how proceeds are used when green products / services are provided to capital raiser 102. The information that technology provider 104 receives from system 100 includes information generated to facilitate technology provider 104's decision-making and activities, such as evaluation and / or certification results of green technologies. In this green finance market, capital flows from investor 103 to capital raiser 102 as an investment, and from capital raiser 102 to technology provider 104 when green products and services are purchased.The user interface 107 can send and receive data via a cloud network. An example of the user interface 107 is a REST API. The user interface 107 can be accessed, for example, from a web application in a browser.

[0040] The processor 120 is configured to benchmark / evaluate the performance of the green facility. The processor 120 can consist of multiple computing units to support multiple functions and services of the platform. These computing units include: - Green product evaluation unit 101a1, which evaluates and / or certifies green products and generates and / or updates green functionality datasets. - Benchmark dataset generation unit 101a2 that generates benchmark datasets, - Prediction / Benchmark Unit 101a3: Predicts the performance of green facilities and obtains at least one benchmark. - Green Finance Definition Recommendation Section 101a4 recommends eligibility criteria, KPIs, and SPTs for green products. - Financial product recommendation section 101a5 recommends green finance products to investors and capital raisers. - Green Function Recommendation Unit 101a6 recommends appropriate green functions (including products and services provided by technology providers) to capital raisers by performing a cost-benefit analysis using forecast results and benchmark results. - The interest rate recommendation section 101a7 recommends bond and loan interest rates to capital raisers and investors using statistical and / or machine learning methods based on information such as project information, maturity, general market interest rates, and similar green bond interest rates. - Property value prediction unit 101a8 predicts the value of a property using machine learning and other methods based on data related to the value of the property, such as the type of property, location, and environmental impact. - Verification unit 101a9, which verifies and selects green projects based on predictions and benchmark results. - Sales monitoring and verification unit 101a10 monitors and verifies the use of revenue based on green spending information from capital investors and green technology providers. - Performance verification unit 101a that verifies the environmental impact of green facilities based on operating data. - Asset impact calculation unit 101a12 that calculates the impact of green investments on investors based on the environmental impact of green investments and - Report creation unit 101a13 prepares reports on verification results, revenue verification results, and environmental impact verification results.

[0041] System 100 may further include data storage 101b, which is a data storage device. Data storage 101b may be a centralized or distributed data storage system such as a blockchain. Data storage 101b also supports multiple functions and services of System 100. Stored data may include green facility operation information, facility design information, green function datasets, benchmark datasets, forecast and benchmark results (including forecast performance, at least one calculated benchmark, and benchmark results), KPI datasets, validation results, performance validation results, financial product information, forecast property value, recommended interest rates, revenue records, reports, and / or report templates.

[0042] Figure 3A shows an example of a blockchain implementation of system 100 for benchmarking / evaluating the performance of green facilities. Full node 1710 is connected to green facility 105 via green facility interface 106. Green facility interface 106 is configured to automatically receive data from the data collection and processing system (e.g., smart energy management system) of green facility 105 via a cloud network. An example of green facility interface 106 is a REST API. Full node 1710 is also connected to different types of users, including capital raisers 102, investors 103, and technology providers 104, via user interface 107. User interface 107 is configured to send and receive data via a cloud network. An example of user interface 107 is a REST API. User interface 107 can be accessed, for example, from a web application in a browser.

[0043] A blockchain network can include other types of nodes that have only partial functionality compared to a full node (1710). For example, a light node stores and provides only the data necessary for daily activities and faster transactions. Light nodes do not participate in block verification and only store the block header.

[0044] A full node 1710 includes transaction management 1710a, distributed ledger 1710b, smart contract 1710c, consensus management 1710d, security management 1710e, and replication management 1710f. Transaction management 1710a manages the process of transaction requests, transaction receipts, transaction verification, transaction storage, and block generation by the consensus algorithm. It also manages the execution of smart contracts. Transactions in this application include, for example, capital investors committing to KPIs and SPTs, generating evaluation results for green projects, and verifying the use of revenue. Consensus management 1710d defines how network participants reach consensus so that a transaction is considered valid. There are three categories of consensus protocols: computationally intensive consensus protocols, capacity-based consensus protocols, and voting-based consensus protocols. In the proposed solution, capacity-based and voting-based consensus protocols are recommended. The distributed ledger 1710b stores transactions, is updated by the transaction management function 1710a, and synchronized between nodes in the network by the replication management 1710f. The security management 1710e is responsible for user authentication using encryption technology and data privacy using hashing technology.

[0045] Smart contract 1710c is used to assist in the generation of transactions that can then be distributed to each node in the network. In this application, smart contract 1710c is used to implement functions within system 100 for defining, selecting, monitoring, reporting, and verifying green finance. Smart contract 1710c defines the rules of what activities each type of user can and should perform. It also defines the rules for generating outputs for defining, selecting, monitoring, reporting, and verifying green finance. Smart contract 1710c may be integrated with machine learning models to generate results. Smart contract 1710c may interact with data outside the blockchain network by technologies such as oracles. Figure 3B shows an example of smart contract 1710c with the contact name "Performance Prediction". To support the performance prediction process, several functions are defined with their respective variable names, such as an energy prediction function and an emission_prediction function. The calculation logic is also defined, which performs calculations and sequentially calls the defined functions to generate desired outputs such as predicted energy and predicted emissions.

[0046] System 100 can be implemented as a platform for providing services to key stakeholders, such as capital raisers 102 and investors 103, regarding the definition, selection, monitoring, reporting, and verification of sustainable / green finance products, including but not limited to green bonds, green loans, sustainability bonds, sustainability-linked bonds, and sustainability-linked loans. System 100 can also be used by technology (product / service) providers 104 to demonstrate their products and services to customers (e.g., capital raisers 102) in order to grow their business.

[0047] Potential users of System 100 include: - Capital investors, such as green building developers, 102 may want to use this system 100 to demonstrate their green finance products and attract investment. This system 100 has the potential to significantly reduce the time and cost of research, reporting, and services. - Corporate investors such as banks 103 may want to use System 100 to ensure the sustainability impact of green products. - Individual investors 103 may want to use System 100 for green / sustainability investing while saving on research costs. - Policymakers, such as those in government agencies, who want to use System 100 to regulate the green finance market. - Technology (product / service) providers 104 will demonstrate their products or services.

[0048] Furthermore, System 100 can be used as a platform to promote carbon pricing, such as carbon taxes and carbon trading, by providing functions for benchmarking, predicting, and verifying carbon emissions from green facilities.

[0049] System 100 not only provides technical evaluations of green finance projects but can also be integrated into larger systems that manage investment and revenue flows, such as stock exchanges. Figure 4 shows an integrated system 1810 combining System 100 and the stock exchange system 1820. System 100 and the stock exchange system 1820 exchange information on financial transactions and results generated by System 100. This allows the integrated system 1810 to provide relevant information to users (capital raisers 102, investors 103, and technology providers 104) via the user interface 107. For example, the stock exchange system 1820 can update System 100 regarding changes in ownership of green bonds. System 100 can then update access control to green facility information in response to changes in ownership.

[0050] Figures 5A and 5B illustrate a method 200 for benchmarking / evaluating the performance of a green facility using system 100, according to some embodiments of the present disclosure. In step 210, the processor 120 of system 100 generates a green function dataset using operational information collected from at least one green facility 105, green function information collected from at least one technology provider 104, and green facility information collected from at least one capital provider 102. The green function information collected from at least one technology provider 104 may consist of information about green products (e.g., product efficiency and materials) and information about green services (e.g., energy audit service information). The green facility information collected from at least one capital provider 102 may consist of information about green projects, green facility design, and operation. For example, the collected green facility information may include facility characteristics, system specifications and green functions, and historical performance data of existing facilities.

[0051] In step 220, the processor 120 of system 100 generates a benchmark dataset using operational data collected from at least one green facility, green facility information collected from at least one capital source 102, and the green functionality dataset generated in step 210. In step 230, the processor 120 of system 100 inputs the proposed green facility information and the generated green function dataset into a performance prediction model, so that the performance parameters of the proposed green facility are predicted using the performance prediction model. The performance parameters are output from the performance prediction model for evaluating the performance of the proposed green facility. The proposed green facility information may consist of the facility characteristics, system specifications, and green functions of the proposed green facility. At least one benchmark of the proposed green facility is calculated using a benchmark calculation model based on the proposed green facility information and the generated benchmark dataset. The predicted performance parameters of the proposed green facility are compared with the calculated at least one benchmark to obtain benchmark results for evaluating the performance of the proposed green facility. In step 240, the processor 120 of system 100 may recommend green finance products to capital raisers and / or green facilities to investors based on the obtained forecast and benchmark results. Based on the forecast and benchmark results obtained in step 230, the processor 120 provides capital raisers 102 and / or investors 103 with information on proposed green facilities, such as the definition of green finance, i.e., eligibility criteria for green projects or green activities (hereinafter, "project" will be used to represent both projects and activities), revenue-based models (e.g., green bonds, green loans), Key Point Indicators (KPIs), Sustainability Performance Targets (SPTs), and Performance-based models (e.g., sustainability-linked bonds, sustainability-linked loans). In step 250, the processor 120 of system 100 evaluates the green project based on the predictions and benchmark results obtained in step 230 and selects it for revenue use and / or implementation. In step 260, the processor 120 of system 100 monitors and verifies the use of revenue for the revenue-based model. In step 270, the processor 120 of system 100 verifies the achieved performance of the proposed green facility based on the predictions and benchmark results obtained in step 230, using the green facility's operating data. To verify the achieved performance of the proposed green facility, at least one updated benchmark of the proposed green facility is calculated based on the updated information and generated benchmark dataset of the proposed green facility, and the calculated updated benchmark is compared with the achieved performance of the proposed green facility. The achieved performance may be the performance achieved by the proposed green facility after the completion of the green project. In step 280, the processor 120 of system 100 generates reports including a green finance product report and an investor carbon impact report.

[0052] Figure 6 shows a prediction and benchmarking process (step 230) by the benchmark unit 101a3 according to some embodiments of the present disclosure. Each step is performed by the processor 120 of the system 100. In step 310, information on the proposed green facility (e.g., facility characteristics, system specifications and green functions, and historical performance data of existing facilities) is obtained from at least one capital source 102. In step 320, at least one benchmark is calculated based on the acquired information and generated benchmark dataset of the proposed green facility. In step 330, the performance parameters of the proposed green facility are predicted based on the information of the proposed green facility and the generated green function dataset. In step 340, the calculated benchmark is compared with the predicted performance parameters of the proposed green facility to obtain the benchmark result.

[0053] Figure 7A shows a predictive benchmarking process (step 230) by a predictive benchmarking unit according to some embodiments of the present disclosure. Each step is performed by the processor 120 of the system 100. In Step 410, the benchmark type is determined based on the KPIs, SPTs, and / or project eligibility criteria determined in the green finance definition process. Examples of benchmark types are as follows: • Comparison over time. (Applicable only to existing facilities) • Compare with statistics from similar facilities. • Consider the characteristics of the facility, the efficiency of the baseline system, the schedule, etc., and compare them with the benchmark generated by the predictive model.

[0054] Step 420 checks whether the type of benchmark is "self-referenced". In step 430a, if the benchmark type is "relative to itself," historical performance data of the existing facility is processed and used together with information about the characteristics of the existing and proposed new facility (e.g., space type and area, schedule), and a performance prediction model is used to calculate "business as usual" performance parameters. The calculated "business as usual" performance parameters are used as at least one benchmark. "Business as usual" performance means performance when new facility utilization information such as schedule, space type, and area is proposed, while maintaining the historical / existing system efficiency.

[0055] The methods used to calculate "normal" performance may be based on performance prediction models, such as using system modeling and / or machine learning approaches. System modeling approaches can range from simplified engineering calculations based on the physics of the system (usually less accurate but easier) to complex computer simulation models (usually more accurate but resource-intensive), such as EnergyPlus or IES VE. Machine learning approaches can be used for relatively accurate and low-resource predictions. Machine learning approaches generate and validate models using relevant data and machine learning algorithms.

[0056] Facility characteristics (building type, air-conditioned area, etc.), system specifications (lighting efficiency, etc.), green features (smart lighting control, etc.), and a green feature dataset (effect of green features on performance, e.g., energy saving rate of smart lighting control products) can be used as input to the performance prediction model. The performance prediction model generates predicted performance parameters (e.g., energy use intensity of a green building) based on the input information. Figure 7B shows an example of a performance prediction model. In the performance prediction model shown in Figure 7B, the building type can be an office, the air-conditioned area can be 50%, the lighting efficiency can be 160 lm / W, the green feature can be smart lighting control, and the predicted energy use intensity (EUI) can be 50 kWh / m2.yr.

[0057] In step 430b, if the benchmark type is not “against-itself”, at least one “non-against-self” benchmark is determined using a benchmark calculation model based on information about the proposed green facility, including the generated benchmark dataset and / or proposed facility characteristics (e.g., space type and area, schedule). In some embodiments, both “against itself” and “against others” benchmarking methods may be applied, depending on the committed definition of the capital raker.

[0058] In step 440, based on the information of the proposed green facility (proposed facility characteristics (space type, area, schedule, etc.), proposed system specifications and green functions, existing facility performance data (existing facilities only), green function dataset, etc.), the performance parameters of the proposed green facility are predicted using a performance prediction model. The proposed performance parameters of the green facility may be proposed by the user or generated by the system when making recommendations from the green function recommendation unit 101a6, etc. Performance data of existing facilities can be collected from IoT platforms (smart energy management systems, etc.), power companies, and other available sources. Performance data collected from existing facilities may be stored in data storage to generate benchmark datasets and used to evaluate green product performance in green function dataset generation (green product evaluation and certification).

[0059] In step 450, the predicted performance parameters of the proposed green facility are compared to at least one benchmark generated from 430a and / or 430b in order to obtain benchmark results.

[0060] Figure 8 shows a method 200 for benchmarking / evaluating green facility performance using an example of a green bond, according to several embodiments. Each step is performed by the processor 120 of system 100.

[0061] In Step 510, after receiving a green finance project request from the capital seeker, a new green finance project is created. The capital seeker is then asked to enter basic information about the green finance project. This basic information includes, but is not limited to, key applicant information (identity, company information, etc.) and key project information (project name, project type, location, required funding amount, etc.).

[0062] Step 520 provides recommendations to facilitate decision-making by capital raisers, based on key project information. These recommendations include, but are not limited to, appropriate green finance products, green project eligibility criteria, KPIs, SPTs, and green functions, based on the green finance product dataset. Recommendations for green project eligibility criteria, KPIs, SPTs, and green functions are based on the obtained forecast and benchmark results.

[0063] In Step 530, the capital raiser selects a green financial product and receives commitments regarding the project's eligibility criteria or KPIs and SPTs.

[0064] In step 540, information on green finance projects is displayed and / or recommended to investors based on their preferences and the predicted green real estate value generated by the Real Estate Value Prediction Unit using machine learning and other methods based on a dataset of real estate values. Subsequently, the investor's consent to invest in the green finance project is obtained.

[0065] In step 550, green facility information (e.g., design information and operational information) is collected from capital providers, for example, manually via a web-based portal and / or automatically via an IoT platform. Based on the proposed green facility information, the generated green functionality dataset, and the generated benchmark dataset, the performance parameters of the proposed green facility are predicted using the obtained prediction and benchmark results, the green project is validated, and validation results are obtained.

[0066] In Step 560, the use of revenue is monitored using, for example, green spending information and evidence of green spending (invoices, photos / videos of installed products / services) collected from capital providers. Image / video recognition technology may be implemented to analyze the implementation of products / services. The use of revenue is verified based on confirmation of green spending collected from technology providers, and the results of the revenue use verification (revenue verification results) are obtained.

[0067] In Step 570, the environmental impact is verified using predictions and benchmark results based on operational information collected from the green facility after its construction or upgrade, and the results of the environmental impact verification are obtained.

[0068] In Step 580, based on the acquired verification results, revenue verification results, and environmental impact verification results, green finance reports are generated periodically or upon request to support the verification reports, revenue verification reports, and performance verification reports, respectively.

[0069] Step 590 stipulates that investor carbon impact reports will be prepared periodically or upon request, based on the investor's identity and asset / green investment information, such as the environmental impact of the green investment.

[0070] In other embodiments, the order of steps 510, 520, 530, 540, and 550 can be changed or omitted, and the user can modify or omit steps to adapt to different types of green finance products, such as green loans. For example, in step 510, instead of having a capital source create a green finance project for green bonds, an investor such as a bank may create a green loan scheme. In step 540, instead of recommending green bond products to investors, the developed green loan scheme is recommended to the capital borrowers. Depending on the green finance model, steps 560 and 570 may be omitted. For revenue-based models, step 570 may be optional. For performance-based models, step 560 is optional.

[0071] Figure 9A shows the green function dataset generation process (step 210) performed by the Green Product Evaluation Department 101a1. This green function dataset generation process also serves as the green product evaluation and certification service.

[0072] A green functionality dataset can include the name of each product, the green functionality type to which the product belongs, the product's efficiency, and other characteristics related to the green functionality, such as the design of natural ventilation for green buildings. Figure 9B shows an example of a green functionality dataset.

[0073] Step 710 involves receiving product information from technology providers who wish to have their green products evaluated, demonstrated, and / or certified. The green functionality dataset is updated based on the received product information. This information includes details about raw materials, manufacturing, use, and end-of-use.

[0074] In Step 720, product performance is evaluated using statistical methods, machine learning methods, and / or performance prediction models as exemplified above, based on pre- and post-installation performance data of the facility where the specific product is installed, as well as other information that may influence the efficiency assessment, such as weather data. The evaluation results (such as validated product efficiency and lifecycle emissions) are added to or updated in the Green Functionality Dataset.

[0075] In step 730, the evaluation results are compared against the green product standards. If the standards are met, the green product is certified, and a certificate is sent to the applicant in step 740. If the standards are not met, the applicant is notified of the result in step 750.

[0076] Figure 10A shows the benchmark dataset generation process (step 220). Figure 10B shows an example of a benchmark dataset. Figure 10C shows an example of a benchmark calculation model. Benchmarks can be obtained based on the type of the entire facility (e.g., building type or category) or a subsystem of the facility (e.g., type of space within a building, system type belonging to a building such as a lighting system). For each benchmark type, the best performance value, typical / average performance value, and / or a certain percentile performance value may be generated for use as at least one benchmark. For each benchmark type, conditional information can be added to define where and how the data can be applied, such as being applicable only to a specific city.

[0077] In some embodiments, a benchmark calculation model can be used to generate at least one benchmark for a green facility. Information such as building type, space type, and air-conditioned area can be input into the benchmark calculation model to generate at least one benchmark, such as maximum performance or typical performance. The benchmark calculation model uses the same methodology as the performance prediction model, but the input to the benchmark calculation model is benchmark values ​​for subsystems such as the maximum / standard efficiency of the air conditioning system.

[0078] In step 810, relevant data for generating benchmark datasets is collected. This data may include existing facility performance datasets, existing facility design datasets, and green functionality datasets. In step 820, the data is processed and / or cleaned in preparation for generating the benchmark dataset. In step 830, a benchmark dataset and / or benchmark computation model are generated using statistical and / or machine learning methods. In step 840, the new / updated benchmark dataset and benchmark calculation model are saved.

[0079] Figure 11 shows the recommendation process (steps 240, 520) by the financial product recommendation unit 101a5, the green finance definition recommendation unit 101a4, the green function recommendation unit 101a6, the interest rate recommendation unit 101a7, and the property value prediction unit 101a8. In step 910, user information and project information are received. User information may consist of information collected from funders 102, investors 103, and / or technology providers 104. Step 920 identifies appropriate financial instruments based on user information, project information, and the green finance product dataset. The green finance product dataset can include various types of green finance products available, such as green bonds, sustainability-linked bonds, green loans, and sustainability-linked loans. Bonds require a capital source to determine their definition. Loans are defined by investors, and appropriate loans are identified and recommended to capital sources. Step 920 can be skipped if the user has already determined the type of green product. In Step 930, depending on the type of green finance product recommended / selected, KPIs, SPTs, and / or eligibility criteria are identified using the obtained forecasts and benchmark results based on project information, KPI datasets (defining KPIs for different project types), and benchmark datasets. In step 940, appropriate green functions are identified, and based on project information, green function datasets, and predictions by the prediction / benchmarking unit 101a3, the green function recommendation unit 101a6 performs a cost and benefit analysis. Step 950 generates a recommended interest rate for a green finance product, such as a green bond, based on project information and an interest rate dataset that includes interest rates for existing financial products in the market. In step 960, the recommendations from steps 920 through 950 are sent to the user, and the user needs to make selections / decisions to define a green finance product. In step 970, the user's selections / decisions are received and stored in data storage 101b as financial product information.

[0080] Figure 11 shows the green bond recommendation process defined by capital raiser 102. However, depending on the type of user, some steps may be skipped and / or modified. For example, if the user is an investor such as a bank that wants to define a green loan scheme, step 940 may be skipped for the investor, as the green loan applicant, i.e., the capital raiser, will receive recommendations for green features, cost-benefit analysis, and projected results.

[0081] Figure 12 shows the project selection (verification) process (steps 250, 550) performed by the verification unit 101a9. In step 1010, project information, including facility characteristics (space type, area, schedule, etc.), system specifications, green functions, and past performance data of existing facilities, as well as information on the proposed green facilities, is received from the capital source. In step 1020, based on the received project information, the benchmark type determined by the green finance product definition, the benchmark dataset, and the green function dataset, the prediction / benchmarking unit 101a3 uses a performance prediction model to predict the performance parameters of the proposed green facility and performs benchmarking. In step 1030, the eligibility of the proposed project is checked against the determined KPIs and SPTs or defined eligibility criteria. If the project is eligible to use and / or is suitable to implement green procurement, the project is displayed as "Eligible" in the database and the user is notified in step 1040. If the project is not eligible, in step 1050 the Green Function Recommendation Unit 101a6 recommends additional green functions, the user revises the project design, and resubmits the project verification application.

[0082] Figure 13 shows the performance verification process (step 270) performed by the performance verification unit 101a11.

[0083] Step 1110 involves receiving updates from the capital financier after project completion, including facility characteristics (e.g., type and area of ​​space, schedule), system specifications, green features, and facility performance data.

[0084] In Step 1120, a calibration benchmark (explained in the following diagram) is performed to benchmark the performance of the green facility based on the actual design and operation of the green facility, using updated information, benchmark datasets, and green functionality datasets. The achieved green facility performance is the performance that the proposed green facility will achieve after the completion of the green project.

[0085] In Step 1130, the performance of the green facility achieved is compared against the determined KPIs and SPTs, or the defined eligibility criteria.

[0086] In step 1140, if the project meets the requirements, the project is marked as “Verified” in the database. In some embodiments, individual green features / products used by the green facility can be evaluated and marked as “Verified.” In some embodiments, each revenue used for a green feature and / or green facility can be marked as “Performance Verified.” Performance refers to energy savings, carbon emission reductions, etc. Performance may be on a facility-wide basis or on an individual green feature basis. For example, a daylight product can increase indoor illumination levels, and as a result, reduce the operating lighting power density (LPD). If the reduction in operating LPD is verified based on the power meter data of the lighting system, the performance of the daylight product is considered verified, and the revenue used for the daylight product can be displayed as “Performance Verified.”

[0087] Step 1150 requires that if a project does not meet the requirements, the capital seeker (capitalizer) take actions such as verifying the correctness of the implemented green functions in order to achieve the expected performance of the green facility.

[0088] Figure 14 shows the benchmark process (step 1120) calibrated by the benchmark unit 101a3. In step 1210, the benchmark type is determined based on the KPIs, SPTs, and / or project eligibility criteria determined in the green finance definition process. In step 1220, it is checked whether the benchmark type is for itself.

[0089] In step 1230a, if the benchmark type is "relative to itself," the history and / or new performance data of the existing facility are processed and used together with information on existing and implemented facility characteristics (e.g., space type and area, schedule) to recalculate the "normal" performance used as the benchmark. Normal performance means the performance when new facility usage information such as schedule, space type, and area is introduced, while maintaining past / existing system efficiency. The performance prediction model for recalculating normal performance should use the same method as described above.

[0090] In step 1230b, if the benchmark type is not “against itself”, at least one “non-against-self” benchmark is recalculated using the benchmark dataset and / or benchmark calculation model based on the implemented facility characteristics (e.g., space type and area, schedule) and / or new performance data. In some embodiments, depending on the capital raker’s committed definition, both “against itself” and “non-against itself” benchmarking methods may be applied.

[0091] In step 1240, the performance of the green facility is determined by processing new performance data and the characteristics of the implemented facility. In steps 1230a, 1230b, and 1240, historical / existing and / or new performance data of the green facility may be collected through IoT platforms (e.g., smart energy management systems), power companies, and other available sources. The data collected from the green facility is stored in data storage 101b and used in step 220 to generate a benchmark dataset, which is used to evaluate green product performance in the green functionality dataset generation (green product evaluation and certification) process (step 210).

[0092] In step 1250, the green facility performance achieved from 1240 is compared to at least one recalculated benchmark from 1230a and / or 1230b to generate a calibrated benchmark result.

[0093] Figure 15 shows the sales monitoring and verification process 260 performed by the sales monitoring and verification unit 101a10.

[0094] In step 1310, updates on the capitalizer's expenditures are received, which may include proof of expenditures and / or proof of implementation of green functions. In step 1320, a request is sent to each product / service provider to confirm the supply of products / services to the capitalizer and / or receipt of payment from the capitalizer from step 1310. In step 1330, confirmation from the product / service provider is received. In step 1340, the investor's claim and the product / service provider's confirmation are compared. If they match, in step 1350, the use of the revenue is marked as confirmed. If they do not match, the investor is required to resubmit the claim.

[0095] Figure 16 shows the process of creating a green financial product report by the report creation unit 101a13 (steps 280, 580). Step 1410 retrieves report types such as verification reports, revenue verification reports, and performance verification reports based on user or system requirements, such as to support periodic reporting functionality. Based on the report type, a template is selected. In step 1420, the necessary information for the template is retrieved from data storage 101b. This information includes verification information, performance verification information, revenue information, and other verification information. A report is generated based on the retrieved information, report type, and report template. In step 1430, the generated report is sent to each user.

[0096] Figure 17 shows the process of creating investor carbon impact reports by the report generation unit 101a13 and the asset impact calculation unit 101a12 (steps 280, 590). In step 1510, the investor's ID is obtained, and all relevant asset information for the investor for the corresponding period is retrieved from the data storage 101b. In step 1520, the asset impact calculation unit 101a12 calculates the overall performance and emissions of the assets for the corresponding period based on the expected and / or achieved performance and emissions information of the green facilities for each investment. In step 1530, a report is generated based on the calculation results in step 1520 and sent to each user.

[0097] System 100 can be implemented using a conventional centralized data storage and processing system, but it can also be implemented using a distributed data storage and processing system, such as a blockchain network system. Figure 18 shows an example of project selection (verification) (step 250) using blockchain implementation. In step 1610, the blockchain node receives input from the capital maker regarding project information from the user interface 107. The capital maker's input may include facility characteristics (e.g., space type and area, schedule), system specifications and green features, and historical performance data (existing facilities only). In step 1620, this input is used along with data stored on the blockchain network, including benchmark datasets, green functionality datasets, determined KPIs and SPTs, and / or defined green project criteria, to predict the performance of the green facility, perform benchmarks, and check eligibility by executing smart contracts on the blockchain nodes. In step 1630, the node hashes and encrypts the data and broadcasts the transaction data (including input data, prediction results, prediction and benchmark results, and eligibility results) and signature to the network. In step 1640, the validator node validates the transaction and broadcasts it to the block generator. In step 1650, a block generator is selected based on consensus, verifies the validated transactions, groups them into a block, and broadcasts that block to the network. In step 1660, the validator node verifies the validity of the block. In step 1670, the new block is added to the blockchain network and becomes viewable by the relevant users via the user interface 107.

[0098] Furthermore, one or more steps of the methods described herein can be carried out in parallel rather than sequentially.

[0099] While this disclosure has been specifically shown and described with reference to certain embodiments, it should be understood by those skilled in the art that various modifications can be made in form and detail without departing from the spirit and scope of this disclosure as defined by the appended claims. Accordingly, the scope of this disclosure is indicated by the appended claims, and all modifications that fall within the meaning and equivalence of the claims are therefore intended to be included. Finally, the language used in this disclosure has been selected primarily for readability and reference, and may differ from that selected 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 to which an application based herein may be filed. Accordingly, this disclosure of embodiments of the invention is intended to illustrate, rather than limit, the scope of the invention as described in the following claims. While various aspects and embodiments are described in this disclosure, other aspects and embodiments will also be apparent to those skilled in the art. The various aspects and embodiments described in this disclosure are for illustrative purposes only and are not intended to be limiting. The actual scope is defined by the following claims. [Explanation of Symbols]

[0100] 100 Systems 101b Data Storage 102 Capital Raisers 102a Green Project Information 103 Investor 103a Green Finance Product Information 104 Technology provider 104a Green Technology / Service Information 105 Green Facilities 106 Green Facility Interface 107 User Interface

Claims

1. A method for a computer to evaluate the performance of a green facility, The computer includes the steps of acquiring green function information collected from at least one technology provider, green facility information collected from at least one capital source, and operational information collected from at least one green facility, The computer generates a green function dataset by associating the green function information, green facility information, and operation information with each green facility that is the same subject of evaluation, The computer stores the green function dataset in a data storage device, The computer, using the green function dataset as input, predicts performance parameters, including energy consumption in the proposed green facility, as prediction results using a performance prediction model. The computer generates a benchmark dataset that includes building history information, The computer evaluates the performance of the proposed green facility based on the difference between the performance parameters predicted by the performance prediction model and the benchmark calculated from the benchmark dataset, and generates benchmark results. A method for evaluating the performance of green facilities, including those mentioned above.

2. In a method for evaluating the performance of a green facility as described in claim 1, The computer generates the benchmark dataset using the collected driving information, the collected green function information, the green facility information, and the generated green function dataset. The computer uses a benchmark calculation model to calculate at least one of the benchmarks for the proposed green facility, based on information about the proposed green facility including facility characteristics, system specifications and green functions, historical performance data of existing facilities, and the generated benchmark dataset. The computer compares the predicted performance parameters of the proposed green facility with the calculated benchmark in order to obtain the difference. A method for evaluating the performance of green facilities.

3. In a method for evaluating the performance of a green facility as described in claim 1, The computer recommends green finance products to capital raisers and / or green facilities to investors based on the obtained prediction and benchmark results. A method for evaluating the performance of green facilities.

4. In a method for evaluating the performance of a green facility as described in claim 1, The computer evaluates and selects green projects related to the use of funds based on the obtained prediction results and benchmark results. The aforementioned computer monitors and verifies how the proceeds are used. We will evaluate and select green projects related to the use of funds. A method for evaluating the performance of green facilities.

5. In a method for evaluating the performance of a green facility as described in claim 2, The computer verifies the performance achieved by the proposed green facility, The computer calculates at least one updated benchmark for the proposed green facility based on the updated information for the proposed green facility and the generated benchmark dataset. The computer compares the calculated updated benchmark with the performance achieved by the proposed green facility. A method for evaluating the performance of green facilities.

6. In a method for evaluating the performance of a green facility as described in claim 2, The computer determines the type of benchmark based on the key indicators, sustainability performance targets, and / or project eligibility criteria determined by the definition of green finance. If the type of benchmark is one to itself, the computer calculates at least one of the benchmarks using the performance prediction model based on the historical performance data of the existing facility, information about the existing facility, and information about the proposed green facility. If the type of benchmark is not one of itself, the computer determines at least one benchmark using the generated benchmark dataset and / or benchmark calculation model based on the proposed green facility information. The computer predicts the performance parameters of the proposed green facility using the performance prediction model, based on the information of the proposed green facility, the historical performance data of the existing facility, and the green function dataset. The computer compares the predicted performance parameters of the proposed green facility with at least one benchmark and obtains benchmark results. A method for evaluating the performance of green facilities.

7. In a method for evaluating the performance of a green facility as described in claim 1, The performance prediction model is a simplified engineering calculation, a complex computer simulation model, or a model based on machine learning. A method for evaluating the performance of green facilities.

8. A system for evaluating the performance of green facilities, A green facility interface configured to collect operational information from at least one green facility, A user interface configured to connect with one or more users, A processor connected to the green facility interface and the user interface, A data storage device configured to store data, The aforementioned processor, The system acquires green function information collected from at least one technology provider, green facility information collected from at least one capital source, and the said operational information collected from at least one green facility. The aforementioned green function information, green facility information, and operational information are linked and integrated for each green facility that is the same subject of evaluation to generate a green function dataset. The green function dataset is stored in the data storage device, Using the aforementioned green function dataset as input, the performance prediction model predicts performance parameters, including energy consumption, in the proposed green facility. Generate a benchmark dataset that includes building history information. The performance of the proposed green facility is evaluated based on the difference between the performance parameters predicted by the performance prediction model and the benchmark calculated from the benchmark dataset. system.

9. In a system for evaluating the performance of a green facility as described in claim 8, The aforementioned processor, Using the collected driving information, the collected green function information, the green facility information, and the generated green function dataset, the benchmark dataset is generated. Based on the information of the proposed green facility, including facility characteristics, system specifications and green functions, historical performance data of existing facilities, and the generated benchmark dataset, a benchmark calculation model is used to calculate at least one of the benchmarks for the proposed green facility. To obtain the difference, the predicted performance parameters of the proposed green facility are compared with the calculated benchmark. system.

10. In a system for evaluating the performance of a green facility as described in claim 9, The data stored in the data storage device includes the operation information, the green function information, the green facility information, the generated green function dataset, the generated benchmark dataset, prediction results, and benchmark results. system.

11. In a system for evaluating the performance of a green facility as described in claim 10, The aforementioned data storage device is a centralized or distributed data storage system. system.

12. In a system for evaluating the performance of a green facility as described in claim 11, The aforementioned data storage device is a blockchain. system.

13. In a system for evaluating the performance of a green facility as described in claim 9, The aforementioned processor, Based on the obtained forecast and benchmark results, we recommend green finance products to capital raisers and / or green facilities to investors. system.

14. In a system for evaluating the performance of a green facility as described in claim 9, The aforementioned processor, Based on the obtained forecast and benchmark results, green projects related to the use of funds will be evaluated and selected. Monitor and verify how the proceeds are used. system.

15. In a system for evaluating the performance of a green facility as described in claim 9, The aforementioned processor, The performance achieved by the proposed green facilities will be verified. Based on the updated information of the proposed green facility and the generated benchmark dataset, calculate at least one updated benchmark for the proposed green facility. The calculated updated benchmark is compared with the performance achieved by the proposed green facility. system.

16. In a system for evaluating the performance of a green facility as described in claim 9, The aforementioned processor, Based on the project eligibility criteria determined by key indicators, sustainability performance targets, and / or the definition of green finance, the type of benchmark is determined. If the type of benchmark is for itself, then, based on the historical performance data of the existing facility, the information of the existing facility, and the information of the proposed green facility, the performance prediction model is used to calculate at least one of the benchmarks. If the type of benchmark is not one of itself, then, based on the information of the proposed green facility, at least one benchmark is determined using the generated benchmark dataset and / or the benchmark calculation model. Based on the information on the proposed green facility, the historical performance data of existing facilities, and the green function dataset, the performance prediction model is used to predict the performance parameters of the proposed green facility. The predicted performance parameters of the proposed green facility are compared with at least one benchmark, and benchmark results are obtained. system.

17. In a system for evaluating the performance of a green facility as described in claim 8, The performance prediction model is a simplified engineering calculation, a complex computer simulation model, or a model based on machine learning. system.

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