A forestry carbon sink dynamic measurement method and system

Abstract

The application discloses a forestry carbon sink dynamic measurement method and system, and relates to the technical field of forestry carbon sink measurement. The method comprises the following steps: dividing the target forest carbon sink management into a plurality of continuous compliance periods, and identifying compliance deviation events; dividing the target forest into a plurality of structure gradient blocks, evaluating the forest credit level of each structure gradient block, and generating a structure-credit correction coefficient; obtaining the carbon sink purpose information of the target forest, and generating a structure-purpose cross weight; collecting the forestry management behavior of the target forest in the compliance period, and calculating a behavior influence factor; periodically generating a risk integration level factor for each structure gradient block according to environmental uncertainty factors; and jointly correcting the original carbon sink growth value to obtain a standard carbon sink effective measurement result. The application realizes real-time and accurate measurement of the forestry carbon sink, and significantly improves the reliability and transaction value of carbon sink compliance management.

Description

Technical Field

[0001] This application relates to the field of forestry carbon sequestration technology, and in particular to a dynamic measurement method and system for forestry carbon sequestration. Background Technology

[0002] With global climate change, forestry carbon sinks, as an important natural carbon sink system, play an irreplaceable role. Forestry carbon sinks refer to the carbon that forests absorb carbon dioxide from the atmosphere through photosynthesis and fix in vegetation and soil as biomass. The total amount of forest carbon sinks is affected by a variety of factors, including forest age structure, growth status, management practices, and natural disturbances. Therefore, scientifically and accurately measuring the increase in forestry carbon sinks has become a core foundation for the implementation of carbon trading mechanisms, the confirmation of ecological asset ownership, and the assessment of compliance responsibilities.

[0003] In related technologies, most forestry carbon sink measurement methods rely on single-report calculations and lack a mechanism for analyzing the cyclical differences between compliance targets and actual carbon storage growth. This makes it difficult to identify risks of non-compliance or excessive fluctuations in carbon sinks in a timely manner, hindering carbon credit management and compliance verification. Furthermore, current carbon sink measurement results are often used as unified carbon asset data outputs, failing to consider the impact of subsequent application scenarios on redemption capacity and compliance risks. This makes it difficult to provide a basis for carbon asset management by application and tiered market trading, thus requiring improvement. Summary of the Invention

[0004] The purpose of this invention is to provide a dynamic measurement method and system for forestry carbon sequestration to solve the problems mentioned in the background art.

[0005] Firstly, this application provides a dynamic measurement method for forestry carbon sequestration, which adopts the following technical solution: The carbon sink management of the target forest area is divided into several consecutive compliance cycles. A carbon storage growth target is set for each compliance cycle. After the compliance cycle ends, compliance deviation events are identified. The target forest area is divided into several structural gradient blocks. Based on the performance deviation events, the forest land credit rating of each structural gradient block is evaluated, and a structure-credit correction coefficient is generated. Obtain carbon sink usage information for the target forest area, establish a correlation between each carbon sink usage and the corresponding structural gradient block, and generate structure-use cross weights; Collect forestry management behavior data from the target forest area during the contract fulfillment period, and calculate the behavior impact factor based on the degree of impact of the forestry management behavior on carbon sequestration results; Based on environmental uncertainties, a risk interference factor system is constructed to periodically generate risk score level factors for each structural gradient block. By combining the structure-credit correction coefficient, the structure-use cross-weight, the behavior impact factor, and the risk score level factor, the original carbon sink growth value is jointly corrected to obtain the effective measurement result of the standard carbon sink.

[0006] Preferably, the carbon sequestration management of the target forest area is divided into several consecutive compliance cycles, with a corresponding carbon storage growth target set for each compliance cycle. The specific steps for identifying compliance deviation events after the compliance cycle ends are as follows: The carbon sequestration management of the target forest area is divided into several consecutive compliance cycles, and a clear start and end time is set for each compliance cycle as an independent carbon sequestration accounting unit. Before the start of the compliance period, a carbon storage growth target is set for each compliance period, and after the end of the compliance period, the actual carbon storage growth value is collected. By comparing and analyzing the actual carbon storage growth value with the carbon storage growth target value, compliance deviation events within the compliance period can be identified.

[0007] Preferably, the step of comparing and analyzing the actual carbon storage growth value with the carbon storage growth target value to identify compliance deviation events within the compliance period specifically includes: The actual carbon storage growth value is compared and analyzed with the carbon storage growth target value to determine the type of deviation event; If the actual carbon storage growth value is lower than the carbon storage growth target value, then there is a situation where the carbon storage target is not met, and the deviation event type is recorded as a negative deviation event. If the actual carbon storage growth value is higher than the carbon storage growth target value, then there is a carbon storage excess situation, and the deviation event type is recorded as a positive deviation event. Calculate the absolute difference between the actual carbon storage growth value and the target carbon storage growth value, determine the deviation event level, and generate a performance deviation event based on the deviation event type.

[0008] Preferably, the step of dividing the target forest area into several structural gradient blocks, assessing the forest land credit rating of each structural gradient block in conjunction with the performance deviation events, and generating a structure-credit correction coefficient is as follows: Obtain the age information of forest land units within the target forest area and divide the target forest area into several structural gradient blocks; Based on the spatial distribution characteristics of each structural gradient block, including forest type, canopy structure and stand density, the structural gradient level of each structural gradient block is set. Based on the aforementioned performance deviation events, determine whether there are performance deviations in each structural gradient block; If there are any deviations in performance, the deviation event type and level of each structural gradient block are obtained, and the forest land credit rating of each structural gradient block is assessed. By combining the structural gradient level and forest land credit level, a structure-credit correction coefficient is generated to correct the original carbon sink growth value for credit.

[0009] Preferably, the steps of obtaining carbon sequestration use information of the target forest area, establishing a correlation between each carbon sequestration use and the corresponding structural gradient block, and generating structure-use cross-weights are as follows: Obtain information on the use of carbon sinks in the target forest area. Carbon sink uses include compliance offsetting, ecological compensation, long-term storage, and short-term trading. Based on the compliance risk level and redemption stability of various carbon sink uses, attribute weights for various carbon sink uses are constructed. The redemption stability is used to characterize the ability of such carbon sinks to achieve continuous and reliable redemption under the given use, and is determined by the sustainability, volatility and predictability of such carbon sinks within a preset historical period. A mapping relationship is established between each structural gradient block and the carbon sink use it generates. Based on the carbon sink use attribute weight, a structure-use cross-weight model is established to generate the structure-use cross-weight for each structural gradient block, which is used to correct the use of the original carbon sink growth value.

[0010] Preferably, the step of collecting forestry management behavior data from the target forest area during the compliance period and calculating the behavior impact factor based on the degree of impact of the forestry management behavior on carbon sequestration results is as follows: During the contract period, forestry management behaviors occurring in each structural gradient block of the target forest area are collected, including positive and negative management behaviors. A behavioral impact model is established to set corresponding carbon storage impact weight values ​​for different forestry management behaviors. The carbon storage impact weight values ​​reflect the degree of their impact on carbon sink growth. All forestry management behaviors that occur in each structural gradient block during the performance period are classified and statistically analyzed. Based on the carbon storage impact weight value, the total impact value of the behaviors is calculated by weighting. The total value of the behavioral impact is standardized to generate a behavioral impact factor, which is used to correct the original carbon sink growth value.

[0011] Preferably, the step of constructing a risk interference factor system based on environmental uncertainties and periodically generating risk score level factors for each structural gradient block is as follows: The environmental uncertainties that may have a potential impact on the carbon sink of the target forest area during the compliance period are identified. These environmental uncertainties include extreme weather factors, fire hazard factors, pest and disease outbreak factors, and human-caused damage factors. For each type of uncertainty, a risk interference factor is set, and a risk interference factor system is constructed. Each risk interference factor includes risk type, risk level, and risk frequency. The risk interference factors are spatially matched with the structural gradient blocks in the target forest area to identify the impact of risk factors on each structural gradient block during the performance period. The risk level and risk frequency are extracted, the risk integral value of each structural gradient block is calculated, and the risk integral value is standardized into a risk integral level factor for risk correction of the original carbon sink growth value.

[0012] Preferably, the steps for jointly correcting the original carbon sink growth value by combining the structure-credit correction coefficient, structure-use cross-weight, behavioral impact factor, and risk score level factor to obtain the effective measurement result of the standard carbon sink are as follows: The actual carbon sequestration growth value of each structural gradient block during the compliance period is extracted as the original carbon sequestration growth value and labeled as follows. ; Extract the combined structure-credit adjustment coefficient, structure-use cross-weight, behavioral influence factor, and risk score level factor, and label them as a, b, c, and d, respectively; Through formula The effective measurement result C of the standard carbon sink is calculated, and the original carbon sink growth value is jointly corrected.

[0013] Secondly, the forestry carbon sequestration dynamic measurement system provided in this application adopts the following technical solution: The compliance cycle management module divides the carbon sink management of the target forest area into several consecutive compliance cycles. A carbon storage growth target is set within each compliance cycle, and compliance deviation events are identified after the compliance cycle ends. The structural credit assessment module divides the target forest area into several structural gradient blocks, and, in conjunction with the performance deviation events, assesses the forest land credit rating of each structural gradient block, generating a structural-credit correction coefficient. The application adaptation module obtains carbon sink application information for the target forest area, establishes a relationship between each carbon sink application and the corresponding structural gradient block, and generates structure-application cross weights. The management behavior assessment module collects forestry management behavior data from the target forest area during the contract fulfillment period and calculates the behavior impact factor based on the degree of impact of the forestry management behavior on carbon sequestration results. The risk interference assessment module constructs a risk interference factor system based on environmental uncertainties, and periodically generates risk score level factors for each structural gradient block. The carbon sink joint correction module combines the structure-credit correction coefficient, the structure-use cross-weight, the behavior impact factor, and the risk score level factor to jointly correct the original carbon sink growth value and obtain the effective measurement result of the standard carbon sink.

[0014] In summary, this application includes at least one of the following beneficial technical effects: 1. By introducing a compliance cycle, a carbon storage growth target is clearly set for each cycle to achieve phased target management. After the cycle ends, carbon sink compliance deviation events are identified by comparing the actual carbon storage increment with the target value. Based on forest heterogeneity, forest areas are divided into multiple structural gradient blocks according to forest age characteristics to achieve spatially differentiated management; simultaneously, compliance deviation events from the previous cycle are correlated with each block to reflect the differences in compliance performance of different forest areas. By integrating structural status and compliance credit, a structure-credit correction coefficient is set to adjust the structural risk of the original carbon storage increment, effectively improving the objectivity and credibility of carbon sink measurement results. Use information is introduced into the measurement system and cross-matched with structural gradient blocks to establish a use credibility-structure fit model, forming a structure-use cross-weight, used to differentiate and correct carbon sink value, improving the matching degree and compliance between measurement results and actual carbon sink uses. By collecting and classifying forestry management behaviors during the compliance period, the system identifies their positive or negative effects on carbon sink formation. Behavioral influencing factors are introduced as quantitative indicators of management performance, enabling carbon sink measurement to respond to human intervention. This not only incentivizes high-quality management but also enhances the ability to punish abnormal behavior, thus strengthening the behavioral sensitivity of the measurement system. By constructing an environmental risk factor system and classifying it into integral levels based on the degree of interference to each block, the carbon sink measurement results fully reflect the uncertainty of the implementation process, thereby enhancing the overall accounting system's resilience and practical reference value. By integrating four influencing factors—structure, use, behavior, and risk—the system systematically, multidimensionally, and dynamically corrects the original carbon sink growth value. The resulting standard carbon sink effective value not only possesses comprehensive adjustment capabilities based on spatial structure, use attributes, human behavior, and risk levels but also exhibits good comparability and policy alignment, improving the scientific rigor and objectivity of the forestry carbon sink management and accounting system.

[0015] 2. Based on forest age information, forest land is structurally divided to achieve spatial control through age-based management and zonal assessment, enhancing the granularity and spatial adaptability of measurement results. By introducing spatial distribution parameters such as forest type, canopy structure, and stand density, the structural complexity of each block is assessed, and a structural gradient level is set. Blocks with different structural levels correspond to different carbon sink stability and growth potential. This level reflects the structural integrity and carbon storage service capacity of the forest land itself, which helps to identify highly sensitive areas or key carbon sink areas. Previously generated compliance deviation events are correlated with structural gradient blocks to form a structure-compliance cross-identification mechanism. By judging whether each block has compliance anomalies, it is possible to identify blocks with structural potential but poor compliance, or to discover positive examples of simple structures but proper management. This ensures that the carbon sink credit system no longer relies solely on the inherent conditions of the forest land, but also considers the ecological foundation and compliance performance, enhancing the system's ability to reflect the interaction between management behavior and ecological structure. Assess the forest land credit rating of each structural gradient block, integrate the structural gradient rating with the forest land credit rating to generate a structure-credit correction coefficient, which is used to adjust the original carbon storage growth to avoid overestimating carbon sinks in structurally fragile and poorly compliant areas, or underestimating carbon sinks in high-quality and stable areas.

[0016] 3. Different carbon sink uses correspond to different market demands and management requirements. By obtaining clear information on the types of uses, the actual application scenarios of carbon sinks can be identified, thus ensuring that the measurement results not only reflect carbon reserves but also the value of the use, promoting the upgrade of carbon sink management from an ecological perspective to one that integrates with the use. By introducing a dual-factor system of risk level and realization stability, quantitative attribute weights are established for each type of carbon sink use, thereby reflecting the differences in use and the controllability of realization in value assessment. This helps to suppress the disturbance of the measurement system by speculative carbon sink trading and ensures that carbon sink accounting is more in line with its final ecological and economic performance. Cross-modeling structural attributes and use attributes to generate cross-weight coefficients that reflect the coupling relationship between the two is a key step in building a multi-dimensional dynamic carbon sink adjustment mechanism. This strengthens the comprehensiveness, fairness, and practicality of carbon sink assessment, effectively avoids the errors caused by single-indicator assessment, and ensures that carbon sink measurement results achieve a balance between ecological contribution and use value. Attached Figure Description

[0017] Figure 1 This is a schematic diagram illustrating the specific steps of an embodiment of the dynamic measurement method for forestry carbon sequestration according to the present invention.

[0018] Figure 2 This is a schematic diagram of the module connections of an embodiment of a dynamic measurement system for forestry carbon sequestration according to the present invention. Detailed Implementation

[0019] The following examples and... Figures 1-2The present invention will be described in further detail, but the embodiments of the present invention are not limited thereto.

[0020] This invention discloses a method for dynamic measurement of forestry carbon sequestration, specifically including the following steps: Step S1: Divide the carbon sink management of the target forest area into several consecutive compliance cycles. Set a carbon storage growth target for each compliance cycle. After the compliance cycle ends, identify compliance deviation events. Step S2: Divide the target forest area into several structural gradient blocks, and in conjunction with the performance deviation events, evaluate the forest land credit rating of each structural gradient block to generate a structure-credit correction coefficient. Step S3: Obtain carbon sink usage information for the target forest area, establish a correlation between each carbon sink usage and the corresponding structural gradient block, and generate structure-use cross weights. Step S4: Collect forestry management behavior data of the target forest area during the performance period, and calculate the behavior impact factor based on the degree of impact of the forestry management behavior on the carbon sequestration results; Step S5: Based on environmental uncertainties, construct a risk interference factor system and periodically generate risk score level factors for each structural gradient block. Step S6 involves combining the structure-credit correction coefficient, structure-use cross-weight, behavior impact factor, and risk score level factor to jointly correct the original carbon sink growth value, thereby obtaining the effective measurement result of the standard carbon sink.

[0021] In practical application, by introducing a compliance cycle, a carbon storage growth target is clearly set for each cycle, achieving phased target management. After the cycle ends, by comparing the actual carbon storage increment with the target value, carbon sink compliance deviation events, such as insufficient growth or runaway growth, are identified, enhancing the dynamic adjustment capability of carbon sink accounting. Based on forest land heterogeneity, forest areas are divided into multiple structural gradient blocks according to forest age characteristics, achieving spatially differentiated management; simultaneously, compliance deviation events in the previous cycle are correlated with each block, reflecting the differences in compliance performance of different forest lands. By integrating structural status and compliance credit, a structure-credit correction coefficient is set, thereby achieving structural risk adjustment for the original carbon storage increment, effectively improving the objectivity and credibility of carbon sink measurement results. Carbon sinks can be used in various scenarios such as compliance deduction, market trading, and ecological compensation in practical applications. Their usage attributes directly affect the reliability of fulfillment. Introducing usage information into the metrology system and cross-matching it with structural gradient blocks establishes a usage credibility-structure fit model, forming a structure-use cross-weight. This is used to differentiate and adjust the value of carbon sinks, improving the matching degree and compliance between metrology results and actual carbon sink uses. Carbon sink growth is also affected by the positive and negative impacts of plantation forestry activities. By collecting and classifying management behaviors such as tending, replanting, logging, and pest control during the compliance period, the system identifies their gain or loss effects on carbon sink formation. Introducing behavioral impact factors as quantitative indicators of management performance gives carbon sink metrology a response mechanism to human intervention. This reflects the guiding role of incentivizing high-quality management and improves the ability to punish abnormal behavior, enhancing the behavioral sensitivity of the metrology system. The realization of forestry carbon sinks may be affected by uncontrollable factors such as extreme weather and pests, posing inherent risks. By constructing an environmental risk factor system and classifying it into tiers based on the degree of interference to each area, the carbon sink measurement results can fully reflect the uncertainties of the realization process, thereby enhancing the overall accounting system's resilience and practical reference value. By integrating four influencing factors—structure, use, behavior, and risk—the original carbon sink growth value is systematically, multidimensionally, and dynamically revised. The resulting standard carbon sink effective value not only possesses comprehensive adjustment capabilities based on spatial structure, use attributes, human behavior, and risk levels, but also exhibits good comparability and policy alignment capabilities, improving the scientific rigor and objectivity of the forestry carbon sink management and accounting system.

[0022] The carbon sequestration management of the target forest area is divided into several consecutive compliance cycles. A corresponding carbon storage growth target is set for each compliance cycle. After the compliance cycle ends, the steps for identifying compliance deviation events are as follows: Step S11: Divide the carbon sequestration management of the target forest area into several consecutive compliance cycles, and set a clear start and end time for each compliance cycle as an independent carbon sequestration accounting unit. Step S12: Before the start of the compliance period, set a carbon storage growth target value for each compliance period; after the end of the compliance period, collect the actual carbon storage growth value. Step S13: Compare and analyze the actual carbon storage growth value with the carbon storage growth target value to identify compliance deviation events within the compliance period.

[0023] In practical application, the compliance period length is determined based on the forest type, growth stage, and carbon trading compliance cycle of the target forest area. Shorter cycles are used for rapidly changing forest stands, while longer cycles are used for stable or mature stands. The compliance period length is aligned with the carbon market compliance settlement cycle to ensure that measurement results match carbon trading compliance requirements. By introducing a compliance period, carbon sink management is transformed from a long-term, ambiguous state to a clear, phased control model, making forestry carbon sinks responsible units with time boundaries. Each compliance period serves as an independent carbon sink accounting unit, facilitating dynamic process management and periodic performance evaluation. Based on historical carbon storage growth data and the current forest age of the target forest area, carbon storage growth targets are set. By setting these targets, managers can make quantitative predictions based on factors such as forest type, growth potential, and initial investment, improving the planning and controllability of carbon sink growth and maximizing the realization of carbon sink value potential. By collecting the actual carbon storage growth value at the end of the compliance period and comparing it with the target value, compliance deviation events can be objectively identified and used to evaluate compliance performance. This ensures that carbon sequestration is no longer limited to the result level, but has a closed-loop capability of process control, target setting, and result feedback, thereby improving the sustainability, fairness, and dynamic adjustment capability of the entire carbon sequestration system.

[0024] The steps for comparing and analyzing the actual carbon storage growth value with the target carbon storage growth value to identify compliance deviation events within the compliance period are as follows: Step S131: Compare and analyze the actual carbon storage growth value with the carbon storage growth target value to determine the type of deviation event; Step S132: If the actual carbon storage growth value is lower than the carbon storage growth target value, then there is a situation where the carbon storage target is not met, and the deviation event type is recorded as a negative deviation event. Step S133: If the actual carbon storage growth value is higher than the carbon storage growth target value, then there is a carbon storage excess situation, and the deviation event type is recorded as a positive deviation event. Step S134: Calculate the absolute difference between the actual carbon storage growth value and the carbon storage growth target value, determine the deviation event level, and generate a performance deviation event based on the deviation event type.

[0025] In practical application, by comparing the actual carbon sequestration growth with the pre-set target value on a periodic basis, a closed-loop system of planning-execution-deviation identification is constructed. This not only provides core evidence for carbon sink performance evaluation but also provides input conditions for subsequent credit correction. By clearly defining the types of deviation events, the compliance evaluation has a basis for classified management, strengthening the responsiveness and adaptability of the measurement system and helping to achieve dynamic identification and graded response to the compliance performance of forest areas. If carbon sequestration growth falls short of expectations, it is called a negative deviation event. This not only reflects deficiencies in forest productivity or management strategies but may also provide early warnings of potential risks (such as forest degradation, disturbance events, etc.), helping to downgrade the area in subsequent credit rating assessments, thereby improving the authenticity, fairness, and fraud prevention capabilities of the overall carbon sequestration accounting system. If carbon sequestration growth exceeds targets, it is called a positive deviation event. This reflects positive incentives for high-quality forest areas or those with effective management, which is conducive to stimulating forestry operators' enthusiasm for continuously improving carbon sequestration capacity. By quantifying the degree of deviation, the absolute difference between the actual carbon storage growth value and the carbon storage growth target value is calculated. Using the carbon storage growth target value as a benchmark, the absolute difference is normalized to obtain the deviation ratio value. The deviation level division interval is preset, and the deviation ratio value is set to the corresponding interval to determine the deviation level. This ensures that the deviation event not only has direction (positive / negative) but also intensity (mild / moderate / severe). This leads to the construction of a more accurate and expressive compliance data system, which can be used to guide the setting of subsequent correction coefficients. The final generated compliance deviation events will serve as basic input items in the system, running through the entire carbon sink measurement process and constituting an important basis for credit assessment and management performance.

[0026] The steps of dividing the target forest area into several structural gradient blocks, assessing the forest land credit rating of each structural gradient block based on the aforementioned performance deviation events, and generating a structure-credit correction coefficient are as follows: Step S21: Obtain the age information of forest units within the target forest area and divide the target forest area into several structural gradient blocks; Step S22: Based on the spatial distribution characteristics of each structural gradient block, including forest type, canopy structure and stand density, set the structural gradient level of each structural gradient block. Step S23: Based on the performance deviation event, determine whether there is a performance deviation in each structural gradient block; Step S24: If there is a performance deviation, obtain the deviation event type and deviation event level of each structural gradient block, and evaluate the forest land credit rating of each structural gradient block. Step S25: Combining the structural gradient level and forest land credit level, a structure-credit correction coefficient is generated to correct the original carbon sink growth value.

[0027] In practical applications, stand age, as a crucial indicator of forest ecological succession, determines the carbon storage potential and dynamic capacity of forest stands. Using stand age information as a basis for structural division of forest land enables spatial control through age-based management and zonal assessment, enhancing the granularity and spatial adaptability of measurement results. By introducing spatial distribution parameters such as forest type, canopy structure, and stand density, the structural complexity of each block is assessed, and a structural gradient level is established. Different structural levels correspond to different carbon sink stability and growth potential. This level reflects the structural integrity and carbon storage service capacity of the forest land itself, aiding in the subsequent identification of highly sensitive areas or key carbon sink regions. Linking previously generated compliance deviation events with structural gradient blocks forms a structure-compliance cross-identification mechanism. By determining whether each block exhibits compliance anomalies, it is possible to identify blocks with structural potential but poor compliance, or to discover positive examples of simple structures that are well-managed. This ensures that the carbon sink credit system no longer relies solely on the inherent conditions of the forest land but also considers the ecological foundation and compliance performance, enhancing the system's ability to reflect the interaction between management behavior and ecological structure. The structural gradient level and forest land credit level are numerically processed, and corresponding structural level and credit level values ​​are assigned according to preset level mapping rules. The structural level and credit level values ​​are combined to calculate the basic correction value. The combination method includes weighted superposition, piecewise mapping, or matrix mapping. Then, the correction direction is set according to different level combination relationships: when both structural level and credit level are high, the correction coefficient is increased; when either level is low, the correction coefficient is reduced. Finally, the basic correction value is normalized and range-constrained to ensure that the structural-credit correction coefficient falls within a preset range (e.g., 0.6 to 1.2). The resulting structural-credit correction coefficient is used to adjust the original carbon storage growth, avoiding overestimation of carbon sinks in structurally fragile areas and areas with poor compliance, or underestimation of carbon sinks in high-quality and stable areas.

[0028] The steps for obtaining carbon sequestration use information for the target forest area, establishing a correlation between each carbon sequestration use and its corresponding structural gradient block, and generating structure-use cross-weights are as follows: Step S31: Obtain information on the use of carbon sinks in the target forest area. The uses of carbon sinks include compliance offsetting, ecological compensation, long-term storage, and short-term trading. Step S32: Based on the compliance risk level and redemption stability of various carbon sink uses, construct the attribute weights of various carbon sink uses. The redemption stability is used to characterize the ability of such carbon sinks to achieve continuous and reliable redemption under the given use, and is determined by the continuity, volatility and predictability of such carbon sinks within a preset historical period. Step S33: Establish a mapping relationship between each structural gradient block and the carbon sink use it generates. Based on the carbon sink use attribute weight, establish a structure-use cross-weight model to generate the structure-use cross-weight of each structural gradient block, which is used to correct the use of the original carbon sink growth value.

[0029] In practical applications, different carbon sink uses correspond to different market demands and management requirements. For example, compliance offset carbon sinks emphasize stable redemption, ecological compensation carbon sinks focus more on ecological continuity, and short-term trading carbon sinks emphasize liquidity and rapid monetization. By obtaining clear information on the type of use, the system can identify the actual application scenarios of carbon sinks, thus ensuring that the measurement results reflect not only carbon reserves but also the value of the use, promoting the upgrade of carbon sink management from an ecological perspective to a use-integrated approach. By introducing a dual-factor system of risk level and redemption stability, quantitative attribute weights are established for each type of carbon sink use, such as higher weights for long-term reserves and lower weights for short-term trading uses. This reflects the differences in use and the controllability of redemption in value assessment, helping to suppress the disturbance of speculative carbon sink trading to the measurement system and ensuring that carbon sink accounting is more in line with its final ecological and economic performance. Cross-modeling structural attributes and use attributes to generate cross-weight coefficients that reflect the coupling relationship between the two is a key step in building a multi-dimensional dynamic carbon sink adjustment mechanism. This strengthens the comprehensiveness, fairness, and practicality of carbon sink assessment, effectively avoids errors caused by single-indicator assessment, and ensures that carbon sink measurement results achieve a balance between ecological contribution and use value.

[0030] The steps for collecting data on forestry management practices in the target forest area during the compliance period, and calculating behavioral impact factors based on the degree of impact of these practices on carbon sequestration results, are as follows: Step S41: During the performance period, collect forestry management behaviors occurring in each structural gradient block in the target forest area, including positive management behaviors and negative management behaviors; Step S42: Establish a behavioral impact model and set corresponding carbon storage impact weight values ​​for different forestry management behaviors. The carbon storage impact weight values ​​reflect the degree of their impact on carbon sink growth. Step S43: Classify and statistically analyze all forestry management behaviors that occur in each structural gradient block during the performance period, and calculate the total value of behavior impact based on the carbon storage impact weight value. Step S44: Standardize the total value of the behavioral impact to generate a behavioral impact factor, which is used to correct the original carbon sink growth value.

[0031] In practical applications, by classifying and collecting management behaviors within each structural gradient block, such as tending, thinning, regeneration, logging, and pest and disease control, positive behaviors (promoting carbon sink growth) and negative behaviors (leading to carbon storage loss) can be accurately identified. This effectively enhances the system's responsiveness to human factors and enables a leap in carbon sink assessment from static ecological observation to dynamic behavioral mapping. To construct a behavioral impact model, forestry management behaviors within the target forest area are first categorized and coded, classified into positive and negative management behaviors based on their direction of influence on carbon storage changes. Then, based on historical monitoring data, comparative data from similar forest stands, and existing forestry management experience, the impact of various management behaviors on carbon storage changes under different forest age stages and structural gradients is statistically analyzed to determine the direction and relative degree of influence of each behavior on carbon sink growth. On this basis, a mapping relationship between behavior types and carbon storage changes is constructed, assigning corresponding carbon storage impact weights to each type of forestry management behavior. Positive behaviors are assigned positive weights, and negative behaviors are assigned negative weights. The weights are graded according to their intensity of promoting or inhibiting carbon storage growth, forming a behavioral impact model that can be used to quantitatively evaluate various forestry management behaviors. A functional relationship or weighting rule is established between various management behaviors and their carbon storage change effects. By converting behavior types into weighted quantitative parameters, the system has the ability to distinguish between good and bad management practices and supports subsequent calculations for higher-precision carbon sink correction. A comprehensive statistical analysis of the frequency and intensity of forestry behaviors is conducted, and weighted using the aforementioned weighting system to generate a total behavioral impact value that reflects the overall management effect within a cycle. This value comprehensively describes the positive and negative contributions of management behaviors in each structural gradient block during the compliance cycle, elevating carbon sink management activities from qualitative description to quantitative assessment, thus enhancing the system's supervisory, evaluative, and regulatory adaptability. Standardization transforms the impact results of management behaviors in different blocks and cycles into behavioral impact factors on a unified scale. These factors can be directly used as adjustment coefficients to positively amplify or negatively reduce the original carbon sink growth value, not only improving the behavioral sensitivity of the measurement results but also promoting a shift in forestry carbon sink accounting from a results-oriented approach to a process- and results-oriented approach.

[0032] Based on environmental uncertainties, a risk interference factor system is constructed, and risk score level factors are periodically generated for each structural gradient block. The specific steps are as follows: Step S51: Obtain environmental uncertainties that may have a potential impact on the carbon sink of the target forest area during the performance period. The environmental uncertainties include extreme weather factors, fire hazard factors, pest and disease outbreak factors, and human-caused damage factors. Step S52: Set risk interference factors for each type of uncertainty and construct a risk interference factor system. Each risk interference factor includes risk type, risk level and risk frequency. Step S53: Spatial matching of the risk interference factors with the structural gradient blocks in the target forest area to identify the impact of risk factors on each structural gradient block during the performance period. Step S54: Extract the risk level and risk frequency, calculate the risk integral value of each structural gradient block, and standardize the risk integral value into a risk integral level factor for risk correction of the original carbon sink growth value.

[0033] In practical applications, carbon sequestration systems are not only affected by management practices but also significantly impacted by environmental events, such as droughts, lightning strikes, fires, and pest infestations, which can lead to carbon sequestration losses. Incorporating these uncertainties into the data collection process helps prevent overestimation of carbon sequestration and improves the accuracy and prudence of assessment results. Parameterizing various environmental disturbances and constructing a systematic risk disturbance factor system enables the classification and quantitative management of risks. By assigning different risks numerical weights based on their level (strong / medium / weak) and frequency (frequent / occasional), the system can accurately assess their impact on carbon sequestration stability and redemption capability, enabling it to adjust synchronously with real-world environmental fluctuations and enhancing the flexibility and fault tolerance of the measurement model. Spatially locating environmental risk factors and matching them with structural gradient blocks allows for precise perception of spatially heterogeneous risks in carbon sequestration measurement. For example, within the same region, low-lying areas are more susceptible to flooding than high-lying areas, while older forest areas are more prone to pest infestations. Spatial matching ensures that risk correction is no longer a one-size-fits-all approach, but rather involves zoned value assignment and differentiated weighting, thereby improving the geographical adaptability and risk reflection capability of the measurement results and facilitating the construction of risk-sensitive carbon sink block maps. Various risk interference factors acting on the target structural gradient blocks are extracted, along with their corresponding risk levels and frequencies. The risk level characterizes the intensity of a single risk event, while the risk frequency characterizes the frequency or probability of occurrence of that type of risk within the compliance period. Different types of risk interference factors are then classified and coded, and risk type weights are assigned based on their differences in impact on carbon sinks. Based on this, the risk levels, frequencies, and risk type weights of various risk factors are combined and calculated. All risk factors within the same block are weighted and accumulated to obtain the risk integral value of that structural gradient block within the compliance period. This quantifies and integrates multi-dimensional risk factors into a single risk integral value, which is then standardized into a risk integral level factor for dynamic adjustment of the original carbon sink growth value. This allows the measurement results to reflect the potential impact of environmental volatility on fulfillment capacity in real time, ensuring that carbon sink value assessment is more sustainable and forward-looking.

[0034] The steps for jointly correcting the original carbon sink growth value by combining the structure-credit correction factor, the structure-use cross-weight, the behavioral impact factor, and the risk score level factor to obtain the effective measurement result of the standard carbon sink are as follows: Step S61: Extract the actual carbon sequestration growth value of each structural gradient block during the compliance period as the original carbon sequestration growth value, and label it as... ; Step S62: Extract the combined structure-credit correction coefficient, structure-use cross-weight, behavioral impact factor, and risk score level factor, and label them as a, b, c, and d, respectively. Step S63, using the formula The effective measurement result C of the standard carbon sink is calculated, and the original carbon sink growth value is jointly corrected.

[0035] In practical applications, the four correction factors are applied to the original carbon sink growth value through formula calculation, thereby achieving a joint adjustment of the carbon sink accounting results. This ensures that any abnormality in any dimension (such as low credit, mismatched use, high risk, etc.) will result in a moderate reduction in the carbon sink value to reflect the actual risk, and ensure that the final carbon sink measurement results have environmental authenticity and scientific validity.

[0036] A dynamic measurement system for forestry carbon sequestration, which applies the dynamic measurement method for forestry carbon sequestration described above, includes: The compliance cycle management module divides the carbon sink management of the target forest area into several consecutive compliance cycles. A carbon storage growth target is set within each compliance cycle, and compliance deviation events are identified after the compliance cycle ends. The structural credit assessment module divides the target forest area into several structural gradient blocks, and, in conjunction with the performance deviation events, assesses the forest land credit rating of each structural gradient block, generating a structural-credit correction coefficient. The application adaptation module obtains carbon sink application information for the target forest area, establishes a relationship between each carbon sink application and the corresponding structural gradient block, and generates structure-application cross weights. The management behavior assessment module collects forestry management behavior data from the target forest area during the contract fulfillment period and calculates the behavior impact factor based on the degree of impact of the forestry management behavior on carbon sequestration results. The risk interference assessment module constructs a risk interference factor system based on environmental uncertainties, and periodically generates risk score level factors for each structural gradient block. The carbon sink joint correction module combines the structure-credit correction coefficient, the structure-use cross-weight, the behavior impact factor, and the risk score level factor to jointly correct the original carbon sink growth value and obtain the effective measurement result of the standard carbon sink.

[0037] In practical applications, the compliance cycle management module divides the carbon sink management process into multiple compliance cycles and sets target carbon storage levels, enabling periodic tracking of carbon sink changes and identification of compliance deviations. The structural credit assessment module divides tiered blocks based on forest age and structural characteristics, assesses forest land credit ratings in conjunction with compliance performance, and generates structure-credit correction parameters to adjust carbon sink reliability. The use-adaptation construction module identifies carbon sink use types and establishes correlations with block structural attributes, constructing structure-use cross-weights to reflect the difficulty of carbon sink realization and its practical application value. The management behavior assessment module collects forestry management behaviors and quantifies their positive and negative impacts on carbon storage growth, generating behavior impact factors to reflect the contribution of human management performance to carbon sinks. The risk interference assessment module identifies and quantifies the potential impact of environmental uncertainties on carbon sinks in each block, generating risk score level factors to correct the stability assessment of carbon sink realization. The carbon sink joint correction module integrates four types of correction factors—structure, use, behavior, and risk—to jointly correct the original carbon storage growth value, generating scientific and reliable standard carbon sink effective measurement results.

[0038] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A method for dynamic measurement of forestry carbon sequestration, characterized in that, Includes the following steps: The carbon sink management of the target forest area is divided into several consecutive compliance cycles. A carbon storage growth target is set for each compliance cycle. After the compliance cycle ends, compliance deviation events are identified. The target forest area is divided into several structural gradient blocks. Based on the performance deviation events, the forest land credit rating of each structural gradient block is evaluated, and a structure-credit correction coefficient is generated. Obtain carbon sink usage information for the target forest area, establish a correlation between each carbon sink usage and the corresponding structural gradient block, and generate structure-use cross weights; Collect forestry management behavior data from the target forest area during the contract fulfillment period, and calculate the behavior impact factor based on the degree of impact of the forestry management behavior on carbon sequestration results; Based on environmental uncertainties, a risk interference factor system is constructed to periodically generate risk score level factors for each structural gradient block. By combining the structure-credit correction coefficient, the structure-use cross-weight, the behavior impact factor, and the risk score level factor, the original carbon sink growth value is jointly corrected to obtain the effective measurement result of the standard carbon sink.

2. The method for dynamic measurement of forestry carbon sequestration according to claim 1, characterized in that, The steps of dividing the carbon sequestration management of the target forest area into several consecutive compliance cycles, setting corresponding carbon storage growth targets for each compliance cycle, and identifying compliance deviation events after the compliance cycle ends are as follows: The carbon sequestration management of the target forest area is divided into several consecutive compliance cycles, and a clear start and end time is set for each compliance cycle as an independent carbon sequestration accounting unit. Before the start of the compliance period, a carbon storage growth target is set for each compliance period, and after the end of the compliance period, the actual carbon storage growth value is collected. By comparing and analyzing the actual carbon storage growth value with the carbon storage growth target value, compliance deviation events within the compliance period can be identified.

3. The method for dynamic measurement of forestry carbon sequestration according to claim 2, characterized in that, The step of comparing and analyzing the actual carbon storage growth value with the carbon storage growth target value to identify compliance deviation events within the compliance period specifically includes: The actual carbon storage growth value is compared and analyzed with the carbon storage growth target value to determine the type of deviation event; If the actual carbon storage growth value is lower than the carbon storage growth target value, then there is a situation where the carbon storage target is not met, and the deviation event type is recorded as a negative deviation event. If the actual carbon storage growth value is higher than the carbon storage growth target value, then there is a carbon storage excess situation, and the deviation event type is recorded as a positive deviation event. Calculate the absolute difference between the actual carbon storage growth value and the target carbon storage growth value, determine the deviation event level, and generate a performance deviation event based on the deviation event type.

4. The method for dynamic measurement of forestry carbon sequestration according to claim 3, characterized in that, The steps of dividing the target forest area into several structural gradient blocks, assessing the forest land credit rating of each structural gradient block in conjunction with the performance deviation events, and generating a structure-credit correction coefficient are as follows: Obtain the age information of forest land units within the target forest area and divide the target forest area into several structural gradient blocks; Based on the spatial distribution characteristics of each structural gradient block, including forest type, canopy structure and stand density, the structural gradient level of each structural gradient block is set. Based on the aforementioned performance deviation events, determine whether there are performance deviations in each structural gradient block; If there are any deviations in performance, the deviation event type and level of each structural gradient block are obtained, and the forest land credit rating of each structural gradient block is assessed. By combining the structural gradient level and forest land credit level, a structure-credit correction coefficient is generated to correct the original carbon sink growth value for credit.

5. The method for dynamic measurement of forestry carbon sequestration according to claim 1, characterized in that, The steps of obtaining carbon sequestration use information of the target forest area, establishing a correlation between each carbon sequestration use and the corresponding structural gradient block, and generating structure-use cross-weights are as follows: Obtain information on the use of carbon sinks in the target forest area. Carbon sink uses include compliance offsetting, ecological compensation, long-term storage, and short-term trading. Based on the compliance risk level and redemption stability of various carbon sink uses, attribute weights for various carbon sink uses are constructed. The redemption stability is used to characterize the ability of such carbon sinks to achieve continuous and reliable redemption under the given use, and is determined by the sustainability, volatility and predictability of such carbon sinks within a preset historical period. A mapping relationship is established between each structural gradient block and the carbon sink use it generates. Based on the carbon sink use attribute weight, a structure-use cross-weight model is established to generate the structure-use cross-weight for each structural gradient block, which is used to correct the use of the original carbon sink growth value.

6. The method for dynamic measurement of forestry carbon sequestration according to claim 1, characterized in that, The steps for calculating the behavioral impact factor based on the degree of influence of the forestry management behavior of the target forest area during the contract period are as follows: During the contract period, forestry management behaviors occurring in each structural gradient block of the target forest area are collected, including positive and negative management behaviors. A behavioral impact model is established to set corresponding carbon storage impact weight values ​​for different forestry management behaviors. The carbon storage impact weight values ​​reflect the degree of their impact on carbon sink growth. All forestry management behaviors that occur in each structural gradient block during the performance period are classified and statistically analyzed. Based on the carbon storage impact weight value, the total impact value of the behaviors is calculated by weighting. The total value of the behavioral impact is standardized to generate a behavioral impact factor, which is used to correct the original carbon sink growth value.

7. The method for dynamic measurement of forestry carbon sequestration according to claim 1, characterized in that, The step of constructing a risk interference factor system based on environmental uncertainties and periodically generating risk score level factors for each structural gradient block is as follows: The environmental uncertainties that may have a potential impact on the carbon sink of the target forest area during the compliance period are identified. These environmental uncertainties include extreme weather factors, fire hazard factors, pest and disease outbreak factors, and human-caused damage factors. For each type of uncertainty, a risk interference factor is set, and a risk interference factor system is constructed. Each risk interference factor includes risk type, risk level, and risk frequency. The risk interference factors are spatially matched with the structural gradient blocks in the target forest area to identify the impact of risk factors on each structural gradient block during the performance period. The risk level and risk frequency are extracted, the risk integral value of each structural gradient block is calculated, and the risk integral value is standardized into a risk integral level factor for risk correction of the original carbon sink growth value.

8. The method for dynamic measurement of forestry carbon sequestration according to claim 1, characterized in that, The steps for jointly correcting the original carbon sink growth value by combining the structure-credit correction coefficient, structure-use cross-weight, behavioral impact factor, and risk score level factor to obtain the effective measurement result of standard carbon sink are as follows: The actual carbon sequestration growth value of each structural gradient block during the compliance period is extracted as the original carbon sequestration growth value and labeled as follows. ; Extract the combined structure-credit adjustment coefficient, structure-use cross-weight, behavioral influence factor, and risk score level factor, and label them as a, b, c, and d, respectively; Through formula The effective measurement result C of the standard carbon sink is calculated, and the original carbon sink growth value is jointly corrected.

9. A dynamic measurement system for forestry carbon sequestration, characterized in that, The application of the dynamic measurement method for forestry carbon sequestration as described in any one of claims 1-8 includes: The compliance cycle management module divides the carbon sink management of the target forest area into several consecutive compliance cycles. A carbon storage growth target is set within each compliance cycle, and compliance deviation events are identified after the compliance cycle ends. The structural credit assessment module divides the target forest area into several structural gradient blocks, and, in conjunction with the performance deviation events, assesses the forest land credit rating of each structural gradient block, generating a structural-credit correction coefficient. The application adaptation module obtains carbon sink application information for the target forest area, establishes a relationship between each carbon sink application and the corresponding structural gradient block, and generates structure-application cross weights. The management behavior assessment module collects forestry management behavior data from the target forest area during the contract fulfillment period and calculates the behavior impact factor based on the degree of impact of the forestry management behavior on carbon sequestration results. The risk interference assessment module constructs a risk interference factor system based on environmental uncertainties, and periodically generates risk score level factors for each structural gradient block. The carbon sink joint correction module combines the structure-credit correction coefficient, the structure-use cross-weight, the behavior impact factor, and the risk score level factor to jointly correct the original carbon sink growth value and obtain the effective measurement result of the standard carbon sink.

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