Urban carbon sink construction evaluation method and system

Abstract

The invention provides an urban carbon sink construction evaluation method and system. The method belongs to the technical field of urban construction evaluation, and comprises the steps of determining an evaluation target and an evaluation range of urban carbon sink construction, and constructing a multi-dimensional evaluation index system based on the determined evaluation target and evaluation range; a dynamic evaluation model is designed through the constructed multi-dimensional evaluation index system, and various index data in the urban carbon sink construction process are monitored in real time through the evaluation system. Based on the actual demand of urban carbon sink construction, a multi-dimensional evaluation index system is constructed, a plurality of key dimensions such as natural ecological system carbon sink capacity, urban planning and management level and social and economic development conditions are covered, comprehensiveness and scientificity of evaluation are ensured, and the method is suitable for popularization and application. And public institutions and decision makers are helped to deeply understand the current situation and potential of urban carbon sink construction from multiple angles.

Description

Technical Field

[0001] The invention proposes an urban carbon sink construction evaluation method and system, belonging to the technical field of urban construction evaluation. Background Art

[0002] The carbon sink elements dominated by the natural ecological environment in the carbon cycle of the urban system are important measures to achieve the goal of low-carbon urban construction. In general, the natural ecosystem plays a major role in the carbon cycle, including forest carbon sink, green land carbon sink and water carbon sink. Enhancing the carbon sink capacity of urban forests, urban green land carbon sinks and urban waters is an important measure to offset urban carbon emissions and achieve the goal of low-carbon urban construction. The implementation of these measures is an important part of diagnosing the level of urban carbon sink construction. However, the current research on the evaluation of urban carbon sink construction level is insufficient, making it difficult to comprehensively and objectively evaluate the status of urban carbon sink construction. Summary of the invention

[0003] The present invention provides a method and system for evaluating urban carbon sink construction, which are used to solve the problems mentioned in the above background technology:

[0004] The present invention proposes a method for evaluating urban carbon sink construction, the method comprising:

[0005] Determine the evaluation objectives and scope of urban carbon sink construction, and build a multi-dimensional evaluation indicator system based on the determined evaluation objectives and scope;

[0006] Through the construction of a multi-dimensional evaluation index system, a dynamic evaluation model is designed to monitor the various indicator data in the process of urban carbon sink construction in real time through the evaluation system;

[0007] Input the data of various indicators into the dynamic evaluation model for dynamic evaluation and monitoring; adjust the evaluation indicators and weights according to the evaluation results;

[0008] The evaluation results are output in a visual way, and the evaluation results of different cities or regions are compared and analyzed, and carbon sink construction experience data is generated based on the comparative analysis results.

[0009] Furthermore, the evaluation objectives and scope of urban carbon sink construction are determined, and based on the determined evaluation objectives and scope, a multi-dimensional evaluation indicator system is constructed, including:

[0010] Determine the evaluation objectives and scope based on the actual situation and development needs of urban carbon sink construction;

[0011] Determine key evaluation dimensions, including carbon sequestration capacity of natural ecosystems, urban planning and management level, and social and economic development status;

[0012] Based on the determined evaluation objectives and scope, a multi-dimensional evaluation index system consisting of five-level parameters is constructed, and the urban carbon sink level value of each parameter is calculated.

[0013] Furthermore, the multi-dimensional evaluation index system is constructed, a dynamic evaluation model is designed, and various index data in the process of urban carbon sink construction are monitored in real time through the evaluation system, including:

[0014] Construct a data collection system based on multiple means, including remote sensing monitoring, Internet of Things devices and online surveys, and collect indicator data through the data collection system;

[0015] Preprocessing the collected indicator data includes cleaning, integration and standardization. Through data mining and machine learning algorithms, the preprocessed raw data is deeply analyzed to mine potential information and patterns.

[0016] Combining time series analysis and grey prediction, a dynamic evaluation model is constructed to reflect the historical trend and future direction of urban carbon sink construction;

[0017] Through the evaluation system, the urban carbon sink construction process is monitored in real time, and early warning thresholds are set to provide timely warnings and disposal of risk factors that affect carbon sink construction.

[0018] Furthermore, the data of various indicators are input into the dynamic evaluation model for dynamic evaluation and monitoring; and the evaluation indicators and weights are adjusted according to the evaluation results, including:

[0019] Verify and update the collected indicator data, and input the verified data into the dynamic evaluation model for analysis and evaluation;

[0020] According to the model output results, analyze the overall situation and performance of various dimensions of urban carbon sink construction, and combine historical data and trend analysis to predict the development trend of future carbon sink construction;

[0021] Based on the forecast results, the evaluation indicators and weights are adjusted and optimized regularly; and based on dynamic factors, the timeliness and accuracy of the evaluation system are ensured.

[0022] Furthermore, the evaluation results are output in a visual manner, and the evaluation results of different cities or regions are compared and analyzed, and carbon sink construction experience data is generated according to the comparative analysis results, including:

[0023] Present the evaluation results through visualization, including charts, maps, and animations; and design interactive interfaces;

[0024] Conduct horizontal comparison and vertical analysis of the evaluation results of different cities or regions;

[0025] Organize and analyze the comparative analysis results, generate an experience data report on urban carbon sink construction, and publish the experience data report.

[0026] The present invention proposes an urban carbon sink construction evaluation system, the system comprising:

[0027] System construction module: determine the evaluation objectives and scope of urban carbon sink construction, and build a multi-dimensional evaluation indicator system based on the determined evaluation objectives and scope;

[0028] Model design module: through the construction of a multi-dimensional evaluation index system, a dynamic evaluation model is designed, and the evaluation system is used to monitor the various indicator data in the process of urban carbon sink construction in real time;

[0029] Indicator adjustment module: input various indicator data into the dynamic evaluation model for dynamic evaluation and monitoring; adjust the evaluation indicators and weights according to the evaluation results;

[0030] Data generation module: Output the evaluation results in a visual way, conduct comparative analysis on the evaluation results of different cities or regions, and generate carbon sink construction experience data based on the comparative analysis results.

[0031] Furthermore, the system building module includes:

[0032] Scope determination module: Determine the evaluation objectives and scope according to the actual situation and development needs of urban carbon sink construction;

[0033] Dimension determination module: determine key evaluation dimensions, including carbon sequestration capacity of natural ecosystems, urban planning and management level, and social and economic development status;

[0034] Level value calculation module: Based on the determined evaluation objectives and evaluation scope, a multi-dimensional evaluation index system consisting of five-level parameters is constructed, and the urban carbon sink level value of each parameter is calculated.

[0035] Furthermore, the model design module includes:

[0036] Data collection module: Build a data collection system based on multiple means, including remote sensing monitoring, Internet of Things devices and online surveys, and collect indicator data through the data collection system;

[0037] Preprocessing module: preprocesses the collected indicator data, including cleaning, integration and standardization. Through data mining and machine learning algorithms, the preprocessed raw data is deeply analyzed to mine potential information and rules.

[0038] Model building module: Combine time series analysis and grey prediction to build a dynamic evaluation model that reflects the historical trend and future direction of urban carbon sink construction;

[0039] Threshold setting module: Through the evaluation system, the urban carbon sink construction process is monitored in real time, and early warning thresholds are set to provide timely warnings and disposal of risk factors that affect carbon sink construction.

[0040] Furthermore, the indicator adjustment module includes:

[0041] Analysis and evaluation module: Verify and update the collected indicator data, and input the verified data into the dynamic evaluation model for analysis and evaluation;

[0042] Trend prediction module: Analyze the overall status and performance of urban carbon sink construction in various dimensions based on the model output results, and predict the development trend of carbon sink construction in the future by combining historical data and trend analysis;

[0043] Optimization and adjustment module: regularly adjust and optimize the evaluation indicators and weights according to the prediction results; and based on dynamic factors, ensure the timeliness and accuracy of the evaluation system.

[0044] Furthermore, the data generation module includes:

[0045] Visualization module: present the evaluation results through visualization methods, including charts, maps and animations; and design interactive interfaces;

[0046] Comparative analysis module: conduct horizontal comparison and vertical analysis of the evaluation results of different cities or regions;

[0047] Report generation module: organize and analyze the comparative analysis results, generate urban carbon sink construction experience data report, and publish the experience data report.

[0048] Beneficial effects of the present invention:

[0049] Based on the actual needs of urban carbon sink construction, a multi-dimensional evaluation index system was constructed, covering multiple key dimensions such as natural ecosystem carbon sink capacity, urban planning and management level, and social and economic development status, ensuring the comprehensiveness and scientificity of the evaluation, which will help public institutions and decision makers to gain an in-depth understanding of the current status and potential of urban carbon sink construction from multiple perspectives;

[0050] By designing a dynamic evaluation model and real-time monitoring system, this method achieves continuous tracking and immediate feedback of the urban carbon sink construction process. By using multiple means of data collection such as remote sensing monitoring, Internet of Things devices and online surveys, combined with data mining and machine learning technology, it can quickly identify risk factors and take corresponding measures, enhancing the foresight and initiative of management;

[0051] Setting early warning thresholds and dynamic assessment models enables early warning of potential problems and risks in the process of urban carbon sink construction, effectively avoiding or reducing negative impacts, ensuring the smooth progress of carbon sink construction projects, and improving the city's ability to adapt to climate change;

[0052] By regularly adjusting the evaluation indicators and weights, we ensure that the evaluation system can timely reflect the new progress and new requirements of urban carbon sink construction, providing a scientific basis for policy formulation and resource allocation. Predictive analysis and trend insights help cities formulate more accurate carbon sink construction strategies and promote the efficient achievement of carbon reduction targets;

[0053] The visualization of the evaluation results not only improves the transparency and readability of the information, but also promotes horizontal comparison and vertical analysis among different cities, and builds a platform for communication and cooperation among cities. The release of the experience data report provides other cities with valuable practical cases and learning resources, accelerates the dissemination and application of carbon sink construction experience, and promotes the common progress of low-carbon development in cities. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a step diagram of the method of the present invention;

[0055] Figure 2 This is a system module diagram of the present invention. DETAILED DESCRIPTION

[0056] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0057] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. The embodiments described are only a part of the embodiments of the present invention, rather than 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.

[0058] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0059] One embodiment of the present invention, as Figure 1 As shown, a method for evaluating urban carbon sink construction comprises:

[0060] Determine the evaluation objectives and scope of urban carbon sink construction, and build a multi-dimensional evaluation indicator system based on the determined evaluation objectives and scope;

[0061] Through the construction of a multi-dimensional evaluation index system, a dynamic evaluation model is designed to monitor the various indicator data in the process of urban carbon sink construction in real time through the evaluation system;

[0062] Input the data of various indicators into the dynamic evaluation model for dynamic evaluation and monitoring; adjust the evaluation indicators and weights according to the evaluation results;

[0063] The evaluation results are output in a visual way, and the evaluation results of different cities or regions are compared and analyzed, and carbon sink construction experience data is generated based on the comparative analysis results.

[0064] The working principle of the above technical scheme is as follows: the main goal of the evaluation is to measure and improve the performance of cities in carbon sink construction in order to achieve the goal of low-carbon development. The scope of the evaluation covers the key areas of urban carbon sink construction, including the carbon sink capacity of natural ecosystems (such as forest carbon sink, green land carbon sink, and water carbon sink); ensure that the evaluation covers all aspects of urban carbon sink construction; and establish a detailed evaluation index system based on the determined evaluation goals and scope. For example, for the forest carbon sink dimension, an indicator matrix of five links, namely planning, implementation, inspection, results and feedback, is constructed. Each link is further subdivided into specific indicators (such as the "planning to improve the level of forest carbon sink" in the planning link) and scoring variables (such as the "target system for protecting forest vegetation carbon storage"). Each dimension and link has a corresponding scoring variable, which is assigned based on actual planning, statistical data, etc. to ensure the scientificity and accuracy of the evaluation; a set of calculation models is developed, which quantitatively evaluates the performance of each city in carbon sink construction from the five-level parameters of "city layer-dimension layer-link layer-indicator layer-score variable layer". The model design takes into account the correlation and hierarchy between indicators, and can dynamically adjust and adapt to the specific conditions of different cities; through various data sources such as statistical yearbooks and ecological and environmental bulletins, actual data on carbon sink construction in cities are collected. These data are input into the dynamic evaluation model for real-time monitoring and evaluation. The model calculates the scores of each link, each indicator, and each scoring variable through a series of formulas, and finally summarizes the overall score of urban carbon sink construction, that is, the carbon sink level value; the evaluation process is not static, but based on the feedback of the evaluation results, the evaluation indicators and weights are continuously adjusted to ensure the accuracy and adaptability of the evaluation system. If it is found that a city has a low score in a certain link or indicator, the city can be guided to strengthen construction in these weak links by increasing the weight of the corresponding indicator or optimizing the evaluation criteria; after the evaluation is completed, the evaluation results are output in a visual way such as charts and maps, which intuitively shows the level and gap of carbon sink construction in different cities. Through comparative analysis between different cities, it is possible to identify which cities have performed well in carbon sink construction, summarize best practices and lessons learned, and form carbon sink construction experience data that can be used as a reference for other cities.

[0065] The effects of the above technical solution are: by constructing a multi-dimensional evaluation index system, it covers the carbon sink capacity of natural ecosystems, urban planning and management levels, and social and economic development conditions, ensuring the comprehensiveness and scientificity of the evaluation, and helping cities to comprehensively consider and plan from multiple angles and levels during the carbon sink construction process; the application of dynamic evaluation models makes the management and decision-making of urban carbon sink construction more accurate and flexible. Real-time monitoring of various indicator data provides cities with timely feedback information, improving management efficiency and decision-making quality; the evaluation results are fed back into the process of indicator adjustment, which can promote the continuous optimization of the evaluation system itself, ensure that the evaluation standards are synchronized with the actual situation, and contribute to the continuous improvement and development of urban carbon sink construction; the visual output of the evaluation results facilitates the public to understand the effectiveness of urban carbon sink construction. By comparing the evaluation results of different cities or regions, it can identify excellent cases and backward areas, promote exchanges and cooperation between cities, share successful experiences in carbon sink construction, accelerate the process of backward areas learning from advanced areas, and overall improve the average level of urban carbon sink construction in my country; this evaluation method provides a powerful tool for achieving the goal of urban low-carbon development. Through continuous monitoring and evaluation, it effectively guides the planning and implementation of urban carbon reduction and carbon absorption projects and promotes the green and low-carbon transformation of cities; transparent evaluation results and visual displays can enhance the public's awareness and participation in urban carbon sink construction, enhance social responsibility, and promote the formation of low-carbon lifestyles and consumption patterns.

[0066] In one embodiment of the present invention, the evaluation target and evaluation scope of urban carbon sink construction are determined, and a multi-dimensional evaluation index system is constructed based on the determined evaluation target and evaluation scope, including:

[0067] Determine the evaluation objectives and scope based on the actual situation and development needs of urban carbon sink construction;

[0068] Determine key evaluation dimensions, including carbon sequestration capacity of natural ecosystems, urban planning and management level, and social and economic development status;

[0069] Based on the determined evaluation objectives and evaluation scope, a multi-dimensional evaluation index system consisting of five-level parameters is constructed, and the urban carbon sink level value of each parameter is calculated. The five-level parameters are city layer-dimensional layer-link layer-indicator layer-score variable layer. Among them, the calculation formula of the carbon sink level value of each parameter is as follows:

[0070] 1. Calculation of carbon sink construction level at the city scale:

[0071] In the construction of urban carbon sinks, the performance of the three construction dimensions contributes differently to the overall carbon sink construction level of the city, so different weight values ​​should be assigned to them. The carbon sink construction level value of the overall urban scale can be expressed by the following formula:

[0072]

[0073] In the above formula, Indicates the comprehensive value of the city’s reporting carbon sink construction level; Represents the city in three dimensions d{ }'s annual carbon sink construction level; Indicates the weights of the three dimensions in urban carbon sink construction. The weights of the three carbon sink construction dimensions are 0.4, 0.3, and 0.3 respectively.

[0074]

[0075] In the above formula, It represents the carbon sink construction level of a city from the perspective of carbon sink.

[0076] (II) Calculation of urban carbon sink construction level values ​​at dimensional scales:

[0077] The level of a city in each dimension of carbon sink construction is determined by the performance values ​​of the five construction management process links. The value of the city's carbon sink construction level on the dimensional scale is calculated using the following formula:

[0078]

[0079] In the above formula, Indicates the carbon sink performance level of the city in the s{P,I,C,O,F} link of the d dimension, including the performance value of the planning link ( )、Implementation performance value( )、Inspection link performance value( ), result performance value ( ) and feedback link performance value ( ).

[0080] The weights of the five links s{P, I, C, O, F} in the process of urban carbon sink construction are 0.2, 0.2, 0.15, 0.3, and 0.15, respectively. ={ , , , , }={0.2,0.2,0.15,0.3,0.15}.

[0081] (III) Calculation of urban carbon sink construction level at the link scale:

[0082] Urban carbon sink construction level value at link scale ( ) is calculated using the following formula:

[0083] ①P link

[0084]

[0085] ②I stage

[0086]

[0087] ③C link

[0088]

[0089] ④O stage

[0090]

[0091] ⑤F stage

[0092]

[0093] In the formula, is the score value of indicator i in link s{P,I,C,O,F}; n refers to the number of indicators i in link s, and the value of n varies in different links in different dimensions; It is the correction coefficient of each urban carbon sink construction dimension d in the P link and O link.

[0094] (IV) Calculation of carbon sink construction level value based on indicator scale:

[0095] In terms of indicator scale, the value of urban carbon sink construction level ( ) is calculated using the following formula:

[0096]

[0097] In the above formula, is the value of the score variable j in the indicator i in the s{P,I,C,O,F} link; m refers to the number of score variables j, and the number of score variables m will vary for different indicators.

[0098] When applied to each specific link, the calculation formula for each indicator in each management process link can be obtained:

[0099] ①The calculation formula of the index i value in the P link is as follows:

[0100]

[0101] ②The calculation formula of the index i value in the I stage is as follows:

[0102]

[0103] ③The calculation formula of the index i value in the C link is as follows:

[0104]

[0105] ④The calculation formula of the index i value in the O stage is as follows:

[0106]

[0107] ⑤The calculation formula of the index i value in the F link is as follows:

[0108]

[0109] (V) Calculation of carbon sink construction level value based on score variable scale:

[0110] The value of the evaluation index is determined by many factors, which are called score variables (j). Different evaluation indicators include score variables of different properties and numbers. These score variables have both quantitative and qualitative attributes and need to be processed for calculation.

[0111] Score variables for quantitative attributes:

[0112] In the above technical solution, the value of the urban carbon sink construction level is based on the percentage system, and the score variable of the quantitative attribute needs to be standardized from 0 to 100. In order to exclude the extreme outliers of the score variable of the quantitative attribute in the natural data between cities, the maximum and minimum values ​​need to be processed. The specific method is: the maximum value is based on Calculated, the minimum value is based on Calculated, where xj is the value of the score variable j, represents the average value of score variable j among a group of cities, Represents the standard deviation of this set of data.

[0113] After processing the extreme outliers, the score variable value is calculated using the following formula:

[0114] =

[0115] is the value of the score variable j. is the optimal value of the score variable j in a group of cities. There are two ways to obtain it Value: (a) If there is a relevant standard that specifies a benchmark or optimal value for the scoring variable j, (b) If there is no relevant standard, then .

[0116] is the worst value of the score variable j in a group of cities. There are two ways to obtain it. Value: (a) If there is a relevant standard that specifies the maximum value of the score variable j, (b) If there is no relevant standard, then = .

[0117] (VI) Correction factor:

[0118] Among the three dimensions of urban carbon sink construction, the level of carbon sink construction is greatly affected by objective conditions, so it needs to be corrected by applying correction coefficients. The various parameters and properties of the correction coefficients for constructing the level of urban carbon sink construction can be summarized in Table 1.

[0119] Table 1. Characteristics of urban carbon sink construction level correction coefficients based on urban objective conditions

[0120]

[0121]

[0122] According to the mechanism of constructing the correction coefficient of urban carbon sink construction level based on the objective conditions of cities discussed earlier in this section, when diagnosing the carbon sink construction level of a group of sample cities, different correction coefficients should be assigned to different cities according to the objective conditions of carbon sink construction. Cities with favorable objective conditions should be assigned correction coefficients with values ​​less than 1, and cities with unfavorable objective conditions should be assigned correction coefficients with values ​​greater than 1, to ensure that the diagnosed urban carbon sink construction level value can relatively truly reflect the urban carbon sink construction level. The specific correction coefficient calculation is shown in the following formula:

[0123] =

[0124] It represents the correction coefficient applied to the P and O links of the carbon sink construction dimension d, It represents the logarithm of the characteristic index value of city j in dimension d. Logarithmic processing will not change the relative relationship between a set of data, but it compresses the scale of the data set's variation range, making the data more stable and eliminating the extreme differences in the natural data set. However, it should be noted that the characteristic index value of some dimensions is less than the value of the natural logarithm (e), in which case the logarithmic result will be negative. Therefore, for such characteristic indicators, before performing logarithmic calculation, all characteristic index values ​​of the dimension need to be uniformly multiplied by a constant to ensure that all All are positive values.

[0125] It represents the median of the logarithmic values ​​of the characteristic indicators of all cities in dimension d. By applying the median, it can be achieved that: cities with logarithmic values ​​of characteristic indicators below the median are given a correction coefficient greater than 1, and cities with logarithmic values ​​of characteristic indicators above the median are given a correction coefficient less than 1.

[0126] Finally, considering that the correction coefficient should be within a certain reasonable range, this report uses (μ-σ) and (μ+σ) as the lower and upper limits of the correction coefficient, respectively. μ and σ represent the mean and standard deviation of the logarithmic values ​​of the objective condition characteristic indicators of a group of sample cities in various dimensions.

[0127] The working principle of the above technical scheme is as follows: the goal of urban carbon sink construction evaluation is to systematically evaluate the performance of cities in terms of carbon sink capacity of natural ecosystems such as forests, green spaces, and waters, urban planning and management levels, etc., aiming to discover construction results and problems, put forward improvement suggestions, and promote cities to move towards sustainable development. The evaluation scope is wide, covering key areas such as natural ecology and urban management of carbon sink construction; based on the above goals and scope, a multi-dimensional evaluation index system is constructed. The system is divided into five levels: city level, dimension level, link level, indicator level, and scoring variable level. Among them, the dimension level corresponds to three key evaluation dimensions: carbon sink capacity of natural ecosystems, urban planning and management level, and social and economic development status. Each dimension is subdivided into specific links, such as planning (P), implementation (I), inspection (C), results (O), and feedback (F), to ensure that the evaluation covers all links of the management process; in each link, specific evaluation indicators and scoring variables are designed for different dimensions. For example, in the planning phase of the forest carbon sink dimension, there are two scoring variables, namely, "the target system for protecting forest vegetation carbon storage" and "the target system for improving forest vegetation carbon storage", while in the implementation phase, the focus is on the indicator of "the guarantee for implementing the work of improving the level of forest carbon sinks", covering scoring variables in terms of mechanisms, funds, manpower and technology. Similarly, the green space carbon sink and water carbon sink dimensions also have their own specific evaluation indicators and scoring variables; according to official materials and research results such as planning documents, statistical yearbooks, and work reports, relevant data are collected, and each scoring variable is scored according to pre-set scoring points and rules. For example, the scoring points of forest carbon sinks such as "planning the target value of forest coverage" and "establishing a city-level forest chief" have clear scoring standards; using a calculation model composed of five-level parameters, from the city level to the scoring variable level, the scores of each city in various dimensions, links, indicators and even scoring variables are calculated. The scores are integrated by weighted average and other methods to obtain the comprehensive level of carbon sink construction at the city level. Among them, different dimensions and links have different weights in the total score, reflecting the differences in the importance of different aspects to carbon sink construction.

[0128] The effects of the above technical scheme are as follows: by constructing a multi-dimensional evaluation index system, the technical scheme can systematically and comprehensively evaluate the effectiveness of cities in carbon sink construction, which is conducive to urban managers to clarify the advantages and disadvantages of carbon sink construction and put forward targeted improvement measures, thereby promoting urban low-carbon transformation and sustainable development; due to the wide range of evaluation, covering multiple aspects such as natural ecosystem carbon sinks, the scheme can reveal the imbalanced state of urban carbon sink construction, identify which cities or regions have shortcomings in forest, green space, water carbon sink construction, or planning, management, public participation, etc., and then balance resource input and promote balanced development of various cities; the calculation model constructed based on five-level parameters can accurately calculate the carbon sink construction level of cities in different dimensions and links, providing a quantitative reference for optimizing resource allocation. For example, for links with low scores, technical assistance can be increased in a targeted manner to ensure the efficient use of carbon sink construction resources; incorporating public participation into the evaluation system can enhance the public's understanding of urban carbon sink construction, encourage more people to participate in low-carbon actions, form a good social atmosphere, and promote social consensus on carbon sink construction.

[0129] In one embodiment of the present invention, the multi-dimensional evaluation index system is constructed, a dynamic evaluation model is designed, and various index data in the process of urban carbon sink construction are monitored in real time through the evaluation system, including:

[0130] Construct a data collection system based on multiple means, including remote sensing monitoring, Internet of Things devices and online surveys, and collect indicator data through the data collection system;

[0131] Preprocessing the collected indicator data includes cleaning, integration and standardization. Through data mining and machine learning algorithms, the preprocessed raw data is deeply analyzed to mine potential information and patterns.

[0132] Combining time series analysis and grey prediction, a dynamic evaluation model is constructed to reflect the historical trend and future direction of urban carbon sink construction;

[0133] Through the evaluation system, the urban carbon sink construction process is monitored in real time, and early warning thresholds are set to provide timely warnings and disposal of risk factors that affect carbon sink construction.

[0134] The working principle of the above technical solution is: using remote sensing satellites, IoT sensor networks and online questionnaires to build a data collection system. Remote sensing monitoring can remotely perceive the coverage area and growth status of ecosystems such as forests, green spaces and waters; IoT devices monitor local environmental parameters in real time, such as soil moisture and air quality; online surveys collect social and economic information such as public opinions and participation. In this way, from macro to micro, data information related to carbon sink construction is fully covered; the collected raw data is cleaned to remove invalid, erroneous or duplicated information, and then the data from different sources are integrated to make it uniform in format for easy analysis. Standardization ensures the comparability of data across cities and time. Using data mining technology and machine learning algorithms, such as clustering and regression analysis, hidden patterns and associations are mined from a large amount of data to provide a basis for subsequent modeling; combined with time series analysis, historical data of urban carbon sink construction is analyzed to identify development trends and periodic laws. Using methods such as gray prediction models, the possible development trajectory of future carbon sink construction is predicted to form a dynamic evaluation model. This model not only reflects the current situation, but also predicts future trends, providing forward-looking references for decision makers. The dynamic evaluation model is embedded in the evaluation system to continuously monitor the urban carbon sink construction process and update various indicator data in real time. The system sets an early warning threshold. Once certain indicators are detected to deviate from the normal range, the early warning signal is immediately triggered to prompt managers to pay attention to specific problems or risk factors and take timely response measures, such as adjusting management strategies and increasing resource investment, to prevent potential negative impacts. Through the above process, urban managers can obtain instant and accurate information on the status of carbon sink construction and make scientific decisions based on data analysis results. At the same time, the evaluation results and early warning information can be fed back to the data collection and analysis stage to form a closed-loop management, continuously optimize the evaluation indicator system and model, improve the accuracy of prediction, and ultimately promote the continuous improvement and efficient management of urban carbon sink construction.

[0135] The effects of the above technical solutions are as follows: the multi-dimensional evaluation index system covers multiple aspects such as the carbon sink capacity of natural ecosystems, ensuring the comprehensiveness and scientificity of the evaluation, and being able to more accurately reflect the comprehensive level of urban carbon sink construction; through remote sensing monitoring, Internet of Things equipment, online surveys and other multi-means data collection systems, first-hand data can be obtained in real time, ensuring the timeliness and accuracy of the evaluation results, and providing decision makers with data-based decision support; the pre-processing and deep analysis steps use data mining and machine learning algorithms to mine potential information and laws from massive data, further improving the depth and breadth of data analysis, and providing a scientific basis for the optimization of carbon sink construction; the dynamic evaluation model combines time series analysis and gray prediction to not only reflect The historical trends of urban carbon sink construction can also predict future development directions, help identify risks in advance and take preventive measures, and improve the foresight and initiative of management; by setting early warning thresholds, the system can automatically identify and warn of potential risk factors, help urban managers respond in a timely manner, reduce uncertainty in the carbon sink construction process, and improve management efficiency and effectiveness; this technical solution can help cities discover deficiencies in carbon sink construction through real-time monitoring and scientific evaluation, adjust strategies in a timely manner, promote the implementation of carbon emission reduction and carbon absorption projects, and promote the transformation of cities to low-carbon and green development, in line with global climate change response and sustainable development goals; the introduction of online surveys and other means has increased public participation, made carbon sink construction more transparent, and helped to enhance public environmental awareness.

[0136] In one embodiment of the present invention, the data collection system based on multiple means is constructed, and the multiple means include remote sensing monitoring, Internet of Things devices and online surveys, and the index data is collected through the data collection system, including:

[0137] Clarify the specific indicator data required for the evaluation of urban carbon sink construction, including vegetation coverage, soil carbon storage, and greenhouse gas emissions, and set the goals and scope of data collection based on the indicators;

[0138] Different collection strategies are formulated for different types of data. For key indicator data, a timed collection strategy is adopted. For data that is more important but more difficult to collect, a high-frequency collection priority is set. For special events or emergencies, an emergency data collection plan is formulated.

[0139] Utilize cloud computing and big data technologies to build a distributed data collection platform and design a data quality monitoring mechanism to ensure the accuracy and reliability of collected data through real-time verification and anomaly detection;

[0140] During the data collection process, the data collection process is monitored in real time, and once data anomalies or equipment failures are found, intervention and processing are carried out;

[0141] After data collection is completed, the collected data is initially verified to check the integrity and consistency of the data; data cross-validation and outlier detection are used to conduct in-depth analysis and processing of the data, and data that does not meet quality requirements is eliminated or corrected.

[0142] The working principle of the above technical scheme is as follows: clarify the goal of urban carbon sink construction evaluation, and determine the specific indicator data to be collected based on this goal, such as vegetation coverage, soil carbon storage, greenhouse gas emissions, etc. The selection of these indicators is intended to fully reflect the current situation and effectiveness of urban carbon sink construction and provide a basis for subsequent analysis. Set the scope and standards of data collection according to the indicators to ensure the pertinence and comprehensiveness of data collection; formulate differentiated data collection strategies based on the characteristics of different indicators. For key indicators that need to be tracked continuously (such as vegetation coverage), timed automatic collection is adopted to ensure the continuity and stability of the time series of data. For important but difficult to collect data, high priority is set and more frequent collection frequency is adopted to ensure data quality. At the same time, an emergency collection plan is established to respond quickly to emergencies and obtain data in a timely manner, such as vegetation damage after natural disasters; a distributed data collection platform built using cloud computing and big data technology is used to achieve efficient and large-scale data collection. The platform has a built-in data quality monitoring mechanism, which automatically identifies data errors and anomalies through real-time verification and anomaly detection algorithms to ensure data accuracy and reliability. The monitoring system during the data collection process can detect and solve the fault problems of the data collection equipment in real time, ensuring the continuity and stability of data collection; after the data collection is completed, the system automatically performs a preliminary verification procedure to check the integrity and consistency of the data to ensure the availability of the data set. Data cross-validation and outlier detection technology are used to further screen out or correct data that does not meet the requirements and improve the quality of the data set. Through these steps, the data used for evaluation is accurate and provides reliable data support for scientific analysis; the entire data collection process also includes a continuous feedback and optimization mechanism for collection strategies and data processing methods. Based on the actual collection effect and data quality analysis, the collection strategy and optimization processing algorithm are continuously adjusted to ensure that the data collection system can adapt to the dynamic changes in the process of urban carbon sink construction and improve the accuracy and timeliness of the evaluation.

[0143] The effects of the above technical solutions are: through multi-means data collection, including remote sensing monitoring, Internet of Things equipment, online surveys, etc., the comprehensive coverage of data required for urban carbon sink construction evaluation is ensured, and key indicators such as vegetation coverage, soil carbon storage and greenhouse gas emissions can be monitored in real time. Different collection strategies are used for different data types to improve the accuracy and representativeness of data, especially the regular collection of key indicators to ensure the continuity and timeliness of data, providing a solid data foundation for carbon sink assessment; setting high-frequency collection priorities for data with high importance and high collection difficulty, and emergency data collection plans for special events or emergencies, significantly improve the system's response speed to data changes and ability to respond to emergencies, ensure the timeliness and flexibility of data collection, and facilitate rapid decision-making and implementation of response measures; the distributed data collection platform built using cloud computing and big data technology not only enhances data processing capabilities, but also realizes real-time data verification and anomaly detection through data quality monitoring mechanisms, effectively ensuring the accuracy and reliability of data. Real-time monitoring and rapid intervention in problems during data collection further ensure the stability and quality of data collection; after data collection is completed, the data is deeply analyzed and cleaned through multi-level data processing steps such as preliminary verification, data cross-validation and outlier detection, and data that does not meet quality requirements is eliminated, ensuring the high quality of the final data set used for evaluation, providing reliable data support for scientific analysis and decision-making; high-quality data provides accurate information support for the evaluation of urban carbon sink construction, enabling decision makers to identify the effectiveness and shortcomings of carbon sink construction based on detailed analysis results, adjust management strategies in a timely manner, optimize resource allocation, and promote continuous improvement and efficiency improvement of carbon sink construction; through refined data management and analysis, this technical solution helps cities identify the best practices and potential risks of carbon sink construction and promote the implementation of low-carbon development strategies.

[0144] In one embodiment of the present invention, the evaluation system is used to monitor the urban carbon sink construction process in real time, and to set an early warning threshold to promptly warn and deal with risk factors that affect the carbon sink construction, including:

[0145] Analyze the entire life cycle of urban carbon sink construction, subdivide risk factors into specific links and fields, and use the analytic hierarchy process (AHP) and expert scoring to assign weights to risk factors;

[0146] For each risk factor, a warning threshold matrix based on historical data and dynamic scenario analysis is constructed;

[0147] Combining the Internet of Things and big data, a multi-dimensional real-time monitoring system is constructed. The real-time monitoring system adopts a composite early warning algorithm to conduct in-depth mining of monitoring data, and early identification and early warning of risk factors;

[0148] For each identified risk, a corresponding risk response plan library is established, and the plan library is continuously optimized through retrospective analysis of historical warning cases;

[0149] Use complex network theory to analyze the interactions and transmission paths between risk factors, identify systemic risks, establish risk transmission models, evaluate the impact of a single risk event on the entire carbon sink construction system, and implement systemic risk prevention and control strategies;

[0150] Regularly evaluate the performance of the early warning system, and use machine learning algorithms to allow the system to self-learn historical warning cases and continuously optimize the warning model and threshold settings;

[0151] Build a public participation platform, release early warning information, and provide interactive feedback channels;

[0152] Establish a cross-departmental coordinated early warning and response mechanism, and adjust relevant policies in a timely manner based on the early warning results.

[0153] The working principle of the above technical solution is: by systematically analyzing the entire life cycle of urban carbon sink construction, potential risk factors are subdivided into different categories such as planning rationality, implementation efficiency, ecological technology, and policy implementation. The analytic hierarchy process (AHP) is used in combination with expert opinions to quantify and assign values ​​to various risk factors to ensure comprehensive identification and reasonable weight distribution, providing a scientific basis for subsequent early warning and management. Among them, the analytic hierarchy process (AHP) is a multi-criteria decision-making method, which is mainly used to solve complex problems in the decision-making process, especially when there are multiple goals or standards. For each type of risk factor, an early warning threshold matrix is ​​constructed. The matrix integrates historical data and dynamic situation analysis, sets static thresholds, and dynamically adjusts the threshold range according to external conditions such as seasonal changes, policy adjustments, and economic fluctuations to ensure the adaptability and flexibility of the early warning system; the Internet of Things technology is used to collect real-time data, combined with a big data analysis platform, and a composite early warning algorithm such as fusion neural network, fuzzy logic, and gray theory is used to deeply mine and analyze the data to achieve early identification of risk factors. The algorithm design comprehensively considers historical trends, seasonal fluctuations, anomaly detection, etc. to improve the accuracy and timeliness of early warnings; for the identified risks, a risk response plan library including emergency measures, scenario simulation, impact assessment and recovery strategies is established. The plan library will be continuously optimized based on historical case analysis to ensure the practicality and pertinence of the plan, and to be able to respond to risk events quickly and effectively; complex network theory is used to analyze the interaction and transmission path between risks, to build a risk transmission model, to evaluate the impact of single-point risks on the overall situation, to implement systematic prevention and control, to reduce risk diffusion, and to enhance the resilience of urban carbon sink construction; regular evaluation of the performance of the early warning system, including key indicators such as early warning accuracy and response speed, and through machine learning technology, the system can self-learn based on historical cases, continuously optimize the early warning model and threshold setting, and achieve self-evolution and continuous optimization; build a public participation platform to increase the awareness and participation of society in carbon sink construction risks, and at the same time disclose early warning information and processing processes to form a multi-party risk management ecosystem; establish a coordinated early warning and response mechanism across environmental, forestry, urban construction, finance and other departments to ensure that they can quickly link up when risks occur, and to ensure the smooth implementation and sustainable development of urban carbon sink construction projects.

[0154] The effects of the above technical solutions are as follows: by subdividing the risk factors of the entire process of urban carbon sink construction, using the analytic hierarchy process (AHP) and expert evaluation, the comprehensiveness and accuracy of risk identification are ensured, which helps urban managers to accurately locate potential problems and take targeted preventive measures; the constructed warning threshold matrix takes into account dynamic factors such as seasonal changes, policy changes, and economic fluctuations, so that the warning system can flexibly adapt to various situational changes, ensure the timeliness and accuracy of warnings, and effectively reduce the suddenness and severity of risks; combining the real-time monitoring system of the Internet of Things and big data, and using a composite warning algorithm, the intelligence level of risk identification is improved, and risk factors can be identified and warned at an early stage. With a rich risk response plan library, the emergency response capacity of urban carbon sink construction is enhanced; using complex The use of complex network theory to analyze risk transmission paths and assess systemic risks helps to formulate prevention and control strategies from a global perspective, reduce the chain reaction of risks, and enhance the resilience of urban carbon sink construction. Through machine learning, the early warning system is evaluated for performance and self-learning is carried out to continuously improve the accuracy and response speed of early warnings, ensuring the efficient operation and continuous evolution of the early warning system to adapt to new risks and challenges that may be encountered in the future. The public participation platform constructed not only enhances the public's awareness of the risks of carbon sink construction, but also creates a good social environment for urban carbon sink construction. The cross-departmental coordination mechanism established ensures rapid response and effective collaboration in the face of risks, and provides solid support for urban carbon sink construction by timely adjusting policies and providing financial guarantees, which promotes the smooth implementation and long-term sustainable development of carbon sink construction projects.

[0155] In one embodiment of the present invention, the step of inputting the data of various indicators into a dynamic evaluation model for dynamic evaluation and monitoring; and adjusting the evaluation indicators and weights according to the evaluation results, comprises:

[0156] Verify and update the collected indicator data, and input the verified data into the dynamic evaluation model for analysis and evaluation;

[0157] According to the model output results, analyze the overall situation and performance of various dimensions of urban carbon sink construction, and combine historical data and trend analysis to predict the development trend of future carbon sink construction;

[0158] Based on the forecast results, the evaluation indicators and weights are adjusted and optimized regularly; and based on dynamic factors, the timeliness and accuracy of the evaluation system are ensured.

[0159] The working principle of the above technical solution is: using a variety of data collection methods, including remote sensing monitoring, Internet of Things devices and online surveys, to obtain various indicator data related to urban carbon sink construction. These data are strictly verified to ensure their accuracy and timeliness, and then updated to the dynamic evaluation model. The verification process includes removing outliers and filling missing values ​​to ensure data quality; the verified data is input into the multi-dimensional evaluation model, and the model conducts a comprehensive assessment of the current status of urban carbon sink construction based on the five-level parameter structure of city layer-dimension layer-link layer-indicator layer-score variable layer. This includes not only the diagnosis of the current level of carbon sink construction, but also a detailed evaluation of each link of the management process such as planning, implementation, inspection, results and feedback. The model can monitor data changes in real time and dynamically evaluate the progress and effectiveness of urban carbon sink construction; based on the evaluation results output by the model, the overall situation and individual performance of the city in forest carbon sink, green land carbon sink, water carbon sink, planning management, implementation guarantee, inspection supervision, and result feedback are systematically analyzed. Combining historical data and time series analysis, using methods such as grey prediction, we predict the future development trend of urban carbon sink construction and identify possible bottlenecks and opportunities; regularly adjust the evaluation indicators and weights based on the prediction results and actual changes. Dynamic factors, such as urban development dynamics (such as population growth), are taken into consideration to ensure that the evaluation system can reflect the latest urban conditions and policy orientations, and maintain the timeliness and accuracy of the evaluation. The adjustment process involves adding, deleting or modifying evaluation indicators, as well as reallocating the weights of each indicator to better reflect the current focus and challenges of urban carbon sink construction; the evaluation results and the adjusted indicator system are output in a visual way to facilitate decision makers to intuitively understand the strengths and weaknesses of urban carbon sink construction. Comparative analysis between different cities can promote experience sharing and the promotion of best practices, and guide the optimization strategy and resource allocation of urban carbon sink construction.

[0160] The effects of the above technical solutions are as follows: by inputting the verified indicator data into the dynamic evaluation model, the accuracy and reliability of the evaluation results can be ensured, and the evaluation bias caused by data errors or obsolescence can be avoided, making the evaluation of urban carbon sink construction more scientific and accurate; the solution regularly adjusts the evaluation indicators and weights according to the model output results and forecast trends, which means that the evaluation system can be updated in a timely manner with the development dynamics of the city (such as population growth, land use changes), ensuring the timeliness and adaptability of the evaluation; by combining historical data and trend analysis for future predictions, urban managers can predict the challenges and opportunities that carbon sink construction may face in advance, and make early arrangements based on this, formulate forward-looking strategies and plans, avoid short-term behavior, and promote long-term sustainable development; the adjustment of evaluation indicators based on forecast results will help cities identify priority areas and weak links in carbon sink construction, thereby rationally allocating resources, improving the efficiency of the use of funds, technology, manpower and other resources, and avoiding waste of resources; with the openness and transparency of the evaluation results, it can enhance the public's cognition and participation in carbon sink construction, enhance the public's environmental awareness, and promote the formation of synergy among all sectors of society to jointly promote urban low-carbon development. Comparative analysis of carbon sink construction levels among different cities can stimulate healthy competition among cities, prompt backward cities to learn advanced experiences, promote overall improvement, and form a good demonstration effect of urban carbon sink construction.

[0161] In one embodiment of the present invention, the evaluation results are output in a visual manner, and the evaluation results of different cities or regions are compared and analyzed, and carbon sink construction experience data is generated according to the comparative analysis results, including:

[0162] Present the evaluation results through visualization, including charts, maps, and animations; and design interactive interfaces;

[0163] Conduct horizontal comparison and vertical analysis of the evaluation results of different cities or regions to extract the successful experiences and failed lessons in carbon sink construction of cities.

[0164] Organize and analyze the comparative analysis results, generate an experience data report on urban carbon sink construction, and publish the experience data report.

[0165] The working principle of the above technical solution is as follows: the system obtains detailed data on forest carbon sinks, green land carbon sinks, and water carbon sinks from various cities, including indicators and score variables for each link such as planning, implementation, inspection, results, and feedback. These data come from planning documents, statistical yearbooks, ecological environment bulletins, academic research, etc., and are processed through data cleaning and standardization to ensure the accuracy and consistency of the data; using the constructed urban carbon sink construction evaluation framework, the processed data is input into the calculation model composed of five levels of parameters, namely "city layer-dimension layer-link layer-indicator layer-score variable layer", and the carbon sink construction level scores of each city in different dimensions and links are calculated through quantitative scoring and weighted calculation; according to the objective conditions and characteristics of the city, such as annual precipitation, water area ratio, etc., the correction coefficient is calculated, and the scores of P (planning) and O (results) links are adjusted to reflect the actual carbon sink construction efforts of the city under different natural conditions, and avoid evaluation bias caused by regional differences; the evaluation results are displayed in a visual way such as charts, maps and animations, and the designed interactive interface allows users to easily query and compare the carbon sink construction performance of different cities or regions. These visualization tools intuitively display information such as city rankings, score distribution, and development trends, making it easier for users to understand and analyze. The system compares data between cities horizontally to identify cities with excellent or insufficient carbon sink construction performance, and analyzes the successful experiences and failure lessons behind them. Through longitudinal analysis, we observe the changing trends of cities over time and evaluate the effectiveness of carbon sink construction measures. Based on the results of comparative analysis, we organize analysis reports and summarize them into a report on urban carbon sink construction experience data. This report records in detail the effective practices, existing problems, and improvement directions of various cities in carbon sink construction, providing reference and reference for other cities. The report is released through official channels for use by public agency decision makers, researchers, and the public.

[0166] The effect of the above technical solution is: through visualization means such as charts, maps, animations, etc., the complex evaluation results of urban carbon sink construction can be intuitively displayed, so that non-professionals can easily understand the progress and results of carbon sink construction in various cities. The introduction of the interactive interface further enhances the user experience. Users can flexibly query and compare data from different cities according to their own needs, which improves the accessibility and transparency of information; horizontal comparison of the performance of different cities in forest carbon sinks, green land carbon sinks, water carbon sinks and other dimensions can quickly identify cities with significant construction results and backward cities, providing public institutions with clear benchmarks and improvement directions. Vertical analysis reveals the dynamic changes in urban carbon sink construction, which helps to adjust strategies in a timely manner; comparative analysis not only reveals the differences between cities, but also provides a basis for extracting successful experiences and lessons from failures; visual output is easy to spread, which can attract public attention and improve social awareness of carbon sink construction and climate change issues. The public participates in the supervision and discussion of carbon sink construction through the interactive interface, forming a good social atmosphere, which is conducive to promoting the popularization of low-carbon lifestyles; the publication of experience data reports provides valuable first-hand information for scientific research institutions, which helps to deepen scientific research. The data and case studies in the report can serve as a basis for future planning and accelerate the achievement of urban low-carbon transformation and sustainable development goals. Through comparative analysis, it is possible to identify which cities have potential or face challenges in specific carbon sink construction areas, thereby improving the overall efficiency and effectiveness of carbon sink construction.

[0167] One embodiment of the present invention, as Figure 2 As shown, a system for evaluating urban carbon sink construction includes:

[0168] System construction module: determine the evaluation objectives and scope of urban carbon sink construction, and build a multi-dimensional evaluation indicator system based on the determined evaluation objectives and scope;

[0169] Model design module: Through the construction of a multi-dimensional evaluation index system, a dynamic evaluation model is designed to monitor the various indicator data in the process of urban carbon sink construction in real time through the evaluation system;

[0170] Indicator adjustment module: input various indicator data into the dynamic evaluation model for dynamic evaluation and monitoring; adjust the evaluation indicators and weights according to the evaluation results;

[0171] Data generation module: Output the evaluation results in a visual way, conduct comparative analysis on the evaluation results of different cities or regions, and generate carbon sink construction experience data based on the comparative analysis results.

[0172] The working principle of the above technical scheme is as follows: the main goal of the evaluation is to measure and improve the performance of cities in carbon sink construction in order to achieve the goal of low-carbon development. The scope of the evaluation covers the key areas of urban carbon sink construction, including the carbon sink capacity of natural ecosystems (such as forest carbon sink, green land carbon sink, and water carbon sink), ensuring that the evaluation covers all aspects of urban carbon sink construction; based on the determined evaluation goals and scope, a detailed evaluation index system is established. For example, for the forest carbon sink dimension, an indicator matrix of five links, namely planning, implementation, inspection, results and feedback, is constructed, and each link is further subdivided into specific indicators (such as the "planning to improve the level of forest carbon sink" in the planning link) and scoring variables (such as the "target system for protecting forest vegetation carbon storage"). Each dimension and link has a corresponding scoring variable, which is assigned based on actual planning, statistical data, etc. to ensure the scientificity and accuracy of the evaluation; a set of calculation models is developed, which quantitatively evaluates the performance of each city in carbon sink construction from the five-level parameters of "city layer-dimension layer-link layer-indicator layer-score variable layer". The model design takes into account the correlation and hierarchy between indicators, and can dynamically adjust and adapt to the specific conditions of different cities; through various data sources such as statistical yearbooks and ecological and environmental bulletins, actual data on carbon sink construction in cities are collected. These data are input into the dynamic evaluation model for real-time monitoring and evaluation. The model calculates the scores of each link, each indicator, and each scoring variable through a series of formulas, and finally summarizes the overall score of urban carbon sink construction, that is, the carbon sink level value; the evaluation process is not static, but based on the feedback of the evaluation results, the evaluation indicators and weights are continuously adjusted to ensure the accuracy and adaptability of the evaluation system. If it is found that a city has a low score in a certain link or indicator, the city can be guided to strengthen construction in these weak links by increasing the weight of the corresponding indicator or optimizing the evaluation criteria; after the evaluation is completed, the evaluation results are output in a visual way such as charts and maps, which intuitively shows the level and gap of carbon sink construction in different cities. Through comparative analysis between different cities, it is possible to identify which cities have performed well in carbon sink construction, summarize best practices and lessons learned, and form carbon sink construction experience data that can be used as a reference for other cities.

[0173] The effects of the above technical solution are: by constructing a multi-dimensional evaluation index system, it covers the carbon sink capacity of natural ecosystems, urban planning and management levels, and social and economic development conditions, ensuring the comprehensiveness and scientificity of the evaluation, and helping cities to comprehensively consider and plan from multiple angles and levels during the carbon sink construction process; the application of dynamic evaluation models makes the management and decision-making of urban carbon sink construction more accurate and flexible. Real-time monitoring of various indicator data provides cities with timely feedback information, improving management efficiency and decision-making quality; the evaluation results are fed back into the process of indicator adjustment, which can promote the continuous optimization of the evaluation system itself, ensure that the evaluation standards are synchronized with the actual situation, and contribute to the continuous improvement and development of urban carbon sink construction; the visual output of the evaluation results facilitates the public to understand the effectiveness of urban carbon sink construction; by comparing the evaluation results of different cities or regions, it is possible to identify excellent cases and backward areas, promote exchanges and cooperation between cities, share successful experiences in carbon sink construction, accelerate the process of backward areas learning from advanced areas, and overall improve the average level of urban carbon sink construction in my country; this evaluation method provides a powerful tool for achieving the goal of urban low-carbon development. Through continuous monitoring and evaluation, it effectively guides the planning and implementation of urban carbon reduction and carbon absorption projects and promotes the green and low-carbon transformation of cities; transparent evaluation results and visual displays can enhance the public's awareness and participation in urban carbon sink construction, enhance social responsibility, and promote the formation of low-carbon lifestyles and consumption patterns.

[0174] In one embodiment of the present invention, the system building module includes:

[0175] Scope determination module: Determine the evaluation objectives and scope according to the actual situation and development needs of urban carbon sink construction;

[0176] Dimension determination module: determine key evaluation dimensions, including carbon sequestration capacity of natural ecosystems, urban planning and management level, and social and economic development status;

[0177] Level value calculation module: Based on the determined evaluation objectives and evaluation scope, a multi-dimensional evaluation index system consisting of five-level parameters is constructed, and the urban carbon sink level value of each parameter is calculated.

[0178] The working principle of the above technical scheme is as follows: the goal of urban carbon sink construction evaluation is to systematically evaluate the performance of cities in terms of carbon sink capacity of natural ecosystems such as forests, green spaces, and waters, urban planning and management levels, and social and economic development, aiming to discover construction results and problems, put forward improvement suggestions, and promote cities to move towards sustainable development. The evaluation scope is wide, covering the three key areas of natural ecology, urban management, and social economy in carbon sink construction; based on the above goals and scope, a multi-dimensional evaluation index system is constructed. The system is divided into five levels: city level, dimension level, link level, indicator level, and scoring variable level. Among them, the dimension level corresponds to three key evaluation dimensions: carbon sink capacity of natural ecosystems, urban planning and management level, and social and economic development status. Each dimension is subdivided into specific links, such as planning (P), implementation (I), inspection (C), results (O), and feedback (F), to ensure that the evaluation covers all links of the management process; in each link, specific evaluation indicators and scoring variables are designed for different dimensions. For example, in the planning phase of the forest carbon sink dimension, there are two scoring variables, namely, "the target system for protecting forest vegetation carbon storage" and "the target system for improving forest vegetation carbon storage", while in the implementation phase, the focus is on the indicator of "the guarantee for implementing the work of improving the level of forest carbon sinks", covering scoring variables in terms of mechanisms, funds, manpower and technology. Similarly, the green space carbon sink and water carbon sink dimensions also have their own specific evaluation indicators and scoring variables; according to official materials and research results such as planning documents, statistical yearbooks, and work reports, relevant data are collected, and each scoring variable is scored according to pre-set scoring points and rules. For example, the scoring points of forest carbon sinks such as "planning the target value of forest coverage" and "establishing a city-level forest chief" have clear scoring standards; using a calculation model composed of five-level parameters, from the city level to the scoring variable level, the scores of each city in various dimensions, links, indicators and even scoring variables are calculated. The scores are integrated by weighted average and other methods to obtain the comprehensive level of carbon sink construction at the city level. Among them, different dimensions and links have different weights in the total score, reflecting the differences in the importance of different aspects to carbon sink construction.

[0179] The effects of the above technical solution are: by constructing a multi-dimensional evaluation index system, this technical solution can systematically and comprehensively evaluate the effectiveness of cities in carbon sink construction, provide scientific and quantitative decision-making basis for public institutions, and help urban managers to clarify the advantages and disadvantages of carbon sink construction and propose targeted improvement measures, thereby promoting urban low-carbon transformation and sustainable development; due to the wide range of evaluation, covering natural ecosystem carbon sinks, urban planning management, social economy and other aspects, this solution can reveal the unbalanced state of urban carbon sink construction, and help public institutions identify which cities or regions have shortcomings in forest, green space, water carbon sink construction, or planning, management, public participation, etc., and then balance resource input and promote balanced development of cities; the calculation model constructed based on five-level parameters can accurately calculate the carbon sink construction level of cities in different dimensions and links, which provides a quantitative reference for public institutions to optimize resource allocation. For example, for links with low scores, public institutions can increase financial support, technical assistance or policy guidance to ensure the efficient use of carbon sink construction resources; incorporating social and economic development conditions and public participation into the evaluation system can enhance public awareness of urban carbon sink construction, encourage more public participation in low-carbon actions, form a good social atmosphere, and promote social consensus on carbon sink construction.

[0180] In one embodiment of the present invention, the model design module includes:

[0181] Data collection module: Build a data collection system based on multiple means, including remote sensing monitoring, Internet of Things devices and online surveys, and collect indicator data through the data collection system;

[0182] Preprocessing module: preprocesses the collected indicator data, including cleaning, integration and standardization. Through data mining and machine learning algorithms, the preprocessed raw data is deeply analyzed to mine potential information and rules.

[0183] Model building module: Combine time series analysis and grey prediction to build a dynamic evaluation model that reflects the historical trend and future direction of urban carbon sink construction;

[0184] Threshold setting module: Through the evaluation system, the urban carbon sink construction process is monitored in real time, and early warning thresholds are set to provide timely warnings and disposal of risk factors that affect carbon sink construction.

[0185] The working principle of the above technical solution is: using remote sensing satellites, IoT sensor networks and online questionnaires to build a data collection system. Remote sensing monitoring can remotely perceive the coverage area and growth status of ecosystems such as forests, green spaces and waters; IoT devices monitor local environmental parameters in real time, such as soil moisture and air quality; online surveys collect social and economic information such as public opinions and participation. In this way, from macro to micro, data information related to carbon sink construction is fully covered; the collected raw data is cleaned to remove invalid, erroneous or duplicated information, and then the data from different sources are integrated to make it uniform in format for easy analysis. Standardization ensures the comparability of data across cities and time. Using data mining technology and machine learning algorithms, such as clustering and regression analysis, hidden patterns and associations are mined from a large amount of data to provide a basis for subsequent modeling; combined with time series analysis, historical data of urban carbon sink construction is analyzed to identify development trends and periodic laws. Using methods such as gray prediction models, the possible development trajectory of future carbon sink construction is predicted to form a dynamic evaluation model. This model not only reflects the current situation, but also predicts future trends, providing forward-looking references for decision makers. The dynamic evaluation model is embedded in the evaluation system to continuously monitor the urban carbon sink construction process and update various indicator data in real time. The system sets an early warning threshold. Once certain indicators are detected to deviate from the normal range, the early warning signal is immediately triggered to prompt managers to pay attention to specific problems or risk factors and take timely response measures, such as adjusting management strategies and increasing resource investment, to prevent potential negative impacts. Through the above process, urban managers can obtain instant and accurate information on the status of carbon sink construction and make scientific decisions based on data analysis results. At the same time, the evaluation results and early warning information can be fed back to the data collection and analysis stage to form a closed-loop management, continuously optimize the evaluation indicator system and model, improve the accuracy of prediction, and ultimately promote the continuous improvement and efficient management of urban carbon sink construction.

[0186] The effects of the above technical solutions are as follows: the multi-dimensional evaluation index system covers multiple aspects such as the carbon sink capacity of natural ecosystems, urban planning and management level, etc., ensuring the comprehensiveness and scientificity of the evaluation, and being able to more accurately reflect the comprehensive level of urban carbon sink construction; through multi-means data collection systems such as remote sensing monitoring, Internet of Things equipment and online surveys, it is possible to obtain first-hand data in real time, ensuring the timeliness and accuracy of the evaluation results, and providing decision makers with data-based decision support; the preprocessing and in-depth analysis steps use data mining and machine learning algorithms to mine potential information and rules from massive data, further improving the depth and breadth of data analysis, and providing a scientific basis for the optimization of carbon sink construction; the dynamic evaluation model combines time series analysis and gray prediction, It can not only reflect the historical trends of urban carbon sink construction, but also predict future development directions, help identify risks in advance and take preventive measures, and improve the foresight and initiative of management; by setting early warning thresholds, the system can automatically identify and warn potential risk factors, help urban managers respond in a timely manner, reduce uncertainties in the carbon sink construction process, and improve management efficiency and effectiveness; this technical solution can help cities discover deficiencies in carbon sink construction through real-time monitoring and scientific evaluation, adjust strategies in a timely manner, promote the implementation of carbon emission reduction and carbon absorption projects, and promote the transformation of cities to low-carbon and green development, in line with global climate change response and sustainable development goals; the introduction of online surveys and other means has increased public participation, made carbon sink construction more transparent, and helped to enhance public environmental awareness.

[0187] In one embodiment of the present invention, the indicator adjustment module includes:

[0188] Analysis and evaluation module: Verify and update the collected indicator data, and input the verified data into the dynamic evaluation model for analysis and evaluation;

[0189] Trend prediction module: Analyze the overall status and performance of urban carbon sink construction in various dimensions based on the model output results, and predict the development trend of carbon sink construction in the future by combining historical data and trend analysis;

[0190] Optimization and adjustment module: regularly adjust and optimize the evaluation indicators and weights according to the prediction results; and based on dynamic factors, ensure the timeliness and accuracy of the evaluation system.

[0191] The working principle of the above technical solution is: using a variety of data collection methods, including remote sensing monitoring, Internet of Things devices and online surveys, to obtain various indicator data related to urban carbon sink construction. These data are strictly verified to ensure their accuracy and timeliness, and then updated to the dynamic evaluation model. The verification process includes removing outliers and filling missing values ​​to ensure data quality; the verified data is input into the multi-dimensional evaluation model, and the model conducts a comprehensive assessment of the current status of urban carbon sink construction based on the five-level parameter structure of city layer-dimension layer-link layer-indicator layer-score variable layer. This includes not only the diagnosis of the current level of carbon sink construction, but also a detailed evaluation of each link of the management process such as planning, implementation, inspection, results and feedback. The model can monitor data changes in real time and dynamically evaluate the progress and effectiveness of urban carbon sink construction; based on the evaluation results output by the model, the overall situation and individual performance of the city in forest carbon sink, green land carbon sink, water carbon sink, planning management, implementation guarantee, inspection supervision, and result feedback are systematically analyzed. Combining historical data and time series analysis, using methods such as grey prediction, we predict the future development trend of urban carbon sink construction and identify possible bottlenecks and opportunities; regularly adjust the evaluation indicators and weights based on the prediction results and actual changes. Dynamic factors, such as urban development dynamics (such as population growth, economic transformation) and policy changes (such as new environmental protection regulations, fiscal investment adjustments) are taken into consideration to ensure that the evaluation system can reflect the latest urban conditions and maintain the timeliness and accuracy of the evaluation. The adjustment process involves adding, deleting or modifying evaluation indicators, as well as reallocating the weights of each indicator to better reflect the current focus and challenges of urban carbon sink construction; the evaluation results and the adjusted indicator system are output in a visual way to facilitate decision makers to intuitively understand the strengths and weaknesses of urban carbon sink construction. Comparative analysis between different cities can promote experience sharing and the promotion of best practices, and guide the optimization strategy and resource allocation of urban carbon sink construction.

[0192] The effects of the above technical solution are: by inputting the verified indicator data into the dynamic evaluation model, the accuracy and reliability of the evaluation results can be ensured, and the evaluation bias caused by data errors or obsolescence can be avoided, making the evaluation of urban carbon sink construction more scientific and accurate; the solution regularly adjusts the evaluation indicators and weights according to the model output results and forecast trends, which means that the evaluation system can be updated in time with the development dynamics of the city (such as population growth, land use changes), ensuring the timeliness and adaptability of the evaluation; by combining historical data and trend analysis for future predictions, urban managers can foresee the challenges and opportunities that carbon sink construction may face in advance, and make arrangements in advance and formulate forward-looking strategies and plans accordingly. Avoid short-term behavior and promote long-term sustainable development; the adjustment of evaluation indicators based on the prediction results will help cities identify the priority areas and weak links of carbon sink construction, so as to rationally allocate resources, improve the efficiency of the use of funds, technology, human resources and other resources, and avoid waste of resources; through the interactive feedback of evaluation results and policy changes, public institutions can timely adjust and improve relevant policies, improve the pertinence and effectiveness of policies, and at the same time, provide a scientific basis for urban planning and management, and promote the optimization and upgrading of management measures; with the openness and transparency of evaluation results, it can enhance the public's cognition and participation in carbon sink construction, enhance the public's environmental awareness, and promote the formation of joint forces among all sectors of society to jointly promote low-carbon development in cities. The comparative analysis of carbon sink construction levels among different cities can stimulate healthy competition among cities, prompt backward cities to learn advanced experience, promote overall level improvement, and form a good demonstration effect of urban carbon sink construction.

[0193] In one embodiment of the present invention, the data generation module includes:

[0194] Visualization module: present the evaluation results through visualization methods, including charts, maps and animations; and design interactive interfaces;

[0195] Comparative analysis module: conduct horizontal comparison and vertical analysis of the evaluation results of different cities or regions to extract the successful experiences and failed lessons of cities in carbon sink construction.

[0196] Report generation module: organize and analyze the comparative analysis results, generate urban carbon sink construction experience data report, and publish the experience data report.

[0197] The working principle of the above technical solution is as follows: the system obtains detailed data on forest carbon sinks, green land carbon sinks, and water carbon sinks from various cities, including indicators and score variables for each link such as planning, implementation, inspection, results, and feedback. These data come from statistical yearbooks, ecological and environmental bulletins, academic research, etc., and are processed through data cleaning and standardization to ensure the accuracy and consistency of the data; using the constructed urban carbon sink construction evaluation framework, the processed data is input into the calculation model composed of five levels of parameters, namely "city layer-dimension layer-link layer-indicator layer-score variable layer", and the carbon sink construction level scores of each city in different dimensions and links are calculated through quantitative scoring and weighted calculation; according to the objective conditions and characteristics of the city, such as annual precipitation, water area ratio, etc., the correction coefficient is calculated, and the scores of P (planning) and O (results) links are adjusted to reflect the actual carbon sink construction efforts of the city under different natural conditions, and avoid evaluation bias caused by regional differences; the evaluation results are displayed in a visual way such as charts, maps and animations, and the designed interactive interface allows users to easily query and compare the carbon sink construction performance of different cities or regions. These visualization tools intuitively display information such as city rankings, score distribution, and development trends, making it easier for users to understand and analyze. The system compares data between cities horizontally to identify cities with excellent or insufficient carbon sink construction performance, and analyzes the successful experiences and failure lessons behind them. Through longitudinal analysis, we observe the changing trends of cities over time and evaluate the effectiveness of carbon sink construction measures. Based on the results of comparative analysis, we organize analysis reports and summarize them into a report on urban carbon sink construction experience data. This report records in detail the effective practices, existing problems, and improvement directions of various cities in carbon sink construction, providing reference and reference for other cities. The report is released through official channels for use by public agency decision makers, researchers, and the public.

[0198] The effect of the above technical solution is: through visualization means such as charts, maps, animations, etc., the complex evaluation results of urban carbon sink construction can be intuitively displayed, so that non-professionals can easily understand the progress and results of carbon sink construction in various cities. The introduction of the interactive interface further enhances the user experience. Users can flexibly query and compare data from different cities according to their own needs, which improves the accessibility and transparency of information; horizontally comparing the performance of different cities in forest carbon sinks, green land carbon sinks, water carbon sinks and other dimensions can quickly identify cities with significant construction results and lagging cities. Vertical analysis reveals the dynamic changes in urban carbon sink construction, which helps to adjust strategies in a timely manner; comparative analysis not only reveals the differences between cities, but also provides a basis for extracting successful experiences and lessons from failures; visual output is easy to spread, which can attract public attention and improve social awareness of carbon sink construction and climate change issues. The public participates in the supervision and discussion of carbon sink construction through the interactive interface, forming a good social atmosphere, which is conducive to promoting the popularization of low-carbon lifestyles; the release of experience data reports provides valuable first-hand information for scientific research institutions, etc., which helps to deepen scientific research and accurately formulate policies. The data and case studies in the report can serve as a basis for future planning and policy adjustments, accelerating the realization of urban low-carbon transformation and sustainable development goals. Through comparative analysis, it is possible to identify which cities have potential or face challenges in specific carbon sink construction areas, thereby improving the overall efficiency and effectiveness of carbon sink construction.

[0199] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for evaluating urban carbon sink construction, characterized in that: The method comprises: Determine the evaluation objectives and scope of urban carbon sink construction, and build a multi-dimensional evaluation indicator system based on the determined evaluation objectives and scope; Through the construction of a multi-dimensional evaluation index system, a dynamic evaluation model is designed to monitor the various indicator data in the process of urban carbon sink construction in real time through the evaluation system; Input the data of various indicators into the dynamic evaluation model for dynamic evaluation and monitoring; adjust the evaluation indicators and weights according to the evaluation results; The evaluation results are output in a visual way, and the evaluation results of different cities or regions are compared and analyzed, and carbon sink construction experience data is generated based on the comparative analysis results.

2. The urban carbon sink construction evaluation method according to claim 1, characterized in that: The evaluation objectives and scope of urban carbon sink construction are determined, and based on the determined evaluation objectives and scope, a multi-dimensional evaluation indicator system is constructed, including: Determine the evaluation objectives and scope based on the actual situation and development needs of urban carbon sink construction; Determine key evaluation dimensions, including carbon sequestration capacity of natural ecosystems, urban planning and management level, and social and economic development status; Based on the determined evaluation objectives and scope, a multi-dimensional evaluation index system consisting of five-level parameters is constructed, and the urban carbon sink level value of each parameter is calculated.

3. The urban carbon sink construction evaluation method according to claim 1, characterized in that: The multi-dimensional evaluation index system is constructed, a dynamic evaluation model is designed, and the evaluation system is used to monitor various index data in the process of urban carbon sink construction in real time, including: Construct a data collection system based on multiple means, including remote sensing monitoring, Internet of Things devices and online surveys, and collect indicator data through the data collection system; Preprocessing the collected indicator data includes cleaning, integration and standardization. Through data mining and machine learning algorithms, the preprocessed raw data is deeply analyzed to mine potential information and patterns. Combining time series analysis and grey prediction, a dynamic evaluation model is constructed to reflect the historical trend and future direction of urban carbon sink construction; Through the evaluation system, the urban carbon sink construction process is monitored in real time, and early warning thresholds are set to provide timely warnings and disposal of risk factors that affect carbon sink construction.

4. The urban carbon sink construction evaluation method according to claim 1, characterized in that: The data of various indicators are input into the dynamic evaluation model for dynamic evaluation and monitoring; According to the evaluation results, the evaluation indicators and weights are adjusted, including: Verify and update the collected indicator data, and input the verified data into the dynamic evaluation model for analysis and evaluation; According to the model output results, analyze the overall situation and performance of various dimensions of urban carbon sink construction, and combine historical data and trend analysis to predict the development trend of future carbon sink construction; Based on the forecast results, the evaluation indicators and weights are adjusted and optimized regularly; and based on dynamic factors, the timeliness and accuracy of the evaluation system are ensured.

5. The urban carbon sink construction evaluation method according to claim 1, characterized in that: The evaluation results are output in a visual manner, and the evaluation results of different cities or regions are compared and analyzed. Based on the comparative analysis results, carbon sink construction experience data is generated, including: Present the evaluation results through visualization, including charts, maps, and animations; and design interactive interfaces; Conduct horizontal comparison and vertical analysis of the evaluation results of different cities or regions; Organize and analyze the comparative analysis results, generate an experience data report on urban carbon sink construction, and publish the experience data report.

6. An urban carbon sink construction evaluation system, characterized in that: The system comprises: System construction module: determine the evaluation objectives and scope of urban carbon sink construction, and build a multi-dimensional evaluation indicator system based on the determined evaluation objectives and scope; Model design module: Through the construction of a multi-dimensional evaluation index system, a dynamic evaluation model is designed to monitor the various indicator data in the process of urban carbon sink construction in real time through the evaluation system; Indicator adjustment module: input various indicator data into the dynamic evaluation model for dynamic evaluation and monitoring; adjust the evaluation indicators and weights according to the evaluation results; Data generation module: Output the evaluation results in a visual way, conduct comparative analysis on the evaluation results of different cities or regions, and generate carbon sink construction experience data based on the comparative analysis results.

7. The urban carbon sink construction evaluation system according to claim 6 is characterized in that: The system building modules include: Scope determination module: Determine the evaluation objectives and scope according to the actual situation and development needs of urban carbon sink construction; Dimension determination module: determine key evaluation dimensions, including carbon sequestration capacity of natural ecosystems, urban planning and management level, and social and economic development status; Level value calculation module: Based on the determined evaluation objectives and evaluation scope, a multi-dimensional evaluation index system consisting of five-level parameters is constructed, and the urban carbon sink level value of each parameter is calculated.

8. The urban carbon sink construction evaluation system according to claim 6 is characterized in that: The model design module includes: Data collection module: Build a data collection system based on multiple means, including remote sensing monitoring, Internet of Things devices and online surveys, and collect indicator data through the data collection system; Preprocessing module: preprocesses the collected indicator data, including cleaning, integration and standardization. Through data mining and machine learning algorithms, the preprocessed raw data is deeply analyzed to mine potential information and rules. Model building module: Combine time series analysis and grey prediction to build a dynamic evaluation model that reflects the historical trend and future direction of urban carbon sink construction; Threshold setting module: Through the evaluation system, the urban carbon sink construction process is monitored in real time, and early warning thresholds are set to provide timely warnings and disposal of risk factors that affect carbon sink construction.

9. The urban carbon sink construction evaluation system according to claim 6, characterized in that: The indicator adjustment module includes: Analysis and evaluation module: Verify and update the collected indicator data, and input the verified data into the dynamic evaluation model for analysis and evaluation; Trend prediction module: Analyze the overall status and performance of urban carbon sink construction in various dimensions based on the model output results, and predict the development trend of carbon sink construction in the future by combining historical data and trend analysis; Optimization and adjustment module: regularly adjust and optimize the evaluation indicators and weights according to the prediction results; and based on dynamic factors, ensure the timeliness and accuracy of the evaluation system.

10. The urban carbon sink construction evaluation system according to claim 6, characterized in that: The data generation module comprises: Visualization module: present the evaluation results through visualization methods, including charts, maps and animations; and design interactive interfaces; Comparative analysis module: conduct horizontal comparison and vertical analysis of the evaluation results of different cities or regions; Report generation module: organize and analyze comparative analysis results, generate urban carbon sink construction experience data report, and publish the experience data report.

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