Multi-scene land gradient utilization carbon emission prediction method, coupling model, system and storage medium

Abstract

The invention relates to the technical field of land utilization change evaluation, in particular to a multi-scene land gradient utilization carbon emission prediction method and system and a readable storage medium. A multi-scenario land gradient utilization carbon emission prediction coupling model under three scenarios of historical development trend, future climate change and policy planning effect is considered to comprehensively evaluate the influence of land gradient utilization change on carbon emission under different development paths. And a decision basis is provided for formulating scientific and reasonable regional space planning, a carbon emission reduction policy and a sustainable development strategy. The invention aims to solve the problem of how to improve the simulation accuracy of the carbon emission management regulation simulation system.

Description

Technical Field

[0001] The present application relates to the technical field of land use change assessment, and particularly to a multi-scenario land gradient utilization carbon emission prediction method, system and readable storage medium. Background Art

[0002] Land use is the way and condition of human utilization of the natural attributes of land, and is also the most direct manifestation of the interaction between humans and nature. As the embodiment of the surface height and shape, terrain drives the distribution of resources and human activities through the migration of surface materials and energy conversion, and determines the formation of the national land space pattern. Clarifying the role of terrain in the development and utilization of national land space is the basis and premise for analyzing the carbon emission effect and its regulation of land gradient utilization.

[0003] Currently, the carbon emission management and control schemes for land use change lack the following considerations:

[0004] First, the unique regional differentiation characteristics of mountains are not fully reflected in the research on land use carbon emissions. As a regional complex with complex human-land relationships, mountains show obvious vertical distribution characteristics compared with plain areas. However, in the current research on land use carbon emissions with mountains as the research object, the limitations of terrain gradient on the endowment of mountain land resources, ecological environment protection and economic and social development are often ignored, and the importance of terrain gradient in land use carbon emission research is not considered;

[0005] Second, the research on land use carbon emission simulation and prediction is still weak. When constructing simulation and prediction models, most studies tend to set development scenarios with ecological priority and cultivated land protection as the core. This single-perspective simulation focusing on land resource protection ignores the uncertainty of future climate change and the key role of policy optimization and adjustment in achieving the carbon neutrality goal.

[0006] Therefore, a carbon emission regulation method for land use change combining terrain gradient and multiple perspectives is needed to improve the simulation accuracy of the carbon emission management and control simulation system. Summary of the Invention

[0007] The main purpose of the present application is to provide a multi-scenario land gradient utilization carbon emission prediction method, aiming to solve the problem of how to improve the simulation accuracy of the carbon emission management and control simulation system.

[0008] To achieve the above object, a multi-scenario land gradient utilization carbon emission prediction method provided by the present application includes:

[0009] Optionally, the land use types include cultivated land, forest land, grassland, water area, urban land, rural settlements, industrial and mining and transportation construction land, and unused land. The target driving factor for cultivated land is GDP, the target driving factor for forest land is the surface vegetation coverage value, the target driving factors for grassland and water area are both the distance from the river, the target driving factor for urban land is the distance from the county government, the target driving factors for rural settlements and industrial and mining and transportation construction land are both night lights, and the target driving factor for unused land is elevation.

[0010] Optionally, the calculation expression for the land development probability is:

[0011]

[0012] In the formula, is the development probability of each land use type; i is the cell, k is the land use type, the T value is 0 or 1, where 1 indicates that there is a conversion from other land use types to the k land use type, and 0 indicates no conversion; p n (x) is the predicted type of the nth decision tree of the vector x; I[·] is the indicator function of the decision tree set; M is the total number of decision trees.

[0013] Optionally, the calculation expression for the total land use development probability is:

[0014]

[0015] In the formula, represents the total development probability of the k land use type in the i cell; is the land development probability of the k land use type in the i cell; r is a random value within 0 to 1; μ k is the threshold for generating new patches of the k land use type; is the neighborhood effect of the i-th cell, representing the coverage ratio of land use types within the k neighborhood; is the adaptive inertia coefficient, representing the influence of the future demand of the k land use type;

[0016] Among them, The calculation expression of is:

[0017]

[0018] In the formula, represents the total number of grid cells occupied by the k land use type at the last iteration within the n×n window, ω k is the neighborhood weight of different land use types, representing the proportion of the expansion area of the land use type in the total land expansion area;

[0019] Among them, The calculation expression of is:

[0020]

[0021] In the formula, and are respectively the difference between the current quantity and the future demand of the land use type k at the (d - 1)-th iteration and the (d - 2)-th iteration.

[0022] Optionally, the expression of the objective function is as follows:

[0023]

[0024] In the formula, F(x) is the objective function that minimizes comprehensive carbon emissions, maximizes economic benefits, and maximizes ecological benefits, MinF1(x) is the minimum value of carbon emissions, MaxF2(x) is the maximum value of economic benefits, MaxF3(x) is the maximum value of ecological benefits; x i is the land use type, a i is the carbon emission / carbon absorption intensity of different land use types; b i is the economic benefit per unit area of different land use types;, c i is the value of ecosystem services per unit area of land.

[0025] Optionally, the land use types include cultivated land, forest land, grassland, water area, urban land, rural settlements, industrial and mining, transportation construction land, and unused land, and the constraint conditions for each land use type include:

[0026]

[0027] 21141.730 ≤ x1 ≤ 22524.830

[0028] 54614.861 ≤ x2 ≤ 66087

[0029] 29883.522 ≤ x3 ≤ 30063.402

[0030] x4 ≥ 1456.177

[0031] 2661.630 ≤ x5 + x6 + x7 ≤ 3460.119

[0032] 945.214 ≤ x5 ≤ 1661.521

[0033] 935.612 ≤ x6 ≤ 949.655

[0034] 780.804 ≤ x7 ≤ 848.943

[0035] x8 < 160.511

[0036] In the formula, x1 is the cultivated land area, x2 is the forest land area, x3 is the grassland area, x4 is the water area, x5 is the urban land area, x6 is the rural residential area, x7 is the industrial and mining, transportation and construction land area, x8 is the unused land area, and x5 + x6 + x7 represents the construction land area.

[0037] In addition, to achieve the above object, the present application further provides a multi-scenario land gradient utilization carbon emission prediction coupling model, and the multi-scenario land gradient utilization carbon emission prediction coupling model includes:

[0038] A historical development scenario module, configured to calculate the land development probability corresponding to each land use type based on the land expansion situation and target driving factors of each land use type in each area to be predicted during the historical period, and calculate the total land use development probability according to the land development probability corresponding to each land use type, so as to evaluate the first land gradient utilization change trend of the area to be predicted under the historical development scenario based on the total land use development probability; and,

[0039] A future climate scenario module, configured to obtain the predicted future population change, economic change, precipitation change and temperature change of the area to be predicted under the future climate scenario, and evaluate the second land gradient utilization change trend of the area to be predicted under the future climate scenario according to the future population change, the economic change, the precipitation change and the temperature change, wherein the future climate scenario includes at least three sub-future climate scenarios caused by different carbon emission levels; and,

[0040] A policy planning scenario module, configured to use each land use type in the area to be predicted as a decision variable, use carbon emission minimization, economic benefit maximization and ecological benefit maximization as objective functions, and the area of each land use type as a constraint condition to evaluate the third land gradient utilization change trend of the area to be predicted under the policy planning scenario;

[0041] A carbon emission prediction module, configured to calculate the predicted carbon emission values of the area to be predicted in the target future year under the first land gradient utilization change trend, the second land gradient utilization change trend and the third land gradient utilization change trend respectively, so as to determine the carbon emission prediction result of the area to be predicted in the target future year based on the first land gradient utilization change trend, the second land gradient utilization change trend and the third land gradient utilization change trend and the predicted carbon emission values corresponding to each trend.

[0042] In addition, to achieve the above object, the present application also provides a computer system, which includes: a memory, a processor, and a multi-scenario land gradient utilization carbon emission prediction program stored on the memory and operable on the processor. When the multi-scenario land gradient utilization carbon emission prediction program is executed by the processor, the steps of the multi-scenario land gradient utilization carbon emission prediction method described in any one of the above are implemented.

[0043] In addition, to achieve the above object, the present application also provides a computer-readable storage medium, on which a multi-scenario land gradient utilization carbon emission prediction program is stored. When the multi-scenario land gradient utilization carbon emission prediction program is executed by a processor, the steps of the multi-scenario land gradient utilization carbon emission prediction method described in any one of the above are implemented.

[0044] The present application at least has the following beneficial effects:

[0045] A multi-scenario land gradient utilization carbon emission prediction coupling model considering three scenarios of historical development trends, future climate change, and policy planning effects is used to comprehensively evaluate the impact of land gradient utilization changes on carbon emissions under different development paths, providing a decision-making basis for formulating scientific and reasonable regional spatial plans, carbon emission reduction policies, and sustainable development strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a schematic flowchart of the first embodiment of the multi-scenario land gradient utilization carbon emission prediction method of the present application;

[0047] Figure 2 It is a schematic diagram of the PLUS model architecture involved in the embodiment of the present application;

[0048] Figure 3 It is a schematic diagram of the contribution degree of cultivated land driving factors in the central Yunnan urban agglomeration involved in the embodiment of the present application;

[0049] Figure 4 It is a schematic diagram of the contribution degree of forest land driving factors in the central Yunnan urban agglomeration involved in the embodiment of the present application;

[0050] Figure 5 It is a schematic diagram of the contribution degree of grassland driving factors in the central Yunnan urban agglomeration involved in the embodiment of the present application;

[0051] Figure 6 It is a schematic diagram of the contribution degree of water area driving factors in the central Yunnan urban agglomeration involved in the embodiment of the present application;

[0052] Figure 7 It is a schematic diagram of the contribution degree of urban land driving factors in the central Yunnan urban agglomeration involved in the embodiment of the present application;

[0053] Figure 8Schematic diagram of contribution degrees of driving factors for rural residential areas, industrial and mining areas, and construction and transportation land in the central Yunnan urban agglomeration involved in the embodiments of this application;

[0054] Figure 9 Schematic diagram of contribution degrees of driving factors for unused land in the central Yunnan urban agglomeration involved in the embodiments of this application;

[0055] Figure 10 Schematic diagram of the architecture of a computer system for the multi-scenario land gradient utilization carbon emission prediction method of this application;

[0056] Figure 11 Schematic diagram of the architecture of the multi-scenario land gradient utilization carbon emission prediction coupling model of this application.

[0057] The realization, functional features, and advantages of the purpose of this application will be further described in conjunction with the embodiments with reference to the accompanying drawings. Detailed implementation manners

[0058] To better understand the above technical solution, the exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0059] First Embodiment

[0060] In this embodiment, a multi-scenario land gradient utilization carbon emission prediction coupling model is constructed to predict the carbon emissions of the area to be predicted with obvious land gradient utilization effects under three scenarios: historical development scenario, future climate scenario, and policy planning scenario. The multi-scenario land gradient utilization carbon emission prediction coupling model described in this embodiment is the SD-GMOP-PLUS coupling model, that is, by integrating and applying the SD model, GMOP model, and PLUS model, to realize the simulation of multiple scenarios such as the historical development scenario, policy planning scenario, and future climate scenario mentioned in the exploration.

[0061] The SD (System Dynamics) model can perfectly combine the system function and structure. When dealing with complex and non-linear system problems, it can combine qualitative and quantitative analysis to construct a model reflecting the internal causal feedback relationship of the system. In the process of constructing this model, the complex system is regarded as a system with a multiple information causal feedback mechanism, and then non-linear and time-varying problems are processed, and quantitative simulation is carried out, aiming to solve system problems based on information feedback. In this embodiment, it is used to simulate the changing trend of land gradient utilization under the future climate scenario.

[0062] GMOP (Grey multi-objective optimization) applies the grey system theory to the mathematical optimization model of multi-objective linear programming. This model can simultaneously optimize multiple objective functions on the premise that part of the information is fuzzy and unclear (i.e., grey information), aiming to find a decision-making plan that meets a series of constraints and makes multiple objective functions reach the optimal simultaneously. As an optimization method integrating the grey prediction GM(1,1) model and multi-objective linear programming, the GMOP mathematical model usually includes objective functions, constraints, and decision variables. Therefore, in this embodiment, it is used to simulate the changing trend of land gradient utilization in the policy planning scenario;

[0063] The PLUS (Patch-generating Land Use Simulation) model is a cellular automaton model based on raster data, mainly used to simulate future land use changes at the patch scale. This model integrates the Land Use Expansion Analysis Strategy (LEAS) and the Cellular Automata with Random Seeds of Multiple Types (CARS), retains the advantages of the Transformation Analysis Strategy (TAS) and the Pattern Analysis Strategy (PAS), can better reveal the driving factors of land use changes and predict the evolution ability of land use types at the patch scale, make up for the deficiencies in aspects such as the transformation rule mining strategy and the land use dynamic change simulation strategy of the cellular automaton model, and improve the model simulation accuracy.

[0064] In addition, the Markov module is integrated into the PLUS model. The Markov module is a mathematical model describing a special stochastic motion process, referring to a series of processes in which a metastable system transforms from the state at time t to the state at time t + 1 at a series of specific time intervals. This transformation requires that the state at time t + 1 is only related to the state at time t. In other words, the future state only depends on the current state and is not affected by the past state. As a discrete-time stochastic process with Markov properties. In the study of land use changes, the Markov model can analyze the probability of land use changing over time by establishing a transition probability matrix, and then predict the quantity of land use types in the next period according to the transition probability matrix of the previous period. Therefore, in this embodiment, the Markov module of the PLUS model is used to simulate the changing trend of land gradient utilization in the historical development scenario.

[0065] It should be noted that the changing trend of land gradient utilization described in this embodiment includes the changing trends in both the quantity structure and the spatial distribution pattern of land gradient utilization.

[0066] The changing trends of the quantity structure include the changes in the area of land use types, land use intensity, and land use structure in the region; the changing areas of the spatial distribution pattern include the spatial transfer of land use, the transfer of the land use center of gravity, and the changes in the degree of spatial agglomeration and dispersion of land use in the region.

[0067] Referring to Figure 1 , in this embodiment, the multi-scenario land gradient utilization carbon emission prediction method includes the following steps:

[0068] Step S10, based on the land expansion situation and target driving factors of each land use type in each area to be predicted during the historical period, calculate the land development probability corresponding to each land use type, and calculate the total land use development probability according to the land development probability corresponding to each land use type, so as to evaluate the first land gradient utilization change trend of the area to be predicted under the historical development scenario based on the total land use development probability; and,

[0069] For the historical development scenario module, the historical development scenario is a spatio-temporal distribution feature and evolution trend of land gradient utilization of the land to be predicted in the past historical period, and a future-oriented land development benchmark framework is constructed. This scenario is based on the past land gradient utilization characteristics as a reference, and evaluates and compares the expected effects of future land gradient utilization strategies by continuing the historical development trend.

[0070] In this embodiment, the land expansion situation refers to the area change situation of different land use types in the area to be predicted during the historical period, which can be quantified by the corresponding area change amount of this land use type.

[0071] The driving factor refers to the influencing factor that causes the land use change in the area to be predicted. There are usually multiple driving factors for one land use type. The target driving factor in this embodiment refers to the driving factor with the highest contribution degree.

[0072] After calculating the total land use development probability of the area to be predicted, evaluate the first land gradient utilization change trend of the area to be predicted under the historical development scenario based on the total land use development probability.

[0073] It should be noted that the first land gradient utilization change trend described in this embodiment is a visual dataset that can be displayed by a computer system for researchers to analyze based on the first land gradient utilization change trend.

[0074] Step S20: Obtain the future population change amount, economic change amount, precipitation change amount, and temperature change amount predicted for the area to be predicted under future climate scenarios. Based on the future population change amount, the economic change amount, the precipitation change amount, and the temperature change amount, determine the second land gradient utilization change trend of the area to be predicted under future climate scenarios, where the future climate scenarios include at least three sub-future climate scenarios caused by different carbon emission levels; and,

[0075] For the future climate scenario module, this module constructs Shared Socioeconomic Pathways (SSPs) parallel to Representative Concentration Pathways (RCPs) from perspectives related to the scientific basis, impacts, vulnerabilities, risks, adaptation, and mitigation of climate change, elevating the simple setting of original socioeconomic changes to pathway scenarios. SSPs consist of five future socioeconomic scenarios resulting from five different carbon emission level development models, namely SSP1, SSP2, SSP3, SSP4, and SSP5, representing sustainable development scenarios, intermediate development scenarios, interregional competition scenarios, unbalanced development scenarios, and fossil fuel-based development scenarios respectively. RCPs describe different paths of air pollutant concentrations and greenhouse gas emissions when future population, economy, and land use change. The four representative paths in RCPs are RCP2.6, RCP4.5, RCP6.0, and RCP8.5, and the numbers in each path represent the radiative forcing values of greenhouse gases in 2100.

[0076] In this embodiment, the future climate scenarios include at least three sub-future climate scenarios caused by different carbon emission levels, and at least three sub-future climate scenarios under the coupling of SSP and RCP are set to emphasize the driving effect of different socioeconomic development models on climate change.

[0077] Step S30: Use each land use type in the area to be predicted as a decision variable, take carbon emission minimization, economic benefit maximization, and ecological benefit maximization as objective functions, and the area of each land use type as a constraint condition to determine the third land gradient utilization change trend of the area to be predicted under the policy planning scenario;

[0078] For the policy planning scenario, the policy planning scenario is guided by the current land policy of the area to be predicted, centered around the core development goals of the area to be predicted, to minimize land waste and enhance the comprehensive carrying capacity of the land. This scenario plays a key role in the sustainable land use by formulating and implementing a series of incentive and restraint measures, guiding all sectors of society to actively participate in low-carbon and environmentally friendly land use practices, and effectively controlling its carbon emissions.

[0079] In this embodiment, an optimization objective function for land gradient utilization that integrates three objectives, namely minimizing carbon emissions, maximizing economic value, and maximizing ecological value, is constructed from three aspects: decision variable setting, objective function construction, and constraint condition establishment. The aim is to find the optimal solution or optimal plan that simultaneously meets the above three objectives, so as to characterize the future land use demand of the area to be predicted under the policy planning scenario.

[0080] It should be noted that the third land gradient utilization change trend described in this embodiment is a visual dataset that can be displayed by a computer system for researchers to analyze based on the third land gradient utilization change trend.

[0081] Step S40: Calculate the predicted carbon emission values of the area to be predicted in the target future year under the first land gradient utilization change trend, the second land gradient utilization change trend, and the third land gradient utilization change trend respectively, so as to determine the carbon emission prediction result of the area to be predicted in the target future year based on the first land gradient utilization change trend, the second land gradient utilization change trend, the third land gradient utilization change trend, and the corresponding predicted carbon emission values of each trend.

[0082] In this embodiment, based on the first land gradient utilization change trend, the second land gradient utilization change trend, and the third land gradient utilization change trend predicted by the multi-scenario land gradient utilization carbon emission prediction coupling model, calculate the corresponding carbon emission values of each trend as the predicted carbon emission values.

[0083] It should be noted that the relationship between steps S10, S20, and S30 is parallel, and the description order in this embodiment does not form a limitation on the execution order of these steps.

[0084] It should be noted that the corresponding carbon emission values of each trend can be calculated through existing technologies. The focus of this embodiment is to propose three different scenarios for predicting and analyzing the carbon emissions of the area to be predicted.

[0085] In the technical solution provided in this embodiment, a multi-scenario land gradient utilization carbon emission prediction coupling model considering three scenarios of historical development trend, future climate change, and policy planning effect is proposed to comprehensively evaluate the impact of land gradient utilization change on carbon emissions under different development paths, and provide a decision-making basis for formulating scientific and reasonable regional spatial planning, carbon emission reduction policies, and sustainable development strategies.

[0086] Second Embodiment

[0087] Based on the first embodiment, in this embodiment, the PLUS model is used as the basic framework to construct the historical development scenario module. In this embodiment, referring to Figure 2Schematic diagram of the PLUS model architecture shown.

[0088] First, to ensure that the row and column numbers of land use data are consistent in different periods, the land use data format is converted to "unsigned char", and cultivated land, forest land, grassland, water area, urban land, rural settlements, industrial and mining, construction and transportation land, and unused land are numbered 1-8 in sequence. The land use data of two periods (2000 and 2020) in the area to be predicted are superimposed to extract the changed areas of each land use type, that is, land expansion is extracted.

[0089] Furthermore, since the land gradient use change is a complex non-linear process affected by multiple factors, and the influencing factors have different degrees of influence at different times and locations. On the basis of comprehensively considering the current development characteristics, data availability and applicability of the area to be predicted, 14 driving factors such as temperature, precipitation, elevation, slope, soil type, normalized difference vegetation index, distance to river, distance to lake, distance to the seat of the county government, distance to railway, distance to road, population, GDP, and night-time light are selected as the driving factor dataset.

[0090] Furthermore, the extracted land expansion data and the driving factor dataset are input into LEAS (Land Expansion Analysis Strategy). The number of regression trees is set to 50; the sampling rate is increased to 0.1 to represent that 10% of the pixels are selected for sample training; mTry is equal to the number of driving factors, that is, 14. The problem of mining the conversion rules of land use types is converted into a binary classification problem, that is, the random forest classification (RFC) algorithm can be used to obtain the development probability and inertia probability of each land use type, and determine the contribution of the driving factors to each land use type. As an ensemble classifier based on decision trees, RFC can extract random samples from the original training dataset, process high-dimensional data, solve the collinearity problem between variables, and finally output the development probability of the kth land use type on the i-cell.

[11] , and the calculation formula is as follows:

[0091]

[0092] In the formula, is the development probability of each land use type; i is the cell, k is the land use type, the T value is 0 or 1, where 1 represents the conversion of other land use types to the k land use type, and 0 means no conversion; p n (x) is the predicted type of the nth decision tree of the vector x; I[·] is the indicator function of the decision tree set; M is the total number of decision trees.

[0093] Furthermore, regarding how to calculate the total probability of land use development, the PLUS model is a CA model based on multi-type random land use seeds, which effectively integrates the influence between macro land use demand (top-down) and local land use competition (bottom-up). During the simulation process, an adaptive inertia coefficient is used to dynamically adjust the competition state of land use in the area to be predicted. The Markov Chain is adopted to determine the land use demand in the study area, and then the automatic generation of land use patches is simulated. The overall probability calculation formula for land use types is as follows:

[0094]

[0095] In the formula, represents the total development probability of land use type k in cell i; is the growth probability of land use type k in cell i; is the neighborhood effect of the i-th cell, that is, the coverage ratio of land use types within the k neighborhood. In this embodiment, the neighborhood range is default set to 3; is the adaptive inertia coefficient, that is, the influence of the future demand for land use type k, and its value depends on the gap between the current land quantity in the area to be predicted and the target demand for land use type k at iteration d.

[0096] The calculation formula for the neighborhood effect is as follows:

[0097]

[0098] In the formula, represents the total number of grid cells occupied by land use type k in the n×n window at the last iteration, ω k is the neighborhood weight of different land use types, which is determined by the proportion of the expansion area of the land use type in the total land expansion area in this embodiment.

[0099] The calculation formula for the adaptive inertia coefficient is as follows:

[0100]

[0101] In the formula, and are the differences between the current quantity and the future demand of land use type k at the (d - 1)-th iteration and the (d - 2)-th iteration respectively. A roulette wheel is constructed based on the overall probability of land use types to select the land use state for the next iteration.

[0102] Finally, a random seed generation and threshold decreasing mechanism are determined to simulate the patch evolution of multiple land use types. That is, when the neighborhood of land use type k is 0, the Monte Carlo method is used to generate change seeds on the development probability surface of each land use type, and its calculation formula is as follows:

[0103]

[0104] In the formula, r is a random value between 0 and 1; μ k Generate new patch thresholds for k land use types. The seed can produce a new land use type and grow into a new patch consisting of a group of cells of the same land use type.

[0105] Third embodiment

[0106] Based on the second embodiment, in this embodiment, based on the LEAS analysis in the PLIUS model, the driving factors of land gradient utilization in the central Yunnan urban agglomeration and their contribution differences can be obtained. The land gradient utilization change process in the central Yunnan urban agglomeration is complex and diverse. The driving factors of different land use types show significant heterogeneity, and the contribution intensity of each factor to land gradient utilization is also different. The target driving factors corresponding to each land use type are as follows:

[0107] Reference Figure 3 , the target driving factor of cultivated land is GDP, and its contribution is 0.11. Economic development has accelerated the process of urbanization and industrialization, resulting in a surge in demand for construction land. For the central Yunnan urban agglomeration, which has complex terrain and interlaced mountains, hills and plains, the expansion of construction land is often accompanied by a reduction in cultivated land, especially when the land demand brought about by economic development is not effectively regulated, and cultivated land with relatively flat terrain is forced to be converted into construction land, forming a trend of decreasing cultivated land resources. However, economic growth has also brought more funds and technical support to the agricultural development of some counties, leading to an increase in demand for agricultural products and agricultural production, promoting agricultural modernization and large-scale operations, and thus increasing the demand for cultivated land, increasing the investment in cultivated land replenishment and reclamation, thereby promoting the further development and expansion of cultivated land.

[0108] Reference Figure 4 , the target driving factor of forest land is NDVI, and its contribution is 0.11. NDVI refers to the degree of surface vegetation coverage. The areas with increased forest area are mainly located in areas with higher NDVI values. The higher the NDVI value, the denser the vegetation and the better the ecological conditions in the area, which provides unique conditions for the natural expansion and artificial afforestation of forest land. The central Yunnan urban agglomeration is located in the core area of ​​Yunnan. It has complex and diverse topography and suitable climatic conditions, and has a good ecological foundation for the growth of trees. In particular, areas with higher NDVI values ​​are usually located in areas with rich natural vegetation such as mountains and hills. Good water, soil and light conditions are conducive to the growth and reproduction of trees.

[0109] Reference Figure 5, the target driving factor for grasslands is the distance from rivers, with a contribution degree of 0.10. The newly added grasslands in the Central Yunnan Urban Agglomeration are mainly located in the areas around rivers. The growth and expansion of grasslands are directly restricted by water conditions. As an important water source, the distance between rivers and grasslands directly affects the water supply of grasslands. In the areas closer to rivers, due to sufficient water, it is conducive to the growth and expansion of grasslands; while in the areas farther from rivers, the growth of grasslands is restricted due to insufficient water. In addition, rivers also affect the soil texture, vegetation distribution and biodiversity of the surface through their flow direction and distribution. There is usually a high biodiversity around rivers, including various aquatic and terrestrial organisms, and complex ecological relationships are formed between these organisms and grasslands. The flow of rivers and the water cycle promote the circulation and distribution of nutrients, providing a good ecological environment for the growth of grasslands.

[0110] Refer to Figure 6 , the target driving factor for water areas is the distance from rivers, with a contribution degree of 0.22. Affected by gravity, the terrain of water areas is relatively low. The closer to the river, the more significant the influence of factors such as river recharge, groundwater level and floods on water areas. The existence of six plateau lakes, namely Dianchi Lake, Fuxian Lake, Yangzonghai Lake, Yilong Lake, Qilu Lake and Xingyun Lake, not only greatly enriches the regional water resource reserves and meets the needs of local economic and social development, but also undertakes multiple ecological service functions such as regulating climate, maintaining biodiversity and providing leisure tourism. The areas around lakes are close to the river system, with more abundant water source recharge, corresponding improvement in soil humidity and fertility, which is conducive to the maintenance and expansion of water areas.

[0111] Refer to Figure 7 , the target driving factor for urban land is the distance from the county government, with a contribution degree of 0.20. The expanding areas of urban land are mainly distributed around the seats of county governments in each county. This also reflects the important role of government agencies in urban development. On the one hand, the intensity and density of economic activities are relatively large in the locations of government agencies, with a large number of commercial service facilities, government agencies and public service facilities gathered; on the other hand, the infrastructure around the county government is usually relatively complete, including transportation, water supply, power supply, communication, etc. The degree of improvement of these infrastructures directly affects the development and utilization value of land. In addition, the aggregation of government agencies also generates population and industrial agglomeration effects, providing a strong economic driving force for the expansion of urban land. Therefore, the land closer to the county government is more likely to be developed into urban land.

[0112] Refer to Figure 8, the target driving factors for rural residential areas, industrial and mining areas, and construction and transportation land are night lights, with contribution degrees of 0.14 and 0.16 respectively. The expansion of rural residential areas, industrial and mining areas, and construction and transportation land is usually closely related to the level of economic activity. The brightness of night lights can reflect the regional economic development level, intensity of human activities, and living standards of residents. In rural areas, brighter areas often have dense population distribution, frequent activities, good infrastructure construction conditions such as roads, power supply, and communication, and residents are willing to settle in these areas. In the case of industrial and mining areas and construction and transportation land, the density of night lights usually indicates the acceleration of the industrialization and urbanization process, leading to a sharp increase in the demand for industrial land and transportation infrastructure land. Therefore, the expansion areas of rural residential areas, industrial and mining areas, and construction and transportation land in the central Yunnan urban agglomeration are mainly distributed in brighter areas.

[0113] Refer to Figure 9 , the target driving factor for unused land is elevation, with a contribution degree of 0.33. The newly added unused land is scattered in the areas with higher terrain in the north. As an important indicator to measure the surface form, elevation directly determines the development difficulty, construction cost, and potential ecological functions of different zones. As the elevation increases, the terrain conditions in the central Yunnan urban agglomeration tend to be complex, and significant changes occur in soil fertility, water resource distribution, and climate characteristics, jointly increasing the difficulty and cost of land development and utilization in high-altitude areas. Therefore, unused land tends to be concentrated in areas with complex terrain, inconvenient transportation, and difficult large-scale development and utilization.

[0114] Fourth Embodiment

[0115] Based on the first embodiment, in this embodiment, the SD model is used to reflect the relationship between the structure, function, and behavior of the land system, so as to predict the changes and trends of the system under future climate scenarios and clarify the changing trends of regional land gradient utilization.

[0116] In this embodiment, three sub-future climate scenarios, namely SSP1-2.6, SSP2-4.5, and SSP5-8.5, are set under the coupling of SSP and RCP. Among them, SSP1-2.6 refers to a sustainable development scenario with low emissions and low radiative forcing; SSP2-4.5 refers to a development scenario with medium levels of social and economic development and greenhouse gas emissions, and the SSP5-8.5 scenario refers to a high-emission, high-speed development scenario dominated by fossil fuels.

[0117] The SSP1-2.6 scenario is the future development path of social and economic sustainable development and low radiative forcing. The core goal of this scenario is to ensure net-zero emissions of CO2 after 2050, and the global radiative forcing increment remains at about 2.6 W·m -2Within. Compared with the pre-industrial revolution multi-model ensemble average global average temperature, the global average temperature rise in this scenario will be controlled within 2°C. The realization of the SSP1-2.6 scenario depends on close cooperation globally, promoting economic development with a relatively high growth rate while the total population stabilizes or decreases, prompting countries to transform from a model that simply pursues high-speed economic growth and focus more on the overall well-being of humanity and long-term sustainable development.

[0118] The SSP2-4.5 scenario is the future development path of intermediate socio-economic development and medium radiative forcing. In this scenario, socio-economic development and greenhouse gas emissions are at intermediate levels, neither highly sustainable nor laissez-faire growth. Most economies are stable in their political systems, and their development paths more follow historical development models rather than significantly deviating from the "business as usual" model. It is expected that by 2100, the global radiative forcing increment is expected to exceed that of the SSP1-2.6 scenario, about 4.5 W·m -2 . Compared with the pre-industrial revolution multi-model ensemble average global average temperature, the global average temperature rise in the SSP2-4.5 scenario is expected to exceed 2°C. But it can be controlled at a lower level than the SSP5-8.5 scenario.

[0119] The SSP5-8.5 scenario is the future path of promoting economic development by consuming a large amount of fossil energy. Along with the intensification of high radiation intensity, socio-economic activities continue to grow in this scenario, but there is a lack of effective greenhouse gas emission reduction strategies and policy support. Compared with relatively mild emission reduction scenarios such as SSP1-2.6 and SSP2-4.5, CO2 emissions in the SSP5-8.5 scenario will continue to increase. This high-growth, high-emission model will lead to a significant increase in global radiative forcing. It is expected that the global radiative forcing increment in 2100 will reach or exceed 8.5 W·m -2 . Compared with the pre-industrial revolution multi-model ensemble average global average temperature, the global average temperature rise in the SSP5-8.5 scenario will be very significant, far exceeding the 2°C temperature control target set by the Paris Agreement.

[0120] Optionally, for the predicted future population changes, economic changes, precipitation changes, and temperature changes under future climate scenarios, first, the original climate models can be uniformly interpolated onto a grid that matches the resolution of the climate station observation data to ensure data spatial consistency. Then, with the set first historical time period range as the calibration period and the set second historical time period range (the range is greater than the first historical time period range) as the verification period, the quantile mapping method can be used to correct the biases of future precipitation and temperature data.

[0121] Next, the Delta model is used to perform downscaling analysis on the corrected precipitation and temperature data to improve the data resolution and reduce the errors generated when the data is directly used for climate change analysis in medium and small-scale regions. Then, the precipitation of all meteorological stations in the region at different future times is interpolated using GIS to obtain the future precipitation and temperature data of the study area, which is used for the simulation analysis of land gradient utilization under future climate scenarios. Further and optionally, to ensure the consistency and comparability of the data, the monthly data is synthesized into annual data in this embodiment to be unified with the population and economic data under future climate scenarios.

[0122] Finally, the future population, GDP, precipitation, and temperature data under each future climate scenario are linearly fitted to obtain the fitting equations of each variable with time, and then the population growth rate, GDP growth rate, annual average precipitation change, and annual average temperature change are determined.

[0123] Exemplarily, in some specific embodiments, three scenarios such as SSP1-2.6, SSP2-4.5, and SSP5-8.5 are used as sub-future climate scenarios. The simulation parameter settings for the SSPs future climate change scenarios can refer to Table 1 below:

[0124] Table 1. Simulation parameter settings for SSPs future climate change scenarios

[0125]

[0126] It should be noted that the second land gradient utilization change trend described in this embodiment is a visual dataset that can be displayed by a computer system for researchers to analyze based on the second land gradient utilization change trend.

[0127] In addition, to ensure the simulation accuracy of the SD model, the relative error is used to test the simulation effect of the SD model in this embodiment, and its expression is:

[0128]

[0129] Exemplarily, in a specific embodiment, the central Yunnan urban agglomeration in 2020 is used as the area to be predicted, and the actual measured values are tested against the predicted values obtained using the SD model proposed in this embodiment. The test results are shown in Table 2 below:

[0130] Table 2. Test of SD model simulation results

[0131]

[0132]

[0133] It can be seen that the relative errors of the historical simulation test results are all less than 5%, indicating that the model has high accuracy.

[0134] The Fifth Embodiment

[0135] Based on the First Embodiment, in this embodiment, a policy planning scenario module is constructed based on the GMOP model.

[0136] First of all, as an important part of the GMOP model construction, the setting of decision variables should not only conform to the current land use characteristics, planning requirements and development trends of the research area, but also be independent of each other, with characteristics such as typicality, difference, comprehensiveness and feasibility. In this embodiment, 8 decision variables are set, namely cultivated land (x1), forest land (x2), grassland (x3), water area (x4), urban land (x5), rural settlements (x6), industrial and mining, construction and transportation land (x7) and unused land (x8) (Table 3).

[0137] Table 3. Land Use Structure and Variable Settings of the Area to be Predicted in 2020

[0138]

[0139] Furthermore, under the guidance of the low-carbon economy theory, the core of the land gradient use optimization strategy lies in fully exploring and enhancing the economic value and ecological value of land through scientific planning and management, so as to achieve the unity of economic, ecological and social benefits. Therefore, the construction of the objective function needs to be comprehensively considered from three aspects: the carbon emission reduction potential, land economic value and ecological value of the research area, so as to construct the optimal comprehensive benefit objective function under the background of policy optimization:

[0140] (1) The goal of minimizing carbon emissions emphasizes optimizing the land gradient use structure, enhancing the carbon sink function of the terrestrial ecosystem, and reducing the proportion of high-carbon emission activities to achieve low-carbon emissions or even zero carbon emissions. Its expression is as follows:

[0141]

[0142] In the formula, MinF1(x) is the minimum value of carbon emissions, x i is the land use type, and a i is the carbon emission / carbon absorption intensity (t / km 2 ) of different land use types.

[0143] Optionally, in some specific embodiments, GM(1,1) is used to obtain the carbon emission and carbon absorption intensity coefficients of the land gradient use in the area to be predicted in the target year, and finally the objective function of minimizing carbon emissions is established, which can be expressed as:

[0144] MinF1(x) = -677.356x1 - 379.495x2 + 21.079x3 - 668.687x4 + 7418.129x5 + 17677.193x6 + 13721.105x7

[0145] (2) Maximizing economic benefits aims to seek the maximum economic benefits of land under the constraints of carbon emissions control and carbon absorption capacity improvement, ensuring the sustainability of land use activities and thus providing strong support for social and economic development. The objective function expression for maximizing economic benefits is as follows:

[0146]

[0147] In the formula, MaxF2(x) is the maximum value of economic benefits, and b i is the economic benefit coefficient, that is, the economic benefit per unit area of different land use types (100 million yuan / km 2 ).

[0148] Optionally, in some specific embodiments, the ratio of the output values of agriculture, forestry, animal husbandry, and fishery to the areas of cultivated land, forest land, grassland, and water areas is set as the economic benefit coefficient of these four land use types. The economic benefit coefficients of urban land, rural residential areas, and industrial, mining, and construction transportation land are determined by the ratio of the output values of the secondary and tertiary industries to the areas of these three land use types. The economic benefit coefficient of unused land is set to 0.001 billion yuan / km [30,31] on the basis of referring to previous studies 2 . The economic benefit coefficients of different land use types in the area to be predicted in the target year are obtained after GM(1,1) prediction. The objective function for maximizing the economic benefits of the area to be predicted can be expressed as:

[0149] MaxF2(x) = 0.102x1 + 0.004x2 + 0.068x3 + 0.047x4 + 13.223x5 + 10.927x6 + 2.624x7 + 0.001x8

[0150] (3) The objective of maximizing ecological benefits takes into account the impact of land and its use patterns on the ecosystem. By reasonably allocating land resources, it ensures that the ecological value of the regional land reaches the maximum. The objective function expression for maximizing ecological benefits is as follows:

[0151]

[0152] In the formula, MaxF3(x) is the maximum value of ecological benefits, and c i is the ecosystem service value coefficient of each land use type, that is, the ecosystem service value per unit area of land (10,000 yuan / km 2 ).

[0153] Optionally, in some specific embodiments, in this embodiment, the ecosystem service value per unit area of the area to be predicted is calculated by the regional equivalent correction method. Among them, the ecological service value of construction land (urban land, rural settlements, industrial and mining, and construction transportation land) is set to 0. Then, the GM(1,1) is used to predict the land ecosystem service value coefficient in the target year. The objective function for maximizing the ecological benefits of the area to be predicted can be expressed as:

[0154] MaxF3(x)=152.517x1+760.541x2+465.794x3+2418.214x4+25.132x8

[0155] (4) The policy planning scenario takes into account the dual needs of resource and environmental protection and economic development. The main goal is to minimize regional carbon emissions, maximize the potential of carbon sinks through reasonable land planning, and pursue the maximum comprehensive value of the joint output of economic and social development and ecological services to achieve the green, low-carbon and sustainable development of the central Yunnan urban agglomeration. Its expression is as follows:

[0156]

[0157] In the formula, F(x) is the optimal objective function that minimizes comprehensive carbon emissions, maximizes economic benefits, and maximizes ecological benefits, that is, the policy planning scenario objective function.

[0158] Furthermore, in this embodiment, the allowable land area change in the area to be predicted in the relevant policies is used as a constraint condition, so that the carbon emissions predicted by the policy scenario module change under this constraint, thereby scientifically and reasonably optimizing and adjusting the land use structure in the target future year.

[0159] Sixth Embodiment

[0160] In this embodiment, based on the land area proposed in the fifth embodiment as the constraint condition for the area to be predicted.

[0161] In this embodiment, the central Yunnan urban agglomeration in five prefecture-level cities of Kunming, Qujing, Yuxi, Chuxiong, and Honghe in the central and eastern regions of Yunnan Province is used as the area to be predicted, and 2035 is used as the target future year, and the following constraint conditions including specific values are set:

[0162] (1) Total area constraint

[0163] The total land area of the central Yunnan urban agglomeration is 111301.530 km2, and the area of each land type is greater than 0. Therefore, the total area constraint can be established as:

[0164]

[0165] (2) Cultivated land area constraint

[0166] According to the relevant data set, the permanent population in this region is expected to reach 3305 million in 2035, accounting for 55% of the total population of the province. Considering the core position of cultivated land in maintaining national food security and ensuring the supply of important agricultural products, it is necessary to continuously strengthen the protection of cultivated land. Combining the overall territorial space planning of the five prefecture-level cities of Kunming, Qujing, Yuxi, Chuxiong and Honghe, the reserved amount of cultivated land in the central Yunnan urban agglomeration in 2035 shall not be less than 21141.730 km 2 . However, with the acceleration of the urbanization process, the demand for construction land is increasing continuously, which will inevitably occupy some cultivated land resources. Therefore, in order to ensure the sustainable utilization of cultivated land resources, in this embodiment, the predicted value of the cultivated land area in 2035 is used as the lower limit of cultivated land protection after 2020, and the constraint equation of the cultivated land area can be expressed as:

[0167] 21141.730≤x1≤22524.830

[0168] (3) Constraint on forest land area

[0169] As the most important carbon sink in the terrestrial ecosystem, forest land not only absorbs CO2 in the atmosphere through photosynthesis, converts it into organic matter and fixes it in vegetation or soil, effectively reducing greenhouse gas emissions, but also plays an irreplaceable role in maintaining ecological balance and protecting biodiversity. In 2020, the forest land area of the central Yunnan urban agglomeration reached 54614.861 km 2 . With the in-depth implementation of the "dual carbon" goal and the continuous strengthening of the intensity of ecological protection and restoration, it is expected that the forest land area will continue to increase in the future. The relevant policies point out that during the "14th Five-Year Plan" period, the goals of completing 3.5 million mu of artificial afforestation and returning cultivated land to forests or grasslands, closing hillsides for afforestation of 5.5 million mu, the forest coverage rate reaching more than 67%, and the forest stock volume reaching more than 2.75 billion cubic meters shall be achieved. Accordingly, the forest land area of the central Yunnan urban agglomeration in 2035 is expected to be 66087 km 2 . In this embodiment, it is used as the upper limit of the forest land area growth, and the current value in 2020 is used as the lower limit, and the constraint equation can be established as:

[0170] 54614.861≤x2≤66087

[0171] (4) Constraint on grassland area

[0172] The grassland area of the central Yunnan urban agglomeration shows a downward trend, which to a certain extent affects its positive role in carbon emission reduction. The grassland area in 2000 was 30620.964 km 2 , and by 2020 it was only 29883.522 km 2 , with a reduction of up to 737.442 km 2, and during the research period, the CO2 absorption of grasslands did not fully offset the emissions generated by autotrophic and heterotrophic respiration, which also weakened their carbon sink potential. Given the government's emphasis on grassland ecological restoration, the degradation of grasslands will be effectively curbed in the future. Relevant policies clearly state that the work of returning farmland to forests and grasslands should be strengthened to enhance water conservation and soil and water conservation functions, and it is planned to complete the ecological restoration of 500,000 mu of degraded grasslands. Accordingly, the grassland area in the central Yunnan urban agglomeration is expected to increase by 179.880 km in 2035. 2 , which can be set as the upper limit of the grassland, and the constraint equation is established as:

[0173] 29883.522 ≤ x3 ≤ 30063.402

[0174] (5) Water area constraint

[0175] As an important carbon pool in the ecosystem, water areas play a key role in mitigating global climate change through the carbon storage and emission reduction functions of natural water bodies such as wetlands, lakes, and rivers. Protecting and restoring water area ecosystems can not only enhance the carbon sink capacity of water bodies but also reduce greenhouse gas emissions through the rational use of water resources and green technologies for sewage treatment. The water areas in the central Yunnan urban agglomeration are mainly expanding, increasing by 162.050 km from 2000 to 2020. 2 , especially from 2015 to 2020, the water area increased by as much as 132.134 km. 2 . Accordingly, the water area in the target year should not be less than the current value in 2020, and the constraint equation can be established as:

[0176] x4 ≥ 1456.177

[0177] (6) Construction land area constraint

[0178] As an important production factor for human activities, construction land is the most important natural resource investment in the process of industrialization and urbanization, including urban land, rural residential areas, and industrial and construction transportation land. The construction land area in the central Yunnan urban agglomeration in 2020 was 2661.630 km. 2 , with the acceleration of the new urbanization process, it is expected that the construction land will still show an expanding trend in the future. However, relevant policies clearly state that the expansion multiple of the urban development boundary in 2035 should be controlled within 1.3 times the urban construction land scale in 2020. Combining the overall land use plans of Kunming City, Qujing City, Yuxi City, Chuxiong Prefecture, and Honghe Prefecture, the construction land area in the central Yunnan urban agglomeration in the target year is expected to be 3460.119 km. 2 , taking this value as the upper limit of future construction land expansion, the constraint equation is established as:

[0179] 2661.630 ≤ x5 + x6 + x7 ≤ 3460.119

[0180] (7) Urban land area constraint

[0181] From 2000 to 2020, the construction land area of the central Yunnan urban agglomeration increased significantly, reaching 945.2142 km 2 in 2020. The per capita urban land area was 69.313 m 2 / person. Given the strong demand for urban land due to economic growth, urban land will continue to show a rapid expansion trend in the future. Therefore, in this embodiment, the GM(1,1) model is used to predict that the urban land area of the central Yunnan urban agglomeration will reach 1661.521 km 2 in 2035. The relevant data set points out that for the development goal in 2035, the permanent population is expected to reach 30.5 million, and the urbanization rate will exceed 70%. Accordingly, the planned per capita urban land area in 2035 is 77.823 m 2 / person, which meets the planned per capita urban land standard in the "Standard for Classification of Urban Land Use and Planning Construction Land ((GB 50137-2011))". Therefore, the predicted value in 2035 can be used as the upper limit of urban land, and the constraint condition equation is established as:

[0182] 945.214 ≤ x5 ≤ 1661.521

[0183] (8) Rural residential area constraint

[0184] By 2035, 55% of the province's population will be concentrated in this area. This population concentration will lead to a reduction in the area of some rural residential areas due to urbanization. However, with the in-depth implementation of the rural revitalization strategy, the rural modernization development centered on agricultural industrialization and rural livability will bring new development opportunities to rural areas. The return of the population and foreign investment will also promote a moderate increase in the area of some rural residential areas. This growth is more of a stable and orderly expansion achieved on the basis of ensuring ecological environmental protection and stable agricultural production. Based on the development trend of rural residential areas in the central Yunnan urban agglomeration from 2000 to 2020, it is expected that the rural residential area in this area will be 949.655 km 2 in 2035, an increase of 14.043 km 2 compared with 2020. This predicted value can be used as the upper limit of expansion, and the established constraint condition equation is:

[0185] 935.612 ≤ x6 ≤ 949.655

[0186] (9) Industrial and mining, construction and transportation land area constraint

[0187] In the future, the demand for land for transportation facilities in the Central Yunnan Urban Agglomeration will inevitably increase with the acceleration of the urbanization process. At the same time, the current problems of scattered industrial space and homogenized industrial functions will be effectively alleviated, and the industrial layout will be more intensive, guiding the development of industrial and mining construction land towards a more compact and efficient direction. To meet the demand for transportation and industrial and mining land, the area of industrial and mining and construction transportation land will inevitably expand to a certain extent. According to the current development trend, the industrial and mining and construction transportation land in the Central Yunnan Urban Agglomeration is expected to be 848.943 km 2 in 2035, which can be used as the upper limit of expansion, and a constraint equation can be established:

[0188] 780.804 ≤ x7 ≤ 848.943

[0189] (10) Constraint on the area of unused land

[0190] With the continuous advancement of the national territorial space planning, the territorial space of the Central Yunnan Urban Agglomeration will be further optimized to achieve a more scientific and reasonable allocation. Under this background, the area of unused land will show a steady shrinking trend, significantly lower than the current value in 2020. This not only meets the national strategic requirements for improving the utilization efficiency of land resources and promoting sustainable development but also demonstrates the important role played by the Central Yunnan Urban Agglomeration in optimizing the territorial space layout and promoting regional coordinated development. Accordingly, a constraint equation can be established as:

[0191] x8 < 160.511

[0192] Sort out the various constraint conditions in (1)-(10) to obtain the set of constraint conditions shown in Table 4

[0193] Table 4. Constraint conditions

[0194]

[0195] Sixth Embodiment

[0196] Based on any of the above embodiments, for regions with obvious vertical zonality and altitude gradient, various land use types in this type of region are significantly affected by altitude. When evaluating the carbon emission effect, it is easy to ignore the change in the carbon emission intensity of the land caused by the spatial distribution change of the region due to land vertical differentiation and / or gradient stratification in this type of region, thereby resulting in inaccurate evaluation of the carbon emission effect within the region. Therefore, in this embodiment, the region to be predicted is divided by gradient levels, and the regions with the same gradient level in the region to be predicted are used as research units. After calculating the carbon emission / absorption amount in each research unit, the carbon emission effect of each gradient level region is evaluated respectively.

[0197] Specifically, the gradient levels are divided by the topographic position index calculated from the elevation value and slope value of the land. The topographic position index (TPI) is an index used to describe topographic features. The traditional topographic position index is mainly obtained by comparing the elevation value of a grid cell with the average elevation value of its surrounding area. However, the topographic position index proposed in this embodiment is calculated using the elevation value and slope value.

[0198] Optionally, the calculation steps of the topographic position index in this embodiment are as follows:

[0199] First step, select any pixel in the to-be-predicted area as the target pixel in units of pixels. Taking the target pixel as the center, take the average of the elevation values and slope values of all pixels within the target radius range to obtain the average elevation value and average slope value of the target pixel;

[0200] Second step, based on the elevation value, slope value of the target pixel, as well as the average elevation value and the average slope value, calculate the topographic position index within the target radius range:

[0201]

[0202] In the formula, T ij (R) is the local window topographic position index of the pixel in the i-th row and j-th column under the local window with the target radius range R; u ij is the local window centered on the target pixel at the (i, j) position, H ij and S ij are respectively the elevation value and slope value of the target pixel, is the average elevation value, is the average slope value.

[0203] Optionally, both the elevation value and slope value in the embodiment can be obtained through Digital Elevation Model (DEM) data.

[0204] In a specific embodiment, the initial DEM data downloaded from the Earth Science Data website of the National Aeronautics and Space Administration (NASA) can be selected. The spatial resolution is 12.5m × 12.5m (that is, the size of one pixel is 12.5m). Compared with the SRTM and ASTER product data, its resolution and three-dimensional details are the best, which can meet the research requirements for higher-precision data analysis and mapping, and can display the three-dimensional characteristics of the central Yunnan urban agglomeration as much as possible to improve the calculation accuracy. After preprocessing the downloaded initial DEM product data, such as projection, mosaicking, extraction, resampling, surface analysis, etc., the digital elevation model data containing elevation values and slope values required in this embodiment can be obtained.

[0205] Optionally, for how to divide the target sub-region based on the terrain position index T ij (R), with the same target radius range R as the unit, first divide the area to be predicted into multiple sub-regions, and then apply the aforementioned terrain position index calculation formula to calculate the terrain position index T ij (R) of each sub-region. According to the numerical interval where the terrain position index corresponding to each sub-region is located, determine the target gradient where each of the said sub-regions is located.

[0206] In some specific embodiments, the area to be predicted is the Central Yunnan Urban Agglomeration in the central and eastern regions of Yunnan Province, and its terrain position index is between 0 and 1.22. It is divided into five gradient levels: I (0 - 0.40), II (0.40 - 0.53), III (0.53 - 0.63), IV (0.63 - 0.74), and V (0.74 - 1.22). When the value of the terrain position index corresponding to the sub-region is within the above corresponding interval, it can be determined which target sub-interval corresponding to which target gradient level the sub-region belongs to.

[0207] The seventh embodiment

[0208] Based on any of the above embodiments, in this embodiment, specific examples of the carbon emission prediction results of the area to be predicted in the target future year are provided based on the trends in three different scenarios: the first land gradient utilization change trend, the second land gradient utilization change trend, and the third land gradient utilization change trend.

[0209] In this example, the area to be predicted is the Central Yunnan Urban Agglomeration, the target future year is 2035, and the future climate scenarios include three sub-future climate scenarios: SSP1 - 2.6 scenario, SSP2 - 4.5 scenario, and SSP5 - 8.5 scenario. The carbon emissions per unit area and carbon absorption of different land use types in 2035 are set. Multiply the land gradient utilization simulation results of the Central Yunnan Urban Agglomeration under different scenarios by the future land gradient utilization carbon emission intensity to obtain the carbon emissions, carbon absorption, and net carbon emissions of each land use type in the Central Yunnan Urban Agglomeration in 2035 under different scenarios, as shown in Table 5 below:

[0210] Table 5. Carbon emissions from land gradient utilization in the Central Yunnan Urban Agglomeration in 2035 under different scenarios (10,000 tons)

[0211]

[0212] As can be seen from Table 4, the carbon emissions of land gradient utilization in the central Yunnan urban agglomeration in 2035 under the five scenarios have decreased significantly compared with the past, but there are significant differences in the emission reduction effects among different scenarios. The scenario with the smallest carbon emissions of land gradient utilization is SSP1-2.6, followed by the policy planning scenario, then the SSP2-4.5 scenario, the SSP5-8.5 scenario ranks fourth, and the historical development scenario has the largest carbon emissions. The carbon emissions of land gradient utilization under the historical development scenario are expected to be 3.06743 million tons, showing a significant decrease compared with 2020. However, compared with the emissions under the other four scenarios simulated in the same year, it is still relatively high. The reason is that there is a large-scale expansion in urban and rural production and living spaces such as urban land, rural residential areas, industrial and mining, and construction and transportation land. Based on the principle of constant total amount and balance of land type increase and decrease, the increase in construction land will inevitably reduce the scale of ecological land, thereby weakening the carbon sink capacity of ecological land such as forests. The SSP5-8.5 scenario represents a future development path mainly based on fossil fuel extraction and energy-intensive labor methods. The carbon emissions of land gradient utilization in the central Yunnan urban agglomeration in 2035 under this scenario are 2.72589 million tons. Although it is 0.34155 million tons lower than the historical development scenario, it still faces relatively high risks in the future. The carbon emissions of land gradient utilization under the SSP2-4.5 scenario are 1.67051 million tons. It is a relatively mild emission reduction path that can not only ensure economic growth but also control the incremental carbon emissions. Although its carbon emissions are slightly higher than the policy planning scenario, they are still lower than the historical development scenario and the SSP5-8.5 scenario, reflecting a good balance between economic development and carbon emission reduction. In 2035, the carbon emissions of land gradient utilization in the central Yunnan urban agglomeration under the policy planning scenario are significantly lower than the other three scenarios, namely the historical development, SSP5-8.5, and SSP2-4.5 scenarios, only 1.23328 million tons, fully demonstrating the key role of policy guidance and scientific planning in the process of carbon emission reduction. By reasonably allocating land resources and optimizing the land use structure, carbon emission reduction can be effectively promoted. However, according to the current policy planning goals, there is still a certain gap in achieving carbon neutrality in 2035. On the contrary, under the SSP1-2.6 scenario, the carbon emissions generated by each land use type are offset by the carbon absorption amount, achieving a perfect balance between carbon emissions and carbon absorption, and even showing a carbon ecological surplus (i.e., the carbon absorption amount exceeds the carbon emissions), which also demonstrates the practical feasibility of the central Yunnan urban agglomeration to achieve the carbon neutrality goal in advance. The SSP1-2.6 scenario is also the optimal development path to achieve the carbon neutrality goal in advance.

[0213] In addition, in this embodiment, each region in the central Yunnan urban agglomeration is divided according to the gradient level, and the carbon emissions of land use in the central Yunnan urban agglomeration at each gradient level are measured, and the results are shown in Table 6 as follows:

[0214] Table 6. Carbon emissions of land use in the central Yunnan urban agglomeration at different gradient levels (10,000 tons)

[0215]

[0216]

[0217] In 2035, the gradient distribution characteristics of carbon emissions and carbon absorption under different scenarios continued the development trend from 2000 to 2020 and showed significant vertical zonality. Under the five scenarios, the I-level and II-level gradients were still mainly carbon emissions, while the III-V-level gradients showed significant carbon sink effects. At the same time, there were significant differences in carbon emissions and carbon absorption among these five gradients under different scenarios. Under the I-level gradient, the carbon emissions predicted by the SSP5-8.5 scenario were the highest, reaching 17.15945 million tons, highlighting the urgency of reducing greenhouse gas emissions under the high-emission path. Followed by the policy planning scenario, the SSP2-4.5 scenario, and the historical development scenario, with carbon emissions between 16.79715 and 16.93180 million tons, reflecting the volatility of the impact of different policy orientations and economic development paths on carbon emissions. In contrast, the carbon emissions of the SSP1-2.6 scenario were the lowest, only 14.58114 million tons, indicating the great potential of the sustainable development path in reducing carbon emissions and promoting green and low-carbon transformation. It is worth noting that the carbon emission difference between the scenarios with the largest and smallest emissions at this gradient level was as high as 2.57831 million tons, further emphasizing the importance of optimizing the energy consumption structure and controlling fossil energy consumption for achieving the carbon emission reduction target. The carbon emissions of the II-level gradient decreased significantly under the five scenarios, accounting for only 8.10-13.95%, indicating obvious emission reduction effects in this gradient area. The carbon absorption of the III-V-level gradients was generally higher than the carbon emissions, jointly constituting an important carbon sink area in the central Yunnan urban agglomeration. Especially the IV-level gradient showed the strongest carbon sink capacity in 2035, which benefited from its rich natural vegetation resources and relatively low human activity interference, enabling vegetation to more effectively absorb and store CO2. Although the III-level gradient also had carbon sink characteristics, with the advancement of mountain town construction in Yunnan, the intensity of human activities in this gradient increased, resulting in a weakening of its carbon sink effect. Although the carbon absorption of the V-level gradient was lower than that of the III-level and IV-level gradients, it had increased significantly compared with 2000-2020, stabilizing between 3.81172 and 3.91396 million tons, fully reflecting the positive results achieved by ecological restoration and protection measures in this gradient area.

[0218] In addition, based on the above data analysis results, we further obtained the following conclusions:

[0219] (1) The land gradient utilization change in the central Yunnan urban agglomeration is the result of the combined action of natural and socio-economic factors. There are significant differences in the driving factors and their contribution intensities for different land use types. The main driving factor for cultivated land is GDP, with a contribution degree of 0.11. Economic development will lead to changes in the demand for agricultural land. The main driving factor for forest land is NDVI, with a contribution degree of 0.11. The greenness and growth status of vegetation affect the expansion of forest land. The main driving factor for grassland is the distance to the river, with a contribution degree of 0.10. Proximity to the river provides sufficient water for grassland, which is beneficial to its growth and expansion. The main driving factor for water area is the distance to the river, with a contribution degree of 0.22. Water source replenishment is extremely important for the maintenance and expansion of water areas. The main driving factor for urban land is the distance to the county government, with a contribution degree of 0.20. The dividends brought by proximity to government agencies provide impetus for urban expansion. The main driving factors for rural settlements and industrial, mining, construction, and transportation land are night lights, with contribution degrees of 0.14 and 0.16 respectively. High economic activity levels are conducive to the expansion of these two land use types. The main driving factor for unused land is elevation, with a contribution degree of 0.33. It is difficult and costly to develop and utilize land in high-altitude areas, making large-scale development and utilization difficult.

[0220] (2) There are significant differences in the simulation results of the quantity structure of land gradient utilization in the central Yunnan urban agglomeration under five scenarios. Under the historical development scenario, the acceleration of the urbanization process promotes the expansion of construction land, resulting in the compression of the natural ecological space. Under the SSP1-2.6 scenario, the scales of forest land and grassland expand, the intensity of ecosystem protection and restoration increases, and the land use demand transforms into a green and sustainable mode. Under the SSP2-4.5 scenario, except for the reduction in the areas of cultivated land and unused land, there are varying degrees of expansion for the remaining land types. Under the SSP5-8.5 scenario, the scales of production and living land both increase. Under the policy planning scenario, the areas of urban land and forest land increase significantly, showing a dual orientation of ecological protection and intensive land use. Although the change trends of each land use type are basically the same under the five scenarios, there are still obvious gradient differentiations. The areas of cultivated land, water area, urban land, rural settlements, and industrial, mining, construction, and transportation land decrease with the increase in gradient. The areas of forest land and grassland show an inverted U-shaped change, while the area of unused land increases with the increase in gradient.

[0221] (3) The spatial distribution pattern of land gradient utilization in the Central Yunnan Urban Agglomeration presents a relatively stable state. Cultivated land, forest land, and grassland are the main land use types in the study area. Among them, forest land and grassland are widely distributed, while cultivated land shows a clustered distribution. The scale of urban land, rural settlements, and industrial and construction transportation land is small, but the quantity is large, mainly clustering in a small area around the locations of prefecture-level and county-level governments. Affected by topography and hydrological conditions, water areas are distributed in strips or patches. Unused land is scattered at the edge of the study area or in areas with complex terrain. The formation of this spatial pattern is closely related to the natural conditions of the Central Yunnan Urban Agglomeration, which is mainly composed of high plateaus and mountains with fragmented and steep terrain. In addition, there are differences in the distribution of various land use types at different gradient levels. The I-level gradient is the main distribution area of cultivated land, water areas, urban land, rural settlements, and industrial and construction transportation land; under the II-level gradient, cultivated land, forest land, and grassland are widely distributed, and the land use types are more complex and diverse; under the III-level gradient, cultivated land, forest land, and grassland are intertwined, forming a balanced and dense distribution pattern; under the IV-level gradient, forest land has an absolute advantage, forming a contiguous distribution trend with grassland and cultivated land in the northwest and southwest regions of the study area; under the V-level gradient, forest land and grassland are clustered in the northwest region of the study area.

[0222] (4) The carbon emissions from land gradient utilization in the Central Yunnan Urban Agglomeration have decreased significantly compared with the past, and there are differences in the emission reduction effects under different scenarios. The carbon emissions in the historical development scenario are the largest, followed by the SSP5-8.5 scenario and the SSP2-4.5 scenario. Then comes the policy planning scenario, and the SSP1-2.6 scenario has the smallest emissions, achieving a perfect balance between carbon emissions and carbon absorption, and even showing a carbon ecological surplus. From the perspectives of science and sustainability, the policy planning scenario still needs to be further optimized. The future development of the Central Yunnan Urban Agglomeration should tend to the SSP1-2.6 scenario to effectively connect with the carbon neutrality goal. In addition, the gradient distribution characteristics of carbon emissions and carbon absorption under different scenarios continue the historical development trend, showing a significant vertical zonality law: the I-level and II-level gradients are mainly carbon emissions, while the III-V level gradients show a carbon sink effect.

[0223] It should be noted that in the above example, the Central Yunnan Urban Agglomeration is taken as an example for analysis, but the analysis results can also be applied to the analysis of carbon emission effects in areas with obvious vertical zonality and altitude gradients.

[0224] As an implementation solution, Figure 10 It is a schematic diagram of the architecture of the hardware operating environment of the computer system involved in the solution of the embodiment of the present application.

[0225] Such as Figure 10As shown in the figure, the computer system may include: a processor 1001, such as a CPU, a memory 1005, a user interface 1003, a network interface 1004, and a communication bus 1002. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display) and an input unit such as a keyboard (Keyboard). Optionally, the user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface). The memory 1005 may be a high-speed RAM memory or a stable memory (non-volatile memory), such as a disk memory. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0226] Those skilled in the art can understand that Figure 1 the computer system architecture shown in the figure does not constitute a limitation on the computer system, and may include more or fewer components than shown in the figure, or combine some components, or have different component arrangements.

[0227] As Figure 10 shown in the figure, the memory 1005, as a storage medium, may include an operating system, a network communication module, a user interface module, and a multi-scenario land gradient utilization carbon emission prediction program. Among them, the operating system is a program that manages and controls the hardware and software resources of the computer system, and runs the multi-scenario land gradient utilization carbon emission prediction program and other software or programs.

[0228] In Figure 10 the computer system shown in the figure, the user interface 1003 is mainly used to connect to the terminal and communicate with the terminal for data; the network interface 1004 is mainly used to connect to the background server and communicate with the background server for data; the processor 1001 can be used to call the multi-scenario land gradient utilization carbon emission prediction program stored in the memory 1005.

[0229] In this embodiment, the computer system includes: a memory 1005, a processor 1001, and a multi-scenario land gradient utilization carbon emission prediction program stored on the memory and executable on the processor, where:

[0230] When the processor 1001 calls the multi-scenario land gradient utilization carbon emission prediction program stored in the memory 1005, the following operations are performed:

[0231] Based on the land expansion situation and target driving factors of each land use type in each area to be predicted within the historical period, calculate the land development probability corresponding to each land use type, and calculate the total land use development probability based on the land development probability corresponding to each land use type, so as to evaluate the first land gradient utilization change trend of the area to be predicted under the historical development scenario; and,

[0232] Obtain the predicted future population change, economic change, precipitation change, and temperature change of the area to be predicted under the future climate scenario. According to the future population change, the economic change, the precipitation change, and the temperature change, evaluate the second land gradient utilization change trend of the area to be predicted under the future climate scenario, where the future climate scenario includes at least three sub-future climate scenarios caused by different carbon emission levels; and,

[0233] Taking each land use type in the area to be predicted as a decision variable, with carbon emission minimization, economic benefit maximization, and ecological benefit maximization as the objective functions, and the area of each land use type as the constraint conditions, evaluate the third land gradient utilization change trend of the area to be predicted under the policy planning scenario;

[0234] Calculate the predicted carbon emission values of the area to be predicted in the target future year under the first land gradient utilization change trend, the second land gradient utilization change trend, and the third land gradient utilization change trend respectively, so as to determine the carbon emission prediction result of the area to be predicted in the target future year based on the first land gradient utilization change trend, the second land gradient utilization change trend, the third land gradient utilization change trend, and the predicted carbon emission values corresponding to each trend.

[0235] In addition, referring to Figure 11 This embodiment also proposes a multi-scenario land gradient utilization carbon emission prediction coupling model, and the multi-scenario land gradient utilization carbon emission prediction coupling model includes:

[0236] A historical development scenario module 100, which is used to calculate the land development probability corresponding to each land use type based on the land expansion situation and target driving factors of each land use type in each area to be predicted within the historical period, and calculate the total land use development probability based on the land development probability corresponding to each land use type, so as to determine the first land gradient utilization change trend of the area to be predicted under the historical development scenario based on the total land use development probability;

[0237] The future climate scenario module 200 is used to obtain the predicted future population change amount, economic change amount, precipitation change amount, and temperature change amount in the area to be predicted under the future climate scenario, and determine the second land gradient utilization change trend in the area to be predicted under the future climate scenario according to the future population change amount, the economic change amount, the precipitation change amount, and the temperature change amount, where the future climate scenario includes at least three sub-future climate scenarios caused by different carbon emission levels; and,

[0238] The policy planning scenario module 300 is used to use each land use type in the area to be predicted as a decision variable, and use carbon emission minimization, economic benefit maximization, and ecological benefit maximization as objective functions, and the area of each land use type as a constraint condition to determine the third land gradient utilization change trend in the area to be predicted under the policy planning scenario;

[0239] The carbon emission prediction module 400 is used to calculate the predicted carbon emission values of the area to be predicted under the first land gradient utilization change trend, the second land gradient utilization change trend, and the third land gradient utilization change trend respectively, and determine the carbon emission prediction result of the area to be predicted in the target future year based on the first land gradient utilization change trend, the second land gradient utilization change trend, the third land gradient utilization change trend, and the predicted carbon emission values corresponding to each trend.

[0240] In addition, those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program includes program instructions, and the computer program can be stored in a storage medium, and the storage medium is a computer-readable storage medium. The program instructions are executed by at least one processor in the computer system to implement the process steps of the embodiments of the above methods.

[0241] Therefore, the present application also provides a computer-readable storage medium, and the computer-readable storage medium stores a multi-scenario land gradient utilization carbon emission prediction program, and when the multi-scenario land gradient utilization carbon emission prediction program is executed by a processor, it implements each step of the multi-scenario land gradient utilization carbon emission prediction method as described in the above embodiments.

[0242] Among them, the computer-readable storage medium can be various computer-readable storage media such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a magnetic disk, or an optical disc that can store program codes.

[0243] It should be noted that since the storage medium provided in the embodiments of the present application is the storage medium used to implement the methods in the embodiments of the present application, based on the methods introduced in the embodiments of the present application, those skilled in the art can understand the specific structure and variations of the storage medium, and thus will not be elaborated herein. Any storage medium used in the methods of the embodiments of the present application falls within the scope of protection of the present application.

[0244] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0245] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can also be implemented. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to generate a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks.

[0246] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in Figure 1 one or more of the flows Figure 1 or blocks.

[0247] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks.

[0248] It should be noted that in the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in the claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present application can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In a unit claim listing several devices, several of these devices may be embodied by the same item of hardware. The use of the words first, second, and third, etc. does not denote any order. These words may be interpreted as names.

[0249] Although the preferred embodiments of the present application have been described, additional changes and modifications can be made by those skilled in the art once they learn the basic inventive concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present application.

[0250] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to include these changes and modifications.

Claims

1. A multi-scenario carbon emission prediction method for land gradient utilization, characterized in that, The method includes the following steps: Based on the land expansion situation and target driving factors of each land use type in each area to be predicted within the historical period, calculate the land development probability corresponding to each land use type, and calculate the total land use development probability according to the land development probability corresponding to each land use type, so as to evaluate the first land gradient utilization change trend of the area to be predicted under the historical development scenario; and, Obtain the predicted future population change, economic change, precipitation change and temperature change of the area to be predicted under the future climate scenario, and evaluate the second land gradient utilization change trend of the area to be predicted under the future climate scenario according to the future population change, the economic change, the precipitation change and the temperature change, wherein the future climate scenario includes at least three sub-future climate scenarios caused by different carbon emission levels; and, Taking each land use type in the area to be predicted as a decision variable, taking carbon emission minimization, economic benefit maximization and ecological benefit maximization as objective functions, and the area of each land use type as a constraint condition, evaluate the third land gradient utilization change trend of the area to be predicted under the policy planning scenario; Calculate the predicted carbon emission values of the area to be predicted in the target future year under the first land gradient utilization change trend, the second land gradient utilization change trend and the third land gradient utilization change trend respectively, so as to determine the carbon emission prediction result of the area to be predicted in the target future year based on the first land gradient utilization change trend, the second land gradient utilization change trend and the third land gradient utilization change trend and the predicted carbon emission values corresponding to each trend.

2. The multi-scenario land gradient utilization carbon emission prediction method according to claim 1, wherein The land use types include cultivated land, forest land, grassland, water area, urban land, rural settlements, industrial and mining and transportation construction land and unused land. The target driving factor of the cultivated land is GDP, the target driving factor of the forest land is the surface vegetation coverage value, the target driving factors of the grassland and the water area are both the distance from the river, the target driving factor of the urban land is the distance from the county government, the target driving factors of the rural settlements and the industrial and mining and transportation construction land are both the night lights, and the target driving factor of the unused land is the elevation.

3. The multi-scenario land gradient utilization carbon emission prediction method according to claim 1 or 2, characterized in that The calculation expression of the land development probability is: In the formula, is the development probability of each land use type; i is the cell, k is the land use type, the T value is 0 or 1, where 1 indicates that there is a conversion from other land use types to the k land use type, and 0 indicates no conversion; p n (x) is the predicted type of the nth decision tree of the vector x; I[·] is the indicator function of the decision tree set; M is the total number of decision trees.

4. The multi-scenario land gradient utilization carbon emission prediction method according to claim 3, characterized in that, The calculation expression of the total land use development probability is: In the formula, represents the total development probability of land use type k in the i-th cell; is the land development probability of land use type k in the i-th cell; r is a random value within 0 to 1; μ k is the threshold for generating new patches of land use type k; is the neighborhood effect of the i-th cell, representing the coverage ratio of land use types within the k neighborhood; is the adaptive inertia coefficient, representing the influence of future demand for land use type k; Among them, The calculation expression is: In the formula, represents the total number of grid cells occupied by the k land use type in the last iteration within the n×n window, and ω k is the neighborhood weight of different land use types, representing the proportion of the expansion area of the land use type in the total land expansion area; Among them, The calculation expression is: In the formula, and respectively represent the difference between the current quantity and the future demand of the land use type k at the (d - 1)-th iteration and the (d - 2)-th iteration.

5. The multi-scenario land gradient utilization carbon emission prediction method according to claim 1, characterized in that The expression of the objective function is as follows: In the formula, F(x) is the objective function that minimizes comprehensive carbon emissions, maximizes economic benefits, and maximizes ecological benefits. MinF1(x) is the minimum value of carbon emissions, MaxF2(x) is the maximum value of economic benefits, and MaxF3(x) is the maximum value of ecological benefits; x i is the land use type, a i is the carbon emission / carbon absorption intensity of different land use types; b i is the economic benefit per unit area of different land use types; c i is the value of ecosystem services per unit area of land.

6. The multi-scenario land gradient utilization carbon emission prediction method according to claim 1, characterized in that The land use types include cultivated land, forest land, grassland, water area, urban land, rural settlements, industrial and mining and transportation construction land and unused land. The constraint conditions of each land use type include: 21141.730≤x1≤22524.830 54614.861≤x2≤66087 29883.522≤x3≤30063.402 x4≥1456.177 2661.630 ≤ x5 + x6 + x7 ≤ 3460.119 945.214≤x5≤1661.521 935.612≤x6≤949.655 780.804≤x7≤848.943 x8<160.511 In the formula, x1 is the cultivated land area, x2 is the forest land area, x3 is the grassland area, x4 is the water area, x5 is the urban land area, x6 is the rural settlement area, x7 is the industrial and mining and transportation construction land area, x8 is the unused land area, and x5 + x6 + x7 represents the construction land area.

7. A carbon emission prediction coupling model for multi-scenario land gradient utilization, characterized in that, The multi-scenario land gradient utilization carbon emission prediction coupling model includes: A historical development scenario module, configured to calculate the land development probability corresponding to each land use type based on the land expansion of each land use type in each area to be predicted and the target driving factors within a historical period, and calculate the total land use development probability based on the land development probability corresponding to each land use type, so as to evaluate the first land gradient utilization change trend of the area to be predicted under the historical development scenario; and, A future climate scenario module, configured to obtain the predicted future population change, economic change, precipitation change and temperature change of the area to be predicted under the future climate scenario, and evaluate the second land gradient utilization change trend of the area to be predicted under the future climate scenario according to the future population change, the economic change, the precipitation change and the temperature change, wherein the future climate scenario includes at least three sub-future climate scenarios caused by different carbon emission levels; and, A policy planning scenario module, configured to use each land use type in the area to be predicted as a decision variable, and use carbon emission minimization, economic benefit maximization and ecological benefit maximization as objective functions, and the area of each land use type as a constraint condition, to evaluate the third land gradient utilization change trend of the area to be predicted under the policy planning scenario; A carbon emission prediction module, configured to calculate the predicted carbon emission values of the area to be predicted in the target future year under the first land gradient utilization change trend, the second land gradient utilization change trend and the third land gradient utilization change trend respectively, so as to determine the carbon emission prediction result of the area to be predicted in the target future year based on the first land gradient utilization change trend, the second land gradient utilization change trend and the third land gradient utilization change trend and the predicted carbon emission values corresponding to each trend.

8. A computer system, characterized in that, The computer system includes: a memory, a processor, and a multi-scenario land gradient utilization carbon emission prediction program stored on the memory and executable on the processor. When the multi-scenario land gradient utilization carbon emission prediction program is executed by the processor, the steps of the multi-scenario land gradient utilization carbon emission prediction method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a multi-scenario land gradient utilization carbon emission prediction program. When the multi-scenario land gradient utilization carbon emission prediction program is executed by a processor, the steps of the multi-scenario land gradient utilization carbon emission prediction method according to any one of claims 1 to 6 are implemented.

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