56 results about "Climate change" patented technology

The invention discloses a vegetation-submerged embankment coupled tough coast protection method and system and a storage medium, and the method comprises the steps: firstly, determining a research region, and systematically collecting the near-shore terrain, water and sand power conditions, vegetation parameters and other data; secondly, constructing a water-sand-vegetation coupling numerical simulation model based on the collected data, and performing precision verification on the model; then selecting a suitable vegetation type in combination with environmental characteristics of the research area, carrying out artificial seeding and planting work in a vegetation potential habitat of the tidal flat, and determining a design elevation and an offshore distance of the submerged dike; and finally, evaluating the coast protection effect under the synergistic effect of the submerged embankment and the vegetation, and quantitatively analyzing the improvement degree of the coast protection effect on wave attenuation and beach stability. A sustainable tough coast protection system can be formed under the climate change background through a collaborative protection mechanism integrating natural vegetation and an artificial submerged embankment. The method has the advantages of low cost, remarkable wave reduction effect and the like, and the capability of coping with climate change challenges and comprehensive protection of coastal zones can be effectively improved.

The invention relates to the related technical field of climate monitoring, in particular to a freezing circle climate change monitoring method and system suitable for an Asian alpine region, and the method comprises the steps: configuring a monitoring network, receiving a frozen soil multi-source monitoring data set, constructing a frozen soil and climate coupling response model, embedding a region climate assessment engine, and generating a frozen soil degradation risk grade distribution map. And judging whether risk reminding is triggered or not according to the dynamic frozen soil degradation threshold value, and if the risk reminding is triggered, marking an abnormal region in the frozen soil degradation risk level distribution diagram. The technical problems that Asian alpine region freezing circle climate change monitoring abnormity reminding mostly depends on static data, the dynamic evolution process of frozen soil cannot be accurately captured, and the frozen soil degradation risk identification precision is limited are solved, a frozen soil and climate coupling response model constructed based on multi-source data is achieved, and the frozen soil degradation risk identification accuracy is improved in combination with a dynamic degradation threshold value set according to the frozen soil type. And risk reminding is triggered in time and an abnormal region is marked, so that the frozen soil degradation risk identification precision is improved.

The invention provides a fusion method of ground station monitoring and satellite remote sensing inversion PM2.5 data, and relates to the technical field of PM2.5 pollution prevention and control. Ground station data and satellite remote sensing data are subjected to time matching and space matching and then are fused by using a universal Kriging method to obtain a high-precision and large-area coverage PM2.5 product. The PM2.5 data spatial correlation is considered, so that the fusion result is closer to reality, the calculation complexity is reduced, and meanwhile, the reliability of the fusion result can be judged by giving an error after data fusion. According to the method, the limitations of insufficient spatial consideration and high calculation complexity of a traditional method are made up, and the PM2.5 product can be used for pollution process analysis, climate change and other researches.

The invention discloses a lake and reservoir future carbon income and expenditure scenario prediction accounting method based on climate change influence, and relates to the technical field of ecological environment monitoring and carbon cycle research, and the method comprises the steps: S1, collecting climate data, social economic data and lake and reservoir ecological data of a target lake and reservoir and a surrounding region, and carrying out the cleaning and missing value supplementation of the data; according to the method, the social economy-climate coupling model is constructed, social economy factors such as population growth and economic development and climate change factors are cooperatively incorporated into lake and reservoir carbon income and expenditure prediction, comprehensive simulation of lake and reservoir carbon income and expenditure multi-driving factors is realized, the fitting degree of a prediction result and an actual situation is improved, and the prediction efficiency is improved. By setting multiple groups of social economy-climate scene combinations, lake and reservoir carbon income and expenditure prediction results under different development paths are output, a carbon income and expenditure accounting sub-model based on a process mechanism is embedded, and model parameters are verified and optimized in combination with historical data, so that the accuracy and reliability of lake and reservoir carbon income and expenditure prediction accounting are further improved.

The invention discloses a method for analyzing influence of climate change and human activity on space-time evolution of water resources. The method comprises the following steps: acquiring meteorological information, hydrological information, land utilization information, soil information, social economic information and water resource information of a to-be-detected area; performing trend analysis on the water resource information to obtain a space-time evolution rule of the water resource information; performing attribution analysis on the time-space evolution rule to obtain influence factors of the time-space evolution of the water resource; obtaining the correlation degree of the influence factors and the space-time evolution rule, and completing the analysis of the climate change and human activity on the space-time evolution of the water resource. According to the method, the influence factors of water resource evolution of the to-be-detected area are researched through the multivariate statistical stepwise regression analysis model, the multicollinearity between the related influence factors of climate change and human activity is reduced through the characteristics of stepwise regression analysis, the calculation accuracy is improved, and the sustainable development of water resources is promoted.

The invention discloses a storm surge coupling numerical simulation method and system based on climate change, and relates to the technical field of numerical simulation, and the method comprises the steps: determining the size of a regional grid according to a pre-obtained regional feature, and carrying out the data reconstruction processing of the obtained heterogeneous spatial data based on the size of the regional grid, and obtaining a comprehensive heterogeneous data set; constructing a comprehensive heterogeneous model, performing model coupling to obtain a comprehensive coupling model, and performing friction coefficient adjustment to obtain a storm surge coupling numerical simulation model; performing storm surge simulation on the comprehensive observation data acquired in real time, and evaluating and optimizing the storm surge coupling numerical simulation model to obtain a storm surge simulation model for storm surge coupling numerical simulation under climate change. Through the comprehensive coupling model, the limitation of traditional single water adding simulation is broken through, and the simulation capacity of extreme water level, offshore water adding and composite disaster processes is remarkably improved.

The invention relates to the technical field of climate change prediction, in particular to a climate change prediction method and system based on artificial intelligence, and the method comprises the following steps: S1, multi-source climate data space-time alignment processing: obtaining satellite remote sensing data, ground observation station data and ocean buoy data, generating a standardized space-time matrix covering the globe through a space-time interpolation algorithm; s2, multi-scale climate feature collaborative extraction: outputting global scale prediction data by a global branch, and outputting regional scale prediction data by a regional branch; and S3, cross-scale dynamic coupling prediction: inputting the global scale prediction data into a cross-scale coupling layer to generate a region correction vector, and outputting a final coupling prediction result after fusing the global scale prediction data with the region scale prediction data. According to the invention, through climate data standardization processing, double-branch multi-scale feature extraction and cross-scale dynamic coupling prediction, the accuracy and regional response capability of climate change modeling are improved.

The invention discloses a corn planting suitability comprehensive evaluation method which comprises the following steps: step 1, data collection and index system construction: a system collects multi-source information of meteorological data, crop growth data and historical disaster data, and a comprehensive corn planting suitability evaluation index system is constructed, the method comprises the following steps of 1, selecting a corn growth period, 2, dividing the growth period: determining four key stages of the corn growth period in combination with related data and local production practice experience, 3, constructing a comprehensive evaluation model, 4, evaluating the planting potential and carrying out weighting processing, 5, carrying out comprehensive evaluation and zoning, and 6, carrying out comprehensive evaluation and zoning. According to the method, the blank of the existing research in comprehensive evaluation of the corn planting suitability is filled, a new thought and method are provided for coping with agricultural risks caused by climate changes, a scientific regionalization reference is provided for agricultural decision makers by constructing a perfect corn planting suitability evaluation system, and the corn planting suitability evaluation method has a wide application prospect. The guarantee of global grain safety and the sustainable development of an agricultural ecosystem are facilitated.

The present application relates to the technical field of climate change warning, and discloses a mangrove blue carbon ecological risk identification and warning method and system for climate change, comprising the following steps: acquiring time series of global land surface characteristic satellite leaf area index data and environmental factors, performing detrending, deseasonalization and standardization processing to extract abnormal sequences; determining climate oscillation windows and neutral periods according to multivariate El Nino-Southern Oscillation index and dipole mode index, and calculating the mean value of the environmental factors in the neutral period as a reference; inputting the environmental factors into a long short-term memory network to extract attention weights, calculating the factor difference between the mean value of the climate oscillation window and the reference, mapping the factor difference by using a nonlinear saturation kernel function, combining the weights to obtain contribution amplitude, and selecting a dominant risk factor by comparing the direction; processing the absolute value of the leaf area index anomaly by using an S function to output risk intensity, and predicting future leaf area index anomaly based on the dominant risk factor to generate a warning evaluation.

A kind of high-strength road of water resources adjustment system in response to climate change, including the structure system template with hollow unit body is combined into underground structure space by pouring and solidifying concrete slurry, and then is paved as road or pavement, the hollow unit body is combined by at least one piece of structure template and multiple side plates;The upper surface of the template is provided with a plate, and a through hole and at least one pipe are provided on the plate, pour concrete slurry after the structure system template and the side plate are combined and paved, and form an underground structure space with high support strength after solidification;Therefore, replace the drainage system infrastructure such as the traditional road drain and drain cover, and use the reserved hole or drill hole of the road, or the water permeable pavement, or the water storage and drainage system of the rainwater collected from the roadside drainage hole and stored in the underground space, effectively store water to prevent regional waterlogging, and provide larger drainage space system, when no drain is set, the urban renewal appearance of the road can be planned to be wider, safer and more beautiful, and as the best method of water resources adjustment, it can also prevent mosquito breeding and foul odor in traditional drain.

Systems, methods, and computer-readable storage media for forecasting the impact of climate change, and more specifically to the impact on water quality and / or quantity. The system receives, from a plurality of sensors within a predefined geographic area, environmental data. The system normalizes the environmental data and executes an artificial intelligence algorithm, where inputs to the artificial intelligence algorithm include the normalized environmental data, and outputs of the artificial intelligence algorithm include environmental risks, consequences, and probabilities associated with at least one environmental event. The system then modifies a planned project using the environmental risks, consequences, and probabilities associated with the at least one environmental event.

The present invention discloses an existing structure strength detection device under climate change, and relates to the technical field of strength detection, the existing structure strength detection device comprises a first carrier plate, the right side of the first carrier plate is fixedly provided with a connection seat, the bottom of the connection seat is fixedly provided with a first U-shaped seat, and the top of the first U-shaped seat is provided with a driving assembly; a supporting rod is fixed to the outer wall of the first rotating rod, a fourth carrying rod is fixed to the left side of the supporting rod, an impact ball is fixed to the left end of the fourth carrying rod, a third carrying rod is fixed to the right side of the supporting rod, and an iron block is fixed to the right end of the third carrying rod. The impact ball is used for impacting the concrete structure under the climate change, and then the detection assembly is used for detecting the concrete structure before and after the impact, so that the change condition of the strength of the concrete structure under the climate change is obtained, and the concrete structure can be correspondingly maintained subsequently.

The invention discloses a climatic change response type modular micro-landscape self-adaptive monitoring system, which comprises a micro-landscape module, a climatic change monitoring module, a data processing module and a self-adaptive adjustment module, the micro-landscape module is made of a sustainable material subjected to multi-dimensional testing and is integrated with climatic change sensitive type indicative organisms; the climatic biological monitoring module is used for collecting climatic environment parameter data and indicative biological quantitative characteristic data; the data processing module is used for receiving climate environment parameter data and indicative biological quantitative characteristic data acquired by the climate biological monitoring module, predicting climate trend and micro-landscape growth state pre-judgment through a CNN-LSTM network, and outputting an optimization scheme in combination with a genetic algorithm; and the adaptive module dynamically adjusts the module structure and generates environmental parameters, thereby realizing accurate response to climate change and micro-landscape ecological efficiency optimization.

The present application relates to a kind of based on multi-source data and machine learning ground observation evapotranspiration complete sequence acquisition method and system.The steps of the method include: preparing high-quality evapotranspiration data and time-continuous reference variable;Training machine learning model, construct the nonlinear relationship of evapotranspiration and reference variable, and evaluate the performance of machine learning model;Using the machine learning model trained completely to interpolate the evapotranspiration of missing time, produce time-continuous evapotranspiration data.The present application effectively solves the problem of how to obtain more real day-scale data, can provide high-quality continuous true value for ground verification;At the same time, it solves the problem of how to obtain long-time complete sequence data, can provide powerful data support for climate change analysis.

The present application belongs to the technical field of surface water heat flux inversion, in particular to a multi-temporal surface water heat flux collaborative inversion method, device and medium. The method comprises: calculating the change rate of surface radiation with vegetation coverage by using multi-temporal remote sensing surface temperature; realizing multi-temporal remote sensing surface temperature collaborative decomposition; solving the joint balance equation of multi-temporal water heat flux and its components; and inverting multi-temporal surface sensible heat flux, water heat flux and its components. The present application realizes multi-temporal surface temperature joint decomposition based on surface temperature and vegetation coverage time series information, thereby proposing a new idea of multi-temporal surface water heat flux joint cutting sharing reference temperature, establishing multi-temporal balance equation considering surface water heat flux and its components, overcoming the deficiency that current remote sensing model is only suitable for single temporal surface water heat flux inversion, improving the efficiency of surface water heat flux remote sensing estimation, and providing technical services for climate change, agricultural production, ecological construction, water resources management and the like.

The invention relates to a light energy battery technology based on ferroelectric polarization. The invention belongs to the technical field of renewable energy sources, new energy sources and clean energy sources, and belongs to national key industries. The technical problems to be solved are that the conversion efficiency of a silicon electric p-n junction solar cell is relatively low, and a photovoltaic power generation system of the silicon electric p-n junction solar cell has intermittency, volatility and uncertainty. The technical scheme for solving the problem is characterized in that ferroelectric photovoltaic positive and negative polarization is opposite in conductive property, generated positive and negative photoelectric conversion batteries form a battery unit, and formed positive and negative magnetic fields are combined into a unified magnetic field. The conversion efficiency of the battery unit is improved, all-optical and all-time fixed mode power generation is realized, and the problems of intermittency, volatility and uncertainty are avoided. The solar power generation device is mainly used for solar power generation to cope with climate changes. In the field of aerospace, energy is provided for space stations and satellites. The invention relates to the field of military weapons, and relates to cold light missiles made of energy power and cold light materials to prevent heat tracking.

The invention discloses a method and system for quantizing the influence of temperature increase on farmland emission reduction potential, and relates to the technical field of crossing of agricultural ecology and climate change, and the method comprises the following steps: S1, collecting multi-dimensional temperature increase characteristics and basic data, dividing farmland regions and crop growth periods, extracting temperature increase characteristic parameters, and calculating the temperature increase characteristic parameters; greenhouse gas emission, soil, crop and meteorological basic data are synchronously collected; s2, contrast case matching and coupling model construction: matching a same-period contrast case based on a random forest algorithm, measuring and calculating a temperature increase response coefficient, and introducing a time-dependent attenuation coefficient and a region regulation factor to construct a multi-factor coupling quantification model; s3, emission reduction potential dynamic quantification and scene prediction; according to the method and the system for quantifying the influence of the temperature increase on the farmland emission reduction potential, the dynamic influence of the temperature increase on the farmland emission reduction potential is accurately captured, the shortages of the time effect and the regional suitability in the prior art are effectively made up, and the evaluation precision is remarkably improved.

A sea ice habitat restoration system is configured to rapidly extract pack ice and raft it onto level ice to establish disturbance corridors that augment the ecological carrying capacity and resistance to continuing climate change. The deployable system is configured to be mounted on an icebreaking ship and includes a inclined-plane ice ramp supported by an ice rafting device. The system applies a hogging moment to the extracted sea ice and laterally rafts it with minimal damage, facilitating the restoration of sea ice habitats of threatened ice-obligate and ice-associated species. This ship-mounted version produces significantly greater volumes, additional vertically and horizontally arranged layers, and more widely distributed composite masses of freeze-bonded rafted ice over the course of a w inter compared to the singly rafted ice passively produced by the moored version referenced herein.

The application discloses a seabed bubble plume flux estimation method based on seismic oceanology, and is characterized in that: based on a seabed bubble plume parameter model, numerical simulation and attribute analysis are mainly used as research means to accurately identify and analyze the seismic response of the seabed bubble plume, a function equation is used to quantitatively analyze the relationship between the bubble volume fraction and the seismic response characteristics (instantaneous amplitude, instantaneous frequency and instantaneous phase); the bubble volume fraction is obtained by combining the function equation and the parameters such as the width, height and shape of the seabed bubble plume in the actual seismic data to further obtain the quantitative index of the seabed bubble plume flux, which provides important theoretical reference and technical support for the identification and analysis of the seabed bubble plume in the actual seismic data, the positioning of the seabed cold spring activity, the research on the enrichment rule and the occurrence area of the seabed resources and the climate change of the greenhouse gas.

The invention discloses an ecological hydrological benign relationship maintenance threshold defining method and system, and relates to the technical field of ecological hydrology, and the method comprises the steps: obtaining multi-source data of a target drainage basin, and selecting a core decision variable; constructing a dual-objective optimization model comprising a hydrological objective function and an ecological objective function based on the core decision variable; constructing a plurality of constraint conditions based on the multi-source data; based on all constraint conditions, a multi-objective evolutionary algorithm is adopted to solve the dual-objective optimization model, and a Pareto optimal solution set is obtained; and determining a weight based on an expert scoring method, and selecting an optimal threshold value from the Pareto optimal solution set based on an ideal point method and a comprehensive satisfaction index. And collaborative optimization of ecological protection and economic development is realized, so that the stability and practicability of the threshold value under the climate change background are ensured.

The invention discloses a drainage basin base flow change attribution analysis method and system, and relates to the field of drainage basin water resource management, and the method comprises the steps: obtaining multi-source basic data of a to-be-researched region; based on daily flow data in the multi-source basic data, a heuristic segmentation algorithm is adopted to determine an annual runoff abrupt change point set; dividing the data according to the first mutation point to obtain reference period data and influence period data; based on the reference period multi-source data and an improved SWAT model of an embedded base flow extraction module, obtaining a base flow simulation sequence; and obtaining the contribution rate of the climate change and the human activity to the basic flow change of the drainage basin through a scene reconstruction and difference decomposition calculation method in combination with the data of the reference period and the influence period. According to the method, by accurately depicting the basin base flow process and the base flow-runoff synergistic effect, multi-time-scale refined quantitative analysis of the contribution rate of human activities and sub-items thereof and climate changes to the base flow change is realized, and the scientificity of basin water resource management decision is remarkably improved.

The invention provides a water vapor prediction method based on TCN and Transform, and the method comprises the following steps: S1, collecting water vapor data, and carrying out the preprocessing of the collected water vapor data; s2, performing local feature extraction on the preprocessed water vapor data by using TCN; s3, carrying out global fusion on the extracted local features through a Transform algorithm; and S4, predicting the PWV by using the multi-layer full-connection neural network. According to the method, the algorithm structure is optimized, the prediction precision and the real-time performance are improved, and more accurate data support is provided for weather forecast and climate change analysis.

The invention discloses a runoff change attribution analysis method based on a Boudike framework, and belongs to the technical field of hydrology, water resource and environment change analysis. The runoff change attribution analysis method comprises the following steps: obtaining annual scale hydrology sequences of a target drainage basin in a reference period and a change period; based on the annual scale hydrological sequences of the reference period and the change period, combining a Bedike equation, and adopting a decomposition method or an elastic coefficient method to quantitatively separate the total runoff variation of the target drainage basin into runoff variation caused by climate change and runoff variation caused by human activity; calculating the contribution rate of the climatic change and the human activity to the total runoff change of the target drainage basin based on the runoff change caused by the climatic change and the runoff change caused by the human activity, and taking the contribution rate as a runoff change attribution analysis result; wherein the Buddy equation is used for describing the physical relationship among the actual evapotranspiration, the precipitation and the potential evapotranspiration of the target drainage basin. The method has both physical mechanism clarity and calculation efficiency, and can effectively reduce parameter calibration complexity and data dependence.

A system is provided.SOLUTION: The system includes a means for acquiring vermin appearance / disappearance information from each local government or organization, a means for acquiring weather change data from a weather information providing service, a means for acquiring mountain village management or development information from a local government, a means for storing the vermin appearance / disappearance information, the weather change data and the development information in a database, a means for cooperatively and mutually analyzing the various kinds of stored data, a means for predicting the appearance / disappearance risk of vermin, a means for generating a countermeasure proposal on the basis of a prediction result, and a means for notifying a related user of the generated countermeasure proposal.SELECTED DRAWING: Figure 1

The invention discloses a method and a system for inverting global sea level change based on satellite gravity data, and belongs to the field of geophysical monitoring and remote sensing. The method comprises the following steps: acquiring satellite gravity data and multi-source auxiliary data, calculating time-varying characteristics of a gravity field after preprocessing, and constructing an inversion model coupling land mass change and ocean thermal expansion; and finally outputting high-resolution global sea level change data by combining a machine learning and data fusion technology optimization result. According to the method, the problems of insufficient precision and low resolution of a traditional method are solved, the sea level change monitoring accuracy can be effectively improved, and key data support is provided for climate change research and disaster prevention and reduction.

The invention discloses a fish habitat water flow structure restoration method, and belongs to the technical field of water conservancy and hydropower ecological protection and fishery resource protection. The method comprises the following steps: S01, analyzing the climate change trend of a target water area; s02, establishing a fish demand model; s03, predicting possible flow velocity change, depth change and area expansion and reduction conditions of the target water area by adopting hydrodynamic simulation software; s04, a set of self-adjustable water flow structure repairing scheme is designed; s05, the water flow structure repairing scheme is visually displayed; s06, carrying out water flow structure repair scheme trial points in the target water area, and adjusting and perfecting a water flow structure repair scheme; and S07, evaluating the effect of the repair measures, and adjusting and optimizing the water flow structure repair scheme according to the evaluation result. Climate change, fish ecological requirements and hydrodynamic simulation are deeply coupled, a self-adjustment optimization mechanism is integrated in the restoration method, and an innovative solution is provided for long-term stability of the fish habitat.

Disclosed is a multi-factor benefit analysis method and system for resilience-oriented power system planning schemes, including acquiring new power system planning schemes; evaluating the abilities of the acquired schemes to withstand extreme events within a set period of years, to obtain resilience indicators; estimating the acquired schemes by using the carbon emission flow method, to obtain carbon emission indicators; calculating the cost of constructing power facilities and the cost of reinforcing or renovating substations and lines, to obtain economic indicators; and evaluating and comparing the planning schemes under the evaluation and decision-making framework of new power system planning schemes for resilience enhancement by comprehensively considering the resilience indicators, carbon emission indicators, and economic indicators, to form a closed loop of decision-making, and give feedback to and correct the planning schemes, thereby achieving adaptive planning for the construction of new power systems under climate changes.

The invention discloses a climate change long-term trend assessment method based on periodic data decomposition, and relates to the technical field of climate change assessment, and the method comprises the steps: collecting multi-source climate data, and constructing a multi-source climate data time sequence; performing periodic decomposition, extracting disturbance of long-term trend, periodic fluctuation and short-term random fluctuation components on the long-term trend, optimizing the long-term trend, and generating a long-term change trend of the target climate; and for the influence of the new energy power volatility, the regional power generation efficiency and the power grid load matching degree, generating an influence evaluation result, and generating a visual report. According to the method, the technical problems that the climate trend prediction precision is insufficient and the multi-dimensional influence on the power system is difficult to accurately evaluate in the prior art are solved, and the technical effects of improving the climate trend prediction precision and accurately quantifying the comprehensive disturbance influence on the power system are achieved.

The application relates to the technical field of civil engineering, and discloses a foundation pit simulation analysis method based on a finite element method, which comprises the following steps: S1, collecting foundation pit engineering data and environmental information: using geological exploration equipment, environmental monitoring sensors, climate monitoring equipment, underground water level sensors and settlement monitoring sensors to collect and store real-time monitoring data of the geological conditions, soil mechanical properties, underground water level, climate change and settlement of the foundation pit engineering; S2, establishing a multi-physical field coupling model of the foundation pit: according to the foundation pit engineering data collected in step S1, a foundation pit simulation analysis system based on the finite element method, a foundation pit simulation analysis equipment based on the finite element method and a computer readable storage medium are further provided. Through the combination of the finite element method, the multi-physical field coupling model and deep learning optimization, the application realizes efficient and accurate foundation pit stability analysis, automatically generates optimization suggestions, and significantly improves the calculation efficiency, accuracy and real-time dynamic adjustment capability.

This invention provides a method for evaluating forest carbon storage potential based on site factors. It employs the Mitscherlich equation to construct the relationship between average diameter at breast height (DBH) and corresponding survey time intervals, thus inversely determining stand age. Based on the growth of average stand carbon storage, site quality grades are classified, and a graded growth model for carbon storage is established. After differentiation, stand age is used as a predictor variable to determine the annual growth rate of carbon storage. Based on a general linear regression model, a backward elimination method is used to screen out factors affecting carbon sequestration rates, quantifying the direction and extent of the influence of stand factors and environmental drivers on forest carbon storage potential. Furthermore, the differences in the influence of tree species on carbon sequestration potential are explored based on site factors. This invention establishes a linear relationship between annual carbon storage growth and stand factors and environmental drivers, providing a new method for quantitatively assessing the carbon sequestration potential of suitable afforestation sites at a future time under climate change, and providing technical support for formulating appropriate afforestation plans and carbon reduction policies.