New energy project full life cycle carbon reduction comprehensive efficiency evaluation method

Abstract

The invention discloses a new energy project full-life-cycle carbon reduction comprehensive efficiency evaluation method, and the method comprises the steps: calculating an initial value of an input-output type index according to the carbon reduction basic data of a new energy project full-life cycle, and carrying out the standardization processing, and obtaining a standardization score; based on a data envelope analysis C2R model, calculating a carbon reduction comprehensive efficiency evaluation coefficient of each project; and if the coefficient is not lower than a preset threshold value, determining that the full-life-cycle carbon reduction comprehensive efficiency of the corresponding project is qualified. According to the new energy project full-life-cycle carbon reduction comprehensive efficiency evaluation method, input-output type indexes are distinguished, standardization processing is carried out to ensure evaluation scientificity, weight imbalance is avoided depending on the objective weighting characteristic of the C2R model, and the problem that in the prior art, evaluation of new energy project carbon reduction comprehensive efficiency from the full-life-cycle perspective is lacked can be solved. And a traditional evaluation method is difficult to effectively evaluate the comprehensive efficiency due to the fact that input and output type indexes are not distinguished and the weight is easy to be unbalanced.

Description

Technical Field

[0001] This invention relates to the field of new energy, and in particular to a method for evaluating the comprehensive carbon reduction efficiency of new energy projects throughout their entire life cycle. Background Technology

[0002] With their core advantages of zero carbon emissions and sustainable recycling, new energy projects have become a core pathway for global carbon reduction. From photovoltaic power plants and wind farms to energy storage systems and ultra-high voltage transmission projects, various new energy projects cover the entire energy production, transmission, and storage chain. They are not only gradually replacing traditional fossil fuel consumption but also widely penetrating key carbon emission sectors such as industry, transportation, and construction, becoming a crucial support for countries to fulfill their carbon reduction commitments and achieve coordinated ecological and economic development. However, the carbon reduction effectiveness assessment of new energy projects requires professional evaluation techniques. Existing mainstream methods mainly include two categories: the Analytic Hierarchy Process (AHP) decomposes the overall evaluation objective into various evaluation indicators, constructs a multi-level analytical structure model based on the correlation and membership relationships between indicators, and ultimately determines the importance weight or superiority / inferiority order of the bottom-level indicators relative to the top-level overall objective; the fuzzy comprehensive evaluation method, based on fuzzy mathematical membership theory, transforms qualitative evaluation into quantitative evaluation. It is used to judge things constrained by multiple factors, and the weights of each factor need to be subjectively allocated during the evaluation, with the weights assumed to be the same unless otherwise specified.

[0003] While existing technologies have provided relatively comprehensive research on the full life-cycle management of new energy projects, they lack specific evaluation studies on the overall carbon reduction efficiency from this perspective. Furthermore, mainstream methods such as the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation are prone to imbalances in indicator weights when faced with scenarios involving numerous risk factors and complex indicator relationships in the carbon reduction evaluation of new energy projects. This leads to deviations between evaluation results and actual conditions. Moreover, they fail to distinguish the essential differences between input and output indicators before uniformly assigning weights, violating the actual logic of assessment. Additionally, due to their reliance on subjective weight allocation, they are ill-suited to the core requirements of objectivity and accuracy in the evaluation of overall carbon reduction efficiency. Summary of the Invention

[0004] This invention provides a method for evaluating the comprehensive carbon reduction efficiency of new energy projects throughout their entire life cycle, in order to address the problem that existing technologies lack a comprehensive evaluation of the carbon reduction efficiency of new energy projects from a life cycle perspective, and that traditional evaluation methods are difficult to effectively evaluate this comprehensive efficiency because they do not distinguish between input and output indicators and the weights are easily unbalanced.

[0005] To achieve the above objectives, this invention provides a method for evaluating the comprehensive carbon reduction efficiency of new energy projects throughout their entire life cycle, comprising: To obtain basic carbon reduction data for several new energy projects throughout their entire life cycle; Based on the aforementioned carbon reduction baseline data, initial values ​​for input and output indicators are calculated, and the initial values ​​are standardized to obtain a standardized indicator score set. Based on the C2R model of data envelopment analysis, the comprehensive carbon reduction efficiency evaluation coefficients corresponding to the several new energy projects are calculated according to the standardized index score set. If the carbon reduction comprehensive efficiency evaluation coefficient is greater than or equal to the preset threshold, then the carbon reduction comprehensive efficiency of the corresponding new energy project throughout its entire life cycle is deemed qualified.

[0006] This invention incorporates the entire lifecycle dimension into the basic data collection process, directly acquiring carbon reduction-related data for new energy projects throughout their entire lifecycle. This fills the gap in existing technologies that lack a lifecycle perspective, providing a complete and coherent data source for comprehensive efficiency assessment. Secondly, by clearly dividing the assessment indicator system into input-type and output-type categories, it accurately solves the problem of traditional methods failing to distinguish indicator attributes, establishing a clear correspondence between resource input and carbon reduction effectiveness. Simultaneously, by standardizing the initial values ​​of the indicators, it eliminates interference caused by differences in the dimensions of different indicators, laying the foundation for subsequent objective assessment. Finally, it employs a C2R model based on data envelopment analysis for comprehensive efficiency calculation. This model inherently eliminates the need for subjective setting of indicator weights, automatically achieving objective weight allocation through relative efficiency analysis of input and output. This fundamentally avoids the weight imbalance problem caused by subjective weighting in traditional evaluation methods, ensuring the scientific validity and effectiveness of the comprehensive carbon reduction efficiency assessment results. Finally, through a preset threshold judgment mechanism, it achieves a precise and comprehensive assessment of the comprehensive carbon reduction efficiency of new energy projects.

[0007] Compared to existing technologies, this invention ensures the scientific nature of the evaluation dimensions by clearly distinguishing and standardizing input and output indicators. It avoids the risk of weight imbalance by leveraging the objective weighting characteristics of the C2R model. Ultimately, it achieves a comprehensive, accurate, and effective evaluation of the overall carbon reduction efficiency of new energy projects. Therefore, it can solve the problems of existing technologies lacking an evaluation of the overall carbon reduction efficiency of new energy projects from a life-cycle perspective, and the difficulty in effectively evaluating this overall efficiency due to the failure to distinguish between input and output indicators and the tendency for weight imbalance in traditional evaluation methods.

[0008] As a preferred option, the C2R model based on data envelopment analysis calculates the comprehensive carbon reduction efficiency evaluation coefficients for the various new energy projects according to the standardized index score set, specifically as follows: The standardized scores of input indicators and output indicators of the several new energy projects are extracted from the standardized indicator score set and sorted to form input indicator vector and output indicator vector for each project. The results are then integrated to obtain the overall input indicator matrix and the overall output indicator matrix. Based on the C2R model of data envelopment analysis, a two-stage linear programming model is established according to the overall input index matrix and the overall output index matrix. By solving the established model, the target parameter values ​​of each new energy project are obtained. The target parameter values ​​of each new energy project are substituted into a preset formula to calculate the comprehensive carbon reduction efficiency evaluation coefficient of each new energy project.

[0009] This preferred solution refines the application process of the C2R model, transforming standardized indicators into structured input-output vectors and matrices, providing a clear data foundation for model calculations. The construction of a two-stage linear programming model achieves a closed-loop logic from indicator extraction to efficiency coefficient calculation, avoiding the problems of chaotic data utilization and disconnected calculation steps in traditional assessments. By solving for the target parameters step-by-step and substituting them into the formula, the calculation of the comprehensive carbon reduction efficiency evaluation coefficient becomes more traceable, reducing human calculation errors. This standardized process not only improves the reliability of the assessment results but also makes the efficiency comparison of different new energy projects more equitable, providing accurate and operable quantitative basis for project selection and optimization, effectively supporting scientific decision-making for carbon reduction projects.

[0010] As a preferred approach, based on the C2R model of data envelopment analysis, a two-stage linear programming model is established according to the overall input index matrix and the overall output index matrix. By solving the established model, the target parameter values ​​for each new energy project are obtained, specifically: Based on the C2R model of data envelopment analysis, a first linear programming model is constructed according to the overall input index matrix and the overall output index matrix. For each new energy project, the corresponding exclusive input index vector and output index vector are substituted into the first linear programming model for solution, so as to obtain the target value, the output relaxation vector reflecting the degree of input redundancy, and the input relaxation vector reflecting the degree of output gap for each new energy project. The input-output of new energy projects with a target value of zero is determined to be valid, thus obtaining a set of projects with valid input-output. Select a target benchmark project from the set of effective input-output projects as a benchmark, construct a second linear programming model based on the overall input index matrix, the overall output index matrix, the output relaxation vector and input relaxation vector corresponding to the target benchmark project, solve the second linear programming model, and obtain the target parameter value for each new energy project.

[0011] The proposed two-stage linear programming model construction and solution method in this preferred scheme first-stage the model to screen out the effective set of input-output projects, accurately eliminating inefficient projects and focusing on core samples for subsequent evaluation. Using benchmark projects in the effective project set as a reference, the second-stage model is constructed in conjunction with relaxation vectors, solving the problems of benchmark ambiguity and large parameter calculation deviations in traditional C2R models during multi-project evaluation. The introduction of output and input relaxation vectors clearly reflects the redundancy of project inputs and the gap in outputs, pointing the way for project optimization. The step-by-step solution method ensures the accuracy of target parameter values ​​and allows the evaluation process to have both screening and optimization functions, extending carbon reduction efficiency evaluation from simple result judgment to process improvement, thus enhancing the practical value of the method.

[0012] As a preferred embodiment, the selection rules for the target benchmark items are as follows: The target benchmark project is selected from the set of effective input-output projects where the solution result of the first linear programming model is zero and both the output relaxation vector and the input relaxation vector are zero.

[0013] This preferred approach prioritizes projects with zero target values ​​and zero relaxation vectors by clearly defining the selection rules for benchmark projects. This ensures that the benchmark projects possess optimal input-output efficiency and resource allocation capabilities. This precise benchmark selection method avoids the problems of arbitrary and insufficient representativeness in traditional assessments, providing a reliable reference standard for subsequent parameter calculations. Using the optimal project as the benchmark minimizes assessment bias, allowing the carbon reduction efficiency evaluation coefficients of each project to more accurately reflect its relative level. Simultaneously, the clear selection rules enhance the standardization and repeatability of the assessment method. Different assessors using these rules can obtain consistent benchmark projects, strengthening the credibility and cross-scenario applicability of the assessment results.

[0014] As a preferred embodiment, the first linear programming model includes a constraint set, which is constructed based on the overall input index matrix, the overall output index matrix, the input index vectors and output index vectors of each new energy project, combined with the weight coefficient vector, the output relaxation vector and the input relaxation vector. The constraint set includes output constraints, input constraints and weight coefficient summation constraints.

[0015] This preferred solution defines the scientific boundaries of the model solution by clearly defining the constraint set of the first linear programming model, thus avoiding unrealistic parameter results during the solution process. The combination of output constraints, input constraints, and weight coefficient summation constraints comprehensively considers the input-output logic of new energy projects, ensuring that the model calculations closely match the actual operation scenarios of the projects. The constraint set is based on core indicator matrices and vectors, making the constraints more targeted and solving the problem of distorted calculation results caused by fuzzy constraints in traditional models. The clear constraint system not only improves the reliability of results such as target values ​​and relaxation vectors, but also makes the model solution process more logical and interpretable, providing solid theoretical support for subsequent benchmark project selection and target parameter calculation, and enhancing the rigor of the entire evaluation method.

[0016] As a preferred approach, the initial values ​​are standardized to obtain a standardized index score set, specifically as follows: Based on the initial values ​​of the input indicators and the output indicators, the average value and standard deviation of each of the input indicators and the output indicators are calculated respectively. Based on the mean and standard deviation, different standard deviation standardization formulas are used according to the indicator type to calculate the standardized scores of input-type indicators and output-type indicators for each new energy project, and integrate them to obtain a standardized indicator score set. The standard deviation standardization formula is designed by adjusting the direction of the numerator difference so that when the standardized score is positive, the standardized score is positively correlated with the overall carbon reduction efficiency level of the corresponding new energy project.

[0017] This preferred scheme effectively eliminates differences in dimensions and orders of magnitude between different indicators by calculating the average and standard deviation of the indicators and adjusting the direction of the difference based on the indicator type, thus solving the problem of comparability of indicators in traditional standardization. The positive correlation between standardized scores and overall carbon reduction efficiency ensures that scores directly reflect project efficiency levels, enhancing the intuitiveness of the evaluation results. Compared to simple normalization methods, this approach better preserves the distribution characteristics of indicator data, reducing the impact of data distortion on subsequent calculations. The formation of standardized indicator score sets provides high-quality input data for the C2R model, ensuring the accuracy of subsequent efficiency coefficient calculations, while also enabling comparison of indicators for different types of new energy projects, broadening the applicability of the evaluation method.

[0018] As a preferred option, the input indicators include the intensity of land use, initial investment cost, and installed capacity of equipment; the output indicators include carbon emission reduction, annual power generation, and cost per kilowatt-hour.

[0019] This optimal solution comprehensively covers the core dimensions of the entire lifecycle of new energy projects. Input-type indicators such as land resource intensity and initial investment accurately reflect the project's resource and cost consumption; output-type indicators such as carbon emission reduction and annual power generation directly reflect the project's carbon reduction benefits and energy value. The selection of indicators balances economic efficiency with a focus on the core carbon reduction objective, avoiding the problems of single indicators or unbalanced emphasis in traditional assessments. The inclusion of the cost per kilowatt-hour indicator enables a linked consideration of benefits and costs, making the assessment more aligned with the actual operational needs of the project. This indicator system provides a comprehensive and focused core basis for the assessment, ensuring that the comprehensive carbon reduction efficiency evaluation can truly reflect the project's overall value and providing precise support for project investment and policy formulation.

[0020] As a preferred embodiment, the carbon emission reduction includes direct carbon emission reduction and indirect carbon emission reduction; The direct carbon emission reduction is calculated based on the annual on-grid electricity and the regional average carbon emission coefficient of thermal power; the indirect carbon emission reduction is calculated based on the carbon emission intensity of traditional processes and the carbon emission intensity of the actual process of the project.

[0021] This preferred approach categorizes carbon emission reductions into direct and indirect categories, overcoming the limitations of traditional assessments that focus solely on emission reductions through power generation substitution. It achieves a comprehensive accounting of carbon reduction benefits. Direct carbon emission reductions reflect the substitution value of new energy sources for thermal power, while indirect carbon emission reductions reflect the carbon reduction contribution of project process optimization. The combination of these two approaches fully covers the carbon reduction path throughout the project's entire lifecycle. Clear accounting criteria make carbon emission reduction calculations more operational, avoiding issues such as ambiguity, duplication, or omissions in emission reduction statistics. Comprehensive carbon emission reduction data makes output-based indicators more realistic and reliable, improving the accuracy of the overall carbon reduction efficiency evaluation coefficient. Simultaneously, the refined emission reduction classification provides direction for exploring the project's carbon reduction potential, helping the project improve carbon reduction benefits from both power generation substitution and process optimization, thus enhancing the practical guidance significance of the methodology.

[0022] The present invention also provides a device for evaluating the comprehensive carbon reduction efficiency of new energy projects throughout their entire life cycle, including a data acquisition module, an indicator module, a coefficient module, and an evaluation module; The acquisition module is used to acquire basic carbon reduction data of several new energy projects throughout their entire life cycle. The indicator module is used to calculate the initial values ​​of input and output indicators based on the carbon reduction basic data, and to standardize the initial values ​​to obtain a standardized indicator score set. The coefficient module is used to calculate the comprehensive carbon reduction efficiency evaluation coefficients corresponding to the several new energy projects based on the C2R model of data envelopment analysis and the standardized index score set. The evaluation module is used to determine that the overall carbon reduction efficiency of the corresponding new energy project is qualified if the carbon reduction comprehensive efficiency evaluation coefficient is greater than or equal to a preset threshold.

[0023] The present invention also provides a storage medium storing a computer program, which is called and executed by a computer to realize the comprehensive carbon reduction efficiency assessment method for the entire life cycle of a new energy project as described above.

[0024] The present invention also provides a computer program product, including a computer program or instructions, which, when executed by a communication device, implements the comprehensive carbon reduction efficiency assessment method for the entire life cycle of a new energy project as described above. Attached Figure Description

[0025] Figure 1 This is a flowchart illustrating a method for evaluating the comprehensive carbon reduction efficiency of a new energy project throughout its entire life cycle, as provided in an embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of a comprehensive carbon reduction efficiency assessment device for the entire life cycle of a new energy project provided in an embodiment of the present invention. Detailed Implementation

[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] In the description of this invention, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" and "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a number" means two or more.

[0028] The present invention provides a method for evaluating the comprehensive carbon reduction efficiency of new energy projects throughout their entire life cycle. This method aims to address the lack of research in the existing technology on evaluating the comprehensive carbon reduction efficiency of new energy projects from the perspective of the entire life cycle. Furthermore, traditional methods such as the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation have problems such as subjective weighting leading to an imbalance in indicator weights, failure to distinguish between input and output indicators, and deviation from the actual evaluation situation, resulting in evaluation results that do not match reality. In addition, it cannot eliminate the interference of investment scale and unfair evaluation of sensitive indicators.

[0029] Example 1: Please see Figure 1 The present invention provides a method for evaluating the comprehensive carbon reduction efficiency of a new energy project throughout its entire life cycle, comprising steps S1 to S4, and the specific implementation steps are as follows: S1. Obtain basic carbon reduction data for several new energy projects throughout their entire life cycle.

[0030] Step S1 in this embodiment of the invention specifically includes: Acquire basic carbon reduction data for several new energy projects throughout their entire lifecycle, including input-related raw data and output-related raw data. Input-related raw data refers to the basic data related to "inputs" throughout the entire lifecycle of a new energy project; output-related raw data refers to the basic data related to "outputs" throughout the entire lifecycle of a project. "Entire lifecycle" refers to the period from construction and preparation to operation.

[0031] It should be noted that, in Embodiment 1 of the present invention, new energy projects specifically refer to energy development and operation projects with solar photovoltaic power generation and wind power generation as their core businesses, and which need to cover the entire life cycle.

[0032] S2. Calculate the initial values ​​of input and output indicators based on the carbon reduction basic data, standardize the initial values, and obtain the standardized indicator score set.

[0033] Step S2 in this embodiment of the invention includes S2.1 to S2.3, specifically as follows: S2.1 Construct an evaluation index system for the comprehensive carbon reduction efficiency of new energy projects throughout their entire life cycle, specifically including two categories: input indicators and output indicators. (1) In terms of constructing output-type indicators, five core output-type indicators are set: carbon emission reduction, annual power generation, annual operating revenue, internal rate of return, and cost per kilowatt-hour, and the quantitative logic is improved: ① Carbon emission reduction includes annual direct carbon emission reduction and indirect carbon emission reduction. Annual direct carbon emission reduction refers to the reduction in annual carbon dioxide emissions directly achieved by the project through replacing fossil fuel power generation with new energy power generation. Indirect emission reduction refers to the reduction in carbon emissions throughout the project's entire life cycle due to optimization in equipment manufacturing, transportation, construction, and other processes. Carbon emission reduction is a positive indicator; the higher the value, the more significant the project's carbon reduction performance. The formulas for calculating each parameter are as follows: ; ; ; For the calculation formula of annual direct carbon emission reduction, the unit of measurement for "regional thermal power average carbon emission coefficient" is "tons of CO2 / 10,000 kilowatt-hours". The specific value and unit can be determined or obtained by referring to the "Guidelines for the Compilation of Provincial Greenhouse Gas Inventories".

[0034] ② Annual power generation refers to the total electrical energy actually produced by the project each year, used to reflect the project's energy output capacity. This indicator is a positive indicator, and the specific calculation formula is as follows: ; There are two optional methods to obtain the annual power generation: in addition to calculating it using the formula mentioned above, you can also directly read the cumulative metering data of the project's power generation meter without any additional calculation. Both methods can accurately obtain the annual power generation value.

[0035] ③ Annual operating revenue refers to the total revenue obtained by a project through various operating activities within one year. It is a core indicator for measuring the rationality of the project's technological investment. In addition to the core revenue from grid-connected electricity, it also includes diversified revenue streams such as national / local new energy project subsidies, energy storage frequency regulation / backup service revenue, green certificate trading revenue, and waste heat utilization revenue. This indicator can more comprehensively reflect the project's profit structure and market competitiveness, and is particularly suitable for new energy projects with multi-business operation models. It is a key basis for judging the overall financial health of the project. This indicator is a positive indicator, and the specific calculation formula is as follows: ; ; Among them, "Other operating income" includes revenue from green certificate trading. Green certificates are certifications of the green attributes of new energy power. The income obtained by a project through the sale of green certificates is supplementary operating income in addition to core income. The "grid-connected electricity price" used when calculating annual grid-connected electricity revenue already includes special subsidies provided by the state or local governments to new energy projects, and does not only refer to the basic grid-connected electricity price, ensuring that revenue accounting covers the benefits brought by policy support.

[0036] ④ Internal rate of return (IRR) and cost per kilowatt-hour are two core financial indicators. The former can directly reflect the long-term profit potential of a project, while the latter can accurately reflect the level of cost control. The two complement carbon reduction and energy output indicators.

[0037] (2) In terms of constructing input-type indicators, four core input-type indicators are set: land input (the intensity of land resource occupation by the project), initial investment cost, annual operation and maintenance cost, and equipment installed capacity, and the quantitative logic is improved: ① Land input refers to the total land area occupied by a new energy project throughout its construction, preparation, and operation. This includes the main power generation area (such as photovoltaic array areas and wind turbine foundation areas), auxiliary facilities areas (such as substations, operation and maintenance office buildings, and on-site roads), and temporary land occupation. It is a core indicator for measuring the intensity of land resource occupation by a project, directly reflecting the rationality of the project's layout and resource utilization efficiency under land resource constraints. Significant differences exist in land use patterns and unit installed capacity land occupation coefficients among different types of new energy projects. The quantitative formula is: ; This quantitative formula can comprehensively cover all types of land occupation scenarios mentioned in the description, ensuring that the indicator calculation and definition are completely consistent; in addition, the land occupation of auxiliary facilities covers specific areas such as roads and power distribution rooms.

[0038] ② Initial investment cost refers to the total capital investment in a project from preliminary planning, surveying and design, equipment procurement, construction to final acceptance and readiness for power generation. It is a concentrated reflection of resource consumption during the project's startup phase. Specifically, it includes equipment procurement costs, construction costs, preliminary expenses, and other costs. This indicator directly affects the project's return on investment and capital recovery period; its calculation formula is: ; ③ Annual operation and maintenance costs refer to the expenses incurred annually during the project's operation period to ensure continuous, safe, and efficient power generation. It is a core indicator reflecting the cost pressures during the project's operational phase. These mainly include equipment maintenance costs, personnel salaries, consumable and power costs, management fees, and other unforeseen operation and maintenance expenses. This indicator is closely related to the aging of the project's equipment and the level of operation and maintenance technology, directly affecting the project's continued profitability and operational stability. Its calculation formula is: ; ④ The installed capacity of the equipment refers to the total maximum power output that all the power generation equipment installed in the project can stably output under the design rated conditions.

[0039] S2.2 Based on the established evaluation index system, initial values ​​for input-type indicators and output-type indicators are calculated using basic carbon reduction data. Input-type indicators include land input, initial investment cost, annual operation and maintenance cost, and installed equipment capacity; output-type indicators include carbon emission reduction, annual power generation, annual operating revenue, internal rate of return, and cost per kilowatt-hour. Carbon emission reduction includes direct and indirect carbon emission reduction; direct carbon emission reduction is calculated based on annual on-grid electricity and the regional average carbon emission coefficient of thermal power; indirect carbon emission reduction is calculated based on the carbon emission intensity of traditional processes and the actual carbon emission intensity of the project's processes. The specific calculation methods for each indicator are shown in the evaluation index system above.

[0040] This embodiment, S2.2, comprehensively covers the core dimensions of the entire lifecycle of new energy projects. Input-type indicators such as land input and initial investment accurately reflect the project's resource and cost consumption; output-type indicators such as carbon emission reduction and annual power generation directly reflect the project's carbon reduction benefits and energy value. The selection of indicators balances economic efficiency with highlighting the core carbon reduction objective, avoiding the problems of single indicators or unbalanced emphasis in traditional assessments. The inclusion of the cost per kilowatt-hour indicator enables a linked consideration of benefits and costs, making the assessment more aligned with the actual operational needs of the project. This indicator system provides a comprehensive and focused core basis for the assessment, ensuring that the comprehensive carbon reduction efficiency evaluation can truly reflect the project's comprehensive value and providing precise support for project investment and policy formulation. Furthermore, by refining carbon emission reductions into direct and indirect categories, this approach breaks through the limitations of traditional assessments that only focus on emission reductions through power generation substitution, achieving a comprehensive accounting of carbon reduction benefits. Direct carbon emission reduction accounting reflects the substitution value of new energy sources for thermal power, while indirect carbon emission reduction reflects the carbon reduction contribution of project process optimization. The combination of these two approaches fully covers the carbon reduction path throughout the project's entire lifecycle. Clear accounting criteria make carbon emission reduction calculations more operational, avoiding problems such as ambiguity, duplication, or omissions in emission reduction statistics. Comprehensive carbon emission reduction data makes output-based indicators more realistic and reliable, improving the accuracy of the comprehensive carbon reduction efficiency evaluation coefficient. At the same time, the refined emission reduction classification provides direction for exploring the project's carbon reduction potential, helping projects improve carbon reduction benefits from both power generation substitution and process optimization, and enhancing the practical guiding significance of the methodology.

[0041] S2.3 Based on the initial values ​​of the input indicators and the output indicators, calculate the average value and standard deviation of each of the input and output indicators respectively. Based on the mean and standard deviation, different standard deviation standardization formulas are used according to the indicator type to calculate the standardized scores of input-type indicators and output-type indicators of each new energy project, and then integrate them to obtain a set of standardized indicator scores. The standard deviation standardization formula is designed by adjusting the direction of the numerator difference so that when the standardization score is positive, the standardization score and the overall carbon reduction efficiency level of the corresponding new energy project are positively correlated. This design stems from the clear difference in meaning between positive and negative indicators: the larger the value of a positive indicator, the stronger the carbon reduction effect, economic benefits, or energy output capacity of the new energy project, and the higher the overall carbon reduction efficiency. Carbon emission reduction, annual power generation, annual operating income, and internal rate of return in this invention all belong to this category, and their value growth will directly drive the improvement of overall efficiency; the negative indicator is the opposite, the smaller the value, the more conducive it is to the improvement of overall efficiency.

[0042] Therefore, the standardized formulas for the standard deviation of positive and negative indicators are as follows: For positive indicators: ; ; For negative indicators: ; ; in, For the first k The first evaluated new energy project Standardized scores of output indicators For the first k The first evaluated new energy project Initial values ​​for output-related indicators and For all evaluated new energy projects k The mean and standard deviation of each output-related indicator; For the first The first evaluated new energy project Standardized scores for each input-type indicator, For the first The first evaluated new energy project Initial values ​​for input-type indicators, and For all evaluated new energy projects The mean and standard deviation of each input indicator.

[0043] In this embodiment, S2.3 calculates the average and standard deviation of the indicators and adjusts the direction of the difference based on the indicator type, effectively eliminating the differences in dimensions and orders of magnitude between different indicators and solving the problem of comparability of indicators in traditional standardization. The positive correlation between the standardized score and the overall carbon reduction efficiency design allows the score to directly reflect the efficiency level of the project, improving the intuitiveness of the evaluation results. Compared with simple normalization methods, this approach better preserves the distribution characteristics of the indicator data and reduces the impact of data distortion on subsequent calculations. The formation of the standardized indicator score set provides high-quality input data for the C2R model, ensuring the accuracy of subsequent efficiency coefficient calculations, while also making it possible to compare indicators of different types of new energy projects, thus broadening the applicability of the evaluation method.

[0044] S3. Based on the C2R model of data envelopment analysis, the carbon reduction comprehensive efficiency evaluation coefficients of several new energy projects are calculated according to the standardized index score set.

[0045] Step S3 in this embodiment of the invention is specifically as follows: Extract the standardized scores of input-type indicators and output-type indicators from the standardized indicator score set of m new energy projects, sort them, and form the input indicator vector for each project. and output-type index vector The overall input indicator matrix is ​​obtained by integration. X and overall output index matrix Y ;in, , is a 4-dimensional vector; , is a 5-dimensional vector; (another notation) This is the summation vector, and subsequent model calculations involving the summation of weight coefficients and relaxation vector operations are all based on this vector.

[0046] Using the C2R model of Data Envelopment Analysis as the core framework, based on the overall input indicator matrix X and overall output index matrix Y Construct the first linear programming model for each new energy project (denoted as the i-th). j0 (For each project), two types of key data need to be identified: one is the project-specific input-type indicator vector. Output-type index vector Right now The evaluation object itself , is a necessary item for the core constraints of the model. The second is the reference matrix. and Its upper right corner is marked as j0 , used to distinguish the vector of the evaluation object itself; To remove the first j0 The remaining values ​​after evaluating the output index vectors of each new energy project are... 5 ×( m-1 A 3D output index matrix, To remove the first j0 The remaining values ​​after evaluating the input index vector of each new energy project 4 ×( m-1 The input indicator matrix is ​​3D, which is composed of the overall input indicator matrix. X and overall output index matrix Y Remove the first j0 The indicator vectors for each project are derived from this. Their core function is to avoid comparing the evaluated object with itself, thus improving the rigor of the evaluation. They are optional auxiliary items for optimizing the evaluation logic. Their application logic is as follows: [The reference matrix is ​​then used for...] , (The reference group excluding the evaluation subjects) serves as the "implicit reference benchmark set" for the model. When solving for the weight coefficients λ, the model will select valid peer data from this implicit reference benchmark set and compare it with the first... j0 Project , By comparing with the object being evaluated itself, we can determine whether its input and output are relatively optimal, thus avoiding evaluation distortion caused by self-comparison.

[0047] Vector of specific input indicators for each new energy project and output-type index vector Substituting these values ​​into the first linear programming model yields three types of results for each new energy project: first, the model's objective value, used to determine the effectiveness of the project's input-output; this objective value is the result of solving the objective function in the first linear programming model; second, the output relaxation vector. , The solution yields specific numerical values, which reflect the degree of input redundancy of the project at the current output level; thirdly, the input relaxation vector. , The solution yields a specific value, which is used to reflect the degree of output gap of the project under the current input level. The first linear programming model includes an objective function and a set of constraints. The objective function is set as follows: The constraint set is constructed based on the overall input-type indicator matrix, the overall output-type indicator matrix, and the input-type and output-type indicator vectors of each new energy project, combined with weight coefficient vectors, output relaxation vectors, and input relaxation vectors. The constraint set includes output constraints. Input constraints and the constraint of summing weight coefficients ; New energy projects with a target value of 0 are deemed to have valid input-output ratios, resulting in a set of projects with valid input-output ratios. If the target value is not 0, the project is deemed relatively invalid. A target benchmark project is selected from the set of projects with effective input-output ratios. A second linear programming model is constructed based on the overall input index matrix, the overall output index matrix, the output relaxation vector corresponding to the target benchmark project, and the input relaxation vector. Solving the second linear programming model yields the target parameter values ​​for each new energy project. The selection criteria for the target benchmark project are as follows: New energy projects within the set of effective input-output projects where the solution result of the first linear programming model is 0, and both the output relaxation vector and the input relaxation vector are zero, are selected as the target benchmark projects. Furthermore, the second linear programming model includes an objective function and a constraint set; the objective function is... The constraint set retains the core logic of the first stage and adds a 4-dimensional unit vector d. 4 and 5-dimensional unit vector d 5 Constraints.

[0048] The target parameter values ​​for each new energy project Substituting into the preset formula, the comprehensive carbon reduction efficiency evaluation coefficient of each new energy project is calculated. ,and, The range is between [0,1]. The larger the value, the higher the overall efficiency of carbon reduction from new energy sources.

[0049] The first linear programming model is as follows: ; ; in, , is the summation vector; To produce relaxation vectors (The dimension is 5, corresponding to 5 output indicators), fully represented as: ; For input relaxation vector (The dimension is 4-dimensional, corresponding to 4 input indicators), fully represented as: ; X As a matrix of overall input indicators, Y For the overall output indicator matrix ， , These are the weighting coefficients of the input-output indicators. The rule for determining the effectiveness of project input-output is: when the minimum value of the solution to the first linear programming model is 0, the project is considered valid. j0 The evaluated new energy projects are considered relatively effective in terms of input and output; if the minimum value is not 0, they are judged to be relatively ineffective. Furthermore, This is the canonical representation of the input relaxation vector in the Data Envelopment Analysis (C2R) model, which differs from the input relaxation vector defined earlier. (i.e., here) ) are different ways of writing the same vector, and ;in Emphasis is placed on the overall representation of the vector. Both focus on the transpose of the vector and have the same core meaning. They are used to measure the redundancy and inefficient use of input resources in the evaluated new energy project under the current output level. They are the core variables for quantifying the optimization space of input.

[0050] The second linear programming model is: ; ; The default formula is: ; It should be noted that the C2R model, short for Charnes-Cooper-Rhodes model, is a core classic model of Data Envelopment Analysis (DEA). It is specifically designed for evaluating the relative efficiency of multi-input, multi-output systems and is used to measure the degree of input-output optimization of the evaluated object. Its core logic is: by constructing a linear programming model, based on the input and output data of all evaluated objects, the "optimal efficiency frontier" is determined, and then the gap between a single object and this frontier is compared to quantify its relative efficiency. At the same time, input redundancy and output gaps can be identified through relaxation vectors.

[0051] The two-stage linear programming model construction and solution method proposed in Example S3 of this embodiment uses the first-stage model to screen out the effective set of input-output projects, accurately eliminating inefficient projects and focusing on core samples for subsequent evaluation. Using the benchmark projects in the effective project set as a reference, the second-stage model is constructed in conjunction with relaxation vectors, solving the problems of benchmark ambiguity and large parameter calculation deviations in the traditional C2R model during multi-project evaluation. The introduction of output relaxation vectors and input relaxation vectors clearly reflects the redundancy of project inputs and the gap in outputs, pointing the way for project optimization. The step-by-step solution method ensures the accuracy of the target parameter values ​​and allows the evaluation process to have both screening and optimization functions, extending carbon reduction efficiency evaluation from simple result judgment to process improvement, thus enhancing the practical value of the method. Furthermore, by employing clearly defined target benchmark selection rules, projects with zero target values ​​and zero relaxation vectors are prioritized, ensuring that benchmark projects possess optimal input-output efficiency and resource allocation capabilities. This precise benchmark selection method avoids the problems of arbitrary and insufficient representativeness in traditional assessments, providing a reliable reference standard for subsequent parameter calculations. Using the optimal project as the benchmark minimizes assessment bias, allowing the comprehensive carbon reduction efficiency evaluation coefficients of each project to more accurately reflect its relative level. Simultaneously, the clear selection rules enhance the standardization and repeatability of the assessment method; different assessors using these rules can obtain consistent benchmark projects, strengthening the credibility and cross-scenario applicability of the assessment results. Furthermore, by clearly defining the constraint set of the first linear programming model, a scientific boundary was established for the model solution, avoiding unrealistic parameter results during the solution process. The combination of output constraints, input constraints, and weight coefficient summation constraints comprehensively considers the input-output logic of new energy projects, ensuring that the model calculations closely match the actual operational scenarios of the projects. The constraint set, based on core indicator matrices and vectors, makes the constraints more targeted and solves the problem of distorted calculation results caused by fuzzy constraints in traditional models. The clear constraint system not only improves the reliability of results such as target values ​​and relaxation vectors but also makes the model solution process more logical and interpretable, providing solid theoretical support for subsequent benchmark project selection and target parameter calculation, and enhancing the rigor of the entire evaluation method.

[0052] S4. If the comprehensive carbon reduction efficiency evaluation coefficient is greater than or equal to the preset threshold, the comprehensive carbon reduction efficiency of the corresponding new energy project throughout its entire life cycle is deemed qualified.

[0053] Step S4 in this embodiment of the invention is specifically as follows: For several new energy projects, an evaluation is conducted according to a grading mechanism, based on the comprehensive carbon reduction efficiency evaluation coefficient. If the value is greater than or equal to a preset threshold, the overall carbon reduction efficiency of the corresponding new energy project throughout its entire life cycle is deemed qualified; simultaneously, it can be determined according to... The specific value range is further refined according to the grading mechanism to determine its specific efficiency level, such as excellent, good, good, or satisfactory, comprehensively quantifying the project's overall carbon reduction efficiency level. If the overall carbon reduction efficiency evaluation coefficient... If the project is deemed to be in need of improvement, it is recommended to strengthen the equipment operation and maintenance technology upgrades for several new energy projects.

[0054] It should be noted that the grading mechanism divides efficiency into five levels: Excellent, Good, Fair, Satisfactory, and Needs Improvement, clearly defining the corresponding level for each. The value range is defined, and the core characteristics of each level are described in detail from aspects such as input-output effectiveness, performance of core indicators, and resource utilization, which is used to intuitively determine the overall carbon reduction efficiency level of the evaluated new energy project. The following will explain the level classification mechanism in detail: ①Excellent level The range of values ​​is: =1.0, the core feature is described as: 1. Input and output are fully efficient (input relaxation vector S in the C2R model) - , Output relaxation vector S + 1. All are zero vectors, with no resource redundancy and no benefit gap; 2. Core output indicators such as carbon emission reduction and annual power generation have reached the optimal value under the current input level, and input indicators such as land input and initial investment cost are accurately configured; 3. The carbon reduction benefits and the rationality of technology input are optimally coordinated, which fully meets the evaluation goal of "concentrating resources on key links" and is a benchmark project in the industry.

[0055] ② Excellent level The value range is: 0.8≤ <1.0, the core characteristics are described as follows: 1. The input and output are almost completely effective, with minimal input redundancy or output gap (absolute value of non-zero component of relaxation vector ≤5%), and outstanding performance of core indicators; 2. Significant carbon emission reduction effect, with annual operating income, internal rate of return and other technical input rationality indicators at the upper level of the industry, and reasonable control of input costs; 3. Only slight optimization space exists in non-core indicators (such as auxiliary facility land occupation and a small amount of operation and maintenance consumable expenditure), and the overall efficiency level can serve as a regional demonstration project.

[0056] ③Good level The value range is: 0.6≤ <0.8, the core characteristics are described as follows: 1. Good input-output efficiency, with a small amount of input redundancy or output gap (absolute value of non-zero component of relaxation vector ≤15%), and no key indicator shortcomings; 2. Core output indicators such as carbon emission reduction and annual power generation meet the standards, and there is no significant waste of input costs, with optimization potential only in a single dimension (such as expanding green certificate trading revenue or improving equipment operation and maintenance efficiency); 3. The carbon reduction benefits are basically matched with the rationality of technology investment, which is above the industry average level, and efficiency improvement can be achieved without major adjustments.

[0057] ④ Qualified level The value range is: 0.4≤ <0.6, the core characteristics are described as follows: 1. The input-output efficiency basically meets the standard, but there is obvious input redundancy or output gap (absolute value of non-zero component of relaxation vector ≤30%), and some core indicators are poor; 2. Carbon emission reduction meets the basic requirements, but indicators such as annual power generation and internal rate of return are lower than the industry average, or there is excessive land investment and high initial investment costs; 3. There is a certain imbalance between carbon reduction benefits and the rationality of technology investment, and 1-2 key links need to be optimized in a targeted manner (such as optimizing land layout and improving operation and maintenance technology level) to improve efficiency.

[0058] ⑤ Level to be improved The range of values ​​is: <0.4 indicates the following core characteristics: 1. Low input-output efficiency, significant input redundancy or output gap (absolute value of non-zero component of relaxation vector >30%), and serious shortcomings in multiple dimensions; 2. Carbon emission reduction effect not meeting expectations, or serious waste of input costs (such as land idling, inefficient initial investment expenditure), and indicators such as annual operating revenue and cost per kilowatt-hour deviating from reasonable range; 3. Imbalance between carbon reduction and profit logic, requiring a comprehensive review of the input-output configuration logic, and fundamental efficiency improvement through restructuring the indicator optimization plan (such as adjusting equipment installed capacity and optimizing construction technology).

[0059] In this embodiment, S3-S4 refines the application process of the C2R model, transforming standardized indicators into structured input-output vectors and matrices, providing a clear data foundation for model calculation. The construction of the two-stage linear programming model achieves a closed-loop logic from indicator extraction to efficiency coefficient calculation, avoiding the problems of chaotic data utilization and disconnected calculation steps in traditional assessments. By solving the target parameters step-by-step and substituting them into the formula, the calculation of the comprehensive carbon reduction efficiency evaluation coefficient becomes more traceable, reducing human calculation errors. This standardized process not only improves the reliability of the assessment results but also makes the efficiency comparison of different new energy projects more fair, providing accurate and operable quantitative basis for project selection and optimization, effectively supporting scientific decision-making for carbon reduction projects.

[0060] Overall, the embodiments of the present invention have the following beneficial effects: This invention incorporates the entire lifecycle dimension into the basic data collection process, directly acquiring carbon reduction-related data for new energy projects from planning, construction, operation to decommissioning. This fills the gap in existing technologies lacking a lifecycle perspective, providing a complete and coherent data source for comprehensive efficiency assessment. Secondly, by clearly dividing the assessment indicator system into input-type and output-type categories, it accurately solves the problem of traditional methods failing to distinguish indicator attributes, establishing a clear match between resource input and carbon reduction effectiveness. Simultaneously, by standardizing the initial values ​​of the indicators, it eliminates interference caused by differences in the dimensions of different indicators, laying the foundation for subsequent objective assessment. Finally, it employs a C2R model based on data envelopment analysis for comprehensive efficiency calculation. This model inherently eliminates the need for subjective setting of indicator weights, automatically achieving objective weight allocation through relative efficiency analysis of input and output. This fundamentally avoids the weight imbalance problem caused by subjective weighting in traditional evaluation methods, ensuring the scientific validity and effectiveness of the comprehensive carbon reduction efficiency assessment results. Finally, through a preset threshold judgment mechanism, it achieves a precise and comprehensive assessment of the comprehensive carbon reduction efficiency of new energy projects. In summary, this invention constructs an evaluation index system for the comprehensive carbon reduction efficiency of new energy projects throughout their entire life cycle and introduces a C2R model based on data envelopment analysis for efficiency calculation. This not only effectively avoids unfair treatment of sensitive indicators during the evaluation process and ensures the weight balance among various evaluation indicators, but also eliminates interference caused by differences in investment scale, guides resource elements to focus on key links and key areas of production and operation, and ultimately significantly improves the carbon reduction efficiency and technological effects of new energy projects. It also solves the problem of distorted results caused by traditional evaluation methods that fail to distinguish the nature of input and output indicators and suffer from subjective weighting imbalances.

[0061] Example 2: Please see Figure 2 The present invention provides a device for evaluating the comprehensive carbon reduction efficiency of a new energy project throughout its entire life cycle, comprising a data acquisition module 10, an indicator module 20, a coefficient module 30, and an evaluation module 40. Among them, the data acquisition module 10 is used to acquire basic carbon reduction data of several new energy projects throughout their entire life cycle; Indicator module 20 is used to calculate the initial values ​​of input and output indicators based on carbon reduction basic data, and to standardize the initial values ​​to obtain a standardized indicator score set. Coefficient module 30 is used for the C2R model based on data envelopment analysis to calculate the comprehensive carbon reduction efficiency evaluation coefficients corresponding to several new energy projects based on the standardized index score set. The evaluation module 40 is used to determine that the carbon reduction comprehensive efficiency of the corresponding new energy project is qualified if the carbon reduction comprehensive efficiency evaluation coefficient is greater than or equal to the preset threshold.

[0062] It should be noted that the technical concept of this second embodiment is completely consistent with that of the first embodiment. The two maintain a high degree of synergy at the technical logic level. The specific technical details can be referred to the relevant description of the first embodiment, which will not be repeated here.

[0063] Example 3: This invention provides a computer-readable storage medium, which includes a stored computer program, wherein the computer program, when running, controls the device where the computer-readable storage medium is located to execute the aforementioned method for evaluating the comprehensive carbon reduction efficiency of a new energy project throughout its entire life cycle. The method for evaluating the comprehensive carbon reduction efficiency of a new energy project throughout its entire life cycle, if implemented as a software functional unit and used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0064] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present invention are performed entirely or partially. The computer can be a general-purpose computer, a special-purpose computer, a computer network, a network device, a user equipment, or other programmable device. The computer program or instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, the computer program or instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium, such as a floppy disk, hard disk, or magnetic tape; it can also be an optical medium, such as a digital video optical disc; or it can be a semiconductor medium, such as a solid-state drive. The computer-readable storage medium may be a volatile or non-volatile storage medium, or may include both types of storage media.

[0065] The above are preferred embodiments of the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for evaluating the comprehensive carbon reduction efficiency of a new energy project throughout its entire life cycle, characterized in that, include: To obtain basic carbon reduction data for several new energy projects throughout their entire life cycle; Based on the aforementioned carbon reduction baseline data, initial values ​​for input and output indicators are calculated, and the initial values ​​are standardized to obtain a standardized indicator score set. Based on the C2R model of data envelopment analysis, the comprehensive carbon reduction efficiency evaluation coefficients corresponding to the several new energy projects are calculated according to the standardized index score set. If the carbon reduction comprehensive efficiency evaluation coefficient is greater than or equal to the preset threshold, then the carbon reduction comprehensive efficiency of the corresponding new energy project throughout its entire life cycle is deemed qualified.

2. The method for evaluating the comprehensive carbon reduction efficiency of a new energy project throughout its entire life cycle as described in claim 1, characterized in that, Based on the C2R model using data envelopment analysis, the comprehensive carbon reduction efficiency evaluation coefficients for the aforementioned new energy projects are calculated according to the standardized index score set, specifically as follows: The standardized scores of input indicators and output indicators of the several new energy projects are extracted from the standardized indicator score set and sorted to form input indicator vector and output indicator vector for each project. The results are then integrated to obtain the overall input indicator matrix and the overall output indicator matrix. Based on the C2R model of data envelopment analysis, a two-stage linear programming model is established according to the overall input index matrix and the overall output index matrix. By solving the established model, the target parameter values ​​of each new energy project are obtained. The target parameter values ​​of each new energy project are substituted into a preset formula to calculate the comprehensive carbon reduction efficiency evaluation coefficient of each new energy project.

3. The method for evaluating the comprehensive carbon reduction efficiency of a new energy project throughout its entire life cycle as described in claim 2, characterized in that, Based on the C2R model of data envelopment analysis, a two-stage linear programming model is established according to the overall input index matrix and the overall output index matrix. By solving the established model, the target parameter values ​​for each new energy project are obtained, specifically: Based on the C2R model of data envelopment analysis, a first linear programming model is constructed according to the overall input index matrix and the overall output index matrix. For each new energy project, the corresponding exclusive input index vector and output index vector are substituted into the first linear programming model for solution, so as to obtain the target value, the output relaxation vector reflecting the degree of input redundancy, and the input relaxation vector reflecting the degree of output gap for each new energy project. The input-output of new energy projects with a target value of zero is determined to be valid, thus obtaining a set of projects with valid input-output. Select a target benchmark project from the set of effective input-output projects as a benchmark, construct a second linear programming model based on the overall input index matrix, the overall output index matrix, the output relaxation vector and input relaxation vector corresponding to the target benchmark project, solve the second linear programming model, and obtain the target parameter value for each new energy project.

4. The method for evaluating the comprehensive carbon reduction efficiency of a new energy project throughout its entire life cycle as described in claim 3, characterized in that, The selection rules for the target benchmark items are as follows: The target benchmark project is selected from the set of effective input-output projects where the solution result of the first linear programming model is zero and both the output relaxation vector and the input relaxation vector are zero.

5. The method for evaluating the comprehensive carbon reduction efficiency of a new energy project throughout its entire life cycle as described in claim 3, characterized in that, The first linear programming model includes a constraint set, which is constructed based on the overall input index matrix, the overall output index matrix, the input index vectors and output index vectors of each new energy project, combined with the weight coefficient vector, the output relaxation vector and the input relaxation vector. The constraint set includes output constraints, input constraints and weight coefficient summation constraints.

6. The method for evaluating the comprehensive carbon reduction efficiency of a new energy project throughout its entire life cycle as described in claim 1, characterized in that, The initial values ​​are standardized to obtain a standardized index score set, specifically: Based on the initial values ​​of the input indicators and the output indicators, the average value and standard deviation of each of the input indicators and the output indicators are calculated respectively. Based on the mean and standard deviation, different standard deviation standardization formulas are used according to the indicator type to calculate the standardized scores of input-type indicators and output-type indicators for each new energy project, and integrate them to obtain a standardized indicator score set. The standard deviation standardization formula is designed by adjusting the direction of the numerator difference so that when the standardized score is positive, the standardized score is positively correlated with the overall carbon reduction efficiency level of the corresponding new energy project.

7. A method for evaluating the comprehensive carbon reduction efficiency of a new energy project throughout its entire life cycle, as described in any one of claims 1-6, characterized in that... The input indicators include the intensity of land use, initial investment cost, and installed capacity of equipment; the output indicators include carbon emission reduction, annual power generation, and cost per kilowatt-hour.

8. The method for evaluating the comprehensive carbon reduction efficiency of a new energy project throughout its entire life cycle as described in claim 7, characterized in that, The carbon emission reductions include direct carbon emission reductions and indirect carbon emission reductions; The direct carbon emission reduction is calculated based on the annual on-grid electricity and the regional average carbon emission coefficient of thermal power; the indirect carbon emission reduction is calculated based on the carbon emission intensity of traditional processes and the carbon emission intensity of the actual process of the project.

9. A device for evaluating the comprehensive carbon reduction efficiency of a new energy project throughout its entire life cycle, characterized in that, It includes a data collection module, an indicator module, a coefficient module, and an evaluation module; The acquisition module is used to acquire basic carbon reduction data of several new energy projects throughout their entire life cycle. The indicator module is used to calculate the initial values ​​of input and output indicators based on the carbon reduction basic data, and to standardize the initial values ​​to obtain a standardized indicator score set. The coefficient module is used to calculate the comprehensive carbon reduction efficiency evaluation coefficients corresponding to the several new energy projects based on the C2R model of data envelopment analysis and the standardized index score set. The evaluation module is used to determine that the overall carbon reduction efficiency of the corresponding new energy project is qualified if the carbon reduction comprehensive efficiency evaluation coefficient is greater than or equal to a preset threshold.

10. A storage medium, characterized in that, The storage medium stores a computer program, which is called and executed by a computer to implement a comprehensive carbon reduction efficiency assessment method for the entire life cycle of a new energy project as described in any one of claims 1 to 8.

11. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed by the communication device, they implement the comprehensive carbon reduction efficiency assessment method for the entire life cycle of a new energy project as described in any one of claims 1 to 8.

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