Island city circular economy efficiency evaluation method based on data envelope analysis method

Abstract

The invention discloses an island city circular economy efficiency evaluation method based on a data envelope analysis method, which relates to the technical field of island city evaluation, and is characterized in that decision units participating in evaluation and an evaluation target are clarified in combination with the actual condition of island city circular economy, and the decision units are circular economy projects of island cities; according to the evaluation target, screening an input index and an output index of island city circular economy efficiency evaluation to construct an evaluation index system; and for the screened input indexes and output indexes, collecting related data of the decision-making unit in a fixed evaluation period. By calculating the efficiency value of each decision-making unit, the efficiency bottleneck of the island city in the circular economy can be identified, the slack variable result is further analyzed, the influence degree of the bottleneck on the overall efficiency is further quantified, and therefore a targeted resource optimization configuration strategy is formulated. Therefore, maximum utilization and minimum waste of resources in island city circular economy are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of island city evaluation, and in particular to an island city circular economy efficiency evaluation method based on a data envelopment analysis method. Background Art

[0002] The circular economy emphasizes the reuse of resources and environmental sustainability, which is particularly important for island cities. The ecosystems of island cities are relatively fragile and resources are limited. Therefore, achieving resource recycling and reducing environmental damage are important ways to ensure their sustainable development. The development of circular economy in island cities involves multiple links and departments, such as resource mining, processing, reuse and waste disposal. When formulating circular economy strategies, island cities need to understand the efficiency of various departments and links in order to identify bottlenecks and problems and propose targeted improvement measures. Data envelopment analysis, as a cutting-edge efficiency evaluation method, has the advantages of non-parametric, multi-indicator evaluation, flexibility and result interpretability. These advantages make data envelopment analysis widely used in circular economy efficiency evaluation in island cities.

[0003] Since the circular economy efficiency evaluation of island cities involves multiple input and output indicators, the amount of data for data envelopment analysis is too large, which makes it difficult to distinguish the relative efficiency of each indicator when evaluating the circular economy efficiency, making it difficult to formulate effective improvement measures. Therefore, how to distinguish the relative efficiency of each indicator and more accurately identify high-efficiency and low-efficiency indicators is the problem we need to solve. To this end, this paper proposes a circular economy efficiency evaluation method for island cities based on data envelopment analysis. Summary of the invention

[0004] The purpose of the present invention is to provide a method for evaluating the circular economy efficiency of island cities based on a data envelopment analysis method, so as to solve the problems raised in the above-mentioned background technology.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is: The circular economy efficiency evaluation method for island cities based on data envelopment analysis method includes the following steps: Step 1: Based on the actual situation of circular economy in island cities, clarify the decision-making units involved in the evaluation and the evaluation objectives, where the decision-making units are the various circular economy projects in island cities; Step 2: According to the evaluation objectives, select the input indicators and output indicators for the evaluation of circular economy efficiency of island cities to construct an evaluation indicator system; Step 3: For the selected input indicators and output indicators, collect relevant data of the decision-making unit within a fixed evaluation period and pre-process the data; Step 4: According to the scale return characteristics of the circular economy of island cities, based on the constant scale return model (CCR model) and the evaluation index system, a linear programming problem of the data envelopment analysis (DEA) model is constructed; Step 5: Using MaxDEA software, the processed data is input into the data envelopment analysis (DEA) model for solution to obtain the efficiency value and slack variable results of each decision-making unit; Step 6: Identify the relative efficiency of each input-output indicator through the calculated efficiency value and slack variable results to determine the high-efficiency indicator and the low-efficiency indicator, and formulate targeted improvement measures based on the analysis results to improve the circular economy efficiency of island cities.

[0006] A further improvement of the technical solution of the present invention is that in step 1, the process of determining the decision unit and the evaluation target is as follows: Step 101, according to the characteristics of the circular economy of the island city and the requirements of sustainable development, set specific evaluation goals, set the resource recycling rate improvement ratio and waste reduction ratio; Step 102, collect relevant information of all circular economy projects in the island city, conduct preliminary screening based on project scale, technology maturity and environmental impact factors, identify the relative efficiency level of each circular economy project, find out the high-efficiency and low-efficiency projects, and use the screened circular economy projects in the island city as decision-making units, with each project as an independent evaluation object; Step 103, classify the selected decision-making units according to project types and goals, analyze the input-output situation of each project, and extract the project operation information of each decision-making unit, including the project's construction time, investment scale, operation cycle, technical process and management model.

[0007] A further improvement of the technical solution of the present invention is that in step 2, the construction process of the evaluation index system is: Step 201, according to the determined evaluation target (the ratio of resource recycling rate improvement and the ratio of waste reduction), screen the input indicators and output indicators, wherein the input indicators include water resource input indicators, energy input indicators and waste input indicators, and the output indicators include resource recycling rate indicators and waste reduction indicators; Step 202, further analyze the selected input indicators and output indicators to determine their respective sub-indicators, wherein the sub-indicators of the water resource input indicator include the total water use quantum indicator, the fresh water use quantum indicator and the wastewater reuse quantum indicator; the sub-indicators of the energy input indicator include the total energy consumption sub-indicator, the renewable energy utilization quantum indicator and the energy recovery quantum indicator; the sub-indicators of the waste input indicator include the domestic waste input quantum indicator, the industrial waste input quantum indicator and the construction waste input quantum indicator; the sub-indicators of the resource recycling rate indicator include the water resource recycling rate sub-indicator, the energy recycling rate sub-indicator and the waste recycling rate sub-indicator; the sub-indicators of the waste reduction indicator include the domestic waste reduction quantum indicator, the industrial waste reduction quantum indicator and the construction waste reduction quantum indicator; Step 203, integrate the screened input indicators and output indicators to construct a complete evaluation indicator system, and standardize indicators of different dimensions to eliminate the impact of dimensions and make the indicators comparable.

[0008] A further improvement of the technical solution of the present invention is that in step 3, the process of collecting relevant data of the decision-making unit is as follows: Step 301, according to the input indicators and output indicators, analyze the sub-indicators of the input indicators and the output indicators, traverse the relevant reports and records, and collect relevant data, wherein the reports and records include water resource use records, energy consumption records, and waste treatment records, and the sources of the reports and records are government statistical departments, corporate financial statements, environmental monitoring reports, and project operation records; Step 302, pre-processing operations of data cleaning are performed on the extracted data of input indicators and output indicators, and inaccurate and untrue data, vague, superficial and one-sided data, identical, repeated, redundant and outdated data are eliminated through data cleaning, and missing data are processed by deletion and interpolation methods, and data from different data sources are integrated into a unified data sequence; Step 303, convert the data type of the integrated data, convert the data into a unified format, and re-check the data to ensure the accuracy and consistency of the data, and then store the data in the data warehouse.

[0009] A further improvement of the technical solution of the present invention is that in step 4, the construction process of the linear programming problem of the data envelopment analysis model is: Step 401, based on the preprocessed data, combined with the scale return characteristics of the island city circular economy, a constant scale return model (CCR model) is constructed, and the expression form of the constant scale return model (CCR model) is determined, wherein the constant scale return means that the scale return of the island city circular economy remains unchanged during the evaluation period; Step 402, combining the constructed constant returns to scale model (CCR model) and the evaluation index system, constructing the linear programming problem of the data envelopment analysis (DEA) model, including setting decision variables, constructing the objective function and adding constraints; Step 403, the setting of decision variables includes efficiency variables, weight variables and slack variables, the construction of objective function, the analysis of the ratio of maximized output to input, and the determination of the maximized efficiency value The added constraints include input-output ratio constraints, efficiency value constraints, weight non-negative constraints and slack variable constraints.

[0010] A further improvement of the technical solution of the present invention is that the constant returns to scale model (CCR model) is expressed as: ; in, is the maximum efficiency value, is the output weight variable, is the input weight variable, is the i-th input index value of the j-th decision-making unit, is the rth output indicator value of the jth decision-making unit, m is the number of input indicators, and s is the number of output indicators.

[0011] A further improvement of the technical solution of the present invention is that in step 5, the process of obtaining the efficiency value and slack variable result of each decision unit is as follows: Step 501, organize the pre-processed data into the format required by the MaxDEA software, organize the data according to the decision-making unit (DMU), each row represents a decision-making unit, and each column represents an indicator (including input indicators and output indicators). Use the MaxDEA software to create a new project and import the organized data file into the software; Step 502, starting data envelopment analysis (DEA) in the MaxDEA software, and calculating the input data according to the set constant returns to scale model (CCR model) and parameters; Step 503, after the analysis is completed, the MaxDEA software outputs the relative efficiency value of each decision-making unit, wherein the decision-making unit with an efficiency value of 1 is effective in data envelopment analysis (DEA), indicating that its output has reached the optimal level under given input conditions, and the decision-making unit with an efficiency value less than 1 is invalid in data envelopment analysis (DEA), and there is room for efficiency improvement; Step 504, analyzing the slack variable results output by the MaxDEA software, including input redundancy and output shortfall. Input redundancy indicates the amount of input that can be reduced while keeping the current output level unchanged, and output shortfall indicates the amount of output that can be increased while keeping the current input level unchanged.

[0012] A further improvement of the technical solution of the present invention is that the calculation expression of the relative efficiency value is: ; in, is the relative efficiency value, which indicates the output efficiency of the decision-making unit under given input conditions. is the output weight, which is used to measure the importance of the output indicator in the overall efficiency evaluation. is the actual value of the rth output indicator, indicating the actual performance of the decision-making unit on this output indicator. is the input weight, which is used to measure the importance of the input indicator in the overall efficiency evaluation. is the actual value of the ith input indicator, indicating the actual investment of the decision-making unit on the input indicator, m is the number of input indicators, s is the number of output indicators, The value range is ,when When , it means that the decision-making unit is DEA effective and the output reaches the optimal level; The calculation expression of the input redundancy is: ; in, is the redundancy of the i-th input indicator of the k-th decision-making unit, indicating the amount of input that can be reduced while keeping the current output level unchanged. is the actual value of the i-th input indicator of the k-th decision-making unit, is the weight variable in the linear programming problem, which indicates the reference weight of the j-th decision-making unit when evaluating the k-th decision-making unit. is the actual value of the i-th input indicator of the j-th decision-making unit, and n is the total number of decision-making units; The calculation expression of the output shortage is: ; in, is the shortfall of the rth output indicator of the kth decision-making unit, indicating the output that can be increased while keeping the current input level unchanged. is the actual value of the rth output indicator of the kth decision-making unit, is the actual value of the rth output indicator of the jth decision-making unit.

[0013] A further improvement of the technical solution of the present invention is that in step 6, the process of formulating the improvement measures is as follows: Step 601, based on the efficiency values ​​output by the MaxDEA software, sort the decision making units (DMUs) from high to low according to the efficiency values, identify the efficient DMUs with an efficiency value of 1 and the inefficient DMUs with an efficiency value lower than 1, and for the inefficient DMUs, check the slack variable results, including the redundancy of inputs and the shortage of outputs, and identify the input index with the largest redundancy and the output index with the largest shortage; Step 602, combining the efficiency value and the slack variable results, identifying the relative efficiency between the input-output indicators. The high efficiency indicator is characterized by maintaining low redundancy or high output in multiple decision-making units, while the low efficiency indicator is the opposite, characterized by high redundancy or low output in multiple decision-making units. Step 603, based on the analysis results, formulate improvement measures for the high-efficiency indicators and the low-efficiency indicators respectively, analyze the causes of the input indicators with large redundancy and the output indicators with large shortage, formulate targeted improvement measures, set improvement goals, and monitor the implementation effects of the improvement measures; Step 604, implement the formulated improvement measures to ensure that each measure is effectively implemented, and regularly monitor and evaluate the implementation effect of the improvement measures, and then adjust and improve the measures to achieve continuous improvement and efficiency enhancement.

[0014] Due to the adoption of the above technical solution, the present invention has the following technical advances compared with the prior art: The present invention provides an island city circular economy efficiency evaluation method based on a data envelopment analysis method. By calculating the efficiency value of each decision-making unit, the efficiency bottleneck of the island city in the circular economy can be identified, and then the slack variable results can be deeply analyzed to further quantify the impact of the bottleneck on the overall efficiency, thereby formulating a targeted resource optimization allocation strategy to ensure the maximum utilization of resources and minimized waste in the island city circular economy.

[0015] The present invention provides an island city circular economy efficiency evaluation method based on the data envelopment analysis method. Starting from multiple input and output indicators, it provides a comprehensive efficiency evaluation perspective for the island city circular economy, and comprehensively considers various input indicators such as water resources, energy and waste, as well as various output indicators such as resource recycling rate and waste reduction. It helps to comprehensively identify the advantages and disadvantages of the island city circular economy, thereby more accurately evaluating the overall efficiency level of the project. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0017] Figure 1 is a flow chart of the method of the present invention; Figure 2 It is a flow chart for constructing the linear programming problem of the data envelopment analysis model of the present invention; Figure 3 The figure is a flow chart for obtaining the efficiency values ​​and slack variable results of each decision-making unit in the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] Embodiment 1, as Figure 1 , Figure 2 As shown, the present invention provides an island city circular economy efficiency evaluation method based on a data envelopment analysis method, comprising the following steps: Step 1: Based on the actual situation of circular economy in island cities, clearly define the decision-making units involved in the evaluation and the evaluation objectives. The decision-making units are the circular economy projects in island cities. According to the characteristics of circular economy in island cities and the requirements of sustainable development, set specific evaluation objectives, set the resource recycling rate increase ratio and waste reduction ratio. The resource recycling rate covers the recycling of water resources, energy and waste (such as plastics, metals, paper, etc.), and the waste generation volume covers domestic waste, industrial waste and construction waste. Collect relevant information on all circular economy projects in island cities, conduct preliminary screening based on project scale, technology maturity and environmental impact factors, identify the relative efficiency level of each circular economy project, find out efficient and inefficient projects, and use the selected circular economy projects in island cities as decision-making units. Each project is an independent evaluation object. Classify the selected decision-making units according to project type and objectives, analyze the input-output situation of each project, and extract the project operation information of each decision-making unit, including the construction time, investment scale, operation cycle, technical process and management mode of the project; Step 2: According to the evaluation objectives, screen the input indicators and output indicators of the circular economy efficiency evaluation of island cities to construct an evaluation indicator system. According to the determined evaluation objectives (the proportion of resource recycling rate improvement and the proportion of waste reduction), screen the input indicators and output indicators. The input indicators include water resource input indicators, energy input indicators and waste input indicators. The output indicators include resource recycling rate indicators and waste reduction indicators. Further analyze the screened input indicators and output indicators to determine their respective sub-indicators. The sub-indicators of the water resource input indicator include the total water consumption quantum indicator, the fresh water consumption quantum indicator and the wastewater reuse quantum indicator. The total water consumption measures the project's consumption of water resources, the fresh water consumption measures the project's direct consumption of fresh water resources, and the wastewater reuse measures the project's recycling of wastewater. The sub-indicators of the energy input indicator include the total energy consumption sub-indicator, the renewable energy utilization quantum indicator and the energy recovery quantum indicator. The total energy consumption measures the project's energy consumption, the renewable energy utilization measures the project's utilization of renewable energy, and the energy recovery measures the project's recycling and reuse of energy. The sub-indicators of the waste input index include the domestic waste input quantum index, the industrial waste input quantum index and the construction waste input quantum index. The domestic waste input measures the amount of domestic waste treated by the project, the industrial waste input measures the amount of industrial waste treated by the project, and the construction waste input measures the amount of construction waste treated by the project; the sub-indicators of the resource recycling rate index include the water resource recycling rate sub-indicator, the energy recycling rate sub-indicator and the waste recycling rate sub-indicator. The water resource recycling rate measures the degree of recycling of water resources by the project, and the energy recycling rate measures the degree of recycling of energy by the project. , the waste recycling rate measures the degree of waste recycling and reuse of the project; the sub-indicators of the waste reduction indicator include the domestic waste reduction quantum indicator, the industrial waste reduction quantum indicator and the construction waste reduction quantum indicator. The domestic waste reduction measures the contribution of the project to the reduction of domestic waste, the industrial waste reduction measures the contribution of the project to the reduction of industrial waste, and the construction waste reduction measures the contribution of the project to the reduction of construction waste. The screened input indicators and output indicators are integrated to construct a complete evaluation indicator system, and the indicators of different dimensions are standardized to eliminate the impact of the dimension and make the indicators comparable; Step 3: For the selected input indicators and output indicators, collect relevant data of the decision-making unit within a fixed evaluation period and pre-process the data. According to the input indicators and output indicators, analyze the sub-indicators of the input indicators and output indicators, traverse the relevant reports and records, and collect relevant data. The reports and records include water resource use records, energy consumption records, and waste treatment records. The sources of the reports and records are government statistical departments, corporate financial statements, environmental monitoring reports, and project operation records. Perform data cleaning pre-processing operations on the extracted input indicators and output indicators. Through data cleaning, inaccurate and untrue data are eliminated, and vague, superficial, and one-sided data are eliminated. Eliminate identical, repeated, redundant, and outdated data, use deletion and interpolation methods to process missing data, and integrate data from different data sources into a unified data sequence. Perform data type conversion on the integrated data, convert the data into a unified format, and re-verify the data to ensure the accuracy and consistency of the data, and then store the data in the data warehouse; Step 4. According to the scale returns characteristics of the circular economy of island cities, based on the constant scale returns model (CCR model) and the evaluation index system, a linear programming problem of the data envelopment analysis (DEA) model is constructed. Based on the preprocessed data, a constant scale returns model (CCR model) is constructed in combination with the scale returns characteristics of the circular economy of island cities, and the expression form of the constant scale returns model (CCR model) is determined. Among them, the constant scale returns means that the scale returns of the circular economy of island cities remain unchanged during the evaluation period. Combined with the constructed constant scale returns model (CCR model) and the evaluation index system, the linear programming problem of the data envelopment analysis (DEA) model is constructed, including the setting of decision variables, the construction of the objective function and the addition of constraints. The setting of decision variables includes efficiency variables, weight variables and slack variables. For the construction of the objective function, the ratio of maximized output to input is analyzed to determine the maximized efficiency value. , the added constraints include input-output ratio constraints, efficiency value constraints, weight non-negative constraints and slack variable constraints, and the input-output ratio constraints are implemented to ensure that the output-input ratio of each decision-making unit does not exceed 1, that is, , for all decision making units , the efficiency value is constrained to ensure that the efficiency value is within a reasonable range, that is, , perform non-negative weight constraints to make the output weight variable and input weight variables All are greater than or equal to 0; Furthermore, the constant returns to scale model (CCR model) is expressed as: ; in, is the maximum efficiency value, is the output weight variable, is the input weight variable, is the i-th input index value of the j-th decision-making unit, is the rth output indicator value of the jth decision-making unit, m is the number of input indicators, and s is the number of output indicators; Step 5: Using MaxDEA software, the processed data is input into the data envelopment analysis (DEA) model for solution to obtain the efficiency value and slack variable results of each decision-making unit; Step 6: Identify the relative efficiency of each input-output indicator through the calculated efficiency value and slack variable results to determine the high-efficiency indicator and the low-efficiency indicator, and formulate targeted improvement measures based on the analysis results to improve the circular economy efficiency of island cities.

[0020] Embodiment 2, as Figure 3 As shown, based on Example 1, the present invention provides a technical solution: Preferably, in step 5, the process of obtaining the efficiency value and slack variable result of each decision unit is: Arrange the preprocessed data into the format required by MaxDEA software, organize the data according to the decision-making unit (DMU), each row represents a decision-making unit, and each column represents an indicator (including input indicators and output indicators). Use MaxDEA software to create a new project and import the organized data file into the software. Start data envelopment analysis (DEA) in MaxDEA software, and calculate the input data according to the set constant returns to scale model (CCR model) and parameters. After the analysis is completed, MaxDEA software outputs the relative efficiency value of each decision-making unit. Among them, the decision-making unit with an efficiency value of 1 is effective in data envelopment analysis (DEA), indicating that its output has reached the optimal level under given input conditions. The decision-making unit with an efficiency value less than 1 is invalid in data envelopment analysis (DEA), and there is room for efficiency improvement. Analyze the slack variable results output by MaxDEA software, including input redundancy and output shortage. Input redundancy indicates the amount of input that can be reduced while keeping the current output level unchanged, and output shortage indicates the amount of output that can be increased while keeping the current input level unchanged. Furthermore, the calculation expression of the relative efficiency value is: ; in, is the relative efficiency value, which indicates the output efficiency of the decision-making unit under given input conditions. is the output weight, which is used to measure the importance of the output indicator in the overall efficiency evaluation. is the actual value of the rth output indicator, indicating the actual performance of the decision-making unit on this output indicator. is the input weight, which is used to measure the importance of the input indicator in the overall efficiency evaluation. is the actual value of the ith input indicator, indicating the actual investment of the decision-making unit on the input indicator, m is the number of input indicators, s is the number of output indicators, The value range is ,when When , it means that the decision-making unit is DEA effective and the output reaches the optimal level; when When , it means that the decision-making unit is DEA ineffective and there is room for efficiency improvement. If the decision-making unit performs well in terms of output and the input is relatively small or reasonable, then The value will tend to 1, or even reach 1, indicating that the efficiency of the decision-making unit is high. If the decision-making unit performs poorly in terms of output, or too much input leads to low output efficiency, then The value will be less than 1, and the greater the difference from 1, the lower the efficiency of the decision-making unit and the greater the room for improvement; The calculation expression of input redundancy is: ; in, is the redundancy of the i-th input indicator of the k-th decision-making unit, indicating the amount of input that can be reduced while keeping the current output level unchanged. is the actual value of the i-th input indicator of the k-th decision-making unit, is the weight variable in the linear programming problem, which indicates the reference weight of the j-th decision-making unit when evaluating the k-th decision-making unit. is the actual value of the i-th input indicator of the j-th decision-making unit, n is the total number of decision-making units, when When , it means that the i-th input index of the k-th decision-making unit has no redundancy and the input amount has reached the optimal level; when When , it indicates that there is redundancy and the corresponding input can be reduced; The calculation expression of output shortfall is: ; in, is the shortfall of the rth output indicator of the kth decision-making unit, indicating the output that can be increased while keeping the current input level unchanged. is the actual value of the rth output indicator of the kth decision-making unit, is the actual value of the rth output indicator of the jth decision-making unit, when When , it means that the rth output indicator of the kth decision-making unit has reached the optimal level without any deficiency; when When , it means that the output is insufficient and the corresponding output can be increased; In step 6, the process of formulating improvement measures is as follows: Based on the efficiency value output by MaxDEA software, the decision-making units (DMUs) are sorted from high to low according to the efficiency value, and the efficient decision-making units with an efficiency value of 1 and the inefficient decision-making units with an efficiency value lower than 1 are identified. For the inefficient decision-making units, the slack variable results are checked, including the redundancy of input and the shortage of output, and the input index with the largest redundancy and the output index with the largest shortage are identified. Combining the efficiency value and the slack variable results, the relative efficiency between the input and output indicators is identified. The efficient indicators are characterized by maintaining low redundancy or high output in multiple decision-making units. On the contrary, the inefficient indicators are characterized by high redundancy or low output in multiple decision-making units. According to the analysis results, improvement measures are formulated for the efficient indicators and the inefficient indicators respectively. For the input indicators with large redundancy and the output indicators with large shortage, the reasons are analyzed, targeted improvement measures are formulated, improvement goals are set, and the implementation effect of the improvement measures is monitored. Among them, for inefficient input indicators and projects with inefficient water resource input, water-saving equipment and technology will be used to strengthen water resource management and allocation, and improve water resource utilization efficiency. For projects with inefficient energy input, energy-saving transformation will be implemented, and energy-saving technologies and equipment will be used to improve energy utilization efficiency. For inefficient output indicators and projects with low resource recycling rates, technological research and development and innovation will be strengthened, resource recycling and utilization processes will be improved, and resource recovery and reuse rates will be increased. For projects with low waste reduction, waste source reduction and classification management will be strengthened, green production methods and green consumption patterns will be promoted, and waste generation will be reduced. The improvement measures formulated will be implemented to ensure that various measures are effectively implemented, and the implementation effects of the improvement measures will be monitored and evaluated regularly, and then the measures will be adjusted and improved to achieve continuous improvement and efficiency improvement.

[0021] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. The circular economy efficiency evaluation method for island cities based on data envelopment analysis is characterized by: The following steps are involved: Step 1: Based on the actual situation of circular economy in island cities, clarify the decision-making units involved in the evaluation and the evaluation objectives, where the decision-making units are the various circular economy projects in island cities; Step 2: According to the evaluation objectives, select the input indicators and output indicators for the evaluation of circular economy efficiency of island cities to construct an evaluation indicator system; Step 3: For the selected input indicators and output indicators, collect relevant data of the decision-making unit within a fixed evaluation period and pre-process the data; Step 4: According to the scale return characteristics of the circular economy of island cities, based on the scale return invariant model and evaluation index system, a linear programming problem of the data envelopment analysis model is constructed; Step 5: Using MaxDEA software, the processed data is input into the data envelopment analysis model for solution to obtain the efficiency value and slack variable results of each decision-making unit; Step 6: Identify the relative efficiency of each input-output indicator through the calculated efficiency value and slack variable results to determine the high-efficiency indicator and the low-efficiency indicator, and formulate targeted improvement measures based on the analysis results.

2. The method for evaluating circular economy efficiency of island cities based on data envelopment analysis according to claim 1 is characterized by: In step 1, the process of determining the decision unit and the evaluation target is as follows: Step 101, according to the characteristics of the circular economy of the island city and the requirements of sustainable development, set specific evaluation goals, set the resource recycling rate improvement ratio and waste reduction ratio; Step 102, collect relevant information of all circular economy projects in the island city, conduct preliminary screening based on project scale, technology maturity and environmental impact factors, identify the relative efficiency level of each circular economy project, find out the high-efficiency and low-efficiency projects, and use the screened circular economy projects in the island city as decision-making units; Step 103, classify the selected decision-making units according to project types and goals, analyze the input-output situation of each project, and extract the project operation information of each decision-making unit, including the project's construction time, investment scale, operation cycle, technical process and management model.

3. The method for evaluating circular economy efficiency of island cities based on data envelopment analysis according to claim 2 is characterized by: In step 2, the construction process of the evaluation index system is as follows: Step 201, according to the determined evaluation target, screen the input indicators and output indicators, wherein the input indicators include water resource input indicators, energy input indicators and waste input indicators, and the output indicators include resource recycling rate indicators and waste reduction indicators; Step 202, further analyze the selected input indicators and output indicators to determine their respective sub-indicators, wherein the sub-indicators of the water resource input indicator include the total water use quantum indicator, the fresh water use quantum indicator and the wastewater reuse quantum indicator; the sub-indicators of the energy input indicator include the total energy consumption sub-indicator, the renewable energy utilization quantum indicator and the energy recovery quantum indicator; the sub-indicators of the waste input indicator include the domestic waste input quantum indicator, the industrial waste input quantum indicator and the construction waste input quantum indicator; the sub-indicators of the resource recycling rate indicator include the water resource recycling rate sub-indicator, the energy recycling rate sub-indicator and the waste recycling rate sub-indicator; the sub-indicators of the waste reduction indicator include the domestic waste reduction quantum indicator, the industrial waste reduction quantum indicator and the construction waste reduction quantum indicator; Step 203, integrating the screened input indicators and output indicators, constructing a complete evaluation indicator system, and standardizing indicators of different dimensions.

4. The method for evaluating circular economy efficiency of island cities based on data envelopment analysis according to claim 3 is characterized by: In step 3, the process of collecting the decision-making unit related data is as follows: Step 301, according to the input index and the output index, analyzing the sub-indicators of the input index and the output index, traversing the relevant reports and records, and collecting relevant data, wherein the reports and records include water resource use records, energy consumption records, and waste treatment records; Step 302, performing a pre-processing operation of data cleaning on the extracted data of input indicators and output indicators, and integrating the data from different data sources into a unified data sequence; Step 303, convert the data type of the integrated data, convert the data into a unified format, verify the data again, and then store the data in the data warehouse.

5. The method for evaluating circular economy efficiency of island cities based on data envelopment analysis according to claim 4 is characterized by: In step 4, the construction process of the linear programming problem of the data envelopment analysis model is as follows: Step 401, based on the preprocessed data, a constant returns to scale model is constructed in combination with the scale returns characteristics of the circular economy of the island city, and the expression form of the constant returns to scale model is determined; Step 402, combining the constructed invariant returns to scale model and the evaluation index system, constructing a linear programming problem of the data envelopment analysis model, including setting decision variables, constructing an objective function, and adding constraints; Step 403, the setting of decision variables includes efficiency variables, weight variables and slack variables, the construction of objective function, the analysis of the ratio of maximized output to input, and the determination of the maximized efficiency value The added constraints include input-output ratio constraints, efficiency value constraints, weight non-negative constraints and slack variable constraints.

6. The method for evaluating circular economy efficiency of island cities based on data envelopment analysis according to claim 5 is characterized by: The expression of the constant returns to scale model is: ； in, is the maximum efficiency value, is the output weight variable, is the input weight variable, is the i-th input index value of the j-th decision-making unit, is the rth output indicator value of the jth decision-making unit, m is the number of input indicators, and s is the number of output indicators.

7. The method for evaluating the circular economy efficiency of island cities based on data envelopment analysis according to claim 6 is characterized by: In step 5, the process of obtaining the efficiency value and slack variable results of each decision-making unit is as follows: Step 501, organize the pre-processed data into the format required by the MaxDEA software, organize the data according to the decision-making unit, use the MaxDEA software to create a new project, and import the organized data file into the software; Step 502, start data envelopment analysis in MaxDEA software, and calculate the input data according to the set constant returns to scale model and parameters; Step 503, after the analysis is completed, the MaxDEA software outputs the relative efficiency value of each decision-making unit, wherein the decision-making unit with an efficiency value of 1 is effective in data envelopment analysis, indicating that its output has reached the optimal level under given input conditions, and the decision-making unit with an efficiency value less than 1 is invalid in data envelopment analysis; Step 504, analyzing the slack variable results output by the MaxDEA software, including input redundancy and output shortfall. Input redundancy indicates the amount of input that can be reduced while keeping the current output level unchanged, and output shortfall indicates the amount of output that can be increased while keeping the current input level unchanged.

8. The method for evaluating circular economy efficiency of island cities based on data envelopment analysis according to claim 7 is characterized by: The calculation expression of the relative efficiency value is: ； in, is the relative efficiency value, is the output weight, is the actual value of the rth output indicator, is the input weight, is the actual value of the ith input indicator, m is the number of input indicators, s is the number of output indicators, The value range is ,when When , it means that the decision-making unit is DEA effective and the output reaches the optimal level; The calculation expression of the input redundancy is: ； in, is the redundancy of the i-th input indicator of the k-th decision-making unit, is the actual value of the i-th input indicator of the k-th decision-making unit, is the weight variable in the linear programming problem, which indicates the reference weight of the j-th decision-making unit when evaluating the k-th decision-making unit. is the actual value of the i-th input indicator of the j-th decision-making unit, and n is the total number of decision-making units; The calculation expression of the output shortage is: ； in, is the shortage of the rth output indicator of the kth decision-making unit, is the actual value of the rth output indicator of the kth decision-making unit, is the actual value of the rth output indicator of the jth decision-making unit.

9. The method for evaluating circular economy efficiency of island cities based on data envelopment analysis according to claim 8 is characterized by: In step 6, the process of formulating improvement measures is as follows: Step 601, based on the efficiency values ​​output by the MaxDEA software, sort the decision-making units from high to low according to the efficiency values, identify the efficient decision-making units with an efficiency value of 1 and the inefficient decision-making units with an efficiency value lower than 1, and for the inefficient decision-making units, check the slack variable results, including the redundancy of input and the shortage of output, and identify the input index with the largest redundancy and the output index with the largest shortage; Step 602, combining the efficiency value and the slack variable result, identifying the relative efficiency between the input-output indicators; Step 603, based on the analysis results, formulate improvement measures for the high-efficiency indicators and the low-efficiency indicators respectively, analyze the causes of the input indicators with large redundancy and the output indicators with large shortage, formulate targeted improvement measures, set improvement goals, and monitor the implementation effects of the improvement measures; Step 604, implement the formulated improvement measures, monitor and evaluate the implementation effects of the improvement measures regularly, and then adjust and improve the measures to achieve continuous improvement and efficiency enhancement.

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