A method for assessing carbon sink enhancement potential in coastal areas

Abstract

The present invention relates to the field of environmental and ecological engineering technology, and in particular to a method for assessing the carbon sink potential in coastal areas, comprising the following steps: S1: collecting multi-dimensional marine environmental data to form a preliminary data set; S2: conducting an in-depth analysis of the carbon sink increase characteristics and long-term change trends, and identifying factors that affect the offshore carbon sink pattern; S3: constructing a static relationship model between the carbon sink pattern and the factors; S4: constructing a dynamic relationship model of the carbon sink pattern; S5: combining the static relationship model with the dynamic relationship model to conduct a comprehensive assessment to determine the carbon sink spatial carrying capacity of the coastal area; S6: determining the carbon sink potential of each region based on the spatial carrying capacity assessment result obtained in S5. The present invention can provide more accurate carbon sink management and prediction by integrating static and dynamic models to assess the carbon sink capacity of the coastal area, thereby supporting scientific policy formulation and resource optimization, and enhancing environmental protection effects.

Description

Technical Field

[0001] The invention relates to the technical field of environment and ecological engineering, and in particular to a method for evaluating the carbon sink enhancement potential in a coastal area. Background Art

[0002] With the deepening of global carbon cycle research, the carbon sink function of coastal areas and their role in the global carbon balance have received widespread attention. As the junction of land and open sea, the interaction between the complex ecosystem and human activities in the coastal areas makes the carbon sink capacity have significant spatial heterogeneity. Historically, the assessment of the carbon sink capacity of these areas has mostly relied on static, point-in-time data analysis, lacking continuous monitoring and real-time assessment of dynamic changes, which has limited the accurate prediction and effective management of the changing trend of carbon sink potential.

[0003] The main problem with existing technologies is that, although a single static model or dynamic model can provide carbon sink assessment results for a certain period of time, it is often unable to comprehensively reflect the real-time changes in carbon sink capacity caused by changes in natural conditions and human activities. In addition, existing assessment methods lack models that integrate static and dynamic factors, resulting in the inability to comprehensively assess and predict changes in the spatial carrying capacity of carbon sinks, which poses an obstacle to the formulation of long-term and effective environmental management and protection policies.

[0004] Therefore, there is an urgent need to develop an assessment method that can integrate static and dynamic relationships to improve the accuracy and effectiveness of carbon sink management, especially in the rapidly changing near-coastal environment, to ensure flexibility in responding to environmental changes and to develop more forward-looking and adaptive management measures. Summary of the invention

[0005] Based on the above purpose, the present invention provides a method for evaluating the carbon sink enhancement potential in coastal areas.

[0006] A method for evaluating the carbon sink enhancement potential in a coastal area comprises the following steps:

[0007] S1: Deploy a sensor network in the coastal area to collect multi-dimensional marine environmental data, and combine it with data on land-based pollutants entering the sea and sea area use to form a preliminary data set;

[0008] S2: Preprocess the preliminary data set formed by S1, and conduct in-depth analysis on the carbon sink increase characteristics and long-term change trends through factor analysis methods to identify factors affecting the offshore carbon sink pattern;

[0009] S3: Based on the factors identified in S2, a static relationship model between carbon sink pattern and factors is constructed using multivariate regression analysis techniques to assess the spatial distribution of carbon sink capacity under current environmental conditions;

[0010] S4: Combining historical data with the factors in S2, a dynamic relationship model of carbon sink pattern is constructed to analyze the dynamic change law of factors and predict the change trend of carbon sink capacity under different scenarios;

[0011] S5: Combine the static relationship model of S3 with the dynamic relationship model of S4 to conduct a comprehensive assessment to determine the carbon sink spatial carrying capacity of the coastal area;

[0012] S6: Based on the spatial carrying capacity assessment results obtained in S5, the analytic hierarchy process is used to conduct spatial suitability analysis to determine the carbon sink enhancement potential of each region.

[0013] Optionally, the S1 specifically includes:

[0014] S11: Deploy a sensor network in the intertidal zone, nearshore shallow water area, estuary area, typical seagrass bed, and coral reef area near the coast, wherein the sensor network includes water quality monitoring sensors, temperature sensors, salinity sensors, nutrient concentration sensors, sediment characteristic sensors, and biodiversity monitoring sensors;

[0015] S12: Continuous data collection through the sensor network, including water quality parameters, temperature, salinity, nutrient concentration, sediment characteristics and biodiversity. The data collection frequency is set to once an hour, the cycle is 24 hours, and the data is collected once a day;

[0016] S13: Pollutant sensors are installed at land-based sewage outlets, farmland runoff confluence areas and industrial emission points to collect data on pollutant types, concentrations and emissions. Satellite remote sensing technology and drone cruise image data are combined to obtain spatial distribution information on sea area use, including fishing density, shipping route distribution and distribution of coastal industrial facilities. All data are standardized through a unified interface protocol and integrated with the multi-dimensional marine environmental data collected in S12 to form a preliminary data set.

[0017] Optionally, the S2 specifically includes:

[0018] S21: Perform data preprocessing on the preliminary data set formed in S1, including data cleaning, data normalization and outlier processing;

[0019] S22: Prepare the preliminary data set after data preprocessing for factor analysis, including constructing the correlation matrix between variables and using KMO test and Bartlett sphericity test to confirm that the data are suitable for factor analysis;

[0020] S23: After confirming that the data are suitable for factor analysis, principal component analysis is used to extract key factors, and the extracted factors are optimized through the rotation matrix;

[0021] S24: Based on the results of factor analysis, the weights and influence directions of each factor are analyzed through the factor loading matrix to identify the factors affecting the offshore carbon sink pattern, and the factors are matched with the actual carbon sink data to confirm the role of the factors in the carbon sink pattern.

[0022] Optionally, the S23 specifically includes:

[0023] S231: After confirming that the data is suitable for factor analysis, the principal component analysis method is used to extract key factors. First, the covariance matrix C of the original data set is calculated; then the covariance matrix C is decomposed by eigenvalue to obtain the eigenvalue and the corresponding eigenvector ;principal component That is the eigenvector The linear combination is expressed as: ,in, represents the jth principal component, and x is the original variable vector;

[0024] S232: Optimize the extracted factors and use the rotation matrix to rotate the principal components to improve the explanatory power. Specifically, the Varimax rotation method is used to optimize the factors. The goal of the Varimax rotation is to maximize the variance of each factor. The Varimax rotation is optimized by the following objective function Q, and the formula is: , where Q represents the objective function, represents the elements in the factor loading matrix, p is the number of variables, m is the number of extracted factors, and by maximizing Q, the optimized rotated factor loading matrix is ​​obtained.

[0025] Optionally, the S24 specifically includes:

[0026] S241: Based on the factor analysis results, construct the factor loading matrix L, whose elements Represents the load of the i-th variable on the j-th factor; according to the load value in the factor loading matrix L , analyze the weight and direction of each factor on different variables, that is, by comparing the size and sign of the load value, identify the positive and negative impact of each factor on each variable and its intensity;

[0027] S242: Calculate the weight of each factor , and according to the calculated weight Rank the factors to determine the most important ones;

[0028] S243: Match the identified factors with the actual carbon sink data and calculate the factor scores Quantify the role of each factor in the actual carbon sink pattern, factor score The formula is: ,in, represents the loading vector of the jth factor, X represents the original data vector, and the factor scores are calculated by The results are compared with the spatial distribution of carbon sink data to confirm the specific role of each factor in the offshore carbon sink pattern.

[0029] Optionally, the S3 specifically includes:

[0030] S31: Based on the factors identified in S2, a static relationship model between carbon sink pattern and factors is constructed using multivariate regression analysis technology. First, the carbon sink capacity Y is set as the dependent variable, and the identified key factors are As the independent variable, a linear regression model is constructed; and the regression coefficients are estimated by the least squares method. , the static impact of each factor on carbon sink capacity can be quantified through the static relationship model;

[0031] S32: After constructing the multivariate regression model, The model was evaluated using the F test. Used to measure the explanatory power of the model; the F test is used to test the overall significance of all independent variables in the regression model. By performing a statistical test on the F value of the regression equation, the overall fitting effect of the model can be judged;

[0032] S33: Using the regression model constructed in S31, input the factor data under current environmental conditions to calculate the carbon sink capacity of each region , and draw a spatial distribution map of carbon sequestration capacity.

[0033] Optionally, the S4 specifically includes:

[0034] S41: Combine the historical carbon sink data and the factor data identified in S2, first integrate the data according to the time series to form a complete time series data set; and use linear interpolation or time series completion technology to fill in the missing data;

[0035] S42: Based on the integrated historical time series data, the vector autoregression model is used to construct a dynamic relationship model of the carbon sink pattern. The expression of the vector autoregression model is: ,in, represents the carbon sink capacity and factor vector at time t, is the coefficient matrix of the model, p is the lag order, is a random disturbance term; and the coefficient matrix is ​​estimated by the least squares method , construct a dynamic relationship model reflecting the carbon sink capacity and factors;

[0036] S43: Evaluate the constructed dynamic relationship model through autocorrelation function and partial autocorrelation function, and specifically use AIC or BIC to determine the optimal back-order p of the model;

[0037] S44: Based on the dynamic relationship model constructed in S42, the predicted values ​​of factors under different scenarios are input, and the changing trend of carbon sequestration capacity under each scenario is calculated through model simulation. The prediction formula is:

[0038] ,in, Indicates time The carbon sink capacity and factor prediction values ​​are calculated and the carbon sink capacity prediction results at future points are obtained through iterative calculation.

[0039] Optionally, the S5 specifically includes:

[0040] S51: Integrate the static relationship model constructed in S3 with the dynamic relationship model constructed in S4. The integration method is to construct a weighted combination model to comprehensively consider the impact of static and dynamic factors on carbon sequestration capacity. The specific formula is: ,in, represents the comprehensive carbon sink capacity, represents the carbon sink capacity predicted by the static model, represents the carbon sink capacity predicted by the dynamic model, and is the weight coefficient of the static and dynamic models;

[0041] S52: Evaluate the comprehensive model constructed in S51 by calculating the mean square error (MSE) and coefficient of determination of the comprehensive model To evaluate the goodness of fit of the model, the mean square error is defined as:

[0042] ,in, is the actual carbon sink capacity value of the i-th sample, is the predicted value of the comprehensive model, n is the number of samples;

[0043] S53: Using the evaluated and optimized comprehensive model, input the factor data under current environmental conditions and calculate the carbon sink capacity of each region , and based on this, evaluate the carbon sink spatial carrying capacity of each region .

[0044] Optionally, S6 specifically includes:

[0045] S61: Based on the spatial carrying capacity assessment results obtained in S5, the analytic hierarchy process is used to analyze the carbon sink suitability of each region. A hierarchical structure model is first constructed to divide the carbon sink enhancement potential into the target layer, the criterion layer, and the program layer;

[0046] S62: Compare the factors of the criterion layer pairwise and construct the judgment matrix A. The elements of the judgment matrix A It represents the importance ratio of the i-th factor to the j-th factor;

[0047] S63: Calculate the weight vector w of each criterion using the judgment matrix A;

[0048] S64: Perform a consistency test on the judgment matrix A, calculate the consistency index CI and the consistency ratio CR to verify the rationality of the judgment matrix; the formula of the consistency index CI is: ,in, is the maximum eigenvalue of the judgment matrix A; the formula for calculating the consistency ratio CR is: , where RI is the random consistency index, which is obtained from a fixed table according to the order of the judgment matrix; When , the consistency of the judgment matrix meets the requirements, otherwise the matrix needs to be readjusted;

[0049] S65: Based on the calculated weight vector w and the spatial carrying capacity assessment results obtained in S5, a spatial suitability analysis is conducted on each region. Combining the weights of each criterion and the actual data of the region, a comprehensive assessment is made of the carbon sink enhancement potential of each region.

[0050] Optionally, the S65 specifically includes:

[0051] S651: Combine the weight vector w calculated in S64 with the actual data of each region obtained in S5. Specifically, for each region i, perform a weighted summation of the weights of each criterion and the corresponding actual data to calculate the comprehensive score of the region. , the formula is: ,in, represents the comprehensive score of region i, is the weight of the jth criterion, is the actual data of region i on the jth criterion, and n is the total number of criteria;

[0052] S652: Comprehensive scores of each region calculated in S651 , sort all regions, obtain the relative ranking of each region, and divide each region into different levels to determine the carbon sink enhancement potential of each level.

[0053] Beneficial effects of the present invention:

[0054] The present invention, by constructing a comprehensive static and dynamic model, provides a more comprehensive and accurate method to evaluate the carbon sink capacity and spatial carrying capacity of near-coastal areas. This method can not only capture the dynamic changes of carbon sink capacity over time, but also effectively predict possible future changes. Through this invention, we can better understand the changes in carbon sink capacity under the influence of different natural and human factors, thereby providing a scientific basis for carbon sink management and policy formulation, and enhancing the adaptability and foresight of related decisions.

[0055] The present invention, by accurately identifying and predicting areas with high carbon sink potential, can guide related environmental protection and restoration work to focus more accurately on key areas and maximize the benefits of environmental intervention. This not only helps to improve the overall health and stability of regional ecosystems, but also provides strong support for addressing global climate change. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0057] Figure 1 This is a schematic diagram of a method for assessing carbon sink enhancement potential according to an embodiment of the present invention;

[0058] Figure 2 This is a schematic diagram of a process for identifying factors that affect offshore carbon sink patterns according to an embodiment of the present invention. DETAILED DESCRIPTION

[0059] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0060] It should be noted that the references to "one embodiment", "embodiment", "exemplary embodiments", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0061] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0062] like Figure 1-Figure 2 As shown, a method for assessing the carbon sink enhancement potential in coastal areas includes the following steps:

[0063] S1: Deploy a sensor network in coastal areas to collect multi-dimensional marine environmental data, including water quality parameters, temperature, salinity, nutrient concentration, sediment characteristics and biodiversity, and combine it with data on land-based pollutants entering the sea and sea area use to form a preliminary data set to provide basic information for subsequent analysis;

[0064] S2: Preprocess the preliminary data set formed in S1, and conduct in-depth analysis on the carbon sink increase characteristics and long-term change trends through factor analysis methods, identify the factors affecting the offshore carbon sink pattern, and provide a basis for subsequent model construction;

[0065] S3: Based on the factors identified in S2, a static relationship model between carbon sink pattern and factors is constructed using multivariate regression analysis techniques to assess the spatial distribution of carbon sink capacity under current environmental conditions;

[0066] S4: Combine historical data with the factors in S2 to build a dynamic relationship model of carbon sink pattern, which is used to analyze the dynamic change law of factors, predict the changing trend of carbon sink capacity under different scenarios, and enhance the adaptability of the model to environmental changes;

[0067] S5: Combine the static relationship model of S3 with the dynamic relationship model of S4 to conduct a comprehensive assessment to determine the carbon sink spatial carrying capacity of the coastal area;

[0068] S6: Based on the spatial carrying capacity assessment results obtained in S5, the analytic hierarchy process (AHP) was used to conduct spatial suitability analysis to determine the carbon sink enhancement potential of each region.

[0069] S1 specifically includes:

[0070] S11: Deploy sensor networks in intertidal zones, nearshore shallow waters, estuaries, typical seagrass beds, and coral reefs near the coast. The sensor networks include water quality monitoring sensors, temperature sensors, salinity sensors, nutrient concentration sensors, sediment characteristic sensors, and biodiversity monitoring sensors. The sensors are precisely positioned based on the terrain characteristics, tidal influences, and distribution of major pollution sources in the area to ensure representativeness and comprehensiveness of data collection.

[0071] S12: Continuous data collection through sensor networks, including water quality parameters, temperature, salinity, nutrient concentration, sediment characteristics and biodiversity. The data collection frequency is set to once an hour, the cycle is 24 hours continuously, and the data is collected once a day. The collection frequency and cycle can be dynamically adjusted according to seasonal changes and special events such as typhoons and floods. The frequency and cycle of data collection are set according to the needs of environmental changes to ensure the timeliness and accuracy of the data; each of the above sensors collects data through a specific collection principle. For example, the water quality monitoring sensor measures the concentration of dissolved ions in water through the conductivity method, the temperature sensor uses a thermistor or thermocouple to measure the seawater temperature, the salinity sensor measures the salinity through the conductivity method, the nutrient concentration sensor measures the concentration of nutrients such as nitrate and phosphate through the colorimetric method or electrochemical sensor, the sediment characteristic sensor measures the particle size and concentration of sediments through a turbidity meter or a laser particle size analyzer, and the biodiversity monitoring sensor records the distribution and density of marine organisms through acoustic or optical sensors;

[0072] S13: Pollutant sensors are installed at sewage outlets, farmland runoff confluence areas and industrial emission points on land to collect data on pollutant types, concentrations and emissions. Satellite remote sensing technology and drone cruise image data are combined to obtain spatial distribution information on sea area use, including fishing density, shipping route distribution and distribution of coastal industrial facilities. All data are standardized through a unified interface protocol and integrated with the multi-dimensional marine environmental data collected in S12 to form a preliminary data set. The above steps form an accurate and comprehensive preliminary data set by deploying different types of sensors in intertidal zones, shallow waters, estuaries and typical ecological areas, and using clear collection principles and set frequencies, combined with the integration of land pollution sources and sea area use data, which provides a reliable data basis for subsequent factor analysis and model construction, and improves the scientificity and practicality of the evaluation.

[0073] S2 specifically includes:

[0074] S21: Data preprocessing is performed on the preliminary data set formed in S1, including data cleaning, data normalization and outlier processing. Data cleaning improves the accuracy of the data set by removing noise data, duplicate data and incomplete data. Data normalization converts data of different dimensions into a unified scale for subsequent analysis. Outlier processing identifies and removes outliers in the data set that do not conform to the normal distribution based on statistical methods (such as the Z-score method or the IQR method) to ensure the reliability of the analysis results.

[0075] S22: Prepare for factor analysis on the preliminary data set after data preprocessing, including constructing a correlation matrix between variables, and confirming that the data is suitable for factor analysis through KMO (Kaiser-Meyer-Olkin) test and Bartlett sphericity test. The KMO test result should be controlled above 0.6, and the significance of Bartlett sphericity test should be less than 0.05 to confirm that the data is suitable for factor analysis.

[0076] S23: After confirming that the data is suitable for factor analysis, principal component analysis (PCA) is used to extract key factors. The variables are linearly combined to reduce the dimension while retaining the most important variation information in the data. The extracted factors are optimized through rotation matrices (such as orthogonal rotation or oblique rotation) to better explain the carbon sink increase characteristics and long-term change trends.

[0077] S24: Based on the results of factor analysis, the weights and influence directions of each factor were analyzed through the factor loading matrix, the factors affecting the offshore carbon sink pattern were identified, and the factors were matched with the actual carbon sink data to confirm the role of the factors in the carbon sink pattern, thereby providing a basis for subsequent model construction; the accuracy and consistency of the data were ensured by cleaning, normalizing and processing the preliminary data set; the KMO test and Bartlett sphericity test were combined to ensure that the data were suitable for factor analysis; the key factors were extracted through principal component analysis, and the key factors affecting the offshore carbon sink pattern were identified, which laid a scientific foundation for the subsequent model construction, thereby improving the reliability and scientificity of the entire evaluation method.

[0078] S23 specifically includes:

[0079] S231: After confirming that the data is suitable for factor analysis, principal component analysis (PCA) is used to extract key factors. First, the covariance matrix C of the original data set is calculated. The formula is: , where C represents the covariance matrix, n is the number of samples, is the i-th sample data vector, Sample mean vector; then perform eigenvalue decomposition on the covariance matrix C to obtain the eigenvalue and the corresponding eigenvector ,in, represents the jth eigenvalue, is the eigenvector corresponding to the eigenvalue; the principal component That is the eigenvector The linear combination is expressed as: ,in, represents the jth principal component, x is the original variable vector; by selecting the principal components with higher cumulative contribution rates (usually the first few larger eigenvalues The corresponding principal component ), key factors can be extracted;

[0080] S232: Optimize the extracted factors and use the rotation matrix to rotate the principal components to improve the explanatory power. Specifically, the Varimax rotation method is used to optimize the factors. The goal of the Varimax rotation is to maximize the variance of each factor, making the explanatory power of each factor clearer. The Varimax rotation is optimized through the following objective function Q, and the formula is: , where Q represents the objective function, represents the elements in the factor loading matrix, p is the number of variables, and m is the number of extracted factors. By maximizing Q, the optimized rotated factor loading matrix is ​​obtained, so that each factor is highly correlated with a few variables, thereby improving the interpretability of the factor; through detailed principal component extraction steps, combined with the calculation of the covariance matrix and eigenvalue decomposition, the key factors are scientifically extracted, and the explanatory power of the factors is maximized through Varimax rotation, ensuring the accuracy and interpretability of the factor analysis results, providing a more solid data foundation for subsequent model construction, and improving the scientificity and application value of the entire analysis method.

[0081] S24 specifically includes:

[0082] S241: Based on the factor analysis results, construct the factor loading matrix L, whose elements represents the load of the i-th variable on the j-th factor. The formula of the load matrix L is: ,in, represents the i-th original variable, represents the jth factor, For variables With factor The covariance between and Respectively represent variables With factor The variance of the factor loading matrix L , analyze the weight and direction of each factor on different variables, that is, by comparing the size and sign of the load value, identify the positive and negative impact of each factor on each variable and its intensity;

[0083] S242: Calculate the weight of each factor , the formula is: ,in, Factor The eigenvalue of , m is the total number of factors, and the weight It reflects the relative importance of the factors in explaining the original data and is calculated based on the weights Rank the factors to determine the most important ones;

[0084] S243: Match the identified factors with the actual carbon sink data and calculate the factor scores Quantify the role of each factor in the actual carbon sink pattern, factor score The formula is: ,in, represents the loading vector of the jth factor, X represents the original data vector, and the factor score It reflects the impact strength of each factor in the actual carbon sink data. The spatial distribution of carbon sink data was compared to confirm the specific role of each factor in the offshore carbon sink pattern; the key factors affecting the offshore carbon sink pattern were accurately identified by constructing a factor loading matrix and conducting a detailed analysis; the role of the factor in the carbon sink pattern was quantified by calculating the factor weights and scores, and matched with the actual carbon sink data, ensuring the accuracy and pertinence of the analysis results, and providing a scientific basis for the precise prediction and management of the carbon sink pattern.

[0085] S3 specifically includes:

[0086] S31: Based on the factors identified in S2, a static relationship model between carbon sink pattern and factors is constructed using multivariate regression analysis technology. First, the carbon sink capacity Y is set as the dependent variable, and the identified key factors are As the independent variable, a linear regression model is constructed, and the model expression is: , where Y represents carbon sink capacity, is the intercept of the regression model, is the regression coefficient corresponding to each factor, is the error term; and the regression coefficient is estimated by the least squares method (OLS) , the formula is: ,in, is the estimated regression coefficient vector, F is the factor matrix, and Y is the observed value vector of carbon sink capacity. The static influence of each factor on carbon sink capacity can be quantified through the static relationship model;

[0087] S32: After constructing the multivariate regression model, The model was evaluated using the F test. It is used to measure the explanatory power of the model and is defined as: ,in, is the actual carbon sink capacity value, is the model prediction value, is the mean value of carbon sequestration capacity; the F test is used to test the overall significance of all independent variables in the regression model. The overall fitting effect of the model is judged by performing a statistical test on the F value of the regression equation;

[0088] S33: Using the regression model constructed in S31, input the factor data under current environmental conditions to calculate the carbon sink capacity of each region , and draw a spatial distribution map of carbon sink capacity. By analyzing the spatial distribution map, the carbon sink capacity of different regions can be intuitively evaluated; by constructing a multivariate regression model, the static impact of key factors on carbon sink capacity is quantified, and the reliability of the model is ensured through model evaluation and verification; the model is used to predict and evaluate the spatial distribution of carbon sink capacity under current environmental conditions, which provides a scientific basis for determining the spatial carrying capacity of carbon sinks in various regions, thereby improving the accuracy of carbon sink management and decision-making.

[0089] S4 specifically includes:

[0090] S41: Combine the historical carbon sink data and the factor data identified in S2, first integrate the data according to the time series to form a complete time series data set; and use linear interpolation or time series completion technology to fill in the missing data to ensure the integrity and continuity of the time series data;

[0091] S42: Based on the integrated historical time series data, the vector autoregression (VAR) model is used to construct a dynamic relationship model of the carbon sink pattern. The expression of the vector autoregression model is: ,in, represents the carbon sink capacity and factor vector at time t, is the coefficient matrix of the model, p is the lag order, is a random disturbance term; and the coefficient matrix is ​​estimated by the least squares method , construct a dynamic relationship model reflecting the carbon sink capacity and factors;

[0092] S43: The constructed dynamic relationship model is evaluated by the autocorrelation function (ACF) and partial autocorrelation function (PACF) to test the applicability and accuracy of the model. Specifically, the AIC (Akaike Information Criterion) or BIC (Bayesian Information Criterion) is used to determine the optimal back-order p of the model to ensure that the model has good predictive ability while balancing complexity and goodness of fit;

[0093] S44: Based on the dynamic relationship model constructed in S42, the predicted values ​​of factors under different scenarios are input, and the changing trend of carbon sequestration capacity under each scenario is calculated through model simulation. The prediction formula is:

[0094] ,in, Indicates time The carbon sink capacity and factor prediction values ​​are obtained, and the carbon sink capacity prediction results at future points are obtained through iterative calculation; and by comparing the prediction results under different scenarios, the impact of the dynamic changes of factors on the carbon sink capacity is analyzed to determine the carbon sink capacity trend under different scenarios; by integrating historical data and key factor data, a dynamic relationship model of carbon sink pattern based on the VAR model is constructed, and the dynamic relationship between carbon sink capacity and key factors is quantified. Using scenario analysis and trend prediction methods, the changing trend of carbon sink capacity under different scenarios can be effectively predicted, which provides a scientific basis for carbon sink management and strategy formulation, and improves the prediction ability and practicality of the model in complex environments.

[0095] S5 specifically includes:

[0096] S51: Integrate the static relationship model constructed in S3 with the dynamic relationship model constructed in S4. The integration method is to construct a weighted combination model to comprehensively consider the impact of static and dynamic factors on carbon sequestration capacity. The specific formula is: ,in, represents the comprehensive carbon sink capacity, represents the carbon sink capacity predicted by the static model, represents the carbon sink capacity predicted by the dynamic model, and is the weight coefficient of the static and dynamic models, satisfying ; Determined by historical data fitting results or expert scoring and The value of enables the comprehensive model to accurately reflect the overall trend of carbon sink capacity;

[0097] S52: Evaluate the comprehensive model constructed in S51 by calculating the mean square error (MSE) and coefficient of determination of the comprehensive model To evaluate the goodness of fit of the model, the mean square error is defined as:

[0098] ,in, is the actual carbon sink capacity value of the i-th sample, is the predicted value of the comprehensive model, n is the number of samples; by optimizing the weight coefficient in the model and , minimize MSE, ensuring that the comprehensive model has high prediction accuracy;

[0099] S53: Using the evaluated and optimized comprehensive model, input the factor data under current environmental conditions and calculate the carbon sink capacity of each region , and based on this, evaluate the carbon sink spatial carrying capacity of each region , the calculation formula is: ,in, is the predicted value of the comprehensive carbon sink capacity of region i, and area i is the area of ​​region i. By weighted integration of the static relationship model and the dynamic relationship model, a comprehensive model that can comprehensively reflect the changing trend of carbon sink capacity is constructed, and the prediction accuracy is ensured through evaluation and optimization of the model. Finally, the carbon sink spatial carrying capacity of each region is calculated based on the comprehensive model, which provides a scientific and reliable decision-making basis for carbon sink management and spatial planning in near-coastal areas.

[0100] The spatial suitability analysis using the analytic hierarchy process in S6 specifically includes:

[0101] S61: Based on the spatial carrying capacity assessment results obtained in S5, the analytic hierarchy process (AHP) was used to analyze the carbon sink suitability of each region. A hierarchical structure model was first constructed to divide the carbon sink enhancement potential into a target layer, a criterion layer, and a program layer. The target layer is used to determine the carbon sink enhancement potential of the region, the criterion layer includes factors such as carbon sink spatial carrying capacity, environmental sensitivity, and ecological restoration potential, and the program layer is for each specific region.

[0102] S62: Compare the factors of the criterion layer pairwise and construct the judgment matrix A. The elements of the judgment matrix A It represents the importance ratio of the i-th factor to the j-th factor; the expression of the judgment matrix A is:

[0103] ,

[0104] in, The value of is based on expert scoring or relevant data analysis results, indicating the importance of the i-th criterion relative to the j-th criterion;

[0105] S63: Calculate the weight vector w of each criterion using the judgment matrix A. First, normalize the judgment matrix A so that the sum of the elements in each column is 1. Then calculate the average value of each row to obtain the weight vector w. The formula is: ,in, represents the weight of the ith criterion, n is the number of criteria, and the weight vector w reflects the relative importance of each criterion in determining the carbon sink enhancement potential;

[0106] S64: Perform a consistency test on the judgment matrix A, calculate the consistency index CI and the consistency ratio CR to verify the rationality of the judgment matrix; the formula of the consistency index CI is: ,in, is the maximum eigenvalue of the judgment matrix A; the formula for calculating the consistency ratio CR is: , where RI is the random consistency index, which is obtained from a fixed table according to the order of the judgment matrix; When , the consistency of the judgment matrix meets the requirements, otherwise the matrix needs to be readjusted;

[0107] S65: Based on the calculated weight vector w and the spatial carrying capacity assessment results obtained in S5, a spatial suitability analysis is conducted on each region, and the carbon sink enhancement potential of each region is comprehensively assessed in combination with the weights of each criterion and the actual data of the region; the carbon sink enhancement potential of each region is accurately quantified by standardizing the actual data of each region and calculating the weighted comprehensive score, and these results are intuitively displayed on the spatial distribution map through spatial suitability analysis, so that decision makers can clearly identify areas with greater carbon sink enhancement potential, thereby providing scientific, data-driven support for carbon sink management and regional planning.

[0108] The comprehensive assessment of carbon sink enhancement potential in each region in S65 specifically includes:

[0109] S651: Combine the weight vector w calculated in S64 with the actual data of each region obtained in S5, where the actual data of each region includes specific values ​​corresponding to criteria such as carbon sink spatial carrying capacity, environmental sensitivity, and ecological restoration potential. For each region i, perform a weighted summation of the weights of each criterion and the corresponding actual data to calculate the comprehensive score of the region. , the formula is: ,in, represents the comprehensive score of region i, is the weight of the jth criterion, is the actual data of region i on the jth criterion, n is the total number of criteria; comprehensive score It reflects the overall performance of the region under each criterion;

[0110] S652: Comprehensive scores of each region calculated in S651 , sort all regions, obtain the relative ranking of each region, and divide each region into different levels to determine the carbon sink enhancement potential of each level; specifically, divide the regions with higher comprehensive scores into first-level regions, which represent larger carbon sink enhancement potential; the regions with medium comprehensive scores are divided into second-level regions, which represent medium carbon sink enhancement potential; the regions with lower comprehensive scores are divided into third-level regions, which represent smaller carbon sink enhancement potential; the sorting and grading results of each region are used in actual carbon sink management and planning, determine the regions where carbon sink enhancement measures are implemented first based on the analysis results, and formulate corresponding management strategies according to the regional levels to ensure that the effect of carbon sink enhancement is maximized.

[0111] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for assessing the carbon sink enhancement potential in coastal areas, characterized in that: The following steps are involved: S1: Deploy a sensor network in the coastal area to collect multi-dimensional marine environmental data, and combine it with data on land-based pollutants entering the sea and sea area use to form a preliminary data set; S2: Preprocess the data set formed by S1, and conduct in-depth analysis of the carbon sink increase characteristics and long-term change trends through factor analysis methods to identify factors affecting the offshore carbon sink pattern, including: S21: Perform data preprocessing on the preliminary data set formed in S1, including data cleaning, data normalization and outlier processing; S22: Prepare the preliminary data set after data preprocessing for factor analysis, including constructing the correlation matrix between variables and using KMO test and Bartlett sphericity test to confirm that the data are suitable for factor analysis; S23: After confirming that the data are suitable for factor analysis, the principal component analysis method is used to extract key factors, and the extracted factors are optimized through the rotation matrix; S24: Based on the results of factor analysis, the weights and influence directions of each factor are analyzed through the factor loading matrix to identify the factors that affect the offshore carbon sink pattern, and the factors are matched with the actual carbon sink data to confirm the role of the factors in the carbon sink pattern; The S23 specifically includes: S231: After confirming that the data is suitable for factor analysis, the principal component analysis method is used to extract key factors. First, the covariance matrix C of the original data set is calculated; then the covariance matrix C is decomposed by eigenvalue to obtain the eigenvalue λ j and the corresponding eigenvector v j ;Principal component y j That is the eigenvector v j The linear combination is expressed as: Among them, y j represents the jth principal component, and x is the original variable vector; S232: Optimize the extracted factors and use the rotation matrix to rotate the principal components to improve the explanatory power. Specifically, the Varimax rotation method is used to optimize the factors. The goal of the Varimax rotation is to maximize the variance of each factor. The Varimax rotation is optimized by the following objective function Q, and the formula is: Among them, Q represents the objective function, l ij represents the elements in the factor loading matrix, p is the number of variables, m is the number of extracted factors, and by maximizing Q, the optimized rotated factor loading matrix is ​​obtained; The S24 specifically includes: S241: Based on the factor analysis results, construct the factor loading matrix L, whose element l ij Represents the load of the i-th variable on the j-th factor; according to the load value l in the factor loading matrix L ij , analyze the weight and direction of each factor on different variables, that is, by comparing the size and sign of the load value, identify the positive and negative impact of each factor on each variable and its intensity; S242: Calculate the weight ω of each factor j , and according to the calculated weight ω j Rank the factors to determine the most important ones; S243: Match the identified factors with the actual carbon sink data and calculate the factor score S j Quantify the role of each factor in the actual carbon sink pattern, factor score S j The formula is: S j =L j ·X, where L j represents the loading vector of the jth factor, X represents the original data vector, and the factor score S is calculated by j Compare with the spatial distribution of carbon sink data to confirm the specific role of each factor in the offshore carbon sink pattern; S3: Based on the factors identified in S2, a static relationship model between carbon sink pattern and factors is constructed using multivariate regression analysis techniques to assess the spatial distribution of carbon sink capacity under current environmental conditions; S4: Combining historical data with the factors in S2, a dynamic relationship model of carbon sink pattern is constructed to analyze the dynamic change law of factors and predict the changing trend of carbon sink capacity under different scenarios; S5: Combine the static relationship model of S3 with the dynamic relationship model of S4 to conduct a comprehensive assessment to determine the carbon sink spatial carrying capacity of the coastal area; S6: Based on the spatial carrying capacity assessment results obtained in S5, the analytic hierarchy process is used to conduct spatial suitability analysis to determine the carbon sink enhancement potential of each region.

2. The method for evaluating the carbon sink enhancement potential in coastal areas according to claim 1, characterized in that: The S1 specifically includes: S11: Deploy a sensor network in the intertidal zone, nearshore shallow water area, estuary area, typical seagrass bed, and coral reef area near the coast, wherein the sensor network includes water quality monitoring sensors, temperature sensors, salinity sensors, nutrient concentration sensors, sediment characteristic sensors, and biodiversity monitoring sensors; S12: Continuous data collection through the sensor network, including water quality parameters, temperature, salinity, nutrient concentration, sediment characteristics and biodiversity. The data collection frequency is set to once an hour, the cycle is 24 hours, and the data is collected once a day; S13: Pollutant sensors are installed at land-based sewage outlets, farmland runoff confluence areas and industrial emission points to collect data on pollutant types, concentrations and emissions. Satellite remote sensing technology and drone cruise image data are combined to obtain spatial distribution information on sea area use, including fishing density, shipping route distribution and distribution of coastal industrial facilities. All data are standardized through a unified interface protocol and integrated with the multi-dimensional marine environmental data collected in S12 to form a preliminary data set.

3. The method for evaluating the carbon sink enhancement potential in coastal areas according to claim 1, characterized in that: The S3 specifically includes: S31: Based on the factors identified in S2, a static relationship model between carbon sink pattern and factors is constructed using multivariate regression analysis technology. First, the carbon sink capacity Y is set as the dependent variable, and the identified key factors F1, F2, …, F m As the independent variable, a linear regression model is constructed; and the regression coefficient β is estimated by the least squares method. j , the static impact of each factor on carbon sink capacity can be quantified through the static relationship model; S32: After constructing the multivariate regression model, 2 The model was evaluated by the F test and R 2 Used to measure the explanatory power of the model; the F test is used to test the overall significance of all independent variables in the regression model. By performing a statistical test on the F value of the regression equation, the overall fitting effect of the model can be judged; S33: Using the regression model constructed in S31, input the factor data under current environmental conditions to calculate the carbon sink capacity of each region And draw a spatial distribution map of carbon sequestration capacity.

4. The method for evaluating the carbon sink enhancement potential in coastal areas according to claim 1, characterized in that: The S4 specifically includes: S41: Combine the historical carbon sink data and the factor data identified in S2, first integrate the data according to the time series to form a complete time series data set; and use linear interpolation or time series completion technology to fill in the missing data; S42: Based on the integrated historical time series data, the dynamic relationship model of carbon sink pattern is constructed using the vector autoregression model. The expression of the vector autoregression model is: Y t =A1Y t-1 +A2Y t-2 +…+A p Y t-p +∈ t , where Y t Represents the carbon sink capacity and factor vector at time t, A1, A2, …, A p is the coefficient matrix of the model, p is the lag order, ∈ t is a random disturbance term; and the coefficient matrix A is estimated by the least squares method i , construct a dynamic relationship model reflecting the carbon sink capacity and factors; S43: Evaluate the constructed dynamic relationship model through autocorrelation function and partial autocorrelation function, and specifically use AIC or BIC to determine the optimal back-order p of the model; S44: Based on the dynamic relationship model constructed in S42, the predicted values ​​of factors under different scenarios are input, and the changing trend of carbon sequestration capacity under each scenario is calculated through model simulation. The prediction formula is: Y t+h =A1Y t+h-1 +A2Y t+h-2 +…+A p Y t+h-p , where Y t+h It represents the carbon sink capacity and factor prediction value at time t+h, and the carbon sink capacity prediction result at future time point is obtained through iterative calculation.

5. The method for evaluating the carbon sink enhancement potential in coastal areas according to claim 1, characterized in that: The S5 specifically includes: S51: Integrate the static relationship model constructed in S3 with the dynamic relationship model constructed in S4. The integration method is to construct a weighted combination model to comprehensively consider the impact of static and dynamic factors on carbon sequestration capacity. The specific formula is: Y 综合 =α·Y 静态 +β·Y 动态 , where Y 综合 represents the comprehensive carbon sink capacity, Y 静态 represents the carbon sink capacity predicted by the static model, Y 动态 represents the carbon sink capacity predicted by the dynamic model, α and β are the weight coefficients of the static and dynamic models; S52: Evaluate the comprehensive model constructed in S51 by calculating the mean square error and determination coefficient R of the comprehensive model. 2 To evaluate the goodness of fit of the model, the mean square error is defined as: Among them, Y 实际,i is the actual carbon sink capacity value of the ith sample, Y 综合,i is the predicted value of the comprehensive model, n is the number of samples; S53: Using the evaluated and optimized comprehensive model, input the factor data under current environmental conditions and calculate the carbon sink capacity of each region Based on this, the carbon sink space carrying capacity C of each region is evaluated. i .

6. The method for evaluating the carbon sink enhancement potential in coastal areas according to claim 1, characterized in that: The S6 specifically includes: S61: Based on the spatial carrying capacity assessment results obtained in S5, the analytic hierarchy process is used to analyze the carbon sink suitability of each region. A hierarchical structure model is first constructed to divide the carbon sink enhancement potential into the target layer, the criterion layer, and the program layer; S62: Compare the factors of the criterion layer in pairs and construct a judgment matrix A. The element a of the judgment matrix A ij It represents the importance ratio of the i-th factor to the j-th factor; S63: Calculate the weight vector w of each criterion using the judgment matrix A; S64: Perform a consistency test on the judgment matrix A, calculate the consistency index CI and the consistency ratio CR to verify the rationality of the judgment matrix; the formula of the consistency index CI is: Among them, λ max is the maximum eigenvalue of the judgment matrix A; the formula for calculating the consistency ratio CR is: Among them, RI is the random consistency index, which is obtained from a fixed table according to the order of the judgment matrix; when CR<0.1, the consistency of the judgment matrix meets the requirements, otherwise the matrix needs to be readjusted; S65: Based on the calculated weight vector w and the spatial carrying capacity assessment results obtained in S5, a spatial suitability analysis is conducted on each region. Combining the weights of each criterion and the actual data of the region, a comprehensive assessment is made of the carbon sink enhancement potential of each region.

7. A method for assessing carbon sink potential in coastal areas according to claim 6, characterized in that: The S65 specifically includes: S651: Combine the weight vector w calculated in S64 with the actual data of each region obtained in S5. Specifically, for each region i, perform a weighted summation of the weights of each criterion and the corresponding actual data to calculate the comprehensive score S of the region. i , the formula is: Among them, S i represents the comprehensive score of region i, w j is the weight of the jth criterion, X ij is the actual data of region i on the jth criterion, and n is the total number of criteria; S652: Based on the comprehensive score S of each area calculated in S651 i , sort all regions, obtain the relative ranking of each region, and divide each region into different levels to determine the carbon sink enhancement potential of each level.

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