Risk quantitative evaluation method and system for full-period development of forestry carbon sink project

Abstract

The invention provides a risk quantitative evaluation method and system for full-cycle development of a forestry carbon sink project, and the method comprises the following steps: dividing the risk factors of the full-cycle development of the forestry carbon sink project into a preparation stage risk, an implementation stage risk and a carbon sink transaction stage risk, and obtaining the risk data of each stage. By scientifically and systematically determining risk factors, constructing an evaluation index system, determining index weights and carrying out risk quantitative scoring, accurate quantitative evaluation of the full-cycle development risk of the forestry carbon sink project is realized, the defect of strong subjectivity of a traditional qualitative evaluation method is overcome, and accurate data support is provided for project developers; through risk level division, a project developer can visually understand the project risk degree, a targeted risk coping strategy can be conveniently formulated according to the risk level, the project risk management level is improved, the project development risk is reduced, smooth implementation and income of the project are guaranteed, and sustainable development of the forestry carbon sink project is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of forestry carbon sink project development assessment, and particularly relates to a risk quantification assessment method and system for the whole-cycle development of forestry carbon sink projects. Background Art

[0002] With the increasing global attention to climate change issues, forestry carbon sink projects, as an important carbon emission reduction means, are playing an increasingly important role in addressing climate change. Forestry carbon sink projects absorb carbon dioxide in the atmosphere and fix it in forest vegetation and soil through activities such as afforestation and forest management, thereby achieving the goal of carbon emission reduction. However, during the whole-cycle process of forestry carbon sink projects from project planning, implementation to finally generating carbon emission reduction benefits, they face many risks.

[0003] In the project planning stage, there are risks of policy and regulation changes; for example, changes in national or local subsidy policies and access standards for forestry carbon sink projects will affect the feasibility and economic benefits of the project; market risks cannot be ignored either. The carbon market price fluctuates greatly. If the price is overestimated in the early stage of project development and the price drops during actual transactions, the project's income will be reduced; In the project implementation stage, there are natural risks. For example, natural disasters such as forest fires and pests and diseases will cause a large number of trees to die, directly affecting the carbon sink volume; in addition, technical risks are also relatively prominent. The immaturity of afforestation technology and forest monitoring technology will lead to inaccurate carbon sink measurement and poor project implementation effects; In the project operation stage, management risks are crucial; poor project operation management, such as unreasonable personnel allocation and low capital use efficiency, will increase project costs and reduce project profitability; at the same time, social risks will also occur. The resistance of local residents to the project will lead to obstacles in project implementation; Currently, for the assessment of forestry carbon sink project risks, qualitative analysis methods are mostly used, which are highly subjective and lack accurate quantitative data support. This makes it difficult for project developers to accurately grasp the magnitude of project risks, unable to make scientific and reasonable decisions, and is not conducive to the sustainable development of forestry carbon sink projects; Therefore, a risk quantification assessment method and system for the whole-cycle development of forestry carbon sink projects are proposed. Summary of the Invention

[0004] In view of this, embodiments of the present invention hope to provide a risk quantification assessment method and system for the whole-cycle development of forestry carbon sink projects to solve or alleviate the technical problems existing in the prior art and at least provide a beneficial option.

[0005] To solve the above technical problems, a technical solution adopted by this application is: a risk quantification assessment method for the whole-cycle development of forestry carbon sink projects, including the following steps: Step 1: Divide the risk factors in the full-cycle development of forestry carbon sink projects into risks in the preparation stage, implementation stage, and carbon sink trading stage, and obtain the risk data for each stage; Step 2: Perform data standardization processing on the risk data obtained for each stage to eliminate the dimension difference; Step 3: Based on the risk data for each stage after processing, construct a risk assessment index system, and select the assessment indicators for each stage for the risks in the preparation stage, implementation stage, and carbon sink trading stage respectively; Step 4: Based on the selected assessment indicators, use the analytic hierarchy process combined with the dynamic Bayesian network to determine the weights of each assessment indicator; Step 5: Invite experts to score each assessment indicator, and summarize and average to obtain the quantitative scores of each assessment indicator; Step 6: According to the quantitative scores of each assessment indicator and the weights of each assessment indicator, calculate the risk values of each risk factor and the total risk value of the full-cycle development of the project; Step 7: According to the total risk value, divide the risks in the full-cycle development of forestry carbon sink projects into risk levels.

[0006] Preferably as a further refinement of this technical solution, in Step 4, the method for determining the weights of each assessment indicator includes the following steps: Step 401: Through expert experience and historical data, compare each assessment indicator at the same level pairwise, and construct a judgment matrix for the risk assessment indicators at each level; Step 402: Use the square root method or the sum-product method to calculate the eigenvector and the largest eigenvalue of the judgment matrix; Step 403: Calculate the consistency index and calculate the consistency ratio according to the average random consistency index to perform a consistency test on the judgment matrix; Step 404: Based on the processed risk data, construct a dynamic Bayesian network to describe the dynamic dependence relationship and causal connection between each assessment indicator, and generate an inference result; Step 405: According to the inference result of the dynamic Bayesian network, dynamically adjust the preliminary weights determined by the analytic hierarchy process.

[0007] Preferably as a further refinement of this technical solution, in Step 5, the calculation formula for the risk value of each risk factor is: ; where, represents the risk value of the th risk factor, represents the weight of the th assessment indicator under the th risk factor, represents the The quantified score of the th evaluation index under a risk factor, is the number of evaluation indexes under the th risk factor; The calculation formula for the total risk value of the full-cycle development of the project is: ; Among them, represents the total risk value of the full-cycle development of the project, is the number of risk factors, represents the th risk value of the risk factor, is the transmission coefficient of the th risk factor to the th risk factor in the risk transmission coefficient matrix. The transmission coefficient reflects the degree of mutual influence between risk factors.

[0008] As a further optimization of the present technical solution, in step two, the data standardization process adopts an adaptive fuzzy standardization formula; the specific formula is: ; Among them, is the original data, is the mean value of this group of data, is the standard deviation, is a coefficient dynamically adjusted according to the degree of data uncertainty, and its value range is [0, 1]. When the data uncertainty is high, tends to 1; when the data uncertainty is low, tends to 0; is the fuzzy membership function. The fuzzy membership function is used to describe the degree to which the data belongs to a certain fuzzy set, and a suitable function form can be selected according to the actual data distribution, such as the normal distribution membership function, trapezoidal membership function, etc.

[0009] As a further optimization of the present technical solution, in step three, the evaluation indexes for each stage are respectively selected for the risks in the preparation stage, implementation stage, and carbon sink trading stage, specifically including: For the risks in the preparation stage, evaluation indexes are selected from aspects of policies and regulations and land resources; For the risks in the implementation stage, evaluation indexes are selected from aspects of the natural environment and technical solutions; For the risks in the carbon sink trading stage, evaluation indexes are selected from the perspectives of the market and trading counterparts.

[0010] Preferably, as a further aspect of the present technical solution, in step one, the risks in the preparation stage include policy and regulation risks and land ownership risks, the risks in the implementation stage include natural factor risks and technical solution risks, and the risks in the carbon sink trading stage include market fluctuation risks and counterparty credit risks; The risk data for each stage is obtained by consulting materials, on-site research, and expert interviews.

[0011] Preferably, as a further aspect of the present technical solution, in step seven, the risk levels include three levels: low risk, medium risk, and high risk; if , it is determined as low risk; if , it is determined as medium risk; if , it is determined as high risk; where is the total risk value, and are pre-set thresholds.

[0012] To solve the above technical problems, another technical solution adopted by the present application is: a risk quantification and assessment system for the full-cycle development of a forestry carbon sink project, the system includes: a risk data acquisition module, a data standardization processing module, an index system construction module, a weight determination module, an index scoring module, a risk value calculation module, and a risk level classification module; The risk data acquisition module is configured to divide the risk factors of the full-cycle development of a forestry carbon sink project into risks in the preparation stage, risks in the implementation stage, and risks in the carbon sink trading stage, and acquire the risk data for each stage; The data standardization processing module is configured to perform data standardization processing on the acquired risk data for each stage to eliminate the dimension difference; The index system construction module is configured to construct a risk assessment index system based on the processed risk data for each stage, and select the evaluation indexes for each stage for the risks in the preparation stage, risks in the implementation stage, and risks in the carbon sink trading stage respectively; The weight determination module is configured to determine the weights of each evaluation index based on the selected evaluation indexes by using the analytic hierarchy process combined with a dynamic Bayesian network; The index scoring module is configured to invite experts to score each evaluation index, and summarize and average to obtain the quantitative scores of each evaluation index; The risk value calculation module is configured to calculate the risk values of each risk factor and the total risk value of the full-cycle development of the project according to the quantitative scores of each evaluation index and the weights of each evaluation index; The risk level classification module is configured to classify the risks of the full-cycle development of a forestry carbon sink project according to the total risk value.

[0013] As a further preferred embodiment of the present technical solution, the system further includes a result display and output module, which is used to display the risk assessment results in the form of charts or reports, and at the same time, supports the export function of the risk assessment results.

[0014] As a further preferred embodiment of the present technical solution, the risk data acquisition module uses multi-source data fusion technology to acquire risk data at each stage.

[0015] Due to the adoption of the above technical solutions in the embodiments of the present invention, it has the following advantages: 1. By scientifically and systematically determining risk factors, constructing an evaluation index system, determining index weights, and conducting risk quantification scoring, the present invention realizes the accurate quantification evaluation of the full-cycle development risks of forestry carbon sink projects, overcomes the defect of strong subjectivity of traditional qualitative evaluation methods, and provides accurate data support for project developers; 2. By dividing risk levels, the present invention enables project developers to intuitively understand the project risk level, facilitates formulating targeted risk response strategies according to the risk level, improves the project risk management level, reduces project development risks, ensures the smooth implementation and benefits of the project, and promotes the sustainable development of forestry carbon sink projects; 3. The evaluation method of the present invention has universality and scalability, and can appropriately adjust risk factors and evaluation indexes according to the characteristics of different regions and different types of forestry carbon sink projects, and is applicable to the risk assessment of various forestry carbon sink projects.

[0016] The above summary is only for the purpose of the specification and is not intended to be limiting in any way. In addition to the above-described illustrative aspects, embodiments, and features, further aspects, embodiments, and features of the present invention will be readily apparent by reference to the drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0018] Figure 1 It is a flowchart showing the process of a risk quantification evaluation method for the full-cycle development of a forestry carbon sink project of the present invention; Figure 2 It is a flowchart showing the process of a method for determining the weights of each evaluation index of the present invention; Figure 3Schematic diagram of the functional modules of a risk quantification and assessment system for the full-cycle development of a forestry carbon sink project according to the present invention. Specific implementation manners

[0019] The following describes the embodiments of the present disclosure in detail with reference to the accompanying drawings.

[0020] It should be clear that the following illustrates the implementation manners of the present disclosure through specific specific examples, and those skilled in the art can easily understand the other advantages and effects of the present disclosure from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all the embodiments. The present disclosure can also be implemented or applied through other different specific implementation manners, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that, without conflict, the following embodiments and the features in the embodiments can be combined with each other. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present disclosure without creative efforts belong to the scope of protection of the present disclosure.

[0021] It should be noted that the following describes various aspects of the embodiments within the scope of the appended claims. It should be obvious that the aspects described herein can be embodied in a wide variety of forms, and any specific structure and / or function described herein is illustrative only. Based on the present disclosure, those skilled in the art should understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects described herein can be used to implement the device and / or practice the method. Additionally, this device and / or this method can be implemented using other structures and / or functionality in addition to one or more of the aspects described herein.

[0022] It should also be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present disclosure in a schematic manner, and only the components related to the present disclosure are shown in the diagrams, rather than being drawn according to the number, shape, and size of the components in actual implementation. The type, quantity, and ratio of each component in its actual implementation can be an arbitrary change, and the component layout type may also be more complex.

[0023] In addition, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, those skilled in the art will understand that the described aspects can be practiced without these specific details.

[0024] Figure 1 It is a flowchart of a risk quantification and assessment method for the full-cycle development of a forestry carbon sink project according to an embodiment of the present invention. It should be noted that if there are substantially the same results, the method of the present application does not depend on Figure 1subject to the shown process sequence. As Figure 1 - Figure 2 shown: A risk quantification and assessment method for the full-cycle development of forestry carbon sink projects includes the following steps: Step 1: Divide the risk factors in the full-cycle development of forestry carbon sink projects into risks in the preparation stage, risks in the implementation stage, and risks in the carbon sink trading stage, and obtain risk data for each stage; Step 2: Perform data standardization processing on the obtained risk data for each stage to eliminate the dimension difference; Specifically, first, summarize all types of risk data obtained in Step 1 for the preparation stage, implementation stage, and carbon sink trading stage to ensure the integrity and accuracy of the data. Conduct preliminary data cleaning to remove duplicate, incorrect, or missing data. For example, if record errors are found in the policy subsidy data for certain years during the collection of policy and regulation risk data, corrections or deletions are required; Then, for each set of risk data, calculate its mean and standard deviation . The mean reflects the central tendency of the data, and the standard deviation measures the dispersion degree of the data. The calculation formulas are as follows: The calculation formula for the mean is: ; where represents the th data point, is the number of data; The calculation formula for the standard deviation is: ; where represents the th data point, is the mean, is the number of data; Next, dynamically adjust the coefficient according to the uncertainty degree of the data, and its value range is [0, ]; when the data uncertainty is high, tends to 1; when the data uncertainty is low, tends to 0; The uncertainty of the data can be judged in the following ways: Reliability of data sources: If the data comes from multiple different and uneven-quality channels, its uncertainty is relatively high, and the value of can be relatively large; if the data comes from an authoritative and reliable single channel, the uncertainty is low, and the value of The value should be relatively large; conversely, if the data is relatively stable, the value is relatively small; Subsequently, select an appropriate fuzzy membership function , and the fuzzy membership function is used to describe the degree to which the data belongs to a certain fuzzy set. An appropriate function form can be selected according to the actual data distribution. Common ones include the normal distribution membership function, trapezoidal membership function, etc.; The formula for the normal distribution membership function is: ; The normal distribution membership function is applicable to the case where the data approximately follows a normal distribution; among them, is the natural constant, an important mathematical constant, whose value is approximately 2.71828. It is widely used in many fields such as mathematics, science, and engineering and has special mathematical properties, is the original data, is the mean of this group of data, is the standard deviation; According to the actual distribution range and characteristics of the data, determine the four parameters a, b, c, d of the trapezoid. The form of the trapezoidal membership function is: ; Finally, use the adaptive fuzzy normalization formula: ; Perform normalization processing on the original data. Among them, is the original data, is the mean of this group of data, is the standard deviation, is the coefficient dynamically adjusted according to the degree of data uncertainty, is the fuzzy membership function, is the data after normalization.

[0025] Step 3: Based on the processed risk data at each stage, construct a risk assessment index system, and select the assessment indicators for each stage for the risks in the preparation stage, implementation stage, and carbon sink trading stage respectively; Specifically, first, clarify the construction principles. When constructing a risk assessment index system, some basic principles need to be followed to ensure the scientificity, rationality, and effectiveness of the index system; the specific construction principles are as follows: Principle of comprehensiveness: The indicators should cover all kinds of risk factors faced in each stage of the whole-cycle development of forestry carbon sink projects to avoid missing important information; Principle of independence: The indicators should be kept as independent as possible among each other to reduce the overlap and correlation between indicators so as to accurately reflect the influence of different risk factors; Principle of operability: The indicators should have clear definitions and data sources to facilitate actual operation and data collection; Principle of dynamics: Considering that risk factors change over time during the project development process, the indicator system should have a certain degree of dynamic adaptability to be able to reflect the changes in risks in a timely manner; Then, select the evaluation indicators for each stage for the risks in the preparation stage, implementation stage, and carbon sink trading stage respectively; Among them, the preparation stage mainly involves various preparatory work before the project starts, and the risks in this stage mainly come from policies, regulations, and land resources; the implementation stage is the process of the project's specific implementation, mainly facing risks in the natural environment and technical solutions; the carbon sink trading stage mainly involves risks in the market and trading counterparts; Finally, integrate the selected evaluation indicators for each stage to form a complete risk assessment indicator system. At the same time, optimize the indicator system. Through methods such as expert consultation and data analysis, rank the importance of the indicators, and delete some indicators with weak correlations or difficult-to-obtain data to ensure the simplicity and effectiveness of the indicator system.

[0026] Step 4: Based on the selected evaluation indicators, use the analytic hierarchy process combined with the dynamic Bayesian network to determine the weights of each evaluation indicator; Specifically, first, decompose the risk assessment problem of the full-cycle development of the forestry carbon sink project into an objective layer, a criterion layer, and a scheme layer; the objective layer is to evaluate the risks of the full-cycle development of the forestry carbon sink project; the criterion layer includes various risk factors in the preparation stage, implementation stage, and carbon sink trading stage, such as policy regulations and land resource risks in the preparation stage; the scheme layer is the specific evaluation indicators, such as policy stability and land ownership clarity; Then, through expert experience and historical data, make pairwise comparisons of each evaluation indicator at the same level to construct the judgment matrix of the risk assessment indicators at each level; let the judgment matrix be: ; Among them, represents the importance degree of indicator relative to indicator . Usually, the 1-9 scale method is used to determine the value of . The specific scale meanings are as follows:

[0027] Next, use the square root method or the sum-product method to calculate the eigenvector and the largest eigenvalue of the judgment matrix; Among them, the specific calculation steps of the square root method are as follows: Calculate the product of each element in each row of the judgment matrix : ; Calculation of the nth root is normalized to obtain the eigenvector : ; Calculate the maximum eigenvalue : ; where is the th element of the vector The specific calculation steps of the sum-product method are as follows: Normalize each column element of the judgment matrix to obtain ; Sum the elements of the normalized matrix by row to obtain : ; Normalize to obtain the eigenvector : ; Calculate the maximum eigenvalue : ; where is the th element of the vector Finally, calculate the consistency index ; And calculate the consistency ratio according to the average random consistency index (the corresponding values for different values can be obtained by referring to relevant materials): ; When the judgment matrix is considered to have satisfactory consistency, otherwise the judgment matrix needs to be adjusted again; The combination for the dynamic Bayesian network (DBN) is as follows: First, based on the processed risk data, analyze the dynamic dependence relationships and causal connections among various evaluation indicators, and construct the structure of the dynamic Bayesian network. Methods such as expert knowledge and data mining algorithms (such as constraint-based algorithms and scoring-based algorithms) can be used to determine the network structure; Then, use historical risk data to learn the parameters of the dynamic Bayesian network, that is, the conditional probability distributions of each node, through methods such as maximum likelihood estimation and Bayesian estimation; Next, when the states of some indicators are known, use the dynamic Bayesian network for inference to obtain the dynamic influence relationships and probability distributions among the indicators, and generate the inference results; Finally, according to the inference results of the dynamic Bayesian network, dynamically adjust the initial weights determined by the analytic hierarchy process. For example, if the DBN inference results show that the correlation degree between some indicators has changed, or the influence degree of a certain indicator on other indicators has increased or decreased, then adjust the weights of these indicators accordingly to reflect the latest risk situation and the dependence relationships among the indicators.

[0028] Step Five: Invite experts to score each evaluation indicator, and summarize and average to obtain the quantitative scores of each evaluation indicator; Specifically, first, carefully select experts with profound professional knowledge and rich practical experience in the field of forestry carbon sink projects. These experts can come from different units such as forestry scientific research institutions, relevant universities, government forestry departments, and carbon sink trading enterprises. They should be familiar with all aspects of forestry carbon sink projects, including policies and regulations, afforestation techniques, carbon sink measurement, and market transactions. Generally, the number of invited experts is 5 - 15 to ensure that the evaluation results have a certain degree of representativeness and reliability; Then, in order to ensure the accuracy and consistency of experts' scoring, a unified and clear scoring standard needs to be formulated. The scoring standard usually adopts a 1 - 10 point system, and the specific meanings are as follows:

[0029] Next, provide experts with detailed project materials and descriptions of each evaluation indicator, and let experts independently score each evaluation indicator according to their professional judgment and experience in accordance with the scoring standard. The scoring results of experts can be collected by means of online questionnaires, offline meetings, or written materials; after collecting the scoring results of all experts, summarize the scores of each evaluation indicator and calculate the average value; Finally, after calculating the quantitative scores of each evaluation indicator, analyze the results, check the indicators with higher scores. These indicators are the main risk points faced by the project and need to be focused on and corresponding countermeasures should be taken; at the same time, further communication with experts can be carried out to verify the rationality of the scoring results and ensure that the evaluation results can truly reflect the risk situation of the project.

[0030] Step 6: Calculate the risk value of each risk factor and the total risk value of the project's full-cycle development based on the quantitative scores of each evaluation index and the weights of each evaluation index; Specifically, after obtaining the quantitative scores and weights of each evaluation index, the risk value of each risk factor and the total risk value of the project's full-cycle development can be calculated. This step is a key link in the quantitative assessment of project risks and can intuitively reflect the degree of project risks. The specific calculation process is as follows: The calculation formula for the risk value of each risk factor is: ; Where, represents the risk value of the th risk factor, represents the weight of the th evaluation index under the th risk factor, represents the quantitative score of the th evaluation index under the th risk factor, is the number of evaluation indexes under the th risk factor; The calculation formula for the total risk value of the project's full-cycle development is: ; Where, represents the total risk value of the project's full-cycle development, is the number of risk factors, represents the risk value of the th risk factor, is the transmission coefficient of the th risk factor to the th risk factor in the risk transmission coefficient matrix. The transmission coefficient reflects the degree of mutual influence between risk factors.

[0031] Step 7: Divide the risks of the full-cycle development of the forestry carbon sink project into risk levels according to the total risk value; Specifically, the risk levels are usually divided into three levels: low risk, medium risk, and high risk. In some complex projects or scenarios with high risk sensitivity, further subdivision can also be carried out, such as adding lower risk and higher risk levels, for a total of five levels; the risk levels provide key basis for project decisions; if the project is at a low risk level, the project developer can promote the implementation of the project, reasonably allocate resources, and ensure the smooth progress of the project; for medium-risk projects, targeted risk response strategies need to be formulated, and key risk factors need to be monitored and managed, such as strengthening policy and regulation tracking, optimizing technical solutions, etc.; high-risk projects require cautious decision-making, and it is necessary to re-evaluate the project feasibility, or suspend the project for adjustment and improvement; at the same time, the results of risk level division can be used for information communication and sharing among relevant parties such as within the project team, between project developers and investors, and regulatory authorities. Clear risk levels can enable all parties to quickly understand the project risk status, promote effective communication and decision-making synergy, and ensure the smooth progress of the project.

[0032] In one embodiment, specifically, in step four, the method for determining the weights of each evaluation index includes the following steps: Step 401: Through expert experience and historical data, make pairwise comparisons of each evaluation index at the same level, and construct a judgment matrix for risk evaluation indexes at each level; Specifically, assume that when evaluating the risks in the preparation stage of a forestry carbon sink project, three evaluation indexes are selected: policy stability, subsidy policy strength, and strictness of access threshold; Invite 5 experts in the field of forestry carbon sinks. According to their experience and the historical data of past projects in terms of policies collected, make pairwise comparisons of these three indexes; For example, the experts believe that policy stability is slightly more important than subsidy policy strength. According to the 1-9 scale method, the corresponding element in the judgment matrix is , ; policy stability is significantly more important than the strictness of the access threshold, , ; subsidy policy strength is slightly more important than the strictness of the access threshold, , , , thus constructing the judgment matrix : .

[0033] Step 402: Use the square root method or the sum-product method to calculate the eigenvector and the maximum eigenvalue of the judgment matrix; Specifically, first, calculate the product of each element in each row of the judgment matrix ; ; ; ; Then, calculate to the power root , where ; ; ; ; Next, normalize to obtain the eigenvector ; ; ; ; ; Finally, calculate the maximum eigenvalue ; First, calculate : ; Then, calculate : ; .

[0034] Step 403: Calculate the consistency index and calculate the consistency ratio based on the average random consistency index to perform a consistency test on the judgment matrix; Specifically, first, calculate the consistency index ; ; Here , , then: ; Then, check the average random consistency index table. When , ; Calculate the consistency ratio ; ; Because , the judgment matrix has satisfactory consistency and the preliminary weights are reasonable.

[0035] Step 404: Based on the processed risk data, construct a dynamic Bayesian network to describe the dynamic dependencies and causal relationships between the evaluation indicators and generate inference results; Specifically, first, collect the risk data of the forestry carbon sink project over the years during the preparation stage, including policy changes, subsidy disbursement, adjustment of access standards, and the actual implementation of the project. Using this data, with the help of a professional Bayesian network construction tool (such as GeNIe), construct a dynamic Bayesian network based on the logical relationships between various evaluation indicators. For example, it is found that changes in policy stability will affect the adjustment of subsidy policy intensity, which in turn affects the strictness of the project's access threshold. Establish corresponding nodes and directed edges in the network to represent these relationships; Then, by inputting some known risk data (such as recent policy changes), use the inference algorithm of the Bayesian network (such as variable elimination method) for inference. The inference results show that under the current policy environment, the impact of subsidy policy intensity on project risk is greater than previously expected, while the impact of the strictness of the access threshold becomes relatively smaller.

[0036] Step 405: Dynamically adjust the initial weights determined by the analytic hierarchy process according to the inference results of the dynamic Bayesian network; Specifically, adjust the initial weights determined by the analytic hierarchy process according to the inference results of the dynamic Bayesian network. Since the inference results show that the impact of subsidy policy intensity on project risk increases and the impact of the strictness of the access threshold decreases, appropriately increase the weight of subsidy policy intensity and decrease the weight of the strictness of the access threshold; Assume the initial weights are , , ; After adjustment, the new weights become , , ; The adjusted weights will be used in subsequent calculations of the risk values of each risk factor and the total risk value of the project's full-cycle development to more accurately evaluate the project risk.

[0037] In one embodiment, specifically, in step five, the calculation formula for the risk value of each risk factor is: ; Where, represents the risk value of the th risk factor, represents the weight of the th evaluation indicator under the th risk factor, represents the quantified score of the th evaluation indicator under the th risk factor, is the number of evaluation indicators under the th risk factor; Specifically, assume that during the preparation stage of the forestry carbon sink project, the risk factor For policy and regulation risks, it includes 3 evaluation indicators: policy stability, subsidy policy strength, and strictness of access thresholds. . After calculation or determination, the weight of policy stability , the weight of subsidy policy strength , and the weight of strictness of access thresholds ; after expert scoring, the quantified score of policy stability points, the quantified score of subsidy policy strength points, and the quantified score of strictness of access thresholds points; Substitute the above data into the formula, and the risk value of the policy and regulation risk factor: ; This indicates that in the preparation stage, only considering the risk factors in terms of policies and regulations, its risk value is 6.9, at a medium to high risk level.

[0038] The calculation formula for the total risk value of the full-cycle development of the project is: ; Among them, represents the total risk value of the full-cycle development of the project, is the number of risk factors, represents the th risk value of the risk factor, is the transmission coefficient of the th risk factor to the th risk factor in the risk transmission coefficient matrix. The transmission coefficient reflects the degree of mutual influence between risk factors; Specifically, assume that there are 6 risk factors in the full cycle of the forestry carbon sink project, including policy and regulation risks in the preparation stage, land resource risks in the preparation stage, natural factor risks in the implementation stage, technical solution risks in the implementation stage, market fluctuation risks in the carbon sink trading stage, and counterparty credit risks in the carbon sink trading stage, that is ; the risk values of each risk factor have been calculated , , , , , ; the risk transmission coefficient matrix is assumed to be: ; First calculate: ; Then calculate: , ; For example, calculate Conduction impact on other risk factors: , and then calculate successively to the conduction impact on other risk factors and sum them up; finally, add the results of the two parts to obtain the total risk value of the project's full-cycle development . If the calculation result , it indicates that the overall project is at a relatively high risk level, and it is necessary to comprehensively review the risks in each stage of the project and formulate coping strategies.

[0039] In one embodiment, specifically, in step two, the data standardization process adopts an adaptive fuzzy standardization formula; the specific formula is: ; wherein, is the original data, is the mean value of this group of data, is the standard deviation, is the coefficient dynamically adjusted according to the degree of data uncertainty, and its value range is [0, 1]. When the data uncertainty is high, tends to 1; when the data uncertainty is low, tends to 0; is the fuzzy membership function, and the fuzzy membership function is used to describe the degree to which the data belongs to a certain fuzzy set. A suitable function form can be selected according to the actual data distribution, such as the normal distribution membership function, trapezoidal membership function, etc.; Specifically, taking the land acquisition cost data in the preparation stage of a certain forestry carbon sink project as an example, assuming the original data has 100, 120, 150, etc. (unit: yuan / mu); first calculate the mean value and the standard deviation of this group of data; assume , ; if the data uncertainty is evaluated to be relatively high, , select the normal distribution membership function; for the data , calculate the fuzzy membership function value: ; Then substitute it into the adaptive fuzzy standardization formula: ; It can be obtained that: ; Perform such calculations for each value in this group of data to complete the standardization process; The dimensional differences are eliminated by the standardized data, and the uncertainty and ambiguity of the data are considered. In subsequent risk assessments, different types of data (such as land costs, policy stability scores, etc.) can be compared and analyzed under a unified standard, making the assessment results more accurately reflect the project risk status.

[0040] In one embodiment, specifically, in step three, evaluation indicators for each stage are selected for the risks in the preparation stage, implementation stage, and carbon sink trading stage, specifically including: For the risks in the preparation stage, evaluation indicators are selected from aspects of policies and regulations and land resources; Among them, in terms of policies and regulations, it includes policy stability, subsidy policy strength, and strictness of access thresholds, specifically as follows: Policy stability: Evaluate whether the policies of the national and local governments regarding forestry carbon sink projects change frequently, which can be measured by counting the number of relevant policy introductions and the number of major adjustments within a certain period of time; Subsidy policy strength: Examine the amount of financial subsidies, subsidy methods, and subsidy periods for forestry carbon sink projects by the government. The greater the subsidy strength, the higher the economic feasibility of the project; Strictness of access thresholds: Analyze the conditions and standards that the project needs to meet in aspects such as project establishment and approval, such as regulations on project scale, technical requirements, environmental protection standards, etc.; In terms of land resources, it includes clarity of land ownership, land suitability, and land acquisition costs, specifically as follows: Clarity of land ownership: By consulting land property certificates, relevant contracts, and on-site investigations, judge whether the land ownership, use right, and management right are clear and whether there are disputes; Land suitability: Evaluate whether the soil quality, topography, climate conditions, etc. of the land are suitable for forestry planting. It can be judged based on soil test reports, meteorological data, etc.; Land acquisition costs: Include land lease fees, land expropriation fees, etc. Excessive costs will increase the upfront investment and risks of the project; For the risks in the implementation stage, evaluation indicators are selected from aspects of natural environment and technical solutions; Among them, in terms of the natural environment, it includes the frequency of natural disasters, the incidence of pests and diseases, and the stability of climate conditions, specifically as follows: Frequency of natural disasters: Count the historical occurrence times and intensities of natural disasters such as forest fires, floods, droughts, typhoons, etc. in the project area, and predict the probability of future occurrences; Incidence of pests and diseases: Monitor the situation of trees in the project area being invaded by pests and diseases, and calculate the proportion of the area affected by pests and diseases to the total area; Climate condition stability: Analyze the interannual and seasonal variations of climate factors such as temperature, precipitation, and sunlight in the project area. Unstable climate conditions can affect tree growth and carbon sink volume; In terms of technical solutions, it includes the maturity of afforestation technology, the accuracy of forest monitoring technology, and the speed of technology upgrading and replacement, as follows: Maturity of afforestation technology: Evaluate whether the technologies such as the adopted afforestation methods, tree species selection, and planting density have been verified by practice and are suitable for the local environment; Accuracy of forest monitoring technology: Examine the accuracy and reliability of technologies such as carbon sink measurement and forest growth monitoring used in the project to ensure that the carbon sink effect of the project can be accurately evaluated; Speed of technology upgrading and replacement: Pay attention to the technological development trends in the field of forestry carbon sinks, judge whether the technologies adopted by the project are easily replaced by new technologies, and the costs and difficulties of updating technologies; For the risks in the carbon sink trading stage, select evaluation indicators from the perspectives of the market and trading counterparts; Among them, in terms of the market, it includes the fluctuation range of carbon sink prices, market supply and demand relationships, and the impact of the macroeconomic environment, as follows: Fluctuation range of carbon sink prices: Analyze the historical fluctuations of carbon sink prices in the carbon market, calculate the standard deviation or coefficient of variation of the prices to measure the price stability; Market supply and demand relationships: Study the supply and demand conditions in the carbon sink market, including the number, scale of carbon sink projects, and the market demand for carbon sinks, and judge the degree of market competition and price trends; Impact of the macroeconomic environment: Consider the impact of factors such as the macroeconomic situation, energy policies, and climate change policies on the carbon market, and evaluate the potential risks to the project's carbon sink trading revenue; In terms of trading counterparts, it includes the credit rating of trading counterparts, the operational stability of trading counterparts, and the rationality of trading contract terms, as follows: Credit rating of trading counterparts: Query the credit reports of trading counterparts to understand their credit history, debt repayment ability, and default records; Operational stability of trading counterparts: Analyze the operating conditions, financial conditions, and market competitiveness of trading counterparts to judge whether they have the ability to fulfill trading contracts; Rationality of trading contract terms: Examine the rationality and fairness of terms such as price, quantity, delivery time, and quality standards in trading contracts to avoid potential contract disputes.

[0041] In one embodiment, specifically, in step one, the risks in the preparation stage include policy and regulation risks and land ownership risks, the risks in the implementation stage include natural factor risks and technical solution risks, and the risks in the carbon sink trading stage include market fluctuation risks and trading counterpart credit risks; the risk data of each stage are obtained through consulting materials, on-site research, and expert interviews; Among them, the policy and regulation risks mainly stem from the changes in relevant national and local policies. The adjustment of subsidy policies will change the financial support for projects, and the changes in access standards will affect the legality and feasibility of projects. To obtain data on such risks, it is necessary to consult the official websites of government departments, such as the website of the National Forestry and Grassland Administration and the websites of local forestry authorities, and collect the latest policy documents, notices and other materials related to forestry carbon sinks. The land ownership risk involves the issues of the ownership, use right and management right of land. Unclear land ownership will lead to disputes during the project implementation process, increase project costs and even cause the project to stagnate. When obtaining data, it is necessary to consult the archival materials of the land management department, including land property certificates, land transfer contracts, land registration information, etc., to clarify the land ownership status. The natural factor risks mainly include the impacts of forest fires, pests and diseases, extreme weather, etc. on forestry carbon sink projects. To obtain relevant risk data, it is necessary to consult the historical data archives of the forestry department and the meteorological department to understand the number of forest fires, the burned area, the types of pests and diseases, the outbreak frequency and the damage degree in the project area in a certain period in the past, as well as the occurrence frequency and intensity of extreme weather (such as heavy rain, drought, typhoon, etc.). The technical solution risks are reflected in the imperfections or inapplicabilities of afforestation technologies, forest monitoring technologies, etc. Consult relevant technical literature and research reports to understand the commonly used technical solutions in current forestry carbon sink projects and their advantages and disadvantages, compare the effects of different technologies in actual applications, conduct on-site inspections of other similar forestry carbon sink projects, communicate with the project technical responsible persons to understand the problems and solutions they encounter in the process of technology application, invite technical experts for interviews, evaluate the feasibility, reliability and advancement of the technical solutions proposed for the project, and analyze the existing technical risks. The market fluctuation risks are affected by various factors, such as the supply and demand relationship in the carbon market, the macroeconomic situation, policy changes, etc. To obtain market fluctuation risk data, it is necessary to consult the transaction data of the carbon trading platform, including the historical trend of carbon sink prices, trading volume, turnover and other information. The credit risk of trading counterparts is related to the smooth progress of project carbon sink transactions. To obtain data on such risks, it is necessary to collect materials such as the enterprise registration information, financial statements, credit rating reports of trading counterparts, and evaluate their economic strength and debt repayment ability.

[0042] In one embodiment, specifically, in step seven, the risk levels include three levels: low risk, medium risk and high risk; if , it is determined as low risk; if , it is determined as medium risk; if , it is determined as high risk; where is the total risk value, and are pre-set thresholds. Specifically, after calculating the total project risk value it is compared with a pre-set threshold and compared; if the calculated of a certain forestry carbon sink project is due to , , , then the project is determined to be of low risk; if , , it is determined to be of medium risk; if , , it is determined to be of high risk.

[0043] In summary, the risk quantification and assessment method for the full-cycle development of a forestry carbon sink project provided by the embodiments of the present invention divides the full-cycle risk of the project into the preparation, implementation, and carbon sink trading stages, obtains risk data for each stage by consulting materials, conducting on-site investigations, and interviewing experts, processes the data using an adaptive fuzzy standardization formula, selects evaluation indicators from multiple aspects to construct an index system, determines the index weights using the analytic hierarchy process combined with a dynamic Bayesian network, invites experts to score and calculate the risk value, and divides the risk level based on the total risk value, realizing the accurate quantification and assessment of the risks of forestry carbon sink projects; this method overcomes the defect of strong subjectivity in traditional qualitative assessments, provides accurate data support for project developers, facilitates their formulation of response strategies according to the risk level, improves the project risk management level, promotes the sustainable development of forestry carbon sink projects, and has universality and scalability, and can adapt to the risk assessment needs of different forestry carbon sink projects.

[0044] Figure 3 is a schematic diagram of the functional modules of a risk quantification and assessment system for the full-cycle development of a forestry carbon sink project according to an embodiment of the present application. As Figure 3 shown, a risk quantification and assessment system for the full-cycle development of a forestry carbon sink project includes: a risk data acquisition module, a data standardization processing module, an index system construction module, a weight determination module, an index scoring module, a risk value calculation module, and a risk level division module; The risk data acquisition module is configured to divide the risk factors of the full-cycle development of a forestry carbon sink project into risks in the preparation stage, risks in the implementation stage, and risks in the carbon sink trading stage, and obtain the risk data for each stage; The data standardization processing module is configured to perform data standardization processing on the obtained risk data for each stage to eliminate the dimension difference; The index system construction module is configured to construct a risk assessment index system based on the processed risk data for each stage, and select the evaluation indicators for each stage for the risks in the preparation stage, risks in the implementation stage, and risks in the carbon sink trading stage respectively; A weight determination module, configured to determine the weights of each evaluation index based on the selected evaluation indexes by using the analytic hierarchy process combined with a dynamic Bayesian network; An index scoring module, configured to invite experts to score each evaluation index, and summarize and average to obtain the quantitative scores of each evaluation index; A risk value calculation module, configured to calculate the risk values of each risk factor and the total risk value of the full-cycle development of the project according to the quantitative scores of each evaluation index and the weights of each evaluation index; A risk level division module, configured to divide the risk levels of the full-cycle development of the forestry carbon sink project according to the total risk value.

[0045] In one embodiment, specifically, the system further includes a result display and output module, which is used to display the risk assessment results in the form of charts or reports. At the same time, it supports the export function of the risk assessment results.

[0046] In one embodiment, specifically, the risk data acquisition module uses multi-source data fusion technology to acquire risk data at each stage. In addition to obtaining data by consulting materials, on-site investigations, and expert interviews, it also integrates satellite remote sensing data, meteorological monitoring data, social media public opinion data, etc.; uses data mining and machine learning algorithms to clean, fuse, and analyze multi-source data, and extracts valuable risk information; at the same time, this module has a data quality assessment function to evaluate the accuracy, integrity, and reliability of data from different sources, and provides a high-quality data basis for subsequent data processing and analysis.

[0047] For other details of the implementation technical solutions of each module in the risk quantitative assessment system for the full-cycle development of a forestry carbon sink project in the above embodiments, reference can be made to the description in the risk quantitative assessment method for the full-cycle development of a forestry carbon sink project in the above embodiments, which will not be elaborated here.

[0048] It should be noted that the embodiments in this specification are all described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The same or similar parts among the embodiments can be referred to each other. For system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiments.

[0049] The basic principles of the present disclosure have been described above in combination with specific embodiments. However, it should be pointed out that the advantages, advantages, effects, etc. mentioned in the present disclosure are only examples and not limitations. It cannot be considered that these advantages, advantages, effects, etc. are essential for each embodiment of the present disclosure. In addition, the above-mentioned specific details are only for the purpose of illustration and easy understanding, rather than limitations. The above details do not limit the present disclosure to necessarily adopt the above specific details to implement.

[0050] In this disclosure, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. The block diagrams of devices, apparatuses, equipment, and systems involved in this disclosure are only illustrative examples and do not intend to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open-ended words, meaning "including but not limited to", and can be used interchangeably with each other. The words "or" and "and" used herein refer to the word "and / or" and can be used interchangeably with it, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to" and can be used interchangeably with it.

[0051] In addition, as used herein, the "or" used in the listing of items starting with "at least one" indicates a disjunctive listing. So, for example, the listing of "at least one of A, B, or C" means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Furthermore, the term "exemplary" does not mean that the described examples are preferred or better than other examples.

[0052] It should also be noted that in the systems and methods of this disclosure, each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent solutions of this disclosure.

[0053] Various changes, substitutions, and alterations to the technologies described herein can be made without departing from the teachings defined by the appended claims. In addition, the scope of the claims of this disclosure is not limited to the specific aspects of the processes, machines, manufactures, compositions of events, means, methods, and acts described above. Current or later-developed processes, machines, manufactures, compositions of events, means, methods, or acts that perform substantially the same function or achieve substantially the same result as the corresponding aspects described herein can be utilized. Thus, the appended claims include such processes, machines, manufactures, compositions of events, means, methods, or acts within their scope.

[0054] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this disclosure. Various modifications to these aspects are very obvious to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of this disclosure. Therefore, this disclosure is not intended to be limited to the aspects shown herein, but rather to the broadest scope consistent with the principles and novel features disclosed herein.

[0055] The foregoing description has been presented for purposes of illustration and description. In addition, this description is not intended to limit embodiments of the present disclosure to the form disclosed herein. Although several example aspects and embodiments have been discussed above, those skilled in the art will recognize some variations, modifications, alterations, additions, and subcombinations thereof.

Claims

1. A risk quantification assessment method for the full-cycle development of forestry carbon sink projects, characterized in that, It includes the following steps: Divide the risk factors in the whole-cycle development of forestry carbon sink projects into risks in the preparation stage, implementation stage, and carbon sink trading stage, and obtain the risk data for each stage; Conduct data standardization processing on the risk data obtained for each stage to eliminate the dimension differences; Based on the risk data for each stage after processing, construct a risk assessment index system, and select the evaluation indicators for each stage for the risks in the preparation stage, implementation stage, and carbon sink trading stage respectively; Based on the selected evaluation indicators, use the analytic hierarchy process combined with a dynamic Bayesian network to determine the weights of each evaluation indicator; Invite experts to score each evaluation indicator, and summarize and average to obtain the quantitative scores of each evaluation indicator; According to the quantitative scores of each evaluation indicator and the weights of each evaluation indicator, calculate the risk values of each risk factor and the total risk value of the whole-cycle development of the project; According to the total risk value, divide the risks in the whole-cycle development of forestry carbon sink projects into risk levels.

2. The risk quantification assessment method for the full-cycle development of a forestry carbon sink project according to claim 1, wherein, The method for determining the weights of each evaluation indicator includes the following steps: Through expert experience and historical data, make pairwise comparisons of the evaluation indicators at the same level, and construct a judgment matrix for the risk assessment indicators at each level; Use the square root method or the sum-product method to calculate the eigenvector and the maximum eigenvalue of the judgment matrix; Calculate the consistency index and calculate the consistency ratio according to the average random consistency index to conduct a consistency test on the judgment matrix; Based on the processed risk data, construct a dynamic Bayesian network to describe the dynamic dependence relationship and causal connection between each evaluation indicator, and generate an inference result; According to the inference result of the dynamic Bayesian network, dynamically adjust the preliminary weights determined by the analytic hierarchy process.

3. A risk quantification and assessment method for the full-cycle development of a forestry carbon sink project according to claim 1, characterized in that, The calculation formula for the risk value of each risk factor is: ； Among them, represents the risk value of the th risk factor, represents the weight of the th evaluation index under the th risk factor, represents the quantified score of the th evaluation index under the th risk factor, is the number of evaluation indexes under the th risk factor; The calculation formula for the total risk value of the whole-cycle development of the project is: ； Among them, represents the total risk value of the full-cycle development of the project, is the number of risk factors, represents the risk value of the th risk factor, is the transmission coefficient of the th risk factor to the th risk factor in the risk transmission coefficient matrix.

4. A risk quantification and assessment method for the full-cycle development of a forestry carbon sink project according to claim 1, wherein, The data standardization processing adopts an adaptive fuzzy standardization formula; the specific formula is: ； Among them, is the original data, is the mean value of this group of data, is the standard deviation, is a coefficient dynamically adjusted according to the degree of data uncertainty, and its value range is [0, 1], is the fuzzy membership function.

5. The risk quantification and assessment method for the full-cycle development of a forestry carbon sink project according to claim 1, characterized in that, The selection of the evaluation indicators for each stage for the risks in the preparation stage, implementation stage, and carbon sink trading stage respectively specifically includes: For the risks in the preparation stage, select evaluation indicators from aspects of policies, regulations, and land resources; For the risks in the implementation stage, select evaluation indicators from aspects of the natural environment and technical solutions; For the risks in the carbon sink trading stage, select evaluation indicators from the perspectives of the market and trading counterparts.

6. A risk quantification and assessment method for the full-cycle development of a forestry carbon sink project according to claim 1, characterized in that: The risks in the preparation stage include policy and regulation risks and land ownership risks, the risks in the implementation stage include natural factor risks and technical solution risks, and the risks in the carbon sink trading stage include market fluctuation risks and trading counterpart credit risks; The risk data for each stage is obtained through consulting materials, on-site research, and expert interviews.

7. A risk quantification and assessment method for the full-cycle development of a forestry carbon sink project according to claim 1, characterized in that: The risk levels include three levels: low risk, medium risk, and high risk; if , it is determined as low risk; if , it is determined as medium risk; if , it is determined as high risk; where is the total risk value, and are pre-set thresholds.

8. A risk quantification and assessment system for the full-cycle development of forestry carbon sink projects, which is applied to the risk quantification and assessment method for the full-cycle development of forestry carbon sink projects according to any one of claims 1-7, and is characterized in that, The system includes: a risk data acquisition module, a data standardization processing module, an index system construction module, a weight determination module, an index scoring module, a risk value calculation module, and a risk level division module; The risk data acquisition module is configured to divide the risk factors in the whole-cycle development of forestry carbon sink projects into risks in the preparation stage, implementation stage, and carbon sink trading stage, and obtain the risk data for each stage; The data standardization processing module is configured to perform data standardization processing on the risk data of each stage obtained, eliminating the dimension differences; The index system construction module is configured to construct a risk assessment index system based on the risk data of each stage after processing, and select the evaluation indexes of each stage for the risks in the preparation stage, implementation stage and carbon sink trading stage respectively; The weight determination module is configured to determine the weights of each evaluation index based on the selected evaluation indexes, using the analytic hierarchy process combined with the dynamic Bayesian network; The index scoring module is configured to invite experts to score each evaluation index, and summarize and average to obtain the quantitative scores of each evaluation index; The risk value calculation module is configured to calculate the risk values of each risk factor and the total risk value of the full-cycle development of the project according to the quantitative scores of each evaluation index and the weights of each evaluation index; The risk level division module is configured to divide the risk levels of the full-cycle development of the forestry carbon sink project according to the total risk value.

9. The risk quantification and assessment system for the full-cycle development of a forestry carbon sink project according to claim 8, characterized in that: The system further includes a result display and output module, which is used to display the risk assessment results in the form of charts or reports, and at the same time, supports the export function of the risk assessment results.

10. A risk quantification and assessment system for the full-cycle development of a forestry carbon sink project according to claim 8, characterized in that: The risk data acquisition module uses multi-source data fusion technology to acquire the risk data of each stage.

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